ALBERT

Description: Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.

Description: Due to the fast attenuation of the magnetic field along with the distance, the magnetic anomaly generated by the remote magnetic target is usually buried in the magnetic noise. In order to improve the performance of magnetic anomaly detection (MAD) with low SNR, we propose an adaptive method of MAD with ensemble empirical mode decomposition (EEMD) and minimum entropy (ME) feature. The magnetic data is decomposed into the multiple intrinsic modal functions (IMFs) with different scales by EEMD. According to a defined criterion, the magnetic noise and magnetic signal are reconstructed based on IMFs, respectively. Entropy feature of reconstructed magnetic signal is extracted based on the probability density function (PDF) of the noise which is updated by the reconstructed magnetic noise. Compared to the traditional minimum entropy method, the entropy feature extracted by the proposed method is more obvious. The magnetic anomaly is detected whenever the entropy feature drops below the threshold. Thus, it is effective for revealing the weak magnetic anomaly by the proposed method. The measured magnetic noise is used to validate the performance of the proposed method. The results show that the detection probability of the proposed method is higher with low input SNR.

Description: In order to study the regularity of overburden deformation and mining pressure changes in the stope. Take the geological conditions of Hanglaiwan coal mine in Yushen mining area as the background, a 3000 mm×1340 mm×200 mm physical similar material model was established in laboratory. Eight fiber Bragg grating sensors were embedded in the model, used to monitor the movement and deformation of the overburden key stratum and the change of mining abutment pressure in real time and then to determine the influencing factors and strain transfer coefficients of the fiber grating through theoretical analysis and calibration experiments. The results show the following. (1) The changes in value, position, and shape of the strain can reflect the dynamic evolution process of bending deformation, breaking, and rotation of the key strata. When the key layer breaks for the first time, the strain curve shows a peak shape; when the key layer breaks periodically, the strain curve of the fiber grating sensor showed a plateau shape for a period of time. (2) The strain curve has a good corresponding relationship with the change in supporting pressure. When the strain curve of the fiber Bragg grating sensor was at the trough stage, the abutment pressure was at the peak stage, with the increase of sensor buried height, and the supporting pressure and the strain value had a linear relationship within the range of the advanced abutment pressure. The conclusion shows that the fiber Bragg grating sensor monitoring technology has good effect on the internal strain and key supporting pressure monitoring of the overburden in the model test; it provides new monitoring methods and means for the model tests.

Description: Generally, there are mainly two methods to solve the image restoration task in low-level computer vision, i.e., the model-based optimization method and the discriminative learning method. However, these two methods have clear advantages and disadvantages. For example, it is flexible for the model-based optimization method to handle different problems, but large quantity of computing time is required for better performance. The discriminative learning approach has high computing efficiency, but the application scope is seriously limited by the fixed training model. It would be better to combine the advantages of these two methods. Luckily, with the variable splitting techniques, we insert the trained convolutional neural network (CNN) for denoising as one model to the model-based optimization method to solve other image restoration problems (e.g., deblurring and super-resolution). Final experimental results show that our denoising network is able to provide strong prior information for image restoration tasks. The image restoration effects can reach or approximate the most advanced algorithm in such three tasks as denoising, deblurring, and super-resolution. Moreover, the algorithm proposed in this paper is also the most competitive in terms of computational efficiency.

Description: With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

Description: Particulate matters (PM) have become one of the important pollutants that deteriorate public health. Since PM is ubiquitous in the atmosphere, it is closely related to life quality in many different ways. Thus, a system to accurately monitor PM in diverse environments is imperative. Previous studies using digital images have relied on individual atmospheric images, not benefiting from both spatial and temporal effects of image sequences. This weakness led to undermining predictive power. To address this drawback, we propose a predictive model using the deep dehazing cascaded CNN and temporal priors. The temporal prior accommodates instantaneous visual moves and estimates PM concentration from residuals between the original and dehazed images. The present method also provides, as by-product, high-quality dehazed image sequences superior to the nontemporal methods. The improvements are supported by various experiments under a range of simulation scenarios and assessments using standard metrics.

Description: Rapid urbanization has become a major urban sustainability concern due to environmental impacts, such as the development of urban heat island (UHI) and the reduction of urban security states. To date, most research on urban sustainability development has focused on dynamic change monitoring or UHI state characterization, while there is little literature on UHI change analysis. In addition, there has been little research on the impact of land use and land cover changes (LULCCs) on UHI, especially simulates future trends of LULCCs, UHI change, and dynamic relationship of LULCCs and UHI. The purpose of this research is to design a remote sensing-based framework that investigates and analyzes how the LULCCs in the process of urbanization affected thermal environment. In order to assess and predict the impact of LULCCs on urban heat environment, multitemporal remotely sensed data from 1986 to 2016 were selected as source data, and Geographic Information System (GIS) methods such as the CA-Markov model were employed to construct the proposed framework. The results showed that (1) there has been a substantial strength of urban expansion during the 40-year study period, (2) the farthest distance urban center of gravity moves from north-northeast (NEE) to west-southwest (WSW) direction, (3) the dominate temperature was middle level, sub-high level, and high level in the research area, (4) there was a higher changing frequency and range from east to west, and (5) there was a significant negative correlation between land surface temperature and vegetation and significant positive correlation between temperature and human settlement.

Description: “Tea” is a beverage which has a unique taste and aroma. The conventional method of tea manufacturing involves several stages. These are plucking, withering, rolling, fermentation, and finally firing. The quality parameters of tea (color, taste, and aroma) are developed during the fermentation stage where polyphenolic compounds are oxidized when exposed to air. Thus, controlling the fermentation stage will result in more consistent production of quality tea. The level of fermentation is often detected by humans as “first” and “second” noses as two distinct smell peaks appear during fermentation. The detection of the “second” aroma peak at the optimum fermentation is less consistent when decided by humans. Thus, an electronic nose is introduced to find the optimum level of fermentation detecting the variation in the aroma level. In this review, it is found that the systems developed are capable of detecting variation of the aroma level using an array of metal oxide semiconductor (MOS) gas sensors using different statistical and neural network techniques (SVD, 2-NM, MDM, PCA, SVM, RBF, SOM, PNN, and Recurrent Elman) successfully.

Description: We present experimental results of fluctuation-enhanced gas sensing by low-cost resistive sensors made of a mixture of graphene flakes and TiO2 nanoparticles. Both components are photocatalytic and activated by UV light. Two UV LEDs of different wavelengths (362 and 394 nm) were applied to modulate the gas sensing of the layers. Resistance noise was recorded at low frequencies, between 8 Hz and 10 kHz. The sensors’ response was observed in an ambient atmosphere of synthetic air and toxic NO2 at selected concentrations (5, 10, and 15 ppm). We observed that flicker noise changed its frequency dependence at different UV light wavelengths, thereby providing additional information about the ambient atmosphere. The power spectral density changed by a few times as a result of UV light irradiation. The sensors were operated at 60 and 120°C, and the effect of UV light on gas sensing was most apparent at low operating temperature. We conclude that UV light activates the gas-sensing layer and improves gas detection at low concentrations of NO2. This result is desirable for the detection of the components of gas mixtures, and the modulated sensor can replace an array of independent resistive sensors which would consume much more energy for heating. We also suggest that a more advanced technology for preparing the gas-sensing layer, by use of spin coating, will produce corresponding layers with thickness of about a few μm, which is about ten times less than that for the tested samples. The effects induced by the applied UV light, having a penetration depth of only a few μm, would then be amplified.

Description: Impervious surface (IS) is a key indicator to measure the urbanization process and ecological environment. Many studies have observed an urbanization process based on IS at the city scale. Understanding the changes in the IS over a period at a regional level offers an alternative and effective approach to characterize and quantify the spatial process of urban agglomeration. This study focuses on the urban agglomeration of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) by utilizing the sensor-based Landsat data during 1987-2017 and investigates the spatiotemporal distribution of IS expansion at both regional and city scales. The modified linear spectral mixture analysis (MLSMA) method is used to extract the IS of the GBA. Then, the IS mapping accuracies were assessed after comparison with high-resolution historical data. The spatiotemporal and directional changes of IS surfaces for GBA are analyzed by using Gravity Center (GC) and Standard Deviational Ellipse (SDE). Finally, Shannon’s Diversity Index (SHDI) is used to analyze the overall characteristics of landscape level, and the Patch Density (PD) and Landscape Shape Index (LSI) are used to describe the characteristics of different classes of the IS. The results show that the IS of the whole region experienced rapid and massive expansion during the past 30 years and exhibited a distinct characteristic along the Pearl River Delta (PRD) and the coastline. Furthermore, the IS area increased rapidly in the PRD, while it is relatively stable in Hong Kong and Macao. We believe that the findings of this study can help policy makers to better understand and maintain the sustainable development of the GBA.

Description: Structural health monitoring (SHM) technology is a monitoring process and early warning method for the health status or damage of special workpiece structures by deploying sensors. In recent years, there have been many studies on SHM, such as ultrasonic, pulsed eddy current, optical fiber, magnetic powder, and other nondestructive testing technologies. Due to their sensor deployment, testing environment, power supply, and transmission line wiring mechanism, they bring problems such as detection efficiency, long-term monitoring, and unreliable systems. The combination of wireless sensing technology and intelligent detection technology is used to solve the above problems. Therefore, this paper studies the tag antenna smart sensor, which is used to characterize the extension of metal defects in SHM. Then, it presents a wireless passive three-dimensional sensing antenna, and simulations verify the feasibility of the antenna. The simulation results show that the antenna can characterize the two extension directions of depth and width of the metal surface structure smooth defect. At the same time, the antenna can characterize the position of smooth defects on the surface of metal structures relative to the antenna and then realize the smooth defect positioning.

Description: High-intensity underground mining generates considerable surface subsidence in mining areas, including ground cracks and collapse pits on roads and farmland, threatening the safety of buildings. Large-amplitude subsidence (e.g., 〉2 m) is usually characterized by a large phase gradient in interferograms, leading to severe phase decorrelation and unwrapping errors. Therefore, the subsidence on the surface cannot be well derived simply using conventional differential interferometric synthetic aperture radar (DInSAR) or other geodetic measurements. We propose a new method that combines both DInSAR and subpixel offset-tracking technology to improve mine subsidence monitoring over large areas. We utilize their respective advantages to extract both the spatial boundaries and the amplitude of displacements. Using high-resolution RADARSAT-2 SAR images (5 m) acquired on February 13, 2012, and November 27, 2012, in the Shendong Coalfield located at the border between Shaanxi Province and Inner Mongolia Province, China, we obtain the subcentimetre-level subsidence of the mine boundary by DInSAR and resolve the metre-level mine subsidence centre based on subpixel offset tracking. The whole subsidence field is obtained by combining and analyzing the subcentimetre-level and the metre-level subsidence. We use the probability integral method (PIM) function model to fit the boundary and central mine subsidence to reconstruct the spatial distribution of the mine subsidence. Our results show that the maximum central subsidence reaches ~4.0 m (beyond the monitoring capabilities of DInSAR), which is generally in agreement with the maximum subsidence of ~4.0-5.0 m from field investigation. We also model the boundary and the central subsidence (the final fitting coefficient is 0.978). Our findings indicate that the offset-tracking method can compensate for the deficiency of DInSAR in large-amplitude subsidence extraction, and the inclusion of the PIM technique helps reconstruct the whole subsidence field in mining areas.

Description: To increase productivity in agricultural production, speed, and accuracy is the key requirement for long-term economic growth, competitiveness, and sustainability. Traditional manual paddy rice seed classification operations are costly and unreliable because human decisions in identifying objects and issues are inconsistent, subjective, and slow. Machine vision technology provides an alternative for automated processes, which are nondestructive, cost-effective, fast, and accurate techniques. In this work, we presented a study that utilized machine vision technology to classify 14 Oryza sativa rice varieties. Each cultivar used over 3,500 seed samples, a total of close to 50,000 seeds. There were three main processes, including preprocessing, feature extraction, and rice variety classification. We started the first process using a seed orientation method that aligned the seed bodies in the same direction. Next, a quality screening method was applied to detect unusual physical seed samples. Their physical information including shape, color, and texture properties was extracted to be data representations for the classification. Four methods (LR, LDA, k-NN, and SVM) of statistical machine learning techniques and five pretrained models (VGG16, VGG19, Xception, InceptionV3, and InceptionResNetV2) on deep learning techniques were applied for the classification performance comparison. In our study, the rice dataset were classified in both subgroups and collective groups for studying ambiguous relationships among them. The best accuracy was obtained from the SVM method at 90.61%, 82.71%, and 83.9% in subgroups 1 and 2 and the collective group, respectively, while the best accuracy on the deep learning techniques was at 95.15% from InceptionResNetV2 models. In addition, we showed an improvement in the overall performance of the system in terms of data qualities involving seed orientation and quality screening. Our study demonstrated a practical design of rice classification using machine vision technology.

Description: In this paper, an IoT-based indoor air quality monitoring platform, consisting of an air quality-sensing device called “Smart-Air” and a web server, is demonstrated. This platform relies on an IoT and a cloud computing technology to monitor indoor air quality in anywhere and anytime. Smart-Air has been developed based on the IoT technology to efficiently monitor the air quality and transmit the data to a web server via LTE in real time. The device is composed of a microcontroller, pollutant detection sensors, and LTE modem. In the research, the device was designed to measure a concentration of aerosol, VOC, CO, CO2, and temperature-humidity to monitor the air quality. Then, the device was successfully tested for reliability by following the prescribed procedure from the Ministry of Environment, Korea. Also, cloud computing has been integrated into a web server for analyzing the data from the device to classify and visualize indoor air quality according to the standards from the Ministry. An application was developed to help in monitoring the air quality. Thus, approved personnel can monitor the air quality at any time and from anywhere, via either the web server or the application. The web server stores all data in the cloud to provide resources for further analysis of indoor air quality. In addition, the platform has been successfully implemented in Hanyang University of Korea to demonstrate its feasibility.

Description: Broilers produce abnormal sounds such as cough and snore when they suffer from respiratory diseases. The aim of this research work was to develop a method for broiler abnormal sound detection. The sounds were recorded in a broiler house for one week (24/7). There were 20 thousand white feather broilers reared on the floor in a building. Results showed that the developed recognition algorithm, using wavelet transform Mel frequency cepstrum coefficients (WMFCCs), correlation distance Fisher criterion (CDF), and hidden Markov model (HMM), provided an average accuracy, precision, recall, and F1 of 93.8%, 94.4%, 94.1%, and 94.2%, respectively, for broiler sound samples. The results indicate that sound analysis can be used in broiler respiratory assessment in a commercial broiler farm.

Description: Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the average measuring error, the variance of measuring error, and the overall sorting accuracy are 204.175 ms, 1.48 mm, 0.07 mm, and 93%, respectively, implying the feasibility and satisfied accuracy of the proposed approach.

Description: A novel passive wireless integrated SAW-based antenna sensor for strain sensing is presented in this paper. A SAW delay line scheme is proposed for signal modulation, which could distinguish the backscattered data from environmental clutter in time domain. The theoretical relationship between the antenna resonance frequency shifts, the temperature, and the applied strain was established. A multiphysical coupled simulation process is proposed to improve the simulation accuracy. By comparing the phase shift of adjacent echoed data which is generated by SAW reflective grating, the temperature information could be extracted exactly and the effect of temperature fluctuation on the resonance frequency could be compensated. A more accurate passive (battery-free) wireless strain sensing could be provided by this proposed integrated antenna sensor than the previous proposed methods. Simulation and experimental results demonstrate the effectiveness of the sensor.

Description: The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.

Description: Diabetes is one of today’s greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.

Description: One of the advantages of a netted airborne radar system (NARS) is escaping interception of the passive detection system (PDS) while tracking a target. A significant tactic to realize tracking without PDS interception is to study the low probability of interception (LPI) time of NARS. Firstly, this paper analyses the power, frequency, and platform interception probabilities of a combined PDS consisting of a radar-warning receiver (RWR) and an electronic support measurement (ESM). Secondly, this paper takes interactive multiple models (IMM) to describe the target tracking process and introduces a binary hypothesis test for chi square as well as noncentralized chi square distributions as a detection criterion of NARS during target tracking after the design of adaptive dwell time and the maximum illumination interval algorithm. Finally, based on experiential moving platform interception probabilities of a RWR and an ESM, a simplified math model is presented to estimate LPI time of NARS when the parameters are partially known. Simulations illustrate that the simultaneous management of radiation power and time is crucial for NARS against combined PDS interception.

Description: To achieve a rational allocation of limited water resources, and formulation of an appropriate irrigation system, this research studied the change characteristics of stem water content (StWC) in plant and its response to environmental factors. In this study, the StWC and environmental factors of Lagerstroemia indica in Beijing were continuously observed by a BD-IV plant stem water content sensor and a forest microclimate monitoring station from 2017 to 2018. The variation of StWC and its correlation with environmental factors were analyzed. The results showed that the StWC of Lagerstroemia indica varies regularly day and night during the growth cycle. Meanwhile, the rising time, valley time, and falling time of StWC were various at the different growth stages of Lagerstroemia indica. The results of correlation analysis between StWC and environmental factors indicated that the StWC of Lagerstroemia indica was positively correlated with air relative humidity, while it was negatively correlated with total radiation and air temperature. The multiple regression equation of StWC and environmental factors of Lagerstroemia indica was , and the coefficient of determination of the equation was of 0.87. Furthermore, the results illustrated that the irrigation should pay attention to supplementing irrigation in time during the peak growing season of fruit.

Description: At present, precision agriculture and smart agriculture are the hot topics, which are based on the efficient data collection by using wireless sensor networks (WSNs). However, agricultural WSNs are still facing many challenges such as multitasks, data quality, and latency. In this paper, we propose an efficient solution for multiple data collection tasks exploiting edge computing-enabled wireless sensor networks in smart agriculture. First, a novel data collection framework is presented by merging WSN and edge computing. Second, the data collection process is modeled, including a plurality of sensors and tasks. Next, according to each specific task and correlation between task and sensors, on the edge computing server, a double selecting strategy is established to determine the best node and sensor network that fulfills quality of data and data collection time constraints of tasks. Furthermore, a data collection algorithm is designed, based on set values for quality of data. Finally, a simulation environment is constructed where the proposed strategy is applied, and results are analyzed and compared to the traditional methods. According to the comparison results, the proposal outperforms the traditional methods in metrics.

Description: This paper proposes Lion Optimized Cognitive Acoustic Network (LOCAN) to reduce packet delay and packet loss during packet transmission in Underwater Acoustic Sensor Network (UWASN). Packet delay and packet loss in UWASN are because of water column variations such as Doppler effect and geometric spreading (GS). Doppler effect forms due to sensor node’s motion and sea surface variations such as salinity and temperature. Geometric spreading (GS) occurs due to sediment drift wave fronts and frequent changes in node’s location and depth. Water column variations change the amplitude of sound propagation, causing channel coherence and multipath interference, which affect packet transmission. The existing UWASN algorithms focus only on temperature and salinity variations. In LOCAN, channel selection through Lion Optimization Algorithm solves the problems of water column variation and improves the battery life, network lifetime, and throughput. The proposed algorithms show a better result in terms of efficiency, when compared to existing UWASN algorithms.

Description: Human motion capture is widely used in exoskeleton robots, human-computer interaction, sports analysis, rehabilitation training, and many other fields. However, soft-sensor-based wearable dynamic measurement has not been well achieved. In this paper, the dynamic measurements of legs were investigated by using dielectric elastomers as stain sensors, and an alternating signal was applied to detect the dynamic rotational angles of the legs. To realize a quick response, parameters of the sensors were optimized by circuit analysis. The sensor can detect hip, knee, and ankle joint motions with a sample frequency of 200 Hz. The measurements of the sensors were compared with a commercial motion capture system from PhaseSpace, and dynamic errors between them were smaller than 3° when squatting and walking at low speed and smaller than 5° when walking at high speed. Experiments therefore demonstrate the feasibility of the integrated wearable stretch sensors with pants.

Description: Leymus chinensis is a perennial rhizomatous clonal plant with strong tolerance to mowing, grazing, drought, and salt-alkali. However, with patchy soil environment, how mowing affect the photosynthesis of L. chinensis in heterogeneous patches is largely unknown. In this experiment, we tested the effects of mowing intensity (0, 35%, and 70% removal of aboveground biomass) on plant photosynthesis under different heterogeneous patches with different saline-alkali soil. We found that moderate concentrations of salt-alkali under heterogeneous patches have no significant effect on the photosynthesis of L. chinensis. Moderate mowing can maintain photosynthesis under no heterogeneity soil and moderate salt-alkali patch condition. In addition, heavy mowing and high salt-alkali soil under heterogeneous patches both inhibited net photosynthetic rate () and transpiration rate (), reduced leaf area, and plant height. L. chinensis responded to extreme soil conditions and strong disturbance by increasing water-use efficiency (WUE), reducing relative water content (RWC), and changing stomatal conductance (Gsw). Therefore, our results imply that moderate grazing or mowing can be used to maintain the productivity and economic benefits of grassland when the soil heterogeneous patches with moderate saline-alkali conditions.

Description: We report on a small-size light detection and ranging (LiDAR) sensor, which offers the possibility of being used in the field during oil spill incidents. In the present study, we develop an algorithm that can distinguish between seawater and oil through the use of a laser at 905 nm wavelength. We investigate the ability of the sensor to detect three different oil types (light crude, bunker A, and bunker C) through experiments and analyze the differences between the types and volumes of spilled oil (1, 5, 10, 15, 20, 25, 30, 35, 40, and 50 mL). The results showed that our algorithm for detecting oil spills over seawater was successful: the LiDAR sensor was able to detect different oil types and volumes. Spilled oil area coverage ranged by more than 50% of the detected area, and the viscosity of bunker C oil reached up to 73%. In addition, the experimental oil spills were mainly formed of oil films of 1 mm and 2 mm thicknesses, which confirmed geometrical properties. Follow-up research should further investigate the characteristics of oil slick thickness measured by the LiDAR system and undertake field tests to assess the feasibility of using the LiDAR system in pollution incidents.

Description: The land surface model SURFEX 7.3 was used to study climate effect of urban expansion located in oasis in arid area of Northwest China by surface and 2 m urban heat island (UHI) intensity and available energy ratio (). We performed a true regional development scenario and three assumed scenario simulations in 1978, 1993, 2004, and 2014, respectively. The results show that 2 m UHI always displays positive twin peaks during whole day, while surface UHI only displays a positive single peak with several hours during daytime at four seasons in the four years. Moreover, 2 m UHI intensity during night is higher than that during daytime, indicating that UHI intensity is contributed more by “trap effect” from urban complex geometry or anthropogenic heat and that surface UHI according to land surface temperature cannot reflect UHI comprehensively. The oasis-urban development resulted in local warming and increasing of , and compared with the original undeveloped environment, local climate in the study area was in a relatively balanced state in 1978 and 1993 due to the “heating effect” of urban area and the “cooling effect” of oasis, but the offsetting effect from oasis would become weaker after1993.

Description: Guided wave transducers, such as electromagnetic acoustic transducers and piezoelectric transducers, generate multimode waves at a given excitation frequency in a cylindrical structure, making it difficult to detect flaws in such structures. To accurately identify the flaws, the transducers must be well designed to suppress the nonaxisymmetric modes. Instead of using the normal mode expansion (NME) method, a spatial Fourier transform (SFT) method is proposed to analyze source influence on the guided wave excitation in a cylindrical structure. A two-dimensional SFT is performed on the spatial distribution function of the surface loading applied to the cylindrical structure. The spatial distribution function is represented in a cylindrical coordinate system. The circumferential-direction SFT is carried out from the angular coordinate to the circumferential orders of the guided wave modes. The axial-direction SFT is carried out from the axial coordinate to the wavenumbers of the guided wave modes. The results of the two-dimensional SFT represent guided wave excitation capabilities for different circumferential orders and wavenumbers. The specific surface loading conditions on the outer surface of a pipe are analyzed to predict source influence on the guided wave excitation. The results are consistent with those obtained using the NME method. Experiments corresponding to the specific surface loading conditions are carried out on a stainless steel pipe. The results confirm the effectiveness of the SFT method.

Description: Falls from a bed often occur when an elderly patient attempts to get out of bed or comes close to the edge of a bed. These mishaps have a high possibility of serious injuries, such as bruises, soreness, and bone fractures. Moreover, a lack of repositioning the body of a bedridden elderly person may cause bedsores. To avoid such a risk, a continuous activity monitoring system is needed for taking care of the elderly. In this study, we propose a bed position classification method based on the sensor signals collected from only four sensors that are embedded in a panel (composed of two piezoelectric sensors and two pressure sensors). It is installed under the mattress on the bed. The bed positions considered are classified into five different classes, i.e., off-bed, sitting, lying center, lying left, and lying right. To collect the training dataset, three elderly patients were asked for consent to participate in the experiment. In our approach, a neural network combined with a Bayesian network is adopted to classify the bed positions and put a constraint on the possible sequences of the bed positions. The results from both the neural network and Bayesian network are combined by the weighted arithmetic mean. The experimental results have a maximum accuracy of position classification of 97.06% when the proportion of coefficients for the neural network and the Bayesian network is 0.3 and 0.7, respectively.

Description: Wireless sensor networks (WSNs) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multihop routing. Agriculture sector also adopted these networks to promote innovations for environmental friendly farming methods, lower the management cost, and achieve scientific cultivation. Due to limited capabilities, the sensor nodes have suffered with energy issues and complex routing processes and lead to data transmission failure and delay in the sensor-based agriculture fields. Due to these limitations, the sensor nodes near the base station are always relaying on it and cause extra burden on base station or going into useless state. To address these issues, this study proposes a Gateway Clustering Energy-Efficient Centroid- (GCEEC-) based routing protocol where cluster head is selected from the centroid position and gateway nodes are selected from each cluster. Gateway node reduces the data load from cluster head nodes and forwards the data towards the base station. Simulation has performed to evaluate the proposed protocol with state-of-the-art protocols. The experimental results indicated the better performance of proposed protocol and provide more feasible WSN-based monitoring for temperature, humidity, and illumination in agriculture sector.

Description: Recently, there has been increasing interest in the field of underwater wireless sensor networks (UWSNs), which is a basic source for the exploration of the ocean environment. A range of military and civilian applications is anticipated to assist UWSN. The UWSN is being developed by the extensive wireless sensor network (WSN) applications and wireless technologies. Therefore, in this paper, a review has been presented which unveils the existing challenges in the underwater environment. In this review, firstly, an introduction to UWSN is presented. After that, underwater localizations and the basics are presented. Secondly, the paper focuses on the architecture of UWSN and technologies used for underwater acoustic sensor network (UASN) localization. Various localization techniques are discussed in the paper classified by centralized and distributed localizations. They are further classified into estimated and prediction-based localizations. Also, various underwater localization algorithms are discussed, which are grouped by the algorithms based on range and range-free schemes. Finally, the paper focuses on the challenges existing in underwater localizations, underwater acoustic communications with conclusions.

Description: Inertial navigation systems/Doppler velocity log (INS/DVL) integrated navigation systems are widely used in underwater environments where GPS is unavailable. An INS/DVL integrated navigation system is generally loosely coupled; however, this does not work if any of the DVL transducers do not work. If a system is tightly coupled, velocity error can be estimated with fair accuracy even if some of the transducers fail. However, despite the robustness of a tightly coupled system compared to a loosely coupled one, velocity error estimation accuracy of the former decreases as the number of faulty transducers increases. Therefore, this paper proposes an INS/DVL/revolutions per minute (RPM) integrated navigation filter designed to improve the performance of conventional tightly coupled integrated systems by estimating data from faulty transducers using RPM data. Two salient features of the proposed filter are (1) estimating RPM data accounting for error from the effect of tidal currents and (2) continuous estimation of error in RPM data by selectively converting only the measurements of faulty transducers. The performance of the proposed filter was first verified using Monte Carlo numerical simulations with the analysis range set to 1 standard deviation (1σ, 68%) and then with real sea test measurement data.

Description: Control accuracy significantly affects the performances of boom sprayer. In this study, we develop a precise autocontrol technology based on the vehicle speed feedback. We utilize the auxiliary antidrift system of wind-curtain type air flow and the variable spraying control system for adaptive fertilizing and online measuring of working conditions. Experimental results demonstrate that the variable spraying control system could keep the speed error less than 3%. The air flow significantly improves the penetration of spraying, decreases the fog drip, and increases the pesticide utility. Benefitting from the auxiliary air flow, the average utility of pesticide is improved from 26.76% to 37.98%. Additionally, the speed feedback control reduces the consumption of pesticide by more than 12%.

Description: The availability of suitable native plant species for local animal husbandry development and ecological restoration is limited on the Qinghai-Tibetan Plateau. Therefore, comparisons of the ecological adaptability of native species to alternative habitats and their introduction into new habitats are of high importance. This study is aimed at identifying the alteration in morphological and physiological characteristics by measuring photosynthetic physiology, nutrient content, and growth associated with adaptation of plants to conditions at different altitudes 2450, 2950, 3100, and 3300 m above sea level (a. s. l.) on the plateau. Seeds of the dominant grass, Elymus nutans, were collected from locations at these altitudes and grown at a test location of 2950 m a. s. l. Results indicated that altitude had no significant effect on plant height and root depth. However, the leaf area and total root surface area of plants derived from 2950 and 3300 m a. s. l. showed a parabolic response, being greater than those of plants derived from the lowest (2450 m) and highest (3300 m a. s. l.). Total (root plus shoot) dry matter reduced progressively from 2450 to 3300 m a. s. l, while root : shoot ratio increased progressively with altitude. Seed yield of plants originating from the test altitude (2950 m a. s. l) was significantly higher than at any other altitude, being 20% lower at 2450 m, and 38% and 58% less in populations originating from the higher altitudes (3100 and 3300 m a. s. l.). There was also a parabolic decline in response of Elymus nutans germplasm from 3100, 3300, and 2450 m, compared with plants from 2950 m a. s. l., to photosynthetic rate, total N, soluble sugar, and starch contents. Germplasm from 2450 m a. s. l. had significantly lower shoot and higher root carbon content, lower shoot nitrogen, and lower root carbon-to-nitrogen ratio compared with plants derived from the other three altitudes. It is suggested that the stable, genetically determined morphological and physiological features of ecotypes showed parabolic responses which means these ecotypes have become adapted to local habitats, whereas parameters such as dry matter, total root : shoot ratio, photosynthetic rate, and intercellular CO2 concentration of plants reflected phenotypic linear response to current abiotic conditions. It is postulated that introduced ecotypes from 2450, 3100, and 3300 m could adapt to the environment at 2950 m a. s. l. gradually. We conclude that the increased thermal regime experienced by plants introduced from high altitude to low altitude may facilitate the increased growth of Elymus nutans subtypes. It is important to preserve local strains of native species, or ecotypes, for reintroduction into degraded environments and to maintain the greatest ecosystem stability in the northeastern Tibetan Plateau.

Description: Traditionally, routing decisions have been based on minimizing travel time as the associated cost. Ecorouting considers the environmental aspects (e.g., emissions and fuel) as part of the travel cost to mitigate the undesirable impact of transportation systems on the environment. Unlike the exiting ecorouting review papers, this research work is aimed at providing a three-factor taxonomy at a more disaggregated level from the optimization prospective and map ecorouting studies to the proposed taxonomy. Furthermore, the strengths and weaknesses of the presented models are summarized. Our main findings include (a) a majority of studies optimized one objective at a time; (b) the microscopic level of aggregation of the flow and emission/fuel models was rarely employed for large case studies, due to the associated complexity; and (c) all of the reviewed studies were applied in a centralized routing system environment. In the near future, when intelligent vehicles will be on the roads, a multiobjective distributed routing framework can be employed with a microscopic level of aggregation for both traffic and emission models, which is capable of operating on largescale networks in real time. Additionally, short-term spatiotemporal prediction of GHG cost is a crucial aspect to be tackled.

Description: Ultrahigh-resolution optical coherence tomography provides an axial resolution of 1-2 μm for resolving cellular structures of biological tissues critical for the diagnosis of diseases. However, it requires a relatively large spectral bandwidth which is not supported by the key components of the imaging system. We propose a novel spectral-domain OCT design, termed interferometer-in-spectrometer, which is able to compensate the bandwidth limitations of the grating and the line scan sensor by spectral shaping without compromising the signal intensity and adding the system cost. The advantage of axial resolution and ranging depth over the standard design is experimentally validated using the standard testing method and fresh swine cornea ex vivo. Moreover, opportunities that opened up by this new scheme for improving the performances of spectral-domain OCT are also discussed.

Description: Tractor front-end loaders are an essential part of the equipment used on farms. At present, there are an important number of small- and medium-sized companies involved in the manufacturing of this equipment. These companies rely heavily on experience for innovative designs, as in the vast majority of cases they lack access to adequate methodology for the optimal design of new front-end loaders. The study conducted has developed a methodology to design tractor front-end loaders with a view of obtaining their accurate design during the bucket loading process. The methodology comprises two phases: the first phase involves a numerical analysis of the structural behaviour of the front-end loader components by means of the Finite Element Method; the second phase, the experimental phase, makes use of low-cost sensors, in particular, strain gauges, to analyse existing strains at selected points in the front-end loader structure. The experimental results obtained by means of low-cost sensors fitted onto the front-end loader allow analysing the existing strains at the points measured, as well as validate the numerical model developed. This methodology is validated by applying it to a commercial front-end loader, more specifically to model 430E2 of the company Maquinaria Agrícola El León S.A (Spain).

Description: In recent decades, global and local vegetation phenology has undergone significant changes due to the combination of climate change and human activities. Current researches have revealed the temporal and spatial distribution of vegetation phenology in large scale by using remote sensing data. However, researches on spatiotemporal differentiation of remote sensing phenology and its changes are limited which involves high-dimensional data processing and analysing. A new data model based on data cube technologies was proposed in the paper to efficiently organize remote sensing phenology and related reanalysis data in different scales. The multidimensional aggregation functions in the data cube promote the rapid discovery of the spatiotemporal differentiation of phenology. The exploratory analysis methods were extended to the data cube to mine the change characteristics of the long-term phenology and its influencing factors. Based on this method, the case study explored that the spring phenology of Qinba Mountains has a strong dependence on the topography, and the temperature plays a leading role in the vegetation green-up date distribution of the high-altitude areas while human activities dominate the low-altitude areas. The response of green-up trend slope seems to be the most sensitive at an altitude of about 2000 meters. This research provided a new approach for analysing phenology phenomena and its changes in Qinba Mountains that had the same reference value for other regional phenology studies.

Description: This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.

Description: Wireless sensor networks (WSNs) are a huge number of sensors, which are distributed in area monitoring to collect important signals. WSNs are widely used in several applications such as home automation, environment, and healthcare monitoring. However, most of these applications face various difficulties due to sensor design. Therefore, the major challenge of designing WSNs is saving the energy consumed during communication and extending the network lifetime. Multicriteria Decision Analysis (MCDA) methods have been exploited for saving network energy. However, the majority of researches focus on the Cluster Head (CH) selection. In this paper, we aim to enhance the process of forwarder selection using an efficient combined multicriteria model. The proposed scheme improved the intercluster communication by controlling the distance separating CHs from the sink node. To minimize the cluster density, this work consists of activating only sensor nodes that detect enough strong signals. The activation phase presents a fault-tolerant technique to succeed in the communication process. Moreover, the proposed work is aimed at selecting the most efficient hops, which are responsible for routing data to the sink using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. Simulation results proved that our new protocol maximized the residual energy by 15% and 25% and the network lifetime by 35% and 47% compared to the Distributed Clustering Protocol using Voting and Priority (DCPVP) and Low-Energy Adaptive Clustering Hierarchy (LEACH), respectively.

Description: With the advent of IR (Industrial Revolution) 4.0, the spread of sensors in IoT (Internet of Things) may generate massive data, which will challenge the limited sensor storage and network bandwidth. Hence, the study of big data compression is valuable in the field of sensors. A problem is how to compress the long-stream data efficiently with the finite memory of a sensor. To maintain the performance, traditional techniques of compression have to treat the data streams on a small and incompetent scale, which will reduce the compression ratio. To solve this problem, this paper proposes a block-split coding algorithm named “CZ-Array algorithm,” and implements it in the shareware named “ComZip.” CZ-Array can use a relatively small data window to cover a configurable large scale, which benefits the compression ratio. It is fast with the time complexity O() and fits the big data compression. The experiment results indicate that ComZip with CZ-Array can obtain a better compression ratio than gzip, lz4, bzip2, and p7zip in the multiple stream data compression, and it also has a competent speed among these general data compression software. Besides, CZ-Array is concise and fits the hardware parallel implementation of sensors.

Description: The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson’s disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD.

Description: Pulse waves of a radial artery under different pressures applied through a cuff play an important role in disease diagnosis, especially in traditional chinese medicine (TCM). Pulse waves could be collected by a pressure sensor array affixed to an inflatable cuff. During a process of collecting pulse waves, one sensor of a sensor array moves up and down when the sensor is shocked by a pulse wave. Movement of the sensor leads to the passive displacement of other nearby sensors because of a connecting structure between them. Then, vibration signals will be generated by the nearby sensors although these sensors do not receive radial artery pulse waves. These vibration signals considered an interference are usually superimposed on real signals obtained from these nearby sensors and degrade signal quality. The problem mentioned above does not only generally exist in a pressure sensor array attached to a wristband but also is easy to ignore. This paper proposes a novel interference suppression algorithm based on Welch’s method for estimating and weakening adjacent sensor channel interference to overcome the problem. At first, a sensor array attached to an inflatable cuff and a vibration generator is proposed to establish an experimental platform for simplifying the pulse wave collection process. Then, the interference suppression algorithm is proposed according to mechanical analysis and Welch’s method based on the proposed sensor array and vibration generator. Next anti-interference abilities of the algorithm based on a simplified process are evaluated by different vibration frequencies and applied pressures. The anti-interference abilities of the algorithm based on pulse waves of the radial artery are evaluated indirectly. The results show that the novel interference suppression algorithm could weaken adjacent sensor channel interference and upgrade the signal quality.

Description: In wastewater treatment plants, the degradation of complex substances that contaminate water is carried out by microorganisms, which are fixed by a network formed by filamentous bacteria, creating large flocs that settle easily. However, the excessive growth of said bacteria causes a series of drawbacks such as the reduction of settling velocity, leakage of activated sludge with the effluent, and formation of supernatant, a phenomenon known as bulking. This research work seeks to develop and evaluate a procedure for the physical characterization of the flocs to determine the parameters that affect the settling velocity and thereby detect and control bulking. For this purpose, sedimentation and image analysis tests were carried out from wastewater from the Aguas Antofagasta treatment plant (Chile). The image analysis was performed with images captured from an optical microscope in two magnifications (100x and 50x), which were analyzed by marking each floc individually and characterized by an image processing software. Additionally, sedimentation tests were performed on columns (area of 74 (cm2) and height of 70 (cm)). As a result, an inversely proportional dependence was found on the settling velocity evaluated by the Vesilind equation in the zone of constant fall velocity with respect to the number of flocs connected per cluster, giving an estimate of the settling velocity depending on the number of flocs connected. This would allow predicting settling velocity with image analysis, taking into account that the problem of bulking is determined by the type of filamentous bacteria that causes it and the sedimentation process is affected in large part by local factors. It can be concluded through this study that as the number of flocs connected per cluster increases, the settling velocity decreases. This study provides wastewater treatment plants with a practical tool to determine sedimentation times and thus improve the quality of the treated water, avoiding problems of flocs leaking with the effluent. In addition, the image analysis itself allows rapid detection of the phenomenon of bulking and its severity.

Description: In recent years, methods based on neural network have achieved excellent performance for image segmentation. However, segmentation around the edge area is still unsatisfactory when dealing with complex boundaries. This paper proposes an edge prior semantic segmentation architecture based on Bayesian framework. The entire framework is composed of three network structures, a likelihood network and an edge prior network at the front, followed by a constraint network. The likelihood network produces a rough segmentation result, which is later optimized by edge prior information, including the edge map and the edge distance. For the constraint network, the modified domain transform method is proposed, in which the diffusion direction is revised through the newly defined distance map and some added constraint conditions. Experiments about the proposed approach and several contrastive methods show that our proposed method had good performance and outperformed FCN in terms of average accuracy for 0.0209 on ESAR data set.

Description: The aim of this study is to investigate the flocculation capacity of activated sludge flocculant in chromium ion and nitrobenzene solutions. Besides comparing with activated carbon, we changed some potential factors which may influence the flocculation, such as solution pH value, reaction time, flocculant dosage, and solution initial concentration, and tested the flocculation capacity of this bioflocculant for both chromium and nitrobenzene. In addition, the flocculation of activated sludge after acidic or alkali modification for double solutions was also estimated. Compared with activated carbon, the activated sludge flocculant showed a good flocculation capacity for nitrobenzene, but poor flocculation for chromium following the initial concentration increase. The flocculation for nitrobenzene or chromium increased at initial stage and decreased gradually following the primary dosage of flocculant increase. The flocculation for nitrobenzene increased at the primary stage and decreased after peak, while the flocculation for chromium increased following the pH increase of both solutions. Although the flocculation for nitrobenzene decreased, the flocculation for chromium increased obviously, when we prolonged the reaction time. The flocculant showed a gradual increasing trend in double kinds of solution when they were heating. The flocculation for both nitrobenzene and chromium decreased after acid modification of activated sludge flocculant, while the flocculation increased after basic modification. The present research provided further theoretical basis of the biosorption of heavy metal waste water and nitrobenzene organic toxicants.

Description: We have demonstrated the ability of thin nanocrystalline SiC films with various types of conductivity to detect oxidative (O2), reducing gases (CO, CH4) with the maximum allowable concentrations for human safety. It was shown that n-nc-SiC films with electronic conductivity had a higher gas sensitivity Sn than p-nc-SiC films with hole conductivity sensivity Sp to the action of gases in a wide concentration range. So, for the maximum permissible concentrations of O2 (3%), CO (0.1%), CH4 (10%) the sensitivity ratio of the films Sn/Sp was 2.9; 4.8 and 10, respectively. For research, we used a simple resistor geometry optimizing which it is possible to significantly increase the sensitivity of films to gases in order to detect extremely low gas concentrations. Thus, based on nc-SiC films, it is possible to develop high-temperature gas sensors to detect reactive gases in a wide range of concentrations, including threshold allowable values.

Description: Electric discharges seriously threat the safety of high-voltage switchgear. In this paper, a spectrum-based optical method is proposed for hazardous discharge monitoring. A SiPM-based trinal spectral sensor is developed with good performances in terms of sensitivity, defect resolution, and risk evaluation. Experiments carried out on two types of artificial discharges (i.e., partial discharge and arc discharge) demonstrate that the light intensities coupled in the three spectral bands account for different proportions and the ratio among the three components generally experiences a regular change with increase in severity of discharge. The typical spectral ratio values are then acquired for hazard rating of discharge and recognition of discharge types with high confidence.

Description: With the development of urbanization, land surface temperature (LST), as a vital variable for the urban environment, is highly demanded by urban-related studies, especially the LST with both fine temporal and spatial resolutions. Thermal sharpening methods have been developed just under this demand. Until now, there are some thermal sharpening methods proposed especially for urban surface. However, the evaluation of their accuracy still stopped at the level that only considers the statistical aspect, but no spatial information has been included. It is widely acknowledged that the spatial pattern of the thermal environment in an urban area is relatively critical for urban-related studies (e.g., urban heat island studies). Thus, this paper chose three typical methods from the limited number of thermal sharpening methods designed for the urban area and made a comparison between them, together with a newly proposed thermal sharpening method, superresolution-based thermal sharpener (SRTS). These four methods are analyzed by data from different seasons to explore the seasoning impact. Also, the accuracy for different land covers is explored as well. Furthermore, accuracy evaluation was not only taken by statistical variables which are commonly used in other studies; evaluation of the spatial pattern, which is equally important for urban-related studies, was also carried out. This time, the spatial pattern not only was analyzed qualitatively but also has been quantified by some variables for the comparison of accuracy. It is found that all methods obtained lower accuracies for data in winter than for data in other seasons. Linear water features and areas along it are difficult to be detected correctly for most methods.

Description: The most important aspect of virtual reality (VR) is the degree by which a user can feel and experience virtual space as though it is reality. Until recently, the experience of VR had to be satisfied with operations using a separate controller along with the visual and auditory elements. However, for a far more realistic VR environment, users should be able to experience the delicacy of tactile materials. This study proposes tactile technology, which is inexpensive and easy to use. To achieve this, we analyzed the unique patterns of materials through image filtering and designed a computing model to deliver realistic vibrations to the user. In addition, we developed and tested a haptic glove so that the texture of the material can be sensed in a VR environment.

Description: This study focused on utilizing the Kinect depth sensor to track double-hand gestures and control a real-time robotic arm. The control system is mainly composed of the microprocessor, a color camera, the depth sensor, and the robotic arm. The Kinect depth sensor was used to take photos of the human body to analyze the skeleton of a human body and obtain the relevant information. Such information was used to identify the gestures of the left hand and the left palm of the user. The gesture of left hand was used as an input command device. The gesture of the right hand was used for imitation movement teaching of robotic arm. From the depth sensor, the real-time images of the human body and the deep information of each joint were collected and converted to the relative positions of the robotic arm. Combining forward kinematics and inverse kinematics and D-H link, the gesture information of the right hand was calculated, which was converted via coordinates into each angle of the motor of the robotic arm. From the color camera, when the left palm was not detected, the user could simply use the right hand to control the action and movement of the real-time robotic arm. When the left palm was detected and 5 fingertips were identified, it meant the start of recording the real-time imitation movement of the robotic arm by the right hand. When 0 fingertip was identified, it meant the stoppage of the above recording. When 2 fingertips were identified, the user could not only control the real-time robotic arm but also repeat the recorded actions.

Description: A novel peptide nuclide acid (PNA) electrochemical biosensor based on reduced graphene oxide (NH2-rGO)/2,2,6,6-tetramethylpiperidin-1-yl)oxyl nanocrystalline cellulose (TEMPO-NCC) for the detection of Mycobacterium tuberculosis (M. Tuberculosis) is described. In this study, the nanohybrid films NH2-rGO/TEMPO-NCC were immobilized onto screen-printed carbon electrode (SPE) via a simple drop-coating method. The electrochemical characterization of the designed electrode was investigated using cyclic voltammetry (CV) and impedance spectroscopy (EIS). Meanwhile, the sensitivity and selectivity of the designed biosensor against M. tuberculosis were measured by the differential pulse voltammetry (DPV). The response of the PNA probe-modified (NH2-rGO)/TEMPO-NCC demonstrated that the fabricated biosensor was able to distinguish between complementary, noncomplementary, and one-base mismatch DNA sequences using methylene blue (MB) as the electrochemical indicator. The developed electrochemical biosensor exhibited a linear calibration curve in the concentration range of to with the limit of detection of . The developed electrochemical biosensor has also been tested with a polymerase chain reaction (PCR) product of M. tuberculosis DNA which has shown successful results in distinguishing between negative and positive samples of M. tuberculosis.

Description: Fault detection can increase the reliability and efficiency of power electronic converters employed in power systems. Among the converters in the power system, a Neutral Point Clamped (NPC) three-level inverter is most commonly used to drive electric motors. In this paper, a new approach for open-circuit fault detection and location of the NPC three-level inverter for a shifting process using a constant voltage-to-frequency ratio is proposed. In order to diagnose open-circuit fault in as short a time as possible, an adaptive electrical period partition (AEPP) algorithm is proposed to pick single electrical periods from real-time three-phase current signals. The Maximal Overlap Discrete Wavelet Transformation (MODWT) and Park’s Vector Modulus (PVM) are used for feature analysis and normalization of electrical period signals. The statistical characteristics of the electrical period signals are extracted, and a random forest model is constructed to realize the state classification. Compared with the traditional fault diagnosis method, the proposed algorithm finds fault locations quickly and accurately. The effectiveness and accuracy of the proposed algorithm are verified by experiments.

Description: Building the fingerprint map for indoor localization problems is a labour-intensive and time-consuming process. However, due to its direct influence on the location estimation accuracy, finding a proper mechanism to construct the fingerprint map is essential to enhance the position estimation accuracy. Therefore, in this work, we present a fingerprint map construction technique based on location fix determination and fingerprint matching motivated by the availability of advanced sensing capabilities in smartphones to reduce the time and labour cost required for the site survey. The proposed Location Fixing and Finger Matching (LFFM) method use a landmark graph-based localization approach to automatically estimate the location fixes for the Reference Points and matching the collected fingerprints, without requiring active user participation. Experimental results show that the proposed LFFM is faster than the manual fingerprint map construction method and remarkably improves the positioning accuracy.

Description: Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. Detection and control of infectious diseases is a major problem, especially in developing countries. Lateral flow immunoassay (LFIA) has been introduced as a handheld immunoassay-based point-of-care platform for an automated detection of TB. The CFP10-ESAT6 antigen of M. tuberculosis was used as the target in early detection of TB using LFIA strip-based POC strategy. An interesting platform based on optical signals is implemented as a colour change in the detection area that is visible to the naked eye. The gold nanoparticles (AuNPs) were used as the colour probe for the detection of a target of interest. The high-resolution transmission electron microscopy (HRTEM) image and ultraviolet-visible spectrophotometer (UV-Vis) analysis confirmed that the synthesized AuNPs were appropriate for the immunoassay designed. The platform consists of AuNPs conjugated with specific antibodies (Ab) to capture the antigen of M. tuberculosis. Under the capillary effect, sandwich immunoreactions of AuNP-Ab-antigen were performed on the test pad of the immunostrip, which can be observed by the colour signal on the test line of the strip with a short assay time. Furthermore, the newly developed biosensor was utilized in CFP10-ESAT6 antigen detection in human sputum specimens with satisfactory results. The characteristic coloured bands enable visual detection (naked eye) of target analyte without instrumentation. This noninvasive diagnose system which is sputum-based detection could provide user-friendly and affordable diagnostic tests in developing countries.

Description: Wireless sensor networks (WSNs) have grown considerably in recent years and have a significant potential in different applications including health, environment, and military. Despite their powerful capabilities, the successful development of WSN is still a challenging task. In current real-world WSN deployments, several programming approaches have been proposed, which focus on low-level system issues. In order to simplify the design of the WSN and abstract from technical low-level details, high-level approaches have been recognized and several solutions have been proposed. In particular, the model-driven engineering (MDE) approach is becoming a promising solution. In this paper, we present a survey of existing programming methodologies and model-based approaches for the development of sensor networks. We recall and classify existing related WSN development approaches. The main objective of our research is to investigate the feasibility and the application of high-level-based approaches to ease WSN design. We concentrate on a set of criteria to highlight the shortcomings of the relevant approaches. Finally, we present our future directions to cope with the limits of existing solutions.

Description: Beidou navigation system (BDS) has been developed as an integrated system. The third BDS, BSD-3, will be capable of providing not only global positioning and navigation but also data communication. When the volume of data transmitted through BDS-3 continues to increase, BDS-3 will encounter network traffic congestion, unbalanced resource usage, or security attacks as terrestrial networks. The network traffic monitoring is essential for automatic management and safety assurance of BDS-3. A dynamic traffic detection method including traffic prediction by Long Short-Term Memory (LSTM) and a dynamically adjusting polling strategy is proposed to unevenly sample the traffic of each link. A distributed traffic detection architecture is designed for collection of the detected traffic and its related temporal and spatial information with low delay. A time-varying graph (TVG) model is introduced to represent the dynamic topology, the time-varying link, and its traffic. The BDS-3 network is simulated by STK. The WIDE dataset is used to simulate the traffic between the satellite and ground station. Simulation results show that the dynamic traffic detection method can follow the variation of the traffic of each link with uneven sampling. The detected traffic can be transmitted to the ground station in near real time through the distributed traffic detection architecture. The traffic and its related information are stored by using Neo4j in terms of the TVG model. The nodes, edges, and traffic of BDS-3 can be quickly queried through Neo4j. The presented dynamic traffic detection and representation schemes will support BDS-3 to establish automatic management and security system and develop business.

Description: In this paper, an IoT-based indoor air quality monitoring platform, consisting of an air quality-sensing device called “Smart-Air” and a web server, is demonstrated. This platform relies on an IoT and a cloud computing technology to monitor indoor air quality in anywhere and anytime. Smart-Air has been developed based on the IoT technology to efficiently monitor the air quality and transmit the data to a web server via LTE in real time. The device is composed of a microcontroller, pollutant detection sensors, and LTE modem. In the research, the device was designed to measure a concentration of aerosol, VOC, CO, CO2, and temperature-humidity to monitor the air quality. Then, the device was successfully tested for reliability by following the prescribed procedure from the Ministry of Environment, Korea. Also, cloud computing has been integrated into a web server for analyzing the data from the device to classify and visualize indoor air quality according to the standards from the Ministry. An application was developed to help in monitoring the air quality. Thus, approved personnel can monitor the air quality at any time and from anywhere, via either the web server or the application. The web server stores all data in the cloud to provide resources for further analysis of indoor air quality. In addition, the platform has been successfully implemented in Hanyang University of Korea to demonstrate its feasibility.

Description: Broilers produce abnormal sounds such as cough and snore when they suffer from respiratory diseases. The aim of this research work was to develop a method for broiler abnormal sound detection. The sounds were recorded in a broiler house for one week (24/7). There were 20 thousand white feather broilers reared on the floor in a building. Results showed that the developed recognition algorithm, using wavelet transform Mel frequency cepstrum coefficients (WMFCCs), correlation distance Fisher criterion (CDF), and hidden Markov model (HMM), provided an average accuracy, precision, recall, and F1 of 93.8%, 94.4%, 94.1%, and 94.2%, respectively, for broiler sound samples. The results indicate that sound analysis can be used in broiler respiratory assessment in a commercial broiler farm.

Description: A novel passive wireless integrated SAW-based antenna sensor for strain sensing is presented in this paper. A SAW delay line scheme is proposed for signal modulation, which could distinguish the backscattered data from environmental clutter in time domain. The theoretical relationship between the antenna resonance frequency shifts, the temperature, and the applied strain was established. A multiphysical coupled simulation process is proposed to improve the simulation accuracy. By comparing the phase shift of adjacent echoed data which is generated by SAW reflective grating, the temperature information could be extracted exactly and the effect of temperature fluctuation on the resonance frequency could be compensated. A more accurate passive (battery-free) wireless strain sensing could be provided by this proposed integrated antenna sensor than the previous proposed methods. Simulation and experimental results demonstrate the effectiveness of the sensor.

Description: Control accuracy significantly affects the performances of boom sprayer. In this study, we develop a precise autocontrol technology based on the vehicle speed feedback. We utilize the auxiliary antidrift system of wind-curtain type air flow and the variable spraying control system for adaptive fertilizing and online measuring of working conditions. Experimental results demonstrate that the variable spraying control system could keep the speed error less than 3%. The air flow significantly improves the penetration of spraying, decreases the fog drip, and increases the pesticide utility. Benefitting from the auxiliary air flow, the average utility of pesticide is improved from 26.76% to 37.98%. Additionally, the speed feedback control reduces the consumption of pesticide by more than 12%.

Description: Traditionally, routing decisions have been based on minimizing travel time as the associated cost. Eco-routing considers the environmental aspects (e.g., emissions and fuel) as part of the travel cost to mitigate the undesirable impact of transportation systems on the environment. Unlike the existing eco-routing review papers, this research work is aimed at providing a three-factor taxonomy at a more disaggregated level from the optimization perspective and map eco-routing studies to the proposed taxonomy. Furthermore, the strengths and weaknesses of the presented models are summarized. Our main findings include (a) a majority of studies optimized one objective at a time; (b) the microscopic level of aggregation of the flow and emission/fuel models was rarely employed for large case studies, due to the associated complexity; and (c) all of the reviewed studies were applied in a centralized routing system environment. In the near future, when intelligent vehicles will be on the roads, a multi-objective distributed routing framework can be employed with a microscopic level of aggregation for both traffic and emission models, which is capable of operating on largescale networks in real time. Additionally, short-term spatiotemporal prediction of GHG cost is a crucial aspect to be tackled.

Description: The availability of suitable native plant species for local animal husbandry development and ecological restoration is limited on the Qinghai-Tibetan Plateau. Therefore, comparisons of the ecological adaptability of native species to alternative habitats and their introduction into new habitats are of high importance. This study is aimed at identifying the alteration in morphological and physiological characteristics by measuring photosynthetic physiology, nutrient content, and growth associated with adaptation of plants to conditions at different altitudes 2450, 2950, 3100, and 3300 m above sea level (a. s. l.) on the plateau. Seeds of the dominant grass, Elymus nutans, were collected from locations at these altitudes and grown at a test location of 2950 m a. s. l. Results indicated that altitude had no significant effect on plant height and root depth. However, the leaf area and total root surface area of plants derived from 2950 and 3300 m a. s. l. showed a parabolic response, being greater than those of plants derived from the lowest (2450 m) and highest (3300 m a. s. l.). Total (root plus shoot) dry matter reduced progressively from 2450 to 3300 m a. s. l, while root : shoot ratio increased progressively with altitude. Seed yield of plants originating from the test altitude (2950 m a. s. l) was significantly higher than at any other altitude, being 20% lower at 2450 m, and 38% and 58% less in populations originating from the higher altitudes (3100 and 3300 m a. s. l.). There was also a parabolic decline in response of Elymus nutans germplasm from 3100, 3300, and 2450 m, compared with plants from 2950 m a. s. l., to photosynthetic rate, total N, soluble sugar, and starch contents. Germplasm from 2450 m a. s. l. had significantly lower shoot and higher root carbon content, lower shoot nitrogen, and lower root carbon-to-nitrogen ratio compared with plants derived from the other three altitudes. It is suggested that the stable, genetically determined morphological and physiological features of ecotypes showed parabolic responses which means these ecotypes have become adapted to local habitats, whereas parameters such as dry matter, total root : shoot ratio, photosynthetic rate, and intercellular CO2 concentration of plants reflected phenotypic linear response to current abiotic conditions. It is postulated that introduced ecotypes from 2450, 3100, and 3300 m could adapt to the environment at 2950 m a. s. l. gradually. We conclude that the increased thermal regime experienced by plants introduced from high altitude to low altitude may facilitate the increased growth of Elymus nutans subtypes. It is important to preserve local strains of native species, or ecotypes, for reintroduction into degraded environments and to maintain the greatest ecosystem stability in the northeastern Tibetan Plateau.

Description: The Dempster–Shafer evidence theory has been widely applied in multisensor information fusion. Nevertheless, illogical results may occur when fusing highly conflicting evidence. To solve this problem, a new method of the grouping of evidence is proposed in this paper. This method uses a combination of the belief entropy and the degree of conflict of the evidence as the judgment rule and divides the entire body of evidence into two separate groups. For the grouped evidence, both the credibility weighted factor based on the belief entropy function and the support weighted factor based on the Jousselme distance function are taken into consideration. The two determined weighted factors are integrated to adjust the evidence before applying the DS combination rule. Numerical examples are provided to demonstrate the theoretical feasibility and rationality of the proposed method. The fusion results indicate that the proposed method is more accurate than the compared algorithms in handling the paradoxes. A decision-making case analysis of the biological system is performed to validate the practical applicability of the proposed method. The results confirm that the proposed method has the highest belief degree of the target concentration (50.98%) and has superior accuracy compared to other related methods.

Description: This article proposes an original approach aimed at modelling the noise density in sensors based on a single hot wire or pairs of thermally coupled wires. The model consists in an original combination of a previous electrothermal model of the wire with well-established assumptions on the thermal noise in conductors that carry moderate current densities. A simple method for estimating the model parameters with simple impedance spectroscopy is suggested. The predicted power spectral densities of the wire thermal noise differ from the result of previously presented analytical models, stimulating further experimental studies. The effects of the electrothermal feedback of both hot wires and hot-wire pairs on flicker noise is also intrinsically covered by the proposed approach.

Description: The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.

Description: This paper presents the design of an instrument for measuring the liquid level. The objective of this proposed work is to measure the level of liquid accurately even with variations in liquid concentration. The designed instrument should also be able to compute the concentration of additives in the liquid. For this purpose, a multisensor model comprising a capacitive level sensor (CLS), ultrasonic level sensor (ULS), and capacitance pressure sensor is used to acquire information of the liquid. The data acquired from all these sensors are processed using Pau’s multisensor data fusion framework to compute the level of liquid along with the concentration of additives added to the solution. Pau’s framework consists of alignment, association function, analysis, and representation functions. The designed multisensor technique is tested with real-life data for varying liquid levels and additives. The results obtained show that the successful implementation of the proposed objective producing a root mean square of percentage error is 1.1% over the full scale is possible.

Description: One of the advantages of a netted airborne radar system (NARS) is escaping interception of the passive detection system (PDS) while tracking a target. A significant tactic to realize tracking without PDS interception is to study the low probability of interception (LPI) time of NARS. Firstly, this paper analyses the power, frequency, and platform interception probabilities of a combined PDS consisting of a radar-warning receiver (RWR) and an electronic support measurement (ESM). Secondly, this paper takes interactive multiple models (IMM) to describe the target tracking process and introduces a binary hypothesis test for chi square as well as noncentralized chi square distributions as a detection criterion of NARS during target tracking after the design of adaptive dwell time and the maximum illumination interval algorithm. Finally, based on experiential moving platform interception probabilities of a RWR and an ESM, a simplified math model is presented to estimate LPI time of NARS when the parameters are partially known. Simulations illustrate that the simultaneous management of radiation power and time is crucial for NARS against combined PDS interception.

Description: Inertial navigation systems/Doppler velocity log (INS/DVL) integrated navigation systems are widely used in underwater environments where GPS is unavailable. An INS/DVL integrated navigation system is generally loosely coupled; however, this does not work if any of the DVL transducers do not work. If a system is tightly coupled, velocity error can be estimated with fair accuracy even if some of the transducers fail. However, despite the robustness of a tightly coupled system compared to a loosely coupled one, velocity error estimation accuracy of the former decreases as the number of faulty transducers increases. Therefore, this paper proposes an INS/DVL/revolutions per minute (RPM) integrated navigation filter designed to improve the performance of conventional tightly coupled integrated systems by estimating data from faulty transducers using RPM data. Two salient features of the proposed filter are (1) estimating RPM data accounting for error from the effect of tidal currents and (2) continuous estimation of error in RPM data by selectively converting only the measurements of faulty transducers. The performance of the proposed filter was first verified using Monte Carlo numerical simulations with the analysis range set to 1 standard deviation (1σ, 68%) and then with real sea test measurement data.

Description: Ultrahigh-resolution optical coherence tomography provides an axial resolution of 1-2 μm for resolving cellular structures of biological tissues critical for the diagnosis of diseases. However, it requires a relatively large spectral bandwidth which is not supported by the key components of the imaging system. We propose a novel spectral-domain OCT design, termed interferometer-in-spectrometer, which is able to compensate the bandwidth limitations of the grating and the line scan sensor by spectral shaping without compromising the signal intensity and adding the system cost. The advantage of axial resolution and ranging depth over the standard design is experimentally validated using the standard testing method and fresh swine cornea ex vivo. Moreover, opportunities that opened up by this new scheme for improving the performances of spectral-domain OCT are also discussed.

Description: Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the average measuring error, the variance of measuring error, and the overall sorting accuracy are 204.175 ms, 1.48 mm, 0.07 mm, and 93%, respectively, implying the feasibility and satisfied accuracy of the proposed approach.

Description: Recently, there has been increasing interest in the field of underwater wireless sensor networks (UWSNs), which is a basic source for the exploration of the ocean environment. A range of military and civilian applications is anticipated to assist UWSN. The UWSN is being developed by the extensive wireless sensor network (WSN) applications and wireless technologies. Therefore, in this paper, a review has been presented which unveils the existing challenges in the underwater environment. In this review, firstly, an introduction to UWSN is presented. After that, underwater localizations and the basics are presented. Secondly, the paper focuses on the architecture of UWSN and technologies used for underwater acoustic sensor network (UASN) localization. Various localization techniques are discussed in the paper classified by centralized and distributed localizations. They are further classified into estimated and prediction-based localizations. Also, various underwater localization algorithms are discussed, which are grouped by the algorithms based on range and range-free schemes. Finally, the paper focuses on the challenges existing in underwater localizations, underwater acoustic communications with conclusions.

Description: Diabetes is one of today’s greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.

Description: In the present study, a fluorescent sensor L for sensing of Ce3+ ion was designed and characterized by XRD, 1HNMR, and FTIR. Its fluorescence behavior towards metal ion was examined by fluorescence spectroscopy. Chelation-enhanced fluorescence was shown by the sensor L upon interaction with Ce3+ ion. This fluorescent sensor exhibits high sensitivity and selectivity towards Ce3+ ion in acetonitrile solution, forming 2 : 1 (L : M) complex as determined by Job’s plot. Association constant was found to be 1×107 M−1 estimated from the Benesi-Hildebrand plot. No significant interference was observed in the presence of other studied alkali, alkaline, and transition metal ions. A rapid response was observed when employed for the determination of Ce3+ ion in spiked water samples with a limit of detection equal to 3.4×10−8 M.

Description: The carbon fiber reinforced polymer- (CFRP-) strengthened nanomaterial concrete beam (SNCB) has been increasingly attracting a widespread attention because of the advantages of using the excellent properties of nanomaterials to improve structural properties. An active sensing approach based on a piezoceramic transducer is developed to detect the interfacial debonding performance of CFRP-SNCB. A CFRP-SNCB specimen was fabricated and subjected to periodic loading test to initiate the debonding damage. Three piezoceramic smart aggregates (SAs) and three piezoceramic smart nanomaterial aggregates (SNAs) are embedded in the specimen and used as an actuator and sensor. Experiments show that the nanomaterial concrete becomes a good conduit for wave propagation due to the nucleation and filling effect of nanomaterial. The stress wave signal caused by the embedded SNAs is more sensitive to the debonding performance between CFRP and concrete than SA. The attenuation of stress wave caused by the increase of the severity of debonding damage can be clearly observed from the signals received from SAs and SNAs in the frequency domain analysis. The debonding cracking of the tension end region is earlier than the bond end region, which proves the starting point of structural debonding damage. Furthermore, the debonding state can be evaluated by wavelet packet analysis. The research results demonstrate that the proposed method has potentials to detect the interfacial debonding performance of CFRP-SNCB.

Description: This article proposes an original approach aimed at modelling the noise density in sensors based on a single hot wire or pairs of thermally coupled wires. The model consists in an original combination of a previous electrothermal model of the wire with well-established assumptions on the thermal noise in conductors that carry moderate current densities. A simple method for estimating the model parameters with simple impedance spectroscopy is suggested. The predicted power spectral densities of the wire thermal noise differ from the result of previously presented analytical models, stimulating further experimental studies. The effects of the electrothermal feedback of both hot wires and hot-wire pairs on flicker noise is also intrinsically covered by the proposed approach.

Description: This paper presents the design of an instrument for measuring the liquid level. The objective of this proposed work is to measure the level of liquid accurately even with variations in liquid concentration. The designed instrument should also be able to compute the concentration of additives in the liquid. For this purpose, a multisensor model comprising a capacitive level sensor (CLS), ultrasonic level sensor (ULS), and capacitance pressure sensor is used to acquire information of the liquid. The data acquired from all these sensors are processed using Pau’s multisensor data fusion framework to compute the level of liquid along with the concentration of additives added to the solution. Pau’s framework consists of alignment, association function, analysis, and representation functions. The designed multisensor technique is tested with real-life data for varying liquid levels and additives. The results obtained show that the successful implementation of the proposed objective producing a root mean square of percentage error is 1.1% over the full scale is possible.

Description: The appearance of coverage holes in the network leads to transmission links being disconnected, thereby resulting in decreasing the accuracy of data. Timely detection of the coverage holes can effectively improve the quality of network service. Compared with other coverage hole detection algorithms, the algorithms based on the Rips complex have advantages of high detection accuracy without node location information, but with high complexity. This paper proposes an efficient coverage hole detection algorithm based on the simplified Rips complex to solve the problem of high complexity. First, Turan’s theorem is combined with the concept of the degree and clustering coefficient in a complex network to classify the nodes; furthermore, redundant node determination rules are designed to sleep redundant nodes. Second, according to the concept of the complete graph, redundant edge deletion rules are designed to delete redundant edges. On the basis of the above two steps, the Rips complex is simplified efficiently. Finally, from the perspective of the loop, boundary loop filtering and reduction rules are designed to achieve coverage hole detection in wireless sensor networks. Compared with the HBA and tree-based coverage hole detection algorithm, simulation results show that the proposed hole detection algorithm has lower complexity and higher accuracy and the detection accuracy of the hole area is up to 99.03%.

Description: Blood pressure assessment plays a vital role in day-to-day clinical diagnosis procedures as well as personal monitoring. Thus, blood pressure monitoring devices must afford convenience and be easy to use with no side effects on the user. This paper presents a compact, economical, power-efficient, and convenient wireless plethysmography sensor for real-time blood pressure biosignal monitoring. The proposed sensor facilitates blood pressure signal shape sensing, signal conditioning, and data conversion as well as its wireless transmission to a monitoring terminal. Received data can, subsequently, be compiled and stored on a computer via a Wi-Fi module. During monitoring, users can observe blood pressure signals being processed and displayed on the graphical user interface (GUI)—developed using a virtual instrumentation (VI) application. The proposed device comprises a finger clip optical pulse sensor, analogue signal preprocessing, microcontroller, and Wi-Fi module. It consumes approximately 500 mW power when operating in the active mode and synthesized using commercial off-the-shelf (COTS) components. Experimental results reveal that the proposed device is reliable and facilitates efficient blood pressure monitoring. The proposed wireless photoplethysmographic (PPG) sensor is a preliminary (or first) version of the intended device manifestation. It provides raw blood pressure data for further classification. Additionally, the collected data concerning the blood pressure wave shape can be easily analysed for use in other biosignal observations, interpretations, and investigations. The design approach also allows the device to be built into a wearable system for further research purposes.

Description: In precision agriculture, 3D vision systems are becoming increasingly important. By applying different optical 3D vision techniques, the acquired 3D data can provide information regarding the most important phenotype features in every agricultural scenario. However, most of these 3D vision systems are expensive, except some of the triangulation techniques. In this study, we focus on estimating accurate shapes using shape from focus (SFF), which is a triangulation technique. Typically, the SFF system incurs significant errors from images, including noise. As a solution to this problem, a simple low-pass filter such as the Gaussian filter has generally been used in most studies. However, when a low filter is applied, the noise is depressed but the signals are also blurred, which results in inaccuracies regarding the depth map. In this study, the noise is depressed independently without losing the original signals, and the edge components, which play important roles in finding a focused surface, are enhanced using the independent component analysis (ICA). The edge signals are amplified with a simple basis vector correction in the IC vector space. The experiments are implemented with simulated objects and real objects. The experimental results demonstrate that the obtained accuracy is comparable to that of existing methods.

Description: Water is essential for every life living on the planet. However, we are facing a more serious situation such as water pollution since the industrial revolution. Fortunately, many efforts have been done to alleviate/restore water quality in freshwaters. Numerous sensors have been developed to monitor the dynamic change of water quality for ecological, early warning, and protection reasons. In the present review, we briefly introduced the pollution status of two major pollutants, i.e., pesticides and heavy metals, in freshwaters worldwide. Then, we collected data on the sensors applied to detect the two categories of pollutants in freshwaters. Special focuses were given on the sensitivity of sensors indicated by the limit of detection (LOD), sensor types, and applied waterbodies. Our results showed that most of the sensors can be applied for stream and river water. The average LOD was  ng/ml () for all pesticides, which is significantly higher than that for heavy metals ( ng/ml, ). However, the LODs of a considerable part of pesticides and heavy metal sensors were higher than the criterion maximum concentration for aquatic life or the maximum contaminant limit concentration for drinking water. For pesticide sensors, the average LODs did not differ among insecticides ( ng/ml, ), herbicides ( ng/ml, ), and fungicides ( ng/ml, ). The LODs that differed among sensor types with biosensors had the highest sensitivity, while electrochemical optical and biooptical sensors showed the lowest sensitivity. The sensitivity of heavy metal sensors varied among heavy metals and sensor types. Most of the sensors were targeted on lead, cadmium, mercury, and copper using electrochemical methods. These results imply that future development of pesticides and heavy metal sensors should (1) enhance the sensitivity to meet the requirements for the protection of aquatic ecosystems and human health and (2) cover more diverse pesticides and heavy metals especially those toxic pollutants that are widely used and frequently been detected in freshwaters (e.g., glyphosate, fungicides, zinc, chromium, and arsenic).

Description: The abiotic and biotic stresses including drought, cold, and disease stress are linked by the fact that they all decrease the availability of water to plant cells. In previous studies, some physiological factors related with plant water status, such as stem sap flow, leaf transpiration rate, and water potential, were used to assess the effects of these stresses on plants. But there are few studies about the effects of these stresses on stem water content (StWC) which can be measured by a novel SWR sensor. In this study, crape myrtle was selected as an experimental subject and its StWC was observed in four experiments including no stress, drought, cold, and disease stress. Before conducting stress experiments, the StWC and environmental and physiological parameters were synchronously monitored under unstressed conditions on a typical day in summer. In the experiment of drought stress, the StWC was monitored under different gradients of soil moisture. In the experiment of cold stress, the StWC was monitored in warm and cold weather, respectively. In the experiment of disease stress, the StWC was monitored under different frequencies of disease treatment. The results showed that the correlation coefficients between StWC and PAR and VPD were larger than 0.5 and the correlation coefficients between StWC and Pn, Tr, Gs, and Ci were larger than 0.8 under no stress. The diurnal mean of StWC decreased firstly, then remained stable for a period of time, and eventually continued to fall under drought stress. On the whole, there was a negative correlation between the diurnal mean of StWC and the degree of drought stress. The StWC showed opposite diurnal variation rules in warm and cold weather. There was a positive correlation between the diurnal range of StWC and the degree of cold stress. The diurnal minimum, maximum, and mean of StWC showed a positive correlation with the health level of plants and the diurnal range of StWC showed a negative correlation with the health level of plants. In conclusion, the StWC can be used as a qualitative evaluation index of the degree of the three types of stress.

Description: Copper mining activity is going through big changes due to increasing technological development in the area and the influence of industry 4.0. These changes, produced by technological context and more controls (e.g., environmental controls), are also becoming visible in Chilean mining. New regulations from the Chilean government and changes in the copper mining industry (such as a trend to underground mining) are fostering the search for better results in typical processes such as leaching. This paper describes an experience using artificial intelligence techniques, particularly random forest, to develop predictive models for copper recovery by leaching, using data from an enterprise present in northern Chile for more than 20 years. Two models, one of them with actual operational data and another one with data generated in a controlled environment (piling) are presented. Well-classified values of 98.90% for operational data and 98.72% for pile/piling data were obtained. The methodology devised for the study can be transferred to piling columns or piles with other characteristics, though the operation must focus on copper leaching. It can even be transferred to other leaching processes using another type of mineral, with proper adjustments.

Description: The monitors of oscillometry blood pressure measurements are generally utilized to measure blood pressure for many subjects at hospitals, homes, and office, and they are actively studied. These monitors usually provide a single blood pressure point, and they are not able to indicate the confidence interval of the measured quantity. In this paper, we propose a new technique using a recursive ensemble based on a support vector machine to estimate a confidence interval for oscillometry blood pressure measurements. The recursive ensemble is based on a support vector machine that is used to effectively estimate blood pressure and then measure the confidence interval for the systolic blood pressure and diastolic blood pressure. The recursive ensemble methodology provides a lower standard deviation of error, mean error, and mean absolute error for the blood pressure as compared to those of the conventional techniques.

Description: Early detection and characterization of circulating tumor DNA (ctDNA) can reveal mint of comprehensive biological insights from indicating the presence of tumor, identifying mutational changes of malignant cells, and allowing precision or targeted therapy together with monitoring disease progression, treatment resistance, and relapse of the disease. Apart from these, one of the greatest axiomatic implications of ctDNA detection is that it provides a new shed of light as noninvasive liquid biopsy as a replaceable procedure of surgical tumor biopsy. Despite the tremendous potential of ctDNA in cancer research, there remains a paucity of quantitative study on ctDNA detection and analysis. The majority of previously published microfluidic-based studies have focused on circulating tumor cell (CTC) detection and have failed to address the potential of ctDNA. The studies on microfluidic ctDNA detection are not consistent might be due to the complexity of ctDNA isolation as they present in low concentration in blood plasma. Researchers need to leverage the ability of microfluidic system for ctDNA analysis so that the significant enigma about cancer can be resolved effectively. This study, therefore, highlights the importance of ctDNA as cancer biomarker for liquid biopsy and provides an overview of the current laboratory as well as microfluidic techniques for ctDNA detection. This paper also attempts to show the emergence of new strands of microfluidic ctDNA detection and analysis for personalized cancer chemotherapy.

Description: Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a preprocessing procedure, is necessary for underwater vision. In this paper, a combination of max-RGB method and shades of gray method is applied to achieve the enhancement of underwater vision, and then a CNN (Convolutional Neutral Network) method for solving the weakly illuminated problem for underwater images is proposed to train the mapping relationship to obtain the illumination map. After the image processing, a deep CNN method is proposed to perform the underwater detection and classification, according to the characteristics of underwater vision, two improved schemes are applied to modify the deep CNN structure. In the first scheme, a 1∗1 convolution kernel is used on the 26∗26 feature map, and then a downsampling layer is added to resize the output to equal 13∗13. In the second scheme, a downsampling layer is added firstly, and then the convolution layer is inserted in the network, the result is combined with the last output to achieve the detection. Through comparison with the Fast RCNN, Faster RCNN, and the original YOLO V3, scheme 2 is verified to be better in detecting underwater objects. The detection speed is about 50 FPS (Frames per Second), and mAP (mean Average Precision) is about 90%. The program is applied in an underwater robot; the real-time detection results show that the detection and classification are accurate and fast enough to assist the robot to achieve underwater working operation.

Description: High spatial and temporal resolution remote sensing data play an important role in monitoring the rapid change of the earth surface. However, there is an irreconcilable contradiction between the spatial and temporal resolutions of the remote sensing image acquired from a same sensor. The spatiotemporal fusion technology for remote sensing data is an effective way to solve the contradiction. In this paper, we will study the spatiotemporal fusion method based on the convolutional neural network, which can fuse the Landsat data with high spatial but low temporal resolution and MODIS data with low spatial but high temporal resolution, and generate time series data with high spatial resolution. In order to improve the accuracy of spatiotemporal fusion, a residual convolution neural network is proposed. MODIS image is used as the input to predict the residual image between MODIS and Landsat, and the sum of the predicted residual image and MODIS data is used as the predicted Landsat-like image. In this paper, the residual network not only increases the depth of the superresolution network but also avoids the problem of vanishing gradient due to the deep network structure. The experimental results show that the prediction accuracy by our method is greater than that of several mainstream methods.

Description: In the present study, simultaneous electrochemical behavior of tenofovir disoproxil fumarate (TDF) and lamivudine (LAM) on a glassy carbon electrode (GCE) modified with a novel nanocomposite, nickel-cobalt sulfide-decorated graphene quantum dots (Ni-CoS/GQDs) was investigated. Characterization of different components used for modifications was achieved using UV-Vis and transmission electron microscopy (TEM). The electrochemistry of TDF and LAM at the modified electrode was examined using cyclic voltammetry (CV), linear sweep voltammetry (LSV), chronoamperometry, differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). A substantial (+39 mV TDF; +20 mV LAM) decrease in potential on oxidation reaction in 0.1 M PBS (pH 8.0) was achieved due to synergy between Ni-CoS and GQDs. Using optimized voltammetric parameters and pH, DPV gave a linear calibration over the range 5-18 μM for TDF and LAM. The detection limits were calculated as 2.42×10−8 mM (LAM) and 1.21×10−8 mM (TDF). The estimated values for Gibbs free energy revealed adsorption of TDF and LAM on Ni-CoS/GQDs/GCE as a spontaneous and favorable process. The voltammetric method was applied for determination of LAM and TDF in a pharmaceutical formulation giving average recovery of 99.65%.

Description: Road traffic network (RTN) structure plays an important role in the field of complex network analysis. In this paper, we propose a regional patch detection method from RTN via community detection of complex network. Firstly, the refined Adapted PageRank algorithm, which combines with the influence factors of the location property weight, the geographic distance weight and the road level weight, is used to calculate the candidate ranking results of key nodes in the RTN. Secondly, the ranking result and the shortest path distance as two significant impact factors are used to select the key points of the RTN, and then the Adapted K-Means algorithm is applied to regional patch detection of the RTN. Finally, based on the experimental data of Zhangwu road traffic network, the analysis results are as follows: Zhangwu is divided into 9 functional structures with key node locations as the core. Regional patch structure is divided according to key points, and the RTN is actually divided into nine small functional communities. Nine functional regional patches constitute a new network structure, maintaining connectivity between the regional patches can improve the overall efficiency of the RTN.

Description: This paper deals with the development of the content of Activity of Daily Life (ADL) based on VR (virtual reality) for cognitive function training for the elderly. The proposed ADL content has been developed focusing on a feedback technique based on performance analysis unlike in the existing VR-based cognitive training content wherein the customized training management based on performance evaluation was difficult. Experiments were conducted with 30 elderly people in the welfare center for 3 months. As a result of the effectiveness evaluation, the proposed ADL content showed higher results in all aspects of immersion, satisfaction, and performance. It is expected that user departure from existing ADL content will be prevented, and various artificial intelligence researches will be carried out to improve the technology accuracy to provide customized difficulty which is an important factor.

Description: Source localization is one of the major research contents in the localization research of wireless sensor networks, which has attracted considerable attention for a long period. In recent years, the wireless binary sensor network (WBSN) has been widely used for source localization due to its high energy efficiency. A novel method which is based on WBSN for multiple source localization is presented in this paper. Firstly, the Neyman-Pearson criterion-based sensing model which takes into account the false alarms is utilized to identify the alarmed nodes. Secondly, the mean shift and hierarchical clustering method are performed on the alarmed nodes to obtain the cluster centers as the initial locations of signal sources. Finally, some voting matrices which can improve the localization accuracy are constructed to decide the location of each acoustic source. The simulation results demonstrate that the proposed method can provide a desirable performance superior to some traditional methods in accuracy and efficiency.

Description: This paper shows that the accuracy of azimuth angle measurement for an interferometric localization system used to locate tags in its Line-of-Sight (LoS) can be improved by exploiting Impulse Radio-Ultra WideBand (IR-UWB) signals and without increasing the frequency bandwidth. This solution uses a Phase Correlation (PC) method, initially applied for Continuous Wave (CW) signals, adapted for Ultra WideBand (UWB) pulse signals. The obtained results are compared to those computed by a classical Energy Detection (ED) method where it becomes impossible to estimate azimuth angles for tag positions close to the orthogonal centered axis of the localization system baseline.

Description: A large number of growing digital images require retrieval effectively, but the trade-off between accuracy and speed is a tricky problem. This paperwork proposes a lightweight and efficient image retrieval approach by combining region and orientation correlation descriptors (CROCD). The region color correlation pattern and orientation color correlation pattern are extracted by the region descriptor and the orientation descriptor, respectively. The feature vector of the image is extracted from the two correlation patterns. The proposed algorithm has the advantages of statistic and texture description methods, and it can represent the spatial correlation of color and texture. The feature vector has only 80 dimensions for full color images specifically. Therefore, it is very efficient in image retrieving. The proposed algorithm is extensively tested on three datasets in terms of precision and recall. The experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.

Description: Vision-based traffic sign detection plays a crucial role in intelligent transportation systems. Recently, many approaches based on deep learning for traffic sign detection have been proposed and showed better performance compared with traditional approaches. However, due to difficult conditions in driving environment and the size of traffic signs in traffic scene images, the performance of deep learning-based methods on small traffic sign detection is still limited. In addition, the inference speed of current state-of-the-art approaches on traffic sign detection is still slow. This paper proposes a deep learning-based approach to improve the performance of small traffic sign detection in driving environments. First, a lightweight and efficient architecture is adopted as the base network to address the issue of the inference speed. To enhance the performance on small traffic sign detection, a deconvolution module is adopted to generate an enhanced feature map by aggregating a lower-level feature map with a higher-level feature map. Then, two improved region proposal networks are used to generate proposals from the highest-level feature map and the enhanced feature map. The proposed improved region proposal network is designed for fast and accuracy proposal generation. In the experiments, the German Traffic Sign Detection Benchmark dataset is used to evaluate the effectiveness of each enhanced module, and the Tsinghua-Tencent 100K dataset is used to compare the effectiveness of the proposed approach with other state-of-the-art approaches on traffic sign detection. Experimental results on Tsinghua-Tencent 100K dataset show that the proposed approach achieves competitive performance compared with current state-of-the-art approaches on traffic sign detection while being faster and simpler.

Description: An energy acquisition system for the ground wire of an overhead transmission line can provide a continuous and stable power supply for an on-line monitoring device. Its key issue is how to obtain enough power. To solve this problem, an energy acquisition scheme based on the double-insulated ground wire of an overhead transmission line has been investigated in this study. Three energy acquisition schemes were proposed, equivalent circuit analysis models of the three energy acquisition schemes were established, and the maximum power acquired was theoretically analyzed. The energy acquisition power of the three energy acquisition schemes for different tower-type sizes was also analyzed. A simulation model was built in PSCAD. The effects of load impedance, length of energy acquisition wire, grounding resistance, and load current on the power acquired were analyzed. The research results of this paper provide theoretical guidance for choosing an energy acquisition scheme and for designing key parameters in practice.

Description: COVID-19, a deadly disease that originated in Wuhan, China, has resulted in a global outbreak. Patients infected with the causative virus SARS-CoV-2 are placed in quarantine, so the virus does not spread. The medical community has not discovered any vaccine that can be immediately used on patients infected with SARS-CoV-2. The only method discovered so far to protect people from this virus is keeping a distance from other people, wearing masks and gloves, as well as regularly washing and sanitizing hands. Government and law enforcement agencies are involved in banning the movement of people in different cities, to control the spread and monitor people following the guidelines of the CDC. But it is not possible for the government to monitor all places, such as shopping malls, hospitals, government offices, and banks, and guide people to follow the safety guidelines. In this paper, a novel technique is developed that can guide people to protect themselves from someone who has high exposure to the virus or has symptoms of COVID-19, such as having fever and coughing. Different deep Convolutional Neural Networks (CNN) models are implemented to test the proposed technique. The proposed intelligent monitoring system can be used as a complementary tool to be installed at different places and automatically monitor people adopting the safety guidelines. With these precautionary measurements, humans will be able to win this fight against COVID-19.

Description: Indoor localization based on ultrasound signals has been carried out by several research groups. Most of the techniques rely on a single ultrasound pulse ranging, where the Time of Flight between the ultrasound emitters and a receiver is computed. Ultrasound orthogonal modulation techniques have also been investigated and allow to compute the range between the receiver and multiple simultaneous emitters with increased accuracy. However, no comparative investigation on the possibilities of each of the modulation techniques, comprising Direct Sequence Spread Spectrum, Frequency Hopping Spread Spectrum, and Chirp Spread Spectrum, could be found. Also, common optimized demodulation and correlation techniques for FPGA ready implementations are not widely available. Moreover, the hardware requirements for capturing modulated ultrasound signals could not be found for all the techniques. In this work, the different modulation techniques are optimized and implemented on an FPGA. A dedicated custom ultrasound MEMS-based receiver hardware for broadband ultrasound signal capturing is developed. Several modulation parameters are developed and applied for optimized signal processing. The FPGA resource consumptions are evaluated for the implemented methods. All methods are compared against the regular pulse ranging method, in both single-access and multiple-access ranging mode. Results show that, on average, up to 8 ultrasound-modulated emitters with an orthogonal sequence of length 63 can be demodulated on a Zynq7020 FPGA. In most cases, ranging up to 8 m is demonstrated in both single- and multiple-access mode, with accuracies generally remaining at a centimeter level. The requirements and capabilities for each of the modulation schemes are highlighted in the conclusions.

Description: In the scanning synthetic aperture radar (ScanSAR) mode, the radar antenna sweeps through different range subswaths to image a wide swath. The full-aperture imaging algorithm for ScanSAR data has been widely used because it can be realized by exploiting the existing standard high-precision Stripmap SAR processor and does not require stitch processing in the azimuth. However, both the focused image and the interferogram achieved by full-aperture processing suffer from spikes. The spikes adversely affect the ScanSAR-related applications, such as target detection and interferometry. To effectively suppress the spikes, an improved algorithm based on the missing-data iterative adaptive approach (MIAA) is proposed in this manuscript. Besides, the proposed method can also improve the azimuth resolution of ScanSAR images. Simulation and experimental results demonstrate that this algorithm has better performance when processing ScanSAR data compared with existing methods.

Description: The social and economic harm to North Korea caused by water-related disasters is increasing with the increase in the disasters worldwide. Despite the improvement of inter-Korean relations in recent years, the issue of water-related disasters, which can directly affect the lives of people, has not been discussed. With consideration of inter-Korean relations, a government-wide technical plan should be established to reduce the damage caused by water-related disasters. Therefore, the purpose of this study was to identify remote sensing and GIS techniques that could be useful in reducing the damage caused by water-related disasters while considering inter-Korean relations and the disasters that occur in North Korea. To this end, based on the definitions of disasters in South and North Korea, water-related disasters that occurred during a 17-year period from 2001 to 2017 in North Korea were first summarized and reclassified into six types: typhoons, downpours, floods, landslides, heavy snowfalls, and droughts. In addition, remote sensing- and GIS-based techniques in South Korea that could be applied to water-related disasters in North Korea were investigated and reclassified according to applicability to the six disaster types. The results showed that remote sensing and other monitoring techniques using spatial information, GIS-based database construction, and integrated water-related disaster management have high priorities. Especially, the use of radar images, such as C band images, has proven essential. Moreover, case studies were analyzed within remote sensing- and GIS-based techniques that could be applicable to the water-related disasters that occur frequently in North Korea. Water disaster satellites with high-resolution C band synthetic aperture radar are scheduled to be launched by South Korea. These results provide basic data to support techniques and establish countermeasures to reduce the damage from water-related disasters in North Korea in the medium to long term.