ALBERT

Description: Although advancement has been observed in global navigation satellite systems and these systems are widely used, they cannot provide effective navigation and positioning services in covered areas and areas that lack strong signals, such as indoor environments. Therefore, in recent years, indoor positioning technology has become the focus of research and development. The magnetic field of the Earth is quite stable in an open environment. Due to differences in building and internal structures, this type of three-dimensional vector magnetic field is widely available indoors for indoor positioning. A smartphone magnetometer was used in this study to collect magnetic field data for constructing indoor magnetic field maps. Moreover, an acceleration sensor and a gyroscope were used to identify the position of a mobile phone and detect the number of steps travelled by users with the phone. This study designed a procedure for measuring the step length of users. All obtained information was input into a pedestrian dead reckoning (PDR) algorithm for calculating the position of the device. The indoor positioning accuracy of the PDR algorithm was optimised using magnetic gradients of magnetic field maps with a modified particle filter algorithm. Experimental results reveal that the indoor positioning accuracy was between 0.6 and 0.8 m for a testing area that was 85 m long and 33 m wide. This study effectively improved the indoor positioning accuracy and efficiency by using the particle filter method in combination with the PDR algorithm with the magnetic fingerprint map.

Description: Siphons have been widely used in water supply systems and sewage networks. However, it is difficult to implement non-destructive testing due to structural complexity and limited accessibility. In this paper, a novel condition classification method for water-filled underground siphons is proposed, which uses the acoustic signals received from acoustic sensors installed in the siphon. The proposed method has the advantages of simpler operation, lower cost, and higher detection efficiency. The acoustic wave forms in the siphons reflect on the system characteristics. Seven typical conditions of a water-filled underground siphon were investigated, and a series of experiments were conducted. Acoustic signals were recorded and transformed into acoustic pressure responses for further analysis. The variational mode decomposition (VMD) and the acoustic energy flow density were used for signal processing and feature extraction. The acoustic energy flux density eigenvectors were input to three different classifiers to classify the siphon conditions. The results demonstrate that the proposed acoustic-based approach can effectively classify the blockage and damage conditions of siphons, and the recognition accuracy of the proposed method is higher than 94.4%. Therefore, this research has value for engineering applications.

Description: Parkinson’s disease results in motor impairment that deteriorates patients’ quality of life. One of the symptoms negatively interfering with daily activities is kinetic tremor which should be measured to monitor the outcome of therapy. A new instrumented method of quantification of the kinetic tremor is proposed, based on the analysis of circles drawn on a digitizing tablet by a patient. The aim of this approach is to obtain a tremor scoring equivalent to that performed by trained clinicians. Models are trained with the least absolute shrinkage and selection operator (LASSO) method to predict the tremor scores on the basis of the parameters computed from the patients’ drawings. Signal parametrization is derived from both expert knowledge and the response of an artificial neural network to the raw data, thus the approach was named multimodal. The fitted models are eventually combined into model ensembles that provide aggregated scores of the kinetic tremor captured in the drawings. The method was verified with a set of clinical data acquired from 64 Parkinson’s disease patients. Automated and objective quantification of the kinetic tremor with the presented approach yielded promising results, as the Pearson’s correlations between the visual ratings of tremor and the model predictions ranged from 0.839 to 0.890 in the best-performing models.

Description: Mobile robots are becoming more and more widely used in industry and life, so the navigation of robots in dynamic environments has become an urgent problem to be solved. Dynamic path planning has, therefore, received more attention. This paper proposes a real-time dynamic path planning method for mobile robots that can avoid both static and dynamic obstacles. The proposed intelligent optimization method can not only get a better path but also has outstanding advantages in planning time. The algorithm used in the proposed method is a hybrid algorithm based on the beetle antennae search (BAS) algorithm and the artificial potential field (APF) algorithm, termed the BAS-APF method. By establishing a potential field, the convergence speed is accelerated, and the defect that the APF is easily trapped in the local minimum value is also avoided. At the same time, by setting a security scope to make the path closer to the available path in the real environment, the effectiveness and superiority of the proposed method are verified through simulative results.

Description: A low-power wireless acoustic sensing platform for remote surveillance applications based on a 180 nm CMOS technology is proposed in this paper. The audio signal, which is acquired by a microphone, is first amplified and filtered. Then, the analog signal is converted to a digital signal by a 10-bit analog-to-digital converter (ADC). A digital automatic gain control module is integrated to obtain an optimal input of the ADC. The digital signal is modulated and transmitted at the 433 MHz ISM band after being repacked and encoded. To save power for portable applications, the chip switches to standby mode when no audio is detected. The wireless sensing platform occupies a chip area of 1.76 mm 2 . The supply voltage is 2.5 V for the power amplifier and 1.8 V for other circuits. The measured maximum output power is 5.7 dBm and the transmission distance is over 500 m for real application scenarios. The chip consumes 25.1 mW power in normal work mode and 0.058 mW in standby mode. Compared to existing wireless acoustic sensors, the proposed wireless acoustic sensing platform can achieve features such as compactness, power efficiency, and reliability.

Description: To implement target point cloud segmentation for a polarization-modulated 3D imaging system in practical projects, an efficient segmentation concept of multi-dimensional information fusion is designed. As the electron multiplier charge coupled device (EMCCD) camera can only acquire the gray image, but has no ability for time resolution owing to the time integration mechanism, large diameter electro-optic modulators (EOM) are used to provide time resolution for the EMCCD cameras to obtain the polarization-modulated images, from which a 3D image can also be simultaneously reconstructed. According to the characteristics of the EMCCD camera’s plane array imaging, the point-to-point mapping relationship between the gray image pixels and point cloud data coordinates is established. The target’s pixel coordinate position obtained by image segmentation is mapped to 3D point cloud data to get the segmented target point cloud data. On the basis of the specific environment characteristics of the experiment, the maximum entropy test method is applied to the target segmentation of the gray image, and the image morphological erosion algorithm is used to improve the segmentation accuracy. This method solves the problem that conventional point clouds’ segmentation methods cannot effectively segment irregular objects or closely bound objects. Meanwhile, it has strong robustness and stability in the presence of noise, and reduces the computational complexity and data volume. The experimental results show that this method is better for the segmentation of the target in the actual environment, and can avoid the over-segmentation and under-segmentation to some extent. This paper illustrates the potential and feasibility of the segmentation method based on this system in real-time data processing.

Description: Bacterial biohybrid microswimmers aim at exploiting the inherent motion capabilities of bacteria (carriers) to transport objects (cargoes) at the microscale. One of the most desired properties of microswimmers is their ability to communicate with their immediate environment by processing the information and producing a useful response. Indeed, bacteria are naturally equipped with such communication skills. Hereby, two-component systems (TCSs) represent the key signal transducing machinery and enable bacteria to sense and respond to a variety of stimuli. We engineered a natural microswimmer based on the Gram-positive model bacterium Bacillus subtilis for the development of biohybrids with sensing abilities. B. subtilis naturally adhered to silica particles, giving rise to different motile biohybrids systems with variable ratios of carrier(s)-to-cargo(es). Genetically engineered TCS pathways allowed us to couple the binding to the inert particles with signaling the presence of antibiotics in their surroundings. Activation of the antibiotic-induced TCSs resulted in fluorescent bacterial carriers as a response readout. We demonstrate that the genetically engineered TCS-mediated signaling capabilities of B. subtilis allow for the custom design of bacterial hybrid microswimmers able to sense and signal the presence of target molecules in the environment. The generally recognized as safe (GRAS) status of B. subtilis makes it a promising candidate for human-related applications of these novel biohybrids.

Description: Speech is the most significant mode of communication among human beings and a potential method for human-computer interaction (HCI) by using a microphone sensor. Quantifiable emotion recognition using these sensors from speech signals is an emerging area of research in HCI, which applies to multiple applications such as human-reboot interaction, virtual reality, behavior assessment, healthcare, and emergency call centers to determine the speaker’s emotional state from an individual’s speech. In this paper, we present major contributions for; (i) increasing the accuracy of speech emotion recognition (SER) compared to state of the art and (ii) reducing the computational complexity of the presented SER model. We propose an artificial intelligence-assisted deep stride convolutional neural network (DSCNN) architecture using the plain nets strategy to learn salient and discriminative features from spectrogram of speech signals that are enhanced in prior steps to perform better. Local hidden patterns are learned in convolutional layers with special strides to down-sample the feature maps rather than pooling layer and global discriminative features are learned in fully connected layers. A SoftMax classifier is used for the classification of emotions in speech. The proposed technique is evaluated on Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets to improve accuracy by 7.85% and 4.5%, respectively, with the model size reduced by 34.5 MB. It proves the effectiveness and significance of the proposed SER technique and reveals its applicability in real-world applications.

Description: This work reviews design aspects of liquid metal antennas and their corresponding applications. In the age of modern wireless communication technologies, adaptability and versatility have become highly attractive features of any communication device. Compared to traditional conductors like copper, the flow property and lack of elasticity limit of conductive fluids, makes them an ideal alternative for applications demanding mechanically flexible antennas. These fluidic properties also allow innovative antenna fabrication techniques like 3D printing, injecting, or spraying the conductive fluid on rigid/flexible substrates. Such fluids can also be easily manipulated to implement reconfigurability in liquid antennas using methods like micro pumping or electrochemically controlled capillary action as compared to traditional approaches like high-frequency switching. In this work, we discuss attributes of widely used conductive fluids, their novel patterning/fabrication techniques, and their corresponding state-of-the-art applications.

Description: Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2–4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.

Description: The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

Description: In this paper, a back-illuminated (BSI) time-of-flight (TOF) sensor using 0.2 µm silicon-on-insulator (SOI) complementary metal oxide semiconductor (CMOS) technology is developed for long-range laser imaging detection and ranging (LiDAR) application. A 200 µm-thick bulk silicon in the SOI substrate is fully depleted by applying high negative voltage at the backside for higher quantum efficiency (QE) in a near-infrared (NIR) region. The proposed SOI-based four-tap charge modulator achieves a high-speed charge modulation and high modulation contrast of 71% in a NIR region. In addition, in-pixel drain function is used for short-pulse TOF measurements. A distance measurement up to 27 m is carried out with +1.8~−3.0% linearity error and range resolution of 4.5 cm in outdoor conditions. The measured QE of 55% is attained at 940 nm which is suitable for outdoor use due to the reduced spectral components of solar radiation.

Description: This paper addresses the efficiency of Bluetooth Low Energy (BLE) communication in a network composed of a large number of tags that transmit information to a single hub using advertisement mode. Theoretical results show that the use of advertisements enables hundreds and thousands of BLE devices to coexist in the same area and at the same time effectively transmit messages. Together with other properties (low power consumption, medium communication range, capability to detect a signal’s angle-of-arrival, etc.), this makes BLE a competing technology for the Internet of Things (IoT) applications. However, as the number of communicating devices increases, the advertisement collision intensifies and the communication performance of BLE drops. This phenomena was so far analyzed theoretically, in simulations and in small-scale experiments, but large-scale experiments are not presented in the literature. This paper complements previous results and presents an experimental evaluation of a real IoT-use case, which is the deployment of over 200 tags communicating using advertisements. We evaluate the impact of the number of advertisements on the effective data reception rate and throughput. Despite the advertisement collision rate in our experiment varying between 0.22 and 0.33, we show that BLE, thanks to the multiple transmission of advertisements, can still ensure acceptable data reception rates and fulfill the requirements of a wide range of IoT applications.

Description: Grasping and manipulation with anthropomorphic robotic and prosthetic hands presents a scientific challenge regarding mechanical design, sensor system, and control. Apart from the mechanical design of such hands, embedding sensors needed for closed-loop control of grasping tasks remains a hard problem due to limited space and required high level of integration of different components. In this paper we present a scalable design model of artificial fingers, which combines mechanical design and embedded electronics with a sophisticated multi-modal sensor system consisting of sensors for sensing normal and shear force, distance, acceleration, temperature, and joint angles. The design is fully parametric, allowing automated scaling of the fingers to arbitrary dimensions in the human hand spectrum. To this end, the electronic parts are composed of interchangeable modules that facilitate the mechanical scaling of the fingers and are fully enclosed by the mechanical parts of the finger. The resulting design model allows deriving freely scalable and multimodally sensorised fingers for robotic and prosthetic hands. Four physical demonstrators are assembled and tested to evaluate the approach.

Description: In this work, a high sensitivity micro-thermal conductivity detector (μTCD) with four thermal conductivity cells was proposed. Compared with conventional TCD sensors, the thermal conductivity cell in this work was designed as a streamlined structure; the thermistors were supported by a strong cantilever beam and suspended in the center of the thermal conductivity cell, which was able to greatly reduce the dead volume of the thermal conductivity cell and the heat loss of the substrate, improving the detection sensitivity. The experimental results demonstrated that the μTCD shows good stability and high sensitivity, which could rapidly detect light gases with a detection limit of 10 ppm and a quantitative repeatability of less than 1.1%.

Description: In this paper we present a mobile augmented reality (MAR) application supporting teaching activities in interior design. The application supports students in learning interior layout design, interior design symbols, and the effects of different design layout decisions. Utilizing the latest AR technology, users can place 3D models of virtual objects as e.g., chairs or tables on top of a design layout plan and interact with these on their mobile devices. Students can experience alternative design decision in real-time and increases the special perception of interior designs. Our system fully supports the import of interior deployment layouts and the generation of 3D models based on design artefacts based on typical design layout plan design symbols and allows the user to investigate different design alternatives. We applied John Keller’s Attention, Relevance, Confidence, and Satisfaction (ARCS) learning motivation model to validate our solution to examine the students’ willingness and verify the ability of students to improve learning through MAR technology. We compared a sample experimental group of N = 52 test-subjects with a sample of N = 48 candidates in a control group. Learning indicators as learning interest, confidence, satisfaction and effective have been utilized to assess the students’ learning motivation through the use of MAR technology. The learning results have been determined by the independent sample t testing. The significance of the post-test had a p-value 〈 0.05 difference. The result of the study clearly shows that the reference group utilizing MAR technology as a learning aid show a higher learning effectiveness as the control group. Thus, we conclude that MAR technology does enhance students’ learning ability for interior design and making appropriate design decisions.

Description: Techniques based on ultrasound in nondestructive testing and medical imaging analyze the response of the source frequencies (linear theory) or the second-order frequencies such as higher harmonics, difference and sum frequencies (nonlinear theory). The low attenuation and high directivity of the difference-frequency component generated nonlinearly by parametric arrays are useful. Higher harmonics created directly from a single-frequency source and the sum-frequency component generated nonlinearly by parametric arrays are attractive because of their high spatial resolution and accuracy. The nonlinear response of bubbly liquids can be strong even at relatively low acoustic pressure amplitudes. Thus, these nonlinear frequencies can be generated easily in these media. Since the experimental study of such nonlinear waves in stable bubbly liquids is a very difficult task, in this work we use a numerical model developed previously to describe the nonlinear propagation of ultrasound interacting with nonlinearly oscillating bubbles in a liquid. This numerical model solves a differential system coupling a Rayleigh–Plesset equation and the wave equation. This paper performs an analysis of the generation of the sum-frequency component by nonlinear mixing of two signals of lower frequencies. It shows that the amplitude of this component can be maximized by taking into account the nonlinear resonance of the system. This effect is due to the softening of the medium when pressure amplitudes rise.

Description: Due to the long-term service, Chinese ancient timber buildings show varying degrees of wear. Thus, structural health monitoring (SHM) for these cultural and historical treasures is desperately needed to evaluate the service status. Although there are some FBG sensing-based SHM systems, they are not suitable for Chinese ancient timber buildings due to the differences in architectural types, structural loads, materials, and environment. Besides, a technical gap in Fiber Bragg grating (FBG) sensing-based column inclination monitoring exists. To overcome these weaknesses, this paper develops an FBG sensing-based structural health monitoring system for Chinese ancient Chuan-dou-type timber buildings that aims at monitoring structural deformation, i.e., beam deflection and column inclination, temperature, humidity, and fire around the building. An in-situ test and simulation analyses were conducted to verify the effectiveness of the developed SHM system. To validate the long-term-operation of the developed SHM system, monitoring data within 15 months were analyzed. The results show good agreement between the developed SHM system in this paper and other methods. In addition, the SHM system operated well in the first year after its deployment. This implies that the developed SHM system is applicable and effective in the health state monitoring of Chinese ancient Chuan-dou-type timber buildings, laying a foundation for damage prognosis of such types of timber buildings.

Description: The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.

Description: The mechanical dithered ring laser gyro (RLG) effectively overcomes the lock-in effect and ensures the sensitive accuracy of the low angular rate for the gyro. However, in the inertial measurement unit (IMU) system, the dither excitation of three RLGs causes the coupled vibration of the IMU structure, which could seriously limit the measuring accuracy of RLGs. In this paper, the vibration frequency response characteristic of laser gyro IMU is taken as the focus point, and the method of multi-rigid body dynamics is used to establish the dynamic model of IMU suitable for vibration frequency response analysis. On the basis of the model, the multi-degree-of-freedom coupling vibration of IMU with the gyro dither excitation is clearly described. A new IMU dynamic decoupling optimization method is proposed to minimize the coupled vibration frequency response, and compared with the previous optimal design method. The prototype experimental test results show that the coupled vibration of IMU is restrained more effectively by the proposed new method than by the previous optimal design method. Finally, on the basis of this new method, the measuring accuracy of the RLGs in the IMU system is improved, which is quite useful for practical engineering application.

Description: Musculoskeletal disorders are becoming an ever-growing societal burden and, as a result, millions of bone replacements surgeries are performed per year worldwide. Despite total joint replacements being recognized among the most successful surgeries of the last century, implant failure rates exceeding 10% are still reported. These numbers highlight the necessity of technologies to provide an accurate monitoring of the bone–implant interface state. This study provides a detailed review of the most relevant methodologies and technologies already proposed to monitor the loosening states of endoprosthetic implants, as well as their performance and experimental validation. A total of forty-two papers describing both intracorporeal and extracorporeal technologies for cemented or cementless fixation were thoroughly analyzed. Thirty-eight technologies were identified, which are categorized into five methodologies: vibrometric, acoustic, bioelectric impedance, magnetic induction, and strain. Research efforts were mainly focused on vibrometric and acoustic technologies. Differently, approaches based on bioelectric impedance, magnetic induction and strain have been less explored. Although most technologies are noninvasive and are able to monitor different loosening stages of endoprosthetic implants, they are not able to provide effective monitoring during daily living of patients.

Description: Joint accessory motion testing (JAMT) is a standard procedure used by manual therapists to assess and treat musculoskeletal disorders. Joint accessory motion (JAM) is movement that occurs between joint surfaces, and can be induced by applying force. The motion amount, end feel, symptoms, and resistance perceived by therapists during test procedures are recorded as evidence for the diagnosis, prognosis, treatment decision making, and intervention outcome. However, previous studies have shown that accessory motion tests have insufficient reliability. Recently, many instruments have been developed to increase test reliability, but these instruments quantify the test results with a single probe and utilize the external environment as a reference. Therefore, the measured displacement amount may be affected by other spinal segments. This study proposes an objective portable measurement device with two indenter probes for spinal JAMT, wherein the JAM was quantified by displacement and force measurements between two bones. The instrument was verified with a homemade spinal simulator and computer simulation. The results showed that the force-displacement curves measured by the JAMT device (JAMTD) and those simulated by the computer model exhibited similar characteristics. Moreover, a two-probe measurement could distinguish the differences in stiffness better than a one-probe measurement.

Description: In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.

Description: The point detections obtained from radars or sonars in surveillance environments include clutter measurements, as well as target measurements. Target tracking with these data requires data association, which distinguishes the detections from targets and clutter. Various algorithms have been proposed for clutter measurement density estimation to achieve accurate and robust target tracking with the point detections. Among them, the spatial clutter measurement density estimator (SCMDE) computes the sparsity of clutter measurement, which is the reciprocal of the clutter measurement density. The SCMDE considers all adjacent measurements only as clutter, so the estimated clutter measurement density is biased for multi-target tracking applications, which may result in degraded target tracking performance. Through the study in this paper, a major source of tracking performance degradation with the existing SCMDE for multi-target tracking is analyzed, and the use of the clutter measurement probability is proposed as a remedy. It is also found that the expansion of the volume of the hyper-sphere for each sparsity order reduces the bias of clutter measurement density estimates. Based on the analysis, we propose a new adaptive clutter measurement density estimation method called SCMDE for multi-target tracking (MTT-SCMDE). The proposed method is applied to multi-target tracking, and the improvement of multi-target tracking performance is shown by a series of Monte Carlo simulation runs and a real radar data test. The clutter measurement density estimation performance and target tracking performance are also analyzed for various sparsity orders.

Description: Comfort level in the human body is an index that is always difficult to evaluate in a general and objective manner. Therefore, building owners and managers have been known to adjust environmental physical parameters such as temperature, humidity, and air quality based on people’s subjective sensations to yield satisfactory feelings of comfort. Furthermore, electricity consumption could be reduced by minimizing unnecessary use of heating and cooling equipment based on precise knowledge of comfort levels in interior spaces. To achieve the aforementioned objectives, this study undertook the following four tasks: first, providing visualization and smart suggestion functions to assist building managers and users in analyzing and developing plans based on the demands of space usage and electrical equipment; second, using Internet of Things technology to minimize the difference between real situations and those simulated in building information modeling (BIM); third, accurately evaluating interior environment comfort levels and improving equipment operating efficiency based on quantized comfort levels; and fourth, establishing a persuasive workflow for building energy saving systems. Through developing this system, COZyBIM will help to enhance the satisfactions of comfort level in interior space and operate energy consuming equipment efficiently, to reach the target of energy saving.

Description: Deformation monitoring is of importance to ensure the operation status of concrete-face rockfill dams (CFRD). This paper reported a novel fiber optic gyroscope (FOG) monitoring system for continuously monitoring face slab deformation of CFRD, which consisted of a permanent monitoring pipeline and a sensing vehicle. The monitoring pipeline was made of steel pipes and polyvinyl chloride polymer connectors, which was embedded in a slot of the crushing-type sidewall beneath the concrete face slab of CFRD, forming a permanent monitoring channel. The sensing vehicle was equipped with a high-precision FOG sensor. A low-pass filter was designed to eliminate the vibration noise of the angular velocities of the sensing vehicle during the monitoring process. An in situ test was carried out on the Shuibuya dam, the highest CFRD in the world. The measurements of the FOG monitoring system agreed well with traditional instrument measurements, serving as validation of the system. The FOG monitoring system has the advantages of excellent repeatability, long service life, distributed monitoring, and automatic measurement.

Description: The extraordinary optoelectronic properties of platinum diselenide (PtSe2), whose structure is similar to graphene and phosphorene, has attracted great attention in new rapidly developed two-dimensional (2D) materials beyond the other 2D material family members. We have investigated the surface plasmon resonance (SPR) sensors through PtSe2 with the transfer matrix method. The simulation results show that the anticipated PtSe2 biochemical sensors have the ability to detect analytic. It is evident that only the sensitivities of Ag or Au film biochemical sensors were observed at 118°/RIU (refractive index unit) and 130°/RIU, whereas the sensitivities of the PtSe2-based biochemical sensors reached as high as 162°/RIU (Ag film) and 165°/RIU (Au film). The diverse biosensor sensitivities with PtSe2 suggest that this kind of 2D material can adapt SPR sensor properties.

Description: The ZigBee wireless communication specifications forecast the use of multihop routes between nodes and define that nodes select their routes based on their costs. The specifications define how to compute a route cost from the probability of successfully transmitting on each of the routes’ links; and it is recommended that such probabilities be obtained by counting received link status messages or averaging link quality indicators from received packets. In this paper, we study the performance of these two recommended procedures, show that they can lead to degraded route selections, and propose a procedure that can improve route selections without modifications to the ZigBee protocol or frame formats. Our procedure estimates the probability of successful transmission on each link, based on information from the medium access layer during unicast packet transmissions, and includes a modification into how ZigBee nodes treat routing messages internally in order to reduce variations in the link cost estimates. Focusing on a home environment with one or two hops, our simulation results show that, in several scenarios, our procedure performs better than either of the two procedures recommended in the ZigBee specifications.

Description: A picotesla (PT) level high-sensitivity magnetic sensor with amorphous wire was developed. The magnetic sensor was composed of a (Fe0.06Co0.94)72.5Si2.5B15 (FeCoSiB) amorphous wire with a coil wound around it. The amorphous wire had a diameter of 0.1 mm and a length of 5 mm. The coil was 30 turns. There was no electrical connection with the amorphous wire. The sensor was biased by an alternating current (AC) of about 1 MHz and a direct current (DC). To increase the sensitivity, a resonant circuit was used, and the signal amplitude of the magnetic sensor was increased 10 times from 10 mV/Gauss to about 100 mV/Gauss. The magnetic field resolution was improved 5 times from 30 pT/√Hz to 6 pT/√Hz. An eddy current testing system with a magnetic sensor was developed, and the artificial defects in an aluminum plate were evaluated.

Description: AC arc faults are one of the most important causes of residential electrical wiring fires, which may produce extremely high temperatures and easily ignite surrounding combustible materials. The global interest in machine learning-based methods for arc fault diagnosis applications is increasing due to continuous challenges in efficiency and accuracy. In this paper, a temporal domain visualization convolutional neural network (TDV-CNN) methodology is proposed. The current transformer and high-speed data acquisition system are used to collect the current of a series of arc faults, then the signal is filtered by a digital filter and converted into a gray image in time sequence before being fed into TDV-CNN. Five different electric loads were selected for experimental validation with various signal characteristics, including vacuum cleaner, fluorescent lamp, dimmer, heater, and desktop computer. The experimental results confirm that the classification accuracy of the five loads’ work states in the ten categories could reach 98.7% or even higher by adjusting parameters perfectly. The methodology is believed to be reliable for series arc detection with relatively high accuracy and also has important potential applications in other fault diagnosis fields.

Description: Direction of arrival (DOA) estimation via sensor array is a crucial component of any passive sonar signal processing technology. In certain practical applications, however, the interested far-field targets are frequently affected by near-field interference, which may result in degradation of DOA estimation. Aiming at the direction estimation problems of far-field targets under strong near-field interference, a unified sparse representation model of far-field and near-field hybrid sources is constructed according to the various correlations in steering vectors between the planar wave and spherical wave in this paper. A high-resolution spatial spectrum reconstruction algorithm based on a sparse Bayesian framework is then exploited to constrain the energy of near-field interference in the preset near-field steering vector over-complete dictionary, thus ensuring the accurate detection and estimation of far-field targets. An expectation-maximization (EM) algorithm approach is introduced to estimate the number of sources and noise power iteratively, which will reduce the dependence of the algorithm on such prior information. Several state-of-art algorithms are mentioned and discussed (Minimum Variance Distortionless Response (MVDR) method, Multiple Signal Classification (MUSIC) algorithm and conventional beamforming (CBF) algorithm) to compare with the one proposed in this manuscript that achieves higher accuracy of estimation and resolution under low SNR level with limited samples, which is verified by simulation and for the results obtained in an experimental case study.

Description: This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.

Description: For stable and effective control of the sensor system, analog sensor signals such as temperature, pressure, and electromagnetic fields should be accurately measured and converted to digital bits. However, radiation environments, such as space, flight, nuclear power plants, and nuclear fusion reactors, as well as high-reliability applications, such as automotive semiconductor systems, suffer from radiation effects that degrade the performance of the sensor readout system including analog-to-digital converters (ADCs) and cause system malfunctions. This paper investigates an optimal ADC structure in radiation environments and proposes a successive- approximation-register (SAR) ADC using delay-based double feedback flip-flops to enhance the system tolerance against radiation effects, including total ionizing dose (TID) and single event effects (SEE). The proposed flip-flop was fabricated using 130 nm complementary metal–oxide–semiconductor (CMOS) silicon-on-insulator (SOI) process, and its radiation tolerance was measured in actual radiation test facilities. Also, the proposed radiation-hardened SAR ADC with delay-based dual feedback flip-flops was designed and verified by utilizing compact transistor models, which reflect radiation effects to CMOS parameters, and radiation simulator computer aided design (CAD) tools.

Description: This paper presents a sound source distance estimation (SSDE) method using a convolutional recurrent neural network (CRNN). We approach the sound source distance estimation task as an image classification problem, and we aim to classify a given audio signal into one of three predefined distance classes—one meter, two meters, and three meters—irrespective of its orientation angle. For the purpose of training, we create a dataset by recording audio signals at the three different distances and three angles in different rooms. The CRNN is trained using time-frequency representations of the audio signals. Specifically, we transform the audio signals into log-scaled mel spectrograms, allowing the convolutional layers to extract the appropriate features required for the classification. When trained and tested with combined datasets from all rooms, the proposed model exhibits high classification accuracies; however, training and testing the model in separate rooms results in lower accuracies, indicating that further study is required to improve the method’s generalization ability. Our experimental results demonstrate that it is possible to estimate sound source distances in known environments by classification using the log-scaled mel spectrogram.

Description: In three-dimensional (3D) medical ultrasound imaging with two-dimensional (2D) arrays, sparse 2D arrays have been studied to reduce the number of active channels. Among them, sparse 2D arrays with regular or uniform arrangements of elements have advantages of low side lobe energy and uniform field responses over the entire field of view. This paper presents two uniform sparse array models: sparse rectangular arrays (SRAs) on a rectangular grid and sparse spiral arrays (SSAs) on a sunflower grid. Both arrays can be easily implemented on the commercially available or the custom-made arrays. To suppress the overall grating lobe levels, the transmit (Tx) and receive (Rx) array pairs of both the array models are designed not to have grating lobes at the same locations in the Tx/Rx beam patterns, for which the theoretical design rules are also proposed. Computer simulation results indicate that the proposed array pairs for both the SRAs and the SSAs achieve peak grating lobe levels below –40 dB using about a quarter of the number of elements in the dense rectangular array while maintaining similar beam widths to that of the dense array pair.

Description: This paper proposes a framework to perform the sensor classification by using multivariate time series sensors data as inputs. The framework encodes multivariate time series data into two-dimensional colored images, and concatenate the images into one bigger image for classification through a Convolutional Neural Network (ConvNet). This study applied three transformation methods to encode time series into images: Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Markov Transition Field (MTF). Two open multivariate datasets were used to evaluate the impact of using different transformation methods, the sequences of concatenating images, and the complexity of ConvNet architectures on classification accuracy. The results show that the selection of transformation methods and the sequence of concatenation do not affect the prediction outcome significantly. Surprisingly, the simple structure of ConvNet is sufficient enough for classification as it performed equally well with the complex structure of VGGNet. The results were also compared with other classification methods and found that the proposed framework outperformed other methods in terms of classification accuracy.

Description: Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works.

Description: An in-line and real time chromatic sensor for liquids based on plastic optical fiber was developed. It uses an air gap, fiber to fiber, transmission principle. Its dependency to turbidity and refractive index is studied and characterized. This information will provide the necessary knowledge for future implementation of more complex auto-compensations routines. Due to the predictable behavior of the sensor to variations of turbidity and refractive index, it is shown that a posterior compensation could be applied for the discrimination of color. The real-time color sensor can be used in different turbid liquids and contain different refractive indices.

Description: Knitted fabric sensors have been widely used as strain sensors in the sports health field and its large strain performance and structure are suitable for human body movements. When a knitted structure is worn, different human body movements are reflected through the large strain deformation of fabric structure and consequently change the electrical signal. Here, the mechanical and electrical properties of highly elastic knitted sweatpants were tested under large strain. This sensor has good sensitivity and stability during movement. Compared with traditional motion monitoring, this technique divides the walking cycle into two stages, namely, stance and swing phases, which can be further subdivided into six stages. The corresponding resistance characteristic values can accurately distinguish the gait cycle. Analysis on hysteresis and repeatability revealed that the sensor exhibits a constant electrical performance. Four kinds of motion postures were predicted and judged by comparing the resistance characteristic range value, peak value calculation function and time axis. The measured sensor outputs were transferred to a computer via 4.0 Bluetooth. Matlab language was used to detect the status through a rule-based algorithm and the sensor outputs.

Description: Stretchable conductive materials are originally conceived as radio frequency (RF) and electromagnetic interference (EMI) shielding materials, and, under stretch, they generally function as distributed strain-gauges. These commercially available conductive elastomers have found their space in low power health monitoring systems, for example, to monitor respiratory and cardiac functions. Conductive elastomers do not behave linearly due to material constraints; hence, when used as a sensor, a full characterisation to identify ideal operating ranges are required. In this paper, we studied how the continuous stretch cycles affected the material electrical and physical properties in different embodiment impressed by bodily volume change. We simulated the stretch associated with breathing using a bespoke stress rig to ensure reproducibility of results. The stretch rig is capable of providing constant sinusoidal waves in the physiological ranges of extension and frequency. The material performances is evaluated assessing the total harmonic distortion (THD), signal-to-noise ratio (SNR), correlation coefficient, peak to peak (P-P) amplitude, accuracy, repeatability, hysteresis, delay, and washability. The results showed that, among the three controlled variables, stretch length, stretch frequency and fabric width, the most significant factor to the signal quality is the stretch length. The ideal working region is within 2% of the original length. The material cut in strips of 〉 3 show more reliable to handle a variety of stretch parameter without losing its internal characteristics and electrical properties.

Description: Bruxism is a masticatory muscle activity characterized by high prevalence, widespread complications, and serious consequences but without specific guidelines for its diagnosis and treatment. Although occlusal force-based biofeedback therapy is proven to be safe, effective, and with few side effects in improving bruxism, its mechanism and key technologies remain unclear. The purpose of this study was to research a real-time, quantitative, intelligent, and precise force-based biofeedback detection device based on artificial intelligence (AI) algorithms for the diagnosis and treatment of bruxism. Stress sensors were integrated and embedded into a resin-based occlusion stabilization splint by using a layering technique (sandwich method). The sensor system mainly consisted of a pressure signal acquisition module, a main control module, and a server terminal. A machine learning algorithm was leveraged for occlusal force data processing and parameter configuration. This study implemented a sensor prototype system from scratch to fully evaluate each component of the intelligent splint. Experiment results showed reasonable parameter metrics for the sensors system and demonstrated the feasibility of the proposed scheme for bruxism treatment. The intelligent occlusion stabilization splint with a stress sensor system is a promising approach to bruxism diagnosis and treatment.

Description: Stroke is a cerebral artery disease that negatively affects activities of daily living (ADLs) and quality of life (QoL). Smartphones have demonstrated strong potential in assessing balance performance. However, such smartphone-based tools have thus far not been applied to stroke survivors. The purpose of this study was to develop a smartphone-based balance assessment system for subjects who have experienced strokes and evaluate the system feasibility. The smartphone-based balance assessment application was developed with Android Studio, and reliability and validity tests were conducted. The smartphone was used to record data using a built-in accelerometer and gyroscope, and increased changes represented greater instability. Six postures were tested for 30 s each. Ten healthy adults were recruited in the reliability test, and the intraclass correlation coefficient (ICC) was used to analyze the within-day and between-day reliabilities. Eight subjects with chronic stroke and eight healthy adults were recruited for the validity test, in which balance performance was compared to represent the application validity. The ICC values of the reliability tests were at least 0.76 (p = 0.00). The acceleration data exhibited no difference between individuals who have experienced stroke and healthy subjects; however, all six postures were found to differ significantly between the two groups in the gyroscope data. The study demonstrates that the smartphone application provides a convenient, reliable, and valid tool for the balance assessments of subjects who have experienced chronic stroke.

Description: IEEE 802.11 is one of the most commonly used radio access technologies, being present in almost all handheld devices with networking capabilities. However, its energy-hungry communication modes are a challenge for the increased battery lifetime of such devices and are an obstacle for its use in battery-constrained devices such as the ones defined by many Internet of Things applications. Wake-up Radio (WuR) systems have appeared as a solution for increasing the energy efficiency of communication technologies by employing a secondary low-power radio interface, which is always in the active state and switches the primary transceiver (used for main data communication) from the energy-saving to the active operation mode. The high market penetration of IEEE 802.11 technology, together with the benefits that WuR systems can bring to this widespread technology, motivates this article’s focus on IEEE 802.11-based WuR solutions. More specifically, we elaborate on the feasibility of such IEEE 802.11-based WuR solutions, and introduce the latest standardization efforts in this IEEE 802.11-based WuR domain, IEEE 802.11ba, which is a forthcoming IEEE 802.11 amendment, discussing its main features and potential use cases. As a use case consisting of green Wi-Fi application, we provide a proof-of-concept smart plug system implemented by a WuR that is activated remotely using IEEE 802.11 devices, evaluate its monetary and energy savings, and compare it with commercially available smart plug solutions. Finally, we discuss novel applications beyond the wake-up functionality that IEEE 802.11-enabled WuR devices can offer using a secondary radio, as well as applications that have not yet been considered by IEEE 802.11ba. As a result, we argue that the IEEE 802.11-based WuR solution will support a wide range of devices and deployments, for both low-rate and low-power communications, as well as high-rate transmissions.

Description: Magnetic position and speed sensors are rugged and durable. While DC magnetic sensors use permanent magnets as a field source and usually have only mm or cm range, inductive sensors use electromagnetic induction and they may work up to a distance of 20 m. Eddy current inductive sensors equipped with magnetoresistive sensors instead of inductive coils can operate at low frequencies, allowing detection through a conductive wall. In this paper, we make an overview of existing systems and we present new results in eddy current velocity and position measurements. We also present several types of inductive position sensors developed in our laboratories for industrial applications in pneumatic and hydraulic cylinders, underground drilling, large mining machines, and for detecting ferromagnetic objects on conveyors. While the most precise inductive position sensors have a resolution of 10 nm and linearity of 0.2%, precision requirements on the industrial sensors which we develop are less demanding, but they should have large working distance and large resistance to environmental conditions and interference.

Description: Complex blade responses such as a rotating stall or simultaneous resonances are common in modern engines and their observation can be a challenge even for state-of-the-art tip-timing systems and trained operators. This paper analyses forced vibrations of axial compressor blades, measured during the bench tests of the SO-3 turbojet. In relation to earlier studies conducted in Poland with a small number of sensors, a multichannel tip-timing system let us observe simultaneous responses or higher-order modes. To find possible symptoms of a failure, blade responses in a healthy and unhealthy engine configuration with an inlet blocker were studied. The used analysis methods covered all-blade spectrum and the circumferential fitting of blade deflections to the harmonic oscillator model. The Pearson coefficient of correlation between the measured and predicted tip deflection is calculated to evaluate fitting results. It helps to avoid common operator mistakes and misinterpreting the results. The proposed modal solver can track the vibration frequency and adjust the engine order on the fly. That way, synchronous and asynchronous vibrations are observed and analysed together with an extended variant of least squares. This approach saves a lot of work related to configuring the conventional tip-timing solver.

Description: A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn’t work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the current frame and match the ID of the objects. The nine-point position correction algorithm is presented to adjust the correctness of the Rat-YOLO result and the predicted results. As the training of deep learning needs more datasets than our experiment, and it is time-consuming to process manual marking, automatic software for generating labeled datasets is proposed under a fixed scene and the labeled datasets are manually verified in term of their correctness. Besides this, in an off-line experiment, a mask is presented to remove the highlight. In this experiment, we select the 500 frames of the data as the training datasets and label these images with the automatic label generating software. A video (of 2892 frames) is tested by the trained Rat model and the accuracy of detecting all the three rats is around 72.545%, however, the Rat-YOLO combining the Kalman Filter and nine-point position correction arithmetic improved the accuracy to 95.194%.

Description: This research proposes bipolar junction transistor (BJT)-based log-domain high-order elliptic ladder low-pass (LPF) and band-pass filters (BPF) using a lossless differentiator and lossless and lossy integrators. The log-domain lossless differentiator was realized by using seven BJTs and one grounded capacitor, the lossy integrator using five BJTs and one grounded capacitor, and the lossless integrator using seven BJTs and one grounded capacitor. The simplified signal flow graph (SFG) of the elliptic ladder LPF consisted of two lossy integrators, one lossless integrator, and one lossless differentiator, while that of the elliptic ladder BPF contained two lossy integrators, five lossless integrators, and one lossless differentiator. Log-domain cells were directly incorporated into the simplified SFGs. Simulations were carried out using PSpice with transistor array HFA3127. The proposed filters are operable in a low-voltage environment and are suitable for mobile equipment and further integration. The log-domain principle enables the frequency responses of the filters to be electronically tunable between 10k Hz–10 MHz. The proposed filters are applicable for low-frequency biosensors by reconfiguring certain capacitors. The filters can efficiently remove low-frequency noise and random noise in the electrocardiogram (ECG) signal.

Description: Background. Patients suffering from cerebellar ataxia have extremely variable gait kinematic features. We investigated whether and how wearable inertial sensors can describe the gait kinematic features among ataxic patients. Methods. We enrolled 17 patients and 16 matched control subjects. We acquired data by means of an inertial sensor attached to an ergonomic belt around pelvis, which was connected to a portable computer via Bluetooth. Recordings of all the patients were obtained during overground walking. From the accelerometric data, we obtained the harmonic ratio (HR), i.e., a measure of the acceleration patterns, smoothness and rhythm, and the step length coefficient of variation (CV), which evaluates the variability of the gait cycle. Results. Compared to controls, patients had a lower HR, meaning a less harmonic and rhythmic acceleration pattern of the trunk, and a higher step length CV, indicating a more variable step length. Both HR and step length CV showed a high effect size in distinguishing patients and controls (p 〈 0.001 and p = 0.011, respectively). A positive correlation was found between the step length CV and both the number of falls (R = 0.672; p = 0.003) and the clinical severity (ICARS: R = 0.494; p = 0.044; SARA: R = 0.680; p = 0.003). Conclusion. These findings demonstrate that the use of inertial sensors is effective in evaluating gait and balance impairment among ataxic patients.

Description: The segmentation of buildings in remote-sensing (RS) images plays an important role in monitoring landscape changes. Quantification of these changes can be used to balance economic and environmental benefits and most importantly, to support the sustainable urban development. Deep learning has been upgrading the techniques for RS image analysis. However, it requires a large-scale data set for hyper-parameter optimization. To address this issue, the concept of “one view per city” is proposed and it explores the use of one RS image for parameter settings with the purpose of handling the rest images of the same city by the trained model. The proposal of this concept comes from the observation that buildings of a same city in single-source RS images demonstrate similar intensity distributions. To verify the feasibility, a proof-of-concept study is conducted and five fully convolutional networks are evaluated on five cities in the Inria Aerial Image Labeling database. Experimental results suggest that the concept can be explored to decrease the number of images for model training and it enables us to achieve competitive performance in buildings segmentation with decreased time consumption. Based on model optimization and universal image representation, it is full of potential to improve the segmentation performance, to enhance the generalization capacity, and to extend the application of the concept in RS image analysis.

Description: As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.

Description: A high-performance solar-blind photodetector with a metal–semiconductor–metal structure was fabricated based on amorphous In-doped Ga2O3 thin films prepared at room temperature by radio frequency magnetron sputtering. The photodetector shows a high responsivity (18.06 A/W) at 235 nm with a fast rise time (4.9 μs) and a rapid decay time (230 μs). The detection range was broadened compared with an individual Ga2O3 photodetector because of In doping. In addition, the uneven In distribution at different areas in the film results in different resistances, which causes a quasi-Zener tunneling internal gain mechanism. The quasi-Zener tunneling internal gain mechanism has a positive impact on the fast response speed and high responsivity.

Description: This study is aimed at bridging the gap from a transdisciplinary perspective between cyber-physical systems (CPS) architecture in the field of information science and emotional design in the field of humanistic science for interactive fashion innovation. Information related to a familiar feeling in the process of interactive clothing design is used to explain how the transformation could be realized from data. By creating the cyber-physical-clothing systems (CPCS), the architecture model in the hyper world and takes the development process of an interactive parent-child clothing as a case study for analyzing the transformation from the physical signal input to the social symbol recognition output. The experimental results, which from the perspective of clothing art design rather than information discipline, show that interactive parent-child clothing is not only suitable for the rehabilitation of autistic children but also recognized by most parents. The reasonable embedding of sensing technology can greatly enhance the added value of clothing products. This study provides a fruitful practical application reference for designers who are engaged in the field of art and design but not familiar with the relevant information technology. Furthermore, the application principle and the technical process of CPCS for further interactive clothing design is explained.

Description: Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.

Description: Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment agencies, the number of sensors that can be deployed is limited. In this study we show that machine learning can be used to effectively calibrate lower cost optical particle counters. For this calibration it is critical that measurements of the atmospheric pressure, humidity, and temperature are also made.

Description: Metabolic syndrome is a condition that results from dysfunction of different metabolic pathways leading to increased risk of disorders such as hyperglycemia, atherosclerosis, cardiovascular diseases, cancer, neurodegenerative disorders etc. As this condition cannot be diagnosed based on a single marker, multiple markers need to be detected and quantified to assess the risk facing an individual of metabolic syndrome. In this context, chemical- and bio-sensors capable of detecting multiple analytes may provide an appropriate diagnostic strategy. Research in this field has resulted in the evolution of sensors from the first generation to a fourth generation of ‘smart’ sensors. A shift in the sensing paradigm involving the sensing element and transduction strategy has also resulted in remarkable advancements in biomedical diagnostics particularly in terms of higher sensitivity and selectivity towards analyte molecule and rapid response time. This review encapsulates the significant advancements reported so far in the field of sensors developed for biomarkers of metabolic syndrome.

Description: Non-enzymatic glucose sensing is a crucial field of study because of the current market demand. This study proposes a novel design of glucose sensor with enhanced selectivity and sensitivity by using graphene Schottky diodes, which is composed of graphene (G)/platinum oxide (PtO)/n-silicon (Si) heterostructure. The sensor was tested with different glucose concentrations and interfering solutions to investigate its sensitivity and selectivity. Different structures of the device were studied by adjusting the platinum oxide film thickness to investigate its catalytic activity. It was found that the film thickness plays a significant role in the efficiency of glucose oxidation and hence in overall device sensitivity. 0.8–2 μA output current was obtained in the case of 4–10 mM with a sensitivity of 0.2 A/mM.cm2. Besides, results have shown that 0.8 A and 15 A were obtained by testing 4 mM glucose on two different PtO thicknesses, 30 nm and 50 nm, respectively. The sensitivity of the device was enhanced by 150% (i.e., up to 30 A/mM.cm2) by increasing the PtO layer thickness. This was attributed to both the increase of the number of active sites for glucose oxidation as well as the increase in the graphene layer thickness, which leads to enhanced charge carriers concentration and mobility. Moreover, theoretical investigations were conducted using the density function theory (DFT) to understand the detection method and the origins of selectivity better. The working principle of the sensors puts it in a competitive position with other non-enzymatic glucose sensors. DFT calculations provided a qualitative explanation of the charge distribution across the graphene sheet within a system of a platinum substrate with D-glucose molecules above. The proposed G/PtO/n-Si heterostructure has proven to satisfy these factors, which opens the door for further developments of more reliable non-enzymatic glucometers for continuous glucose monitoring systems.

Description: The paper presents a highly efficient, low cost, ultra-wideband, microstrip monopole antenna for microwave imaging and wireless communications applications. A new structure (z-shape, ultra-wideband (UWB) monopole) is designed, which consists of stepped meander lines to achieve super-wide bandwidth and high efficiency. Three steps are used to design the proposed structure for the purpose to achieve high efficiency and wide bandwidth. The antenna bandwidth is enhanced by varying the length of meander line slots, optimization of the feeding line and with the miniaturization of the ground width. The simulated and measured frequency bands are 2.7–22.5 GHz and 2.8–22.7 GHz (156% fractional bandwidth), respectively. The dimensions of the antenna are 38 mm × 35 mm × 1.57 mm, and its corresponding electrical size is 2.41 λg × 2.22 λg × 0.09 λg, where guided wavelength λg is at the center frequency (12.75 GHz). This antenna achieved a high bandwidth ratio (8.33:1). The realized gain is varying from 1.6–6.4 dBi, while that of efficiency is 70% to 93% for the whole band. Radiation patterns are measured at four operating frequencies. It has an acceptable group delay, fidelity factor, and phase variation results that satisfy the limit of ultra-wideband in the form of the time domain.

Description: Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a fast method of global registration, which is based on RGB (Red, Green, Blue) value by using the four initial point pairs (FIPP) algorithm. First, the number of different RGB values of points in a dataset are counted and the colors in the target dataset having too few points are discarded by using a color filter. A candidate point set in the source dataset are then generated by comparing the similarity of colors between two datasets with color tolerance, and four point pairs are searched from the two datasets by using an improved FIPP algorithm. Finally, a rigid transformation matrix of global registration is calculated with total least square (TLS) and local registration with the iterative closest point (ICP) algorithm. The proposed method (RGB-FIPP) has been validated with two types of data, and the results show that it can effectively improve the speed of 3D point cloud registration while maintaining high accuracy. The method is suitable for points with RGB values.

Description: Sparse iterative covariance-based estimation, an iterative direction-of-arrival approach based on covariance fitting criterion, can simultaneously estimate the angle and power of incident signal. However, the signal power estimated by sparse iterative covariance-based estimation approach is inaccurate, and the estimation performance is limited to direction grid. To solve the problem above, an algorithm combing the sparse iterative covariance-based estimation approach and maximum likelihood estimation is proposed. The signal power estimated by sparse iterative covariance-based estimation approach is corrected by a new iterative process based on the asymptotically minimum variance criterion. In addition, a refinement procedure is derived by minimizing a maximum likelihood function to overcome the estimation accuracy limitation imposed by direction grid. Simulation results verify the effectiveness of the proposed algorithm. Compared with sparse iterative covariance-based estimation approach, the proposed algorithm can achieve more accurate signal power and improved estimation performance.

Description: This paper reports on a portable selective chemical sensor for hazardous vapors at trace levels, which combines a two-stage purge and trap vapor pre-concentration system, a Micro-Electro-Mechanical-System (MEMS) based fast gas-chromatographic (FAST-GC) separation column and a miniaturized quartz-enhanced photoacoustic spectroscopy (QEPAS) detector. The integrated sensing system provides two-dimensional selectivity combining GC retention time and QEPAS spectral information, and was specifically designed to be rugged and suitable to be deployed on unmanned robotic ground vehicles. This is the first demonstration of a miniaturized QEPAS device used as spectroscopic detector downstream of a FAST-GC separation column, enabling real-world analyses in dirty environments with response time of a few minutes. The main modules of the GC/QEPAS sensor device will be described in detail together with the system integration, and successful test results will be reported and discussed.

Description: Diabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient’s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject’s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.

Description: The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy.

Description: The authors wish to make the following corrections to this paper [...]

Description: The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions.

Description: Procalcitonin (PCT) protein has recently been identified as a clinical marker for bacterial infections based on its better sepsis sensitivity. Thus, an increased level of PCT could be linked with disease diagnosis and therapeutics. In this study, we describe the construction of the photoelectrochemical (PEC) PCT immunosensing platform based on it situ grown photo-active CuWO4 nanospheres over reduced graphene oxide layers (CuWO4@rGO). The in situ growth strategy enabled the formation of small nanospheres (diameter of 200 nm), primarily composed of tiny self-assembled CuWO4 nanoparticles (2–5 nm). The synergic coupling of CuWO4 with rGO layers constructed an excellent photo-active heterojunction for photoelectrochemical (PEC) sensing. The platform was then considered for electrocatalytic (EC) mechanism-based detection of PCT, where inhibition of the photocatalytic oxidation signal of ascorbic acid (AA), subsequent to the antibody–antigen interaction, was recorded as the primary signal response. This inhibition detection approach enabled sensitive detection of PCT in a concentration range of 10 pg·mL−1 to 50 ng.mL−1 with signal sensitivity achievable up to 0.15 pg·mL−1. The proposed PEC hybrid (CuWO4@rGO) could further be engineered to detect other clinically important species.

Description: Noise pollution is a problem that affects millions of people worldwide. Over the last few years, many researchers have devoted their attention to the design of wireless acoustic sensor networks (WASNs) to monitor the real data of continuous and precise noise levels and to create noise maps in real time and space. Although WASNs are becoming a reality in smart cities, some research studies argue that very few projects have been deployed around the world, with most of them deployed as pilots for only days or weeks, with a small number of nodes. In this paper, we describe the design and implementation of a complete system for a WASN deployed in the city of Linares (Jaén), Spain, which has been running continuously for ten months. The complete system covers the network topology design, hardware and software of the sensor nodes, protocols, and a private cloud web server platform. As a result, the information provided by the system for each location where the sensor nodes are deployed is as follows: LAeq for a given period of time; noise indicators Lden, Lday, Levening, and Lnight; percentile noise levels (LA01T, LA10T, LA50T, LA90T, and LA99T); a temporal evolution representation of noise levels; and the predominant frequency of the noise. Some comparisons have been made between the noise indicators calculated by the sensor nodes and those from a commercial sound level meter. The results suggest that the proposed system is perfectly suitable for use as a starting point to obtain accurate maps of the noise levels in smart cities.

Description: Person tracking is an important issue in both computer vision and robotics. However, most existing person tracking methods using 3D point cloud are based on the Bayesian Filtering framework which are not robust in challenging scenes. In contrast with the filtering methods, in this paper, we propose a neural network to cope with person tracking using only 3D point cloud, named Point Siamese Network (PSN). PSN consists of two input branches named template and search, respectively. After finding the target person (by reading the label or using a detector), we get the inputs of the two branches and create feature spaces for them using feature extraction network. Meanwhile, a similarity map based on the feature space is proposed between them. We can obtain the target person from the map. Furthermore, we add an attention module to the template branch to guide feature extraction. To evaluate the performance of the proposed method, we compare it with the Unscented Kalman Filter (UKF) on 3 custom labeled challenging scenes and the KITTI dataset. The experimental results show that the proposed method performs better than UKF in robustness and accuracy and has a real-time speed. In addition, we publicly release our collected dataset and the labeled sequences to the research community.

Description: The area covered by Chinese-style solar greenhouses (CSGs) has been increasing rapidly. However, only a few pyranometers, which are fundamental for solar radiation sensing, have been installed inside CSGs. The lack of solar radiation sensing will bring negative effects in greenhouse cultivation such as over irrigation or under irrigation, and unnecessary power consumption. We aim to provide accurate and low-cost solar radiation estimation methods that are urgently needed. In this paper, a method of estimation of solar radiation inside CSGs based on a least mean squares (LMS) filter is proposed. The water required for tomato growth was also calculated based on the estimated solar radiation. Then, we compared the accuracy of this method to methods based on knowledge of astronomy and geometry for both solar radiation estimation and tomato water requirement. The results showed that the fitting function of estimation data based on the LMS filter and data collected from sensors inside the greenhouse was y = 0.7634x + 50.58, with the evaluation parameters of R2 = 0.8384, rRMSE = 23.1%, RMSE = 37.6 Wm−2, and MAE = 25.4 Wm−2. The fitting function of the water requirement calculated according to the proposed method and data collected from sensors inside the greenhouse was y = 0.8550x + 99.10 with the evaluation parameters of R2 = 0.9123, rRMSE = 8.8%, RMSE = 40.4 mL plant−1, and MAE = 31.5 mL plant−1. The results also indicate that this method is more effective. Additionally, its accuracy decreases as cloud cover increases. The performance is due to the LMS filter’s low pass characteristic that smooth the fluctuations. Furthermore, the LMS filter can be easily implemented on low cost processors. Therefore, the adoption of the proposed method is useful to improve the solar radiation sensing in CSGs with more accuracy and less expense.

Description: Vapor sensing via light reflected from photonic crystals has been increasingly studied as a means to rapidly identify analytes, though few studies have characterized vapor mixtures or chemical warfare agent simulants via this technique. In this work, light reflected from the natural photonic crystals found within the wing scales of the Morpho didius butterfly was analyzed after exposure to binary and tertiary mixtures containing dimethyl methylphosphonate, a nerve agent simulant, and dichloropentane, a mustard gas simulant. Distinguishable spectra were generated with concentrations tested as low as 30 ppm and 60 ppm for dimethyl methylphosphonate and dichloropentane, respectively. Individual vapors, as well as mixtures, yielded unique responses over a range of concentrations, though the response of binary and tertiary mixtures was not always found to be additive. Thus, while selective and sensitive to vapor mixtures containing chemical warfare agent simulants, this technique presents challenges to identifying these simulants at a sensitivity level appropriate for their toxicity.

Description: Various gait parameters can be used to assess the risk of falling in older adults. However, the state-of-the-art systems used to quantify gait parameters often come with high costs as well as training and space requirements. Gait analysis systems, which use mobile and commercially available cameras, can be an easily available, marker-free alternative. In a study with 44 participants (age ≥ 65 years), gait patterns were analyzed with three different systems: a pressure sensitive walkway system (GAITRite-System, GS) as gold standard, Motognosis Labs Software using a Microsoft Kinect Sensor (MKS), and a smartphone camera-based application (SCA). Intertrial repeatability showed moderate to excellent results for MKS (ICC(1,1) 0.574 to 0.962) for almost all measured gait parameters and moderate reliability in SCA measures for gait speed (ICC(1,1) 0.526 to 0.535). All gait parameters of MKS showed a high level of agreement with GS (ICC(2,k) 0.811 to 0.981). Gait parameters extracted with SCA showed poor reliability. The tested gait analysis systems based on different camera systems are currently only partially able to capture valid gait parameters. If the underlying algorithms are adapted and camera technology is advancing, it is conceivable that these comparatively simple methods could be used for gait analysis.

Description: Insufficient power supply is a huge challenge for wireless body area network (WBAN). Body channel wireless power transfer (BC-WPT) is promising to realize multi-node high-efficiency power transmission for miniaturized WBAN nodes. However, the behavior of BC-WPT, especially in the multi-node scenario, is still lacking in research. In this paper, the inter-degeneration mechanism of a multi-node BC-WPT is investigated based on the intuitive analysis of the existing circuit model. Co-simulation in the Computer Simulation Technology (CST) and Cadence platform and experiments in a general indoor environment verify this mechanism. Three key factors, including the distance between the source and the harvester, frequency of the source, and area of the ground electrodes, are taken into consideration, resulting in 15 representative cases for simulation and experiments studies. Based on the simulation parameters, an empirical circuit model to accurately predict the received power of multiple harvesters is established, which fits well with the measurement results, and can further provide guidelines for designs and research on multi-node BC-WPT systems.

Description: In a dual-channel circular synthetic aperture radar (CSAR) and ground moving target indication (GMTI) system, the antenna baseline is not parallel with the flight path due to a yaw angle. The angle causes a varying group-phase shift between the dual-channel signals and therefore degrades the correlation between the image pair. Therefore, the group-phase shift needs to be removed before channel equalization. To resolve the problem, the interferometric phase term was deduced and analyzed based on the geometry of a dual-channel CSAR system. Then, the varying phase term with respect to the Doppler frequency and the varying group-phase shift over the range were compensated for in the channel registration. Furthermore, blind channel equalization, including two-dimensional calibration and amplitude equalization, was applied to eliminate the amplitude and residual phase differences between the channels. Finally, the amplitude image obtained using a displaced phase center antenna (DPCA) was multiplied by the phase image obtained with along-track interferometry (ATI) to detect moving targets. The experimental results verified the effectiveness of the method for both uniform and non-uniform clutter suppression.

Description: Isoleucine is one of the branched chain amino acids that plays a major role in the energy metabolism of human beings and animals. However, detailed investigation of specific receptors for isoleucine has not been carried out because of the non-availability of a tool that can monitor the metabolic flux of this amino acid in live cells. This study presents a novel genetically-encoded nanosensor for real-time monitoring of isoleucine in living cells. This nanosensor was developed by sandwiching a periplasmic binding protein (LivJ) of E. coli between a fluorescent protein pair, ECFP (Enhanced Cyan Fluorescent Protein), and Venus. The sensor, named GEII (Genetically Encoded Isoleucine Indicator), was pH stable, isoleucine-specific, and had a binding affinity (Kd) of 63 ± 6 μM. The GEII successfully performed real-time monitoring of isoleucine in bacterial and yeast cells, thereby, establishing its bio-compatibility in monitoring isoleucine in living cells. As a further enhancement, in silico random mutagenesis was carried out to identify a set of viable mutations, which were subsequently experimentally verified to create a library of affinity mutants with a significantly expanded operating range (96 nM–1493 μM). In addition to its applicability in understanding the underlying functions of receptors of isoleucine in metabolic regulation, the GEII can also be used for metabolic engineering of bacteria for enhanced production of isoleucine in animal feed industries.

Description: During open-heart surgery, the status of hemostasis has to be constantly monitored to quickly and reliably detect bleeding or coagulation disorders. In this study, a novel optimized piezo-based measuring system (PIEZ) for rheological monitoring of hemostasis was established. The applicability of the PIEZ for the evaluation of nucleic acid-based drugs influencing coagulation was analyzed. Thrombin aptamers such as NU172 might be used during extracorporeal circulation (ECC) in combination with a reduced heparin concentration or for patients with heparin-induced thrombocytopenia (HIT). Therefore, the effect of the coagulation inhibiting thrombin aptamer NU172 and the abrogation by its complementary antidote sequence (AD) were investigated by this rheological PIEZ system. After the addition of different NU172 concentrations, the coagulation of fresh human blood was analyzed under static conditions and using an in vitro rotation model under dynamic conditions (simulating ECC). The clotting times (CTs) detected by PIEZ were compared to those obtained with a medical reference device, a ball coagulometer. Additionally, after the circulation of blood samples for 30 min at 37 °C, blood cell numbers, thrombin markers (thrombin-antithrombin III (TAT) and fibrinopeptide A (FPA)) and a platelet activation marker (β-thromboglobulin (β-TG)) were analyzed by enzyme-linked immunosorbent assays (ELISAs). The increase of NU172 concentration resulted in prolonged CTs, which were comparable between the reference ball coagulometer and the PIEZ, demonstrating the reliability of the new measuring system. Moreover, by looking at the slope of the linear regression of the viscous and elastic components, PIEZ also could provide information on the kinetics of the coagulation reaction. The shear viscosity at the end of the measurements (after 300 s) was indicative of clot firmness. Furthermore, the PIEZ was able to detect the abrogation of coagulation inhibition after the equimolar addition of NU172 aptamer´s AD. The obtained results showed that the established PIEZ is capable to dynamically measure the hemostasis status in whole blood and can be applied to analyze nucleic acid-based drugs influencing the coagulation.

Description: Indoor navigation systems offer many application possibilities for people who need information about the scenery and the possible fixed and mobile obstacles placed along the paths. In these systems, the main factors considered for their construction and evaluation are the level of accuracy and the delivery time of the information. However, it is necessary to notice obstacles placed above the user’s waistline to avoid accidents and collisions. In this paper, different methodologies are associated to define a hybrid navigation model called iterative pedestrian dead reckoning (i-PDR). i-PDR combines the PDR algorithm with a Kalman linear filter to correct the location, reducing the system’s margin of error iteratively. Obstacle perception was addressed through the use of stereo vision combined with a musical sounding scheme and spoken instructions that covered an angle of 120 degrees in front of the user. The results obtained in the margin of error and the maximum processing time are 0.70 m and 0.09 s, respectively, with obstacles at ground level and suspended with an accuracy equivalent to 90%.

Description: Traffic analyses, particularly speed measurements, are highly valuable in terms of road safety and traffic management. In this paper, an analytical model is presented to measure the speed of a moving vehicle using an off-the-shelf video camera. The method utilizes the temporal sampling rate of the camera and several intrusion lines in order to estimate the probability density function (PDF) of a vehicle’s speed. The proposed model provides not only an accurate estimate of the speed, but also the possibility of being able to study the performance boundaries with respect to the camera frame rate as well as the placement and number of intrusion lines in advance. This analytical model is verified by comparing its PDF outputs with the results obtained via a simulation of the corresponding movements. In addition, as a proof-of-concept, the proposed model is implemented for a video-based vehicle speed measurement system. The experimental results demonstrate the model’s capability in terms of taking accurate measurements of the speed via a consideration of the temporal sampling rate and lowering the deviation by utilizing more intrusion lines. The analytical model is highly versatile and can be used as the core of various video-based speed measurement systems in transportation and surveillance applications.

Description: The use of medium voltage cable joints is mandatory when dealing with power cable faults and the installation of new lines. However, such an accessory is among the top causes of faults among the grid. To this purpose, one of the quantities monitored to understand the causes of such faults is the interfacial pressure between the insulating layers of the cable joint. In this work, the interfacial pressure between Cross-linked polyethylene (XLPE) and silicon rubber has been evaluated when the cable joint experiences thermal cycles. From the results, the pressure variation caused by the thermal cycles is demonstrated. Such a phenomenon may be connected to the generation of voids and weak spots that accelerate cable joint ageing. Therefore, proper comments and conclusions are drawn.

Description: Vibration induces a micro-Doppler effect, which contains crucial motion information and features of the micro-motion target. However, the traditional radar can only acquire micro-motion information in the radial direction. The multi-channel radar is capable of acquiring actual micro-motion information in a three-dimensional space, including amplitude, frequency, and three-dimensional direction of vibration. Moreover, the high-accuracy parameter estimation can be achieved in the terahertz band. In this paper, a method for three-dimensional micro-motion feature exaction and parameter estimation is proposed, including a phase-derived-range algorithm, interference, and auto-correlation. Meanwhile, to prove feasibility of the method, the results of simulations and experiment based on a 0.22 THz multi-channel radar system are presented after theoretical analyses.

Description: In cluster-based wireless sensor networks, cluster heads (CHs) gather and fuse data packets from sensor nodes; then, they forward fused packets to the sink node (SN). This helps wireless sensor networks balance energy effectively and efficiently to prolong their lifetime. However, cluster-based WSNs are vulnerable to selective forwarding attacks. Compromised CHs would become malicious and launch selective forwarding attacks in which they drop part of or all the packets from other nodes. In this paper, a data clustering algorithm (DCA) for detecting a selective forwarding attack (DCA-SF) is proposed. It can capture and isolate malicious CHs that have launched selective forwarding attacks by clustering their cumulative forwarding rates (CFRs). The DCA-SF algorithm has been strengthened by changing the DCA parameters (Eps, Minpts) adaptively. The simulation results show that the DCA-SF has a low missed detection rate of 1.04% and a false detection rate of 0.42% respectively with low energy consumption.

Description: Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

Description: The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user’s head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user’s head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.

Description: This paper demonstrates the use of Universal Software Radio Peripheral (USRP), together with Raspberry Pi3 B+ (RP3) as the brain (or the decision making engine), to develop a distributed wireless network in which nodes can communicate with other nodes independently and make decision autonomously. In other words, each USRP node (i.e., sensor) is embedded with separate processing units (i.e., RP3), which has not been investigated in the literature, so that each node can make independent decisions in a distributed manner. The proposed testbed in this paper is compared with the traditional distributed testbed, which has been widely used in the literature. In the traditional distributed testbed, there is a single processing unit (i.e., a personal computer) that makes decisions in a centralized manner, and each node (i.e., USRP) is connected to the processing unit via a switch. The single processing unit exchanges control messages with nodes via the switch, while the nodes exchange data packets among themselves using a wireless medium in a distributed manner. The main disadvantage of the traditional testbed is that, despite the network being distributed in nature, decisions are made in a centralized manner. Hence, the response delay of the control message exchange is always neglected. The use of such testbed is mainly due to the limited hardware and monetary cost to acquire a separate processing unit for each node. The experiment in our testbed has shown the increase of end-to-end delay and decrease of packet delivery ratio due to software and hardware delays. The observed multihop transmission is performed using device-to-device (D2D) communication, which has been enabled in 5G. Therefore, nodes can either communicate with other nodes via: (a) a direct communication with the base station at the macrocell, which helps to improve network performance; or (b) D2D that improve spectrum efficiency, whereby traffic is offloaded from macrocell to small cells. Our testbed is the first of its kind in this scale, and it uses RP3 as the distributed decision-making engine incorporated into the USRP/GNU radio platform. This work provides an insight to the development of a 5G network.

Description: Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).

Description: C-arm X-ray imaging is commonly applied in operating rooms for guiding orthopedic surgeries. Augmented Reality (AR) with C-arm X-ray images during surgery is an efficient way to facilitate procedures for surgeons. However, the accurate calibration process for surgical AR based on C-arm is essential and still challenging due to the limitations of C-arm imaging systems, such as instability of C-arm calibration parameters and the narrow field of view. We extend existing methods using a depth camera and propose a new calibration procedure consisting of calibration of the C-arm imaging system, and 3D/2D calibration of an RGB-D camera and C-arm system with a new method to achieve reliable data and promising accuracy and, at the same time, consistent with standard surgical protocols. For the calibration procedure, we apply bundle adjustment equations with a 3D designed Lego multi-modal phantom, in contrast to the previous methods in which planar calibration phantoms were applied. By using our method, the visualization of the X-ray image upon the 3D data was done, and the achieved mean overlay error was 1.03 mm. The evaluations showed that the proposed calibration procedure provided promising accuracy for AR surgeries and it improved the flexibility and robustness of existing C-arm calibration methods for surgical augmented reality (using C-arm and RGB-D sensor). Moreover, the results showed the efficiency of our method to compensate for the effects of the C-arm movement on calibration parameters. It was shown that the obtained overlay error was improved for the non-zero rotation movement of C-arm by using a virtual detector.

Description: Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.

Description: The electric power system plays an important role in sensor networks. In the marine seismic exploration streamer system (MSESS), an underwater power system transmits high-voltage direct current to all nodes in the streamer through a daisy chain structure. As offshore oil exploration develops toward deep water, it is necessary to study long streamers with large-scale sensor networks for deep water exploration. When the length of a streamer is increased to a certain value, the output current of the power supply increases sharply. This results in the activation of the overcurrent protection and the power supply shuts down. This paper puts forward an accurate model for an underwater power system applied to MSESS. Using the Newton iteration algorithm and a reverse algorithm, equations established by the model are solved and laboratory test results are used to verify the accuracy of the model. Based on simulation and analysis of the model, we explain why the power system crashes when the streamer is too long. Software that can quickly calculate the maximum number of nodes (the maximum length with which the system works normally) is developed and it is significant for the design of MSESS. The method of research could also be applied to relevant work such as large-scale sensor networks with daisy-chaining power supply in land seismic exploration.

Description: Asymmetry is a cardinal symptom of gait post-stroke that is targeted during rehabilitation. Technological developments have allowed accelerometers to be a feasible tool to provide digital gait variables. Many acceleration-derived variables are proposed to measure gait asymmetry. Despite a need for accurate calculation, no consensus exists for what is the most valid and reliable variable. Using an instrumented walkway (GaitRite) as the reference standard, this study compared the validity and reliability of multiple acceleration-derived asymmetry variables. Twenty-five post-stroke participants performed repeated walks over GaitRite whilst wearing a tri-axial accelerometer (Axivity AX3) on their lower back, on two occasions, one week apart. Harmonic ratio, autocorrelation, gait symmetry index, phase plots, acceleration, and jerk root mean square were calculated from the acceleration signals. Test–retest reliability was calculated, and concurrent validity was estimated by comparison with GaitRite. The strongest concurrent validity was obtained from step regularity from the vertical signal, which also recorded excellent test–retest reliability (Spearman’s rank correlation coefficients (rho) = 0.87 and Intraclass correlation coefficient (ICC21) = 0.98, respectively). Future research should test the responsiveness of this and other step asymmetry variables to quantify change during recovery and the effect of rehabilitative interventions for consideration as digital biomarkers to quantify gait asymmetry.

Description: The “dual probe heat pulse” (DPHP) method using actively heated fiber optic (AHFO) cables combined with distributed temperate sensing (DTS) technology has been developed for monitoring thermal properties and soil water content at the field scale. Field scale application, however, requires the use of robust and thicker fiber optic cables, corroborating the assumption of an infinite thin heat source in the evaluation process. We therefore included a semi-analytical solution of the heat transport equation into the evaluation procedure in order to consider the finite thermal properties of the heating cable without a calibration procedure to estimate effective thermal properties of the soil. To test this new evaluation procedure, we conducted a laboratory experiment and tested different heating scenarios to infer soil moisture from volumetric heat capacity. Estimates were made by analyzing the shift of the temperature amplitude at the sensing cable and the characteristics of the response heating curve. The results were compared with results from the calibrated infinite line source solution and in situ water content point measurements and showed a good approximation of thermal properties for strong and short heat pulses. Volumetric water content estimates are similarly accurate to the results of the calibrated infinite line source solution. Problems arose with the cable spacing and the resettlement process after burying the cable.

Description: In this paper, a multi-frame based homography estimation method is proposed for video stitching in static camera environments. A homography that is robust against spatio-temporally induced noise can be estimated by intervals, using feature points extracted during a predetermined time interval. The feature point with the largest blob response in each quantized location bin, a representative feature point, is used for matching a pair of video sequences. After matching representative feature points from each camera, the homography for the interval is estimated by random sample consensus (RANSAC) on the matched representative feature points, with their chances of being sampled proportional to their numbers of occurrences in the interval. The performance of the proposed method is compared with that of the per-frame method by investigating alignment distortion and stitching scores for daytime and noisy video sequence pairs. It is shown that alignment distortion in overlapping regions is reduced and the stitching score is improved by the proposed method. The proposed method can be used for panoramic video stitching with static video cameras and for panoramic image stitching with less alignment distortion.

Description: The optimization of production processes has always been one of the cornerstones for manufacturing companies, aimed to increase their productivity, minimizing the related costs. In the Industry 4.0 era, some innovative technologies, perceived as far away until a few years ago, have become reachable by everyone. The massive introduction of these technologies directly in the factories allows interconnecting the resources (machines and humans) and the entire production chain to be kept under control, thanks to the collection and the analyses of real production data, supporting the decision making process. This article aims to propose a methodological framework that, thanks to the use of Industrial Internet of Things—IoT devices, in particular the wearable sensors, and simulation tools, supports the analyses of production line performance parameters, by considering both experimental and numerical data, allowing a continuous monitoring of the line balancing and performance at varying of the production demand. A case study, regarding a manual task of a real manufacturing production line, is presented to demonstrate the applicability and the effectiveness of the proposed procedure.

Description: Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson’s correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.

Description: Non-invasive characterization of micro-vibrations in the tympanic membrane (TM) excited by external sound waves is considered as a promising and essential diagnosis in modern otolaryngology. To verify the possibility of measuring and discriminating the vibrating pattern of TM, here we describe a micro-vibration measurement method of latex membrane resembling the TM. The measurements are obtained with an externally generated audio stimuli of 2.0, 2.2, 2.8, 3.1 and 3.2 kHz, and their respective vibrations based tomographic, volumetric and quantitative evaluations were acquired using optical Doppler tomography (ODT). The micro oscillations and structural changes which occurred due to diverse frequencies are measured with sufficient accuracy using a highly sensitive ODT system implied phase subtraction method. The obtained results demonstrated the capability of measuring and analyzing the complex varying micro-vibration of the membrane according to implied sound frequency.

Description: This paper presents LocSpeck, a collaborative and distributed indoor positioning system for dynamic nodes connected using an ad-hoc network, based on inter-node relative range measurements and Wi-Fi fingerprinting. The proposed system operates using peer-to-peer range measurements and does not need ultra-wideband (UWB) fixed anchor, nor it needs a predefined network topology. The nodes could be asymmetric in terms of the available sensors onboard, the computational resources, and the power capacity. This asymmetry adversely affects the positioning performance of the weaker nodes. Collaboration between different nodes is achieved through a distributed estimator without the need of a single centralized computing element. The ranging measurement component of the system is based on the DW1000 UWB transceiver chip from Decawave, which is attached to a set of smartphones equipped with asymmetric sensors. The distributed positioning filter fuses, locally on each node, the relative range measurements, the reading from the internal sensors, and the Wi-Fi received signal strength indicator (RSSI) readings to obtain an estimate of the position of each node. The described system does not depend on fixed UWB anchors and supports online addition and removal of nodes and dynamic node role assignment, either as an anchor or as a rover. The performance of the system is evaluated by real-world test scenarios using a set of four smartphones navigating an indoor environment on foot. The performance is compared to that of a commercial UWB-based system. The results presented in this paper show that weak mobile nodes, in terms of available positioning sensors, can benefit from collaboration with other nearby nodes.

Description: Catching cancer at an early stage is necessary to make it easier to treat and to save people’s lives rather than just extending them. Reactive oxygen species (ROS) have sparked a huge interest owing to their vital role in various biological processes, especially in tumorigenesis, thus leading to the potential of ROS as prognostic biomarkers for cancer. Herein, a non-enzymatic biosensor for the dynamic monitoring of intracellular hydrogen peroxide (H2O2), the most important ROS, via an effective electrode composed of poly (diallyldimethylammonium chloride) (PDDA)-capped reduced graphene oxide (RGO) nanosheets with high loading trimetallic AuPtAg nanoalloy, is proposed. The designed biosensor was able to measure H2O2 released from different cancerous cells promptly and precisely owing to the impressive conductivity of RGO and PDDA and the excellent synergistic effect of the ternary alloy in boosting the electrocatalytic activity. Built upon the peroxidase-like activity of the nanoalloy, the developed sensor exhibited distinguished electrochemical performance, resulting in a low detection limit of 1.2 nM and a wide linear range from 0.05 μM to 5.5 mM. Our approach offers a significant contribution toward the further elucidation of the role of ROS in carcinogenesis and the effective screening of cancer at an early stage.

Description: The purpose of this work is to describe a first approach to a smart bioimpedance spectroscopy device for its application to the estimation of body composition. The proposed device is capable of carrying out bioimpedance measurements in multiple configurable frequencies, processing the data to obtain the modulus and the bioimpedance phase in each of the frequencies, and transmitting the processed information wirelessly. Another novelty of this work is a new algorithm for the identification of Cole model parameters, which is the basis of body composition estimation through bioimpedance spectroscopy analysis. Against other proposals, the main advantages of the proposed method are its robustness against parasitic effects by employing an extended version of Cole model with phase delay and three dispersions, its simplicity and low computational load. The results obtained in a validation study with respiratory patients show the accuracy and feasibility of the proposed technology for bioimpedance measurements. The precision and validity of the algorithm was also proven in a validation study with peritoneal dialysis patients. The proposed method was the most accurate compared with other existing algorithms. Moreover, in those cases affected by parasitic effects the proposed algorithm provided better approximations to the bioimpedance values than a reference device.

Description: Performance of the (CdZn)Te pixelated detectors heavily relies on the quality of the underlying material. Modern laser-induced transient current technique addresses this problem as a convenient tool for characterizing the associated charge distribution. In this paper, we investigated the charge sharing phenomenon in (CdZn)Te pixel detector as a function of the charge collected on adjacent pixels. The current transients were generated in the defined 4 mm2 spots using 660 nm laser illumination. Waveforms measured on the pixel of interest and its surroundings were used to build the maps of the collected charge at different biases. The detailed study of the maps allowed us to distinguish the charge sharing region, the region with a defect, and the finest part in terms of the performance part of the pixelated anode. We observed the principal inhomogeneity complicating the assignment of the illuminated spot to the nearest pixel.

Description: This paper presents an experimental platform allowing in situ measurement in an aqueous medium using an acoustic Love wave sensor. The aim of this platform, which includes the sensor, a test cell for electrical connections, a microfluidic chip, and a readout electronic circuit, is to realize a first estimation of water quality without transportation of water samples from the field to the laboratory as a medium-term objective. In the first step, to validate the ability of such a platform to operate in the field and in Amazonian water, an isolated and difficult-to-access location, namely, the floodplain Logo Do Curuaï in the Brazilian Amazon, was chosen. The ability of such a platform to be transported, installed on site, and used is discussed in terms of user friendliness and versatility. The response of the Love wave sensor to in situ water samples is estimated according to the physical parameters of Amazonian water. Finally, the very good quality of the acoustic response is established, potential further improvements are discussed, and the paper is concluded.