ALBERT

Description: This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

Description: For the problem of joint angle and range estimation with frequency diverse array (FDA), MIMO radar, staggered frequency increment is proposed to expand the range ambiguity and the joint algorithm of ESPRIT and MUSIC is proposed to reduce the computational complexity. The uniformly weighted beampattern of FDA is a SINC-like function. Therefore, the grating lobe of range estimation exists. In this paper, staggered frequency increment is proposed to increase the distance of adjacent grating lobes. The proposed joint estimation algorithm firstly estimates the angle by using ESPRIT algorithm. Then we get the range estimation by MUSIC one-dimensional range search using the above estimated angle. In simulation results section, it is indicated in simulation results that the proposed method improves the range grating lobe and reduces the complexity.

Description: 5G new radio (NR) provides enhanced transmission capabilities to transceivers by utilizing the massive multiple-input multiple-output (MIMO) technology with a significantly increased number of antenna elements. Such transmission requires massive arrays to perform accurate high-gain beamforming over the millimeter-wave frequency band. There is no fixed form of array structures for 5G NR base stations, but they are likely to include multiple subarrays or panels for practicality of implementation and are expected to cover the user equipment (UE) in various locations. In this paper, we propose an array structure to transmit signals over the three-dimensional (3D) space in an isotropic fashion for all types of UEs in ground, aerial, and high-rise building locations, by employing panels on surfaces of a polyhedron. We further derive exact beamforming equations for the proposed array and show the resulting beams provide improved receiver performance over the exiting conventional beamforming. The presented beamforming expressions can be applied to an arbitrary multipanel array with massive antenna elements.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 7, July 2019. 〈br/〉In order to resolve the issue of coverage limitation for the future fifth-generation network, deploying a relay node within a cell is one of the most capable and cost-effective solution, which not only enhances the coverage but also improves the spectral efficiency. However, this solution leads to the undesired interferences from nearby base station and relay nodes that affects user’s signal-to-interference-plus-noise ratio and can cause the ambiguous received signal at the user end. In this article, we have analyzed a relay-based interference-limited network at millimeter wave frequency and proposed a Poisson point process–based model using a stochastic geometrical approach. The results for the proposed Poisson point process model have been evaluated in terms of success probability, network ergodic capacity, and outage probability, compared with the ideal grid model and conventional multiple-antenna ultra-dense network model. The results proved that the success probability and ergodic capacity for the proposed model are 3.5% and 2.3% higher as compared to the most commonly used model for the high-density network, respectively. Furthermore, the results have been analyzed at different multiple-input-multiple-output antenna configuration, which validates the model in the improvement of overall network performance even for higher number of antennas.

Description: Artificial vision systems (AVS) have become very important in precision agriculture applied to produce high-quality and low-cost foods with high functional characteristics generated through environmental care practices. This article reported the design and implementation of a new fuzzy classification architecture based on the RGB color model with descriptors. Three inputs were used that are associated with the average value of the color components of four views of the tomato; the number of triangular membership functions associated with the components and were three and four for the case of component . The amount of tomato samples used in training were forty and twenty for testing; the training was done using the Matlab© ANFISEDIT. The tomato samples were divided into six categories according to the US Department of Agriculture (USDA). This study focused on optimizing the descriptors of the color space to achieve high precision in the prediction results of the final classification task with an error of -6. The Computer Vision System (CVS) is integrated by an image isolation system with lighting; the image capture system uses a Raspberry Pi 3 and Camera Module Raspberry Pi 2 at a fixed distance and a black background. In the implementation of the CVS, three different color description methods for tomato classification were analyzed and their respective diffuse systems were also designed, two of them using the descriptors described in the literature.

Description: Analysis of electromagnetic wave coupling to thin-wire structures plays a very important role in electromagnetic compatibility (EMC). In this paper, a hybrid method, which is integrated parabolic equation (PE) and two-potential integral equation (TPIE), is presented to analyze the coupling problems in terrain environments. To model the realistic scenarios, PE based on the split-step Fourier transform (SSFT) technique is applied to solve the three-dimensional field distribution to obtain the external excitations for the wires. According to the boundary conditions, the high-precision TPIE solved via the moment method (MoM) is developed to simulate the induced currents on the wires. The hybrid method takes the terrain influences into account and provides a more reasonable result compared to the traditional approaches. Numerical examples are given to demonstrate correctness of the proposed method. Simulation experiments of field-to-transmission lines with different frequencies, radiation source heights, conductor radii, and lengths, in a realistic scenario constructed by a digital map, are carried out to investigate the coupling properties.

Description: Traffic data plays a very important role in Intelligent Transportation Systems (ITS). ITS requires complete traffic data in transportation control, management, guidance, and evaluation. However, the traffic data collected from many different types of sensors often includes missing data due to sensor damage or data transmission error, which affects the effectiveness and reliability of ITS. In order to ensure the quality and integrity of traffic flow data, it is very important to propose a satisfying data imputation method. However, most of the existing imputation methods cannot fully consider the impact of sensor data with data missing and the spatiotemporal correlation characteristics of traffic flow on imputation results. In this paper, a traffic data imputation method is proposed based on improved low-rank matrix decomposition (ILRMD), which fully considers the influence of missing data and effectively utilizes the spatiotemporal correlation characteristics among traffic data. The proposed method uses not only the traffic data around the sensor including missing data, but also the sensor data with data missing. The information of missing data is reflected into the coefficient matrix, and the spatiotemporal correlation characteristics are applied in order to obtain more accurate imputation results. The real traffic data collected from the Caltrans Performance Measurement System (PeMS) are used to evaluate the imputation performance of the proposed method. Experiment results show that the average imputation accuracy with proposed method can be improved 87.07% compared with the SVR, ARIMA, KNN, DBN-SVR, WNN, and traditional MC methods, and it is an effective method for data imputation.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 7, July 2019. 〈br/〉Multi-hop wireless sensor networks are widely used in many location-dependent applications. Most applications assume the knowledge of geographic location of sensor nodes; however, in practical scenarios, the high accuracy on position estimates of sensor nodes is still a great challenge. In this research, we propose a hop-weighted scheme that can be useful in distance-based distributed multi-hop localization. The hop-weighted localization approach generates spatial locations around position estimates of unknown sensors and computes local functions that minimize distance errors among hop-weighted and static neighboring sensors. The iterative process of each unknown sensor to re-estimate its own location allows a significant reduction of initial position estimates. Simulations demonstrate that this weighted localization approach, when compared with other schemes, can be suitable to be used as a refinement stage to improve localization in both isotropic and anisotropic networks. Also, under rough initial position estimates, the proposed algorithm achieves root mean square error values less than the radio range of unknown sensors, in average, with only a few iterations.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 7, July 2019. 〈br/〉Due to the increased demand of wireless sensor networks for their characteristics like low energy consumption, robustness, and low cost in several demanding and complex applications like smart grid, health and safety, traffic and weather updates, there is need of monitoring the infrastructure in a timely manner with high reliability. In this article, a cross-layer data communication scheme target-aware cross-layer technique is proposed to enhance reliability and to reduce the latency in wireless sensor networks. The proposed scheme uses connected dominating set at network layer where nodes in the connected dominating set are directly connected to all the other nodes in the network. Each sensor node sends the data to the nodes present in the connected dominating set, which forwards it to their respective destinations. The proposed scheme reduces the chances of collision resulting reduced delays. Higher packet delivery ratio is achieved with the proposed scheme results in improved reliability. The proposed scheme is outperforming other state-of-the-art schemes in terms of packet delivery ratio, latency, and data throughput with the default 802.15.4 and delay-responsive cross layer in both static and mobile scenarios using network simulator tool.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 7, July 2019. 〈br/〉In this article, we present a sensitivity-enhancing feedback control–based damage detection method for piezoelectric actuator and sensor bonded composite laminates with delamination failures. The present method mainly consists of two parts: delamination modeling and feedback controller design. We first introduce the adopted improved layerwise theory–based mathematical model for delamination modeling with finite element implementation. The obtained second-order governing equations are transformed into the state space model for design of state feedback controller. Proper pole placement is required to enhance the sensitivity of frequency shifts to stiffness change caused by delamination. We investigated different delamination interfaces and longitudinal locations for studying the feasibility and efficiency of the present method. The present results clearly demonstrate that with the applied state feedback controller, the frequency shifts of the closed-loop system are significantly enhanced. The proposed sensitivity-enhancing feedback control can be used as an efficient tool for detecting delamination failures in smart composite structures.

Description: This paper proposes the end-to-end detection of a deep network for far infrared small target detection. The problem of detecting small targets has been a subject of research for decades and has been applied mainly in the field of surveillance. Traditional methods focus on filter design for each environment, and several steps are needed to obtain the final detection result. Most of them work well in a given environment but are vulnerable to severe clutter or environmental changes. This paper proposes a novel deep learning-based far infrared small target detection method and a heterogeneous data fusion method to solve the lack of semantic information due to the small target size. Heterogeneous data consists of radiometric temperature data (14-bit) and gray scale data (8-bit), which includes the physical meaning of the target, and compares the effects of the normalization method to fuse heterogeneous data. Experiments were conducted using an infrared small target dataset built directly on the cloud backgrounds. The experimental results showed that there is a significant difference in performance according to the various fusion methods and normalization methods, and the proposed detector showed approximately 20% improvement in average precision (AP) compared to the baseline constant false alarm rate (CFAR) detector.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉With the continuous development of Internet of things, all kinds of smart systems are quickly evolving to make our day-to-day life smoother and safer. Like many other sectors, transportation has entered a period of rapid change. Intelligent Traffic System is one of the fastest-growing fields within the smart systems, which is expected to increase road safety, mitigate traffic congestion, and enable fuel efficiency. The main functionalities of Intelligent Traffic System are as follows: (1) monitoring real-time traffic conditions in specific areas, (2) locating traffic emergencies (i.e. traffic accidents) in specific areas, and (3) dynamic monitoring and managing the continuous use in public transit services (i.e. change in car lanes) that may lead to changes in macro traffic conditions. This article will use the above-mentioned functionalities of the Intelligent Traffic System as underlying simulative scenarios, to design and to implement a smart transportation system based on Internet of things and blockchain—both share inherent distributed technology characteristics—combining both Internet of things sensor nodes and distributed ledger technology, to (1) record the changes in intelligent transportation systems and (2) set up a credit-token mechanism for paying the use and misuses in public transit services accordingly. The Intelligent Traffic System described in this article is intended to be used as experimental project only, given the terms and conditions as depicted in the simulated scenario. In real-life traffic scenarios, it may generate more complex system and data security issues, which will be elaborated and analyzed at the end of this article. Intelligent Traffic System is a comprehensive smart system; it can significantly change and reinvent the wheel for traffic conditions. Based on the system development as discussed in this article, there are still a lot of demands and challenges that need to be addressed in the future. Such topic scope will be explored in depth in our subsequent research.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉3D coverage is not only closer to the actual application environment, but also a research hotspot of sensor networks in recent years. For this reason, a node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network is proposed in this article. According to wireless link-aware area, the link coverage model in three-dimensional wireless sensor network is constructed, and the cube-based network coverage is used to represent the quality of service of the network. This model takes advantage of the principle that the presence of human beings can change the transmission channel of the link. On this basis, the intruder is detected by the data packets transmitted between the wireless links, and then the coverage area is monitored by monitoring the received signal strength of the wireless signal. Based on this new link awareness model, the problem of optimal coverage deployment of the receiving node is solved, that is, how to deploy the receiving node to achieve the optimal coverage of the monitoring area when the location of the sending node is given. In the process of optimal coverage, the traditional genetic algorithm and particle swarm optimization algorithm are introduced and improved. Based on the genetic algorithm, the particle swarm optimization algorithm which integrates the idea of simulated annealing is regarded as an important operator of the genetic algorithm, which can converge to the optimal solution quickly. The simulation results show that the proposed method can improve the network coverage, converge quickly, and reduce the network energy consumption. In addition, we set up a real experimental environment for coverage verification, and the experimental results verify the feasibility of the proposed method.

Description: In this work, we studied the effect of TiO2 sensitization with dry biomass extracted of cyanobacteria on the degradation of methylene blue dye (AM). Cyanobacterial cultures isolated from water samples were collected from the swamp of Malambo in Colombia; two main genera of cyanobacteria were identified, and they were cultivated with BG-11 culture medium. The concentrations of chlorophyll a in the exponential and stationary phases of growth were measured; the phycobilin content was quantified by spectrophotometry. Thin films of TiO2 were deposited by a doctor blade method, and they were sensitized by wet impregnation. Furthermore, a methylene blue (MB) photodegradation process was studied under visible light irradiation on the cyanobacterial biomass sensitized TiO2 material (TiO2/sensitizer); besides, the pseudo-first-order model was used to obtain kinetic information about photocatalytic degradation. The results showed that the BG-11+ treatment reported a higher amount of dry biomass and phycobiliproteins. After the sensitization process, the TiO2/sensitizer thin films showed a significant red shift in the optical activity; besides the thin film roughness decreasing, the TiO2/sensitizer showed photocatalytic activity of 23.2% under visible irradiation, and besides, the kinetic () constant for TiO2/sensitizer thin films was 3.1 times greater than the value of TiO2 thin films. Finally, results indicated that cyanobacterial biomass is a suitable source of natural sensitizers to be used in semiconductor sensitization.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉In the context of fingerprinting applications, this article presents the performance analysis of a type of space labeling based on the binary quantization of the received signal strength indicator. One of the common drawbacks of fingerprinting is the large data size and consequently the large search space and computational load as a result of either vastness of the positioning area or the finer resolution in the fingerprinting grid map. Our approach can be considered, for example, when we use very small, inexpensive beacons, like those based on bluetooth low energy technology, radio frequency identification, or in the future context of the Internet of Things. One of the interesting properties of this deployment is that it can be interpreted as a form of space labeling or encoding since space is divided into cells, and each cell is associated to a binary codeword with the corresponding scalability of the spatial resolution. Here, it developed the performance estimation, exploiting the association of this deployment to an error correcting code. The analysis and numerical and experimental results allow a deeper understanding of the impact of the proposed solution and show that it is robust and computationally efficient with respect to the traditional fingerprinting technique.

Description: The concentration dependence of a microwave frequency radiation from a solution of a functioning enzyme system (ES) (with the example of cytochrome P450 BM3 (CYP102A1) system during lauric acid (LA) hydroxylation) has been studied with a radiothermometric sensor. Registration of the radiation from the enzyme solution has been performed in the frequency range from 3.4 to 4.2 GHz at the enzyme concentrations from 10−10 М to 10−6 М. It has been demonstrated that the catalysis of LA hydroxylation in a reconstituted CYP102A1 system is accompanied by a generation of microwave radiation over the entire range of concentrations studied. It has been found that a transition from a multipulse mode (at nanomolar enzyme concentrations from 10−10М to 10−8М) to a single-pulse mode (at micromolar enzyme concentrations from 10−7М to 10−6М) is observed. This effect is discussed on the basis of assumptions considering possible realization of biomaser-like radiation in the enzyme system. The discovered concentration-based effect of the transition of an unsynchronized pulsed radiation into a synchronized one in ES can further be used in the development of novel methods of noninvasive diagnostics of diseases, in mathematical modeling of the functioning of living systems, and in the development of next-generation quantum computers.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉Accurately predicting the load change of the information system during operation has important guiding significance for ensuring that the system operation is not interrupted and resource scheduling is carried out in advance. For the information system monitoring time series data, this article proposes a load trend prediction method based on isolated forests-empirical modal decomposition-long-term (IF-EMD-LSTM). First, considering the problem of noise and abnormal points in the original data, the isolated forest algorithm is used to eliminate the abnormal points in the data. Second, in order to further improve the prediction accuracy, the empirical modal decomposition algorithm is used to decompose the input data into intrinsic mode function (IMF) components of different frequencies. Each intrinsic mode function (IMF) and residual is predicted using a separate long-term and short-term memory neural network, and the predicted values are reconstructed from each long-term and short-term memory model. Finally, experimental verification was carried out on Amazon’s public data set and compared with autoregressive integrated moving average and Prophet models. The experimental results show the superior performance of the proposed IF-EMD-LSTM prediction model in information system load trend prediction.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉The coverage intensity of sensors is the most important issue on structural health monitoring technique. The geometric configuration of sensors must be optimized based on coverage intensity with proper objectives. In this article, a novel algorithm for optimal sensor placement in various steel frames was evaluated. These frames including moment-resisting frame, moment-resisting frame with base isolation, and moment-resisting frame with base isolation with steel shear wall were selected for case studies. This approach was proposed based on combination of common optimal sensor placement algorithm and nonlinear time history analysis. A new method called transformed time history to frequency domain approach was evaluated to transform nonlinear time history analysis results to frequency domain and then the effective frequencies according the maximum range of Fourier amplitude were selected. The modified type of modal assurance criterion values can be achieved from modal assurance criterion with the exact seismic displacement. All of novel optimal sensor placement processes were done through FEM-MAC-TTFD code modeled and developed in MATLAB by authors of this article. The results show that there is good relative correlation between the sensors number and coverage intensity obtained with modal and modified modal assurance criterion approaches for moment-resisting frame system, but for integrated frame such as moment-resisting frame with base isolation and moment-resisting frame with base isolation with steel shear wall, the modified modal assurance criterion approach is better approach. There is no significant difference between coverage intensity of sensors for top joints between modal assurance criterion and modified modal assurance criterion approaches for moment-resisting frame, moment-resisting frame with base isolation, and moment-resisting frame with base isolation with steel shear wall systems (R2 = 0.994, 0.986, and 0.724, respectively). It was found that if reference point is located in center of frame, there is significant difference between modal assurance criterion and modified modal assurance criterion approaches, and modified modal assurance criterion generated slightly better results.

Description: This research proposes a circularly polarized (CP) single-fed omnidirectional dipole antenna operable in 2.45 GHz frequency for the industrial, scientific, and medical (ISM) radio band applications. The proposed antenna consisted of bisectional dipole core, a pair of quarter-wave baluns, and four diagonally adjoined parasitic braces. The bisectional dipole core was utilized to improve the antenna gain and realize omnidirectional radiation pattern, and the quarter-wave baluns were to symmetrize the current on the bisectional core. The four parasitic braces collectively generated circular polarization. In the study, simulations were conducted using CST Microwave Studio and a prototype antenna fabricated. To validate, experiments were carried out, and simulation and experimental results compared. The finding revealed good agreement between the simulation and experimental results. Essentially, in addition to achieving an antenna gain of 2.07 dBic, the proposed CP single-fed omnidirectional antenna is suited to ISM frequency band applications.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉It is difficult to reconstruct the complete light field, and the reconstructed light field can only recognize specific fixed targets. These have limited the applications of the light field in practice. To solve the problems above, this article introduces the multi-perspective distributed information fusion into light field reconstruction to monitor and recognize the maneuvering targets. First, the light field is represented as sub-light fields at different perspectives (i.e. the Multi-sensor distributed network), and sparse representation and reconstruction are then performed. Second, we establish the multi-perspective distributed information fusion under the condition of regional full-coverage constraints. Finally, the light field data from multiple perspectives are fused and the states of the maneuvering targets are estimated. Experimental results show that the light field reconstruction time of the proposed method is less than 583 s, and the reconstruction accuracy exceeds 92.447% compared with the existing spatially variable bidirectional reflectance distribution function, micro-lens array, and others. In the aspect of maneuvering target recognition, the recognition time of the algorithm in this article is no more than 3.5 s. The recognition accuracy of the algorithm in this article is up to 86.739%. Moreover, the more viewing angles used, the higher the accuracy.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 9, September 2019. 〈br/〉Wireless sensor networks have drawn tremendous attentions from all fields because of their wide application. Maximizing network lifetime is one of the main problems in wireless sensor networks. This article proposes an energy-efficient routing protocol which adopts unequal clustering technology to solve the hot spots problem and proposes double cluster head strategy to reduce the energy consumption of head nodes in the clusters. In addition, to balance the energy consumption between cluster heads and cluster members, a hybrid cluster head rotation strategy based on time-driven and energy-driven is proposed, which can make the timing of rotation more reasonable and the energy consumption more efficient. Finally, we compare the proposed protocol with LEACH, DEBUC, and UCNPD by simulation experiments. The simulation results prove that our proposed protocol can effectively improve the performance in terms of network lifetime, energy consumption, energy balance, stability, and throughput.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 9, September 2019. 〈br/〉Wireless sensor networks require time synchronization, which is the coordination of events or actions to make a system operate in unison. In this work, real experiments and a theoretical analysis of the behavior of the clock sources, most used in wireless sensor networks, have been carried out. The experiments have been performed on two real platforms from two different manufacturers in real environments with sudden changes in temperature. Complementary metal-oxide-semiconductor oscillators have a low accuracy, bigger than 500 ppm, and a high dependency with temperature. External crystal oscillators have good accuracy, around 20 ppm, and are stable with temperature. Temperature-compensated crystal oscillators are very accurate, around 5 ppm, and the temperature has no influence in their drift. The use of phase-locked loop circuits minimizes the impact of temperature and stabilizes oscillators. We highlight and demonstrate the importance of the early stages of design, especially the selection of the clock source, because that decision has a great impact on the performance of the time synchronization in wireless sensor networks.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 9, September 2019. 〈br/〉Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists of diverse components such as smart meters, energy management systems, energy storage systems, and renewable energy resources. In particular, to make an effective energy management strategy for the energy management system, accurate load forecasting is necessary. Recently, artificial neural network–based load forecasting models with good performance have been proposed. For accurate load forecasting, it is critical to determine effective hyperparameters of neural networks, which is a complex and time-consuming task. Among these parameters, the type of activation function and the number of hidden layers are critical in the performance of neural networks. In this study, we construct diverse artificial neural network–based building electric energy consumption forecasting models using different combinations of the two hyperparameters and compare their performance. Experimental results indicate that neural networks with scaled exponential linear units and five hidden layers exhibit better performance, on average than other forecasting models.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 9, September 2019. 〈br/〉Home-based multi-sensor Internet of Things, as a typical application of Internet of Things, interconnects a variety of intelligent sensor devices and appliances to provide intelligent services to individuals in a ubiquitous way. As families become more and more intelligent, complex, and technology-dependent, there is less and less need for human intervention. Recently, many security attacks have shown that Internet home-based Internet of Things have become a vulnerable target, leading to personal privacy problems. For example, eavesdroppers can acquire the identity of specific devices or sensors through public channels, which is not secure, to infer individual public life in the home area network. Authentication is the essential portion of many secure systems processing of verifying and declaring identity. Before providing confidential information, home-based-Internet of Things service authenticates users and devices. The communication and processing capabilities of intelligent devices are limited. Therefore, in home-based Internet of Things, lightweight authentication and key agreement technology are very important to resist known attacks. This article proposes an anonymous authenticated key agreement protocol using pairing-based cryptography. The protocol proposed in this article provides lightweight computation and ensures the security of communication between home-based multi-sensor Internet of Things network and Internet network.

Description: Ground penetrating radar (GPR), as a kind of fast, effective, and nondestructive tool, has been widely applied to nondestructive testing of road quality. The finite-difference time-domain method (FDTD) is the common numerical method studying the GPR wave propagation law in layered structure. However, the numerical accuracy and computational efficiency are not high because of the Courant-Friedrichs-Lewy (CFL) stability condition. In order to improve the accuracy and efficiency of FDTD simulation model, a parallel conformal FDTD algorithm based on graphics processor unit (GPU) acceleration technology and surface conformal technique was developed. The numerical simulation results showed that CUDA-implemented conformal FDTD method could greatly reduce computational time and the pseudo-waves generated by the ladder approximation. And the efficiency and accuracy of the proposed method are higher than the traditional FDTD method in simulating GPR wave propagation in two-dimensional (2D) complex underground structures.

Description: An equivalent circuit method (ECM) is proposed for the design of multilayer frequency-selective surfaces (FSSs). In contrast to the existing ECMs that were developed mainly for the analysis of the properties of a given FSS, the presented ECM aims at providing the initial design parameters of an FSS from the desired frequency response. In this method, four types of basic FSS structures are used as the building blocks to construct the multilayer FSSs, and their surface impedances in both the normal- and the oblique-incidence situations are studied in detail in order to achieve more accurate equivalent circuit (EC) representation of the entire FSS. For a general FSS design with expected frequency response, the EC parameters and the geometrical sizes of the required basic building blocks can be synthesized from a few typical S-parameter (S11/S12) samplings of the response curves via a simple least-square curve-fitting process. The effectiveness and accuracy of the method are shown by the designs of a band-pass FSS with steep falling edge and a miniaturized band-pass FSS with out-of-band absorption. The prototype of one design is fabricated, and the measured frequency response agrees well with the numerical results of the ECM and the full-wave simulations.

Description: A novel Gysel power divider with negative group delay (NGD), good matching, and low insertion loss is proposed. Resistors connected with short-circuited coupled lines (RCSCL) are shunted at output ports of the Gysel power divider to obtain NGD characteristics, and another resistor is shunted at the input port to realize perfect input and output matching. To verify the proposed structure, an NGD Gysel power divider is designed and fabricated. At the center frequency of 1.0 GHz, the measured NGD times for different output ports are −1.94 ns and −1.97 ns, the input/output port return loss is greater than 38 dB, the insertion loss is less than 8.3 dB, and the isolation between output ports is higher than 41 dB. To enhance the NGD bandwidth, two RCSCL networks having slightly different center frequencies are connected in parallel, which provides wider bandwidth with good input matching characteristics.

Description: Constant exposure of a photovoltaic (PV) panel to sunlight causes it to overheat and, consequently, its rated efficiency decreases leading to a drop in its generated power. In this study, a PV panel was tested under standard test conditions in a halogen lamp solar simulator at different solar irradiance values. The PV panel was then fitted with heat dissipating fins and measured under identical test parameters; thereafter, repurposed materials such as high-density polyethylene (HDPE) and plastic bags were, separately, added to the PV panel with fitted heat-extraction fins and the performance was evaluated again. Passively cooling the PV panel with fins and repurposed materials resulted in a 22.7% drop in the PV panel’s temperature, while an 11.6% increase in power output occurred at 1000 W m-2. Utilizing repurposed waste materials in PV cooling improves a panel’s efficiency and saves the environment from the ecological effects of dumping these materials.

Description: Closed-form expression of three-dimensional emitter location estimation using azimuth and elevation measurements at multiple locations is presented in this paper. The three-dimensional location estimate is obtained from three-dimensional sensor locations and the azimuth and elevation measurements at each sensor location. Since the formulation is not iterative, it is not computationally intensive and does not need initial location estimate. Numerical results are presented to show the validity of the proposed scheme.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.

Description: Double modifications of TiO2 by doping with WO3 and by dispersing on a SiO2 support were made by the one-pot sol-gel method. Doping with W shifts the TiO2 band gap energy from 3.2 eV to around 3.06 eV. The surface area of the supported W-TiO2/SiO2 material was significantly increased, by approximately 3 times, in comparison to the bare TiO2. The photocatalytic activities of the catalysts were evaluated in the degradation reaction of p-nitrophenol in aqueous solution and basic medium. After 240 min of photodegradation, more than approximately 99% p-nitrophenol could be mineralized with the most active W-TiO2/SiO2 catalyst. Under UV irradiation, p-nitrophenol was initially photodegraded into hydroquinone and benzosemiquinone intermediates, which were further degraded into smaller fragments such as organic carboxylic acids and finally completely mineralized. A proposed photoreaction mechanism was presented based on the key roles of the surface hydroxyl species and superoxide radicals such as O2- and ⋅OH, together with W6+/W5+ couples and e-/h+ pairs in the catalysts in the p-nitrophenol photodegradation. The one-pot sol-gel synthesis method was proven to be effective to obtain W-TiO2/SiO2 catalyst with large surface area and high photocatalytic activity, and it can be also used for the preparation of other heterogeneous catalysts.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 7, July 2019. 〈br/〉The crowdsourcing schemes which utilize the social network to solve complex tasks are an important part of open cooperation over the Internet. Although blockchain-based crowdsourcing schemes have considerable advantages in decentralization and data sharing, there is still a challenge to gurantee the security of crowdsourced-sensitive information and the fairness of crowdsourcing on the blockchain. To this end, this article investigates a crowdsourcing scheme based on blockchain. First, we define the basic requirements of blockchain-based crowdsourcing schemes including fairness, confidentiality, and integrity. And then, using secure hash, commitment, and homomorphic encryption, we propose a blockchain-based secure and fair crowdsourcing scheme, that is, BFC. The analysis results show that our scheme can satisfy the above requirements. Finally, the experimental results show that the computational overhead of the BFC scheme is acceptable to both the requester and the workers. In a word, our proposed crowdsourcing scheme has good expansibility in reality.

Description: In this paper, a wideband slot antenna element and its array with stereoscopic differentially fed structures are proposed for the radar system. Firstly, a series of slots and a stereoscopic differentially fed structure are designed for the antenna element, which makes it possess a wide bandwidth, stable radiation characteristics, and rather high gain. Moreover, the stereoscopic feeding structure can firmly support the antenna’s radiation structure and reduce the influence of feeding connectors on radiating performance. Secondly, a 4 × 4 array is designed using the proposed antenna element. And a hierarchical feeding network is designed for the array on the basis of the stereoscopic differentially fed structure. For validation, the antenna element and 4 × 4 array are both fabricated and measured: (1) the measured −10 dB impedance bandwidth of the antenna element is 62% (6.8–12.9 GHz) and the gain within the entire band is 5–9.7 dBi and (2) the measured −10 dB impedance bandwidth of the array is approximately 50% (7 to 12 GHz) with its gain being 14–19.75 dBi within the entire band. Notably, measured results agree well with simulations and show great advantages over other similar antennas on bandwidth and gain.

Description: In this paper, a digital elevation model (DEM) was produced for Lop Nur playa produced with the data from TanDEM-X mission. The spatial resolution is 10 m. It covers an area of 38,000 km2 for orthometric height from 785 m to 900 m above sea level, which is composed of 42 interferometric synthetic aperture radar (InSAR) scenes. A least-square adjustment approach was used to reduce the systematic errors in each DEM scene. The DEM produced was validated with data from other sensors including Ice, Cloud, and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) and aerial Structure-from-Motion (SfM) DEM. The results show that global elevation root mean square error to GLAS is 0.57 m, and the relative height error to SfM DEM in complicated terrain is 3 m. The excellent height reliability of TanDEM InSAR DEM in Lop region was proved in this paper. A reliable high-resolution basic topographic dataset for researches of Lop Nur was provided.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉It has been seen that most of the accidents occur due to driver’s fatigue. Drowsiness is a state of mind before the driver falls asleep, which means the driver could not accomplish his actions, such as vehicular braking, controlling vehicular motion, properly. We have built an Internet of things–based medical application to analyze driver’s drowsiness. An architecture has been proposed and a simulation of that scenario in NS3 WSN simulation tool has been done. This simulation shows that the ratio of accidents can be majorly reduced. When drowsiness of drivers is captured, a message alert is delivered to all other drivers of the vehicles that are near to the sleeping driver; for this, different sensor nodes are used. Another unique feature of the sensor network used here is the collaborative effect of sensor nodes. So for measurement and analysis of applications on Google Play, a dataset of the medical applications category was scraped. The scraping was done with 550 applications of each category of medical applications. On each application on Google Play store, almost 70 attributes for each category were scraped. It is envisioned that, in future, wireless sensor networks will be an integral part of our lives, more so than the present-day personal computers.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉This article investigates the possibility of advancing discrete manufacturing using system architectures that are developed for Collaborative Process Automation Systems. Collaborative Process Automation System is a technology that has the potential to achieve production excellence for process industry. However, not much attention has been paid on using the architectures based on Collaborative Process Automation Systems for discrete manufacturing domains. In this article, we propose a base architecture consisting of three layers, and we discuss various alternatives to make the communications among the layers. We consider legacy components in the proposal, in contrast to most of the related works. In order to show the practicality of the proposed alternatives, we present an example that has been implemented in an ongoing project at ABB Robotics in Sweden.

Description: In this paper, distributed constant level detection in wireless sensor networks (WSNs) is investigated. The permuted linear model with a scalar parameter and additive heteroscedastic Gaussian noise is introduced, where the associations between the observations and the sensors are assumed to be unknown. Several detectors such as the approximations of the generalized likelihood ratio test (GLRT) detector, mean detector, and energy detector are proposed, and their receiver operating characteristics (ROCs) are evaluated. Numerical simulations are performed, and it is shown that the performance degradation of the GLRT detector is small, compared to the permutation known as Neyman-Pearson (NP) detector.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉Security of cyber-physical systems against cyber attacks is an important yet challenging problem. Cyber-physical systems are prone to information leakage from the physical domain. The analog emissions, such as magnetic and power, can turn into side channel revealing valuable data, even the crypto key of the system. Template attack is a popular type of side-channel analysis using machine learning technology. Malicious attackers can use template attack to profile the analog emission, then recover the secret key of the system. But conventional template attack requires that the adversary has access to an identical experiment device that he can program to his choice. This study proposes a novel side-channel analysis for physical-domain security in cyber-physical systems. Our contributions are the following three points: (1) Major peak region method for finding points of interests correctly is proposed. (2) A method for establishing templates on the basis of those points of interest still without requiring knowledge of the key is proposed. Several techniques are proposed to improve the quality of the templates as well. (3) A method for choosing attacking traces is proposed to significantly improve the attacking efficiency. Our experiments on three devices show that the proposed method is significantly more effective than conventional template attack. By doing so, we will highlight the importance of performing similar analysis during design time to secure the cyber-physical system.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉In a multi-heterogeneous network with dense deployment and convergence environment, how to efficiently and reasonably allocate idle spectrum resources of the primary network to meet the diversified business demands of secondary users is a difficult problem. In this article, with the goal of maximizing the total transmission rate and minimizing the total cost, a dual-objective optimization mathematical model for network selection and idle spectrum allocation is established in the context of comprehensive consideration of the diversity of spectrum resource attributes and the diversification of secondary users’ business needs. Based on this, two kinds of technical paths to solve the complex network selection and spectrum allocation problem are applied in this article. The first is the simplification method. By preprocessing of objective function, constraint simplification, and standardization, the complex spectrum allocation problem is transformed into a standard form of the 01 programming problem, and the solution is obtained by an improved Hungarian algorithm. Second, an intelligent optimization algorithm named improved non-dominated sorting genetic algorithm II is proposed, which combines the interference constraints of the primary network and the service quality requirements of the secondary users into the objective value evaluation of non-dominated sorting, and corrects the chromosomes that do not meet the constraints. And then makes a decision selection on the optimal solution set to select a compromise solution. Finally, methods proposed in this article are compared with the multi-objective artificial bee colony algorithm through experiments. Experimental results show that the simplified method has higher efficiency, and the improved non-dominated sorting genetic algorithm II can get higher transmission rate, especially the transmission rate–priority strategy.

Description: In this paper, a novel slot antenna array that is based on mirror polarization conversion metasurfaces (MPCM) is proposed. It achieves circular polarization (CP) and effectively reduces the radar cross section (RCS) and increases gain in the entire x-band. This design uses the mirrored composition of the polarization conversion metasurfaces (PCM) on the top surface of the substrate. The MPCM covers a 2 × 2 slot antenna array that is fed with by a sequentially rotating network. The CP radiation is realized by the polarization conversion characteristics of the PCM. At the same time, the reduction of RCS is achieved by 180° (±30°) reflection phase difference between two adjacent PCMs. The improvement in gain is achieved by using a Fabry–Perot cavity, which is constituted by the ground of the antenna and the PCM. Simulated and measured results show that approximately 46.4% of the operating bandwidth is in the range of 7.5–12 GHz (AR 

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉In the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user. Internet of Things usually produces a huge amount of data that to be effectively analyzed, especially with neural networks, demands high computing capabilities. Processing all the data in the cloud may not be sufficient in cases when we need privacy and low latency, and when we have limited Internet bandwidth, or it is simply too expensive. It poses a challenge for creating a new generation of fog computing that supports artificial intelligence and selects the architecture appropriate for an intelligent solution. In this article, we show from four perspectives, namely, hardware, software libraries, platforms, and current applications, the landscape of components used for developing intelligent Internet of Things solutions located near where the data are generated. This way, we pinpoint the odds and risks of artificial intelligence fog computing and help in the process of selecting suitable architecture and components that will satisfy all requirements defined by the complex Internet of Things systems.

Description: A modified multiobjective self-adaptive differential evolution algorithm (MMOSADE) is presented in this paper to improve the accuracy of multiobjective optimization design in the nuclear power system. The performance of the MMOSADE is tested by the ZDT test function set and compared with classical evolutionary algorithms. The results indicate that MMOSADE has a better performance in convergence and diversity. Based on the MMOSADE, a multiobjective optimization design platform for the nuclear power system is proposed, and the application of which is carried out. The evaluation program of the PRHR-HX in AP1000 is developed, and its reliability is verified. The optimal design schemes of PHHR-HX are obtained by utilizing the multiobjective optimization design platform. The results show that the optimal design schemes can envelop the prototype design scheme. This conclusion proves that the optimization design platform proposed in this paper is effective and feasible.

Description: The challenges to be overtaken with alkaline water electrolysis are the reduction of energy consumption, the maintenance, and the cost as well as the increase of durability, reliability, and safety. Having these challenges in mind, this work focused on the reduction of the electrical resistance of the electrolyte which directly affects energy consumption. According to the definition of electrical resistance of an object, the reduction of the space between electrodes could lower the electrical resistance but, in this process, the formation of bubbles could modify this affirmation. In this work, the performance analyses of nine different spaces between stainless steel 316L electrodes were carried out, although the spaces proposed are not the same as those from the positive electrode (anode) to the separator and from the separator to the negative electrode (cathode). The reason why this is studied is that stoichiometry of the reaction states that two moles of hydrogen and one mole of oxygen can be obtained per every two moles of water. The proposed spaces were 10.65, 9.20, 8.25, 7.25, 6.30, 6.05, 4.35, 4.15, and 3.40 millimetres. From the nine different analysed distances between electrodes, it can be said that the best performance was reached by one of the smallest distances proposed, 4.15 mm. When the same distance between electrodes was compared (the same and different distance between electrodes and separator), the one that had almost twice the distance (negative compartment) presented an increase in current density of approximately 33% with respect to that where both distances (from electrodes to separator) are the same. That indicates that the stichometry of the electrolysis reaction influenced the performance.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 7, July 2019. 〈br/〉Internet of Things simulations play significant roles in the diverse kinds of activities in our daily lives and have been extensively researched. Creating and controlling virtual agents in three-dimensional Internet of Things simulations is a key technology for achieving realism in three-dimensional simulations. Given that traditional virtual agent-based approaches have limitations for realism, it is necessary to improve the realism of three-dimensional Internet of Things simulations. This article proposes a Q-Network-based motivation framework that applies a Q-Network to select motivations from desires and hierarchical task network planning to execute actions based on goals of the selected motivations. The desires are to be identified and calculated based on states. Selected motivations will be chosen to determine the goals that agents must achieve. In the experiments, the proposed framework achieved an average accuracy of up to 85.5% when the Q-Network-based motivation model was trained. To verify the Q-Network-based motivation framework, a traditional Q-learning is also applied in the three-dimensional virtual environment. Comparing the results of the two frameworks, the Q-Network-based motivation framework shows better results than those of traditional Q-learning, as the accuracy of the Q-Network-based motivation is higher by 15.58%. The proposed framework can be applied to the diverse kinds of Internet of Things systems such as a training autonomous vehicle. Moreover, the proposed framework can generate big data on animal behaviors for other training systems.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 7, July 2019. 〈br/〉Cognitive radio–based vehicular ad hoc networks can solve the problem of limited spectrum resource and growing vehicular communication service demands in intelligent transportation systems, and thus, it receives much concern recently. In cognitive radio–based vehicular ad hoc networks, the high mobility of vehicles and the dynamic spectrum activity of cognitive radio make routing in such networks a great challenge. Some routing researches have been proposed in cognitive radio–based vehicular ad hoc networks with single-objective optimization and neglecting the nodes’ social behaviors which can improve the network performance. From this perspective, we propose a social-aware routing scheme for cognitive radio–based vehicular ad hoc networks, with the purpose of increasing the packet delivery ratio and decreasing the overhead ratio. First, we analyze the social centrality of primary users to offer an accuracy spectrum hole measurement. Moreover, we develop a social community partition algorithm to divide secondary users into intra-community and inter-community groups. Furthermore, considering the tradeoff between the packet delivery ratio and the overhead ratio, we adopt different replication policies and forwarding ranks in different community communication processes. In the intra-community communication process, we employ the single-copy policy and the contact duration rank. In the inter-community communication process, we utilize the optimized-binary-tree replication policy and the bridge coefficient rank. Simulation results show that our social-aware routing scheme achieves the higher package delivery ratio and the lower overhead ratio when compared with the existing cognitive radio–based vehicular ad hoc networks routing schemes and other standard routing schemes.

Description: COMSOL Multiphysics has been used to conduct thermal-hydraulic analysis in multiple nuclear applications. The aim of this study is to benchmark the prediction accuracy of COMSOL Multiphysics in performing thermal-hydraulic analysis of TRIGA (Training, Research, Isotopes, General Atomics) reactors such as the Geological Survey TRIGA Reactor (GSTR) by comparing its predictions with RELAP5 (a widely used code in nuclear thermal-hydraulic analysis) results and experimental data. The GSTR type is Mark I with a full thermal power of 1 MW, and it resides at the Denver Federal Center (DFC) in Colorado. The numerical investigation of the present work is carried out by developing single-subchannel thermal-hydraulic models of the GSTR utilizing RELAP5 and COMSOL codes. The models estimate the temperatures (fuel, outer clad, and coolant) and water flow patterns in the core as well as fuel element powers at which void starts to form within the coolant subchannels. Then, these models’ predictions are quantitatively evaluated and compared with the measured data.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉This article overviews the emerging use of deep neural networks in data analytics and explores which type of underlying hardware and architectural approach is best used in various deployment locations when implementing deep neural networks. The locations which are discussed are in the cloud, fog, and dew computing (dew computing is performed by end devices). Covered architectural approaches include multicore processors (central processing unit), manycore processors (graphics processing unit), field programmable gate arrays, and application-specific integrated circuits. The proposed classification in this article divides the existing solutions into 12 different categories, organized in two dimensions. The proposed classification allows a comparison of existing architectures, which are predominantly cloud-based, and anticipated future architectures, which are expected to be hybrid cloud-fog-dew architectures for applications in Internet of Things and Wireless Sensor Networks. Researchers interested in studying trade-offs among data processing bandwidth, data processing latency, and processing power consumption would benefit from the classification made in this article.

Description: A practical scale mechanical decladder that can slit spent nuclear fuel rod-cuts (hulls + pellets) of several tens of kg HM/batch is being developed to supply UO2 pellets to a voloxidation process. The mechanical decladder is an apparatus for separating and recovering fuel material and cladding tubes by horizontally slitting the cladding tube of a fuel rod and a defective irradiated fuel rod. In this study, we address the engineering design of the mechanical decladder for the pretesting of rod-cut slitting. To obtain the requirements of the mechanical decladder, we first manufactured a slitter for testing based on the decladding and shearing conditions of hulls and pellets. The performance test of the testing device for decladding was carried out using a 2-CUT blade module and a 3-CUT blade module. We evaluated the decladding methods for the mechanical decladder and selected the 3-CUT blade module based on the results. A buckling measurement instrument was used to perform a buckling verification test according to the length of a rod-cut and to determine decladder dimensions. The optimum decladding rod-cut length for buckling prevention was calculated. Furthermore, we analyzed the decladding mechanism for various slitting methods. Design/fabrication and preliminary tests of the practical scale mechanical decladder were also performed. For this purpose, we constructed the main mechanism by utilizing the SolidWorks modeling and analysis program and fabricated a new mechanical decladder. Based on the derived requirements, a mechanical decladder with three main modules was designed and fabricated for testing. Simulated rod-cuts of zircaloy were also manufactured to test the basic performance of the decladder, and a data acquisition system was constructed using RSC 232 to measure decladding force and velocity. In the basic test, the rod-cut was completely sectioned into three evenly spaced locations by the new mechanical decladder.

Description: An efficient pattern synthesis approach is proposed for the synthesis of a time-modulated sparse linear array (TMSLA) in this paper. Due to the introduction of time modulation, the low/ultralow side lobe level can be obtained with a low amplitude dynamic range ratio. Besides, it helps reduce the difficulty of antenna feeding system effectively. Based on particle swarm optimization (PSO) and convex (CVX) optimization, this paper proposes a hybrid optimization method to suppress the grating lobes of the sparse arrays, peak side lobe level (PSLL), and peak sideband level (PSBL). Firstly, the paper utilizes the CVX optimization as a local optimization algorithm to optimize the elements’ switch-on duration time, which reduces the side lobe of the array. Secondly, with the PSBL as the objective function, the paper adopts the PSO as a global optimization algorithm to optimize the elements’ positions and switch-on time instant, which helps reduce the loss of sideband power caused by time modulation. With respect to the time modulation model, variable aperture sizes (VAS) and more flexible pulse-shifting (PS) schemes are used in this paper. Owing to the introduction of time modulation and CVX optimization, the proposed method is much more feasible and efficient than conventional approaches. Furthermore, it has better array pattern synthesis performance. Numerical examples of the TMSLA and comparisons with the reference are presented to demonstrate the effectiveness of the proposed method.

Description: A Weibull distribution has been proposed for the probability density function (PDF) of the magnitude of the E-field in a reverberation chamber (RC). However, the Weibull distribution has two parameters, and if the parameters are position sensitive, the use of the Weibull distribution could be very limited. We investigate the sensitivity of the estimated parameters of the Weibull distribution in this study; the measurement results show that the parameters of the Weibull distribution depend on the positions of the antenna (or device under test), but not sensitive, and the statistical behavior of the parameters can be quantified. This means that the model of the Weibull distribution has a wider usable frequency range than that of the Rayleigh distribution and the statistical variation of the parameters needs to be considered.

Description: This paper extends a recent theoretical study that was previously presented in the form of a brief communication (Zimont, C&F, 192, 2018, 221-223), in which we proposed a simple splitting method for the derivation of two-fluid conditionally averaged equations of turbulent premixed combustion in the flamelet regime, formulated more conveniently for applications involving unclosed equations without surface-averaged unknowns. This two-fluid conditional averaging paradigm avoids the challenge in the Favre averaging paradigm of modeling the countergradient scalar transport phenomenon and the unusually large velocity fluctuations in a turbulent premixed flame. It is a more suitable conceptual framework that is likely to be more convenient in the long run than the traditional Favre averaging method. In this article, we further develop this paradigm and pay particular attention to the problem of modeling turbulent premixed combustion in the context of a two-fluid approach. We formulate and analyze the unclosed differential equations in terms of the conditions of the Reynolds stresses , and the mean chemical source , which are the only modeling unknowns required in our alternative conditionally averaged equations. These equations are necessary for the development of model differential equations for the Reynolds stresses and the chemical source in the advanced modeling and simulation of turbulent premixed combustion. We propose a simpler approach to modeling the conditional Reynolds stresses based on the use of the two-fluid conditional equations of the standard “” turbulence model, which we formulate using the splitting method. The main problem arising here is the appearance in these equations of unknown terms describing the exchange of the turbulent energy and dissipation rate in the unburned and burned gases. We propose an approximate way to avoid this problem. We formulate a simple algebraic expression for the mean chemical source that follows from our previous theoretical analysis of the transient turbulent premixed flame in the intermediate asymptotic stage, in which small-scale wrinkles in the instantaneous flame surface reach statistical equilibrium, while the large-scale wrinkles remain in statistical nonequilibrium.

Description: This paper presents a high-directivity ultra-wideband beamsteering antenna array. An innovative beamsteering system based on hemispherical dielectric lenses fed by a set of different printed antennas is proposed. Diversity of signals in different spatial positions can be radiated at the same time. A prototype was manufactured and characterized, operating in a bandwidth varying from 8 GHz to 12 GHz with gain up to 13 dBi.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 8, August 2019. 〈br/〉Edge computing has recently emerged as an important paradigm to bring filtering, processing, and caching resources to the edge of networks. However, with the increasing popularity of augmented reality and virtual reality application, user requirements on data access speed have increased. Since the edge node has limited cache space, efficient data caching model is needed to improve the performance of edge computing. We propose a multi-objective optimization data caching model in the edge computing environment using data access counts, data access frequency, and data size as optimization goals. Our model differs from previous data caching schemes that focused only on data access counts or data size. In addition, a cyclic genetic ant algorithm is proposed to solve the multi-objective optimization data caching model. We compare the following three performance indicators: cache hit ratio, average response speed, and bandwidth cost. Simulation results show that the model can improve the cache hit ratio and reduce the response latency and the bandwidth cost.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 3, March 2019. 〈br/〉

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 3, March 2019. 〈br/〉

Description: We present a design of a high-gain quad array of nonuniform helical antennas. The design is obtained by optimization of a 3-D numerical model of four nonuniform helical antennas placed above a ground plane, including a model of a feeding network, utilizing the method of moments with higher-order basis functions. The gain of one optimal nonuniform helical antenna can be about 2.5 dB higher than the gain of a uniform helical antenna of the same axial length. Creating a array further increases the gain up to about 6 dB. The resulting quad array fits into a box whose dimensions are wavelengths, and the gain in the main radiating direction is about 20.5 dBi in the frequency range from 0.9 GHz to 1.1 GHz. The design is verified by measurements of a prototype of the quad array.

Description: The paper deals with the uncertainty quantification of the transient axial current induced along the human body exposed to electromagnetic pulse radiation. The body is modeled as a straight wire antenna whose length and radius exhibit random nature. The uncertainty is propagated to the output transient current by means of the stochastic collocation method. The stochastic approach is entirely nonintrusive and serves as a wrapper around the deterministic code. The numerical deterministic model is based on the time domain Hallen integral equation solved by means of the Galerkin-Bubnov indirect boundary element method (GB-IBEM). The stochastic moments, i.e., the mean and the variance of the transient current, are calculated. Confidence margins are obtained for the whole duration of the transient response as well as for the maximal current value. The presented approach enables the estimation of the probability for the induced current to exceed the basic restrictions prescribed by regulatory bodies. The sensitivity analysis of the input parameters indicates to which extent the variation of the input parameter set influences the output values which is particularly interesting for the design of the human equivalent antenna.

Description: CIPS is a shift in the axial power towards the bottom half of the core, also known as axial offset anomaly (AOA), which results from the deposited of corrosion products during an operation. The main reason of CIPS is the solute particles especially boron compounds concentrated inside the porous deposit. The impact of CIPS is that the axial power distribution control may be more difficult and the shutdown margin can be decreased simultaneously. Besides, it also requires estimated critical condition (ECC) calculations to account for the effects of AOA. In this article, thermal-hydraulic subchannel code and boron deposit model have been combined to analyze the CIPS risk. The neutronics codes deal with the generation of homogenized neutron cross section as well as the calculation of local power factor. A simple rod assembly is analyzed with this combined method and simulation results are presented. Simulation results provide the boron hideout amount inside crud deposits and power shapes. The obtained results clearly show the power shape suppression in regions where crud deposits exist, which is a clear indication of CIPS phenomenon. And the CIPS effects on CHF have also been investigated. Result shows a margin of DNBR decrease in the crud case.

Description: In the current scenario of high-range resolution radar and noncooperative target, the rotational motion parameters of the target are unknown and migration through resolution cells (MTRC) is apparent in the obtained inverse synthetic aperture radar (ISAR)images, in both slant-range and cross-range directions. In the case of the high-speed maneuvering target with a small value of rotation, the phase retrieval algorithm can be applied to compensate for the translational motion to form an autofocusing image. However, when the target has a relatively large rotation angle during the coherent integration time, phase retrieval method cannot get an acceptable image for viewing and analysis as the location of the scatterer will not be true due to the Doppler shift imposed by the target’s rotational motion. In this paper, a novel ISAR imaging method for maneuvering targets based on phase retrieval and keystone transform is proposed, which can effectively solve the above problems. First, the keystone transform is used to solve the MTRC effects caused by the rotation component. Next, phase errors caused by the remaining translational motion will be removed by employing phase retrieval algorithm, allowing the scatterers are always kept in their range cells. Finally, the Doppler frequency shifts of scatterers will be time invariant in the phase of the received signal. Furthermore, this approach does not need to estimate the motion parameters of the target, which simplifies the processing steps. The simulated results demonstrate the validity of this method.

Description: Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 3, March 2019. 〈br/〉

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 3, March 2019. 〈br/〉

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 3, March 2019. 〈br/〉

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 3, March 2019. 〈br/〉

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 3, March 2019. 〈br/〉

Description: After the severe accident (SA) occurred at the Three-Miles Island Nuclear Power Plant (NPP), important efforts on the investigation of the different phenomena during this kind of accidents have been started. Several experimental campaigns investigating one phenomenon at time or the combination of two or more phenomena have been performed. Today, the Phébus experimental campaign is probably the most important activity on the evaluation of the coupling among different phenomena. Four out of five tests investigated the degradation of an intact Pressurized Water Reactor (PWR) fuel bundle and the subsequent transport of Fission Products (FP) and Structural Materials (SM) through the primary circuit and into the containment, while the fifth test was only the degradation of a bed of PWR fuel bundle debris. These tests were performed between 1990 and 2010 at the CEA Cadarache laboratories (France) in a 5000:1 scaled facility. The main four tests varied the employed control rod materials, the fuel burn-up, and the oxidizing conditions of the atmosphere (strongly or weakly). The outcomes of this experimental campaign created a solid base for the understanding of the involved phenomena and allowed the development of models and software codes capable of simulating the evolution of a SA in a real NPP. ASTEC and MELCOR were two of the main SA codes profiting from the results of this Phébus campaign. These two codes were further improved in the latest years to account for the findings obtained in more recent experimental campaigns. A continuous verification and validation work is then necessary to check how the newer code’s versions reproduce the tests performed in these older experimental campaigns such as Phébus one. The present work is intended to be the final step of a series of publications covering the activities carried out at University of Pisa with the ASTEC and the MELCOR SA codes on the four Phébus tests employing an intact PWR fuel bundle. Because of the complexity and the extent of these tests, only the containment aspects were considered in the precedent works, i.e., only the thermal-hydraulics transient and its coupling with the FP and SM behavior. Then, general conclusions based on the outcomes of these precedent works are summarized in this work.

Description: For sequentially collected data, this paper introduces a lag-one differencing method to estimate the random error standard deviation and then uses the estimate to calculate a change detection threshold in a moving window method to detect shifts in the short-term systematic error. Performance results on simulated and real data are presented. Fortunately, the impact of having to perform change detection on the estimated short-term systematic and random error variances is anticipated to be modest or small. The motivating example arises from facilities under nuclear safeguards agreements, where inspector data collected during International Atomic Energy Agency (IAEA) verifications are compared to corresponding operator data. The differences between the operator and inspector values are evaluated using an application of analysis of variance (ANOVA). Typically, it is assumed that short-term systematic errors change across inspection periods, so inspection periods form the groups used in the ANOVA. In some data sets, it appears that the short-term errors have changed at other times, so change detection methods could be used to detect the actual change times.

Description: The large and continuous use of fossil fuels as a primary energy source has led to several environmental problems, such as the increase of the greenhouse effect. In order to minimize these problems, attention has been drawn to renewable energy production. Solar energy is an attractive candidate as renewable source due to its abundance and availability. For this, it is necessary to develop devices able to absorb sunlight and convert it into fuels or electricity in a economical, technical and sustainable way. The so-called artificial photosynthesis has called the attention of researchers due to the possibility of using solar photocatalysts in converting water and CO2 into fuels. This manuscript presents a review of the recent developments of hybrid systems based on molecular photocatalysts immobilized on semiconductor surfaces for solar fuel production through water oxidation and CO2 reduction and also discusses the current challenges for the potential application of these photocatalyst systems.

Description: The health condition of rolling-element bearings is important for machine performance and operating safety. Due to external interferences, the impulse-related fault information is always buried in the raw vibration signal. To solve this problem, a hybrid time-frequency analysis method combining ensemble local mean decomposition (ELMD) and the Teager-Kaiser energy operator (TKEO) is proposed for the fault diagnosis of high-speed train bearings. The ELMD method is a significant improvement over local mean decomposition (LMD) for addressing the mode-mixing problem. The TKEO method is effective for separating amplitude-modulated (AM) and frequency-modulated (FM) signals from a raw signal. But it is only valid for monocomponent AM-FM signals. The proposed time-frequency method integrates the advantages of ELMD and TKEO to detect localized defects in rolling-element bearings. First, a raw signal is decomposed into an ensemble of PFs and a residual component using ELMD. A novel sensitive parameter (SP) is introduced to select the sensitive PF that contains the most fault-related information. Subsequently, the TKEO is applied to extract both the amplitude and frequency modulations from the selected PF. The experimental results of rolling element and outer race fault signals confirmed that the proposed method could effectively recover fault information from raw signals contaminated by strong noise and other interferences.

Description: In this work, a compact affordable and portable spectral imaging system is presented. The system is intended to be employed in general applications, such as material classification or determination of the concentration of chemical species together with colorimetric sensors. The imaging device is reduced to a small digital color detector with an active area of . This device provides a quantification of the incident emission in the form of four digital words corresponding to its averaged components blue, green, red, and near infrared. In this way, the size of the image is reduced to one pixel. The wavelength selection is carried out by means of a LED array disposed surrounding the color detector. The LEDs are selected to cover the wavelength range from 360 to 890 nm. A sequential measurement protocol is followed, and the generated data is transmitted to an external portable device via a Bluetooth link where a classification protocol is implemented in a custom-developed Android™ application. The presented system has been applied in three different scenarios involving material classification, meat freshness monitoring, and chemical analysis. The analysis of the data using principal components shows that it is possible to find a set of wavelengths where the classification of the samples is optimal.

Description: This manuscript deals with the design of a metamaterial-based surface structure for high efficiency wireless power harvesting or collection. Differently from the previously presented structures which require the use of thicker and low-loss (and for this reason high cost) dielectric substrates, the presented work employs a dual-layer structure with a thin low-loss material and an air gap; they allow for the design of very high absorption efficiency metamaterial-based surfaces, with noticeably reduced costs. Furthermore, the air gap thickness can be used as a new degree-of-freedom (more easily adjustable than the thickness of a single-layer structure) for the optimization of other design requirements such as bandwidth or structure sizes. In comparison with other existing designs, the proposed metasurface shows a comparable absorption efficiency of 84.4% but with a larger power collection surface and lower costs.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 9, September 2019. 〈br/〉The study of the robust fatigue feature learning method for the driver’s operational behavior is of great significance for improving the performance of the real-time detection system for driver’s fatigue state. Aiming at how to extract more abstract and deep features in the driver’s direction operation data in the robust feature learning, this article constructs a fuzzy recurrent neural network model, which includes input layer, fuzzy layer, hidden layer, and output layer. The steering-wheel direction sensing time series sends the time series to the input layer through a fixed time window. After the fuzzification process, it is sent to the hidden layer to share the weight of the hidden layer, realize the memorization of the fatigue feature, and improve the feature depth capability of the steering wheel angle time sequence. The experimental results show that the proposed model achieves an average recognition rate of 87.30% in the fatigue sample database of real vehicle conditions, which indicates that the model has strong robustness to different subjects under real driving conditions. The model proposed in this article has important theoretical and engineering significance for studying the prediction of fatigue driving under real driving conditions.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 9, September 2019. 〈br/〉In mobile ad hoc networks, network nodes accomplish a target task usually by cooperative packet forwarding from the source to the destination. It is a challenge to enforce their mutual cooperation for a node’s self-interest. In this article, we focus on cooperative packet forwarding in a one-hop unreliable channel, which leads to packet loss and retransmission. We model the process of packet forwarding with the nodes’ remaining energy and reputation value. We propose a packet-forwarding non-cooperative game model reflecting the utilities of different packet-forwarding strategies, in which an incentive mechanism is introduced to enforce cooperation of packet forwarding. Furthermore, we analyze the packet-forwarding game with replicator dynamics and derive and prove three theorems. If the conditions of the theorems are met, the evolutionarily stable strategies can be attained. Three inferences also reveal how convergence speed to evolutionarily stable states is affected by the cooperative incentive, the probability of successful packet transmissions, and the upper limit of the retransmission number. The simulation results support the proposed theorems and inferences. In addition, we show that our game model with a reputation value and the mechanism of incentive cooperation can improve the probability of successful packet transmissions, and reduce the network overhead.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 9, September 2019. 〈br/〉Recently, many lightweight block ciphers are proposed, such as PRESENT, SIMON, SPECK, Simeck, SPARX, GIFT, and CHAM. Most of these ciphers are designed with Addition–Rotation–Xor (ARX)-based structure for the resource-constrained environment because ARX operations can be implemented efficiently, especially in software. However, if the word size of a block cipher is smaller than the register size of the target device, it may process inefficiently in the aspect of memory usage. In this article, we present a fast implementation method for ARX-based block ciphers, named two-way operation. Moreover, also we applied SPARX-64/128 and CHAM-64/128 and estimated the performance in terms of execution time (cycles per byte) on a 32-bit Advanced RISC Machines processor. As a result, we achieved a large amount of improvement in execution time. The cycles of round function and key schedule are reduced by 53.31% and 31.51% for SPARX-64/128 and 41.22% and 19.40% for CHAM-64/128.

Description: In order to reduce the time of designing microstrip antenna, this paper proposes a self-renewing fitness estimation of particle swarm optimization algorithm (SFEPSO) to improve the design efficiency. Firstly, a fitness predictive model of the particles is constructed according to the evolution formula of particle swarm optimization (PSO). From the third generation of the algorithm, the fitness of particles is given by the prediction model instead of the full-wave electromagnetic simulation. Aiming to keep the right direction of evolution, the prediction model will be proofed every N generation during the optimization process. If the prediction model accuracy is lower than the threshold, it will be updated and then continue iterating until the particles satisfy the demands. Compared with the traditional optimization method, this method greatly reduces the evaluation time and improves the design efficiency. The method is validated by the optimized design of E-type dual-frequency microstrip antenna and WLAN/WiMAX multiband antenna. The optimized results show that the purpose of rapid optimization can be achieved while ensuring the design accuracy.

Description: With the development of the vehicular network, new radio technologies have been in the spotlight for maximizing the utilization of the limited radio spectrum resource while accommodating the increasing amount of services and applications in the wireless mobile networks. New spectrum policies based on dynamic spectrum access technology such as flexible access common spectrum (FACS) have been adopted by the Korea Communications Commission (KCC). 23 GHz bands have been allocated to FACS bands by the KCC, which is expected extensively for vehicular communications. The comprehensive knowledge on the radio channel is essential to effectively support the design, simulation, and development of such radio technologies. In this paper, the characteristics of 23 GHz vehicle-to-infrastructure (V2I) channels are simulated and extracted for the urban environment in Seoul. The path loss, shadow factor, Ricean K-factor, root-mean-square (RMS) delay spread, and angular spreads are characterized from the calibrated ray-tracing simulation results, and it can help researchers have a better understanding of the propagation channel for designing vehicular radio technologies and a communication system in a similar environment.

Description: In order to increase the system capacity of the 5G mobile communication system, multiple-input multiple-output (MIMO) transmission techniques using a large-scale array over the millimeter-wave band have attracted a great amount of attention. To cope with various types of receivers expected in 5G communications such as user equipment (UE) in small cells, indoor Internet-of-Things (IoT) devices at diverse locations, and drones performing aerial navigation, newer types of antenna arrays require all-directional transmission capability. Existing antenna structures with typical panel arrays, however, have restrictions on their transmission angles in both horizontal and vertical directions. In this paper, we propose to employ three-dimensional (3D) array structures composed of multiple triangular panels for efficient massive MIMO transmission of the next-generation wireless systems. We analyze beamforming characteristics of a uniform triangular array (UTA) suitable for such 3D array configurations and present a basic codebook applicable to UTAs. Using antenna structures with multiple UTA panels, multiuser transmission performance is evaluated to demonstrate the effectiveness of the proposal.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 11, November 2019. 〈br/〉Flying ad hoc network is widely used in many military and civilian scenarios. Compared with mobile ad hoc network and vehicular ad hoc network, flying ad hoc network holds some special characteristics such as high mobility, long-distance communication, and sparse node-deployment, which cause an important challenge in the design of routing protocols. In this article, a velocity-aware and stability-estimation–based multi-path routing protocol is proposed for flying ad hoc network. The protocol is mainly composed of two important parts, which are the routing discovery mechanism and routing maintenance mechanism. In routing discovery process, the routing discovery request packet only can be forwarded by the reliable node, which is decided by the calculation of co-direction degree, then the routing overhead is reduced at some extent. Noticeably, the correlation of the survival duration between adjacent links is fully considered, which is very important to the path stability criteria. In routing maintenance progress, a path similarity and path remaining survival duration–based multi-path selection mechanism is proposed. The performance superiority of velocity-aware and stability-estimation–based multi-path routing protocol is also demonstrated by extensive simulations. The results show that velocity-aware and stability-estimation–based multi-path routing protocol is much better than other existing protocols in terms of network throughput, average delay of data transmission, routing overhead, and the convergence time of the routing discovery.

Description: Acoustic emission (AE) is an effective nondestructive evaluation method for assessing damage in materials; however, few works in the literature have focused on one quantification method of damage in concrete under fatigue loading by using AE for characterizing the entire three main deterioration behaviors simultaneously. These deterioration behaviors include Young’s modulus degradation, fatigue total strain, and residual strain development. In this work, an AE quantification method of fatigue damage in concrete was developed, by combining AE and a fiber bundle-based statistical damage model (fiber bundle-irreversible chain model). By establishing a relationship between normalized AE counts and the damage variable based on the fiber bundle-irreversible chain model, the method was proposed. Additionally, this method was verified against the experimental results. It is able to capture the mechanisms of damage accumulation and characterize the three deterioration behaviors simultaneously.

Description: This study presents the time-dependent analyses of transmutations of long-lived fission products (LLFPs) and medium-lived fission products (MLFPs) occurring in thermal reactors in a conceptual helium gas-cooled accelerator-driven system (ADS). In accordance with this purpose, the CANDU-37 and PWR 15 × 15 spent fuels are separately considered. The ADS consists of LBE-spallation neutron target, subcritical fuel zone, and graphite reflector zone. While the considered ADS is fueled with the spent nuclear fuels extracted from each thermal reactor without the use of additional fuel, fission products extracted from same thermal reactor are also placed into transmutation zone in graphite reflector zone. The LLFP transmutation performance of the modified ADS is analyzed by considering three different spent fuels extracted from the thermal reactors. Spent fuels are extracted from CANDU-37 in case A, from PWR-15 × 15 in case B, and from CANDU-37 fueled with mixture of PWR 15 × 15 spent fuel and 46% ThO2 in case C. The LBE target is bombard with protons of 1000 MeV. The proton beam power is assumed as 20 MW, which corresponds to 1.24828·1017 protons per second. MCNPX 2.7 and CINDER 90 computer codes are used for the time-dependent burn calculations. The ADS is operated under subcritical mode until the value of keff increases to 0.984, and the maximum operation times are obtained as 3400, 3270, and 5040 days according to the spent fuel cases of A, B, and C, respectively. The calculations bring out that in the modified ADS, LLFPs and MLFPs, which are extracted from thermal reactors, can be transformed to stable isotopes in significant amounts along with energy production.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 10, October 2019. 〈br/〉Gait analysis is an invaluable tool in diagnosing and monitoring human health. Current techniques often rely on specialists or expensive gait measurement systems. There is a clear space in the field for a simple, inexpensive, quantitative way to measure various gait parameters. This study investigates if useful quantitative gait parameters can be extracted from floor acceleration measurements produced by the input of foot falls. A total of 17 participants walked along a 115-ft-long hallway while underfloor mounted accelerometers measured the vertical acceleration of the floor. Signal-energy-based algorithms detect the heel strike of each step during trials. From the detected footsteps, gait parameters such as the average stride length, the time between steps, and the step signal energy were calculated. In this study, a single accelerometer was shown to be enough to detect steps over a 115-ft corridor. Distributions for all gait parameters measured were generated for each participant, showing a normal distribution with low standard deviation. The success of gait analysis using underfloor accelerometers presents possibilities in the widespread adaptation of gait measurements. The ease of installation and operation offers an opportunity to gather long-term gait measurements. Such data will augment current gait diagnostic approaches by filling the gaps between specialist visits.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 10, October 2019. 〈br/〉

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 10, October 2019. 〈br/〉Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.

Description: In previous studies, the trapped surface wave, which is defined by the residue sums, has been addressed in the evaluation of the Sommerfeld integrals describing electromagnetic field of a vertical dipole in the presence of three-layered or four-layered region. But unfortunately, the existing computational scheme cannot provide analytical solution of the field in the presence of the N-layered region when N 〉 4. The scope of this paper is to overcome the limitations in root finding algorithm implied by the previous approach and provide solution of poles in stratified media. A set of pole equations following with explicit expressions are derived based on the undetermined coefficient method, which enable a graphical approach to obtain initial values of real roots. Accordingly, the generated trapped surface wave components are computed when both the observation point and the electric dipole source are on or near the surface of a dielectric-coated conductor. Validity, efficiency, and accuracy of the proposed method are illustrated by numerical examples.

Description: Buildings consume large amounts of energy, and their transformation from energy users to producers has attracted increasing interest in the quest to help optimize the energy share, increasing energy efficiency and environmental protection. The use of energy-efficient materials is among the proposed approaches to increase the building’s energy balance, thus increasing the performance of building facades. Semitransparent building-integrated photovoltaic (BIPV), being one of the technologies with the potential to increase a building’s energy efficiency, is considered as a feasible method for renewable power generation to help buildings meet their own load, thus serving dual purposes. Semitransparent BIPV integration into buildings not only displaces conventional building facade materials but also simultaneously generates energy while retaining traditional functional roles. The awareness in improving building energy efficiency has increased as well as the awareness in promoting the use of clean or renewable energy technologies. In this study, semitransparent BIPV technology is reviewed in terms of energy generation, challenges, and ways to address limitations which can be used as a reference for the BIPV stakeholders.

Description: Based on the examination of the efficiency of solar plants, this study focuses on three main plants: a photovoltaic (PV) plant, a concentrated solar power (CSP) plant, and a hybrid PV/CSP plant. The modelling of the three plants has been implemented to evaluate the influence of design parameters (orientation angles, solar multiple (SM), thermal energy storage capacity (TES), and fraction of hybridization) on them. Several simulations have been recreated and discussed in details to study the optimal configuration of the two first plants and the profitability of the PV/CSP plants for Ouarzazate (Morocco) location. The findings demonstrate that the optimal orientation angles and TES/SM, respectively, affect the performances of PV and CSP plants, and they also reveal that PV/CSP systems have the benefits to increase the annual energy produced, reduce the cost, and offer a high dispatchability to supply a baseload. The implementing of optimal PV/CSP plant has a great economic impact on Ouarzazate city.

Description: This paper reviews the practical process of designing the electrodes on electroless immersion gold-plated test strips for glucose measurement. We have found that great care must be taken on determining the width and length of the working and reference electrodes, to yield the optimum performance on electroless immersion gold-plated glucose test strips for glucose measurement. Preferably, a width of at least 0.6 mm and a length of at least 24 mm are recommended to prevent variance in electrode resistance during mass production of electroless immersion gold-plated glucose test strips, in order to enhance the accuracy of glucose measurement. This paper also recommended that, if two different geometric sizes of test strips are going to be used on the same glucose meter, both test strips must use different lengths and widths on the working and reference electrodes to yield the same electrical resistance and hence similar electrochemical performance.

Description: In wireless sensor networks, an improved throughput capacity region can be achieved by equipping multiple channels. However, such approach inevitably brings the issue of solving the coupled channel assignment and scheduling problem. This paper put forward a low-complexity distributed channel assignment and scheduling policy for multichannel wireless sensor networks with single-hop traffic flows, named LDCS, as well as its multihop multipath extension. Under the proposed algorithms, random access and backoff time techniques are introduced to keep the complexity low and independent of the number of links and channels. Through theoretical analysis and simulation experiments, it is proved that the proposed algorithms are throughput guaranteed, and in some network scenarios, the achieved capacity region can be larger than that of other comparable distributed algorithms.

Description: International Journal of Distributed Sensor Networks, Volume 15, Issue 10, October 2019. 〈br/〉This article addresses the problem related to the reliability of path after transmitting the given amount of data with the service-level agreement cooperation in the computer communication network. The links have associated with service performance factor parameter during the data transmission, and each node is associated with the requested service performance factor. In this article, first we have considered the single objective to minimize the transmission time of the quickest path problem. An algorithm for quickest path problem has been proposed for results, and furthermore, its time complexity has been shown. The problem has been extended with bi-objective optimization of the quickest path problem, which minimizes the transmission time and hybrid logarithmic reliability. An algorithm is proposed for getting the number of efficient solutions for the quickest path problem using label-correcting algorithm. The algorithms are implemented and tested on different standard benchmark network problems provided with the set of Pareto front of the results.

Description: In directional sensor networks research, target event detection is currently an active research area, with applications in underwater target monitoring, forest fire warnings, border areas, and other important activities. Previous studies have often discussed target coverage in two-dimensional sensor networks, but these studies cannot be extensively applied to three-dimensional networks. Additionally, most of the previous target coverage detection models are based on a circular or omnidirectional sensing model. More importantly, if the directional sensor network does not design a better coverage algorithm in the coverage-monitoring process, its nodes’ energy consumption will increase and the network lifetime will be significantly shortened. With the objective of addressing three-dimensional target coverage in applications, this study proposes a dynamic adjustment optimisation algorithm for three-dimensional directional sensor networks based on a spherical sector coverage model, which improves the lifetime and coverage ratio of the network. First, we redefine the directional nodes’ sensing model and use the three-dimensional Voronoi method to divide the regions where the nodes are located. Then, we introduce a correlation force between the target and the sensor node to optimise the algorithm’s coverage mechanism, so that the sensor node can accurately move to the specified position for target coverage. Finally, by verifying the feasibility and accuracy of the proposed algorithm, the simulation experiments demonstrate that the proposed algorithm can effectively improve the network coverage and node utilisation.

Description: Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.

Description: This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior.

Description: Due to its efficiency in end-to-end communication, wireless sensor network based on the Internet protocol (IP-WSN) is used for monitoring purposes. Nowadays, the concerned agencies are giving their highest priority to monitor its critical infrastructure. Infrastructure health monitoring is the measure of estimating the state of infrastructure health or detecting the changes in structures that affect its performance. The traditional approach to monitor the infrastructure health is done by using centralized data acquisition hub. Installation and commissioning of these systems represent significant concerns, thus moving toward IP-WSN. As cost effectiveness and energy efficiency are major concerns, our proposed approach is to reduce the amount of overhead while keeping the infrastructure health monitoring system accurate. Our contribution in this paper is to reduce the amount of data to be transmitted by compressing the payload of the packets. Thus, we have proposed a double compression algorithm. In this way, the capacity of the sensor node will be increased since less time will be taken to transmit data between the intermediate node as well as the coordinator node. As a consequence, it will also extend the lifetime of the battery.

Description: Ternary nickel-cobalt lithium aluminate LiNiCoAlO2 (NCA, ) is an essential cathode material with many vital advantages, such as lower cost and higher specific capacity compared with lithium cobaltate and lithium iron phosphate materials. However, the noticeably irreversible capacity and reduced cycle performance of NCA cathode materials have restricted their further development. To solve these problems and further improve the electrochemical performance, numerous research studies on material modification have been conducted, achieving promising results in recent years. In this work, the progress of NCA cathode materials is examined from the aspects of surface coating and bulk doping. Furthermore, future research directions for NCA cathode materials are proposed.

Description: In this work, the effect of DIM on the PCE and photostability of PCDTBT:PC71BM PSCs was investigated. DIM is an effective additive in a BHJ PCDTBT:PC71BM solar cell since it fulfills the requirement of a selective PC71BM dissolution. PCE of the device based on PCDTBT:PC71BM processed with DIM is higher than that of the reference device. In terms of the device stability, the PSCs processed with DIM showed poor stability at longer light exposure time. For the device without DIM especially as the light exposure time was increased, the device stability was better because the PCDTBT could be shielded from air by an aggregated PC71BM layer. For the PCDTBT:PC71BM device processed with DIM, the results obtained from measurement indicates that it has a lower recombination rate. The result from IS measurement shows that for pristine PCDTBT:PC71BM devices with 3% DIM, the active layer resistance is lower compared to the device without DIM. However, after irradiating the device for 5 hr, the resistance of the device processed with DIM is higher and it is consistent with decreased PCE of the aged device.

Description: The aim of this work is to study the behaviour of a silicon solar cell under the irradiation of different fluences of high-energy proton radiation (10 MeV) and under constant multispectral illumination. Many theoretical et experimental studies of the effect of irradiation (proton, gamma, electron, etc.) on solar cells have been carried out. These studies point out the effect of irradiation on the behaviour of the solar cell electrical parameters but do not explain the causes of these effects. In our study, we explain fundamentally the causes of the effects of the irradiation on the solar cells. Taking into account the empirical formula of diffusion length under the effect of high-energy particle irradiation, we established new expressions of continuity equation, photocurrent density, photovoltage, and dynamic junction velocity. Based on these equations, we studied the behaviour of some electronic and electrical parameters under proton radiation. Theoretical results showed that the defects created by the irradiation change the carrier distribution and the carrier dynamic in the bulk of the base and then influence the solar cell electrical parameters (short-circuit current, open-circuit voltage, conversion efficiency). It appears also in this study that, at low fluence, junction dynamic velocity decreases due to the presence of tunnel defects. Obtained results could lead to improve the quality of the junction of a silicon solar cell.

Description: We address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped uniform linear array (ULA) using two kinds of approaches represented by the subspace-like method and the sparse reconstruction method. Particular interest emphasizes on exploiting the generalized conjugate symmetry property of L-shaped ULA to maximize the virtual array aperture for two kinds of approaches. The subspace-like method develops the rotational invariance property of the full virtual received data model by introducing two azimuths and two elevation selection matrices. As a consequence, the problem to estimate azimuths represented by an eigenvalue matrix can be first solved by applying the eigenvalue decomposition (EVD) to a known nonsingular matrix, and the angles pairing is automatically implemented via the associate eigenvector. For the sparse reconstruction method, first, we give a lemma to verify that the received data model is equivalent to its dictionary-based sparse representation under certain mild conditions, and the uniqueness of solutions is guaranteed by assuming azimuth and elevation indices to lie on different rows and columns of sparse signal cross-correlation matrix; we then derive two kinds of data models to reconstruct sparse 2-D DOA via M-FOCUSS with and without compressive sensing (CS) involvements; finally, the numerical simulations validate the proposed approaches outperform the existing methods at a low or moderate complexity cost.

Description: Due to the difficulties in actual measurement of sea clutter and uncertainties of experimental data, the electromagnetic (EM) scattering model becomes a better alternative means to acquire the sea clutter. However, the EM scattering model still faces the problems of huge memory consumption and low-computational efficiency when dealing with the large size of sea surface or the long time case. Thus, this paper presents a statistical model to simulate the temporal-spatial correlated three-dimensional (3D) sea clutter, which is based on the statistical properties obtained from the EM scattering model, such as probability density function and correlation function. The comparisons show that the texture feature, autocorrelation function, and PDF of the sea clutter simulated by the statistical model have a good agreement with the results given by the EM model. Furthermore, the statistical model is with high efficiency and can be used to simulate the large scene or long time temporal-spatial correlated 3D sea clutter.

Description: Thermal reactors have been considered as interim solution for transmutation of minor actinides recycled from spent nuclear fuel. Various studies have been performed in recent decades to realize this possibility. This paper presents the neutronic feasibility study on transmutation of minor actinides as burnable poison in the VVER-1000 LEU (low enriched uranium) fuel assembly. The VVER-1000 LEU fuel assembly was modeled using the SRAC code system, and the SRAC calculation model was verified against the MCNP6 calculations and the available published benchmark data. Two models of minor actinide loading in the LEU fuel assembly have been investigated: homogeneous mixing in the UGD (Uranium-Gadolinium) pins and coating a thin layer to the UGD pins. The consequent negative reactivity insertion by minor actinides was compensated by reducing the gadolinium content and boron concentration. The reactivity of the LEU assembly versus burnup and the transmutation of minor actinide nuclides were examined in comparison with the reference case. The results demonstrate that transmutation of minor actinides as burnable poison in the VVER-1000 reactor is feasible as minor actinides could partially replace the functions of gadolinium and boric acid for excess reactivity control.

Description: As key equipment in nuclear power plant, the reactor power control system is adopted to strictly control and regulate the reactor power of a PWR (pressurized water reactor) in a nuclear power plant. A well-optimized predictive control algorithm based on SDMC (stepped dynamic matrix controller) is developed and introduced in this paper and applied to the power regulation of a reactor power model. In addition, the test and verification of this application is conducted by two different methods and devices: the virtual verification platform and the physical DCS (digital control system). The result of the verification suggests that the application of SDMC gains a better performance in the maximum dynamic deviation, adjustment time, overshoot, and so on.