Showing papers in "Electronics in 2018"

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

Abstract: Technology has become ubiquitous, it is all around us and is becoming part of us. Togetherwith the rise of the Internet of Things (IoT) paradigm and enabling technologies (e.g., Augmented Reality (AR), Cyber-Physical Systems, Artificial Intelligence (AI), blockchain or edge computing), smart wearables and IoT-based garments can potentially have a lot of influence by harmonizing functionality and the delight created by fashion. Thus, smart clothes look for a balance among fashion, engineering, interaction, user experience, cybersecurity, design and science to reinvent technologies that can anticipate needs and desires. Nowadays, the rapid convergence of textile and electronics is enabling the seamless and massive integration of sensors into textiles and the development of conductive yarn. The potential of smart fabrics, which can communicate with smartphones to process biometric information such as heart rate, temperature, breathing, stress, movement, acceleration, or even hormone levels, promises a new era for retail. This article reviews the main requirements for developing smart IoT-enabled garments and shows smart clothing potential impact on business models in the medium-term. Specifically, a global IoT architecture is proposed, the main types and components of smart IoT wearables and garments are presented, their main requirements are analyzed and some of the most recent smart clothing applications are studied. In this way, this article reviews the past and present of smart garments in order to provide guidelines for the future developers of a network where garments will be connected like other IoT objects: the Internet of Smart Clothing.

Magnetically Coupled Resonance WPT: Review of Compensation Topologies, Resonator Structures with Misalignment, and EMI Diagnostics

Abstract: Magnetically coupled resonance wireless power transfer systems (MCR WPT) have been developed in recent years. There are several key benefits of such systems, including dispensing with power cords, being able to charge multiple devices simultaneously, and having a wide power range. Hence, WPT systems have been used to supply the power for many applications, such as electric vehicles (EVs), implantable medical devices (IMDs), consumer electronics, etc. The literature has reported numerous topologies, many structures with misalignment effects, and various standards related to WPT systems; they are usually confusing and difficult to follow. To provide a clearer picture, this paper aims to provide comprehensive classifications for the recent contributions to the current state of MCR WPT. This paper sets a benchmark in order to provide a deep comparison between different WPT systems according to different criteria: (1) compensation topologies; (2) resonator structures with misalignment effects; and, (3) electromagnetic field (EMF) diagnostics and electromagnetic field interference (EMI), including the WPT-related standards and EMI and EMF reduction methods. Finally, WPT systems are arranged according to the application type. In addition, a WPT case study is proposed, an algorithm design is given, and experiments are conducted to validate the results obtained by simulations.

Multiple UAV Systems for Agricultural Applications: Control, Implementation, and Evaluation

Abstract: The introduction of multiple unmanned aerial vehicle (UAV) systems into agriculture causes an increase in work efficiency and a decrease in operator fatigue. However, systems that are commonly used in agriculture perform tasks using a single UAV with a centralized controller. In this study, we develop a multi-UAV system for agriculture using the distributed swarm control algorithm and evaluate the performance of the system. The performance of the proposed agricultural multi-UAV system is quantitatively evaluated and analyzed through four experimental cases: single UAV with autonomous control, multiple UAVs with autonomous control, single UAV with remote control, and multiple UAVs with remote control. Moreover, the performance of each system was analyzed through seven performance metrics: total time, setup time, flight time, battery consumption, inaccuracy of land, haptic control effort, and coverage ratio. Experimental results indicate that the performance of the multi-UAV system is significantly superior to the single-UAV system.

A Comprehensive Review of Recent Progress on GaN High Electron Mobility Transistors: Devices, Fabrication and Reliability

Abstract: GaN based high electron mobility transistors (HEMTs) have demonstrated extraordinary features in the applications of high power and high frequency devices. In this paper, we review recent progress in AlGaN/GaN HEMTs, including the following sections. First, challenges in device fabrication and optimizations will be discussed. Then, the latest progress in device fabrication technologies will be presented. Finally, some promising device structures from simulation studies will be discussed.

A Prediction Methodology of Energy Consumption Based on Deep Extreme Learning Machine and Comparative Analysis in Residential Buildings

Abstract: In this paper, we have proposed a methodology for energy consumption prediction in residential buildings. The proposed method consists of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, we have collected real data from four multi-storied residential building. The collected data are provided as input for the acquisition layer. In the pre-processing layer, several data cleaning and preprocessing schemes were deployed to remove abnormalities from the data. In the prediction layer, we have used the deep extreme learning machine (DELM) for energy consumption prediction. Further, we have also used the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) in the prediction layer. In the DELM different numbers of hidden layers, different hidden neurons, and various types of activation functions have been used to achieve the optimal structure of DELM for energy consumption prediction. Similarly, in the ANN, we have employed a different combination of hidden neurons with different types of activation functions to get the optimal structure of ANN. To obtain the optimal structure of ANFIS, we have employed a different number and type of membership functions. In the performance evaluation layer for the comparative analysis of three prediction algorithms, we have used the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results indicate that the performance of DELM is far better than ANN and ANFIS for one-week and one-month hourly energy prediction on the given data.

Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits

Abstract: Advances in manufacturing process technology are key ensembles for the production of integrated circuits in the sub-micrometer region. It is of paramount importance to assess the effects of tolerances in the manufacturing process on the performance of modern integrated circuits. The polynomial chaos expansion has emerged as a suitable alternative to standard Monte Carlo-based methods that are accurate, but computationally cumbersome. This paper provides an overview of the most recent developments and challenges in the application of polynomial chaos-based techniques for uncertainty quantification in integrated circuits, with particular focus on high-dimensional problems.

Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

Abstract: With the popularization and wide application of drones in military and civilian fields, the safety of drones must be considered. At present, the failure and drop rates of drones are still much higher than those of manned aircraft. Therefore, it is imperative to improve the research on the safe landing and recovery of drones. However, most drone navigation methods rely on global positioning system (GPS) signals. When GPS signals are missing, these drones cannot land or recover properly. In fact, with the help of optical equipment and image recognition technology, the position and posture of the drone in three dimensions can be obtained, and the environment where the drone is located can be perceived. This paper proposes and implements a monocular vision-based drone autonomous landing system in emergencies and in unstructured environments. In this system, a novel map representation approach is proposed that combines three-dimensional features and a mid-pass filter to remove noise and construct a grid map with different heights. In addition, a region segmentation is presented to detect the edges of different-height grid areas for the sake of improving the speed and accuracy of the subsequent landing area selection. As a visual landing technology, this paper evaluates the proposed algorithm in two tasks: scene reconstruction integrity and landing location security. In these tasks, firstly, a drone scans the scene and acquires key frames in the monocular visual simultaneous localization and mapping (SLAM) system in order to estimate the pose of the drone and to create a three-dimensional point cloud map. Then, the filtered three-dimensional point cloud map is converted into a grid map. The grid map is further divided into different regions to select the appropriate landing zone. Thus, it can carry out autonomous route planning. Finally, when it stops upon the landing field, it will start the descent mode near the landing area. Experiments in multiple sets of real scenes show that the environmental awareness and the landing area selection have high robustness and real-time performance.

LiDAR and Camera Detection Fusion in a Real-Time Industrial Multi-Sensor Collision Avoidance System

Abstract: Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quarantined by mapping (e.g., GPS) or via beacons that delineate a no-entry area. We propose a delineation method where the industrial vehicle utilizes a LiDAR (Light Detection and Ranging) and a single color camera to detect passive beacons and model-predictive control to stop the vehicle from entering a restricted space. The beacons are standard orange traffic cones with a highly reflective vertical pole attached. The LiDAR can readily detect these beacons, but suffers from false positives due to other reflective surfaces such as worker safety vests. Herein, we put forth a method for reducing false positive detection from the LiDAR by projecting the beacons in the camera imagery via a deep learning method and validating the detection using a neural network-learned projection from the camera to the LiDAR space. Experimental data collected at Mississippi State University’s Center for Advanced Vehicular Systems (CAVS) shows the effectiveness of the proposed system in keeping the true detection while mitigating false positives.

Metamaterial-Based Highly Isolated MIMO Antenna for Portable Wireless Applications

Abstract: In this paper, a metamaterial structure is presented to lower the mutual coupling between the closely spaced microstrip patch antenna elements. Two elements Multiple Input Multiple Output (MIMO) antenna is closely placed with each other at edge to edge separation of 0.135 λ 0 (7 mm). Isolation improvement of 9 dB is achieved by keeping the metamaterial structure in between the MIMO elements. With the proposed structure, the isolation is achieved around −24.5 dB. Due to low ECC, high gain, low channel capacity loss and very low mutual coupling between elements, the proposed antenna is a good candidate for the MIMO applications. The proposed antenna is fabricated and tested. A reasonable agreement between simulated and measured results is observed.

Digital Control Techniques Based on Voltage Source Inverters in Renewable Energy Applications: A Review

Abstract: In the modern era, distributed generation is considered as an alternative source for power generation. Especially, need of the time is to provide the three-phase loads with smooth sinusoidal voltages having fixed frequency and amplitude. A common solution is the integration of power electronics converters in the systems for connecting distributed generation systems to the stand-alone loads. Thus, the presence of suitable control techniques, in the power electronic converters, for robust stability, abrupt response, optimal tracking ability and error eradication are inevitable. A comprehensive review based on design, analysis, validation of the most suitable digital control techniques and the options available for the researchers for improving the power quality is presented in this paper with their pros and cons. Comparisons based on the cost, schemes, performance, modulation techniques and coordinates system are also presented. Finally, the paper describes the performance evaluation of the control schemes on a voltage source inverter (VSI) and proposes the different aspects to be considered for selecting a power electronics inverter topology, reference frames, filters, as well as control strategy.

Acoustic-Based Fault Diagnosis of Commutator Motor

Abstract: In the paper, the author presents acoustic-based fault diagnosis of a commutator motor (CM). Five states of the commutator motor were considered: healthy commutator motor, commutator motor with broken rotor coil, commutator motor with shorted stator coils, commutator motor with broken tooth on sprocket, commutator motor with damaged gear train. A method of feature extraction MSAF-15-MULTIEXPANDED-8-GROUPS (Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups) was described and implemented. Classification methods, such as nearest neighbour (NN), nearest mean (NM), self-organizing map (SOM), backpropagation neural network (BNN) were used for acoustic analysis of the commutator motor. The paper provides results of acoustic analysis of the commutator motor. The results had a good recognition rate. The results of acoustic analysis were in the range of 88.4–94.6%. The NM classifier and the MSAF-15-MULTIEXPANDED-8-GROUPS provided TERCM = 94.6%.

Multi-Agent Cooperative Control Consensus: A Comparative Review

Abstract: Cooperative control consensus is one of the most actively studied topics within the realm of multi-agent systems. It generally aims to drive multi-agent systems to achieve a common group objective. The core aim of this paper is to promote research in cooperative control community by presenting the latest trends in this field. A summary of theoretical results regarding consensus for agreement analysis for complex dynamic systems and time-invariant information exchange topologies is briefly described in a unified way. The application under both non-formation and formation cooperative control consensus for multi-agent system also investigated. In addition, future recommendations and some open problems are also proposed.

Antenna Mutual Coupling Suppression Over Wideband Using Embedded Periphery Slot for Antenna Arrays

Abstract: This paper presents a new approach to suppress interference between neighbouring radiating elements resulting from surface wave currents. The proposed technique will enable the realization of low-profile implementation of highly dense antenna configuration necessary in SAR and MIMO communication systems. Unlike other conventional techniques of mutual coupling suppression where a decoupling slab is located between the radiating antennas the proposed technique is simpler and only requires embedding linear slots near the periphery of the patch. Attributes of this technique are (i) significant improvement in the maximum isolation between the adjacent antennas by 26.7 dB in X-band and >15 dB in Ku and K-bands; (ii) reduction in edge-to-edge gap between antennas to 10 mm (0.37 λ); and (iii) improvement in gain by >40% over certain angular directions, which varies between 4.5 dBi and 8.2 dBi. The proposed technique is simple to implement at low cost.

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

Abstract: The battery is the most ideal power source of the twenty-first century, and has a bright future in many applications, such as portable consumer electronics, electric vehicles (EVs), military and aerospace systems, and power storage for renewable energy sources, because of its many advantages that make it the most promising technology. EVs are viewed as one of the novel solutions to land transport systems, as they reduce overdependence on fossil energy. With the current growth of EVs, it calls for innovative ways of supplementing EVs power, as overdependence on electric power may add to expensive loads on the power grid. However lithium-ion batteries (LIBs) for EVs have high capacity, and large serial/parallel numbers, when coupled with problems like safety, durability, uniformity, and cost imposes limitations on the wide application of lithium-ion batteries in EVs. These LIBs face a major challenge of battery life, which research has shown can be extended by cell balancing. The common areas under which these batteries operate with safety and reliability require the effective control and management of battery health systems. A great deal of research is being carried out to see that this technology does not lead to failure in the applications, as its failure may lead to catastrophes or lessen performance. This paper, through an analytical review of the literature, gives a brief introduction to battery management system (BMS), opportunities, and challenges, and provides a future research agenda on battery health management. With issues raised in this review paper, further exploration is essential.

Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions

Abstract: Cloud computing has significantly enhanced the growth of the Internet of Things (IoT) by ensuring and supporting the Quality of Service (QoS) of IoT applications. However, cloud services are still far from IoT devices. Notably, the transmission of IoT data experiences network issues, such as high latency. In this case, the cloud platforms cannot satisfy the IoT applications that require real-time response. Yet, the location of cloud services is one of the challenges encountered in the evolution of the IoT paradigm. Recently, edge cloud computing has been proposed to bring cloud services closer to the IoT end-users, becoming a promising paradigm whose pitfalls and challenges are not yet well understood. This paper aims at presenting the leading-edge computing concerning the movement of services from centralized cloud platforms to decentralized platforms, and examines the issues and challenges introduced by these highly distributed environments, to support engineers and researchers who might benefit from this transition.

A New Polarization-Reconfigurable Antenna for 5G Applications

Abstract: This paper presented a new circular polarization reconfigurable antenna for 5G wireless communications. The antenna, containing a semicircular slot, was compact in size and had a good axial ratio and frequency response. Two PIN diode switches controlled the reconfiguration for both the right-hand and left-hand circular polarization. Reconfigurable orthogonal polarizations were achieved by changing the states of the two PIN diode switches, and the reflection coefficient |S11| was maintained, which is a strong benefit of this design. The proposed polarization-reconfigurable antenna was modeled using the Computer Simulation Technology (CST) software. It had a 3.4 GHz resonance frequency in both states of reconfiguration, with a good axial ratio below 1.8 dB, and good gain of 4.8 dBic for both modes of operation. The proposed microstrip antenna was fabricated on an FR-4 substrate with a loss tangent of 0.02, and relative dielectric constant of 4.3. The radiating layer had a maximum size of 18.3 × 18.3 mm2, with 50 Ω coaxial probe feeding.

An Extreme Learning Machine Approach to Effective Energy Disaggregation

Abstract: Power disaggregation is aimed at determining appliance-by-appliance electricity consumption, leveraging upon a single meter only, which measures the entire power demand Data-driven procedures based on Factorial Hidden Markov Models (FHMMs) have produced remarkable results on energy disaggregation Nevertheless, these procedures have various weaknesses; there is a scalability problem as the number of devices to observe rises, and the inference step is computationally heavy Artificial neural networks (ANNs) have been demonstrated to be a viable solution to deal with FHMM shortcomings Nonetheless, there are two significant limitations: A complicated and time-consuming training system based on back-propagation has to be employed to estimate the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances In this work, we aim to overcome these limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights We find that the suggested approach outperforms state-of-the-art solutions, namely FHMMs and ANNs, on the UK-DALE corpus Moreover, our solution generalizes better than previous approaches for unseen houses, and avoids a data-hungry training scheme

Electrical circuit modelling of double layer capacitors for power electronics and energy storage applications: a review

Abstract: There has been increasing interests in the use of double layer capacitors (DLCs)—most commonly referred to as supercapacitors (SCs), ultra-capacitors (UCs), or hybrid capacitors (HCs)—in the field of power electronics. This increased interest in the hybridization of energy storages for automotive applications over the past few years is because of their advantage of high power density over traditional battery technologies. To facilitate accurate design and simulation of these systems, there is a need to make use of accurate and well validated models. Several models have been postulated in literature, however, these models have various limitations and strengths, ranging from the ease of use down to the complexity of characterization and parameter identification. The aim of this paper is to review and compare these models, specifically focusing on the models that predict the electrical characteristics of DLCs. The uniqueness of this review is that it focusses on the electrical circuit models of DLCs, highlighting the strengths and weaknesses of the different available models and the various areas for improvement.

Impact of Quality of Service on Cloud Based Industrial IoT Applications with OPC UA

Abstract: The Industrial Internet of Things (IIoT) is becoming a reality thanks to Industry 40, which requires the Internet connection of as many industrial devices as possible The sharing and storing of a huge amount of data in the Cloud allows the implementation of new analysis algorithms and the delivery of new “services” with added value From an economical point of view, several factors can decide the success of Industry 40 new services but, among others, the “short latency” can be one of the most interesting, especially in the industrial market that is used to the “real-time” concept For these reasons, this work proposes an experimental methodology to investigate the impact of quality of service parameters on the communication delay from the production line to the Cloud and vice versa, when gateways with OPC UA (Open Platform Communications Unified Architecture) are used for accessing data directly in the production line In this work, the feasibility of the proposed test methodology has been demonstrated by means of a use case with a Siemens S7 1500 controller exchanging data with the IBM Bluemix platform The experimental results show that, thanks to the proposed method, the solutions based on OPC UA for the implementation of industrial IoT gateways can be easily evaluated, compared and optimized For instance, during the 14-day observation period of the considered use case, the great impact on performance of the Quality of Service parameters emerged Indeed, the average communication delay from the production line to the Cloud may vary from less than 90 ms to about 300 ms

Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges

Abstract: A wireless sensor network (WSN) is a modern technology in radio communication. A WSN comprises a number of sensors that are randomly spread in a specific area for sensing and monitoring physical attributes that are difficult to monitor by humans, such as temperature, humidity, and pressure. Many problems, including data routing, power consumption, clustering, and selecting cluster heads (CHs), may occur due to the nature of WSNs. Various protocols have been conducted to resolve these issues. One of the important hierarchical protocols that are used to reduce power consumption in WSNs is low-energy adaptive clustering hierarchy (LEACH). This paper presents a comprehensive study of clustering protocols for WSNs that are relevant to LEACH. This paper is the first to emphasis on cluster formation and CHs selection methods and their strengths and weaknesses. A new taxonomy is presented to discuss LEACH variants on the basis of different classes, and the current survey is compared with other existing surveys. A complete comparison of the location, energy, complexity, reliability, multi–hop path, and load balancing characteristics of LEACH variants is conducted. Future research guidelines for CHs selection and cluster formation in WSNs are also discussed.

Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats

Abstract: Maritime communications are really challenging due to the adverse transmission conditions and the lack of a pre-provided infrastructure for supporting long range connectivity with land. Communications in high seas are usually covered by satellite links that are expensive and lead to high power consumption by the terminals. However, in areas closer to the shore, other communication options have been adopted for different kinds of services such as boat tracking and telemetry, data collection from moored monitoring systems, etc. In these scenarios, technologies such as cellular communications or wireless sensor networks have been employed so far; nevertheless, all of them present different drawbacks mostly related with the coverage and energy-efficiency of the system. Recently, a novel communication paradigm, so-called Low Power-Wide Area Network (LP-WAN) has gained momentum due to its interesting characteristics regarding transmission distances and end-node’s power consumption. The latter may be of great interest for ships with energetic restrictions such as small sailboats, recreational boats, or radio control ships. For that reason, in this work, we present a boat tracking and monitoring system based on LoRa (Long Range), one of the most prominent LP-WAN technologies. We provide a comprehensive overview of this communication solution as well as a discussion addressing its benefits when applied to maritime scenarios. We present the results extracted from a case of study, where real-training sessions of Optimist Class sailboats have been monitored by means of the presented architecture, obtaining good levels of coverage and link-reliability with limited power consumption. A transmission range study is also presented, demonstrating the validity of this proposal for monitoring activities inside the port or maneuvers close to the shore.

Real-Time Road Lane Detection in Urban Areas Using LiDAR Data

Abstract: The generation of digital maps with lane-level resolution is rapidly becoming a necessity, as semi- or fully-autonomous driving vehicles are now commercially available. In this paper, we present a practical real-time working prototype for road lane detection using LiDAR data, which can be further extended to automatic lane-level map generation. Conventional lane detection methods are limited to simple road conditions and are not suitable for complex urban roads with various road signs on the ground. Given a 3D point cloud scanned by a 3D LiDAR sensor, we categorized the points of the drivable region and distinguished the points of the road signs on the ground. Then, we developed an expectation-maximization method to detect parallel lines and update the 3D line parameters in real time, as the probe vehicle equipped with the LiDAR sensor moved forward. The detected and recorded line parameters were integrated to build a lane-level digital map with the help of a GPS/INS sensor. The proposed system was tested to generate accurate lane-level maps of two complex urban routes. The experimental results showed that the proposed system was fast and practical in terms of effectively detecting road lines and generating lane-level maps.

A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks

Abstract: In this paper, we present a comprehensive study of smart grid security against cyber-physical attacks on its distinct functional components. We discuss: (1) a function-based methodology to evaluate smart grid resilience against cyber-physical attacks; (2) a Bayesian Attack Graph for Smart Grid (BAGS) tool to compute the likelihood of the compromise of cyber components of the smart grid system; (3) risk analysis methodology, which combines the results of the function-based methodology and BAGS to quantify risk for each cyber component of the smart grid; and (4) efficient resource allocation in the smart grid cyber domain using reinforcement learning (extension of BAGS tool) to compute optimal policies about whether to perform vulnerability assessment or patch a cyber system of the smart grid whose vulnerability has already been discovered. The results and analysis of these approaches help power engineers to identify failures in advance from one system component to another, develop robust and more resilient power systems and improve situational awareness and the response of the system to cyber-physical attacks. This work sheds light on the interdependency between the cyber domain and power grid and demonstrates that the security of both worlds requires the utmost attention. We hope this work assists power engineers to protect the grid against future cyber-physical attacks.

Research on Air Confrontation Maneuver Decision-Making Method Based on Reinforcement Learning

Abstract: With the development of information technology, the degree of intelligence in air confrontation is increasing, and the demand for automated intelligent decision-making systems is becoming more intense. Based on the characteristics of over-the-horizon air confrontation, this paper constructs a super-horizon air confrontation training environment, which includes aircraft model modeling, air confrontation scene design, enemy aircraft strategy design, and reward and punishment signal design. In order to improve the efficiency of the reinforcement learning algorithm for the exploration of strategy space, this paper proposes a heuristic Q-Network method that integrates expert experience, and uses expert experience as a heuristic signal to guide the search process. At the same time, heuristic exploration and random exploration are combined. Aiming at the over-the-horizon air confrontation maneuver decision problem, the heuristic Q-Network method is adopted to train the neural network model in the over-the-horizon air confrontation training environment. Through continuous interaction with the environment, self-learning of the air confrontation maneuver strategy is realized. The efficiency of the heuristic Q-Network method and effectiveness of the air confrontation maneuver strategy are verified by simulation experiments.

Charuco Board-Based Omnidirectional Camera Calibration Method

Abstract: In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.

Economical Evaluation and Optimal Energy Management of a Stand-Alone Hybrid Energy System Handling in Genetic Algorithm Strategies

Abstract: Hybrid renewable energy systems are a promising technology for clean and sustainable development. In this paper, an intelligent algorithm, based on a genetic algorithm (GA), was developed and used to optimize the energy management and design of wind/PV/tidal/ storage battery model for a stand-alone hybrid system located in Brittany, France. This proposed optimization focuses on the economic analysis to reduce the total cost of hybrid system model. It suggests supplying the load demand under different climate condition during a 25-years interval, for different possible cases and solutions respecting many constraints. The proposed GA-based optimization approach achieved results clear highlight its practicality and applicability to any hybrid power system model, including optimal energy management, cost constraint, and high reliability.

Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System

Abstract: This paper proposes an artificial neural network (ANN) based maximum power point tracking (MPPT) control strategy for wind energy conversion system (WECS) implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN) based neural network control strategy to extract the maximum available power from the wind velocity. The results are compared with a classical Perturb and Observe (P&O) method and Back propagation network (BPN) method. In order to achieve a high voltage rating, the system is implemented with a quadratic boost converter and the performance of the converter is validated with a boost and single ended primary inductance converter (SEPIC). The performance of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink.

Real-Time Ground Vehicle Detection in Aerial Infrared Imagery Based on Convolutional Neural Network

Abstract: An infrared sensor is a commonly used imaging device. Unmanned aerial vehicles, the most promising moving platform, each play a vital role in their own field, respectively. However, the two devices are seldom combined in automatic ground vehicle detection tasks. Therefore, how to make full use of them—especially in ground vehicle detection based on aerial imagery–has aroused wide academic concern. However, due to the aerial imagery’s low-resolution and the vehicle detection’s complexity, how to extract remarkable features and handle pose variations, view changes as well as surrounding radiation remains a challenge. In fact, these typical abstract features extracted by convolutional neural networks are more recognizable than the engineering features, and those complex conditions involved can be learned and memorized before. In this paper, a novel approach towards ground vehicle detection in aerial infrared images based on a convolutional neural network is proposed. The UAV and the infrared sensor used in this application are firstly introduced. Then, a novel aerial moving platform is built and an aerial infrared vehicle dataset is unprecedentedly constructed. We publicly release this dataset (NPU_CS_UAV_IR_DATA), which can be used for the following research in this field. Next, an end-to-end convolutional neural network is built. With large amounts of recognized features being iteratively learned, a real-time ground vehicle model is constructed. It has the unique ability to detect both the stationary vehicles and moving vehicles in real urban environments. We evaluate the proposed algorithm on some low–resolution aerial infrared images. Experiments on the NPU_CS_UAV_IR_DATA dataset demonstrate that the proposed method is effective and efficient to recognize the ground vehicles. Moreover it can accomplish the task in real-time while achieving superior performances in leak and false alarm ratio.

FPGA-based Chaotic Cryptosystem by Using Voice Recognition as Access Key

Abstract: A new embedded chaotic cryptosystem is introduced herein with the aim to encrypt digital images and performing speech recognition as an external access key. The proposed cryptosystem consists of three technologies: (i) a Spartan 3E-1600 FPGA from Xilinx; (ii) a 64-bit Raspberry Pi 3 single board computer; and (iii) a voice recognition chip manufactured by Sunplus. The cryptosystem operates with four embedded algorithms: (1) a graphical user interface developed in Python language for the Raspberry Pi platform, which allows friendly management of the system; (2) an internal control entity that entails the start-up of the embedded system based on the identification of the key access, the pixels-entry of the image to the FPGA to be encrypted or unraveled from the Raspberry Pi, and the self-execution of the encryption/decryption of the information; (3) a chaotic pseudo-random binary generator whose decimal numerical values are converted to an 8-bit binary scale under the VHDL description of m o d ( 255 ) ; and (4) two UART communication algorithms by using the RS-232 protocol, all of them described in VHDL for the FPGA implementation. We provide a security analysis to demonstrate that the proposed cryptosystem is highly secure and robust against known attacks.

Free-Space Materials Characterization by Reflection and Transmission Measurements using Frequency-by-Frequency and Multi-Frequency Algorithms

Abstract: The knowledge of the electromagnetic constitutive properties of materials is crucial in many applications. Free-space methods are widely used for this purpose, despite their inherent practical difficulties. This paper describes an affordable free-space experimental setup for the characterization of flat samples in 1–6 GHz in a non-anechoic environment. The extracted properties are obtained from the calibrated Scattering Parameters, using a frequency-by-frequency solution or a multi-frequency reconstruction. For the first, we describe how the Time-Domain Gating can be implemented and used for filtering the signals. For the latter, a weighting factor is introduced to balance the reflection and transmission data, allowing one to have a more favorable configuration. The different role of transmission and reflection measurements on the achievable results is analyzed with regard to experimental uncertainties and different noise scenarios. Results from the two strategies are analyzed and compared. Good agreement between simulation, measurement and literature is obtained. According to the reported results for dielectric materials, there is no need of filtering the data by a Time-Domain Gating in case of the multi-frequency approach. Experimental results for Polymethylmethacrylate (PMMA) and Polytetrafluorethylene (PTFE) samples validate both the setup and the processing.