Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analog BJT-Tuned Maximum Power Point Tracking Technique for PV Systems

In this brief, an analog, bipolar junction transistor (BJT)-tuned voltage reference maximum power point tracking (MPPT) method for photovoltaic modules is proposed. The conventional fixed voltage reference method is the simplest method for tracking, but it does not obtain good MPPT efficiency because the maximum power point (MPP) voltage changes at different insolation levels. In reality, an approximately linear slope is formed when connecting the MPPs measured from the highest insolation level to the lowest. Utilizing this characteristic, a BJT, which has a similar electrical property, is used to implement a variable voltage reference that improves the accuracy of the MPP voltage when the insolation changes. The proposed circuit is simple and easy to implement, and it can track the MPP very quickly without the need for a digital controller or PID controller. Hence, the circuits cost and complexity are reduced. Experimental results are given to verify the feasibility of the proposed MPPT method.
Timer Based Digital Implementation of Advanced Bus-Clamping PWM Techniques

Advanced bus-clamping pulse width modulation (ABCPWM) techniques have been shown to outperform continuous and discontinuous (i.e. bus-clamping) pulse width modulation methods in terms of line current distortion, pulsating torque (in case of motor drives) and inverter switching loss at a given average switching frequency under certain operating conditions. These ABCPWM methods clamp a phase to a dc rail (as in discontinuous modulation) and switch another phase at twice the nominal switching frequency (unlike discontinuous modulation). The ‘double-switching’ of the three phases in different intervals makes the implementation of such modulation schemes challenging. Further, the microcontrollers and digital signal processors available for power electronic applications are oriented towards implementation of continuous and discontinuous modulation schemes. This paper discusses the implementation of ABCPWM schemes on such processors. The features of the on-chip pulse-generation module in the digital signal processor are effectively utilized so as to avoid external hardware and to reduce the number of interrupts.
Unique Parametric Ratio Measure for Geometrical Shape Object Detection

The decision-making methodology of an object classifier is based on the values of the measured parameters. The paper proposes an algorithm for target classification with video-based applications, based on ratios of parameters extracted using blob detection. The technique has been developed for different kinds of parametric ratios like ratio of area/major axis, area/minor axis, major axis/minor axis. Whereas, a unique parametric ratio i.e. the ratio of area/minor axis has a specific significance for classifying different categories of objects. The algorithm will differentiate among four major classes of objects i.e. human, two-wheelers, four-wheelers and heavy vehicles like Trucks. The algorithm is trained and tested on videos recorded by day cameras and thermal cameras. The experimental results are shown in tabular form, which are also validated on the basis of published work.
Statistical Detection Theory Approach to Hyperspectral Image Classification

This paper presents a statistical detection theory approach to hyperspectral image (HSI) classification which is quite different from many conventional approaches reported in the HSI classification literature. It translates a multi-target detection problem into a multi-class classification problem so that the well-established statistical detection theory can be readily applicable to solving classification problems. In particular, two types of classification, a priori classification and a posteriori classification, are developed in corresponding to Bayes detection and maximum a posteriori (MAP) detection, respectively, in detection theory. As a result, detection probability and false alarm probability can also be translated to classification rate and false classification rate derived from a confusion classification matrix used for classification. To evaluate the effectiveness of a posteriori classification, a new a posteriori classification measure, to be called precision rate (PR), is also introduced by MAP classification in contrast to overall accuracy (OA) that can be considered as a priori classification measure and has been used for Bayes classification. The experimental results provide evidence that a priori classifier as Bayes classifier which performs well in terms of OA does not necessarily perform well as a posteriori classifier in terms of PR. That is, PR is the only criterion that can be used as a posteriori classification measure to evaluate how well a classifier performs.
Optimal Edge Resource Allocation in IoT-Based Smart Cities

With IoT-based smart cities, massive heterogeneous IoT devices are running diverse advanced services for unprecedented intelligence and efficiency in various domains of city life. Given the exponentially growing number of IoT devices and the large number of smart city services as well as their different QoS requirements, it has been a big challenge for servers to optimally allocate limited resources to all hosted applications for satisfactory performance. Note that by pushing the computing and storage resources to the proximity of end IoT devices, and deploying applications in distributed edge servers, edge computing technology appears to be a promising solution for this challenge. Toward this, we study in this article how to allocate edge resources for average service response time minimization. Besides the proposed algorithms, extensive numerical results are also presented to validate their efficacy.
A Prospect of Achieving Artificial Neural Networks through FPGA

With the beginning of the 21st century, industries and researchers witnessed the end of the Moore’s Law. The stagnation of the size of transistor and other related limitations have compelled researchers to look for alternative approaches in order to maintain the momentum for design of computational intensive, high performing reliable systems focusing applications involving cloud computing, artificial intelligence, big data and IoT. The fuzzy Artificial Neural Network (ANN) based design approach have assisted a lot, in optimizing the systems manifold, for instance optimizing the system design targeting the pattern recognition problem in a large dataset. The capabilities and advantages associated with ANN have made it possible to address varied set of problems. Until now it was being implemented through software. Recent studies have showcased different limitations associated with software level ANN implementations. To address these issues hardware implementation of ANN based algorithms have been proposed and found more efficient vis-a-vis software based implementations. With the advances in technology, hardware available today is more efficient and capable. Devices based on GPUs, FPGAs and ASICs are used for the same, however FPGA shows more potentials as it is closer to wafer processing, providing options for high degree optimizations in power, speed and area; which seems to be perfect for today’s dynamic dataset requirements. This paper focuses on the use of FPGAs for ANN inspired hardware level implementations. Authors have made a comprehensive study of the amalgamations of the technologies. Authors then propound the implementation of processing blocks, activations functions, weight managements and applications on the FPGA augmenting with external data interface.
Mobile as a Sensor in Intelligent Transportation System for Street Route

Rapid increase in the espousal of mobile phones and their ubiquity has significantly reflected upon the Intelligent Transport system (ITS). Some of the major ITS areas of usage include active traffic management, driver information, navigation information, Telematics and so on. Currently there are many apps for destination navigation systems and the navigation apps in Smartphone are more advanced and accurate than any conventional navigation devices. These apps can track location real time, guide to destinations, display maps to points of attraction and interest and even update about real time traffic conditions in some cities. This paper describes the working of location based navigation apps and analyzes the street view services in the navigation apps.
Unsupervised Difference Representation Learning for Detecting Multiple Types of Changes in Multitemporal Remote Sensing Images

With the rapid increase of remote sensing images in temporal, spectral, and spatial resolutions, it is urgent to develop effective techniques for joint interpretation of spatial-temporal images. Multitype change detection (CD) is a significant research topic in multitemporal remote sensing image analysis, and its core is to effectively measure the difference degree and represent the difference among the multitemporal images. In this paper, we propose a novel difference representation learning (DRL) network and present an unsupervised learning framework for multitype CD task. Deep neural networks work well in representation learning but rely too much on labeled data, while clustering is a widely used classification technique free from supervision. However, the distribution of real remote sensing data is often not very friendly for clustering. To better highlight the changes and distinguish different types of changes, we combine difference measurement, DRL, and unsupervised clustering into a unified model, which can be driven to learn Gaussian-distributed and discriminative difference representations for nonchange and different types of changes. Furthermore, the proposed model is extended into an iterative framework to imitate the bottom-up aggregative clustering procedure, in which similar change types are gradually merged into the same classes. At the same time, the training samples are updated and reused to ensure that it converges to a stable solution. The experimental studies on four pairs of multispectral data sets demonstrate the effectiveness and superiority of the proposed model on multitype CD.
Stacked Auto Encoder Based Hybrid Genetic Algorithm for Workforce Optimization

Work area optimization is a sub domain of Workforce optimization where the goal is the efficient utilization of resources (engineers) which leads to significant savings in operational costs and a corresponding increase in revenue. In this paper we present a Hybrid Genetic Algorithm where we will generate prior knowledge about the work area optimization problem using Deep Neural Network to provide good initial estimates to improve the performance of the Genetic Algorithm. The results show that the new approach provides faster convergence as well as more balanced WAs compared to a Conventional Genetic Algorithm.
Incentive Stackelberg Dynamic Game Approach to Transporting a Cable-Suspended Load with Two Quadrotors

Transporting a cable-suspended payload with two quadrotors involves controlling two quadrotors cooperatively. The existing methods formulate the problem into a control system with one cost function. This paper formulates the problem into a control system with two cost functions by considering two quadrotors as two decision makers and exploiting their individual interests as two cost functions. A dynamic game model is developed for this purpose. The benefit is to control the system more efficiently in terms of individual costs. The system dynamics is derived based on the Euler-Lagrange equation, and then linearized into a linear system. Based on the linearized system, an incentive Stackelberg dynamic game approach is applied to the system with one quadrotor as the leader and the other as the follower. Following an incentive Stackelberg game strategy, two state feedback controllers are developed for the leader and the follower. The application scenario is a tracking task where the payload should track a desired trajectory by controlling the two quadrotors. The simulation performance comparison with an LQR controller is provided.
Semisupervised Sparse Subspace Clustering Method With a Joint Sparsity Constraint for Hyperspectral Remote Sensing Images

Sparse subspace clustering (SSC), as an effective subspace clustering technique, has been widely applied in the remote sensing community, demonstrating a superior performance over the traditional methods such as k-means. In this paper, we propose a unified framework for hyperspectral image (HSI) clustering, which incorporates spatial information and label information in an SSC model, aiming at generating a more precise similarity matrix. The spatial information is included through a joint sparsity constraint on the coefficient matrix of each local region. Pixels within a local region are encouraged to select a common set of samples in the subspace-sparse representation, which greatly promotes the connectivity of the similarity matrix. We incorporate the available label information effectively within the same framework, by zeroing the entries of the sparse coefficient matrix, which correspond to the data points from different classes. An optimization algorithm is derived based on the alternating direction method of multipliers for the resulting model. Experimental results on real HSIs demonstrate a superior performance over the related state-of-the-art methods.
Abnormal Heart Rhythm Detection Based on Spectrogram of Heart Sound using Convolutional Neural Network

Based on WHO’s data in 2012, cardiovascular diseases (CVD) is the most leading contributor to mortality, estimated at 17.5 million deaths. There are two effective way to examined heart condition, i.e. electrocardiogram (ECG) and echocardiogram. Although those two methods are effective, both methods are relatively expensive. The other method that can be used to examined heart condition is auscultation method. Auscultation requires substantial clinical experience. This process very subjective because it depends on the experience and the ability of paramedic hearing. Numerous research on developing an automatic system for detecting abnormal heart sound already conducted, but these studies still have some weaknesses making the accuracy in the classification less optimal. So, this paper aims to developed a scheme to classify the heart condition based on spectrogram of heartbeat sounds. To help increase the accuracy of the diagnosis, through this study, a CAD-based scheme was developed by applying a Convolutional Neural Network (CNN) algorithm. The achieved classification accuracy is 82.75%. This indicates that the proposed method has a potential to be implemented in the development of a computer aided diagnosis for CVD.
Development of Balancing Spherical Rolling Robot

Industrial technology and robot have become to be the important things in our life. There have large-scale innovations development and growing fast to meet the demands of people that increases continuously. The purpose of this research is to develop the prototype of Balancing Spherical Rolling Robot aims to work in various areas. Balancing Spherical Rolling Robot has true holonomic and would be able to move in any direction without having to change its orientation. The internal mechanics and the corresponding control systems are designed to change the momentum of the robot. Research show the mechanical design and movement experiment in any behavior.
A Quantitative Approach for Direct Synthesis of Bandpass Filters Composed of Transversal Resonators

This brief aims to propose a quantitative approach for direct synthesis of bandpass filters (BPFs) composed of a few transversal resonators. In conventional synthesis of these BPFs, coupling coefficients between input/output port and resonators are converted to external quality factor (<italic>Q<sub>e</sub></italic>), ignoring inherent characteristics of resonators, such as impedance or reactance/susceptance slopes. As a result, the physical dimensions of resonators could only be determined qualitatively. In this brief, a transversal lowpass prototype derived from coupling matrix is transformed into a bandpass circuit at first, then the coupling coefficients between input/output port and resonators in these BPFs are quantitatively derived as an accurate relationship between <inline-formula> <tex-math notation="LaTeX">${J}$ </tex-math></inline-formula> inverter and susceptance slope of resonators instead. Hence, the physical dimensions of an entire BPF could be directly synthesized by solving the values of <inline-formula> <tex-math notation="LaTeX">${J}$ </tex-math></inline-formula> inverters and susceptance slope in addition to the resonant frequency of resonators. In order to verify the theory, a proof-of-concept example using cross-shaped resonator is designed and fabricated. The measured results are found in good agreement between the synthesized and simulated ones.
SAR Compact Polarimetry for Change Detection and Characterization

Coherent and incoherent exploitation of complex-valued, synthetic aperture radar imagery separated by temporal and/or spatial baselines can provide significant information for change detection. The majority of the existing techniques, however, deal with the single-channel coherency or correlation for change analysis. We develop change detection/change techniques by incorporating the target polarimetric response. The formalism allows straightforward application to quad-pol, dual-pol, and hybrid-pol (compact-pol) imagery. Our aim is to develop polarimetric change detection and change characterization methodologies utilizing a general polarimetric framework that is suitable for quad, dual, and compact polarimetry.
High-Performance SAR Image Matching Using Improved SIFT Framework Based on Rolling Guidance Filter and ROEWA-Powered Feature

Severe speckle noise and complex local deformation in synthetic aperture radar (SAR) images degrade the performance of common scale-invariant feature transform (SIFT) based SAR image matching algorithms. To address this issue, this paper presents a new SIFT framework based algorithm by designing a novel nonlinear multiscale space construction strategy and a new local feature detector and descriptor. In the proposed algorithm, the multiscale images are generated by embedding a rolling guidance filter into the procedure of scale space construction, which can achieve better results in edge-preserving despeckling than other strategies. The improved nonlinear multiscale space construction strategy lays a solid foundation for high-quantity feature point detection with a low false ratio. For feature detection, a new feature detector, Harris–Laplace technique combined with the ratio of exponential weighted average, Harris–Laplace ratio of exponential weighted average (ROEWA), is proposed to effectively suppress the false feature points in SAR images. Moreover, a gradient location and orientation histogram powered by ROEWA is designed to obtain feature descriptors, which is robust to local deformations. Finally, the K-nearest neighbor approach is used to speed up the search for initial matching; the spatially consistent random sample consensus is adopted to remove false match points (outliers). The experimental results using simulated and real SAR images demonstrate that the proposed algorithm can achieve much better performance in terms of the matching accuracy and inlier ratio than other methods.
Variational Bayesian Change Detection of Remote Sensing Images Based on Spatially Variant Gaussian Mixture Model and Separability Criterion

In this paper, we propose a variational Bayesian change detection approach for remote sensing (RS) images that utilizes a spatially variant Gaussian mixture model (SVGMM) in conjunction with separability criterion. The mixture-model-based change detection methods generally have the common mixing coefficients, leading to being sensitive to noise and other environmental factors. To this end, we first introduce SVGMM to accurately characterize the data distribution of the difference image. More importantly, a variational inference algorithm is developed to achieve an effective learning of SVGMM with the closed-form update solutions. Meanwhile, we explore spatial constraint on the hyperparameter in the posteriori distribution of mixing coefficients for improving the accuracy and reliability. Then conditional posteriori probabilities of the changed and unchanged classes are derived from the responsibilities. In addition, a separability criterion, enforcing intraclass compactness and interclass separability, is defined to determine the index integer, which is related to the conditional posteriori probabilities. Finally, the binary change mask (CM), respectively, representing the changed and unchanged classes, is generated by comparing the conditional posteriori probabilities of the changed and unchanged classes. The experiments on the real RS images demonstrate the effectiveness of the proposed method, and also present its convergence observation.
HybridIoT: Integration of Hierarchical Multiple Access and Computation Offloading for IoT-Based Smart Cities

The Internet of Things (IoT) is an emerging technology that proffers to connect massive smart devices together and to the Internet. On the basis of IoT, a smart city is endowed with real-time monitoring, ubiquitous sensing, universal connectivity, and intelligent information processing and control. An IoT-based smart city can offer various smart services to citizens and administrators, thus improving the utilization of public resources regarding transportation, healthcare, environment, entertainment, and energy. The integration of transmitting, computing, and caching is having a profound impact on the development of flexible and efficient IoT in smart cities. However, with the introduction of ultra dense networking (UDN) and mobile edge computing (MEC), we have to carefully consider a joint problem across the physical layer and MAC layer to enable the efficient transmission, computation, and caching of big IoT data generated by massive IoT devices distributed in a city. In doing so, efficient multiple access and computation offloading should be addressed in the physical layer and MAC layer, respectively. In this article, we propose a scalable and sustainable IoT framework that integrates UDN-based hierarchical multiple access and computation offloading between MEC and cloud to support the smart city vision. The proposed integrated framework can substantially reduce the end-to-end delay and energy consumption of computing data from massive IoT devices. Numerical comparison results are presented to show the efficiency of the proposed framework. In addition, we discuss a number of open research issues in implementing the proposed framework. Introduction
Analysis and Scope of Automation in Available Street Light Architecture

Automation of street light and the energy losses occurring in them is an important issue because it requires a large manpower and also leads to increased energy expenses for a city due to which system becomes less efficient. This research paper deals with survey and analysis of system architecture of street light and scope of automation in street light. Automated systems can be employed to make the street lighting system very efficient. This article helps the research community in designing better architecture of street light which helps in reducing the manpower, eliminating the errors which occur due to manual operation and saving in energy consumption of street light.