Miracle-dz/UniFAR-FTUs huggingface dataset:
Zero-Degree TL Based Dual-Band Wilkinson Power Divider for Multi-Band Wireless Applications

In this paper, a new dual-band Wilkinson power divider (PD) featuring simple structure, miniaturized size, and high design flexibility is presented, whose function is mainly realized by a novel dual-band biasing network (DBN). Compared to the conventional one, the half-wavelength transmission lines (TLs) are replaced by zero-degree composite right- and lefthanded (CRLH) TLs, which configuration results in a compact size, and high design flexibility that different with the previous CRLH-TL based dual-band studies, the impedance of the utilized CRLH-TL can be freely selected. Meanwhile, closed-form design equations are derived and can be mathematically utilized to introduce a large number of passbands. For demonstration, a dual-band Wilkinson PD centered at 1.90 GHz and 2.45 GHz is designed and fabricated. It is worth noting that the two frequencies are chosen so that the first frequency is for LTE mobile communication, while the other band is for wireless fidelity (WIFI), Bluetooth, etc. Good agreement between the theoretical and experimental results can be observed. Besides the low in-band insertion loss and full-ports matching properties, the proposed PD also features a good out-of-band rejection performance.
Design and Analysis of Recursive Square Fractal Antenna for WLAN Applications

In this article, a Recursive square fractal antenna has been designed for WLAN applications. A fractal is a self similar structure that enhances the effective length of the patch that transmit/receive electromagnetic radiations. The proposed antenna consists of three layers namely Patch (Perfect Electrical Conductor), Dielectric material (FR-4 with permittivity 4.4) and Ground plane (Perfect Electrical Conductor) and it is fed using a quarter wavelength transmission line for better input impedance. The overall geometry of the fractal antenna is 30mmx30mmx1.6mm and it is designed and simulated in HFSS platform. The output has been analyzed for different iteration made on the radiating patch, the difference in the radiating frequency, return loss, bandwidth, gain and directivity in four different iterations has been compared. The radiating frequency for all different iterations ranges from 4.95 GHz to 5.15 GHz which can be used for WLAN applications. The proposed Recursive square fractal antenna design using Iterated Function System (IFS) shows the improvements in the simulation parameters while increasing the number of iteration. Thus, the simulation results obtained show the S11 output in zeroth iteration is -21.31dB for 5.151GHz, whereas fourth iteration it is -59.66dB for 4.98 GHz.
Security Challenges and Strategies for the IoT in Cloud Computing

The Internet of Things is progressively turning into a pervasive computing service, needing enormous volumes of data storage and processing. However, due to the distinctive properties of resource constraints, self-organization, and short-range communication in Internet of Things (IoT), it always adopts to cloud for outsourced storage and computation. This integration of IoT with cloud has a row of unfamiliar security challenges for the data at rest. Cloud computing delivers highly scalable and flexible computing and storage resources on pay-per-use policy. Cloud computing services for computation and storage are getting increasingly popular and many organizations are now moving their data from in-house data centers to the Cloud Storage Providers (CSPs). Time varying workload and data intensive IoT applications are vulnerable to encounter challenges while using cloud computing services. Additionally, the encryption techniques and third-party auditors to maintain data integrity are still in their developing stage and therefore the data at rest is still a concern for IoT applications. In this paper, we perform an analysis study to investigate the challenges and strategies adapted by Cloud Computing to facilitate a safe transition of IoT applications to the Cloud.
Mushrooming Trends and Technologies to Aid Visually Impaired People

One of the major effect of blindness is the loss of ability to travel from one place to another safely. Visually Impaired People came across various problems in their daily life which make them anxious to do their basic works and to deal with surroundings. This paper provides information on the latest techniques used to assist visually impaired people to assist them. It provides detailed description of tools and techniques which include summary, various merits and demerits followed by the future challenges. This information will aid researchers to develop more efficient solutions for assisting visually impaired persons.
Modeling of Dust Deposition Impact on PV/T Hybrid Collector Performances

Dust deposition on the front of photovoltaic /thermal collectors presents greatest issues in some regions (desertic regions). It's considered as key parameter affecting much more collector's performances. In the present work, a particular attention has been paid to dust deposition effect on the electrical and thermal performances of photovoltaic /thermal collector PV/T.A model of sheet-and-tubes PV/ T collector is developed and validated with experimental data available in literature. The obtained results show that the dust significantly affects the electrical and thermal efficiencies. An increase of dust deposition of 8g/m2 causes an electrical and a thermal output reduction respectively of 28% and 21%. Better electrical and thermal performances have been noted for PV/ T without dust. Moreover, a good agreement has been observed between obtained results and the presented results available in literature.
Lightweight Indoor Modeling Based on Vertical Planes and Lines

Since vertical planes and lines are rich and unique for indoor localization, such as walls, posters, frames of doors and windows, a lightweight hybrid modeling method based on simple spatial features is proposed, specifically for the indoor localization and navigation applications. Firstly, the vertical planes in 3D space are detected and projected to generate a simplified floor plan of indoor environment. Here, a strategy of region segmentation and georeferencing is employed. Moreover, the plane-based model is further optimized by iterative merging algorithm. Then, some remarkable vertical lines are detected and localized based on the plane-based model, which can compensate the inaccuracy of plane ranges and the deficiency of remarkable planes in some areas. The dynamic time warping (DTW) algorithm is applied to match homonymous lines between sequential image frames, in cooperation with Kalman filtering for simultaneous tracking of vertical lines. Finally, a hybrid indoor model consisting of vertical planes and lines is generated, which is much more lightweight than previous 3D modeling methods. Experiments in real indoor scenes using single off-the-shelf smartphone verify the feasibility and effectiveness of the proposed method. The whole model size of an indoor environment with more than 200m2 area is only 302 Bytes, and it achieves an accuracy of about 0.3 meters.
Detection of Economic Minerals in Beach Placer Samples by Machine Learning-Based Microscopic Image Processing

Beach sand minerals (BSM) contains different economic minerals, out of which seven minerals viz. Ilmenitev, Rutile, Leucoxene, Zircon, Sillimanite, Garnet and Monazite are important ones. These minerals have different physical properties based on which these are separated conventionally. In this paper, a separation process using visual information is proposed. Accordingly, microscopic images of sand particles are taken and using machine learning algorithm, the particular economic mineral is accurately detected in the microscopic images. Aggregated Channel Features (ACF) detector-based learning algorithm is used in this paper.
Detection of Cross-Site Scripting Attacks using Dynamic Analysis and Fuzzy Inference System

Many prevalent problems of web applications are induced by injected codes, which pose great security threats. Vulnerabilities found in web applications are commonly typically exploited to perpetrate attacks. With cross-site scripting (XSS), attackers can infuse malevolent contents into website pages, in this way gaining access privileges to sensitive page content of the user such as, session cookies, user’s data or credentials and several other information often kept up by the browser on behalf of the users. This paper presents a hybrid mechanism for detecting XSS attacks using Dynamic Analysis and Fuzzy Inference. The approach scans the website for possible points of injection before generating an attack vector launched via an HTTP request to a web application. The analysis of the HTTP response predicts the presence of an attack vector. The detection capability of the system is evaluated using some active world web applications and the results show a high rate of detection.
RNAi in Agriculturally-Important Arthropods

This chapter was inspired by rapid developments in the field of RNA interference (RNAi), an evolutionarily-conserved cellular mechanism that directs protection against nucleic-acid invaders (e.g., viruses and repetitive DNA sequences including transposable elements) in plants, animals,protozoans and fungi. It is now evident that prokaryotes also possess an RNA-based defense system, though completely distinct from that in eukaryotes. The recent discovery of post-transcriptional gene silencing (PTGS) has generated tremendous interest in basic and applied research, including the development of in vitro and in vivo therapeutic approaches to reduce expression of disease-associated genes. This cutting-edge technology has already been successfully used in genomic manipulations of insect disease vectors, such as development of Dengue-resistant mosquitoes (Blair et al., 2006; Franz et al., 2006 ; Mathur et al., 2010). Our aim in this chapter is to provide an overview of the profound knowledge accumulated in recent years from invertebrate RNAi studies, but with a focus on agriculturally important arthropods. We start with a brief discussion of the RNAi mechanism to introduce readers to key concepts that underlie the practical application of RNAi discussed in the remainder of the chapter. Our discussion will include a number of important issues that should be carefully considered when working with multi-cellular organisms, including bidirectional transport of silencing signals, processing of dsRNA, homology-driven mRNA degradation and subsequent gene silencing. We will look at examples of gene silencing in different arthropod systems to illustrate commonalities and differences found in their silencing machinery. Arthropods are a diverse group of organisms, including ticks, mites, spiders, crabs, and insects, many of which are economically significant pests of agricultural crops, parasites of commercially managed pollinators and vectors of livestock diseases. Considering the huge economic impact of honey bees on crop production, and particularly on pollination of specialty crops, a substantial part of this chapter will be dedicated to honey bee research. Recently, catastrophic losses of honey bee colonies drew national media attention and ignited a renewed interest in basic and applied bee research (Aronstein et al., 2006; Campbell et al., 2010; Dearden et al., 2009; Maori et al., 2009a, 2009b; Nelson et al., 2007). The latest achievements in basic honey bee research provide a better understanding of bee physiology and behavior. Analysis of gene function by selective gene silencing has been
SAR Specific Noise Based Data Augmentation for Deep Learning

Deep learning techniques provide a significant performance increase in automatic target recognition (ATR) of synthetic aperture radar (SAR) images. Due to acquisition complexity, SAR images are often scarce and the performance of deep learning methods is strongly affected by the lack of diversity and low number of images available for training. Data augmentation is a solution that tackles the problem of reduced data by artificially expanding a training set.In this paper, we propose a data augmentation solution that adds Weibull noise to the High Range Resolution Profiles before SAR processing. The resulting noisy images are added to the original training set. A standard CNN is used to evaluate the impact of the proposed data augmentation on the Cranfield University Military Ground Target Dataset (MGTD). The analysis of performance shows are compared with those obtained with the classic translation data augmentation. Results show a 91% correct classification rate is achieved when a combination of the translation and the Weibull noise data augmentation is employed, compared to 86% with a classic translation data augmentation alone and 77% on standard images without data augmentation.
Agribot: A Natural Language Generative Neural Networks Engine for Agricultural Applications

In this paper, we present an artificially intelligent chatbot which would help farmers by providing solutions to various farming related problems and facilitate their decision-making process. The bot not only provides answers to frequently asked questions but also lays emphasis on crop disease detection and weather forecasting. We developed an end to end trainable sequence-to-sequence learning model with the objective of achieving conversational task-oriented system based on minimal assumption on its sequence structure. Our approach exploits a multilayered Long Short-Term Memory (LSTM) unit which maps the input sequence to a corresponding output sequence by converting it into a vector of fixed dimensionality in between. To achieve the disease detection, a Convolutional Neural Network architecture is implemented in which a multilayered architecture is developed and trained from scratch which would classify the plant images into different classes. For conversational system module we have used the Kisan Call Center (KCC) dataset which contains logs of calls at KCC by farmers whereas for disease detection module plant village dataset is used. After training 98% accuracy was achieved for conversational system module on training data and 94% for disease detection module on test data.
An improved Direct Power Control based on SVM strategy of the Doubly Fed Induction Generator

In this paper we propose a control technique called DPC (Direct Power Control) scheme for the doubly-fed induction generator (DFIG) for variable speed windpower generation. The purpose is to directly control the active and reactive power of the DFIG through the converter. The switching states of this converter are selected from a switch table. The latter is fed by two hysteresis comparators whose inputs are the instantaneous errors obtained from the difference between the reference value and the estimated active and reactive power, which represents the DPC. However, because of the variable switching frequency, the conventional C-DPC has the disadvantage of having a high harmonics rate of the currents generated by the DFIG. To remedy this problem a so-called DPC-SVM strategy is proposed. The proposed DPC-SVM technique based on a space vector modulation reduces the current and power ripples. In this control strategy, the active and reactive power are regulated by two conventional PI type regulators using the “SVM” algorithm, then the switching table and the hysteresis correctors have been eliminated. Simulation results of the proposed controller (C-DPC) and (DPC-SVM) scheme are compared for various step changes in the active/reactive power.
Analysis of Quasi-resonant ZCS Boost Converter using State-plane Diagram

In this research work, steady state analysis of quasi-resonant zero current switching (QRZCS) boost converter is investigated using state-plane diagram approach. Expected waveforms of inductor current and capacitor voltage can be mathematically obtained from this approach. For the circuit design purposed, the control frequency characteristics can be derived from the analysis. This paper shows that the use of state-plane diagram for analyzing the resonant converter is easy to understand. However, the computer simulation of the proposed converter is also done to confirm the analysis.
Extracellular Biosynthesis of Gold Nanoparticles by Metal Resistance Bacteria: Streptomyces griseus

The biosynthesis of gold nanoparticles would benefit from the development of clean, nontoxic, and environmentally acceptable procedures concerning microorganisms from bacteria to fungi. Streptomyces are saprophyte bacteria that have the enormous ability to produce secondary metabolites such as vitamin B, antibiotics, and antifungi. The authors investigated biosynthesis of gold nanoparticles by Streptomycec griseus. The UV–vis and XRD spectra of the aqueous medium containing Streptomycec griseus and 1 mM HAuCl4 demonstrated the formation of gold nanoparticles after 48 h. Transmission electron microscopy micrographs showed extracellular production of gold nanoparticles with good monodispersity, spherical shape, and average size of 50 nm.
The Impact of Non-linear NVM Devices on In-Memory Computing

Deep learning has significantly improved the accuracy of large-scale visual/auditory recognition and classification tasks, at the cost of ever-increasing computational resource and storage capacity in hardware. As a result, the data communication between the computing and storage units has been the bottleneck in Artificial Intelligence (AI) computation. The emerging resistive NVMs based in-memory computing architectures have been considered at the promising solution scheme to address the abovementioned issue. However, the non-linearity of the NVM devices has a significant impact on the computing accuracy. In this paper, a non-linear RRAM is modelled and implemented in various in-memory computing architectures. The results show severe accuracy losses caused by the non-linear reading/writing property, mismatch, uncertainty, etc. Several promising solutions are also discussed in this paper.
Optimistic Approach to Initialize Learning Rate

With Deep Neural Networks being the state-of-art architecture to solve complex tasks of Natural Language Processing (NLP) and Image Processing, Convolutional Neural Networks (CNN) have proven to outperform the existing architectures in these domains. Every Neural Network consists of two kinds of basic units called the Non-Trainable and Trainable parameters. The Non-Trainable parameters, cannot be modified and are automatically determined by the neural network. The Trainable parameters, can be explicitly altered which has a direct impact on the performance of the network. To improvise the performance of a neural network, some of the trainable parameters called the Hyper-parameters need to be fine-tuned. The most important hyper-parameter that helps to quickly to identify an Optimal Point, is the Learning Rate. Learning Rate is a parameter based on which the weights of the neurons in the network are adjusted with respect to a loss function. Improper initialization of this parameter could lead the network to get stuck in saddle points or local minimum points. An optimistic approach to initialize the learning rate and following suitable decay policy called Finding Learning Rate’s Optimal Boundaries (FLOB) has been proposed and experimented with GoogleNet on datasets like Fashion-MNIST, CIFAR-10 and Caltech-256 and evaluated with standard metrics in this article.
Borohydride-based Solid-state Electrolytes for Lithium Batteries

Properties of LiBH<inf>4</inf>-LiCl-P<inf>2</inf>S<inf>5</inf> composites are studied regarding their application as solid-state electrolyte for lithium batteries. The studied materials undergo an amorphization at 60°C, accompanied with Li<sup>+</sup> conduction increase. The measured ionic conductivity for the optimized borohydride-halide-sulfide system is about ~10<sup>−3</sup> S cm<sup>−1</sup> at room temperature with low activation energies for ions migration suitable for all-solid-state battery applications near room temperature.
A Deep Reinforcement Learning Based Energy Storage System Control Method for Wind farm Integrating Prediction and Decision

In electricity market, the wind power producers face the challenge that how to maximize their income with the uncertainty of wind power. This paper proposes an integrated scheduling mode that integrates the wind power prediction and the energy storage system (ESS) decision making, avoiding the loss of decision-making information in the wind power prediction. Secondly, deep Q network, a deep reinforcement learning (DRL) algorithm, is introduced to construct the end-to-end ESS controller. The uncertainty of wind power is automatically considered during the DRL-based optimization, without any assumption. Finally, the superiority of the proposed method is verified through the analysis of the case wind farm located in Jiangsu Province.
Radar HRRP Recognition using Attentional CNN with Multi-resolution Spectrograms

A novel high-resolution range profile (HRRP) target recognition method based on attentional convolutional neural network (CNN) model with multi-resolution spectrograms is proposed. Different from time domain HRRP, the multi-resolution spectrograms record the amplitude feature and characterize the phase information among range cells of the target at different time-frequency resolutions. The attentional CNN consists of several feature extractors and an attention mechanism to softly weight features extracted from multi-resolution spectrograms. Experimental results on measured HRRP data demonstrate the effectiveness of the proposed method, and diagnostically visualize the importance of the spectrogram at different resolutions.
Neural Network Based Multiple Object Tracking for Automotive FMCW Radar

Tracking multiple objects from radar data poses several difficulties. In recent work, it has been shown that an algorithm consisting of a thresholding, clustering and multiple object tracking step using the Kalman filter can track multiple objects. Afterwards, features can be extracted from the range-Doppler map to classify the tracked objects. However, this method needs many heuristics on each stage and in the process, information which could be useful in subsequent steps is lost.To overcome these issues, in this paper we introduce a neural network based multiple object tracker. This removes the need for a separate thresholding, clustering, feature extraction and classification step because it combines those into one step which uses a neural network based on the You Only Look Once (YOLO) object detection system to classify and localize objects. The output of the neural network is fed into a Kalman filter based tracker to manage the tracks.We show that a convolutional neural network trained as an object detector can be successfully applied in the radar domain and we show the advantages of our neural network based multiple object tracker over the clustering based method for specific scenarios. These scenarios include tracking objects that cross each other and tracking objects while the radar is non-stationary.
An Improved MPPT Controller for Photovoltaic System Based on Predictive Current Control

Maximum Power Point Tracking (MPPT) gains much attention as an important optimisation field for the photovoltaic (PV) systems. The focus of this paper is to introduce and analyze a novel fast MPPT strategy based on predictive current control. The overall system is designed to improve the energy control performance in tracking the maximum power point (MPP) of a photovoltaic array under irradiation changes condition and fixed load and will be compared to the classical Perturb and observe technique. The proposed control scheme and the classical perturbe and observe (P&O) technique are designed for residential application and implemented in MATLAB/SIMULINK software. The obtained results proves the advantages of this proposed controller in the stability of its output voltage with fast response speed to the required parameters and without power losses.