Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromagnetic Fields and Trajectory of Injected Electron Inside an Elliptical Coaxial Magnetized Plasma Waveguide

In this article, by substituting the dielectric tensor in Maxwell’s equations and using the boundary conditions in a longitudinally magnetized plasma-filled coaxial elliptical waveguide, the dispersion relation in the considered configuration is derived. We study the energy and dynamic of an injected external electron with initial energy using radiation of an electromagnetic source in the considered waveguide having the considered hybrid mode. The trajectory and kinetic energy of an injected electron in the considered waveguide along the propagating axis and hybrid field components are plotted. The obtained differential equations are numerically solved by the fourth-order Runge–Kutta method, and the results are graphically presented. The effects of different parameters on the trajectory and kinetic energy of injected electrons are investigated.
Event-triggered Stabilization for Nonlinear Systems with Center Manifolds

In this work, we consider event-based implementation of control laws designed for local stabilization of nonlinear systems with center-manifolds. The systems being considered possess linearised models with uncontrollable modes on the imaginary axis. The controller chosen decides both the structure of the center-manifold and its stability. Although the control of systems with center-manifolds is well studied, event-based control of such systems is yet to be probed. This involves the exploration of input-to-state stability (ISS) properties of such systems, with respect to measurement errors. Considering the most general structure for the controller, we prove that a controller that locally asymptotically stabilizes the dynamics on the center-manifold, also renders the overall system locally input-to-state stable (LISS) and find the comparison functions involved in the Lyapunov characterization of ISS. This general characterization required a nonlinear change of variables, involving the center-manifold, which can only be approximately determined in most cases. Because of this, it is found to be unsuitable for designing event-triggered controllers. We then explore an approach that does not resort to this change of variables and present our findings. We discuss the possibility of a simpler relative thresholding mechanism and present simulation results for an illustrative example.
Securing the Internet of Everything through Adaptive and Resource-Efficient Mechanisms

The Internet of Everything (IoE) is the intelligent connection of people, process, data and things. In addition to machine-to-machine communications as facilitated in traditional networks and the Internet of Things, IoE enables machine-to-people and technology-assisted people-to-people interactions. An IoE local network may comprise various devices, including resource-constrained devices such as sensors, actuators, and embedded and wearable devices. The protection of such a network (and the data collected by and transmitted between network devices) against security attacks is highly challenging. First, the devices may be deployed in open environments without physical protection. More severely, the security protocols developed for traditional computer networks are ineffective or even inapplicable due to the resource-constrained nature of the end devices in IoE. In this article, we discuss the security challenges and requirements for data communications between IoE devices. We show that a suite of security protocols developed recently may provide an adaptive and resource-ef-ficient solution to IoE security.
Novel Synthesizable eFPGA based on Island Network with Multilevel Switch Boxes

Flexibility, low latency, energy-efficiency, and low area, power, and cost are very important to consider for hardware and software implementation of VLSI circuits. With the advanced technology used to design these circuits especially the System on Chip, embedded FPGA (eFPGA) is one of the fields that has attracted extensive attention to ensure flexibility into SoC. Matrix and hierarchical architectures are the two most popular architectures used to design an FPGA. This paper present firstly an embedded FPGA with hierarchical architecture, the configuration of this eFPGA is ensured by a loader with a proposed model. Then, we propose the first embedded FPGA having a matrix architecture with a multi-level switch box. With the best of our knowledge, this is the first work that presents a decoder which loads the bitstream words to eFPGA SRAMs cells for the mesh of cluster architectures. A series of experimentation is conducted to evaluate the proposed eFPGAs. The static power, latency, and hardware utilization, as well as hardware implementation, are discussed and analyzed. Mesh of cluster eFPGA introduces an area overhead ≈ 2 times of tree eFPGA size which is correlated to static power but has a straightforward benefit in terms of frequency and performance, regularity, and easy to implement.
Anomaly detection in cloud network data

Cloud computing is one of the most rapidly expanding computing concepts in the modern IT world. Cloud computing interconnects data and applications served from multiple geographic locations. A large number of transactions and the hidden infrastructure in cloud computing systems have presented a number of challenges to the research community. Among them, maintaining the cloud network security has become a key challenge. For example, detecting anomalous data has been a key research area in cloud computing. Anomaly detection (or outlier detection) is the identification of suspicious or uncommon data that significantly differs from the majority of the data. Recently, machine learning methods have shown their effectiveness in anomaly detection. However, identifying anomalies or outliers using supervised learning methods still a challenging task due to the class imbalance and the unpredictable nature and inconsistent properties or patterns of anomaly data. One-class classifiers are one feasible solution for this issue. In this paper, we mainly focused on analyzing cloud network data for identifying anomalies using one-class classification methods namely One Class Support Vector Machine(OCSVM) and Autoencoder. Here, we used a benchmark data set, YAHOO Synthetic cloud network data set. To the best of our knowledge, this is the first study that used YAHOO data for detecting anomalies. According to our analysis, Autoencoder achieves 96.02 percent accuracy in detecting outliers and OCSVM achieves 79.05 percent accuracy. In addition, we further investigated the effectiveness of a one class classification method using another benchmarked data set, UNSW-NB15. There we obtained 99.10 percent accuracy for Autoencoder and 60.89 percent accuracy for OCSVM. The above results show the neural network-based methods perform better than the kernel-based methods in anomaly detection in cloud network data.
Link Scheduling in Rechargeable Wireless Sensor Networks with Battery Memory Effects

This paper considers deriving a link schedule for rechargeable WSNs. Unlike past works, it considers: (i) the time required by nodes to harvest energy, and (ii) deterioration in battery lifetime due to memory effects. It presents a greedy heuristic that schedules links according to the earliest time in which the batteries at each link’s end nodes are fully discharged. Our results show that considering memory effects via a battery cycle constraint and energy harvesting time increases the link schedule by up to 30.43% and reduces the number of charge/discharge cycles of battery by up to 84.05%. Hence, it helps to increase a battery’s lifetime. On the other hand, an increase in energy harvesting time linearly increases link schedules but it does not affect the number of charge/discharge cycles. Finally, increasing a battery’s depth of discharge reduces its number of charge/discharge cycles by up to 399.84%, while lengthening the link schedule by up to 7.03% only.
Robust and Efficient Classification for Underground Metal Target Using Dimensionality Reduction and Machine Learning

Underground metal target detection technology has been widely applied in industrial production, resource exploration, and engineering construction, etc. However, due to the influence of non-negligible noise and high dimensionality in collected data, achieving efficient and accurate underground target classification remains a grand challenge for further applications of underground metal target detection on portable devices with limited computing capability and energy supply. This study aimed to seek out robust and efficient data-based strategies to classify the underground metal targets of different shapes and materials based on electromagnetic induction detection. We investigated thirty-three classification strategies based on eleven dimensionality reduction methods, namely, the least absolute shrinkage and selection operator (LASSO), genetic algorithm-support vector machine (GA-SVM), Pearson correlation coefficient (PCC), mutual information (MI), maximal relevance minimal redundancy Pearson correlation (mRMRP), maximal relevance minimal redundancy mutual information (mRMRMI), statistical features (SF), principal component analysis (PCA), kernel principal component analysis (KPCA), locally linear embedding (LLE), and stacked denoising autoencoder (SDAE), and three machine learning models, namely, artificial neural network (ANN), linear support vector machine (L-SVM), and Gaussian Naïve Bayes (GNB). Several parameters, including classification accuracy, the number of features after dimensionality, the feature type importance, and the time consumption were considered to evaluate the data-based classification strategies. Among the classification strategies investigated and considering the above evaluation parameters, the artificial neural network (ANN) classifier assisted with the kernel principal component analysis (KPCA) feature extraction method yielded the best performance in the material-based classification (accuracy:0.99) and the shape-based classification (accuracy:0.99). The locally linear embedding (LLE) improved the robustness of machine learning classifiers and efficiency of the artificial neural network in the material-based classification (improvement of average accuracy:0.17, reduction of classification time cost:14%) and shape-based classification (improvement of average accuracy:0.16, reduction of classification time cost:22%). Our comparative investigation provides a robust and efficient data-based strategy for underground metal target classification, which is significant for applications of underground metal target detection on portable devices with limited computing capability and energy supply. The cross-combination strategy of dimensionality reduction methods and machine learning models provides a way to find the optimal machine learning model for underground target detection.
Dependence on injection temperature and on aquifer’s petrophysical properties of the local stress applying on the pore wall of a crystallized pore in the context of CO2 storage in deep saline aquifers

The development of CCS (carbon capture and storage) currently faces numerous problems and particularly the precipitation of salts induced by the drying of the porous medium during injection of carbon dioxide in deep saline aquifers. This precipitation has several consequences, and particularly the creation of a crystallization pressure which can have an important mechanical impact on the host rock. Literature on crystallization pressure is one century rich of experimental and theoretical works. However, applications have been performed in the field of civil engineering and building science only, and, despite they are of paramount importance in the context of CCS, studies about this phenomenon in deep reservoir conditions are currently lacking. In this paper, we retrieve the classic crystallization pressure equation within the framework of geochemistry and present its explicit form of dependence with temperature, pressure, and composition. Evaluation of the crystallization pressure has then been proceeded considering the injection conditions and a sketch of in-pore crystallization process. The evolution of the local stress transmitted to a crystallized pore wall is found to be strongly related to the petrophysical properties of the medium and to the injection temperature of the carbon dioxide under the assumption of constant salt concentration during the precipitation process. Values differ strongly with the considered mineral, depending particularly on the solubility, and can reach in some conditions 165 MPa, making crystallization pressure a major factor in the mechanical behavior of the aquifer.
Tensor Robust Principal Component Analysis with Low-Rank Weight Constraints for Sample Clustering

With the rapid development of the next-generation sequencing technology, a large amount of genomics information has been obtained. The scale of biological sequencing data is particularly large and complex. The tensor robust principal component analysis (TRPCA) method can effectively preserve the spatial structure of tensor data, so it has received extensive attention. However, the low-rank tensor obtained by TRPCA may be damaged to a certain extent. To solve this problem, this paper proposes a model for weighting low-rank data based on the method of TRPCA. This model has an additional constraint penalty term that can repair corrupted low-rank data and the effective information in it can be fully utilized. In addition, the norm is used to constrain the sparse tensor to make the sparse effect better. In the experimental part, TRPCA model clusters samples by low-rank tensor. The experimental results on cancer omics data show that our method is superior to other methods.
Evaluation of Chinese Sentiment Analysis APIs Based on Online Reviews

Over the last decade, online reviews on social media platforms have become one of the most influential information sources. With the help of sentiment analysis tools, this unstructured information can be converted into structured data and analyzed to extract consumers’ opinions on products and services to assist important business decision-makings. Due to its great practical value, sentiment analysis using technologies like artificial intelligence has attracted attentions of researchers and practitioners. Many major Internet companies have developed their application program interface (API) to provide sentiment analysis services for individuals and businesses. This study aims to test and evaluate current mainstream Chinese sentiment analysis applications. First, authentic online reviews are collected from Ctrip.com, a major online travel Agency in China, using a crawler. These reviews are then analyzed using major Chinese sentiment analysis APIs. Meanwhile, some interviewees were asked to classify and rate these reviews as positive, neutral or negative as well using a survey. After that, human-API discrepancy is compared and evaluated. Our results revealed that classification errors of these APIs are mainly caused by incorrect word segmentation and fail to integrate the context into semantic interpretation.
Control Algorithms Based on Load Angle and Phase Current Difference for Traction Switched Reluctance Motor

The goal of this research is to propose a simple and inexpensive solution to increase the reliability of the electric vehicle with traction switched reluctance motor (SRM) by the modification of control algorithm that is based not only on rotary encoder data but also on single-phase current. The paper presents the research of traction SRM, describes the developed control algorithms based on load angle detection by a rotary encoder and proposes a novel modified algorithm based on the difference of load-dependent variable phase current fragment. Test bench with SRM drive is developed and developed control algorithms are implemented in MCU software. Experimental results show the workability of the developed algorithms proving the opportunity to use single-phase current sensor for control of SRM with variable load.
Systematic and Comprehensive Survey of Genomic Loci Associated with Complex Diseases and Traits

Over the past decade, Genome-wide association studies (GWAS) have been successfully developed and applied to identify many sequence variants that are significantly associated with common diseases and traits. Hundreds of thousands of such trait-associated variants have already been cataloged, making GWASs great resources for complex disease research. The main challenge ahead lies in elucidating disease mechanisms from these findings. In this study, we conducted a systematic survey of the relationships between genes which have been linked to complex diseases and previously researched genetic associations to explore whether any enrichment of known biological knowledge exists. To achieve this, we queried these genes against various collections of previously researched genomic associations through Enrichr. We uncovered many intriguing enrichment patterns between disease-associated genes and biomedical conditions such as the association between Obesity & Hot Drink Temperature, Alzheimer’s and Nose Size, and Body Mass Index & the PCDHA10 Pathway. These findings could potentially shed light on the pathogenicity of these complex diseases and may help in the future development of novel therapeutic treatments.
Performance analysis of ASR Model for Santhali language on Kaldi and Matlab Toolkit

Speech recognition is the ability of devices to respond to spoken commands. In today’s times when we are moving towards an automated world, the area of speech recognition has caught the eye of the researchers. The developments in this area are making waves all around us. Our proposed work presents a method to design a robust digit recognition system in Santhali language using Kaldi toolkit and MATLAB for small vocabulary dataset for varieties of features. Santhali is the most widely spoken local dialect in the state of Jharkhand. Using Kaldi, we trained our system with two training methods; monophone training and triphone training. The triphone method proves to be more efficient than the monophone method because of context mapping. In MATLAB, we obtained 95% accuracy for MFCC+LPC feature extraction applied to a GMM model.
Surrogate-Assisted Evolutionary Multitasking for Expensive Minimax Optimization in Multiple Scenarios

Minimax optimization is a widely-used formulation for robust design in multiple operating or environmental scenarios, where the worst-case performance among multiple scenarios is the optimization objective requiring a large number of quality assessments. Consequently, minimax optimization using evolutionary algorithms becomes prohibitive when each quality assessment involves computationally expensive numerical simulations or costly physical experiments. This work employs evolutionary multitasking optimization and surrogate techniques to address the challenges of the high-dimensional search space and high computation cost of minimax optimization. To this end, finding the worst-case scenario for different candidate solutions is considered as the optimization of multiple problems that can be solved simultaneously using the evolutionary multitasking optimization approach. In order to further speed up the proposed algorithm, a surrogate model in the joint space of the decision and scenario spaces is built to replace part of the expensive function evaluations. A generation-based model management strategy using a statistical hypothesis test is designed to manage the surrogate model. Experimental results on both benchmark problems and an airfoil design application indicate that the proposed algorithm can find satisfactory solutions with a very limited computational budget.
Abnormal Gait Recognition in Real-Time using Recurrent Neural Networks

Abnormal gait recognition plays an important role in diagnosis of musculoskeletal disorders. Suspicious walking behaviours should be detected as early as possible, and possibly in real-time, in order to prevent further deterioration of any part of the body. Analysis tools should provide useful and accurate information via convenient setup procedures. In this work, we conduct a system for recognizing gait abnormalities in real-time where an input is an image frame captured by a single RGB camera at any instance. We view abnormal gait recognition as a time-series problem which requires learning long-term dependencies. Hence, the system is presented with variants of Recurrent Neural Networks (RNNs). The proposed deep neural network model involves extracting 135 human body key points using OpenPose prior to performing recognition task which are quantitatively evaluated based on a simple RNN, Long Short-Term Memory Network (LSTM), and Gated Recurrent Unit Network (GRU). Each deep neural network has a model accuracy about 73.4%, 82.8%, and 81.6%, respectively. According to the confusion matrices of different predictive models, LSTM and GRU provide less confusing predictive results than that of a simple RNN. Therefore, we have discovered that deep neural network based on LSTM is, by far, the suitable model to recognize abnormal gaits due to the high model accuracy with less training and inference time.
Oxygen-Free Methane Aromatization in a Catalytic Membrane Reactor

Low-temperature oxygen-free methane aromatization (OFMA) was carried out in a multifunctional membrane fixed-bed catalytic reactor for the production of benzene and high-purity hydrogen. A 0.5% Ru−3% Mo−HZSM-5 catalyst was used in the temperature range 773−873 K. To assess the beneficial contribution of the membrane, OFMA was conducted without permeation in a fixed-bed (FB) conventional configuration as well as with permeation in a membrane catalytic reactor (MCR) configuration. Selective crosswise withdrawal of produced hydrogen not only led to benzene production in the MCR of twice that in the FB configuration but also, at the highest tested temperature, boosted the conversion of methane to benzene slightly above the thermodynamic limit. However, because of continuous withdrawal of coproduced hydrogen, catalyst deactivation was more pronounced in the MCR configuration than in the FB configuration. Experiments exploring various FB and MCR operating conditions allowed the elaboration of a phenomenological.
CPW-Fed Super-Wideband Antenna With Modified Vertical Bow-Tie-Shaped Patch for Wireless Sensor Networks

In this paper a coplanar waveguide-fed super-wideband antenna is presented for wireless sensor networks. The studied low-profile design is comprised of a modified bow-tie-shaped vertical patch and two asymmetrical ground planes and has been prototyped on a single-sided FR4 microwave substrate. The anticipated antenna has an overall size of $0.25\lambda \times 0.20\lambda $ at 3.035 GHz, the lowest frequency of the operating band. The vertical radiator coupled well with the two coplanar ground planes which enabled the studied antenna to achieve an operating band of 3.035–17.39 GHz (140.56%). The presented antenna demonstrates almost omnidirectional radiation patterns over the entire operating with an average gain of 4.56 dBi and average efficiency of 76.62%. The antenna also features with high fidelity factor, flat group delay, small phase distortion, and good response of transfer function. All these characteristics of the studied antenna make it a robust contender for wireless sensor node applications.