Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Distributed Non-Convex Optimized Support Vector Machine for Vehicular Tracking Systems

This article presents a non-convex optimized support vector machine (NCVX OSVM) algorithm for active steering stability of vehicles on a curved road. Initially, we considered a curved road geometrics formulation and designed a time-distributed (TD) model for NCVX OSVM to compute the steering angle 0°–180° at 10 m/s to follow active navigation at the highest curve entry speed. The proposed TD NCVX OSVM is interconnected with three modules. In the first module, formulated NCVX cost functions and Optimized SVM for smooth steering stability. The second module is based on improving faster training time (IFTT) by using the Naive Bayes probabilistic classifier (NBPC). The third module uses an optimized non-convex (NCVX) cost function to reduce the error phenomenon. The performance of these three modules is evaluated by several 100 data points from vehicle onboard sensors. Further, it is pre-processed in the curved road (start, continue, exit) conditions. The decisive of TD-NCVX OSVM design is demonstrated by using experimental learning on FPGA Zynq 7000 processor and programmed with python script. The empirical calculation shows an accuracy of 98.36%. Furthermore, the proposed design predicts an acceptable upper limit for curved steering whenever the vehicle turning speed is greater than 30 mi/h.
Modeling, Critical Threshold, and Lowest-Cost Patching Strategy of Malware Propagation in Heterogeneous IoT Networks

In heterogeneous Internet of Things (IoT) networks, various communication technologies lead to different transmission ranges of nodes, and they can cooperatively provide seamless communication. Unfortunately, the flexible communication mode offers more chances for malware to invade the IoT devices. To maintain its cyber security, two key issues, i.e. the critical threshold of the onset of malware propagation and the lowest-cost defense strategy after it occurs, need to be addressed. To solve the first challenge, we construct a dynamics model by using the degree-based mean-field theory and point process theory, to study the malware propagation-defense process among heterogeneous IoT devices. We analytically derive the closed-form expression of the critical malware transmission rate which can be used to predict whether the malware can propagate or not. For the latter problem, we investigate the equivalent conditions of degree-related patching strategies in defense effectiveness, which can be used to determine the lowest-cost one more directly. In addition, We compare different transmission ways of malware, and find that the malware infection is hard to avoid under the way of centralized transmission, but the decentralized transmission has a greater risk of large-scale infection. We also explore how the immunization measures and the different communication frequencies affect malware propagation. The results show that it is significant to identify important devices for immunization and infected cluster may form in some cases. Our results offer a theoretical foundation to predict and defense malware propagation in heterogeneous IoT networks.
IoT Based Remote Monitoring of Gas Leakage in Power Plants

Most of the electricity production in the emerging world is focused mainly on generation through power plants. This becomes more important in terms of security. It is important that good safety systems are implemented in training and workplaces. Due to the increasing number of poisonous gases, day by day and the need for minimum control of the pollution of dangerous gases, monitoring, and control of gas leakage are in need. The existing model in power plants and industry discusses the conventional monitoring of workers. The current model is based on microcontroller-based toxic gas detection and alarm systems and IoT industrial LPG and propane gas monitoring systems. The development system has an IOT-based gas and temperature monitoring system. Gas can be analyzed and monitored by the Blynk IoT server. The executive board receives an alarm when the pollution exceeds the critical limit.
Mobile Based Application For Tea Leaf Disease Detection

Tea plant leaf diseases are one of the grand challenges that face in agriculture sector worldwide. In India approximately 20% to 30% of the leaves are lost due to various diseases each year. Tea leaf disease identification is challenging for limited-resources farmers if performed through optical observation of plant leaves symptoms. Therefore, it is important to catch the spread of the disease can spread of the disease in its early stage before they reach epidemic proportion otherwise the disease can spread quickly throughout the entire plantation resulting in huge losses for the farmers. To aid the farmers in the crucial task of identifying tea leaf diseases in its early stage. it is practical to have an intelligent system for detection,identification and classification system in place as a preventive measure. This proposed model is a ML powered mobile based system to automate the plant leaf disease diagnosis process. The developed system uses CNN for feature reduction and classifying five diseases categories. The user interface is developed as a mobile application, allowing farmers to capture a photo of the infected Tea plant leaves. Captured image is then uploaded into the Database and then into the algorithm by specifying image path.It then displays the disease category and symptoms of disease along with the confidence percentage.This solutionwill be user friendly and will perform in an effective and efficient manner. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants.
PMSG Based Standalone Distributed Generation Integrated Universal Active Filtering System

Advancements in digital electronics, process control, and communications have raised the quantity of sensitive loads that need supply voltages to be ideal sinusoidal for proper functioning. So, a power quality (PQ) enhancement strategy is presented in this paper using a universal active filtering system (UAFS) for a standalone distributed generation system (DGS) supplying power to sensitive or nonlinear load. The main components of this standalone DGS consist of a permanent magnet synchronous generator (PMSG) driven by a hydropower prime mover as primary source, and solar photovoltaic as well as battery source with bidirectional DC-DC converter as backup. Here, the qualities of voltages are improved by utilizing a series connected voltage source inverter (VSI) of UAFS by regulating the voltage across load as harmonics free and constant amplitude, irrespective of any PMSG generated voltage deviation. The power indices at the source terminals like current harmonics, power factor, reactive power, etc. are enhanced by the parallel connected VSI, simultaneously with voltage qualities enhancement by series VSI of UAFS. The fundamental signals of nonlinear current through load are obtained by means of the modified generalized integrator (mGI) control technique while extracting real and reactive power terms for efficient PQ improvement during various circumstances. The current qualities at the generation side and voltage qualities across sensitive load are concurrently improved and ensures to follow IEEE standards for the presented DGS though the use of UAFS.
The Cycle-Concentrating PEG Algorithm for Protograph Generalized LDPC Codes

In this paper, we propose the cycle-concentrating progressive edge growth (CC-PEG) algorithm for lifting protograph generalized low-density parity-check (GLDPC) codes. In GLDPC codes, undoped variable nodes (VNs) that are not connected to generalized constraint (GC) nodes are more vulnerable to channel errors than doped VNs protected by GC nodes. We observe that among GLDPC codes sharing the same protograph structure, codes with fewer local cycles at undoped VNs have better decoding performances. Inspired by this observation, the CC-PEG algorithm is proposed to concentrate local cycles at doped VNs and avoid local cycles at vulnerable undoped VNs during the lifting process. Specifically, the CC-PEG algorithm first collects edges that result in the maximum undoped girth, defined as the length of the shortest cycle containing undoped VNs. Following this, the CC-PEG algorithm selects the edge with the lowest concentrated cycle metric. Consequently, the lifted codes exhibit asymmetric cycle distributions concentrated around robust doped VNs. Simulation results for various protographs show that the CC-PEG algorithm achieves a performance gain of up to 20 times lower frame error rate compared to conventional lifting algorithms.
Faster R-CNN-based fire risk sensing technology for communities

As the population of a community grows rapidly, the risk of fire in the community increases. In order to warn or predict the risk of community fire in time and reduce the loss caused by community fire to some extent. We propose a kind of index system for community fire risk assessment, whose data uses sensor data and video data of a community as data sources, labeling to label the image data information and use Faster R-CNN algorithm to process the image data, use the sensor collection data, combined with the index system of community fire risk assessment, to achieve the prediction and evaluation of community fire risk level, before the fire It can effectively improve the level of prevention and control of community fire risk before the occurrence of fire, and facilitate property managers, street and community fire management personnel to carry out daily fire management work, and also play a positive role in reducing the frequency of community fires and reducing the casualties and property losses caused by fire; in addition, it can also identify the flame and smoke in the occurrence of fire, to timely alarm to avoid further expansion of the fire.
Single Channel EEG Based Binary Sleep and Wake Classification using Entropy Based Features

This paper proposes a novel method for binary sleep and Wake classification using entropy-based features extracted from a single-channel electroencephalogram (EEG). This study aims to improve the accuracy of sleep and Wake classification, which has several applications such as in sleep research, sleep tracking, diagnosis of sleep disorders, human performance assessment, human factors engineering. The proposed method is evaluated using the publicly available UCDDB dataset. Results show that the method achieved high classification accuracy, with the Ensemble subspace KNN classifier achieving the highest accuracy of 94.3%, followed by the fine KNN classifier with an accuracy of 92%. A significant improvement in performance can be attributed to the use of entropy-based features in the proposed method. Based on the promising results of this study, it is evident that the proposed method can be applied to sleep medicine for the classification of sleep stages, which can potentially lead to better diagnosis and treatment of sleep disorders.
An Analysis of Traditional Noise Power Spectral Density Estimators Based on the Gaussian Stochastic Volatility Model

Many single- and multi-channel speech enhancement techniques, old and new, rely in one way or another on estimates of the noise power spectral density (PSD). For example, the classical Wiener filter requires that either the speech or noise PSD be estimated. Typically, the noise PSD is estimated, as it is often easier to model and estimate than the speech. As a result, much attention has been paid to this important problem over the past couple of decades, with important scientific milestones being the minimum statistics (MS), the minima controlled recursive averaging (IMCRA), and the minimum mean squared (MMSE) estimators. Despite leading to major progress, these estimators are rather ad hoc, making them difficult to tune and improve in a systematic manner. In this article, we analyse some of the common heuristics employed in such noise PSD estimators to put them on firmer mathematical ground. More specifically, we use the Gaussian stochastic volatility model and show that the MMSE noise PSD estimator can be interpreted as a special case thereof. Moreover, we analyze the related problem of speech presence probability (SPP) estimation and show that the SPP estimation performed in the MMSE noise PSD estimator can be interpreted as an SNR estimator in the context of the Gaussian stochastic volatility model.
Re-Introducing BN Into Transformers for Vision Tasks

In recent years, Transformer-based models have exhibited significant advancements over previous models in natural language processing and vision tasks. This powerful methodology has also been extended to the 3D point cloud domain, where it can mitigate the inherent difficulties posed by the irregular and disorderly nature of the point clouds. However, the attention mechanism within the Transformer presents challenges for utilizing Batch Normalization (BN), as statistical information cannot be extracted efficiently from the data set. Thus, this study proposes a novel residual structure, ResBN, which can effectively handle 3D data. Additionally, to replace BN in the transformer for 2D image processing, we introduce the Patch Normalization (PN) technique. ResBN and PN are evaluated on 3D point cloud and 2D image datasets respectively through statistical experiments, demonstrating their efficacy in enhancing classification performance.
Impulsive Artificial Defense Against Advanced Persistent Threat

Advanced persistent threat (APT) as a new type of cyber espionage poses a severe threat to modern organizations. Artificial APT defense, in which an organization engages experienced cybersecurity experts to artificially check if there exist rootkits implanted by APT actors within the organizational internet and, if so, artificially remove the discovered rootkits, is recognized as an indispensable part of APT defense. There are two different ways of artificial APT defenses: continuous artificial defense (CAD), where the defense work is conducted at all time points, and impulsive artificial defense (IAD), where the defense work is conducted at a scheduled sequence of time points. IAD is superior to CAD in terms of the overall service cost. In the context of IAD, we refer to each sequence of service costs as an IAD policy. This paper addresses the problem of developing a cost-effective IAD policy (the IAD problem). First, by introducing an impulsive state evolutionary model for the organizational intranet, the IAD problem is reduced to an optimal impulsive control model (the IAD model). Second, by deriving the optimality system for the IAD model, an iterative algorithm for solving the IAD model (the IAD algorithm) is presented. Next, the convergence and effectiveness of the IAD algorithm are validated through numerical experiments. Finally, the effect of some factors is inspected. To our knowledge, this is the first time IAD is inspected from the perspective of optimal impulsive control theory.
Improvement of Spectrum Suppression-Based Deep Learning Interpolation Technique

Seismic data are often coarsely or inconsistently sampled along the acquisition geometry due to inherent limitations in survey equipment or insufficient survey budgets. Recently, machine learning techniques have been utilized to acquire compactly sampled seismic data. Among them, the self-supervised learning-based techniques that do not require labels are actively being used, and the interpolation technique based on the blind trace network (BTN) and spectrum suppression using suppression masks through line detection has shown high accuracy. However, the interpolation technique using BTN and the suppression masks through line detection suffers from instability caused by the reconstruction loss and inaccuracy of the suppression masks. To mitigate those problems, we propose suppression masks using a generalized frequency–wavenumber (<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>) trace interpolation (GFKI), patch-based learning, masked UNet, and the equalized learning rate. The suppression masks using GFKI are generated by correlating the zero-padded data with the data obtained by regularly removing traces from the zero-padded data in the <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> domain. In addition, we divide data into patches to enhance the accuracy of the suppression masks. Masked UNet is used to constrain the output to contain the input signals at the designated positions using the binary mask in the space–time domain. Furthermore, we normalize each layer in the network so that the learning speeds for each layer can be commensurate with each other. The synthetic and field data experiments show that the proposed interpolation technique effectively suppresses the aliasing of signals and enables the training process to stably converge.
Improvement of Power Quality through SMES Control in a Standalone Hybrid Microgrid System

An electrochemical hybrid power system composed of superconducting magnetic energy storage (SMES), fuel cells, and electrolyzers is presented in this paper. A novel control algorithm is developed for such a system to maintain a constant DC link voltage by managing converters connected to the fuel cell (FC) and electrolyzer. This algorithm also serves as the Maximum Power Point Tracker (MPPT) for photovoltaics, eliminating the need for a separate MPPT circuit. Due to the slow dynamics of the FC and electrolyzer, proper management allows for instantaneous charging and discharging of the SMES, as well as steady-state power supply and consumption by the FC/electrolyzer. A straightforward DC voltage controller is presented, requiring only a few measurements. This controller employs pulse width modulation to maintain voltages at a predefined reference value at the point of common coupling. Comprehensive MATLAB/SIMULINK simulations demonstrate the excellent performance of these controllers in both transient and steady-state scenarios.
Detection of Fault Inception in a Low Voltage DC Microgrid using Current Differential Protection Method

DC microgrids have gained more attention over the last few years as they have several advantages compared to AC microgrids as it is more reliable, efficient, easy control structure, interconnection of Renewable Energy Sources (RES) and DC loads. In spite of the numerous advantages, designing and implementing an efficient and effective protection scheme for DC microgrids is still an obstacle. The challenge comes from the rapidly rising fault current within few microseconds (upto 17 A/μsec) which has to be quenched off in the non-existence of naturally occurring zero crossing, time constant for control and protection operation are very close and lack of existing guidelines and standards in DC protection. In this paper a LV DC microgrid (DCMG) of 600 V is developed and current differential protection method is implemented to detect the fault and provide operating signal to Solid State Circuit Breaker (SSCB) to isolate the faulty section. The simulation is carried out in MATLAB/SIMULINK environment.
Gated Recurrent Units for Intrusion Detection

Modern automobiles features a plethora of connected and smart features. This has lead to widespread growth of communications within a vehicle which has resulted in larger attack surfaces and different types of attacks. Classic cryptographic solutions are not suitable for the various sensors within a vehicle. Vehicle safety and timing to determine attacks is crucial. This paper proposes the use of Gated Recurrent Units (GRU) to perform intrusion detection in vehicles. Our proposed solution uses the temporal-based prediction properties of GRUs to determine if an intrusion is occurring in a vehicle. We present results of our experiments, conducted using real-world data, and analyze the findings.
Trajectory Tracking of a Quadrotor Underwater Delivery Platform via Adaptive Sliding Mode Control

A new Quadrotor Underwater Delivery Platform (QUDP) was proposed for underwater transportation, in which its configuration has been well used in air quadrotors. But there are still new challenges to controlling such robots underwater. In this paper, a complete dynamics model of QUDP is established first. Then, the trajectory tracker is designed as the control outer loop based on the adaptive sliding mode (ASMC) under an unknown time-varying load, and the stability is proved by using Lyapunov stability theory. Meanwhile, the PD controller is designed as the control inner loop to track the change of attitude angle. Finally, it is verified by numerical simulation. The results show that the proposed controller can perform well with an unknown load and improve the robustness of the system.
Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health (SOH) is a critical factor in evaluating the performance of the lithium-ion batteries (LIBs). Due to various end-user behaviors, the LIBs exhibit different degradation modes, which makes it challenging to estimate the SOHs in a personalized way. In this article, we present a novel particle swarm optimization-assisted deep domain adaptation (PSO-DDA) method to estimate the SOH of LIBs in a personalized manner, where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy. The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method. The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials, ambient temperatures and charge-discharge configurations. Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method. The PyTorch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.
An Ultra-Wideband Ridged Biconical Multibeam Antenna

This paper presents an ultra-wideband multibeam antenna with eight radiating elements that are connected to the central cylindrical part. Each radiating element consists of a TEM horn fed by a coaxial to ridged planar waveguide transition. The connection between the radiating elements and the central cylindrical part equivalently acts as a biconical antenna. The ultra-wideband behavior of the antenna is guaranteed by the wideband properties TEM horn and coaxial to ridged planar waveguide adapter. Comprehensive optimization is performed to minimize the return losses and cross-coupling between ports to reach appropriate performance. A prototype of the antenna is fabricated to use in the experimental verification. An adequate level of agreement observes between the measurement and simulation results. Comparing results confirms that the proposed antenna has proper performance at the frequency band of 2–18 GHz. The proposed antenna has a diameter and height of $9.5 \lambda _{0} \times 1.33 \lambda _{0}$ , and its dimensions, especially the structure’s height, are smaller than the previous investigations. Also, the measurement gain of the proposed antenna changes from 7 to 15.7 dBi in the antenna’s frequency range and has an isolation range between −20 to −70 dB. Another advantage of this antenna is its low losses and high-power handling due to its all-metallic structure. These features make this antenna a suitable choice for high-power applications.