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The Internet of Things (IoT) has witnessed remarkable growth, leading to the emergence of numerous applications and services. However, energy efficiency remains a critical concern in IoT networks due to the resource-constrained nature of many IoT devices. Energy-efficient routing protocols play a pivotal role in mitigating this challenge, optimizing energy consumption, and extending the operational lifetime of IoT deployments. This survey paper aims to provide a holistic understanding of the state-of-the-art approaches, their characteristics, strengths, limitations, and performance metrics. The survey encompasses a wide range of energy-efficient routing protocols proposed in the literature, including traditional routing protocols adapted for IoT scenarios and protocols specifically tailored for energy optimization. We discuss the impact of different network characteristics on the performance of energy-efficient routing protocols, such as network size, node density, mobility, and heterogeneity. Through this systematic survey, we aim to provide researchers, engineers, and practitioners with a comprehensive overview of energy-efficient routing protocols for IoT networks. The survey findings can serve as a valuable reference for protocol selection, performance evaluation, and the development of novel energy-efficient routing strategies in future IoT deployments. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) play a significant role in monitoring changes in the physical environment, relying on a subset of designated nodes known as the Connected Dominating Set (CDS) to facilitate communication and serve as the network's virtual backbone for routing and clustering. Despite the various algorithms for CDS construction, the challenges persist in terms of construction cost and scalability. In this context, this paper introduces the utilisation of the Hunger Games Search Optimisation Algorithm (HGSOA) for Connected Dominating Set Construction in Wireless Sensor Networks (HGSOA-CDS-WSN). The primary intention of this paper is “to develop a technique capable of constructing small-sized CDS with reduced construction costs, particularly suitable for uniformly or randomly distributed nodes”. The proposed HGSOA-CDS-WSN method is implemented in MATLAB based on various metrics. The results are compared with those of existing techniques, such as the Parallel Structure from Motion for UAV Pictures using Weighted CDS (PSfM-CDS-WSN), Polyhedra Associated with Locating-Dominating, Open Locating-Dominating, and Locating Total-Dominating Sets in Graphs (PAL-CDS-WSN), and Sustainable Maintenance of Connected Dominating Set by Solar Energy Harvesting for Internet of Things Networks (SHE-CDS-WSN). The proposed technique achieves lower delay by 13.24%, 25.64%, and 9.96%, and higher throughput by 18.34%, 24.55%, and 23.04% compared to the existing methods. This systematic evaluation underscores the efficacy and superiority of the proposed HGSOA-CDS-WSN technique in terms of connected dominating set size, construction time, energy efficiency, network stability, and overall performance, highlighting its potential to advance wireless sensor network applications. [ABSTRACT FROM AUTHOR]

Detecting intrusions within a network is essential for protecting digital environments; it encompasses the observation and analysis of network traffic to recognize and counteract unauthorized or malicious activities. Conventional methods in network intrusion detection face several challenges such as polymorphic and evasive attacks, scalability issues, and anomaly-based complexity. To address these complexities, this paper proposed a novel method named Attention Long-term Recurrent Network-based Random Chaotic Sine (ALRN-RCS) algorithm for network intrusion detection. In this study, Long Short-Term Memory (LSTM) is utilized to capture complex patterns in network traffic and identify anomalous activities. Also, the attention-based Recurrent Neural Network (RNN) is employed to focus on relevant features within network traffic and enable precise intrusion detection. In this paper, we have incorporated the Chaotic Chimp Sine Cosine optimization algorithm, employing a random update strategy, to optimize hyperparameters and improve the overall efficacy of the proposed approach and the study conducted experiments on the datasets namely the UNSW NB-15, Network Intrusion Detection dataset, and the Segmented Image-based Network Intrusion Detection (SIDD) dataset. Diverse assessment criteria, including accuracy, F1-score, recall, AUC-ROC, and precision, are employed to assess the effectiveness of the ALRN-RCS method and to draw comparisons with established methodologies. The experimental results depict the effectiveness of the ALRN-RCS method for addressing the dynamic and sophisticated nature of modern cyber threats. [ABSTRACT FROM AUTHOR]

Precoding is a critical signal processing technique used in wireless communication systems to enhance transmission performance. This paper initially provides a brief overview of various conventional precoding algorithms. The exploration includes various nonlinear precoding algorithms which outperform linear techniques in high signal-to-noise ratio (SNR) scenarios. Moreover, the paper delves into practical considerations and provides insights into selecting the most suitable technique for specific communication scenarios. The deep learning-based hybrid precoder is designed using the MDL-AltMin algorithm and simulated results show a spectral efficiency of (SE) 40.96 dB compared with different precoding algorithms. The precoder model is modified with a Hybrid Precoding Net algorithm with a spectral efficiency of 21.07 dB for high values of SNR such as −30 and again compared with deep learning precoders. The HPNet model is proposed with a spectral efficiency of 23.04 dB with and without CSI (Channel State Information) at the transmitter. Furthermore, the HPNet model is compared with optimal digital precoders. [ABSTRACT FROM AUTHOR]

This paper presents a new structure of an AC–DC bridgeless single-switch SEPIC converter for battery charging applications. The converter exhibits the features of power factor correction (PFC) by reducing the total harmonic distortion (THD) of AC input current to a considerable extent using the modified structure of the converter. The sizes of inductors are also considerably reduced in the proposed converter. The proposed single-switch converter topology makes it suitable for implementing the complex control algorithms required for battery charging. The circulating current is eliminated by introducing the blocking diodes in the proposed converter. A 200W/56 V prototype converter is synthesised to realise the proposed concept. This paper also includes a comparative study with different similar converters to verify the performance of the proposed converter. [ABSTRACT FROM AUTHOR]

The latest edition of the IETE Journal of Research (Volume 70, Number 6, June 2024) features 45 articles covering a diverse range of topics including communications, electromagnetics, computers and computing, control engineering, electronic circuits, devices and components, instrumentation and measurement, medical electronics, and power electronics. The journal has also appointed three new Deputy Editors-in-Chief to the Editorial Board. The articles delve into various subjects such as wireless communication systems, PAPR reduction in OFDM systems, deep learning classifiers for 5G D2D communication, optimization techniques for IoT relay communication systems, rain attenuation prediction models, adaptive beamforming using convolutional neural networks, and antenna designs for different applications. Other papers explore topics such as geometry word problem resolution, XSS attack detection, video traffic reduction in 5G networks, breast cancer detection in MRI images, multimodal brain image fusion, melanoma skin disease detection, speech enhancement techniques, land cover change detection, medical image encryption, multimedia computation offloading in 5G networks, and priority-based scheduling in hybrid VANETs. These papers provide valuable insights and contribute to the existing body of knowledge in their respective fields. [Extracted from the article]

Multi-Criteria Decision Making (MCDM) approaches have been developed to support a wider range of application areas. For example, channel allocation, among the vehicles in infrastructure-based/hybrid Vehicular Ad hoc NETworks (VANETs), is done by exploiting MCDMs to minimize the waiting time and maximize the request service rate by the Road Side Unit (RSU). The Analytic Hierarchy Process (AHP) developed by Saaty, however, has gained enormous popularity and been used all over the world since its invention in 1977. AHP is capable of prior weight calculation of individual criteria using Criteria Preference Matrix (CPM) and rank computation of alternatives as well. Another MCDM technique, termed Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was created by Hwang and Yoon in 1981. It is a ranking technique that requires inputs of the weights of the criteria. This paper aims to analyze the impact on ranks of various alternatives for different CPMs using the AHP-AHP method and the AHP-TOPSIS method. For both cases weights of criteria have been calculated using AHP before rank computation. In addition to that, this paper deals with the performance evaluation of infrastructure-based VANETs for priority-based channel allocation using the AHP-AHP and the AHP-TOPSIS method in terms of Request Service Rate and Average Waiting Time for various CPMs. It is observed that AHP-TOPSIS performs better in terms of Request Service Rate with the increasing alternatives. Furthermore, the AHP-AHP method shows frequent rank change while increasing the number of alternatives for various CPMs. [ABSTRACT FROM AUTHOR]

A non-contact-based system for estimating the respiration rate of individuals using a mobile phone camera is presented in this paper. Respiration rate is an important vital sign that indicates the number of breaths a person takes per minute and is commonly used in medical assessments. The method involves recording videos of the pit of the neck region using the mobile phone camera and analyzing the changes in light intensity within the video frames. By processing the obtained signals from the video frames, the system accurately estimates the respiration rate. The effectiveness of the proposed method and the estimated respiration rates are compared with rates obtained using two other methods: a spirometer, which is a device used to measure lung function and manual counting. The evaluation is conducted on a group of 34 participants who are instructed to breathe rapidly to simulate varying respiration rates. The results indicate that the presented method achieves an accuracy of over 80% compared with existing state-of-the-art methods. The standard deviation of ±3% suggests a relatively consistent performance across participants. The calculated error between the estimated respiration rates and the reference rates obtained from the spirometer and manual counting validates the reliability of the proposed approach. Additionally, the paper highlights the advantages of this system, including its cost-effectiveness and suitability for remote monitoring and mass screening. The non-contact nature of the method reduces the need for physical contact with patients, which could be especially beneficial in situations where hygiene and infection control are crucial. The paper also outlines plans for future work, which involve validating the method with larger sample sizes and exploring its potential applications in various clinical scenarios. [ABSTRACT FROM AUTHOR]

Various algorithms have been put forth for an efficiency estimation of an in-service induction motor with varying intrusion levels and accuracy. In the field, it is hard to reach the terminals of an in-service motor because of its location. This paper helps a field engineer to choose properly an efficiency estimation method for condition monitoring of an in-service induction motor. In this paper, the author put forth an approach based on symmetrical components to assess the efficiency of induction machine working on unbalanced power supplies at any loading condition. A comparison is done between two offline intelligence techniques like gravitational search (GS) algorithm and cuckoo search (CS) algorithm for estimating the efficiency of an induction machine by using limited measurements. The merit of these techniques is that the motor is not taken out from its place of installation for testing. Also, the highly intrusive no-load test is avoided. Simulations and experimental results support and validate the carried-out research. [ABSTRACT FROM AUTHOR]

Blockchain, an emerging technology benefits the new technological world from its features such as immutability, transparency, and decentralization. The diversity of its domain is from e-commerce, health care, real estate, and enterprise to NFT markets, voting and digital currency trading. The underlying blockchain features make auditing blockchain applications a challenging task. In this paper, we thoroughly analyze the general challenges faced in blockchain forensics, application-specific challenges, and challenges with respect to public and private blockchains. The aim of this paper is to highlight the challenges faced during the forensics of blockchain-based applications. For this, we took a healthcare system, introduced a contract vulnerability in it, and defined two scenarios for attack depending on that vulnerability. One that allows an attacker to make a malicious transaction directly and the other in which the attacker first gives itself access and then adds malicious transactions. The attacks are generated just to analyze the difficulty level of forensics in blockchain applications. To achieve our goal, we performed log analysis for both scenarios and specified the challenges faced during the analysis. Our experiment shows that due to some blockchain-specific features such as anonymity, identification of malicious entities in the system sometimes becomes a hectic challenge. This research benefits the auditors by highlighting the forensics challenges and auditing issues so they can work on these areas to identify the attackers and illegal data modifications accurately in blockchain applications. Experimental results confirmed that our proposed technique enabled efficient forensics investigation of events inside blockchain-based healthcare applications. [ABSTRACT FROM AUTHOR]

With the penetration of DGs in power systems, some restrictions such as economic dispatch in non-dispatchable sources, their control technology and voltage/frequency stability may be challenging. In conventional power systems, the transient stability has been obtained by high-inertia of upstream network in all conditions; however, in modern power grids, the micro grids (MGs) which could operate in both islanded mode and grid-connected mode are taken into account to overcome this issue. AC or DC types of DGs usually connect to the loads through power electronic converters, in which for researchers, this issue has been interesting and applicable to continue. When loads have been changed, the frequency has influenced correspondingly and must not exceed the permissible range. Therefore in this paper, this intention has been studied using conventional droop controller and intelligent sliding surface approach (ISSA) to evaluate the DGs effects on load-frequency control. While two or more areas (as some individual MGs) connect to each other via a transmission line, the voltage/frequency stability and power balancing constrained may be changed, which the ISSA is hired to track the frequency changes. To examine this problem, in this paper there are three MGs operate separately to show if the load may not supply, they should connect to adjacent block. If this important concern has not considered, it may cause global black-out. The simulation results obtained by ISSA compared with droop controller method represent the superiority of proposed technique to achieve best power sharing. [ABSTRACT FROM AUTHOR]

In general, a 5G new radio network is defined by three primary utilization situations, namely upgraded versatile broadband, ultra-reliable, and low latency interchanges of information, as well as huge machine-type correspondences. These utilizations require improved throughput, inertness, dependability contrasted, and a 4G framework. As a coding plan for information, 5G Turbo codes attain high throughput, a variable code rate, and length, and crossover programmed rehash with good error-detecting capability. The execution boundaries of 5G advancements are normal and are tens and thousands of times better compared with 4G. In this paper, the evaluations of puncturing and non-puncturing for turbo code depending on the minimization of penetrating of efficient pieces are discussed. It also provides penetrating of equality pieces considering additive white Gaussian noise (AWGN) Channel with 16 quadrature amplitude modulation (QAM) to attain high spectral efficiency. In addition to this, the super encoder encodes the separated progression with a code speed of 1/3. The code block association progressively connects the yields from the super encoder. The sign mapper employed in this paper adjusts the progression using a 16-QAM balance. Also, the mapper takes effective pieces, one uniformity bit from the upper encoder, and the deinterleaved equity bit from the base encoder. The Orthogonal frequency-division multiplexing (OFDM) mapper parts into more modest equivalent channels, named subcarriers and the information is sent on these equivalent channels at a reduced rate. [ABSTRACT FROM AUTHOR]

Combinational logic circuits (CLCs) are basic building blocks of a system and optimization of these circuits in terms of reduced gates, transistors, or levels will lead to reduced area on chip, reduced power, and improved speed. Simulated annealing (SA) is a thermo-inspired metaheuristic used for solving various engineering and non-engineering problems. SA is also used for the realization and optimization of CLCs. Circuits with a large number of inputs and outputs require more generations for realization. Realization of the optimal circuit with fewer generations is desired as realization time increases with increase in the number of generations. In this paper, an attempt is made to realize circuits using population-based SA with fewer generations. SA with partitioning approach is proposed in this paper for circuits that could not be realized with fewer preset generations. To evaluate the performance of the proposed work, benchmark circuits from LGSynth'91 are considered, and it is observed that the success rate improved and realization time reduced with the proposed partitioning approach. During the evaluation, it is also observed that the gate count was reduced by 2.5–77.39% and the transistor count was reduced by 7.69–95.53% on using proposed work with fewer generations over circuits reported in the literature. [ABSTRACT FROM AUTHOR]

In recent years, the rate of mortality and morbidity increased due to uncontrolled conditions of liver diseases. Liver disorders are mainly caused because of inhalation of toxic gases, consumption of contaminated food, alcohol usage, etc. If the progression of liver disease is not detected at an early stage, it seriously threatens human life. Early diagnosis and timely treatment are the only ways to prevent such life-threatening diseases. This paper mainly aims to design an automated prediction model using ultrasound liver images. Due to non-invasive, cost-effective, and real-time imaging abilities, the ultrasound becomes most preferable modality. Therefore, the main purpose of the paper is at predicting liver diseases using the proposed modified faster region-based convolutional neural network-based hybrid grasshopper tunicate swarm (MFRCNN-HGTS) approach. The distortions in the raw ultrasound liver images are removed and the number of training images is increased by the augmentation process. The augmented data samples are then allowed to pass through a modified faster region-based convolutional neural network (MFRCNN) classifier to predict normal and abnormal instances from the images. To improve classification accuracy, the weight factors are assigned with appropriate values using the hybrid grasshopper tunicate swarm (HGTS) algorithm. The proposed MFRCNN-HGTS approach predicts liver diseases accurately and classifies them based on texture features as normal, cirrhosis, or hepatitis. The experimental setup is performed using MATLAB R2016a software. The proposed MFRCNN-HGTS method is evaluated by comparing it with other traditional methods. The analytic result illustrates that the proposed MFRCNN-HGTS techniques achieve 98.45% accuracy over other techniques. [ABSTRACT FROM AUTHOR]

This paper presents an isolated tiny or micro-bidirectional DC–AC converter (ITBC) that operates in a single-phase, single-stage, bidirectional, and isolated configuration. The converter utilises a voltage-in-phase PWM (IPWM) control scheme to regulate the potential conversion ratio of the converter, enabling it to handle a wide voltage range. The converter also enables smooth power flow direction changes and can achieve soft switching, resulting in high efficiency. A unidirectional current controller can be used for both DC–AC and AC–DC modes. The paper provides detailed information about the micro-inverter's working principles, design procedure, characteristics, and control strategy. The proposed solution is verified by a 500-watt test bench model with a voltage input range of 30 volts to 50 volts. [ABSTRACT FROM AUTHOR]

A simple, robust, economical and highly efficient 8/6 switched reluctance (SR) generator is connected to a wind turbine to increase the amount of power extracted from the wind. Output power of the generator is fed to the PWM rectifier via a four-phase asymmetric half-bridge (AHB) converter and maximum power point tracking (MPPT) of the rectifier is assisted with torque ripple minimization to present an effective control as it allows torque ripple suppression arising from the SR generator nature while capturing maximum wind power. The wide range of speed variation of the SR generator is minimized using the combination of PI and hysteresis current controller. Further, the output of the AHB converter is directed to the conventional PWM rectifier. The purpose of this research is to deliver ripple-free dc power to a dc grid/load or battery. Hence, in this paper, the GWO-based optimized PI controller is implemented as an MPPT method to maintain the constant dc bus voltage. The inherent ability of GWO optimization-based control of the rectifier system ensures better dynamic control. The simulation is performed in a MATLAB environment for a power rating of 1000W standalone system and tested using an FPGA controller. This paper carries out a stability analysis of the system by varying the wind velocity. [ABSTRACT FROM AUTHOR]

Forensics investigation of WordPress applications hosted on containerized cloud environment is critically important due to their increased adaptation and an exponential increase in cybercrimes. Investigation of containerized environments is complex due to their volatile nature and dynamic allocation. This paper explores the forensic analysis of WordPress applications deployed inside a containerized environment. In this paper, we have used a log analysis approach to perform the digital forensics investigation of different attacks on WordPress applications running over Kubernetes. To investigate these events, we collected a large number of operational logs generated by these applications. Log filtrations techniques are applied to analyze only the relevant logs for a specific event to perform the forensic investigation efficiently. The experimental results confirm the proposed approach's effectiveness as we could trace all the malicious events to conduct the digital investigation on the WordPress applications running inside Kubernetes. The scheme successfully identified the attacker's information like incident time, incident source, duration of attack etc. Moreover, the proposed solution also provided forensics readiness for the WordPress applications running in a container-based cloud. [ABSTRACT FROM AUTHOR]

This paper proposes a Modified Chicken Swarm Optimization (MCSO) in which the local optima and early convergence problem of Chicken Swarm Optimization (CSO) is addressed and solved. The CSO adopted the Swarm Intelligence (SI) of chickens to solve the optimization problem in which the behaviour of roosters, hens, and chicks for food search is mathematically formulated. Hens follow their group rooster, whereas mother hen is followed by chicks in search of food. The problem occurs whenever the rooster follows the wrong path and is stuck in the local optima so the mother hen and, therefore, the chick. This situation leads to early convergence and may not provide global optimization. Most of the existing research studies focused on solving the local optima problem of hens. Hence, there is a need to address the local optima problem of roosters as well. The paper offers a solution to this problem by using the randomness phenomenon of Levy's Fight. Levy's flight is offered to guide the roosters, hens, and chicks, which allows the chickens to choose a random direction in a situation when there is no way to find the optimal solution. The inclusion of Levy's flight enhances the self-learning capability of the chicken. The MCSO is tested on the benchmark functions, IEEE CEC-2017 functions and an engineering problem. The results are validated by a comparative analysis with well-known SI agorithms. The results indicate that the MCSO provides competitive performance. The results are statistically verified with the win-tie-loss, Bonferroni-Dunn post-hoc, and Wilcoxon tests. [ABSTRACT FROM AUTHOR]

The application of data deduplication technology reduces the demand for data storage and improves resource utilization. Compared with limited storage capacity and computing capacity of a single node, cluster data deduplication technology has great advantages. However, the cluster data duplication technology also brings new issues on deduplication rate reduction and load balancing of storage nodes. The application of data routing strategy can well balance the problem of deduplication rate and load balancing. The paper introducesa global BloomFilter routing strategy. In order to avoid the communication overhead caused by sending the fingerprints to the data storage node and inquiring about the BloomFilter maintained in the memory, a BloomFilter array is maintained in the memory of the client-server. Each row of the array corresponds to a storage node; before sending the Superchunk to the storage node, it will inquire the BloomFilter array and storage capacity information to get the optimal node. The theoretical analysis and experimental results prove the feasibility of the strategies proposed by this paper. [ABSTRACT FROM AUTHOR]

Long-duration radar signals have a low-range resolution but have high-energy contents, whereas short-duration radar signals have a high-range resolution but have low-energy contents. The pulse compression technique allows the radar signal designers to achieve desired energy corresponding to long-duration signals and range resolution corresponding to short-duration signals simultaneously. The pulse compression technique is used in a high-range resolution radar to discriminate closely located targets in the same vicinity and it also improves radar parameters' measurement accuracy. Polyphase codes are widely used as pulse compression signals in high-range resolution radar systems. The major disadvantage of pulse compression codes is unwanted correlation sidelobes at the output of the matched filter. This paper proposes the sidelobe reduction technique to reduce the Peak Sidelobes Level (PSL) of Frank polyphase codes. The weights of the proposed Sidelobes Reduction Filter (SRF) are optimized by using the Modified Genetic Algorithm (MGA). The proposed technique reduced autocorrelation peak sidelobe level significantly. Finally, the synthesized results of the proposed SRF are presented in this paper. [ABSTRACT FROM AUTHOR]

The paper proposes the design of a seven-level reduced switch multi-level inverter (7LRS-MLI) topology. The design produces a seven-level staircase output waveform with fewer components (seven power switches), thus reducing the cost and area and improving the performance of the design, i.e. increasing efficiency and reducing switching loss effectively. Both symmetric and asymmetric configurations have been implemented and the results are discussed in the paper. A comparison of the proposed structure with other recent seven-level structures has been illustrated. The simulation (using MATLAB/SIMULINK software) and experimental validation of 7LRS-MLI also have been performed for R, RL load, change in load and change in a modulation index (MI). Field programmable gate arrays (FPGA) have been used for digital implementation of the switching waveforms which reduces design time and improves flexibility. For switching devices, the alternative phase opposite disposition pulse width modulation (APOD-PWM) technique is used at 500 Hz and 5 kHz. The voltage and current total harmonic distortion (THD) profiles are also computed during the simulation and experimental validation. [ABSTRACT FROM AUTHOR]

Time-domain effects of UWB disc monopole antenna as transmit antenna, receive antenna, and transmit–receive antenna pair have been investigated using different types of signal excitations. Several noted literature have performed the same by calculating either first or second-order derivatives of the input, thereby creating an ambiguity of computational approaches. The response is also reported to change with a change in pulse width. This paper aims to obviate the ambiguity in time domain response from the transfer function perspective. The concept is established in the paper by implementing a co-planar waveguide (CPW) fed disc monopole antenna with upconverted and baseband input excitation. Findings reveal that the uniqueness of frequency response in determining the effect of antenna rather than the conventional method, and the existing jeopardy of the temporal derivative is henceforth removed. Subsequently, the singularity expansion method has been applied to characterize and optimize the antenna. [ABSTRACT FROM AUTHOR]

Document classification is an open problem in library, information, and computer sciences towards assigning documents to one or more classes. The interest of linguistic researchers in this domain has increased day by day due to interesting applications like language identification, readability assessment, sentiment analysis, spam filtering, etc. However, researchers focussing on natural language processing of resource-scaring languages have faced many hurdles due to the absence of benchmark datasets. Bengali is among the most-spoken resource-scaring or low-resource language. Although Bengali NLP researchers have endeavoured towards creating their own datasets, they are only useful for performance evaluation of their proposed document classification techniques only. Therefore, there is a gap in the literature on the availability of benchmark datasets. To overcome this barrier, this paper presents a benchmark dataset for Bengali document classification, which is publicly accessible and freely available. This dataset consists of a two-tier architecture, the first-tier for hard classification and the second-tier for soft classification techniques. Hard classification techniques follow supervised learning based models for the classification of documents, while on the other hand, soft classification techniques follow unsupervised learning based models for the clustering of documents. The proposed dataset consists of thirteen unique characteristics. This paper also introduces four new feature sets to evaluate the performance of the proposed dataset, namely: location revealing factor, part of speech tagging factor, relative frequency, and prominence factor. [ABSTRACT FROM AUTHOR]

Generally, more Internet of Things (IoT) based sensor networks are used for transportation and traffic signals in a smart city. The need of IoT based networks are increasing day by day. The main issue of IoT networks is poor energy optimization. Offering energy optimization in the IoT will present a number of difficulties. The researchers try to extend the network lifetime by reducing energy consumption. Clustering is a good solution to reduce the consumption of energy in a network. In IoT along wireless sensor networks (WSNs), a cluster is a group of nodes. Using these clusters, Cluster Head (CH) is formed. CH collects information from other nodes and is connected to the Base Station (BS). In this paper, Cluster head selection using a Multi-Dilation Convolutional Neural Network optimized with Balancing Composite Motion Optimization (BCMO) is proposed. The major goal of this work is to increase the normalized energy and optimize the weight, temperature, delay, distance between all nodes, and weight. In this paper, the Multi-Dilation Convolutional Neural Network optimized with Balancing Composite Motion optimization is proposed for energy optimization (CHS-MDCNN-BCMO-IoTN). The efficiency of the proposed CHS-MDCNN-BCMO-IoTN approach is compared with other existing methods in terms of normalized energy, load, temperature, and number of cluster head formations. This CHS-MDCNN-BCMO-IoTN method attains 31.45%, 30.98% and 27.24% highest number of alive nodes, 26.16%, 27.23% and 33.62% lower number of dead nodes when compared with existing methods. [ABSTRACT FROM AUTHOR]

Human-Robot interaction plays a vital role in the service robotics field, especially to support old age-dependent people for socio-economic reasons. In this paper, an indigenous 3D printed 6 DoF robotic arm is proposed to support specially-abled people in their independent-feeding process. The objective of the present paper is to find the combination of optimal positional controllers such as CPID, FC, FPID and FOPID, which can handle the cubic reference input signal and can produce an output signal with minimal overshoot and lesser positional error. The reduced positional error helps the robotic arm to accurately reach the destination with minimal oscillations. This would reduce the wastage of food in the middle of the trajectory as well as at the destination. The technical challenge of the paper is to synchronize machine vision, robot kinematics and trajectory planning with robot control for multiple intermediate points, subjected to the cubic input signal. The feeding robotic arm is equipped with Intel© visual depth camera, which is in synchronization with the microcontroller, servo controller, and servo actuators. Here, six intermediate points were identified in the C-space using FK, among which, fuzzy controller was selected for the first two IPs, the GA optimized FOPID was selected for IP3 to IP5 and FPID was deployed for the last IP. The GA: FOPID produced a negligible overshoot of 0.67%, whereas FC and FPID produced an overshoot of 1.4% and 0.8% respectively. The positional error gap between GA: FOPID, FC and FPID was 0.9, and 0.6 Deg/sec respectively. The selection of combination of optimal controllers helps the manipulator to successfully deliver the food without wasting it. The repeatability of the feeding process is ensured by successfully conducting user testing on 20 users for 25 cycles. [ABSTRACT FROM AUTHOR]

Renewable energy is now in high demand due to the deterioration of the global climate and the depletion of conventional sources. Renewable energy sources (RES) such as wind and solar are extremely intermittent, making it impossible to sustain system reliability with an unacceptably high proportion of renewable energy injection. An intrinsic attribute common to all renewable power plants is that the production of energy relies on environmental conditions such as temperature, pressure, wind speed, humidity, clouds, etc. Therefore, the power from RES cannot be completely regulated or pre-planned. It is important to forecast the amount of electricity that can be produced in a power grid for future demand. Machine learning (ML) is an emerging technology and used in all fields nowadays to perform different tasks. In this paper, the applications of machine learning in renewable energy sources are discussed. These ML techniques are mainly used to predict the power from renewable energy sources like wind, solar, hydro, biomass, tidal, and geothermal. Fault detection is a significant aspect in renewable energy systems to reduce the operation and maintenance cost and to deliver the continuous power to the loads. This paper also focusses on the use of ML techniques in predicting the faults before it occurs, early detection of faults and also to diagnose the faults in renewable energy systems. Along with the above-mentioned applications, these ML techniques used in RES for different purposes are also discussed. [ABSTRACT FROM AUTHOR]

Several advantages have been achieved in distribution systems by adding Distributed Generation (DG). These benefits comprise minimizing power losses and improving the voltage profiles. Such benefits can be enhanced if DGs are optimally sized. This paper presents a DG in the distribution scheme to increase node voltages and reduce the power loss of radial distribution systems by using the Modified Shuffled Frog Leaping Algorithm (MSFLA) technique. The core objective is to decrease active-power losses of the system while keeping the voltage profiles within the defined limits of the system. This paper optimally allocates and sizes three types of DGs in various standard radial test systems and a practical radial feeder. Single and multiple DGs are considered for the proposed work by including some special cases. The standard test systems on which proposed work is performed are 33-bus and 69-bus radial feeders. A practical 43-bus radial feeder of factory area Faisalabad has also been taken under consideration for analysis. The Forward–Backward Sweep (FBS) scheme is selected for the load flow analysis, whereas MSFLA is chosen for finding of optimum size(s) and site(s) of DG(s). Various types of DGs results are retained separately and collectively. We have reduced the losses up to 85.14% and 96.1% for the 33-bus system and 69-bus respectively. The results will depict a significant improvement, both in the minimization of losses and also in improving the voltage profile. The results of multi DGs placement will be much more promising than those achieved after placing them independently. [ABSTRACT FROM AUTHOR]

The IETE Journal of Research's first issue of Volume 70 covers a wide range of topics in communications, electromagnetics, opto-electronics, computers and computing, control engineering, electronic circuits, devices and components, instrumentation and measurement, medical electronics, and power electronics. The articles in this issue discuss various subjects, including improved network performance in wireless sensor networks, machine learning-based classification of cervical cancer, optimal energy reduction in 5G communication systems, and secure data transmission in the Internet of Things. The issue also includes papers on photonic sensors, detection of diabetic retinopathy, optimisation models for home health care scheduling and wind-integrated power systems, and efficient task scheduling in cloud computing environments. One research paper introduces a smartwatch-based machine learning model for monitoring epilepsy and detecting seizures, achieving a high accuracy of 99.40% in cross-validation. The document provides a summary of articles published in the journal, with authors who are experts in their fields and provide experimental findings and simulations to support their research. [Extracted from the article]

A dual-sided metasurface structure, consisting of two different sets of sub-wavelength periodic metallic patterns on its two sides, can offer a promising platform for the design of multifunctional and directional insensitive devices. This paper presents an elaborative study of the structural and mathematical analyses of the frequency response of a dual-sided metasurface structure. It has been found that the ratio between resonating wavelength and the structure's thickness plays a deterministic role in achieving multi-functionality in such designs. The paper further presents an equivalent circuit model of the dual-sided metasurface structure to mathematically analyze the role of impedance in achieving directional insensitivity in such devices. The proposed structural and mathematical analysis has been validated together by designing a directional insensitive rasorber in the latter half of the paper. The analyses done in this paper can open up a broad avenue for the research and development of metasurface-based directional insensitive multifunctional optoelectronics devices. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the sight-threatening diseases entered the top 10 diseases that cause death worldwide. There has been tremendous growth in diabetic patients in the last decade due to lifestyle, uncontrolled diet and genetic causes. Nevertheless, the detection of Diabetic Retinopathy in an earlier stage may save the vision of many people. Imaging technologies in the medical field play a highly vital role in understanding, modelling, detecting and diagnosis of diseases. Detection of abnormalities in the retinal image will help to detect, classify and grade the disease. However, it is necessary to segment the retinal image and extract the optic disc, optic cup, retinal blood vessels and macula. The final image interprets the diagnosis of retinopathy diseases, obliging the professional medical practitioner and representatives. This paper describes a comprehensive survey of data acquisition techniques, diabetic retinopathy and the performance of purposed models in this field. The survey involves several papers comprising the research direction of a screening system and lesion segmentation of DR, disease severity grading and abnormality detection. Furthermore, research challenges are discussed and directions are suggested. [ABSTRACT FROM AUTHOR]

This paper reports the optimization of voltage controlled resistive switching devices in terms of device and circuit level parameters in a memristor-based circuit in order to demonstrate profound non-linear characteristics for high performance NVM applications. The pinched hysteresis loop has been correlated to the different stages of formation and rupture of conductive filaments in memristors. A qualitative study of the VTEAM model has also been presented. The memristor circuit has been simulated for varying device and circuit level parameters and options to optimize the circuit to achieve high degree of power-aware non-linearity in the characteristics of the memristor have been demonstrated in this paper. The effect of window functions on the non-linear behavior of memristors has been presented towards the end of the paper. [ABSTRACT FROM AUTHOR]

Emergency Medical Service (EMS) is an integral part of the healthcare system that works dedicatedly to save the lives of people. The prime responsibility of any EMS provider is to offer a timely response to the person in need. The timely assistance increases the chances of survivability of a person. In India, escalating count of road accidents has increased the demand for ambulance services to provide pre-hospital treatment or transportation of victims to the hospital. This paper presents a new optimization strategy of Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) for ambulance allocation to reduce the ambulance response time. Considering a set of assumptions, the authors have applied the new strategy for allocating 50 ambulances to 11 base stations in Southern Delhi. The working environment of EMS which includes stochastic requests, travel time, and dynamic traffic conditions have been taken into account to attain accurate results. The new optimization strategy of HPSOGA has been implemented in a MATLAB environment to find an optimized allocation plan with minimum response time. With the proposed algorithm the authors have been able to reduce the average response time by 11.61%. The paper also presents the comparison of HPSOGA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the stated problem. The algorithms are compared in terms of objective value (response time), convergence rate, and constancy repeatability to conclude that HPSOGA performs better than the other two algorithms. [ABSTRACT FROM AUTHOR]

This paper presents modeling and nonlinear control of a two-stage 1-MWh battery energy storage system (BESS) connected to a distribution grid. The BESS is based on a cascaded H-bridge (CHB) multilevel converter offering the distribution of the batteries among multiple submodules which provides safer operation and more flexibility in the voltage design of the batteries. The main function of the BESS is to exchange active and reactive power by controlling the current of the CHB using voltage oriented control strategy. Moreover, this paper proposes a nonlinear state feedback exact linearization controller to control the bidirectional buck–boost converter in each submodule to charge/discharge the batteries and stabilize the dc-link capacitor voltage throughout the operation of the BESS. The proposed controller performance is compared with the conventional PI controller and validated through simulation results. [ABSTRACT FROM AUTHOR]

The circular scan architecture (CSA) is a widely used scan design technique in digital circuit testing that involves scanning test data through a circuit in a circular pattern. Several optimization approaches have been suggested so far to lessen the Test Data Volume (TDV), but no one has shown satisfactory results. Therefore, this paper proposes a Modified and Customized Wingsuit Flying Search algorithm to decrease the test data volume time. The proposed MCWFS-SBA-CSA introduces some novel features that distinguish it from traditional circular scan approaches. The concept of wingsuit flying is an innovative approach in the field of scan systems. This allows the scan that moves more dynamically and efficiently, enabling it to cover a larger area in a shorter time than traditional circular-scan approaches. After this phase, only scan-chosen inputs are required to obtain the next test pattern from the captured response. Therefore, required less Automatic test equipment (ATE) memory in this research so there is a Decreased cost in system-on-chip (SOC). The proposed method attains fast global searching capacity. The efficiency of the proposed technique attains 26.42%, 32.45%, and 29.34% higher compression ratio (CR), Test Application Time (TAT) reduction of SC-128 attains 29.45%, 34.67%, and 40.22% higher, TDV of SC-256 attains 32.23%, 36.34%, and 39.23% higher than existing methods, such as TDV for Circular Scan Approaches utilizing Modified Shuffled Shepard Optimization (TDV-CSA-MSSO), Effectual Very-large-scale integration Test data compression system for CSA under modified and colony metaheuristic (ETDC-CSA-MACMH), and Test Data Compression Utilizing Tetrad state Skip System (TDC-DC-TSSC) for Digital Circuits, respectively. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) has grown more important in the domains of pervasive computing, human behavior analysis, assistive health care, and Human–Computer Interaction (HCI). While Deep Learning (DL)-based methods have demonstrated remarkable efficiency in the identification of activities, the management of time series data remains a challenge. Nevertheless, a large number of models currently in use direct the effective extraction of temporal and geographical information from data on human behavior. In this paper, a fusion of a 1-dimensional convolutional neural network (1-DCNN) and Stacked long short-term memory (SLSTM) is proposed for HAR. This model automatically extracts geographical information from the raw body sensor data. 1-D CNN model can extract local features and the SLSTM model can extract long-term relationships in sequence data. In addition, a batch normalization layer was added after the convolution layer to reduce internal covariate shift, accelerate training, and reduce the need for dropout. Moreover, a Max Pooling Layer was applied next to the pooling layer to reduce model parameters. The final step involves applying the learned features to the Softmax layer to fully recognize human activities. The model performance is evaluated on two standard datasets MHEALTH and WISDM and achieves classification accuracies of 99.67% and 99.55% correspondingly. The results demonstrate that the recommended model outperformed the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Blockchain (BC) technology has attracted a lot of interest due to its excellent security and privacy features as well as its immutability. While BC can address security and privacy challenges in IoT, it is often computationally expensive, suffers from limited scalability, and introduces bandwidth overheads and delays, making it unsuitable for IoT applications. This paper proposes a novel lightweight and scalable blockchain (LIGHT-SB) designed specifically for IoT environments. In a smart IoT ecosystem, low-resource devices benefit from a centralized manager that handles communication keys and manages requests. LIGHT-SB enhances decentralization while maintaining robust privacy and security through a hybrid consensus algorithm combining permissionless proof-of-capacity and permissioned Byzantine fault tolerance (PoC-PBFT). Secure message delivery is achieved using SHA-256 and RSA cryptographic techniques. IoT device authentication, communication, and storage are safeguarded within the network. Distributed throughput management is implemented to monitor and adjust BC usage, ensuring it remains efficient. Qualitative evaluations demonstrate that LIGHT-SB effectively mitigates several security threats. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid technique for optimizing Combined Cooling, Heating, and Power (CCHP) systems integrating a diverse set of components including geothermal heat pumps, gas turbines, absorption chillers/heaters, and photovoltaic/thermal collectors, along with storage units like batteries and water tanks. The innovation lies in combining the Capuchin Search Algorithm (CapSA) with the Wild Horse Optimizer (WHO) to concurrently optimize primary energy savings, cost reductions, and carbon dioxide emissions. Parametric distributions are used in probabilistic strategies to resolve uncertainty in solar radiation and building load. CapSA optimizes the CCHP system’s design, while WHO enhances its performance. The effectiveness of the proposed strategy is demonstrated through implementation in MATLAB, where it achieves an average ACSR (Average Comprehensive Saving Rate) enhancement of 5.48%. This numerical result highlights a substantial improvement over existing methodologies, showcasing the superior performance of the hybrid optimization approach in enhancing energy efficiency and environmental sustainability of CCHP systems. [ABSTRACT FROM AUTHOR]

Fibrillation (Fib) is the most well-known sort of sporadic heartbeat. The sporadic termination of electrical impulses causes the atria (the top chambers in the heart) to tremble (or fibrillate). Cardiovascular arrhythmias happen in older people with coronary disease. Now and again, individuals with Fib have no manifestations, and their condition is just recognizable upon actual assessment. The issues include inconsistent heartbeats when the heart is reliably in a sporadic mood, as anticipated through the patient well-being observation framework (PHMS). The PHMS proposed calculation is ready to anticipate the inconsistent pulses by the location of the R top in electrocardiogram signals (ECG) and the unpredictable beat anticipated by utilizing the P wave along with the QRS complex with the T wave for one heart cycle. Hence, this paper proposes a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN), termed hybrid C-RNN, to proceed with the characterization and determination of the arrhythmic beats. This work also analyzes the QRS peak variations, followed by classification. The practicability of the heartbeat characterization utilizing hybrid C-RNN is completed by metrics like accuracy, sensitivity, and specificity. The proposed model achieved an accuracy of 98.57%, sensitivity of 1%, and specificity of 9.565217e-01%. This is compared best with some existing approaches in the result section. [ABSTRACT FROM AUTHOR]

Many PID (Proportional plus Integral plus Derivative) controller tuning methods are proposed in the control literature based on different closed-loop specifications. Also, tuning techniques, based on the maximum sensitivity specification, have been proposed in the literature. In this paper, a novel PID controller design method, based on maximum sensitivity, has been proposed. In this work, the PID controller is viewed as a gain and dynamic part and tuned separately. The PID controller dynamic part is tuned using one of the existing popular tuning methods. The tuned dynamic part is cascaded with the process. After that, the PID controller gain is tuned for the maximum sensitivity specification. The maximum sensitivity versus controller gain graph can be generated. The user can choose the controller gain for the desired maximum sensitivity specification. The popular Ziegler & Nichols and IMC (Internal Model Control) methods tune the dynamic part of the system in this work. These proposed techniques can achieve the desired maximum sensitivity by tuning the controller gain in the subsequent stage. Many illustrative instances are considered to display the applicability and the proposed technique’s simplicity. [ABSTRACT FROM AUTHOR]

The 5G communication network is a promising technology that offers user-centric connectivity to customers, enabling quick, high-capacity, and latency-free access to many applications. Software Defined Networking Controller (SDNC) is an emerging technology that is used for increasing the security and maintaining the dynamic nature of mobile devices in latency and energy-assured 5G wireless communication. Different SDN topology open flow controllers are used in the literature for increasing the throughput and security of the network but they possess some drawbacks such as frequent location update (LU) overhead, high latency, and high energy consumption. This paper proposes a hexagonal software defined network (H-SDN) controller to overcome these challenges. The proposed H-SDN topology is used to preserve Intermediate Message Layer (IML) location during multimedia data transmission. A Core Switch (CS) updates an IML's location within clusters of cells, as long as it remains within the cluster. However, in the proposed method, there will be a location update on the server if the IML departs the cluster. Experiment results show that the proposed method can increase security, which in turn increases throughput, while reduces the location update costs, energy consumption, and transmission latency. [ABSTRACT FROM AUTHOR]

Active switching components are employed to propose a novel compact dual-band matching network (MN) with an adapted frequency in this paper. The design method involves the integration of active switching devices, stepped impedance resonator (SIR), and microstrip transmission line into the frequency response of a dual-band MNs. The PIN diode is employed as an active switching device in the proposed structure to increase the bandwidth of the MNs and attain the desired frequency. The dual-band MNs that are being proposed are fabricated and designed on the Rogers RO4350B substrate for use in Wireless Local Area Networks (WLANs). The simulation results demonstrate a high degree of congruence with the measurements. The maximal insertion losses and return losses in the first band are -1.3 and -24 dB, respectively. In the second band, they are -2.2 and -20.69 dB. The primary benefits of the proposed dual-band MNs are their high attenuation between two passbands, frequency matching capability, suitable return loss, and low insertion loss. [ABSTRACT FROM AUTHOR]

This paper deals with Distributed Generation (DG) system requiring power quality features such as current harmonics, reactive power compensation, and power factor improvement in grid tied operation. The motivation is to combine DG system with the capabilities of shunt active filter to perform real power injection, current harmonics mitigation and power factor improvement. The control algorithm employed in this setup utilizes a reference current generator based on p-q theory together with a pulse width modulation controller for switching pulse generation. The control strategy utilizes PI controller with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for finding optimal PI controller parameters. The effectiveness of the proposed method is verified using MATLAB/Simulink and experimental model developed for 3kWp inverter. The PI-PSO controller injects 0.588kW active power into the grid results in sharing 54% of load power demand and the current harmonics is reduced from 23.70% to 1.63%. The obtained results demonstrate that the grid tied inverter maintains harmonic standards in accordance with the IEEE-519 standard while injecting maximum possible active power to the line. [ABSTRACT FROM AUTHOR]

With large-scale Cyber-Physical Systems (Internet of Things) deployments, the low-cost networked devices are becoming ubiquitous. Time stamping of events is a basic requirement and it is crucial for distributed/isolated systems/devices to work in unison. The system clocks of these devices are not very stable and their behaviour is affected by ageing and environmental conditions. A clock’s timing behaviour is generally analysed independently by comparison to a highly stable standard reference clock using specialized instruments. Network Time Protocol (NTP) is widely used for time synchronization over computer networks. As the NTP server clock is more stable in comparison to a client’s system clock, the variation in the end device’s clock offset values with respect to the NTP server clock over a long time can be considered for analysing the behaviour of the end device’s clock. This paper presents a method for analysing clock behaviour based on NTP messages. A computer programme is developed, which utilizes an open-source NTP client library to exchange NTP messages and records end device’s system clock’s offset variations over time for a given periodicity to analyse the behaviour. This black box analysis approach does not require any direct access to the end device’s clock circuit. The solution may be useful for analysing clock behaviour for long-term stability under actual environmental conditions, for deciding time synchronization periodicity to optimize the number of synchronization packet exchanges, and for pre-deployment timing stability assessment of devices for deployment scenarios having intermittent/non-availability of periodic time synchronization. [ABSTRACT FROM AUTHOR]

In this paper, the graph sample and aggregate-attention network with war strategy optimization algorithm for cyber security in the 5G wireless communication network (CS-5GWCN-GSAAN-WSOA) is proposed in 5G mobile networks to identify cyber threats. Initially, the input data are amassed from the 5G-NIDD dataset. Then the input data are fed to preprocessing, here redundancy elimination and missing value replacement are performed utilizing the contrast limited adaptive histogram equalization filtering (CLAHEF) method. After preprocessing, optimal features are selected by the stochastic gradient boosting based recursive feature elimination process. The selected features are fed to the graph sample and aggregate-attention network (GSAAN) to detect cyber-attacks in 5G wireless communication. Generally, the GSAAN approach does not adopt any optimization techniques for determining optimal parameters and guaranteeing accurate attack detection. Therefore, the war strategy optimization algorithm (WSOA) contemplates to optimize the GSAAN weight parameters. The CS-5GWCN-GSAAN-WSOA method is activated on Python. The CS-5GWCN-GSAAN-WSOA method attains 22.51%, 20.35%, and 35.54% higher accuracy, 19.45%, 27.80%, and 18.93% higher sensitivity analysed with existing models, like cyber security attack identification in 5G wireless systems utilizing machine learning (CS-5GWCN-SVM), enhanced dropping attacks detection in 5G networks depending on deep learning models (CS-5GWCN-KNN), and cyber security issues in 5G enabled attack detection through deep learning (CS-5GWCN-RNN), respectively. [ABSTRACT FROM AUTHOR]

Automated brain tumor detection and classification systems have gained popularity in recent years because traditional diagnosis procedures are time-consuming and costly in nature. Deep learning(DL) methods, specifically pre-trained convolutional neural networks (CNNs), have shown promising results in accurately and rapidly classifying brain tumors. However, the lack of diverse magnetic resonance imaging (MRI) datasets has hindered the ability of DL algorithms to generalize effectively. To address this issue, this paper proposes a DL model using generative adversarial networks (GANs) in conjunction with data augmentation strategies and a structural similarity loss function employed for generating annotated images. A novel DL model inspired from Vision Transformer, Shrinking Linear Time Vision Transformer (SL(t)-ViT) network model is proposed for disease data classification. The model underwent extensive evaluation across multiple datasets, employing standard performance metrics to assess its efficacy in brain tumor identification. The proposed model achieved remarkable testing accuracies of 0.995, 0.996, 0.9954, 0.998, and 0.997 for binary classification tasks across multiple datasets, and 0.986, 0.982, 0.985, and 0.993 for multi-class classification tasks. These results underscore the superior performance of our proposed model, showcasing its capability to outperform state-of-the-art techniques. Specifically, it demonstrated a substantial margin of improvement, ranging from 1-2% for binary classification and 9-10% for multi-class classification, solidifying its position as a leading approach in brain tumor identification. Results demonstrate the efficacy of the proposed model, outperforming other models. Overall, this study highlights the potential of SL(t)-ViT and GANs in improving the accuracy, resource consumption, and efficiency of brain tumor diagnosis. [ABSTRACT FROM AUTHOR]

This paper suggests a new model made of a simple two-section RC ladder for a uniformly distributed RC network and examines its validity under open-circuited condition, as well as when it is terminated in an RC load impedance, using the Elmore-defined rise time as a measure. The model is shown to perform reasonably well under both conditions. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is introduced for the development of a power-efficient voltage differencing buffered amplifier (VDBA) by incorporating an adaptive biasing circuit. The proposed design is specifically tailored to exhibit low power consumption. The VDBA proposed in this study includes a conventional transconductance unit (TU) with an adaptive biasing and a positive feedback circuit. Incorporating adaptive bias cells and cross-coupled positive feedback circuit, the VDBA structure allows for boosted tunable gain based on the input differential voltage. The proposed VDBA shows improved transconductance gain, transient characteristics, linearity for a wide range of inputs, and reduced standby power dissipation. A mathematical formulation has been presented to define the characteristics of the proposed VDBA. To validate the proposed VDBA structure, SPICE simulations were conducted using 180 nm CMOS technology. The input linear range of the proposed VDBA is ± 250 mV. The 3 dB bandwidth of the proposed structure is 200 MHz. The THD has been found low. The Monte Carlo simulations have also been carried out and found the proposed structure is less sensitive to the different parameter variations. The slew rate of the proposed VDBA is found 26.2 V/µS. As an application example, a voltage mode universal MISO biquad filter is also proposed using the proposed structure VDBA. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a broad network of actual, physical objects that can exchange data and communicate with other devices and systems over the Internet. Due to the scalability of the internet, the number of IoT users is increasing, and therefore it is vital to ensure the security of user info. Attacks such as DDoS (Distributed Denial of Service) and MitM (Man-in-the-Middle) can seriously interrupt operations and threaten the confidentiality and integrity of sensitive data. In this paper, a novel Iot based Security Assessment for intrusion using Deep Learning (ISAI-DL) technique has been proposed to identify IoT device vulnerabilities such as DDoS and MitM attacks. Initially, the features are extracted from the API documents using Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques. In proposed ISAI-DL technique, it triggers or activates the vulnerabilities deep within target devices by sending API documents. The vulnerability is detected via the GloVe-CNN-BiLSTM Model; if the attack occurs in an IoT device, the output is classified as either DDoS, MitM, or normal. Evaluation metrics such as accuracy, recall, precision, time efficiency, F1 score, false alarm rate, and detection rate have been used to measure how effective the proposed ISAI-DL technique is. Based on a comparative investigation, the detection rate of the proposed ISAI-DL technique is 17.23%, 18.53%, and 3.24% greater than that of the existing P2ADF, HDA-IDS, and BT-TPF techniques respectively. [ABSTRACT FROM AUTHOR]

A fourth-order generalized integrator (GI)-based frequency locked loop (AFOGI-FLL) controller is proposed to provide switching pulses for a three-phase IGBT-based voltage source converter. Accurate frequency synchronization, lower- and higher-order harmonic elimination, power quality refinement, and reactive power compensation are the features of the presented controller. As a renewable energy solution to meet energy demands, grid-interfaced photovoltaic systems are described in this paper. To stabilize DC link voltage, a novel ANFIS-based hybrid intelligent controller is proposed. In addition, a comparative analysis has been performed between the proposed controller and the third-order generalized integrator (TOGI). The comparative analysis results show that fourth-order generalized integrator has higher DC offset elimination capability and better dynamic performance as well and THD is less compared with the TOGI controllers. The THD of the proposed controller is 1.10% which is less than the IEEE standard. The proposed controller robustness is validated by an experimental prototype setup and test results exhibit the performance under non-linear loading. Additionally, the feasibility of the proposed control scheme is demonstrated by using MATLAB/SIMULINK software. The system is well executed under irradiance variations, load fluctuations, and frequency variations as well. The laboratory prototype is used to demonstrate the proposed system's simulated behavior and experimental test results. [ABSTRACT FROM AUTHOR]

The escalation of Internet of Things (IoT) devices has led to increased data generation at the network edge that has burdened the cloud infrastructure in terms of handling and processing of data. This has led to the rapid adoption of fog computing because of its ability to bring computation and storage closer to the edge and support for real-time applications and services by reducing latency. One of the foremost challenges in the fog computing arena is minimizing turnaround time. This research paper proposes a Modified Levy Flight Firefly Algorithm (MLFFA) to optimize task scheduling for fog computing environments. Specifically, the objective is to minimize the turnaround time of tasks. Moreover, genetic operators like crossover and mutation are also employed to achieve an optimal balance between exploration and exploitation. Experimental observations undertaken show that the proposed method improves the average turnaround time by 55%, 22%, and 13%, average waiting time by 59%, 45%, and 37%, average energy consumption by 19%, 7%, and 4%, and average failure rate by 50%, 28%, and 7% compared to the existing studies, namely Load Balancing and Optimization Strategy (LBOS), Technique for Resource Allocation and Management (TRAM), and Fuzzy Golden Eagle Load Balancing (FGELB), respectively. [ABSTRACT FROM AUTHOR]