Search

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]

SDN architectures are frequently used by organizations for the management of their networks and the detection of anomalous traffic in a single domain. However, in the real world, anomalous traffic might result in attacks like distributed denial of service (DDoS) that affect numerous domains. During intrusion detection, each SDN domain has to send real traffic data of a large volume to the multi-domain controller, exposing its sensitive information. This paper proposes a smart multistage framework for detecting attacks and ensuring privacy at no additional cost. This work utilized the recent unbalanced InSDN dataset for experimentation. It also uses an oversampling technique that reduces the imbalance rate for each attack type and selects the smallest possible training size and feature set size for an increase in detection accuracy and a reduction in computational complexity. Then, a multi-class classifier method for intrusion detection that does not require regularization or hyperparameter tuning, called ensemble-learning-based shallow decision tree (ELSDT) is proposed. Furthermore, the performance of the proposed classifier on the InSDN dataset is assessed on an SDN testbed. Experimental results show the ability of the proposed smart multistage privacy-preserving framework to make a significant reduction in the training sample size and feature set size to 87% and 76%, respectively. It also shows its outperformance in recent literature works by 5.67% improved accuracy. [ABSTRACT FROM AUTHOR]

It also reports simulation-based comparison study with other optimization algorithms such as particle swarm optimization, differential evolution, and whale optimization algorithm. The experiment results of the algorithm in the form of precision are compared with conventional algorithms to draw conclusions about the efficacy of the proposed algorithm. In the paper titled "Analysis of Amplitude Scintillation and Positioning Error of IRNSS/GPS/SBAS Receiver for Heavy Rainy Days", the authors examine the characteristics of amplitude scintillation on an IRNSS/SBAS/GPS receiver with all seven satellites of the Indian Regional Navigation Satellite System (IRNSS) at the low-altitude station IITRAM, Ahmedabad, on days with heavy rain. In the paper "Model Order Reduction Based Power System Stabilizer Design Using Improved Whale Optimization Algorithm", the authors present an optimization technique I viz i . improved whale optimization for designing a power system stabilizer using model-order reduction for a modified single machine infinite bus system. [Extracted from the article]

The paper on "Moving Average Filter-PLL-Based Voltage and Frequency Control of Standalone WECS" a phase locked loop (PLL) is presented to solve the issue of fast and accurate estimation of system frequency during distorted voltage in a wind energy conversion system. The algorithms are the particle swarm optimization, genetic algorithm, imperialist competitive algorithm and selfish herd optimization. The next paper titled "Effective Control of Voltage and Frequency in Microgrid Using Adjustment of PID Coefficients by Metaheuristic Algorithms" presents four evolutionary algorithms to optimize a PID controller's coefficients for use in frequency and voltage control of a microgrid. The paper presents study of performance evaluation of the algorithm for diseases in apple trees and rice crop and compares the performance with other techniques to make inferences about the efficacy of the algorithm. [Extracted from the article]

The current issue of the IETE Journal of Research (Volume 69 Number 12, December 2023) contains seventy articles covering various fields of technology and engineering. These articles discuss topics such as error rate analysis, antenna development, optical DEMUX, computer science, and engineering. Each paper presents a unique study or proposal, highlighting methodologies, techniques, and findings. The articles cover a wide range of topics, including IoT security, keyphrase extraction, deep learning, object detection, emotion recognition, insulin dose prediction, antenna array synthesis, cardiac MRI segmentation, fire detection systems, fault detection in rolling bearings, medical image processing, and optimization techniques for photovoltaic systems. The authors of these articles are experts in their respective fields and have made significant contributions to their areas of study. [Extracted from the article]

In this paper, we present the MU (multi-user) MIMO (multiple-input & multiple-output) system which is capable to serve the maximum number of users without additional bandwidth or power budget. MIMO systems maximize the channel capability as per the DPC scheme (Dirty Paper Coding). According to DPC, several antennas distributed over the base station are used for serving multiple users simultaneously. Nevertheless, DPC is a comprehensive search algorithm employing sequences of encoded user data for transmitting information to multiple users. Due to increased user density and subsequent transmit antennas in the MU-MIMO system, the exhaustive search (i.e. DPC) becomes cumbersome with the expanded search space domain. This search task would not be completed within a coherence time frame of the packet data communication system. The required objective is to consider this as a cost function for optimization to maximize the realizable sum-rate (throughput) of the MU-MIMO systems. This paper illuminates the joint antenna and user scheduling (JAAU) process with binary artificial bee colony (binary ABC) accomplishing nearly 98–99% of system throughput with the suggestively decreased figure in computation complexity and run time. Binary ABC is observed to have the global optimum value swiftly within the stipulated packet time frame of next-generation wireless data communications. Moreover, binary ABC achieves an appreciable system capacity for MU-MIMO systems as compared with DPC (which attains the maximum system throughput with the highest complexity). These findings are corroborated with exhaustive simulation results presented in this article. [ABSTRACT FROM AUTHOR]

By figuring out the best numbers for the inductor, capacitor, and duty cycle of the boost converter, this method makes it possible to match the PV system to the configuration of the DC/AC converter. The suggested numerical antenna model shows good agreement with the performance of the built antenna prototype within the human muscle-mimicking liquid phantom. This paper "Multi -objective Naked Mole -Rat Algorithm for UWB Antenna Design", introduces a novel multi-objective naked mole-rat (MONMR) algorithm for the optimization of two printed monopole patch antenna structures for ultrawideband (UWB) applications. In "Improved Switched-Capacitor Switched-Inductor Z-source Inverter for Increasing Boost Factor and Decreasing Voltage Capacitors Stress", the authors uses a switched-inductor Z-source inverter to improve a switched-capacitor inductor ZSI. [Extracted from the article]

The article discusses the growing interest in establishing smart cities based on the Internet of Things (IoT) and the challenges they face, particularly in terms of security. The article highlights a special issue of the IETE Journal of Research that focuses on IoT, sensor networks, and blockchain technology for smart cities and societies. The special issue contains 10 full papers that cover various topics such as urban land use change detection, air quality data imputation, steganographic approaches for secured communication, and agricultural applications using blockchain. The article expresses gratitude to the organizers and contributors of the special issue and hopes that it will be a valuable resource for researchers and engineers. [Extracted from the article]

In this paper, the radiating element of a patch antenna for energy harvesting application was fabricated using the graphene ink-based screen printed method. Wax-coated paper and polyester were investigated as flexible substrates to mount the graphene radiating element. FR4, the typical material used in electronic circuitry was also considered as a substrate for comparison. The performance of the antenna was measured by using the standard method of Vector network analyzer. Simulation of the antenna performance was carried out using a material-based software tool, COMSOL Multiphysics and the simulated results have been compared with prototyped results. The proposed design is targeted at 2.45 GHz wireless energy harvesting applications. An experimental trial of replacing conventional copper mechanism with state of art material is studied for its performance. [ABSTRACT FROM AUTHOR]

Quantum-Dot-Cellular-Automata (QCA) using Spintronic electrons is a promising alternative to the traditional CMOS technology in this modern era to design any nano-sized electronic structure. The majority of the current digital components are being designed using QCA-cell-interaction. In some cases, reversible logic with the Spintronic-QCA-technique is also being explored to design various digital components. Because of this, the various aspects for the design of a novel hybrid QCA-based Spintronic advance reversible adder and its efficiency measures are presented in this research work by investigating area-occupation, temperature-tolerance, delay, power dissipation, and output strength. The results obtained in this work establish that the proposed design is more efficient in all respect compared to existing designs reported in recent literature. In this paper, 18nm quantum cells are used to design the proposed structure and when the power dissipation based on the total occupied cell area is calculated (100W/cm2), it is found that there is an assurance to get an adder structure with the less occupied area, less power dissipation and higher speed compared to previously published referred design. The temperature-tolerance and output-strength increment with layer separation gap reduction in multilayer platforms are also some additional advancements of the proposed structure, which are also clearly reflected. In this paper, QCA-based "Ripple-Carry-Adder (RCA)" is designed using Spintronic reversible logic in multilayer mode with different composite elements along with a "3-input-majority-voter-gate (MG)" to make the design more compact. QCADesigner 4.0 tool is used as widespread simulation trains to validate the design outcomes and measure different parameters individually. [ABSTRACT FROM AUTHOR]

This paper deals with the design and implementation of a competitive robot arm with an advanced control. A voice-controlled motorized upper limb prosthetics that has been designed with five degrees of freedom can be used to perform various light material handling tasks like a human arm. The robot arm is outfitted with five servomotors at the joints, which can be controlled accurately and precisely for the smooth movement of the arm. A voice recognition module is used to achieve wireless operation. The greatest curse of human life is losing their arms or legs from birth or due to any external calamities. This paper deals with the implementation of a low-cost voice-controlled motorized upper limb prosthetics that could enhance the life of people with upper limb amputation to do their work with some ease. [ABSTRACT FROM AUTHOR]

In this paper, a compact low-profile dual-band 2 × 2 wide band wearable slot antenna (smart watch) for the frequency spectrum of 3.4–3.6 GHz (LTE Band 42) is proposed. For smart watch applications, the antenna should be as small as possible and it should be resonating at the forthcoming 5G technology. Hence, two resonant modes are achieved for each antenna by etching the T-shaped monopole slot in the ground plane of size 40 × 40 mm2, and each slot is coupled fed by the L-shaped tuned stub which is printed on the 0.8 mm-thick FR4 substrate. The proposed antenna is fabricated and its performance is experimentally measured. The measured antenna parameters agree well with the simulated results. The reflection co-efficient and transmission co-efficient of the Multi Input Multi Output (MIMO) antenna are well below −10 dB throughout the band of operation. The efficiency of the MIMO antenna is achieved up to 81% with the overall gain of about 2.4 dB. To validate the proposed MIMO performance, the envelope correlation co-efficient between the antenna elements is calculated. In this paper, the effect of the proposed antenna in the human hand is also studied and discussed along with the specific absorption rate. [ABSTRACT FROM AUTHOR]

The paper provides information about the design, development, and evaluation of hand-eye coordination devices used to test and evaluate hand-eye coordination. Hand-eye coordination is the synchronized control of hand movement with eye movementand the processing of visual input to assist attainment, acquisition, and the use of hand proprioception to guide the eyes. Hand-eye coordination and visual-spatial abilities are essential for a child's development because they are required not only for practical independence but also for scholastic achievement. This paper outlines the development of a hand-eye coordination device using a specially designed pen (sensor), plates with patterns to trace, and a wooden cabinet to enclose and support the auxiliary components. This is a strong diagnostic in-house development in response to meet the requirements of practitioners looking for an accurate technique of measuring the progress of the hand-eye coordination of the patients. The purpose of the study is to assess the validity and dependability/reliability of the suggested device. The practicing physiotherapists conducted the assessments for children. This device was reliable and can be used by physiotherapists. This development provides portable hand-eye coordination equipment with reliable readings for the assessment of children. [ABSTRACT FROM AUTHOR]

Power transformers are one of the most important parts of a power system, and they are crucial to the system's reliability and energy quality. The "health index" idea is used in this research to evaluate and classify transformers based on their conditions in order to optimize transformer upgrading and replacement decisions. And the transformer is inspected and maintained from various angles by collecting the transformer's health factor and utilizing the number of this indicator to determine when and how often the transformer should be inspected and maintained. Finally, the health index described in this paper is utilized to review and regularly classify transformers based on their states and improve the renovation replacement method by applying the fuzzy logic method to research this coefficient precisely. The fuzzy health index used in this work has the advantage of being able to be trained using past transformer data. When the training is complete, it can be applied to other transformers, with the goal of achieving optimal functioning for transformers while considering cost and dependability. The specific results of the present article indicate that the health index criterion presented in this article has a high degree of certainty regarding the selection, arrangement, and timing of maintenance and service steps in the evaluated transformers. In addition, when comparing the proposed method to the conventional method for seven sample transformers, the system availability index of the proposed method improved by about 40%.It also had about 25% higher reliability than the conventional method for 7 sample transformers. [ABSTRACT FROM AUTHOR]

Social media is a great source of communication. People use various social media platforms, such as Twitter, Facebook, and Instagram, for sharing their ideas, opinions, and feelings. Users of different age groups, cultures, education backgrounds manipulate these powerful mediums of communication. Even though it gives all the benefits of knowledge sharing among the users, it has a dark side too. Despite setting restrictions from the corresponding sites, many users use abusive language to blemish the status and image of someone. So it is highly the need of the hour for the government or the particular social media platform to sift out those unwanted hate texts before diffusing them. Finding the hate text is one of the emerging research topics in Natural Language Processing where the model predicts the given text as hate text or not. This automated hate text detection becomes tedious when we consider the Indian languages due to a lack of data. Moreover, Indian people are multilingual and use code-mixed patterns to express their thoughts. The unavailability of the annotated Tamil-English dataset and the lack of a standard model make this task more challenging. In our paper, to handle such code-mixed data, a dataset is created with 10000 Tamil-English code-mixed texts collected from Twitter. These are annotated as hate text/non-hate text. In this paper, we use a synonym-based Bi-LSTM model for classifying hate non-hate text in tweets. [ABSTRACT FROM AUTHOR]

The effects of Electromagnetic Interference (EMI) in Inductive Power Transfer (IPT) for electric vehicle charging circuits are elaborated with Common Mode (CM) emission. Electromagnetic Compatibility (EMC) has to be sustained in the circuits such that it is prone to the effects of interferences, noises and disturbances. These issues can be addressed at the initial product development stage, with accurate identification of the parameters that influence the CM emission in the circuit. The analysis of EMI focuses on three features, namely Source of EMI, Coupling path and Receptor. In the IPT model, the primary source of the EMI is the power converter used for high-frequency supply to the inductive coils. The power converter switches and the heat sink with its Printed Circuit Board (PCB) design are predominant in power electronic circuits. This paper facilitates the reduction of EMI without including complex filters. The system is tested for the violated standard SAEJ2954. In addition, the article corroborates precognitive modeling by implementing compensation networks used in IPT applications and their effectiveness in reducing the leakage current caused by the converters and the coils. The paper insights the air gap variation of the coil with the power transfer efficiency and its influence on electromagnetic interferences. The Fast Fourier Transform (FFT) analysis used in PSIM shows leakage current and harmonic reduction with the compensation network and Pulse Width Modulation (PWM) technique. The fabrication model for the front end power supply to the IPT is tested. The electrical noise emission is estimated using predictive modeling. [ABSTRACT FROM AUTHOR]

The Universal Filtered Multi-Carrier (UFMC) is a derivative of the Orthogonal Frequency Division Multiplexing (OFDM) scheme. It is considered as a candidate waveform for 5G. The considerably high computational complexity restricts the usage of UFMC based systems. This complexity is caused by the filtration process in the system. Since the flexible and efficient use of non-continuous unused spectrum for different network deployment strategies is important in the next generation communication, the selection of UFMC is inevitable because of its improved performance over OFDM and the Filter Bank Multi-Carrier (FBMC) systems. Hence, it is essential to reduce the complexity of a UFMC system. In this paper, we propose a Fast Convolution (FC) based UFMC system. The technique makes use of smaller FFTs and overlapped data frames. The proposed FC-UFMC system achieves a computation minimisation higher than 95% for systems having 64 or more fully utilised sub-channels compared to similar UFMC systems. The Peak to Average Power Ratio (PAPR) for the FC-UFMC system has a 2.27 dB improvement over the UFMC system. The BER performance of the FC-UFMC system in AWGN and ITU-R channels models are superior or similar to the corresponding UFMC system. The paper also presents the relationship of BER dependence on Signal to Noise Ratio (SNR) and Side Lobe Attenuation (SLA) of the sub-band filter. [ABSTRACT FROM AUTHOR]

Automated computer-aided soft computing methods are presently used to detect the tumor regions in brain images. In this paper, the tumor cells are detected in the brain Magnetic Resonance Imaging (MRI) using the Extended Linear Boosting (ELB) classification method as one type of soft computing process. This paper proposes an effective brain tumor detection and segmentation method using the ELB classification method. The Curvelet transform is applied on the source brain MRI image to convert the spatial domain pixels into multi-resolution pixel. The spectral and linear discriminate features are computed from the Curvelet transformed coefficient matrix. The dimensionality of the computed features is reduced using the PCA method and the optimized features are then classified using the ELB classification method. The performance evaluation metrics, sensitivity, specificity, accuracy and detection rate, are used in this paper to evaluate the performance of the proposed brain tumor detection and segmentation system. [ABSTRACT FROM AUTHOR]

Healthcare data analysis has emerged as one of the most promising fields of study in recent years. There are different types of data in the healthcare industry, such as medical test results, blood reports, medical reports, X-rays, CT, MRI, ultrasound, clinical data, omics data, and sensor data. One of the most important and useful techniques for analysing this complicated medical data is machine learning (ML). ML is proving to be a useful artificial intelligence (AI) technique for data analysis. To accurately predict the outcomes of healthcare data, ML employs a variety of statistical techniques and cutting-edge algorithms. In recent years, different ML approaches have been applied to a variety of medical data for disease diagnosis. The paper provides a comprehensive literature survey based on ML techniques to diagnose various diseases. ML importance in the analysis of medical data is discussed with applications. This paper will motivate advanced research in machine intelligence-driven healthcare by showing its potential in healthcare data analysis. We also discuss the challenges that arise when applying ML to healthcare data. Furthermore, this study introduces a new approach to ensemble learning through explainable stacking. By integrating explainable artificial intelligence (XAI) techniques with the stacking method, we aim to not only enhance predictive accuracy but also improve the interpretability of the stacking model. The proposed predictive model outperforms the existing categorisation models, enhancing both the performance and efficiency of the diagnostic process. In addition, we suggest several future directions for further work in this area. [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]

Remote Sensing Infra-Red (IR) imaging sensors operate at very low temperatures (40–100 K). Generally, these types of imaging sensors are integrated with cryo-coolers. These sensors have an inbuilt 2N2222 diode-based cooler temperature sensor (CTS). This paper presents details of low-noise, high=precision processing electronics developed for CTS. Critical design aspects, simulation and hardware results of the system are discussed in this paper. Developed electronics systems achieve ≈5mK resolution and the power dissipation recorded is < 200 mW. [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 current communication deals with the investigation of sucrose concentration in aqueous solution using copper-based metamaterial structure at a wavelength of 1550 nm. In this article, 5×5 air holes are engraved on a copper-based metamaterial structure, which contains the different concentration of sucrose in aqueous solution. Further, the present structure manipulates with finite difference time domain (FDTD) technique to compute the sucrose concentration. Moreover, different losses (absorption loss, dispersion loss, propagation loss, and polarization loss) are measured for the same. Nevertheless, similar type of papers have been published, the present paper has a couple of benefits including the method of computation (FDTD) and the output variation (linear ship) with the concentration. Apart from this, the present research shows the error bar, which deals with an accurate measurement. Again a mathematical model is also obtained, which gives the relationship between the sucrose concentration and the output intensity. Further, output results declared that transmitted intensity varies linearly with the concentration of sucrose. Lastly, a feeble error in the commutation proclaims an accurate investigation of sucrose in their aqueous solution with the help of a photonic structure. [ABSTRACT FROM AUTHOR]

Glaucoma is a critical retinal disorder due to high intraocular pressure (IOP) within the eye. It causes irreversible damage of the optical nerve head (ONH). The available glaucoma detection methods using decomposition techniques with gray images or only green channel images are less accurate. In this paper, a more accurate method for glaucoma detection using image channels (ICs) and discrete wavelet transform (DWT) from fundus images is proposed. Firstly, input images are resized then red channel (RC), green channel (GC), blue channel (BC), and gray scale (Gs) images are extracted. Secondly, these four types of images are enhanced and decomposed separately into sub band images (SBIs) using second level (SL) DWT. Thirdly, most useful features are extracted from each of DWT SBIs. Fourthly, extracted features from each of RC, GC, BC, and Gs images are concatenated and normalized. Finally, robust features are selected and fed to the least square support vector machine (LS-SVM) classifier. The obtained glaucoma detection accuracy of the proposed method is 84.95% which is more than the existing methods using the same image database. The proposed method may become suitable for ophthalmologists to detect glaucoma with better accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an efficient method to improve the far-field radiation pattern of concentric circular antenna array (CCAA) design using two stochastic optimization algorithms known as social spider algorithm (SSA) and modified social spider algorithm (MSSA). Low side lobe level (SLL) plays a crucial role in reducing the interference with the other frequency components along the entire side lobes of the far-field radiation pattern. SSA and MSAA are the state-of-the-art evolutionary optimization techniques which are applied here to determine the optimal current amplitude and the inter-element distance between two consecutive antennae of the 3-ring CCAA. In this paper, the optimal results achieved by using SSA, MSSA for (4, 6, 8) elements and (8, 10, 12) elements 3-ring CCAAs, with and without centre elements are reported. The results achieved by employing SSA and MSSA show a considerable improvement in SLL reduction as compared to the uniform and the other array patterns reported in the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

In this paper, a graph theory-based allocation methodology of capacitor bank is proposed for unbalanced distribution system. A single-stage optimization procedure using electrical centrality measure concept to identify the best suitable locations for the multi-point connection of capacitor is described with its significance. The electrical centrality measure approach gives ranking of all the buses, single capacitor or multiple capacitors can be placed in unbalanced distribution system (UDS) using these indices to fulfill the reactive power demand and maintain voltage profile of the system. To evaluate the performance of the UDS due to capacitor bank connection two parameters are considered – improvement of overall voltage profile and reduction of active power loss. The contribution of the paper is – first, electrical centrality measure is used for identifying the capacitor bank location in the unbalanced distribution system and second it optimizes the size of the capacitor using two different objectives to improve the system voltage profile and reduce the active power loss. The proposed methodology is tested on a modified 123 bus unbalanced distribution system. The active power loss reduction up to 16.50% and total voltage deviation reduction up to 71.53% were achieved using the proposed method. The obtained results corresponding to the two objectives are also compared. [ABSTRACT FROM AUTHOR]

Fourier analysis is well known to provide complete information of the frequencies present in a signal. But in the process, time information is lost. Therefore, its time-frequency representation is required for depicting both time and frequency information simultaneously. Therefore in this paper, fractional wavelet transform (FrWT) is proposed to be used for the first time for extracting the features of various datasets in a standard ECG database by combining the advantages of both fractional domain techniques and wavelets as case-II. Afterwards, Probabilistic Principal Component Analysis (PPCA) is used for detecting R-peaks for diagnosing heart abnormalities in various morphologies of the ECG signal. The proposed technique has been evaluated on the basis of sensitivity (SEN), detection error rate (DER), and positive predictivity (PPR) (of the detected ECG beats) for MIT-BIH Arrhythmia database (M/B Ar DB). Even though both FrFT and FrWT techniques exhibit a high degree of robustness, but SEN of 99.99%, DER of 0.026%, & PPR of 99.99% obtained by latter in case-II are better than SEN of 99.97%, DER of 0.053%, & PPR of 99.98% obtained by the former in case-I for M/B Ar DB. In this paper, average time error (ATE) is also obtained for the considered datasets establishing the effectiveness of the proposed technique further. These encouraging results suggest that the proposed methodology will go a long way in assisting the cardiologists to detect temporal patterns in a wide variety of electrophysiological cases, which is important for improved management of healthcare system. [ABSTRACT FROM AUTHOR]

As spasticity is a major cause of illness in patients that suffer from spinal cord injury (SCI), there are several limitations in the present robotic-based therapy for the treatment of acute SCI patients. A large literature is present for the use of full body exoskeletons, but due to their limitations such as uneasiness due to repetitive motion, does not meet the anthropometric range. As manual therapy is tiresome, a need for ankle-foot therapy exoskeleton was felt and Ankle-Foot Orthosis was developed. This paper presents an ankle-foot therapy exoskeleton device for spasticity treatment of acute SCI patients to reduce the manual efforts of physiotherapists. The proposed device has one Degree of Freedom (DOF) in the sagittal plane in ankle joint. This paper analyses the control design of the prototype and clinical trial results on SCI patients, which show that the proposed prototype is viable in improving the range of ankle motion alleviating the spasticity with p = 0.000365. [ABSTRACT FROM AUTHOR]

This paper mainly studies the financial data management method and edge computing platform based on intelligent edge computing and big computing. This paper first introduces the theoretical basis of edge computing and big data computing, analyzes the collaborative mode of edge computing model and cloud computing, and discusses the cloud data storage scheme. In this paper, edge computing platform is built, and EC Master and Docker private image warehouse are mainly deployed in the data center. In this paper, the edge computing platform constructed was tested. In the mobile edge computing business processing time test, when there was a big gap in computing amount between business 1 and business 2, the total service time of MEC instances was around 0.3 s, showing a good user experience. In the edge acceleration effect test, before and after the adoption of CDN edge acceleration, from the perspective of the overall web page, the average time consumed to open a web page decreased from 1615 ms to 403 ms. From requesting a single HTTP access request, the average time decreased from 520 ms to 74 ms. The financial data management edge computing platform designed in this paper can effectively manage financial data and make an effective contribution to enterprise data management. [ABSTRACT FROM AUTHOR]

Pre-insertion resistor (PIR) is an important component of circuit breaker. The healthiness of the circuit breaker (CB) depends upon that of PIR contact. With the increasing demand of uninterrupted power supply, failure in major equipment like CB may incur huge loss to the utility. The existing study reveals that mechanical failures are more dominant than other types of failures. Thus, many industries prefer to undertake preventive maintenance of equipment rather than break down maintenance. This paper describes the actual problem detected in PIR contact of North Eastern Regional Transmission System, having a 400 kV, 3000 A, SF6 CB through testing and rectification thereafter. The results obtained from the graph helped in detecting mechanical dis-alignment of the connecting link in the link arm and the tubular contact of the PIR. Furthermore, a model has been developed to show the possible threat that would have occurred, had the problem been not rectified. This paper will help the utilities to emphasize on detecting the problem by undergoing preventive maintenance and thereby saving unprecedented failure of equipment. [ABSTRACT FROM AUTHOR]

This paper peruses the Doppler-bearing passive target tracking (DBT), a task that is subject to the nonlinear measurement equations. These measurement equations form the target tracking a nontrivial duty. In order to defeat these problems, many studies have reported various nonlinear estimation techniques, such as sigma-point Kalman filters. These types of filters have demonstrated superior performances for nonlinear system parameter estimation. Consequently, the main contribution of this paper is to improve the performance of the sigma-point Kalman filters. Thereafter, this paper introduces a transformed unscented simplex cubature Kalman filter, in which adaptation techniques are utilized to reduce the dependency of the proposed filter performance on the priori statistical information of the process and measurement noises. Moreover, the proposed filter is initialized by the range-parameterised Unscented Kalman filter. The Cramér–Rao lower bound (CRLB) determines the best accessible accuracy of estimators, which is utilized in this paper to accredit the performance of the proposed filter. The validity and feasibility of the proposed filter are confirmed by Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Recently, telesurgery, as a branch of telemedicine, is growing at great speed. In telesurgery, a remote surgeon can communicate with operating room through communication networks in order to perform remote operation. Because human life is at stake in telesurgery, it is necessary to implement appropriate Traffic Engineering (TE) techniques to manage and route telesurgical data traffics over communication networks. Implementing proper TE technique can increase Quality of Experience (QoE) for the remote surgeon, and as a result, it increases the accuracy and performance of telesurgery. In recent years, the advent of Software Defined Network (SDN) has facilitated network providers to develop efficient TE methods to guarantee QoE for their customer applications. In this paper, a novel multi-objective TE technique called SMOTE is proposed to guarantee QoE for telesurgery applications over SDN. The goal of this paper is to enable a network administrator to provide a satisfactory remote operation by providing QoE for the remote surgeon. The objectives, which are considered in our proposed method, are the minimization of end-to-end delay and packet loss. SMOTE uses Non-dominated Sorting Genetic Algorithm (NSGA-II) a well-known multi-objective algorithm, to solve the optimization model for calculating optimal routes between the remote surgeon and the operating room. We conducted comprehensive simulations to evaluate the performance of SMOTE in terms of QoE. Simulation results confirmed that SMOTE guarantees QoE in telesurgery. [ABSTRACT FROM AUTHOR]

The segmentation of Glioma tumor regions in brain Magnetic Resonance Imaging (MRI) image, using Convolutional Neural Networks (CNN) classification method, is proposed in this paper. The adaptive histogram equalization method is applied on the brain MRI image for enhancing the abnormal pixels with respect to surrounding pixels. This enhanced brain image is transformed into multidirectional scaling image using Gabor transform. Then, features are extracted from this multidirectional scaling image and then these features are trained and classified using CNN deep learning algorithm in order to differentiate the Glioma from normal brain MRI image. Finally, the tumor regions are segmented using morphological operations. The proposed Glioma brain tumor segmentation method using CNN classification approach obtains 96.9% of sensitivity, 99.3% of specificity and 99.2% of accuracy. [ABSTRACT FROM AUTHOR]

Several metamaterial antenna configurations have been proposed in the literature for gain and bandwidth enhancement. However, these antennas are rarely used in commercial applications due to complexity in their design and fabrication process. In this paper, different configurations of electromagnetically coupled microstrip antennas and arrays are proposed as an alternative antenna design for gain and bandwidth enhancement. A comparison is made between the configurations proposed in this paper and the metamaterial configurations reported in the literature. The proposed configurations have better gain and bandwidth than the metamaterial configurations within the smaller volume. [ABSTRACT FROM AUTHOR]

Simulation and emulation of electric motor faults are particularly useful to study, test, and evaluate the motor condition monitoring products based on the motor current signature and vibration analysis. Many researchers have proposed and developed several DAQ & computer-based simulation tools. This paper aims to develop a system to simulate and emulate the maximum types of faults which can occur in a three-phase induction motor. In this paper, an embedded system based induction motor fault simulator and the emulator is proposed and developed. The focus of this paper is to model the induction motor faults mathematically based on the motor current signature and vibration frequency spectrum, which are theoretically derived and proved by the early generation researchers and engineers. The novelty of the proposed approach is a cost-effective, portable, and handy tool for field evaluation of the motor condition monitoring products. This paper presents the hardware and software development for making a stand-alone motor faults simulator and emulator. The system employs a powerful high-end and powerful embedded system INTEL ATOM Minnow Board, which is built around E3826, Dual Core, and 1.46 GHz processor. A dedicated 4-channel Direct Digital Synthesizer (DDS) with 12-bit resolution along with software generates all three-phase induction motor faults for both MCSA and vibration analysis. The simulated and emulated faults signals are compared and evaluated against the Standards with the help of a digital storage oscilloscope. [ABSTRACT FROM AUTHOR]

The September 2024 issue of the IETE Journal of Research contains forty papers covering various areas such as Communications, Electromagnetics, Computers and Computing, Electronic Circuits, Devices and Components, Instrumentation and Measurement, Medical Electronics, Power Electronics, and Opto-electronics. The papers discuss topics ranging from malware detection in 5G and IoT networks to the development of smart systems for antimicrobial susceptibility tests. Authors from diverse backgrounds contribute to research on advanced technologies and applications in the field of electronics and telecommunications. [Extracted from the article]

Generally, 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]

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]

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]

The white blood cell (WBC) plays an instrumental role in protecting the human body from different infectious diseases and unknown biological invaders. Their count and cellular morphology drastically change when the body suffers from any such disease or abnormal conditions. This paper demonstrates the detection of five types of WBC present in human blood smear images. Several authors proposed WBC detection using only one color space. But this paper addresses WBC-type detection using four popular color spaces – RGB (red, green, and blue), HSV (hue, saturation, and intensity), YCbCr (luminance, chroma blue, and chroma red), and L*a*b* (luminosity, chromaticity layer "a*", and chromaticity layer "b*"). The proposed methodology involves color thresholding, K-means clustering, nuclear region analysis, and the implementation of a feedforward neural network. The unsupervised machine learning algorithm, K-means clustering is used to segment nuclei from the blood smear images. The detection of WBC-type using four color spaces is compared in terms of accuracy, sensitivity, specificity, and positive predictive value. It is observed that HSV-based WBC-type detection methodology outperforms methodologies based on RGB, YCbCr, and L*a*b* color spaces. The same methodology mentioned above is then compared with the works proposed by other authors. [ABSTRACT FROM AUTHOR]

This paper analytically investigates both the air gap flux density and the generated power of an Axial Flux Permanent Magnet Vernier Motor (AFPMVM). The AFPMVM has the same operational principle as a magnetic gear. It operates using space harmonics of the air gap flux density due to the magneto-motive force (MMF) and air gap permeance. This paper also examines the effect of various combinations of the rotor magnet pole pairs, stator slots, and winding pole numbers on the power factor and power density using the 3-D finite element method (3D-FEM). It is observed that, although having a higher pole ratio results in low cogging torque and high power density, it makes the machine length longer and has a lower power factor. Therefore, by employing the high pole ratios, the main advantage of the axial flux machines, i.e. having a shorter length, is sacrificed. [ABSTRACT FROM AUTHOR]

The increased short-channel effects in the ultra-nanoscale regime limit the operation of complementary metal oxide semiconductor (CMOS) technology. To combat the issues in the ultra-nanoscale regime, fin-shaped field effect transistor (FinFET) technology is the best choice because of its numerous advantages over CMOS technology. FinFET enhances the further scaling of the devices and control over the channel. Therefore, this research paper presents the implementation of a low leakage 4:2 compressor in FinFET technology. A compressor is a general block, especially used in multiplier units and hence in digital signal processing (DSP) systems. The design of the low power compressor improves the overall efficiency of the DSP systems. An extensive study of the proposed low power 4:2 compressor at 7 nm technology node is done in the paper. Mentor Graphics tools are used for the circuit simulations and analyzes. The key parameters, such as leakage power, delay, power delay product (PDP), and the process voltage temperature (PVT) variations, are estimated for the performance evaluation. The proposed 4:2 compressor saves 87.33% of leakage power and is 75.90% more energy efficient compared to its conventional counterpart. [ABSTRACT FROM AUTHOR]

Pulse compression techniques enable the radar designers to transmit long duration pulse to cater the need of high power at the transmitter and achieve the range resolution of short duration pulse at the radar receiver simultaneously. Due to easy generation and processing, binary codes are widely used as pulse compression codes in radars, sonars and spread-spectrum communication systems. But binary codes have high unwanted pulse compression sidelobes at the output of pulse compression receivers. In this paper, optimum binary codes reported in the literatures are overlaid on Discrete frequency sequences to reduce unwanted peaks sidelobes of optimum binary codes. The proposed approach not only reduced peak sidelobes of optimum binary codes but also increased the pulse compression ratio. In this paper, optimum discrete frequency sequences for overlaying with binary codes are optimized using the Modified Simulated Annealing Algorithm (MSAA). A Discrete Frequency Coded binary signal has a complex signal structure which will be difficult to detect and analyze by enemy Electronics Support Measures (ESM). In this paper, a unified adaptive radar model is also proposed to cater the needs of today's radar signal design requirements. The proposed model will help to improve range resolution, the detection capability of radar, reduce the peak sidelobes significantly, and also improve the ESM capability of radar systems. [ABSTRACT FROM AUTHOR]

In this paper, the electrical design of an RF quartz window for a Ka-band Gyro-TWT amplifier has been carried out using CST microwave studio. The return loss (S11) and insertion loss (S12) of the RF quartz window have been −35.3 dB and −0.02 dB, respectively. The thermal design of the RF quartz window for Gyro-TWT during extreme cases of operation, i.e. at saturation has been carried out using ANSYS software and as discussed in the paper. The temperature 368 K on the disc of the RF window for Gyro-TWT has been obtained. The RF quartz window for the Ka-band Gyro-TWT amplifier has been developed. The cold characterization of the RF quartz window for the Ka-band Gyro-TWT amplifier has also been carried out using a Vector Network Analyzer. A good agreement between simulation and measurement has been obtained. [ABSTRACT FROM AUTHOR]

The number of DGs located at distribution site, which uses renewable energy sources, forms a Micro-grid system. The problem of using DG with the utility is unplanned islanding. This paper has focused on passive islanding detection method (IDM) which is easy for implementation, cheaper and proficient. The suggested method tried to eradicate the disadvantage of passive conventional IDM. Low impedance grid always immersed small deviation of inverter output in the grid-connected mode, but whenever the grid is disconnected (i.e. in islanding condition) this stability effect of the grid is absent, which is picked up for islanding detection in the proposed method. In this paper, voltage ripple-based passive IDM is evaluated and tested under various operating conditions. The proposed method is easily working on condition like generated power and load power are the same and it was found that technique is robust, without NDZ and can detect islanding within 0.3 sec. Also the proposed method is able to differentiate islanding and non-islanding scenarios, which is helpful to stop unnecessary malfunctioning of DGs. [ABSTRACT FROM AUTHOR]

Mission-critical IT systems are utilizing closed source software (CSS) mainly due to reasons related to "quality assurance" and "continued support" despite much better benefits of using Open Source Software (OSS). OSS permits users to access source code for assessment, amendment, and redistribution, which offers low dependency on a vendor without license or maintenance cost. This paper investigates and analyzes OSS adoption factors for "critical IT infrastructure" by conducting a comprehensive review of the relevant literature. Furthermore, this paper proposes a framework that can help the critical IT industry to have increased confidence in OSS. The proposed framework utilizes the organizing logic of the Technology, Organization, and Environment (TOE) framework, recommends factors that were recognized by critically scrutinizing the studies found in the available literature. To validate the framework, a questionnaire-based survey was conducted targeting IT experts in critical sectors. The data integrity of survey results was analyzed using Cronbach's alpha. Framework factors were validated statistically using one sample T-test. The outcome indicated that the factors were statistically significant as the p-value was less than 0.05 for all the factors. [ABSTRACT FROM AUTHOR]

This paper proposes a computer-aided methodology for detecting and segmenting the tumor regions in ultrasound thyroid images using machine and deep learning algorithms. This proposed tumor detection methodology uses Kirsch's edge detector for enhancing the edge region pixels in thyroid image and then Dual Tree Contourlet Transform (DTCT) was applied on the enhanced image for obtaining the coefficients. Then, features are computed from this transformed thyroid image and these features are trained and classified using the Co-Active Adaptive Neuro Expert System (CANFES) classifier. Then, the morphological segmentation method is applied on the abnormal thyroid image to segment the tumor regions. Finally, the Convolutional Neural Network (CNN) algorithm is applied on the segmented tumor regions for diagnosing them into mild, moderate and severe. [ABSTRACT FROM AUTHOR]

This paper aims to obtain the overall equations of the yearly, seasonal, monthly and daily optimum tilt angles worldwide. So, the daily optimum tilt angle variations are examined for 337 locations in the northern hemisphere and 247 locations in the southern hemisphere, and it is shown that the daily optimum tilt angle changes in cosine form over a year. Then, the daily optimum tilt angle is estimated using a cosine function. The daily optimum tilt angle is independent of the longitude and can be estimated using a function that depends on the latitude and the number of days. The resulting model can be used to calculate the optimum tilt angles in desired intervals. In order to verify the validity of the results, the results of the proposed model are compared with the results of the other papers. It is shown that the presented model can calculate the optimum tilt angles with acceptable accuracy in the southern and northern hemispheres. [ABSTRACT FROM AUTHOR]

The GTEM cell is a new version of the modified transmission line, operated at very high-frequency test equipment for EMI or EMC measurement. The equipment is designed for an electromagnetic compatibility, immunity and radiated emissions test. In this paper, GTEM is designed using ANSYS HFSS 13.0 and fabricated for 1 to 3.9 GHz frequency. This paper focuses mainly on the novel validation test for the manufactured GTEM cell characterization without using costly Vector Network Analyzer. The novel test set up comprises of GTEM cell, transmitter and the receiver is designed and tuned at 2.4 GHz. Using this set up field uniformity is checked for fabricated GTEM ranges within +/− 6dB. This makes a compact and cost-effective novel test validation set up for the designed, developed and manufactured GTEM for 1 to 3.9 GHz. [ABSTRACT FROM AUTHOR]