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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]

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]

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]

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]

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]

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]

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]

This study investigated technologies related to a smart-card display and its wireless charging system, where the wireless receiver in the smart card receives not only the data but also the power required for the display circuitry. The goal of this research was to achieve the required energy transfer during the extremely short reading time when a person swipes his or her smart card. This paper describes the design considerations and the analysis procedures of the transmitter and receiver pair for an electronic paper (E-paper) display. A series of magnetic simulations as well as experimental tests were conducted to investigate the various design factors. The design considerations included the shape and effect of using ferromagnetic cores, the component design for frequency tuning, the geometric arrangements, and the number of turns of the coils. A novel design for power transmission is then proposed using a ferromagnetic core to enhance its energy efficiency. In experiments the transmitter unit provided 250 mW of power to the transmitter antenna. The resultant transmission efficiency (S21) was −5.1 dB over a distance of 4.3 mm. Experimental results show that the smart card receives 220 mW, representing a high transmission efficiency of 88%. [ABSTRACT FROM PUBLISHER]

Energy-efficient designs are the need of the hour for signal and image processing applications that are error-tolerant. The paper proposes a new 4:1 approximate arithmetic-based compressor developed by analyzing the probability-based tactics that have a higher degree of precision when implemented. Approximate $ n \times n $ n×n multiplier is designed using the proposed varying probability-based compressors. To validate the effectiveness of the new compressor, a $ 16 \times 16 $ 16×16 multiplier is designed. The $ n \times n $ n×n bit multiplier will have $ 2n $ 2n columns of partial products(PP), in which the least significant n−1 columns are designed using the novel approximate 4:1 compressor and most n + 1 columns are designed with the exact compressor. The results of the 16-bit multiplier are superior to the performance of the exact multiplier in terms of power, area, power-delay-product (PDP), and area-delay-product (ADP) typically by $ 39\% $ 39%, $ 20\% $ 20%, $ 44\% $ 44%, and $ 26\% $ 26% respectively for Design 1 and $ 14\% $ 14%, $ 24\% $ 24%, $ 30\% $ 30%, and $ 21\% $ 21%, respectively for Design 2. The Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE), and Structural Similarity Index Measure(SSIM) for the output images processed by new approximate multipliers are acceptable when compared with precision and energy. [ABSTRACT FROM AUTHOR]

This paper presents the design and analysis of an enhanced tunnel field-effect transistor (TFET) structure. The proposed structure comprises a dual source and a double gate, aiming to enhance the DC characteristics of the TFET. The dual source concept is used in the structure with two homogeneous gates, which play a major role in boosting the tunneling parameters. It also examines the affectability of source dimensions and drain doping on the performance of the proposed device. Furthermore, we have compared our proposed structure with the single source device. The proposed device after optimization exhibits an extraordinary ION/IOFF ratio of 2.33 × 1013, with an IOFF of 6.25 × 10−19A/µm, and decent Ion of 1.46 × 10−5A/µm along with a subthreshold swing (SS) of 43 mV/dec. The device characteristics have been analyzed in the presence of interface traps. The electrical parameters have been quantified against various types and concentrations of traps. The performance of the proposed device has been compared with the existing TFET structures. [ABSTRACT FROM AUTHOR]

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes. [ABSTRACT FROM AUTHOR]

In the current communication era, 2G voice services coexist with higher generation mobile network voice services like Voice over Long Term Evolution (VoLTE) in 4G and Voice over New Radio (VoNR) in 5G. Subscribers registered in the 5G network, but their voice option is in 4G so voice calls can be handled through the Evolved Packet System (EPS) Fallback or Radio access technology (RAT) Fallback. Similarly, subscribers register in a 4G network, but their voice option is in 3G or 2G so voice calls can be handled through Circuit Switched Fallback (CSFB). To provide uninterrupted voice service in such wireless networks usually handoff calls (HCs) receive higher priority than new calls (NCs). So, some guard radio resources (i.e., channels in 2G or RBs in 4G or 5G) may be reserved in each cell to handle this type of HCs. If more channels are reserved for HCs, then the handoff call-dropping probability (HCDP) will be decreased and at that time the new call-blocking probability (NCBP) will be increased. So, by considering the target HCDP if channels are reserved for HC, then HCDP will remain below the target and NCBP will be minimum. In this paper first, we proposed an optimal channel reservation (OCR) policy that reserves guard channels according to the given target HCDP. Second, we compared our OCR policy with fractional channel (FC), limited fractional channel (LFC), and uniform fractional channel (UFC) policies. Finally, we observed the performance of all these policies considering the Global System for the Mobile Communication (GSM) network. [ABSTRACT FROM AUTHOR]

Sparse Ramanujan Sequences (SRS) are periodic series derived from Ramanujan Sums (RS). RS is an exponential summation used to derive infinite series expansions for arithmetic functions, whose application pervades different fields of study from classical mathematics to digital signal processing. SRS has properties similar to RS such as periodicity and orthogonality. SRS transforms allow the sparse representation of signals, hence they have many potential applications, such as denoising and compression. The application of SRS transform to reduce the Peak to Average Power Ratio (PAPR) in Orthogonal Frequency Division Multiplexing (OFDM) is proposed in this paper. The Complementary Cumulative Distribution Function (CCDF) and Bit Error Rate (BER) are used to assess the PAPR reduction performance of the proposed system. The results show that the proposed SRS transform-based OFDM system significantly reduces PAPR and BER with reduced computational complexity in comparison with the existing systems. [ABSTRACT FROM AUTHOR]

In modern telecommunication, there is a growing demand for lightweight, low-cost, and compact microwave components with high transmission functionalities, especially for applications requiring multiple high-performance antennas to provide multi-beam capabilities. Additive manufacturing (AM), also known as three-dimensional (3D) printing, has emerged as a promising solution to meet these demands due to its simplicity, fast prototyping capabilities, and enhanced fabrication accuracy. AM technology has found widespread adoption across various industries, including space-borne, satellite, handheld wireless systems, biomedical, mechanical, electrical, optical, and environmental applications. In particular, AM-based microwave components offer significant advantages, including reduced weight compared to traditional aluminum prototypes, with savings of up to two-thirds of the weight. This paper presents a comprehensive review of the AM process, materials used, and microwave components developed using AM technology. The review encompasses various aspects, including fabrication techniques, performance characteristics, and applications of AM-based microwave components, providing insights into the advancements, and potential future directions of this innovative approach. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and deep learning (DL) are used in numerous fields, particularly to develop effective intrusion detection systems (IDS). Existing wireless network IDS, which rely on a single ML algorithm and have limitations. These include a high rate of false positives, difficulties in recognizing distinct attack patterns, and a high acquisition cost for annotated training datasets. However, hostile threats are always evolving, networks need a smart security solution. In comparison to other ML approaches, DL algorithms are more successful in intrusion detection. This paper presents a DL based ensemble model that combines Multi-verse through Chaotic Atom Search Optimization (MCA) for preprocessing, which eliminates unsolicited/recurrent information in the dataset. The process of optimized feature selection uses Principal Component Analysis (PCA), Chaotic Manta-ray Foraging Optimizations (CMFO), and a grounded grouping method to partition the optimized feature dataset into k-diverse clusters. The recommended model then stacks Support Vector Machine (SVM) as the ensemble model’s meta-learner classifier, pre-training the hybrid DL prototypes using the optimized feature dataset cluster. The CNN-LSTM and CNN-GRU models, which integrate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), are the hybrid DL prototype’s key components. The suggested model’s performance has been enhanced and compared to six ML techniques: NB, SVM, J48, RF, MLP, and kNN models, utilizing measures such as accuracy, precision, recall, and F-measure. The public can access the Aegean Wi-Fi Intrusion Dataset (AWID) which is used for evaluating the recommended model and is outperformed the contemporary models in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes. [ABSTRACT FROM AUTHOR]

Gait design for walking biped robots, that can preserve stability against a known range of disturbances, is very important in real applications. Designing an exponentially stable walking gait with desired features for biped robots has been recently done by an online reinforcement learning method. However, the designed gait might not be robust enough against disturbances. In this paper, we extend a robust version of the method against modeling errors/disturbances. It is done by minimizing the costs of worst rollouts which are generated in the presence of different modeling errors/disturbances. The proposed method's ability to adapt the controller is studied for some robust applications. The simulation shows that the resulted gaits are exponentially stable and robust against modeling errors/disturbances in a feasible range. [ABSTRACT FROM AUTHOR]

In wireless communication systems, the information is transferred as digital symbols these symbols are affected by noise interference while transmitting through the wireless channel. In today’s world, the increased demand for rapid data transmission rates leads to more inter-symbol interference in the received signal. To diminish the effects of inter-symbol interference adaptive channel equalization is utilized in digital communication. In this paper, the inter-symbol interference effect in the finite impulse response channel is reduced in terms of an adaptive equalizer method. The weights/coefficients of the equalizer are optimized through a tunicate swarm algorithm. Channel equalization involves optimizing the channel coefficients to mitigate the impact of inter-symbol interference. This equalization process can be viewed as an iterative optimization task, aimed at lessening the mean square error among the transmitted signal and the equalizer’s output. The implementation of the proposed method is executed using MATLAB software. We assess its effectiveness through a comparative analysis, considering metrics such as mean square error, learning rate, bit error rate, convergence rate, and computational time. Furthermore, we conduct a comparative evaluation with other optimization approaches, including the Bat Algorithm, Slime Mould Algorithm, and Harris Hawks Optimization Algorithm. This comparison demonstrates the superiority of the proposed algorithm over traditional optimization techniques. [ABSTRACT FROM AUTHOR]

In this paper, a five-level inverter-fed Model Predictive Current Control (MPCC) of Five-Phase Permanent Magnet Synchronous Motor (PMSM) Drive is proposed to enhance the performance of the drive by reducing the torque ripples. The MPCC method provides a good steady-state performance and also this method does not require a weighting factor. Among the various torque ripple minimization techniques, the multilevel inverters attracted more attention in electric drives due to rapid development in semiconductor technology. The performance analysis of a five-level (5L) inverter-fed five-phase PMSM drive is compared with two-level (2L) and three-level (3L) inverter drives under various operational conditions. The effectiveness of the proposed method is analyzed using MATLAB/SIMULINK software and also implemented using a real-time simulator. [ABSTRACT FROM AUTHOR]

The injury of blood vessels in brain tissues known as intracranial haemorrhage (ICH) is a major cause of stroke. Computed tomography (CT) and X-ray images are frequently used to estimate the location and size of haemorrhages. Radiologists segment CT scan pictures manually using planimetry, which takes a lot of time. To overcome this issue, an optimal ensemble transfer learning espoused detection and classification of intracranial haemorrhage (CIH-V16-V19-R50-DN121-IV3-XN) is proposed in this paper. Initially, the images are gathered via the Radiological Society of North America (RSNA) ICH dataset. Afterwards, the images are fed to pre-processing. The pre-processed output is fed into a classification segment. The model used for classification is the ensemble transfer learning model such as VGG16, VGG19, Resnet50, InceptionV3, Desnet121, and XceptionNet. With the help of ensemble transfer learning techniques, the image is classified into subdural haemorrhage, epidural haemorrhage, subarachnoidal haemorrhage, intraventricular haemorrhage, intraparenchymal haemorrhage, and normal. With the help of voting classifier the best learning model with accuracy is identified. The proposed CIH-V16-V19-R50-DN121-IV3-XN algorithm is implemented in MATLAB using dataset of the RSNA ICH dataset. The performance metrics, such as accuracy, precision, sensitivity, F1-score, specificity, ROC, and error rate, are examined to analyse the efficiency of the proposed method. The performance of the CIH-V16-V19-R50-DN121-IV3-XN approach attains 10.12%, 22.33%, 21.45% high accuracy and 10.09%, 13.65%, 29.87% high specificity compared to the existing methods. [ABSTRACT FROM AUTHOR]

In this paper, small-signal model parameters and RF characteristics of the GaN-based Dual-Metal Cylindrical Surrounding Gate-Junctionless Accumulation Mode (DM-CSG-JAM) MOSFET has been extracted. Better scattering parameters has been observed for GaN-based DM-CSG-JAM MOSFET over other analogous structures which confirms its applications for RF domain. Also, enhanced drain current, transconductance, cut-off frequency, Ion/Ioff ratio, etc. have been observed for the GaN-based device. The performance of the GaN-based MOSFET has also been compared with the other compound semiconductors (GaAs, InP) and silicon-based DM-CSG-JAM MOSFET. On account of the eminent properties possessed by GaN, the GaN-based device shows outstanding characteristics over the other semiconductor-based device. [ABSTRACT FROM AUTHOR]

Environmental pollution and the rapid decline in the availability of fossil fuels are the major concerns of the electric power industry in recent years. Effective utilization of electrical energy is the need of the hour in building the nation towards sustainable development. This study presents the energy consumption analysis and management performed in a diary firm located in Tamil Nadu, India. Initially, the energy usage of the firm is estimated based on its electricity bills, meter readings, and survey followed by detailed energy consumption analysis and evaluation. The study employs guidelines provided by the Indian Bureau of Energy Efficiency for energy management. The detailed analysis performed finds scopes for improvement in efficient energy usage. Numerical analysis shows that implementation of proposed action plans that include installation of automatic power factor correction, replacement of overheating equipment, and replacement of lighting systems with energy-efficient lighting would save annual energy of 123.96 kWh which in turn results in the reduction of annual CO2 emission by 117.76 tonnes. Furthermore, this paper also proposes a novel optimal process scheduling which optimally schedules different processes in the firm without affecting productivity. This makes an additional savings of INR 4,23,360 per annum in the energy cost by minimizing the maximum demand of the firm with zero investment. A comparison of the proposed approach with existing methodologies available in the literature shows the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Based on the principle of spatial power combination of sparse array time reversed signals, the relationship of distances between constructive interference points to cross angle of beams and viewing angles has been analyzed. Then the formulas of distances between adjacent constructive points at 360-degree views are derived. Considering that the distance between constructive interference points around target is frequently needed to be less than target’s antenna size, The variation law of the view range with beam crossover angle has been simulated and analyzed with MATLAB when the above conditions are met at the same frequency and without errors. On this basis, array deployment recommendations are summed up. In addition, the effects of the received time window length and the difference of the distance between each array element and the target point on the synthesized power have been analyzed. Finally, the spatial power combination simulation experiments of the sparse array time-reversed signal have been carried out. The experimental results show that the methods and conclusions of this paper can provide theoretical basis and reference for the research and application of sparse array power combination technology in long-range wireless energy delivery, high-power microwave weapons, electronic warfare, etc. [ABSTRACT FROM AUTHOR]

Accurately identifying all objects of interest inside the specified frame of reference is crucial for object identification techniques to allow machine vision to interpret images successfully. Theoretical frameworks from computer vision and deep learning have informed many potential solutions to this problem. However, current approaches frequently fail when faced with objects going through random geometric changes and continually show shortcomings when identifying small, dense objects. This research looks at state-of-the-art object detection methods, compares them, and then suggests a convolutional object detection network that has been tweaked to fix the problems with the existing methods. Comparing our research to existing approaches, we find that they perform better. We accomplish this by training deep convolutional networks to detect geometric transformations and adjusting the networks to handle multi-scaled features. The results achieved after the optimization of the You only look once (YOLO) V5 & V7 are stated in this paper. The experiments demonstrated that YOLO v8 achieved higher accuracy than the hyper-tuned YOLO V5 and V7, with an average Precision (mAP) score of 52.8% on the customized military dataset running at 60 Frames Per Second (FPS). Object detection using deep neural networks (DNNs) poses a significant challenge due to the high computational and power requirements, mainly when deployed on general-purpose platforms such as CPUs and GPUs. However, addressing these limitations becomes crucial for efficient edge computing tasks like object detection, where faster, smaller, and energy-efficient solutions are essential. To overcome these challenges, system-on-chip (SoC) designs emerge as a promising solution. [ABSTRACT FROM AUTHOR]

Network security faces challenges, including reduced true positives, increased false positives, and inadequate anomaly detection efficacy. This paper introduces the DeepRoughNetID (DRNID) approach to address these issues and confront the increasing cyberattack threat in modern communication systems. DRNID presents an innovative framework for network intrusion detection, incorporating kNNImputer, GreedyRoughSelector, DeepVAEEnsembler, and IntrusionNet components. kNNImputer enables adaptable data preprocessing by leveraging instance-based learning, facilitating efficient handling of evolving datasets without necessitating full retraining. GreedyRoughSelector enhances classifier performance through systematic attribute selection, focusing on relevant features while eliminating redundancies. DeepVAEEnsembler leverages Variational Autoencoders (VAEs) to learn underlying data distributions, enabling robust anomaly detection. IntrusionNet utilizes VAEs to classify intrusions, providing a comprehensive solution to network security challenges. By integrating these components, DRNID offers a refined approach that promises significant advancements in network intrusion detection. The experimental results demonstrate the effectiveness of DRNID, with an impressive accuracy of 98.8% achieved through 10-fold cross-validation. DRNID exhibits superior performance compared to existing methods across various datasets, showcasing its robust anomaly detection capabilities. This methodology empowers organizations to proactively prevent security violations and safeguard sensitive data from malicious entities. Beyond its theoretical contributions, our research carries tangible implications for strengthening cybersecurity defenses across diverse sectors, including telecommunications, finance, and healthcare. [ABSTRACT FROM AUTHOR]

For energy and cost-efficient computing, many desktops and embedded computing devices these days use voltage frequency islands (VFIs) based processors. An island in VFIs consists of multiple homogeneous cores; however, multiple islands are generally heterogeneous in nature. In contrary to architectures (non-VFI) with per-core dynamic voltage frequency scaling (DVFS), in VFI, all the cores in an island run on the same voltage/frequency at the same time. The energy-aware scheduling of task graphs on VFI architectures is challenging and different from non-VFI architectures in which task-based DVFS is possible. However, in VFI scheduling, the time slot for which an island runs on a particular voltage will be decided. In this paper, we analyse 20 different scheduling heuristics for VFI architectures by varying the size of this time slot based on the workload properties and also by varying the voltage/frequency of the time slots. We also propose a heuristic OptSlotVFI to utilize all the slots optimally. The results show that the VFI scheduling heuristics are able to generate schedules which improve energy consumption up to 25% with an equivalent or shorter schedule length than the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, a novel master-slave flip-flop is designed that incorporates 15 transistors and a single-phase clock, employing topological and adaptive coupling methods. The proposed flip-flop circuit (PFC) is more efficient than other logic-structured flip-flops. PFC is designed using a 45 nm Complementary Metal Oxide Semiconductor (CMOS) technology node. Notably, the PFC exhibits an impressive 34.18%, 42.92%, 39.71%, 44.39%, and 46.91% improvement in average power consumption compared to the adaptive data track flip-flop (ADTFF), Hybrid Flip-Flop (HFF),18-transistor single-phase clocked (18TSPC), Logic Structured Reduction Flip-Flop (LSRFF), and Topologically compressed flip-flop (TCFF). It also improved the C to Q delay, power delay product (PDP). Monte Carlo simulations of average power and C to Q delay have been performed for 1000 samples. By reducing the number of PMOS transistors, the total area of the PFC is minimized. PFC operates effectively within a clock frequency range of up to 1 GHz. [ABSTRACT FROM AUTHOR]

The paper introduces a new approach for designing fractional-order inverse Chebyshev low-pass filters (FOICLF), with an emphasis on filters with an order of 1+α. The coefficients for these filters are determined through Gray Wolf Optimization by approximating filter response with the ideal response of same order FOICLF. Stability tests are conducted, and filters are implemented using Multiple Input Single Output topology with commercial ICs. Comparative analysis reveals close proximity between the ideal response of FOICLF and the designed filters, featuring maximum and mean square errors of –22.48 dB and –61.07 dB, respectively, for 1.7 order. Results are benchmarked against existing literature, highlighting superior performance in stopband and passband magnitude errors and stopband attenuation. Process, Voltage, and Temperature analysis assesses the impact of the parameter variations on the –3 dB frequency, assuring the robustness of designed filters with relative standard deviations ranging from 0.003% to 3.013% for the 1.7 order filter. [ABSTRACT FROM AUTHOR]

Due to the reduction in transistor size, test engineers face a severe issue in power minimization. VLSI circuit's power dissipation has become a vital concern due to the switching activity of the Circuit Under Test (CUT). Switching activity is a significant factor in determining power consumption for CMOS VLSI circuits. In this paper, we formulate a distance estimation technique and reordering using the Tversky index. Here, the distance between the test vectors is calculated using the Tversky index with the formation of an adjacency matrix, and reordering is performed. The experimental results of the proposed method have been compared to different distance measures and existing techniques. It shows that the switching activity is reduced from 52.5% to 21.5%, and the average power reduction is from 42% to 14.6% when the distance is estimated on reordering using the Tversky index measure. Thus, the Tversky index distance measure performs better than the Hamming, gray, and Linear feedback shift register (LFSR) methods. Switching activity estimation for complex ISCAS circuits has been performed and compared with existing methods for superior performance. We have also developed a Built in Self-Test (BIST) for the C17 benchmark circuit, which uses the Tversky counter as a test pattern generator using the TANNER EDA tool. Thus, an efficient deterministic BIST for the C17 benchmark circuit was designed to detect all the stuck-at faults, and the same procedure can be used to create BIST for all CUT and precomputed test sets for real-time field testing. [ABSTRACT FROM AUTHOR]

Optimizing downlink coordinated multipoint (CoMP) performance through advanced scheduling algorithms enhances V2X communication technology, enabling efficient resource allocation, minimizing interference, and maximizing data rates for reliable and synchronized communication between vehicles and infrastructure. In this paper, several scheduling algorithms were compared, including support vector machine (SVM) linear, SVM Radial Basis Function (RBF), SVM Sigmoid, Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Graph Convolutional Networks (GCN). The performance metrics used in this comparison included CoMP decision, throughput, and cell edge throughput. The results showed that the out-rated GCN algorithm had the best-triggering composition for 5G radio networks, outperforming the other algorithms in terms of CoMP decision accuracy and overall throughput. In particular, the GCN algorithm demonstrated significant improvements in cell edge throughput, which is critical for ensuring reliable communication in areas with weaker signal strength. The reported results proves that the integration of advanced scheduling algorithms in the downlink CoMP framework enhances the efficiency of V2X communication, enabling optimized resource allocation, interference mitigation, and maximized throughput, thereby improving system efficiency, reducing latency, and ensuring reliable and seamless information exchange for connected vehicles, smart cities, and industrial automation. [ABSTRACT FROM AUTHOR]

Cybersecurity threat detection in the Internet of Things (IoT) identifies and mitigates risks associated with connected devices. The IoT devices are vulnerable to attacks as they do not contain any robust security mechanism. Traditional methods for cybersecurity-based threat detection face significant problems such as scalability, privacy concerns, and resource constraints when applied to the dynamic IoT environment. To tackle these challenges, this paper proposed a novel Weighted Variational Autoencoder-based Hunter Prey Search (WVA-HPS) algorithm for enhancing cybersecurity threat detection in IoT. In this study, a weighted Variational Autoencoder is employed for regulating weight mechanism using weight regularization and the weight average ensemble method. The Hunter Prey Search optimization (HPSO) algorithm is utilized for minimizing overfitting issues to enhance the efficiency of the WVA method. The proposed WVA-HPS model comprises five different stages such as data collection, data preprocessing, threat detection, model evaluation, and output. The study is validated on diverse datasets namely BoT-IoT, MQTTset, and IoT-23. The WVA-HPS method's performance is analyzed using the metrics namely precision, accuracy, specificity, F-measure, and recall and its performance is compared with existing methods. The experimental results illustrate the performance of the WVA-HPS method for cybersecurity threat detection in an IoT environment. [ABSTRACT FROM AUTHOR]

This paper presents an ECG-based human identification that aims to increase the prediction accuracy with a minimized training dataset using a Block Dense CNN (BDCNN) architecture. This study specifically examines the effect of increasing the number of individuals, while decreasing the amount of training data on the performance of the network. Furthermore, the performance of the proposed network with multisession ECG recordings is also investigated. Comprehensive experimentation on nine ECG public databases that include normal and various abnormal ECG beats has been carried out. Performance of the network is evaluated using metrics such as prediction accuracy, false rejection ratio (FRR), false acceptance ratio (FAR), computational complexity, and training time, and the results are then compared to the results of a residual network. Experimental results show that, not only in a single session but also in multisession ECG recordings, the proposed network proficiently overbears the load of increased persons with decreased training dataset with about a 5.06% increase in identification rate as compared to its close counterpart. Therefore, the proposed BDCNN can be a potential framework for ECG-based human identification systems. [ABSTRACT FROM AUTHOR]

Considering the growing trend toward reducing the size of electronic devices, reducing power consumption has become one of the major concerns of circuit designers. One of the solutions to reduce power consumption is the design of reversible circuits. This paper proposed the design of a low-cost self-control serial adder system. In the proposed circuit, EPOE expressions have been used for the direct and compact design of sequential circuits to reduce the fixed inputs of sequential logic circuits. In addition, to reduce the quantum cost, common terms between the outputs are optimally used. Also, limiting the use of Tofoli gates with a size larger than three inputs in the implementation of the final circuit will reduce the quantum cost of the resulting circuit. The results and comparisons showed that the proposed design has improved efficiency measures including quantum cost, garbage outputs, and fixed inputs compared to existing works. [ABSTRACT FROM AUTHOR]

The impacts of nonlinearities from the I–V curve cause large oscillation and fluctuation occur in the PV attached DC voltage, BDC, load current, and solar current during low penetration of irradiance. PV system nonlinearities, weak damping, and lack of inertia causes lead to stability issues for DC microgrids. The stability of the system has been shifted to an unstable region during low penetration discussed for traditional controllers. In this paper, dual virtual series and parallel emulation with inertia feedforward controller have been introduced to mitigate fluctuations, initial DC notching, and damping issue for the DC microgrid system. This control method simulates the characteristics of the DC machine's robust and versatile method of DC/DC converter. The crossover frequency of the system has been reduced during low penetration with SPIEC which is not sufficient to prevent the oscillation and notching of the system. The system stability is enhanced through the proposed controller during low penetration analysed by pole-zero plots. During low penetration, the system pole has been shifted to a stable region towards the left side of the s-plane by the proposed controller to the traditional controller. The transient performance like oscillation, overshoot, and DC initial notching is resolved by the feed-forward current controller. However, the steady-state error is resolved by the voltage restoration controller. The proposed controller aims to enhance the power sharing among the energy storage and PV system under constant/variable loads. The oscillation, overshoot, and DC initial notching of the energy storage and PV have been discussed and made fast dynamic response by the proposed controller. [ABSTRACT FROM AUTHOR]

In the domain of cloud manufacturing, diverse services and the escalating demand for efficient and cost-effective service composition selection in cloud manufacturing have spurred day by day. Traditional methods face several challenges such as the risk of convergence issues and suboptimal solutions, high dimensionality of decision space, increased computational complexity, and scalability issues. To overcome these complexities, this paper proposed a novel method named the Deep Neural Network (DNN) based Hybrid Many Objective Leopard Seal (HMOLS) algorithm. In this study, DNN is employed to predict future Quality of Service (QoS) for each service based on historical data and integrated features. These predictions are fed back into the optimization model, influencing the dynamic weight calculation for each objective function. In this work, the Leopard Seal Optimization is implemented for hyperparameter optimization to enhance the performance of the HMOLS algorithm. Various evaluation metrics namely accuracy, throughput, reliability, resource utilization, and scalability are utilized to evaluate the performance of the HMOLS method and compare its performance with existing methods. The experimental outcomes depict the effectiveness of the HMOLS approach for the effective selection of services and fusion for cloud manufacturing. DNN-based predictions utilize deep neural networks to predict resource demand or utilization patterns and enable more accurate allocation decisions. These predictions optimize resource allocation by reallocating resources accordingly and address the challenges of resource utilization. Meanwhile, this method is required to overcome the computational overhead for applicability in cloud manufacturing. [ABSTRACT FROM AUTHOR]

Plasmonics-based sensors have grown more popular in the biosensing sector because of their label-free detection feature, which not only makes the sensor easier to use but also eliminates the need for numerous antibodies, which is a requirement for enzyme-linked immunosorbent assays. In this paper, a plasmonic-based refractive index sensor is proposed consisting of a linear rectangular waveguide coupled with a semi-ring arc and a microring resonator on both sides separated by a coupling distance. The Finite Difference Time Domain method is used to study the transmittance characteristics of the sensor. The transmittance curve displays Quintuple Fano resonances, which show sensitivity of 1000, 1184, 1290, 1914, and 2372.1 nm/RIU at 1.037, 1.211, 1.323, 2.051, and 2.383 μm, respectively. Further investigation is carried out by varying structural and geometrical parameters, such as the microring resonator’s radius and the coupling distance between the semi-ring arc and the microring resonator, which may arise due to fabrication intricacies. The performance of the sensor is investigated using the Figure of Merit, Q factor, and Detection limit producing a value of 30.41 RIU−1, 30.2, and 0.0328, respectively. The proposed sensor is further examined for its potential use in the detection of HSV, influenza A, HIV, and M13 Bacteriophage viruses. [ABSTRACT FROM AUTHOR]

In constrained IoT networks, Stateless Address Autoconfiguration (SLAAC) utilizes the Duplicate Address Detection (DAD) protocol to ensure the uniqueness of IPv6 addresses. However, the DAD employed in SLAAC is susceptible to various security vulnerabilities, including issues related to confidentiality, conflicting addresses, and spoofing attacks. Malicious nodes can exploit these weaknesses to perform Denial of Service (DoS) attacks by consistently claiming a tentative address, joining with conflicting address, or disclosing assigned address. Existing measures against DAD attacks have limitations, e.g. high computation, communication overhead, energy consumption, and major protocol modification. To address these challenges, this paper presents an innovative Optimized DAD (O-DAD) that is robust, scalable, and compliant with standard specifications. In O-DAD, the uniqueness of tentative IPv6 addresses is ensured in a way that neither new nor existing nodes have knowledge of each other's exact assigned addresses. O-DAD also hampers the ability of malicious nodes to spoof new/existing nodes. Experimental results demonstrate that the proposed solution effectively mitigates these attacks and exhibits superior performance in terms of Address Success Ratio (ASR), computational complexity, overhead, and energy consumption. When compared to Secure, Improved, and Standard DAD, the proposed scheme reduces overhead and energy by approximately 6%, 8%, and 15%, respectively. [ABSTRACT FROM AUTHOR]

This paper puts forward a low-profile, circularly polarized (CP), wideband Euler’s Spiral Dielectric Resonator Antenna (ES-DRA) having a footprint of 0.68 λ × 0.68 λ × 0.096 λ and operating in X and Ku-band (8.26–14 GHz). A cross-shaped aperture is used to couple the power from 50 Ω walking stick-shaped microstrip line to the ES-DRA. The antenna generates circular polarization radiation in the frequency band 10.47–11.69 GHz and has a peak gain of 8.1 dBic. There is a good matching between radiation and total efficiency in the operating band. The antenna can be put to use in weather mapping and detecting, long-range tracking radar and missile applications. The prototype has been fabricated and tested for experimental validation and the results are in good coherence with the simulated ones. [ABSTRACT FROM AUTHOR]

Online signature verification is one of the biometric authentication techniques. Online signature verification reduces the human error that may occur due to physical signature verification. Many researchers have provided a method of online signature verification still there is a scope for improvement. In this paper, a novel blended feature vector (BFV) is formed by combining two feature vectors. The first of these feature vectors is formed using a raw online signature database and the other feature vector is formed of the features extracted from the grayscale images obtained from the signature database. The algorithm makes use of the probability distribution of the features to provide a feature vector that results in an enhanced verification system. Two online signature databases, namely SVC2004 and ATVS-SSig are used. BFV is used to train the proposed mixed sequence deep neural network (MS-DNN). The use of a blended feature vector in the training of MS-DNN, instead of a whole online signature database, improves the training time of deep neural network to a great extent. This reduction in training time might be useful for new user registration. The performance parameters considered are validation efficiency and equal error rate. Results are compared with the existing state of technology and the comparison shows that the use of a blended feature vector as a classification vector improves the validation efficiency (99.5%) and also there is an improvement in the verification success rate in terms of equal error rate (EER 1.5%) as compared to the existing research. [ABSTRACT FROM AUTHOR]

This paper presents the novel design and analysis of a 25 W broadband Class-AB GaN HEMT-based power amplifier working over 0.9–1.7 GHz. A large signal model of the Wolfspeed-make GaN HEMT CG2H40025 has been used in this design. The comprehensive design process has been carried out with the help of the Advanced Design System software by Keysight. Instead of conjugately matching the load and source impedance obtained from load-pull analysis of a nonlinear model of CG2H40025, the value of load and source impedance has been tuned with the help of a novel design approach of an ideal matching design approach. Thus, to achieve an output power of more than 25 W and a flat large signal gain of 15.425 ± 0.375 dB, a novel design approach has been employed to create input and output matching networks, maintaining a lower Q value results in broad bandwidth and for determining the load and source impedances. The circuit has been fabricated on Roger’s substrate RO4350B having a dielectric constant of 3.66 and a loss tangent of 0.0031. The unique design approach based on electromagnetic simulation of the circuit at each design step yielded experimental results that agreed very well with the simulated results. The fabricated power amplifier achieves a power-added efficiency of over 54.78% and a drain efficiency of over 55.45% throughout the entire frequency range. It also delivers a maximum output power of 32.18 W at 1.3 GHz. This power amplifier is developed for L-band Foliage Penetrating Radar. [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 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]