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We describe a method to estimate coefficients ${{{\bm{h}}}_{\bm{n}}}$ of a power spectral density of the form ${\bm{\Sigma}}{{{\bm{h}}}_{\bm{n}}}/{{{\bm{f}}}^{\bm{n}}}$ (${\bm{f}}\;{\bm{\ }}\text{is}$ the frequency and integer ${\bm{n}} \geq 0$) from corresponding measured time series with periodic gaps. This technique is applied to consistently estimate the amount of $1/{\bm{f}}$ noise present in weather system follow-on microwave microwave imager's (MWI) channels from the time-series data collected with different periodic gaps during prelaunch ground tests. The method assumes that the power spectrum of $1/{\bm{f}}$ noise present in MWI can be represented as a second-order frequency polynomial model of the form ${\bm{\Sigma}}{{{\bm{h}}}_{\bm{n}}}/{{{\bm{f}}}^{\bm{n}}}{\bm{\ }}$ and attempts to retrieve the true spectrum by solving for the ${{{\bm{h}}}_{\bm{n}}}$ coefficients using the power spectrum of the time-series data with periodic gaps. The method also assumes that the periodicity and duration of the data gaps are known and consistent for a given time series. The theoretical basis of the new technique is derived and tested using simulation and the new procedure is then applied to real test data to estimate the coefficients of the frequency polynomial. As a quantitative estimate for the $1/{\bm{f}}$ noise, the radiometer gain fluctuation (${\bm{\Delta}}{\bm{G}}/{\bm{G}}$) at 1 Hz is then solved from the frequency polynomial of the gain fluctuation power spectrum. The 1 Hz $( {{\bm{\Delta}}{\bm{G}}/{\bm{G}}} )$ values were compared between two sets of ground test data for the same MWI channels but with large (92%) and small (4.89%) duty cycles. The similarity of the 1 Hz $( {{\bm{\Delta}}{\bm{G}}/{\bm{G}}} )$ values extracted from these two disparate datasets establishes confidence in the method. The derived noise power spectrum is then used to simulate MWIs radiometric brightness temperature images and predict the level of unwanted striping in the flight data due to $1/{\bm{f}}$ noise content. This method may be applicable to solve for the polynomial coefficients of the power spectrum of any noise process, which can be modeled as a frequency polynomial, given the polynomial form is known a priori.

The ever-changing technology landscape has seen a significant breakthrough with the introduction of edge computing. This innovation has revolutionized various domains, and one of its critical applications is in the domain of accident detection. Edge computing can help enhance road safety and emergency response by enabling real-time processing and analysis of sensory information from onboard sensors, cameras, and other connected devices. By integrating edge computing into accident detection systems, we can overcome the limitations of conventional centralized cloud-based methods and create a safer transportation network. In this article, we have proposed an accident detection framework using Deep Learning (DL) in the edge cloud environment. For accident detection, we have used a Convolutional Neural Network (CNN)- based DL model. The DL model detects the accident in the edge node which is near the data source. The proposed framework provides low latency, minimal network usage, and lower execution time as compared to only cloud-based deployment. Additionally, the proposed accident detection model is accurate up to 95.91% with Precision 0.9574, Recall 0.9574 and F1 score 0.9574 in the cloud-edge environment.

In the development of image forensics, detection of Copy-Move Forgery (CMF) has become a major challenge due to the proliferation of image forgery techniques. The CMF is widely utilized to alter the content of the original image to spread false information or to use such forged digital images for illegal purposes e.g. false evidence in the court of law, or to blackmailing any individual. This paper presents a new method for CMF Detection (CMFD) that uses the power of Graph Convolution Networks (GCNs) and its multiple layers with ReLU activation, for CMFD and analysis. The aim to use GCN is due to its ability to improve the feature extraction process by utilizing the spatial and structural affiliation between elements in the digital images. Also, the GCN aims to store information about images and use it to graphically describe images with pixels or image areas as features, spatial and correlation relationships as edges. By pulling data from this image, GCN is able to obtain content rich features that are very powerful at detecting CMF regions. In proposed methodology, we utilized Support Vector machine (SVM) for classification and the binary cross-entropy loss, and the Adam optimizer for improving accuracy. Our scheme successfully achieves high accuracy and is effective in CMFD. We use the MICC F220, and CoMoFoD datasets to test the GCN in our proposed CMFD method. Through much testing and evaluation, we have found that GCN has the tremendous ability for CMFD in digital images in term of accuracy.

The paper investigates the terahertz performance of a mutually injection-locked multi-element high electron mobility avalanche transit time (HEM-ATT) source based on AlGaN/GaN two-dimensional electron gas (2-DEG). Utilizing a nanostrip patch type planar coupling circuit, mutual injection locking between adjacent elements is achieved. The paper provides a comprehensive analysis of the integrated power combining technique in the mutually injection-locked multi-element HEM-ATT oscillator. A ten-element mutually injection-locked integrated power combined source is designed for operation at 1.0 THz, and simulation studies are conducted to examine its DC, large-signal, and avalanche noise characteristics. The capability of generating a narrow-band terahertz wave is verified by introducing various levels of structural mismatches between the elements. Results indicate that the ten-element HEM-ATT oscillator can deliver 2.27 W peak power with a 17% DC to THz conversion efficiency at 1.0 THz. The average noise measure of the oscillator is found to be 12.54 dB. Additionally, the terahertz performance of the mutually injection-locked ten-element HEM-ATT oscillator is compared with other state-of-the-art THz sources to evaluate its potentiality as an excellent integrated THz radiator.

This paper proposes the mixed sensitivity constraint H $\infty $ based virtual inertia controller (VIC) design for load frequency control (LFC) of islanded AC microgrid (MG). Owing to the increasing integration of renewable energy sources (RESs) in the MG, the power electronic converters required to interface RESs into grid, does not support and maintain required inertia for MG reliable operation. So, system frequency deviates from its nominal value. Virtual synchronous generator (VSG) concept has been introduced to support system inertia in the inverter-based MG. This work introduces the robust control technique based on H $\infty $ approach for design of VIC for the frequency regulation of MG. The mixed-sensitivity concept is utilized for H $\infty $ control-based VIC design. The proposed design aims to strike a balance between achieving high performance in terms of control objectives, such as frequency regulation, while ensuring robust stability in the face of uncertainties and disturbances. The effectiveness and robustness of the suggested VIC for frequency control are assessed under communication time delay, step load demand, parametric uncertainty, and various generation scenarios. Further, the efficacy of the suggested control design is verified in comparison to the existing control approach.

Diagnosing brain tumors using magnetic resonance imaging (MRI) presents significant challenges due to the complexities of segmentation and the variability in tumor characteristics. To address the limitations inherent in traditional methods, this research employs an advanced deep learning approach, integrating ResNet50 for feature extraction and Generative Adversarial Networks (GANs) for data augmentation. A comprehensive evaluation of ten transfer learning algorithms, including GoogLeNet and VGG-16, was conducted for the classification of brain tumors. Model performance was assessed using precision, recall, and F1-score metrics, complemented by additional metrics such as Hamming loss and the Matthews correlation coefficient to provide a more comprehensive insight. To ensure transparency in image predictions, Explainable AI techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME), were utilized. The study involved the analysis of 7023 MRI images, with TumorGANet being trained on a dataset encompassing gliomas, meningiomas, non-tumorous cases, and pituitary tumors. The results demonstrate the exceptional performance of proposed model named TumorGANet, achieving an accuracy of 99.53%, precision and recall rates of 100%, F1 scores of 99%, and a Hamming loss of 0.2%.

As the penetration of inverter-based renewable energy resources increases in the power grid, especially at the distribution and microgrid levels, the need to accurately represent them in planning studies increases as well. However, due to the lack of well-established standard procedures, and vendor reluctance towards the detailed sharing of proprietary models, automated dynamic model validation and parameter calibration tools for inverter based resources (IBRs) remain scarce. This work presents a model validation and parameter calibration platform for representing IBRs with generic phasor-domain models. Phasor measurements of power system events are used for continuous validation using the data playback method, and model parameters are re-calibrated if a significant mismatch between measurements and model response is observed. Unique features of the proposed platform include- 1) an iterative Bayesian optimization approach towards parameter calibration to address potential differences between the structures of generic models implemented in simulation softwares and commercial inverters, 2) error metrics designed to account for a possible mismatch between the time resolution of simulation and measurements, and 3) analysis of the measurement-simulation discrepancies to provide guidance to engineering personnel regarding model shortcomings. The performance of the platform has been illustrated using both simulated data and field measurements to validate/calibrate inverter models in GridLAB-D. Numeric tests show that the proposed iterative calibration algorithm is more accurate and computationally efficient than the standard Bayesian Optimization approach. The computation time required by the iterative calibration algorithm is less than half of that needed by the standard approach.

This study comprehensively analyzes the discontinuous conduction mode (DCM) of a two-phase interleaved buck converter (IBC) with an inversely coupled inductor (ICL). With ICL, nine circuit configurations are possible when the IBC operates in the DCM. Depending on the duty ratio and load condition, DCM can also be classified into seven types. Each type of DCM has a corresponding combination of circuit configurations. Consequently, the application of the combinations for distinguishing DCM types is proposed to analyze the DCM operation of two-phase IBCs with ICL. Different DCM types have different DCM characteristics, such as state space averaged models and time interval calculations, which are needed to be analyzed for small-signal modeling. Therefore, the state space averaged models and time intervals equations are derived for each DCM type, and boundary conditions between adjacent DCM types are analyzed. Experimental tests on the hardware prototype of the IBC are conducted to verify the practical effectiveness of the proposed analysis.

Autonomous vehicles continue to advance, primarily due to the continuous development and improvement of deep learning methods. Motion prediction of road users is a function that humans perform naturally while driving, such as anticipating when a pedestrian is likely to cross an intersection or when a vehicle would like to merge into a lane. Similarly, the ability to predict the motion of other road users has become a key function for autonomous vehicles to optimally plan their path. This publication explores the recent advancements in motion prediction of road users propelled by both deep learning, as well as the release of large datasets. These datasets provide many challenging real-world scenarios which can be leveraged to train and test state-of-the-art deep learning models. This study provides an overview of motion prediction, state-of-the-art datasets, and state-of-the-art models along with a deep dive into their methods. Finally, there is a comparison of model performance, followed by recommendations into future research and concluding remarks.

Research into solar photovoltaic (PV) fed grid-tied multilevel inverters (MLIs) has increased in recent years due to their distinct features compared to two-level voltage source inverters (VSIs). However, maintaining the power quality of MLIs is a challenging problem that encompasses the reduction of total harmonic distortion (THD), dc-link voltage imbalance issues, power loss reduction and thermal stress mitigation; which are significantly affected by the applied pulse width modulation (PWM) technique. In order to tackle the above mentioned power quality problems, this work presents a modified hybrid PWM technique which combines a modified modulating signal with a modified carrier signal. The comparison between the proposed and the existing PWM techniques is performed under different conditions and using a wide range of modulation index variations. A grid-tied three-phase three-level T-type neutral point clamped (NPC) inverter is adopted for the power quality assessment. The proposed hybrid PWM offers 25.57% output line voltage THD and 11.8 V peak-to-peak dc-link capacitor voltage difference which are lower than existing PWM techniques. Additionally, by reducing the switching and conduction losses of the power switches, the proposed hybrid PWM technique offers lower thermal stress than existing PWM techniques. The comparative analysis between the existing and the proposed hybrid PWM technique is carried out using MATLAB/Simulink and PLECS simulation environments. The proposed hybrid PWM technique is further validated using a reduced scale prototype developed in our research laboratory.

In this paper, we address the critical aspect of hardware ageing effects in reconfigurable intelligent surfaces (RISs), whereby the problem of extending the RIS’s life cycle under the impact of non-residual stochastic hardware impairment is considered. Through the replication of the RIS hardware and diverse wireless environments within a statistical simulation environment, we formulate an electronic maintenance framework (EMF) for RIS, where we incorporate non-residual impairments through stochastic modeling and determine the electronic reliability of the system. Accordingly, we derive optimal analytical solutions within the EMF to determine whether systematic maintenance of the RIS hardware should be done immediately or postponed in order to extend the expected life cycle of the RIS system. Furthermore, for the scenario with imperfect maintenance of the RIS-aided system, a reliable communication framework (RCF) is also introduced with residual impairments to assess the error probability of the RIS-aided communication. The RCF is established by deriving the distribution of the received signal-to-interference-plus-noise ratio in the presence of residual hardware impairment arising due to imperfect maintenance of the RIS system. Extensive numerical examples are provided to elucidate the derived solutions and illustrate the reliability performance of the proposed framework under hardware impairments.

The power grid is undergoing massive changes, driven by the need to improve both reliability and resiliency, as well as meeting goals intended to combat climate change. Many solutions to such problems will require vast amounts of data. Almost all measurement, control - and in the future, artificial intelligence (AI) - systems utilize sensing mechanisms designed to capture, transmit, or even act on voltage and/or current measurement parsing and characterization. In this paper, a free, open-access online repository of such grid signatures is presented with the intent of encouraging open sharing of power grid data for the development of artificial intelligence and data-driven applications to meet the goals of tomorrow’s grid. Known as the Grid Event Signature Library, or GESL, this Department of Energy-funded endeavour has seen a growth of over 200 users worldwide since its inception.

Passive microwave remote sensing is a vital tool for acquiring valuable information regarding the Earth's surface, with significant applications in agriculture, water management, forestry, and various environmental disciplines. Precision agricultural (PA) practices necessitate the availability of field-scale, high-resolution remote sensing data products. This study focuses on the design and development of a cost-effective, portable L-band microwave radiometer capable of operating from an unmanned aircraft system platform to measure high-resolution surface brightness temperature ($T_{B}$). This radiometer consists of a dual-polarized (Horizontal polarized, H-pol and Vertical polarized, V-pol) antenna and a software-defined radio-based receiver system with a 30 MHz sampling rate. The post-processing methodology encompasses the conversion of raw in-phase and quadratic (I&Q) surface emissions to radiation $T_{B}$ through internal and external calibrations. Radiometric measurements were conducted over an experimental site covering both bare soil within an agricultural field and a large water body. The results yielded a high-resolution $T_{B}$ map that effectively delineated the boundaries between land and water, and identified land surface features. The radiometric temperature measurements of the sky and blackbody demonstrated a standard deviation of 0.95 K for H-pol and 0.57 K for V-pol in the case of the sky and 0.39 K for both H-pol and V-pol in the case of the blackbody observations. The utilization of I&Q samples acquired via the radiometer digital back-end facilitates the generation of different time–frequency (TF) analyses through short-time Fourier transform and power spectral density (PSD). The transformation of radiometer samples into TF representations aids in the identification and mitigation of radio frequency interference originating from the instrument itself and external sources.

The device dimension down scaling beyond 14 nm technology node utilization of device architecture and new materials is a need of the semiconductors industry. In this paper, three materials, In $_{\mathrm {1-x}}$ GaxAs/ In $_{\mathrm {1-x}}$ GaxP, In $_{1-}$ xGax GAA JL FET developed and analyzed for their analog performance at high temperature. In $_{\mathrm {1-x}}$ GaxAs has a composition of 53% InAs and 47% GaAs with a band gap of 0.75 eV, whereas In $_{\mathrm {1-x}}$ GaxP and In $_{\mathrm {1-x}}$ GaxN exhibit a wider band gap of 1.90 eV and 3.2 eV, respectively, and exhibit different analog performance. An analytical model that is temperature-dependent for the drain current of In $_{\mathrm {1-x}}$ GaxAs, In $_{\mathrm {1-x}}$ GaxP, In $_{\mathrm {1-x}}$ GaxN GAA JL FET with mobility variations has also been developed. The ratio of transconductance-to-normalized drain current (gm/I $_{\mathrm {DS}}$ ) is utilized to examine analog properties. This ratio is a crucial measure of analog performance since it reveals the sensitivity and linearity of the device. All device parameters such as transconductance (g $_{\mathrm {m}}$ ), gm/IDS, cut-off frequency, and intrinsic gain were investigated for higher temperatures ranging from T =300 K to T =500 K. The newly developed device parameters have very low-temperature sensitivity, making these three material devices potential candidates for high temperature applications.

Brain hemorrhages have become increasingly common and can be fatal if left untreated. Current methods for monitoring the progression of the disorder that rely on MRI and PET scans are inconvenient and costly for patients. This has spurred research toward portable and cost-effective techniques for predicting the current stage and malignancy of the hemorrhages. In this study, simulated S-parameter data obtained from a two-antenna system placed over the head is used in conjunction with machine learning to detect the dielectric changes in the brain caused by hemorrhage non-invasively. Several machine learning classifiers are used to analyze the data, and their performance metrics are compared to determine the optimal classifier for this case. The study revealed that Decision Tree, KNN, and Random Forest classifiers are better than SVM and MLP classifiers in terms of accuracy, precision, and recall in predicting Brain hemorrhage at the most probable locations. Contrary to conventional microwave imaging systems requiring several antennas for brain hemorrhage detection, this study demonstrates that integrating machine learning with microwave sensors enables accurate solutions with a reduced antenna count. The results present a transformative strategy for monitoring systems in clinics, where a simple, safe, and low-cost microwave antenna-based system can be intelligently integrated with machine learning to diagnose the presence of Brain hemorrhage.

Rice is one of the foremost food grains that dispenses sustenance to about half of the world’s population. It is cultivated all over the world. The leaf disease detection of this crop is one of the chronic agricultural obstacles that farmers and planting experts have been struggling with for a long time. As a result of the leaf diseases, producing the amount of rice required to feed the world’s rising population has become very challenging. Hence, automatically detecting rice leaf diseases is an inevitable task to increase productivity. Numerous deep learning based methods have been proposed for rice leaf disease detection, which we found rather inefficient considering the size of the models. In this article, we introduce a lightweight deep Convolutional Neural Network (dCNN) based method for rice leaf disease detection, that outperforms contemporary state-of-the-art methods and showcases competitive performance against 21 established benchmark architectures, including AlexNet, MobileNet, ResNet50, DenseNet121, ResNeXt50, ShuffleNet, ConvNext, EfficientNet, GogoleNet, SwinTransformer, VisionTransformer, and MaxVit, to name a few, with significantly lower trainable parameters. Notably, our method achieves an accuracy score of 99.81%, a precision score of 0.99828, a recall score of 0.99826, and an f1-score of 0.99827. Moreover, we enhance the rice leaf disease dataset by merging two existing datasets and supplemented them with an additional 95 manually annotated images gathered from publicly available sources on the internet. We also develop a comprehensive crop health monitoring system for farmers, and develop an open API for the automatic annotation of new instances, benefiting the research community at large.

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries during trips. Simultaneously, personal data use for analytics is growing at an unprecedented rate, raising concerns for privacy. One standard for formalising location privacy is geo-indistinguishability as a generalisation of local differential privacy. However, the noise must be tuned properly, considering the implications of potential utility losses. In this paper, we introduce the notion of approximate geo-indistinguishability (AGeoI), which allows EVs to obfuscate their query locations while remaining within their area of interest. It is vital because journeys are often sensitive to a sharp drop in quality of service (QoS). Our method applies AGeoI with dummy data generation to provide two-fold privacy protection for EVs while preserving a high QoS. Analytical insights and experiments demonstrate that the majority of EVs get “privacy-for-free” and that the utility loss caused by the gain in privacy guarantees is minuscule. In addition to providing high QoS, the iterative Bayesian update allows for a private and precise CS occupancy forecast, which is crucial for unforeseen traffic congestion and efficient route planning.

A multiple quantum barrier (MQB) avalanche photodiode (APD) structure based on GaN/AlxGa $_{1{-}x}\text{N}$ material system has been proposed in this paper which is capable of detecting infrared (IR) signal up to $6.0~\mu \text{m}$ wavelength. A self-consistent quantum drift-diffusion (SCQDD) model developed by the authors, has been used to determine the current-voltage characteristics under dark and illuminated conditions, spectral response, excess noise properties, signal-to-noise ratio, time and frequency responses. Results show that the proposed MQB APD attains peak responsivity of 60 AW $^{-1}$ at $3.0\, \mu \text{m}$ wavelength. Incorporation of a dedicated thin $n$ -type GaN layer for avalanche multiplication in between the $p^{+}$ -GaN contact layer and MQB constant-field drift-layer ensures significantly low noise equivalent power under normal operating conditions at room temperature (300 K). Optical pulse response of the device reveals that special restriction over the charge multiplication able to supress the minor peaks of the current response and consequently significantly narrow pulse response can be achieved. Narrow pulse response leads to broad bandwidth of 274.5 GHz, which is significantly broader than the existing IR photo-detectors.

The variability in different altitudes, geographical variances, and weather conditions across datasets degrade state-of-the-art (SOTA) deep neural network object detection performance. Unsupervised and semisupervised domain adaptations (DAs) are decent solutions to bridge the gap between two different distributions of datasets. The SOTA pseudolabeling process is susceptible to background noise, hindering the optimal performance in target datasets. The existing contrastive DA methods overlook the bias effect introduced from the false negative (FN) target samples, which mislead the complete learning process. This article proposes support-guided debiased contrastive learning for DA to properly label the unlabeled target dataset and remove the bias toward target detection. We introduce: 1) a support-set curated approach to generate high-quality pseudolabels from the target dataset proposals; 2) a reduced distribution gap across different datasets using domain alignment on local, global, and instance-aware features for remote sensing datasets; and 3) novel debiased contrastive loss function that makes the model more robust for the variable appearance of a particular class over images and domains. The proposed debiased contrastive learning pivots on class probabilities to address the challenge of FNs in the unsupervised framework. Our model outperforms the compared SOTA models with a minimum gain of +3.9%, +3.2%, +12.7%, and +2.1% of mean average precision for DIOR, DOTA, Visdrone, and UAVDT datasets, respectively.

The deployment of renewable energy sources has become more frequent in power system networks over the last few years. The prevalence of global warming and some catastrophic climate changes is rising, along with the demand for intricate transport systems, as a result of rapid growth in civilization and modernization in culture. To fight this environmental issue associated with vehicle transmission, almost every nation is promoting electric vehicles (EV). In this article, a novel method for developing a sliding mode maximum power point tracking (MPPT) controller for photovoltaic (PV) systems operating in rapidly varying atmospheric circumstances is put forward. Further, the standard Perturb and observe (Pb&O) algorithm’s variable step is driven by the best sliding mode controller (SLMC) gains, which are determined using the Genetic Algorithm (GAO). Additionally, a PI controller, a grid employing current controlling topology, and an effective charging station constructed with GAO-optimized Sliding Mode-based reconfigurable step size Pb&O as an MPPT controller are executed and tested in MATLAB/Simulink for optimal control of power in the EV charging station. The main contribution of this study is to enhance the created controller’s tracking performance to reach the maximum power point (MPP) with negligible oscillation, low overshoot, minimum ripple, and excellent speed in conditions of air turbulence that change quickly, as well as ensure continuity in supply to the EV. Furthermore, the developed system as a whole shows good efficacy compared with other existing systems reviewed in the literature. Finally, this proposed strategy ensures continuity of power supply to the charging station even in uncertain weather conditions, as grid integration also plays a vital role in the overall demand.

Deep learning (DL) has emerged as a powerful technique for a wide range of computer vision applications. Consequently, DL is also being adopted to process geospatial and remote sensing (RS) images. As these methods are sporadic over different studies, many review papers have also been published to gather the approaches and summarize the existing models in this field. However, a state-of-the-art review paper is still scarce in this field that will present a bibliometric analysis as well as a critical analysis of the recent works. Therefore, this article aims to spur the researchers with a bibliometric analysis to identify the current research trend. As a research sample, in total, 281 related papers were collected from the Web of Science source, and bibliometric analysis was accomplished using VOSviewer software. Among the collection of associated works from the database, 28 papers were selected according to the defined criteria for detailed analysis. Besides this, a few research questions were generated to extract necessary information from the literature for extracting the pros and cons of the selected works. DL techniques were applied in these works and achieved results. Furthermore, the papers were also categorized based on the addressed RS application domain.

Nonlocal calculus-based macroscopic traffic models overcome the limitations of classical local models in accurately capturing traffic flow dynamics. These models incorporate “nonlocal” elements by considering the speed as a weighted mean of downstream traffic density, aligning it more closely with realistic driving behaviors. The primary contributions of this research are manifold. Firstly, we choose a nonlocal LWR model and Greenshields fundamental diagram and prove that this traffic flow model satisfies the well-posed conditions. Furthermore, we prove that the chosen model maintains bounded states, laying the groundwork for developing numerically stable schemes. Subsequently, the efficacy of the proposed nonlocal model is evaluated through extensive field validation using real traffic data from the NGSIM dataset and developing a stable numerical scheme. These validation results highlight the superiority of the nonlocal model in capturing traffic characteristics compared to its local counterpart and establish its enhanced accuracy in reproducing complex traffic behavior. Therefore, this research expands both the theoretical constructs within the field and substantiates its practical applicability.

Monitoring electrical activity from numerous neurons with the help of a high-channel count neural recording system is crucial for the state-of-the-art approaches in neuroscience research and clinical treatment. The performance trade-offs such as channel count, total power consumption, noise, area, and robustness need to be optimized for compatibility in neural signal acquisition system. This work proposes an implantable 32-channel neural signal acquisition system-on-chip (SoC), which includes 32 neural amplifiers, an analog multiplexer (MUX), an analog-to-digital converter (ADC), and an impulse radio ultra-wide band (IR-UWB) transmitter designed using 180 nm CMOS process. The neural amplifier allows to detect local field potentials (LFP) and action potentials (AP) separately in a programmable gain-bandwidth setup. It achieves 48 dB gain within the 0.2–345 Hz for LFPs and a gain of 59.7-dB within the 310–20.7-kHz frequency range for APs. A 10 bit SAR-ADC is designed with reconfigurable sampling rate of 10–40-kSps achieving an FOM (Figure-of-Merit) of 629-fJ/step. The designed IR-UWB transmitter can transmit data at 100-Mbps data rate with an energy efficiency of 12.5-pJ/pulse. The proposed system shows a promising reconfigurable multi-channel neural signal recording paradigm for neuroscience research and Brain Machine Interfaces (BMIs) applications.

This paper describes an open-source library of transmission-level synchrophasor measurements, curated with the aim of accelerating data-driven research and development in the power systems domain. This dataset contains measurements describing both disturbances and ambient conditions, spans two years in time, and is sourced from electric utilities across the United States. Comprised of 1694 unique events, this is the largest open-source repository of real transmission-level phasor measurement unit (PMU) data to date, and will be invaluable for benchmarking new algorithms, testing tools and approaches developed by vendors and researchers, and developing educational tools for university students and control room operators. This paper additionally highlights several potential applications of the dataset that may be useful to the research community.

In this paper, we propose an innovative framework for vehicular communication utilizing reconfigurable intelligent surfaces (RIS) in the millimeter-wave (mmWave) spectrum. Specifically, we consider a scenario where a base station (BS) employs multiple RISs to serve a vehicular user, considering both perfect and imperfect channel state information (ICSI) to account for real-world conditions. The vehicular user sends an uplink training pilot sequence, which is received by the BS through multiple RISs. We formulate an optimization problem to simultaneously optimize the precoder at the BS and the passive beamforming/passive phase shift matrix at each RIS in order to maximize the achievable rate for the vehicle. By leveraging the received signals with varying phase shifts, we provide a solution based on deep learning (DL) that effectively learns to utilize these signals for predicting the optimal phase shift matrix. Through extensive numerical analysis, we validate the effectiveness of our proposed solution by comparing it to the successive refinement (SR) benchmark scheme. Furthermore, it demonstrates that the proposed DL-based beamforming solution attains a performance level near to the maximum achievable rate with the increase in dataset size, eliminating the need for additional training overhead. The incorporation of RIS amplifies the achievable rate, especially in high-mobility scenarios, without necessitating additional complex beamforming solutions. In addition, we also evaluate the performance of the proposed solution in the presence of ICSI. We analyze the impact of the key system parameters, including the number of elements in the RIS, the speed of the vehicle, the transmit power, the distance between devices, and other relevant factors, on the performance of the considered system.

This paper presents an efficient energy management strategy for Fuel Cell Hybrid Electric Vehicles (FCHEV) using a Machine Learning (ML) approach. Petroleum-based fuels are utilised in conventional cars to provide good performance and long-distance speed. There are certain disadvantages to using petrol or diesel, such as poor fuel economy and pollution-causing exhaust gas emissions. Furthermore, there are some limitations with existing available work, and the merger of these different optimisation techniques will be advantageous for achieving optimal performance. To address them, the purpose of this research is to create an efficient energy management approach by combining SVM, KNN, and the Naive Bayes technique. Additionally, by combining these classifier techniques better performing EMS is developed. Using the proposed features, the optimisation approach’s performance accuracy is increased. Furthermore, these individual classifiers comprising of SVM, KNN & Naïve Bayes is giving accuracy percentage of 96%, 92% & 94% respectively. Finally, after combining these three classifiers we have achieved an accuracy percentage of 98%.

In-vehicle controller area network (CAN) is susceptible to various cyberattacks due to its broadcast-based communication nature. An attacker can inject false messages to a vehicle’s CAN via wireless communication, the infotainment system, or the onboard diagnostic port. Thus, an effective intrusion detection system is essential to distinguish authentic CAN messages from false ones. In this study, we developed a hybrid quantum-classical CAN intrusion detection framework using a classical neural network (NN) and a quantum restricted Boltzmann machine (RBM). The classical NN is dedicated to feature extraction from CAN images generated from a vehicle’s CAN bus data. In contrast, the quantum RBM is dedicated to CAN image reconstruction for classification-based intrusion detection. The novelty of the study lies in utilizing the generative ability of an RBM to reconstruct the pixels in a CAN image, a portion of which is dedicated to labeling. Then, that portion of the reconstructed image is used to classify the image as an attack image or a normal image. To evaluate the performance of the hybrid quantum-classical CAN intrusion detection framework, we used a real-world CAN fuzzy attack dataset to create three separate attack datasets, where each dataset represents a unique set of features related to the vehicle. We compared the performance of our hybrid framework to a similar but classical-only framework. Our analyses showed that the hybrid framework performs better in CAN intrusion detection compared to the classical-only framework. For the three datasets considered in this study, the best models in the hybrid framework achieved 97.5%, 97%, and 98.3% intrusion detection accuracies and 94.7%, 93.9%, and 97.2% recalls, respectively. In contrast, the best models in the classical-only framework achieved 92.5%, 95%, and 93.3% intrusion detection accuracies and 84.2%, 89.8%, and 88.9% recalls, respectively.

Green computing, also called sustainable computing, is the process of developing and optimizing computer chips, systems, networks, and software in such a manner that can maximize efficiency by utilizing energy more efficiently and minimizing the negative environmental influence on the surrounding. The term “green computing” refers to practices that lessen the negative effects of technology on the environment. Due to the improvements in modern technology, various devices, mechanisms, and software have been developed, and lots of studies have been conducted to optimize and increase those technologies’ green computing abilities. Thus, review and summarization of green computing-based studies are required to identify the current advancements, challenges, and future research opportunities. This study reviewed and summarized green computing in each area studies, by exploring green computing’s twelve areas. Current research trends, datasets or testing mechanisms, and the construction or implementation of various technologies to accomplish green computing and sustainable development have been discussed. This study, after conducting a thorough comparison and analysis, provides responses to the proposed state-of-the-art research questions. Furthermore, this study presents the current challenges and future research opportunities with respect to each green computing area. This study will provide organizations, researchers, and institutions conducting research on green computing with insights and ideas. Furthermore, environmental organizations, companies, and government agencies concerned with reducing carbon emissions and energy consumption will also benefit from this review study.

The Internet of Things (IoT) paradigm facilitates communication for a multitude of connected smart objects and provisions essential and mission-critical services across diverse sectors. To route packets, IoT networks use Routing Protocol for Low-Power and Lossy Networks (RPL) by default. However, RPL lacks security features by design, making IoT-RPL prone to low-overhead internal attacks such as the rank and version attacks. The attack and normal traffic are found to be identical, making detection challenging for signature-based and anomaly-based Intrusion Detection Systems (IDS). Moreover, a formal proof of correctness of IDS schemes is lacking. In this paper, we propose a novel rank and version attack detection and rank attacker location identification mechanism that utilizes active probing and Discrete Event System (DES) based IDS. Our proposed IDS scheme is centralized with inputs from sensing at the leaf levels. IDS uses as an intelligent probing technique that helps distinguish normal and attack behaviour. Further, DES is used to model the normal and attack specifications. A DES diagnoser, constructed from the DES models, generates an alert when a malicious node is identified. We also prove the correctness and completeness of our scheme. The DES framework is implemented only at root node, therefore using our IDS does not require any heavy deployment, protocol modifications, or training. Proposed method is implemented in simulation and testbed, with a sufficiently large number of IoT devices. We compare our scheme to state-of-the-art approaches. Our performance is found to be energy-efficient, having minimal false positives and achieving more than 99% accuracy in detecting intrusions and identifying the malicious nodes.

The integration of solar photovoltaic (PV) arrays for induction motor drives (IMDs) has attained a lot of research interest due to the versatile applications of IMD including water pumping, cement industries, electric vehicles, and rolling mills etc. Multilevel inverters (MLIs) are a prominent solution for driving medium voltage induction motors (IMs) due to their superior performances over two-level voltage source inverters (VSIs). However, the power quality performance of MLI fed IMDs greatly depends on the pulse width modulation (PWM) strategy employed. Traditional PWM strategies for MLIs suffer from power quality problem such as lower order harmonics, high THD, unbalanced power loss distribution, high switching power loss, thermal instability and electromagnetic interference (EMI) problems. In this paper, a modified PWM strategy is proposed for a solar PV fed 3-level neutral point clamped (NPC) inverter driven IMD. The proposed PWM strategy introduces a new modulating signal with level shifted triangular carriers. The proposed modulating signal is formed by utilizing a $13^{\mathrm {th}}$ harmonic signal and saturated balanced 3-phase sinusoidal signals. The proposed PWM technique is named as $13^{\mathrm {th}}$ harmonic injected 60°-PWM (THISDPWM). The THISDPWM reduces the lower order harmonics contents, overall THD, and switching power losses of the NPC inverter compared to different existing PWM strategies. Apart from that, a robust artificial neutral network (ANN) based incremental conductance maximum power point tracking (MPPT) algorithm is also introduced to control the dc-link voltage of the PV fed NPC inverter driven IMD system. The presented ANN based dc-link controlled technique ensures stable dc-link voltage against wide range of PV parameters variation. All the simulation works are carried out in MATLAB/Simulink environment and a reduced scale laboratory prototype of the IMD is built and tested to validate the performance of the proposed THISDPWM technique.

An active magnetic bearing (AMB) is an evolving research topic in the bearing industry. Compared to the conventional approach, AMB is more advantageous and has different industrial applications. The design of this bearing needs to be improvised to get more optimized performance. This leads us to focus on the better-designed structure of the actuator, which plays an essential role in AMB performance. Thus in this manuscript, we have designed and compared the I-type and U-type actuators’ performances in terms of their resistance, inductance profile, and force profile to show the characteristics of these actuators. These designed actuators are simulated in Ansys Maxwell software, and compared with the simulation results. Further, these results are verified using hardware results, and the tested actuators are used to design two different AMB systems. One current controller is designed and simulated to achieve the stable levitated position of the rotor. This rotor is rotated by inserting an AC magnet in the system and finally, a rotational system is attained, and its speed is observed by the tachometer for both AMB systems. Both the proposed AMB systems are simple and compact in structure as these are having single coil and single power amplifier. These axial AMB systems are used in many high-speed applications as they are having features like less installation space, less cost and more room for heat dissipation.

Information-centric networking (ICN) and its fruition, the named data networking (NDN) is a paradigm shift from host-centric address-based communication architecture to the content-centric name-based one. ICN intends to resolve various major issues faced by today’s internet architecture such as privacy, security, consistent routing, and mobility, to name a few. With the massive increase of mobile data traffic in today’s era, mobility is one of the major concerns in networking. On the one hand, ICN realization i.e., the NDN follows a pull-based communication model and natively supports the consumer (end-user) mobility in wired networks by maintaining the forwarding states on intermediate nodes. Nevertheless, the mobile consumer nodes confront issues in wireless networking environments such as excessive energy consumption as a result of request flooding, content retrieval delays due to intermittent connectivity, and bandwidth consumption due to the broadcasting nature of the wireless medium, among others. The producer (content-generator) mobility, on the other hand, was not initially supported in the original architectural design of NDN for both wired and wireless networks. Therefore, to efficiently address the degradation issues incurred by mobile consumer/producer nodes, a plethora of mobility management schemes have been proposed over the recent few years. In this paper, we provided a detailed survey on the existing research efforts—in the context of producer, consumer, and hybrid mobility, that have been proposed in the literature. Moreover, we outlined various research directions considering the role of mobility in futuristic technologies such as artificial intelligence-enabled smart networks, software-defined networking, edge computing, vehicular-fog computing, autonomous driving, semantic communication, and resource-constrained Internet of Things.

A wideband and dual-polarized dipole antenna design technique is presented. The proposed antenna employs two sets of dipole elements in orthogonal configuration and separated by a dielectric layer. The dipole elements are excited with two separate substrate-integrated-coaxial feeds to achieve high port-to-port isolation and the overall antenna stackup is enclosed in a substrate-integrated-waveguide (SIW) cavity. To validate the proposed substrate-integrated-coaxial fed dual-polarized antenna design method, a prototype antenna was fabricated using standard multi-layer printed circuit board (PCB) manufacturing technologies to operate in the Ku-band. Measured and simulated results exhibit that the designed antenna has a wide impedance bandwidth of less than -10dB reflection coefficients at 10.28-14.8GHz and the port-to-port isolation between the two orthogonally polarized dipole antenna ports is greater than 20dB at most frequencies, making it suitable for many applications such as satellite communications. The antenna has a relatively stable gain pattern with a gain value of 5.5-6.5dBi and a low cross-polarization level (lower than 16dB) over the operating frequency range.

This review addresses emerging literature in the field of Opinion Mining with a particular emphasis on user-generated textual content. The focus is on the various tasks involved in Opinion Mining, which satisfies the comprehension of the essential criteria and methodologies that should be considered prior to embarking on the task. The paper provides an in-depth analysis of benchmarked datasets, widely-used feature sets, algorithms, techniques, open-source tools, challenges, real-world applications, and insights into various dimensions of Opinion Mining. The findings have both theoretical and practical implications, as it highlights the significance of Opinion Mining of textual content in comprehensive society. This review covers both technical and real-world knowledge and offers a comprehensive understanding of available open-source tools that are used in real-time.

Internet-of-Things (IoT)-based Heterogeneous Wireless Sensor Network (HWSN) has emerged as a prevalent technology that plays a significant role in developing various human-centric applications. Like in a wireless sensor network (WSN), energy is also the most crucial resource in IoT-based HWSN. The researchers have proposed many works to achieve energy-efficient network operations by minimizing energy usage. A vast proportion of these works emphasize using the clustering approach, which has proved its worth to a great extent. However, most schemes require the repeated formation of clusters incurring a significant amount of nodes’ energy in the clustering process. The protocol design of such schemes also varies with the changing levels of heterogeneity. In this work, a hybrid clustering scheme- An Energy-Efficient Hybrid Clustering Technique (EEHCT) has been proposed for IoT-based HWSN that minimizes the energy consumption in clusters’ formation and distributes the network load evenly irrespective of the heterogeneity level to prolong network lifetime. It appropriately utilizes dynamic and static clustering strategies to formulate the load-balanced clusters in the network. EEHCT establishes its supremacy over state-of-the-art schemes via an extensive set of simulations and experimentation in terms of multiple network performance metrics like stability, throughput, and network lifetime. Like, it achieves a gain up to 90.27% with respect to network lifetime over its peers in the standard operating conditions and under varying network configurations. In addition to quantitative analysis, a statistical analysis has also been provided to demonstrate the formation of energy-balanced clusters through the proposed scheme.

Due to the intermittent and hazardous nature of lightning impulse, researchers rely on artificial impulse generators to study it. The Tesla coil is a commonly used device to generate artificial lightning. This paper presents a parametric analysis of the primary side coil of the Tesla transformer. The analysis employs seven key electrical properties: skin depth, current density, voltage distribution, Ohmic loss, electric field strength, charge density, and energy content. These parameters are used to evaluate the response of various coil types to high voltage impulses. The Ansys high-frequency electromagnetic solver is used to perform a comparative analysis of essential parameters. The results can aid lightning researchers and Tesla coil designers in determining the optimal designs for Tesla primary coils in the future. The analyzed data is validated using Tesla coil hardware, which is used as an artificial lightning producer to test the feasibility of a lightning energy harvester.

Transformerless grid-connected inverters have attained a lot of research interest in renewable energy interface applications, due to certain promising properties like greater efficiency, light weight, affordable price, and tolerable power density. Among various types of transformerless grid-tied photovoltaic (PV) inverters, multilevel inverters (MLIs) are mostly popular due to their ability to transmit reactive power, small filter size for reducing total harmonic distortion (THD) and their common-ground (CG) configuration to mitigate the detrimental leakage current due to the parasitic capacitances of the PV array. Again, among different types of MLIs for PV systems, switched-capacitor (SC) based multilevel inverter topologies are the burning topic in current decades due to their single source requirements for producing multilevel output voltage. However, for mostly used single-phase five-level inverters, most of the existing SC based topology requires at least two SCs for power conversion. In this paper, a five-level transformerless inverter based on a single SC is proposed, requiring only seven switches, no diode, a single capacitor, and one dc voltage source. The proposed transformerless MLI also has auto-boosting capability. Notably, the number of power switches operating at high frequency is limited to three, which lowers down the switching losses of the inverter. Rather than a new single SC based five-level transformerless inverter topology, a control scheme is also presented to inject a precisely regulated current into the grid that can govern both the active and reactive power support modes. In-depth comparisons between the proposed and cutting-edge MLIs are also provided. All these claims are validated through MATLAB/Simulink and PLECS computer simulation environments. A laboratory-scaled prototype is also built and tested to support the simulated claims further and validate the effectiveness and feasibility of the proposed five-level transformerless inverter topology.

Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral time multiplied absolute error (ITAE), and integral time multiplied square error (ITSE). The significance of an optimization technique and objective function can obtain only by implementing it. As a result, several comparisons are made based on statistical performance, time domain, frequency response behavior, and algorithm execution time. Finally, the applicability of optimization strategies in addition to the performance indices is determined with the aid of the comparative analysis. That could assist in choosing a suitable optimization technique along with a performance index for a high-speed application of an active magnetic bearing system.

Due to the rapid advancement of power semiconductor devices, the use of voltage source inverters (VSIs) has gained widespread acceptance. As a consequence, the performance of the voltage source inverter has emerged as a critical aspect that is highly reliant on the modulation strategy. The pulse width modulation (PWM) technique is the most widely utilized method of controlling power semiconductor switches of VSI. Power quality is always considered as an industrial concern for VSI-based power system such as grid-connected renewable energy systems and industrial motor drives which largely depends on the PWM technique used for switching. However, the existing PWM schemes for VSIs suffer from high total harmonic distortion (THD) and power loss problems. To mitigate the THD and power loss of VSI, a hybrid PWM technique has been proposed in this paper. The proposed hybrid PWM technique introduces a modified modulating signal along with newly shaped carrier signal. A two-level VSI is used to evaluate the performance of the proposed hybrid PWM technique both for with and without filter conditions against RL load. The proposed hybrid PWM technique offers 0.89% filtered voltage THD and 0.69% filtered current THD which are lower than that of existing PWM techniques. On the contrary, at without filter condition, the proposed hybrid PWM technique shows THDs of 45.77% and 12.50% for inverter output voltage and current, respectively which are also lower than those of existing PWM techniques. Apart from these, the proposed hybrid PWM technique reduces the switching and conduction power losses of the VSI as compared to existing PWM techniques. The simulation works are carried out in MATLAB/Simulink environment and a reduced scale experiment is conducted in laboratory to evaluate the performance of the proposed hybrid PWM technique.