The pdf and printed version of article PV-BASED RAPID CHARGING AND BATTERY SWAPPING STATION FOR SMALL TRANSPORT EVS IN RURAL AREAS OF WEST BENGAL Bidrohi Bhattacharjee, Shibabrata Mukherjee, Rupanjali Bhattacharjee, Subha Bhattacharya, Ankur Ganguly, Arabinda Das Facta Universitatis, Series: Electronics and Energetics, Vol. 38, No 3, September 2025, pp. 469-485, contain an incorrect DOI: https://doi.org/10.2298/FUEE2503457B Correct DOI is: https://doi.org/10.2298/FUEE2503469B Link to the corrected article 10.2298/FUEE2503469B
Breast cancer is a widespread and life-threatening disease that occurs when abnormal cells in the breast tissue grow and divide uncontrollably. Breast screening using ultrasound is a good method. In this paper, a modified version of the multi-stage residual U-Net, referred as Residual U-Net-based Architecture for Breast Cancer Detection (RABC-Net), is proposed as a segmentation network for breast tumor detection from ultrasound images. This U-net variant is built using the ResNet architecture. In the model architecture the skip connections at multiple stages within the encoder-decoder network are incorporated strategically. These skip connections facilitate the preservation of fine-grained details from the encoder layers, enhancing the accuracy of the reconstructed images. Additionally, the decoder architecture is employing convolutional layers with regularization techniques and batch normalization, to ensure stable training and preventing overfitting. The model is trained using various types of activation functions. Finally, a modified version of the Mish activation function is proposed in this architecture, named Parametric Mish (P-Mish), which prevents neuron death and loss. The data imbalanced problem has been solved using a generative adversarial networks (GAN) model. The experimental results are demonstrating the effectiveness of the method in producing highfidelity reconstructions, outperforming existing approaches. The effectiveness of the proposed RABC-Net and classical segmentation models is evaluated on two different datasets such as BUSI (Breast Ultrasound Images) & UDIAT. On the BUSI and UDIAT datasets, experimental findings are showing that RABC-Net outperforms the other state-ofthe-art models. The RABC-Net architecture exhibited promising outcomes in automated breast cancer segmentation, demonstrating high accuracy and robustness across diverse imaging conditions.
This paper presents one solution for device configuration and calibration in remote power meter reading and controlling system. The configuration and calibration challenges in mass production settings are discussed, along with the implementation and core features of the Initz software. In real production environments, Initz is used for device configuration in several substations located in the regions of Novi Sad and Zaje & ccaron;ar.
The BT-CEAB2 Pulse Assessment Simulator was evaluatedusing a novelflexible Marija V Perga https://orcid org/0000 0001 6078 200 MXene/polyurethane resistive sensor positioned at the radial site during 1 h sessions in Absrracr: The BT-CEAB2 Puls Asessment Simultor was evalated usng a novel fl three operating modes: Palpation, Auscultation, and Palpation-to-Auscultation. For each MXene/polyurethane r sistive sensor positioned at th ra ial site during 1 h sessions in mode, approximately 1 min recordings acquired at 0, 30, and 60 min were analyzed in operating modes: Palpation, Auscultation, and Palpation-to-Auscultation For each m Python using baseline-drift removal and morphology-oriented filtering, with the HeartPy approx mately 1 min recordings acquired at 0, 30, nd 60 mi were analyoed i Py toolkit used for systolic peak detection and heart-rate estimation. Across all modes, the using baseline-drift removal and morphologyoriented filtering,withthe HeartPyoolki signals exhibited clear pulsatile patterns without dropouts or abrupt discontinuities. Inter-for systolic peak detectionand heart rate esimat on. A rossall modes,the ignals exh beat intervals stayed tightly clustered with only sparse isolated deviations, indicating c e r p l a i pat r highly stable pulse timing during sustained simulator operation. In contrast, systolic peak h u pot r a upt d n e te b t n e s igh ly lu r d i h nly p rs i lated d ia ins,ndi g highly able p l i during t i d i ulat p r i amplitudes showed larger relative variability, plausibly influenced by changes in sensor In cont t sytoli p k a plitud h d fixation, contact pressure,and the coupling between the simulator and the sensor over rltive ibilityluilifl dby cha i en fi atio t t s ur the coupling between the simulator and the sensor over time Representative pulse wavef time. Representative pulse waveforms retained a repeatable morphology with identifiable retained a repeatable morpho ogy with identifiable systolic and diastolic features ena systolic and diastolic features, enabling consistent systolic-to-diastolic timing metrics to consisten systolic o diast verify the simulator output.
Smartphones, as the most adaptable and widely used IoT devices, are increasingly targeted by sophisticated malware due to the sensitive data they carry. Traditional Android malware detection approaches, particularly those relying solely on static features such as permissions or solely on network flow, are often susceptible to evasion techniques. To address these limitations, this study introduces a hybrid deep learning framework named DroidNet, which effectively combines both static (Android permissions) and dynamic (network traffic flow) features through feature-level concatenation. By fusing data from both domains, DroidNet is capable of capturing comprehensive behavioral patterns-leveraging the declarative intent of an application (via permissions) alongside its runtime behavior (via network activity). This hybrid design significantly enhances the model's ability to distinguish between benign and malicious applications, even in cases where permissions alone may be deceptive or insufficient. Feature selection was performed using the Chi-Square test to retain only statistically relevant inputs, followed by standard preprocessing steps to ensure data integrity. While baseline classifiers such as K-Nearest Neighbors, Gaussian Na & uml;& imath;ve Bayes, and Support Vector Machines were tested on static features, they were outperformed by DroidNet. The proposed model achieved superior performance with an F1 Score of 97.05%, Accuracy of 99.7%, Cohen's Kappa Score of 0.9289, and an AUC-ROC Score of 0.9643-demonstrating its robustness and suitability for real-time Android malware detection in both smartphone and IoT environment.
This study examines the cross-sensitivity of methane (CH4) detection via Differential Optical Absorption Spectroscopy (DOAS), concentrating on the interference effects of prevalent atmospheric gases like oxygen (O-2), carbon dioxide (CO2), and water vapor (H2O). By examining CH4 and these gases' absorption spectra in the 870-920 nm wavelength range, the study demonstrates that CO2 and O-2 have very little interference with the detection of CH4, since their absorption spectra barely overlap with the several absorption peaks of CH4. In the same range, water vapor did not exhibit any noticeable absorption. This result demonstrates that the DOAS approach measures CH4 concentrations without being influenced by common ambient gases, particularly in contexts where several gases can be present. Additionally, the results highlight the importance of cross-sensitivity testing in ensuring the reliability of gas detection systems.
AlGaN/GaN-based HEMTs have emerged as a preferred choice over AlGaAs/GaAs devices for high-frequency applications due to their superior electrical properties. In this work, we propose a novel HEMT design integrating an Al2O3 high-K passivation layer, an AlN spacer, and a discrete field plate, which collectively enhance device performance. Using Silvaco TCAD simulations, both DC and AC characteristics were analysed. The proposed device achieves a drain current of 1.6 A/mm, a transconductance of 0.25 S/mm, and a maximum unilateral gain of 160 dB, together with reduced capacitances (CGDMAX) = 4.5 & times; 10(-12) F/mm, CGSMAX) = 1.5 & times; 10(-11) F/mm) and a simulated breakdown voltage of approximately 1200 V. Electric-field and Yparameter analysis indicate strong high-frequency performance. Comparisons with conventional architectures demonstrate that the proposed HEMT exhibits superior high-power and high-frequency behavior, highlighting its potential for advanced RF and power applications.
This study presents an intelligent system designed to regulate ambient temperature and humidity in a typical Indian household room using real-time sensor data and embedded control logic. The system employs an Arduino Nano for data acquisition, communicating with PYNQ-Z2 board over UART interface. Based on predefined environmental thresholds, PYNQ-Z2 board dynamically controls fan speed at three discrete levels through Variable Frequency Drive (VFD) and activates water pump as needed to maintain humidity within target limits. To assess system responsiveness, experiments were conducted at three distinct times of day, capturing natural fluctuations in environmental conditions. The platform supports edge processing using Python on PYNQ-Z2 and allows for remote interaction via Jupyter Notebook and SSH. A transient energy analysis shows that adaptive system reduces cumulative energy consumption by over 25% compared to no-control setup. This work contributes to developing standalone, adaptive climate control systems for use in smart infrastructure and resource-constrained environments.
Electrical Power transmission system is a complex and interconnected network composed of generators, transformers, and transmission lines. The failure of any single component can initiate outages that compromise the reliability and stability of the entire grid. This research introduces a novel, scenario-based approach for enhancing contingency response through the strategic integration of renewable energy sources (RES), particularly solar photovoltaic (PV) systems, into the conventional transmission system. In the proposed methodology, the screening of buses for PV placement is carried out using the Voltage Stability Index (VSI), which is implemented in MATLAB to identify critical busbars prone to instability and to determine the optimal PV deployment locations. Unlike traditional methods, this study evaluates the impact of RES placement not only from a technical perspective but also incorporates economic considerations by correlating system performance with installation cost. After identifying the optimal PV locations through MATLAB-based analysis, ETAP software (version 19.1) is utilized to determine the appropriate PV sizing and to conduct performance evaluation of the system. The N-1 contingency analysis of the IEEE-57 test system has been performed using ETAP, across four structured scenarios involving different combinations of PV integration at the identified weak buses. Key performance indicators-including static voltage stability, real and reactive power variation, line loading, and energy losses-are assessed under N-1 real power deviation, reactive power deviation, and branch overloading indices, is proposed as a comprehensive metric for ranking system reliability. Successive integration of PV at strategic locations, the CPI demonstrated substantial reductions: 39.34% at Bus 45, 87.17% at Bus 46, 23.86% at Bus 49, and 25.72% at Bus 51, highlighting the effectiveness of PV placement in enhancing system performance. The findings demonstrate that optimized multipoint RES integration can significantly reduce contingency severity, enhance resilience, and support cost-effective planning. This work contributes a practical and scalable methodology for utility planners by bridging the gap between contingency analysis and renewable integration strategy, offering both technical and economic insights to inform data-driven decision-making in modern transmission networks.
In this paper, we will present the construction and implementation of a functional prototype of a driver for a six-digit display with 7-segment LED circuits driven by a Microchip 16F84A microcontroller, which is the only integrated circuit in the system. Instead of auxiliary integrated circuits and LED drivers, a network of inexpensive transistors in conjunction with passive components (resistors) was used exclusively, which makes this solution unique and minimalist in terms of hardware resources and costs. The functional complexity of the solution itself has been transferred to the software level. The control program was implemented in machine language using the author's own development system. This approach can be used in general, for the implementation of larger displays without changes in the basic hardware solution.
One square meter of solar surface produces approximately 63x10(6) W/m(2 )of energy and even with all the natural atmospheric filters, this energy is still very little explored, especially in thermal systems. Countries with an abundance of solar radiation still do not properly exploit this natural resource and when we look at poorer countries, this situation is even more worrying. The use of solar collectors for water heating represents significant economic and technological development throughout the world. In this work the PVC-B (Polyvinyl Chloride-Blue) lining honeycomb plate was converted into a solar collector, and its thermal efficiency was then analyzed. The cold-water inlet, hot water outlet and ambient temperatures, as well as the water flow and direct and reflected solar radiation from the ground are measured and these records are used to determine the energy absorbed by the water and incident on the solar collector. The average and maximum thermal efficiency of the PVC-B collector was 38.35% and 68%, respectively, with a maximum temperature of 42.94 degrees C. The collector in this study can be used both as a stand-alone system and as a hybrid system to support electric showers. The behavior of the solar collector studied here is analogous to a swimming pool collector, but with a cost of 73.18% lower. Even with a not so attractive average efficiency (38.35%), the low cost and simple construction make this equipment a great attraction for residential installation, as it can be built by the user himself and used to heat water for low-income families, which is a promising result for a low-investment equipment, compared to commercial solar collectors (FPC).
This paper introduces a novel architecture for signed multiplication, representing a significant advancement in VLSI design for high-end computation. We have implemented signed number multiplier numbers using the Urdhva Tiryagbhyam (UT) algorithm. The purpose of the proposed architecture is to overcome the limitations of previous approaches, through the integration of Virtex-7 and Spartan-7 hardware, in addition to improving power and delay performance. As a result of using vertical and crosswise techniques, the proposed parallel multiplication scheme for signed numbers is implemented in this paper. We have synthesized and simulated this structure using Vivado 2020.1 software and Virtex-7 and Spartan-7 FPGA devices for low-power VLSI computational applications, such as checking the speed and delay of the design post-layout. A comparison is made between the proposed design and existing architectures in terms of area, delay, and power consumption. According to the results, the proposed architecture improves speed by 40% over prior architectures. For faster computing applications, a high-speed proposed multiplier will be useful for complex design architecture. The work contributes significantly to the improvement of high-performance computing and digital signal processing.
This paper presents a machine learning-driven framework for analyzing and predicting potato late blight (caused by Phytophthora infestans) across two distinct cultivation systems-ecological and integrated-using six potato varieties. Traditional statistical methods, including a two-factor Analysis of Variance (ANOVA) and Tukey's Honest Significant Difference (HSD) test, were applied to assess the effects of cultivation systems, potato varieties, and year. To enhance predictive accuracy and model interpretability, an advanced machine learning pipeline, termed the Eco-Integrated Model, was developed. This model integrates SMOTE (Synthetic Minority Oversampling Technique) for handling class imbalance, SHAP (SHapley Additive xPlanations) for interpretability and feature importance analysis, and the CatBoost classifier for robust, high-performance prediction. The dataset, collected over three years (2018-2020), includes multi-varietal and system-specific records of late blight incidence for both ecological integrated-based data, serving as input for model training and evaluation. The proposed Eco-Integrated Model demonstrated high predictive capability, revealing that integrated cultivation systems are generally more effective at suppressing disease progression. Moreover, substantial varietal differences were identified in late blight susceptibility, as highlighted by both statistical and machine learning analyses. These findings underline the value of incorporating explainable, data-driven approaches into plant disease forecasting. The Eco-Integrated Model offers a scalable, interpretable, and accurate predictive solution, contributing to precision agriculture practices and supporting evidence-based decision-making for sustainable potato production and disease management strategies.
The 5(th) and 6(th )(5G and 6G) generation wireless communications exploit large antenna arrays to serve a large number of users over large distances. In 6G sky communication, large antenna arrays will be used for communications with unmanned aerial vehicles (UAV), satellites and high altitude platforms (HAP) along with terrestrial infrastructures. The manuscript at hand dispenses an organized technical survey of the effects of mutual coupling in massive MIMO (mMIMO) (multiple input multiple output) systems, subsuming the effects on te direction-of-arrival (DoA) of the signals and digital beamforming, which substantiate the performance of the design of smart antenna (SA). The mutual coupling distorts the wave front of the incoming signal, resulting in an erroneous DoA estimation and majorly degrading other performances of the antenna array in an mMIMO system. An assortment of compensation techniques is elucidated since it is unfeasible to completely eliminate the mutual coupling. Further, some investigated results of isotropic antennas and dipole arrays are explicated, screening the mutual coupling effects. For practical antenna array design, compensation for the effect of mutual coupling is necessary, especially for densely populated arrays in an mMIMO system. Investigations on various methods of compensation of mutual coupling in antenna array design are surveyed.
Incorporating renewable energy resources (RER) into standard power flow schedules is a complex optimization issue with multiple objectives and nonlinear characteristics. This matter necessitates the careful assessment of a multitude of economic and environmental concerns. This matter requires the evaluation of many restrictions related to disparities between races. The primary goal of generation scheduling is to minimize pollution emissions and costs over a limited time frame. This must be accomplished while ensuring that all system restrictions are adhered to. The Crisscross optimization (CCO) algorithm is used in this research to provide a novel method for solving the short-term hydro-thermal power scheduling (ST-HTPS) and short-term hydro-thermal-wind power scheduling (ST-HTWPS) issues. The suggested CCO method is compared to previously implemented particle swarm optimization (PSO) algorithms, moth-flame optimization (MFO) algorithms, and genetic algorithms (GA). This strategy makes convergence happen faster and solutions more precise while retaining a balance between exploration and exploitation. The proposed model takes into account real-time operational limitations, such as water balance equations, ramp rate limits, and wind uncertainty, to make sure that scheduling is both practical and effective. The suggested systems serve as study examples to evaluate the actual enhancement of the proposed CCO compared to PSO, MFO, PSO, and GA. The simulation outcomes indicate that the recommended CCO modeling offers a more advantageous option than previous heuristic techniques regarding financial considerations (36389.25 $/day) and reduced emissions (9436.29 lb/day). Despite considering the inclusion of several intricate constraints related to ST-HTWPS scenarios, these findings remain unaltered.
In India, the central Government, as well as the respective states, have laid out policies to encourage the adoption of solar energy. For many types of consumers, especially residential and commercial, the Levelized Cost of Energy (LCOE) of solar energy is lesser than the cost of energy imported from the grid. However, solar energy is an intermittent and variable resource, which cannot fulfil the load at all times. Addition of battery storage energy systems (BESS) to solar PV projects can help the consumer better meet their load profile through renewable energy, while improving grid resilience. In many states, time-of-day (ToD) tariffs are offered, which provide incentives for exporting energy beyond sunlight hours. It has been postulated that with ToD tariffs, and falling battery prices, addition of storage can provide benefits to the customer. However, it needs to be analyzed whether hybrid solar-battery storage configurations would make financial sense. In this study, we compare the performance of two popular battery types, Lead Acid and Lithium Ferro Phosphate (LFP), to see which battery type would provide better economic returns for the given customer. The impact of parameters like round trip efficiency, replacement State of Health (SOH), maximum State of Charge (SOC) and cycle time have been considered. It is shown that for current market tariffs and prevalent battery costs, addition of batteries does not improve the Net Present Value (NPV) for residential or commercial customers. The best returns are obtained when the system has only solar, with no batteries attached, while commercial systems achieve better NPV as compared to residential customers. The highest NPV for a residential customer is $1847 for a 6.6 kW system for the given load profile and tariff regime. In contrast, the highest NPV for a commercial customer is $4165 for a 7.7 kW project. It is seen that accelerated depreciation benefits play a major role in improved returns from commercial installations. It is also seen that retail rate dispatch provides higher returns as compared with the peak shaving and self-consumption dispatch algorithms, since it reduces both energy as well as demand charges. It is demonstrated how the retail rate dispatch modulates the battery discharge & charge configuration every hour considering the prevalent tariff at that time and the energy left in the battery.
In the last couple of years, with the advent of emerging load types, the global electricity markets have been witnessing fast-changing consumption behaviors. Across nearly all load sectors, modern nonlinear power electronic loads constitute a significant portion of the total electricity demand. It is becoming a challenging task for network engineers to maintain an uninterrupted power supply without compromising the network's efficiency and grid controllability. The proposed work presents an innovative planning strategy for optimal reactive power allocation in static load models. This load model utilizes exponent-based representations of active and reactive power, considering the seasonal and temporal variations to simulate their impact on transmission network flows. The objective is to minimize the overall operating cost while adhering to constraints imposed by the network. To ensure economic efficiency, the overall operating cost incorporates various components associated with VAr generation, along with the impact of transformer tap settings. The optimal parameters subjected to reactive power planning (RPP) are obtained by using the proposed CTSA (Chaotic Trigonometric Search Algorithm). The effectiveness of the proposed approach is validated on the Indian Utility 62-bus test system. Simulation results show a reduction in overall operating cost across all considered scenarios, demonstrating the efficacy of the proposed method in reactive power management and highlighting the superior performance and versatility of CTSA in addressing diverse operational challenges.
This study explores the effects of high-voltage electrical surges on the performance and structural integrity of thick-film strain sensors developed for structural health monitoring in steel infrastructure. The sensors were fabricated using screen-printing techniques with a bismuth lead ruthenate-based resistive composition deposited on alumina ceramic substrates. To simulate realistic operational conditions, the sensors were mounted on steel beams and subjected to four-point bending to induce mechanical strain. Following mechanical loading, controlled high-voltage surge pulses were applied to emulate extreme electrical events. Sensor response was characterized before and after surge exposure using both static resistance measurements and current noise spectral analysis. While resistance measurements showed limited change, noise spectroscopy revealed microstructural damage undetectable by conventional means. The findings highlight the degradation mechanisms arising from electromechanical stress and demonstrate the effectiveness of noise spectroscopy as a non-destructive diagnostic tool. These results support the use of thick-film sensors in electrically demanding environments.
A microwave sensor array for tumor detection and localization was designed and fabricated on an FR-4 substrate. The sensor consists of three square-shaped split-ring resonators coupled to a microstrip feeding line. Each sensing cell produces a stopband in the transmission spectrum of the device. The sensing cells are mutually decoupled, so that the deposition of the sample on one of the cells induces shift of resonant frequency of only that cell, thereby enabling spatial resolution. The sensing performance was experimentally verified with the samples of animal tissues in an ex vivo setting. Porkfat and lean tissues were used forpreparing the samples, since the dielectric contrast between them roughly corresponds to the contrast between healthy and tumorous tissue. Experimental results suggest that the designed sensor can be effectively used for tumor detection and localization.
This study provides a comprehensive methodological contribution through rigorous comparative analysis between deep learning approaches and traditional optimization algorithms for adaptive beamforming in MIMO systems. Traditional optimization methods face significant challenges in dynamic environments due to computational complexity and convergence issues. Through systematic experimentation with standardized datasets (DeepMIMO, 3GPP TR 38.901, and IEEE MIMO Data Challenge), we evaluate performance using statistically validated metrics including signal-to-interference-plus-noise ratio, bit error rate, computational efficiency, and significantly faster convergence (23.7%, p < 0.01) and higher SINR (18.5%, p < 0.01) in dynamic channel conditions, while traditional algorithms maintain superior performance in steady-state scenarios. Traditional methods outperform deep learning by 12.3% (p < 0.01) in terms of BER in low-SNR environments. Computational complexity analysis shows traditional methods scale as O(N3) with MIMO size N, while deep learning maintains O(N) inference complexity. The main contribution of this work is a novel adaptive selection framework with mathematically proven optimality bounds that dynamically switches between methodologies based on current channel conditions, achieving 15.3% higher average SINR (p < 0.01) in mixed scenarios compared to fixed algorithms.
