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PV-Based Grid Integrated EV with GWO Optimized PI Controller for Boost Integrated Luo Converter

Journal of The Institution of Engineers (India): Series B 2024年第2期105卷 309-321页

作者： Kancherla, Ananda Babu Prasad, N. Bhanu Kishore, D. Ravi Department of Electrical and Electronics Engineering Gandhi Institute of Engineering and Technology Odisha Gunpur India Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology A.P. Rajahmundry India

The superfluous carbon emissions from automobiles and fossil-fuelled power plants are major contributors of global warming and air pollution. It is high time that both these sectors are steadily decarbonized in order to prevent the earth from becoming completely uninhabitable for human life. Thereby, in this work, focus is towards combining photovoltaic (PV) system and grid with an electric vehicle (EV) such as electric train, which successfully satisfies the requirement of both decarbonized energy generation and transportation. For enhancing the voltage level of the PV, a Boost integrated Luo converter is introduced, since it offers wide conversion range along with reduced voltage stress. Moreover, a constant output without fluctuations is obtained from the converter with the employment of Grey Wolf Optimized proportional–integral controller. On the EV side, its speed is controlled by the implementation of PI controller. The PV power is sufficient to run the BLDC motor of EV during daylight hours owing to the presence of sunlight;however, in night time, the BLDC motor is powered by the energy from the single-phase grid. The proposed grid integrated PV-based EV charging system is tested for its effective working in a MATLAB platform. © The Institution of Engineers (India) 2023.

关键词： Global warming

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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

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Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham Science Publishers.

关键词： Quantum computers

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A Generalized Array Factor for Time-Modulated Hexagonal Based Antenna Array Geometry With Novel Trapezoidal Switching

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1967-1972页

作者： Gopi Ram Department of Electronics and Communication Engineering National Institute of TechnologyWarangal 506004India

The concept of the time-modulated array has been emerging as an alternative to the complex phase shifters,which lowers the cost of the array feeding network due to the utilization of radio frequency(RF)*** various forms of hexagonal antenna array geometries can be used for applications like surveillance tracking in phased array radar and wireless communication *** work proposes the generalized array factor(AF)for the hexagonal antenna array geometry based on time *** time modulation in generalized hexagonal geometry can maintain the fixed static amplitude excitation,giving more flexibility over ***,a novel trapezoidal switching function is also proposed and applied to the generalized array factor to enable future researchers to use this array factor in the field of advancement to observe how switching schemes like trapezoidal and rectangular affect the array pattern's side lobe level(SLL).The generalized equation can be utilized for the analysis and synthesis of radiation characteristics of the time-modulated hexagonal array(TMHA),time-modulated concentric hexagonal array(TMCHA),time-modulated hexagonal cylindrical array(TMHCA),and time-modulated hexagonal concentric cylindrical array(TMHCCA).The numerical result illustrates the generation of AF of time-modulated hexagonal structures and also shows that the trapezoidal switching sequence outperforms the rectangular switch using the cat swarm optimization(CSO)approach.

关键词： Hexagonal array geometry radiation pattern time-modulation trapezoidal pulse

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MLRT-UNet:An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation

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computer Modeling in engineering & Sciences 2025年第4期143卷 413-448页

作者： Kaku Haribabu Prasath R Praveen Joe IR Department of Computer Science and Engineering RMK College of Engineering and TechnologyTiruvallur601206India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.

关键词： Thyroid nodules endocrine system multi-level relation transformer U-Net self-attention external attention co-operative transformer fusion thyroid nodules segmentation

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Fault classification and identification through machine learning approaches for a solar PV – battery based water pumping system

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Multimedia Tools and Applications 2025年第14期84卷 13843-13864页

作者： Sowmmiya, U. Abinaya, K. Jhunjhunwala, Rishik Satpathy, Prateek Arul, R. Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur India Department of Electrical and Electronics Engineering Vellore Institute of Technology Chennai India

The world progresses towards enabling renewable sources into the mainstream supply of energy and it is imperative to develop systems that can handle new challenges and disturbances. This paper aims at machine learning model-based fault identification and classification of an islanded Solar PV – battery integrated system feeding a water pumping system. The system is powered through solar PV and battery (discharge mode) which are controlled through Semi Pilot Cell based Fractional Open Circuit Voltage (SFOCV) method and conventional voltage control method respectively. The water pumping system is simulated using MATLAB which is interfaced with RT-LAB software for extracting the real-time data from the OPAL – RT Controller (OP4510) through Software-in-Loop (SiL) approach. All possible faults associated with a stand-alone solar PV-battery system are aggregated to execute the machine learning model. This data is pre-processed and the distribution plots are observed to describe the statistical nature of the data. The processed data is then used to train a Logistic Regression (LR) Model and Artificial Neural Network (ANN) Model to identify whether there is a fault or non-fault condition (Binary Classification) and then classify which fault case has occurred (Multi-class Classification). The models are trained and validated in the controller. The resulting comparative parameters such as accuracy and kappa values indicate that ANN model proves to be a better solution for fault identification and classification. Also, the cost functions (error) seem to be less in ANN model and the comparative results are discussed in this work. The proposed intelligent, fault identification and classification model being implemented with real-time data ensures reliability and immediate indication to the stakeholders on the status of the fault in the system. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： MATLAB

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A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

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IEEE Journal of Solid-State Circuits 2024年第6期59卷 1899-1911页

作者： Chen, Po-Shao Chen, Yen-Lung Lee, Yu-Chi Fu, Zih-Sing Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei10617 Taiwan

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of 25times 25 and 49times 49 pixels for blurred images of 129times 129 and 257times 257 pixels, respectively. Fabricated in 40-nm CMOS, the accelerator's core area is 3.98 mm2. The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of 25times 25 pixels for an image patch with 129times 129 pixels for deblurring a full-HD image in 1.7 s, achieving a 2562times shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5times higher normalized energy efficiency. © 1966-2012 IEEE.

关键词： CMOS integrated circuits

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Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

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computer Systems Science & engineering 2023年第7期46卷 181-193页

作者： V.S.Vaisakhi D.Surendran Department of Electronics and Communication Engineering Nehru Institute of Engineering and TechnologyCoimbatore641105India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641105India

In signal processing and communication systems,digital filters are widely *** some circumstances,the reliability of those systems is crucial,necessitating the use of fault tolerant filter *** strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the *** technology advances,more complicated systems with several filters become *** of the filters in those complicated systems frequently function in parallel,for example,by applying the same filter to various input ***,a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was *** fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the *** primary idea is to use structured authentication scan chains to study the internal states of finite impulse response(FIR)components in order to detect and recover the exact state of faulty modules through the state of non-faulty ***,a simple solution of Double modular redundancy(DMR)based fault tolerance was developed that takes advantage of the availability of parallel filters for image *** approach is expanded in this short to display how parallel filters can be protected using error correction codes(ECCs)in which each filter is comparable to a bit in a standard ECC.“Advanced error recovery for parallel systems,”the suggested technique,can find and eliminate hidden defects in FIR modules,and also restore the system from multiple failures impacting two FIR *** the implementation,Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of *** were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

关键词： Fault tolerance filters efficiency redundancy image denoising error correction codes double modular redundancy

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A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

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Neural Computing and Applications 2024年第21期36卷 12701-12723页

作者： El-Mawla, Nesma Abd Berbar, Mohamed A. El-Fishawy, Nawal A. El-Rashidy, Mohamed A. Communication and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Shebeen El-Kom Egypt Department of Computer Arab East Colleges Riyadh Saudi Arabia

Clinical decision support systems (CDSSs) can effectively detect illnesses such as breast cancer (BC) using a variety of medical imaging techniques. BC is a key factor contributing to the rise in the death rate among women worldwide. Early detection will lessen its impact, which may motivate patients to have quick surgical therapy. computer-aided diagnosis (CAD) systems are designed to provide radiologists recommendations to assist them in diagnosing BC. However, it is still restricted and limited, the interpretability cost, time consumption, and complexity of architecture are not considered. These limitations limit their use in healthcare devices. Therefore, we thought of presenting a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96. In order to decrease carbon emissions while developing the DL model for medical image analysis and meet the objectives of sustainable artificial intelligence, this study seeks to attain high accuracy at the lowest computing cost. It takes into consideration the various characteristics of the pathological variation of BC disease in mammography images to obtain high detection accuracy. It consists of six stages: identifying the region of interest, detecting spatial features, discovering the effective features of the BC pathological types that have infected nearby cells in a concentrated area, identifying the relationships between distantly infected cells in some BC pathological types, weighing the extracted features, and classifying the mammography image. According to experimental findings, Bi-xBcNet-96 beat other comparable works on the benchmark datasets, attaining a classification accuracy of 98.88% in DDSM dataset, 100% in INbreast dataset with 5.08% and 0.3% improvements over the state-of-the-art methods, respectively. Furthermore, a 95.79% reduction in computing complexity was achieved. © The Author(s) 2024.

关键词： computer aided diagnosis

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Noise robust automatic speech recognition: review and analysis

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International Journal of Speech Technology 2023年第2期26卷 475-519页

作者： Dua, Mohit Akanksha Dua, Shelza Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics and Communication Engineering National Institute of Technology Kurukshetra India

Automatic Speech Recognition (ASR) system is an emerging technology used in various fields such as robotics, traffic controls, and healthcare, etc. The leading cause of ASR performance degradation is mismatch between the training and testing environments. The main reason for this mismatch is the presence of noise during the testing phase of an ASR system. Various techniques have been used by different researchers in front and backend phases of ASR, to detect and handle the noise. However, a very few review papers have considered noise as a criterion to present the comparison among the existing research works. Hence, the objective of this survey is to analyze and review all the effective methods proposed by different scientists and researchers to boost the noise robustness of an ASR system. Initially, the paper discusses the basic architecture of an ASR system, the factors affecting the its performance, and noise problem formulation. Secondly, the work analysis existing state of the art noise robust ASR methods in terms of front end feature extraction techniques and backend classification model. Then, a detailed review in terms of various speech databases, that are used by these methods, is given. Finally, an analysis in terms of performance metrics of all these noise-resistant ASR techniques is presented. Also, the paper discusses various feature extraction techniques, backend classification methods, different speech databases and performance metrics in detail, while presenting the analysis. The paper also discusses the existing challenges, and describes future research directions in the area of building noise-resistant ASR systems. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Feature extraction

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Towards optimal path planning for sustainable data collection in real-time wireless sensor networks

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International Journal of computers and Applications 2024年第5期46卷 301-309页

作者： Ranjan, Rajeev Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna India Department of Computer Science and Engineering Bakhtiyarpur College of Engineering Bakhtiyarpur India

Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojourning at rendezvous points (RPs) and gathering data there from nearby sensor nodes (SNs). However, determining the optimal number and location of RPs is a hard problem. Metaheuristic algorithms are well suited to solve such kind of problems. In this paper, we propose a tour planning scheme for the MS by a combined approach that uses K-means clustering and Particle Swarm Optimization algorithm. The proposed scheme first determines a set of near-optimal RPs and then uses them to schedule the tour of the MS. Herein, the energy consumption of the overall scheme is reduced due to the low computational cost of the proposed algorithm. At last, the proposed scheme is shown to outperform the comparative algorithms through simulation. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Motion planning

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