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An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

Computer Systems Science & engineering 2023年第2期44卷 1647-1657页

作者： M.Usharani B.Sakthivel S.Gayathri Priya T.Nagalakshmi J.Shirisha Department of Electronics and Communication Engineering Er.Perunal Manimekalai College of EngineeringKonneripalliHosur635117India Department of Electronics and Communication Engineering Pandian Saraswathi Yadav Engineering CollegeSivagangaiTamilnadu630561India Department of Electronics and Communication Engineering R.M.D Engineering CollegeGummidipundiTamilnadu601206India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur522502India Department of Electronics and Communication Engineering Malla Reddy Engineering CollegeHyderabadTelangana500015India

Approximate computing is a popularﬁeld for low power consumption that is used in several applications like image processing,video processing,multi-media and data *** Approximate computing is majorly performed with an arithmetic circuit particular with a *** multiplier is the most essen-tial element used for approximate computing where the power consumption is majorly based on its *** are several researchers are worked on the approximate multiplier for power reduction for a few decades,but the design of low power approximate multiplier is not so *** seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher *** overcome these issues,the digital circuits are applied to the Deep Learning(DL)approaches for higher *** recent times,DL is the method that is used for higher learning and prediction accuracy in severalﬁ***,the Long Short-Term Memory(LSTM)is a popular time series DL method is used in this work for approximate *** provide an optimal solution,the LSTM is combined with a meta-heuristics Jel-lyﬁsh search optimisation technique to design an input aware deep learning-based approximate multiplier(DLAM).In this work,the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate *** optimal hyperparameters of the LSTM model are identiﬁed by jelly search ***ﬁne-tuning is used to obtain an optimal solution to perform an LSTM with higher *** proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a func-tion of area,delay,power and error *** experimental results on an 8-bit multiplier with an image processing application shows that the proposed approx-imate computing multiplier achieved a superior area and power reduction with very good results on error rates.

关键词： Deep learning approximate multiplier LSTM jellyﬁsh

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Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

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Multimedia Tools and Applications 2024年 1-19页

作者： J, Jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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An indoor IoT-based LiFi system using LEDs for sensor data transfer

AIMS Electronics and Electrical Engineering

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AIMS electronics and Electrical engineering 2025年第2期9卷 118-138页

作者： Eltokhy, Mostafa A.R. Abdel-Hady, Mohamed Haggag, Ayman Hamad, Hisham A. Hosny, Tarek Youssef, Ahmed A.F. El-Rifaie, Ali M. Electronics Technology Department Faculty of Technology and Education Helwan University Cairo Egypt Communication Engineering Dept Al-Safwa High Institute of Engineering High Ministry of Education Cairo Egypt College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait

The Internet of Things (IoT) has an ever more significant function in many areas of our modern world. It improves the overall quality of life, where almost everything, including objects and devices, can be linked to the cloud and thus be hyper-connected. Within this changing IoT system, this paper focuses on data transfer, emphasizing the critical role of light fidelity (LiFi) technology. LiFi technology is an up-and-coming technology that makes use of light-emitting diodes (LEDs) and photodetectors (PDs) as transmitters and receivers (RX), respectively, for concurrent data communications and lighting. This paper proposes a single-input multiple-output IoT-based LiFi system using an LED array, phototransistor, and ambient light sensor-based RXS for simultaneous data transmission. DHT11 sensor is used to measure the environmental conditions and store collected data via Wi-Fi in the cloud server using HTTP protocol. Through indoor LiFi communication, the cloud streaming data is transferred using pulse position modulation (PPM) at a frequency of 2 KHz. The experimental results illustrate transferring and receiving data successfully at a communication distance of 1 meter. MATLAB software is used to evaluate the performance of the spatial radiance pattern and illuminance intensity of an LED Array used and received power depending on a line-of-sight (LOS) channel. © 2025 the Author(s).

关键词： MATLAB

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FPGA-based reflection image removal using cognitive neural networks

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Applied Nanoscience (Switzerland) 2023年第3期13卷 2539-2553页

作者： Saptalakar, Bairu K. Latte, Mrityunjaya V Department of Electronics and Communication Engineering SDM College of Engineering and Technology Karnataka Dharwad580002 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Karnataka Bengaluru560060 India

There is an enormous increase in the resource usage and certain process is required to satisfy the user requirement. Thus, there is a process integration on IoT and data analytics which paves the way for the smart city development. The energy management plays a crucial role based on the processing of image dataset and optimization using the artificial intelligence. To increase the visibility of the image based on the reflection concept, cognitive models are used by relocating the clear glass images. The above process is used widely in computer vision applications as it is ill nature and makes the additional precursors more challenging. Then, eliminating reflections problem is considered for various heuristic observations or other assumptions and it fulfills in practical conditions. In this paper, we generalize the assumptions for issues of reflection based on usage of different information or impose new limitations elimination. The image elimination helps in effective energy utilization by managing the resource through CNN, i.e., optimization techniques of cognitive networks. To overcome the various optimization functions, long computational time required and reflection removal methods, i.e., conventional have not guaranteed their performance. It helps in developing the smart cities as energy plays a significant role as the propose system is integrated with IoT and data analytics. As FPGA system is deployed, it can screen the images by providing effectively the sustainable policies with energy efficient platform through data compression. In the performance analysis, the proposed algorithm is effective as compared with current approaches using CNN methods. Parallel processing of Images in Digital Signal Processing tools will create degradation of the images and it is difficult to achieve high performance, so the implementation is done using FPGA. © 2022, King Abdulaziz City for Science and Technology.

关键词： Field programmable gate arrays (FPGA)

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Performance analysis of optical spectral amplitude CDMA system unipolar codes with double weight construction design

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Journal of Optical Communications 2025年第1期46卷 27-39页

作者： Harshavardhana, M.N. Tarannum, Suraiya Department of Electronics and Communication JSS Academy of Technical Education Uttar Pradesh Noida India Department of Electronics and Communication HKBK College of Engineering Nagavara Karnataka Bengaluru India

Optical Codes Division Multiple Access is a multiplexing approach in which the transmitter a distinguishable optical code is assigned to each user in the communication channel. OCDMA is designed for radio frequency (RF) communication systems and it is a variation of the CDMA system. Subsequently, to develop all OCDMA communication systems, the success of CDMA in wireless networks, as well as the full benefits of fibre optics, such as good security, extremely high bandwidth, low attenuation, and long-haul transmission. In this research Spectral Amplitude Coding-Optical Code Division Multiple Access is proposed. Initially, Modified Double Weight and Zero Cross-Correlation (MDW-ZCC) is proposed in this research to construct the code design of SAC-OCDMA, it produces the best possible autocorrelation and cross-correlation values. Subsequently, a combined Single Photodiode (SPD) and Direct detection technique is introduced to decode the transmitter information and it reduces the noise. Accordingly, a Gaussian approximation technique is presented to estimate the Bit error rate from the eye diagram of BER and Q-factor. Accordingly, the proposed method is simulated using Python software. The performance metrics are Q-factor, and bit error rate and the proposed method is compared with the existing Three-Dimensional Single Weight Zero Cross-Correlation (3D-SWZCC) Code, Two Dimensional Spectral/Spatial Cyclic Shift (2D-SSCS), and Permutation Matrix Zero Cross-Correlation (PM-ZCC) Code. Subsequently, these results exhibit that the proposed method produces the best performance than the other methods and it estimates the BER. Consequently, future work is based on the study of 3D codes with m-sequence code in the spectral/time/spatial OCDMA domain. The approach has elegantly integrated the design of an encoder and a decoder, allowing the system to efficiently create code in 3D bipolar mode, respectively. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Photodiodes

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Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

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Energy engineering 2023年第8期120卷 1747-1761页

作者： Nisha Vasudevan Vasudevan Venkatraman A.Ramkumar T.Muthukumar A.Sheela M.Vetrivel R.J.Vijaya Saraswathi F.T.Josh Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Computer Science Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Electrical and Electronics Engineering Kongunadu College of Engineering and TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Kongu Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Narasu’s Sarathy Institute of TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Panimalar Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Karunya Institute of Technology&SciencesTamil NaduIndia

MigroGrid(MG)has emerged to resolve the growing demand for *** because of its inconsistent output,it can result in various power quality(PQ)*** is a problem that is becoming more and more important for the reliability of power systems that use renewable energy ***,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply ***,this research focuses on power quality enhancement in the MG using hybrid shunt ***,the performance of the filter mainly depends upon the design,and stability of the *** efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)*** performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this ***,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in *** the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

关键词： Artificial intelligence resistive inductive load shunt hybrid filter smart grid adaptive fuzzy back-stepping power factor

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Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

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Computer Systems Science & engineering 2023年第2期44卷 1719-1733页

作者： S.Nandagopal G.Karthy A.Sheryl Oliver M.Subha Department of Computer Science and Engineering Nandha College of TechnologyErode638052TamilnaduIndia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science and Engineering St.Joseph's College of EngineeringChennai600119TamilnaduIndia Department of Electronics and Communication Engineering University College of Engineering NagercoilNagercoil629004TamilnaduIndia

Human Action Recognition(HAR)and pose estimation from videos have gained signiﬁcant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,*** considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difﬁcult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so *** the literature,it is observed that hard to deal with the temporal dimension in the action recognition *** neural network(CNN)models could be used widely to solve *** this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity *** KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose *** the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human ***,an optimal DCNN model is developed to classify the human activities label based on the extracted key *** improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch *** experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.

关键词： Human activity recognition pose estimation key point extraction classiﬁcation deep learning RMSProp

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Hector SLAM Mapping and Localization System using ROS and LIDAR 15

Hector SLAM Mapping and Localization System using ROS and LI...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Patil, Medha Thakur, Aarti Bagul, Priyanka Ajgar, Yogita Department of Electronics and Telecommunication Engineering Modern Education Society's College of Engineering Maharashtra Pune India

ISBN: (纸本)9798331300579

This paper demonstrates the use of laser technology in environment scanning for performing indoor positioning and mapping. A 360-degree LiDAR forms the core of the project and the input device used for understanding its surrounding. Hector SLAM and ROS are the two technologies implemented in this project. Hector SLAM where SLAM stands for Simultaneous Mapping and Positioning, is the algorithm used to map the environment and improve navigation accuracy. The aim of this project is to allow an autonomous mobile robot to understand an unknown environment and map its surroundings to perform further tasks using the created map. © Grenze Scientific Society, 2024.

关键词： Mapping

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A Power Aware Long Short-Term Memory with Deep Brief Network Based Microgrid Framework to Maintain Sustainable Energy Management and Load Balancing

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 1-26页

作者： Gowtham, N. Prema, V. Elmorshedy, Mahmoud F. Bhaskar, M.S. Almakhles, Dhafer J. Department of Electrical and Electronics Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Electrical and Electronics Engineering B.M.S. College of Engineering Bengaluru India Renewable Energy Lab Department of Electrical Engineering College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Electrical Power and Machines Engineering Department Faculty of Engineering Tanta University Tanta31733 Egypt

Microgrids are seen as the future of reliable, sustainable and green energy source for myriad applications. The increasing dependence on microgrid also adds challenges on reliable management of power supply to vividly variant consumers, the major chunk being households coupled with an unprecedented rise in the demand for EV charging. This study aims at presenting a deep Long Short-Term Memory with Deep Brief Network model to reliably predict the grouped energy load and solar energy outcome in a community microgrid. A cutting-edge hybrid metaheuristic algorithm will be taken into consideration for optimizing the load dispatch of community microgrids that are connected to the grid. Three different scheduling scenarios are evaluated to establish an ideal dispatching design for a grid-linked community microgrid with solar elements and energy storage systems feeding electricity loads and charging electric vehicles. The prediction outcomes are integrated into the model to accommodate the uncertainties associated with solar energy outcome and residential energy load and EV charging to achieve a supply-demand equilibrium. The objective of the proposed model is to obtain an energy-efficient system capable of balancing the load and power of microgrid system which remains unperturbed by the aforesaid oscillations. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Long short-term memory

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A New AI Powered Transient Stability Enhancement Scheme for LVRT in Grid-Integrated WPS 2

A New AI Powered Transient Stability Enhancement Scheme for ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Ponrekha, M. Balaraman, Sujatha Manimala, P. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Government College of Engineering Department of Electrical and Electronics Engineering Bodinayakanur India

ISBN: (纸本)9798331534950

Grid disturbances such as line faults can cause a momentary decrease in grid voltage. Reactive power deficiency during voltage dips may cause instability of grid integrated renewable based electric generators or disconnection from the power grid. Therefore, the ability of electric generators to maintain connectivity when the voltage drops is known as LVRT (low-voltage ride-through) functionality. To avoid a short circuit at the high voltage level from resulting in a significant loss of generation, LVRT is necessary at the distribution level of electricity. Current industrial practice is to use Static VAR compensators (SVC) and static synchronous compensators (STATCOM) to enable dynamic reactive power during grid disturbances, but this scheme requires additional investment and also increase system complexity. In this research work, a novel cost-effective AI powered transient stability enhancement scheme using Fuzzy Logic is proposed for LVRT in grid integrated Wind Power System without using any FACTS devices. Fuzzy logic controller is an ideal tool for handling the uncertainty and non-linearity in wind power systems. It enables to model human reasoning, real-time decision-making and offers better adaptability to varying grid conditions. Considering the voltage dip percentage and the fault time during voltage sag, the FLC decides either the WPS should remain connected to the power grid or not as per grid code requirements. Indian grid code is considered in this study. In order to support the grid's voltage recovery and meet the LVRT reactive current requirement, reactive power is also injected. To verify the control system performance, MATLAB/Simulink simulations are run on a 2 MW grid integrated PMSG based WPS. © 2024 IEEE.

关键词： MATLAB

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