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Performance Enhancement of OTFS Massive MIMO Systems using the Optimized Channel Estimation Technique of the Modified 3D Structured Orthogonal Matching Pursuit

Performance Enhancement of OTFS Massive MIMO Systems using t...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Jatangi, Sravan Kumar Kamatham, Yedukondalu JNTUH University College of Engineering Science & Technology Department of Electronics and Communication Engineering Hyderabad India CVR College of Engineering Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350361537

Orthogonal Time Frequency Space (OTFS) modulation has emerged as a potential approach for communications, providing a better performance in circumstances which involve severe Doppler shifts and time-varying channels. However, accurate estimation of channel characteristics in OTFS systems typically requires a large amount of pilot overhead, limiting overall communication system efficiency. This paper introduces the Modified 3D Structured Orthogonal Matching Pursuit (M3D-SOMP) algorithm as a novel approach to address this challenge. The suggested M3D-SOMP method is intended to optimize the allocation as well as the utilization of pilot symbols in the context of OTFS channel estimation. M3D-SOMP uses the hierarchical framework of the 3D channel matrix to effectively select pilot symbols that give rich spatial and temporal information, minimizing the necessary pilot overhead while retaining excellent estimation accuracy. The algorithm adapts to dynamic channel circumstances, enabling stable performance in rapidly changing environments. The simulation results show that the M3D-SOMP algorithm outperforms traditional approaches in terms of channel estimate accuracy and spectral efficiency. M3D-SOMP's lower pilot overhead helps to more effective resource use, making it especially suitable for communication systems operating in bandwidth-limited or interference-prone circumstances. The suggested method expands the possibilities for improving the reliability and efficiency of OTFS communication systems, making them more resilient to demanding channel circumstances. © 2024 IEEE.

关键词： Channel estimation

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A comprehensive review on performance-based comparative analysis, categorization, classification and mapping of text extraction system techniques for images

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Multimedia Tools and Applications 2025年第5期84卷 2327-2484页

作者： Ghai, Deepika Saxena, Sobhit Dhingra, Gittaly Tripathi, Suman Lata School of Electronics and Electrical Engineering Lovely Professional University Phagwara144411 India Department of Electronics and Communication Plaksha University Mohali140306 India

In today’s quick world of images and videos, text extraction became the responsibility of machine/deep learning techniques. The various techniques utilized for text extraction reported by various authors need to be reviewed comprehensively with proper categorization and classification. The performance parameters of all available text extraction techniques need close monitoring in a variety of challenging environments to identify the best performer and also the scope for further improvement. In this work, the mapping of available techniques is accomplished with the processing steps of the text extraction system. Further, the techniques are classified on the basis of image type viz. document text, scene text, and caption text. Challenges in text extraction are segregated on the basis of various text properties available in images. For better illustration and identification of the best, detailed comparative analysis with respect to edge-based, connected component (CC)-based, texture-based, and hybrid-based techniques is presented in tabular form and bar-chart indicating performance evaluation metrics (DR%, PR%, RR%, and processing time) along with processing stage for various image types. Available datasets are summarized highlighting size, and features along with web links for providing better reach to the audience. In this work, the best-performing techniques are identified dataset-wise. The capabilities and limitations of the same techniques are discussed according to obtained parameter percentages, which provides a direction toward future work by highlighting research gaps collectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, and Mobile Edge Computing for Smart Cities 3

Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, an...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Ramkumar, G. Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331509675

An Internet of Things (IoT) network is made up of multiple connected devices to collect and exchange information over the Internet for remote monitoring and control. IoT has drawbacks such as data security vulnerabilities posed by networking devices, data protection issues associated with data collection and sharing, and interoperability challenges among different IOT platforms and devices. A Global Channel Matching (GCM) algorithm is integrated into the Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, and Mobile Edge Computing for Smart Cities (ECMMS) in this article, which aims to optimize relay transmission links for use in IoT networks. In a smart city environment, the GCM algorithm is designed to mimic Firefly behavior and dynamically adjust relay placement for network efficiency and interference reduction. The network model includes IoT devices connected to a Virtual Mobile Edge Computing (MEC) fabric to facilitate task offloading and distributed computing. The proposed framework aims to improve computational efficiency by creating virtual servers that use networked IoT devices based on task requirements. ECMMS has demonstrated through detailed simulations and evaluations that it is more effective in optimizing relay selection, reducing interference, and achieving optimal data rates, which supports the scalability and reliability of IoT applications in smart city infrastructure. Using these parameters with discussed the overcomes of our ECMMS model when compared to MNMD, SHBT, and IHAG models. Such as packet loss, average throughput, average end-to-end delay, energy efficiency, and data success rate where calculate. Here packet loss and data success rates are high rates of 99 % and 95.74 %. Were average throughput, energy efficiency, and average end-to-end delay archives low efficiency of 71.02, 254.06, and 254.06 are calculated. © 2025 IEEE.

关键词： Mobile edge computing

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Reliable network lifetime and energy-aware routing protocol for wireless sensor network using hybrid particle swarm-flower pollination search algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16183-16193页

作者： Vasanthi, G. Prabakaran, N. Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering KLEF University Andhra Pradesh Vijayawada India

In this paper, we propose a trusted cluster based energy and lifetime aware routing (TCELR) protocol for WSN using hybrid swarm swarm-differential search algorithm. In TCELR protocol, first, we introduce an enhanced leader particle swarm optimization (ELPSO) algorithm for cluster formation. Second, we illustrate the flower pollination (FP) algorithm to compute the trust degree of each client in the cluster. The highest trust node is considered as cluster head (CH) in the cluster and it is act as sink node of cluster members to perform intra cluster routing and Third, a group search optimizer with multiple producer (GSOMP) algorithm is used for inter cluster routing to forward sensed data between different clusters. The performance of the proposed TCELR protocol is analyzed and the results are compared with the existing state-of-art routing protocols in terms of throughput, packet delivery ratio, network lifetime are increased and packet loss rate, end to end delay, jitter are decreased. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Routing protocols

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Smart IoT Solutions for Precise COVID-19 Lung and Heart Disease Identification via Bayesian Neural Networks and Hybrid Optimization

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SN Computer Science 2024年第5期5卷 510页

作者： Nandini, K.P. Seshikala, G. School of Electronics and Communication Engineering REVA University Karnataka Bengaluru India

COVID-19 spreads widely, making mitigation and elimination challenging. The illness has worsened due to persistent lung, cardiac, and pulmonary issues. Thus, COVID-19 patients must be identified swiftly and accurately. Deep learning and chest X-rays identify COVID-19. However, various noisy image methods have exhibited unpredictability, overfitting, and erroneous predictions. A Bayesian Neural Network (BNN) and a hybrid Global Sunflower Pollination-based Backtracking Search Optimization (GSP-BSO) algorithm solve these challenges in our unique solution. This approach detects COVID-19 lung and heart problems better. We use Raspberry Pi-based IoT sensors to track patients’ pulse, blood pressure, and body temperature. Analysis data is securely stored on a cloud server. We undertake experiments using MosMed Data and COVID-19 ECG image datasets using Python’s TensorFlow module. The results demonstrate significant time and energy savings and improved detection accuracy, precision, and *** conclusion, BNN and GSP-BSO swiftly and correctly diagnose COVID-19-related lung and heart diseases. Raspberry Pi-based IoT devices improve patient care and administration by monitoring and collecting data in real time. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： BNN COVID-19 Deep learning IoT

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Research on Task Scheduling Model of Ant Colony Optimization Cloud Computing Platform for Online Practical Customer-training Application

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IEIE Transactions on Smart Processing and Computing 2024年第3期13卷 243-253页

作者： Wang, Hongtao School of Electronic and Communication Engineering Jilin Technology College of Electronic Information Jilin132021 China

With the continuous development of internet information technology, cloud-computing task-scheduling platform technology is gradually maturing. Cloud computing is profoundly changing every aspect of people's lives and providing many conveniences. With the application of cloud computing in more fields, more extensive applications and efficient task scheduling algorithms have become increasingly important. This research focuses on the problem of task-scheduling methods for cloud computing platforms in customer-oriented online training systems. Based on the optimization of the ant colony algorithm, an ant colony optimization (ACO) cloud-computing task-scheduling algorithm is proposed. The research results indicate that when the number of tasks is 300, the makespan value of the optimized ant colony cloud scheduling algorithm (OACC) is 340, that of the discrete firefly algorithm (DFA) is 350, that of multi-objective differential evolution (MODE) is 380, and that of improved group search optimization (IGSO) is 409. The overall performance of OACC was 20.3% higher than that of IGSO. OACC maintained a low and stable degree of imbalance (DI) in different task count tests. At a task volume of 300, the overall utility evaluation of the ACO cloud-computing task-scheduling algorithm was 146, which is 31.5% higher than ACO, 18.7% higher than TACO, and 8.1% higher than LB-AACO. The experimental results meet expectations and indicate that the OACC cloud-computing task-scheduling algorithm proposed in the study has high task-processing ability and efficiency and is capable of scheduling tasks on cloud computing platforms for customer-oriented online training systems. Copyrights © 2024 The Institute of electronics and Information Engineers.

关键词： Ant colony optimization

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FusionEXNet: an interpretable fused deep learning model for skin cancer detection

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International Journal of Computers and Applications 2024年第9期46卷 743-753页

作者： Gautam, Yuvika Gupta, Piyush Kumar, Deepika Kalra, Bhavya Kumar, Abhinav Hemanth, Jude D. Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Electronics & Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India

Skin cancer poses a significant burden on mankind and healthcare systems globally, necessitating the development of effective diagnostic and treatment strategies. This paper introduces FusionEXNet, an innovative and interpretable fused deep-learning model for skin cancer detection. FusionEXNet leverages the strengths of EfficientNetV2S and XceptionNet architectures to extract robust features from dermoscopic images, achieving superior performance compared to individual models. While XceptionNet and EfficientNetV2S attained accuracies of 88.82% and 88.01%, respectively, FusionEXNet surpassed them with an impressive accuracy of 90.83%. To enhance model interpretability, Explainable Artificial Intelligence (XAI) techniques, such as SmoothGrad and Faster Score-CAM, are integrated, providing valuable insights into the decision-making process. The model was trained and evaluated using the extensive HAM10000 dataset, consisting of over 10,000 high-resolution images across seven skin lesion categories. The proposed FusionEXNet model offers a reliable, accurate, and interpretable system for skin cancer detection, contributing to improved patient outcomes and more efficient diagnostic processes. This study underscores the potential of combining advanced CNN architectures with XAI methods to create powerful tools for dermatological diagnostics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Deep learning

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Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7

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High Voltage 2025年第1期10卷 69-77页

作者： Yong Yang Shuai Yang Chuan Li Yunxuan Wang Xiaoqian Pi Yuxin Lu Ruohan Wu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and ElectronicsHuazhong University of Science and TechnologyWuhanChina State Grid Hunan Electric Power Company ChangshaChina

Efficient and accurate insulator defect detection is essential for maintaining the safe and stable operation of transmission ***,the detection effectiveness is adversely impacted by complex and changeable environmental backgrounds,particularly under extreme weather that elevates accident ***,this research proposes a high-precision intelligent strategy based on the synthetic weather algorithm and improved YOLOv7 for detecting insulator defects under extreme *** proposed meth-odology involves augmenting the dataset with synthetic rain,snow,and fog algorithm ***,the original dataset undergoes augmentation through affine and colour transformations to improve model's generalisation performance under complex power inspection *** achieve higher recognition accuracy in severe weather,an improved YOLOv7 algorithm for insulator defect detection is proposed,integrating focal loss with SIoU loss function and incorporating an optimised decoupled head *** results indicate that the synthetic weather algorithm processing significantly improves the insulator defect detection accuracy under extreme weather,increasing the mean average precision by 2.4%.Furthermore,the authors’improved YOLOv7 model achieves 91.8%for the mean average precision,outperforming the benchmark model by 2.3%.With a detection speed of 46.5 frames per second,the model meets the requirement of real-time detection of insulators and their defects during power inspection.

关键词： weather extreme algorithm

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Adaptive Bit-Loading and Channel Estimation with IMN For Plain Line and Line with Bridge Taps in Broadband PLC 2

Adaptive Bit-Loading and Channel Estimation with IMN For Pla...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and electronics, ICIITCEE 2024

作者： Smitha Gayathri, D. Rani, K. R. Usha Shirur, Yasha Jyothi BNM Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Rvce Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

The concept of transmitting VDSL2 broadband over power lines has gained substantial attention recently, particularly for its utility in distributing broadband within residential properties. Power lines and telephone lines exhibit distinct characteristics, such as differences in topology and load impedance. In this article, we employ the ABCD parameters of various sections to analyze the power line channel, specifically examining scenarios with up to 10 bridge taps over a 600-meter distance. We consider noise profiles that include periodic impulse noise, which is commonly observed in power line sections, as well as additive white Gaussian noise (AWGN). We also calculate the power spectral density (PSD) of impulse *** loading profiles are determined through the use of Discrete Multitone Transmission (DMT) in the context of VDSL2, with a bandwidth of 30 MHz. Our findings suggest that a lower Transmit Power Spectral Density (PSD) can be sufficient to achieve comparable data speeds to conventional VDSL2 technology, especially in the presence of bridge taps. However, in situations involving inductive loads in the bridge taps, which is common in residential settings, we propose a two-step strategy. Firstly, we recommend enhancing existing VDSL2 modem frontend hybrids by incorporating adjustable impedances that closely match the loaded line. Secondly, we suggest incorporating the ability to incrementally increase the Transmit Power Spectral Density (PSD) and add sub-bands to effectively achieve the desired data rates, similar to the seamless approach used in VDSL2. © 2024 IEEE.

关键词： AWGN channel modelling discrete multitone DMT Power line communication VDSL2

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Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs

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Computer Systems Science & engineering 2023年第1期44卷 99-112页

作者： D.Loganathan M.Balasubramani R.Sabitha S.Karthik Department of Electronics and Communication Engineering Info Institute of EngineeringCoimbatore641107India Department of Computer Science and Engineering SNS College of TechnologyCoimbatore641035India

Sensors are considered as important elements of electronic *** many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop ***,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much ***,reducing energy con-sumption can increase the network lifetime in effective *** that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor *** that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member *** providing equal rate of energy consumption among nodes,the dimensions of framed clusters are ***,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base *** evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head ***,the model also provides higher throughput and minimal delay in delivering data packets.

关键词： Wireless sensor networks energy consumption load balanced clustering finest routing

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