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Optimization Techniques in Electronics: Advances, Challenges, and Future Directions

Optimization Techniques in Electronics: Advances, Challenges...

引用

international conference on ent Computing and Control systems (ICICCS)

作者： Gaurav Mishra Khushi Kumari Chethan Sharma Dept of CSE M.S. Ramaiah Institute of Technology Bangalore Dept of CSE(AI & ML) M.S. Ramaiah Institute of Technology Bangalore Dept of CSE(AI & ML) and Dept of CSE (Cyber Security) M.S. Ramaiah Institute of Technology Bangalore

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Optimization techniques support the solutions of contemporary electronic challenges by managing resources while improving energy conservation and boosting performance together with scalability capabilities. The rapid technological progress of Very Large-Scale Integration (VLSI) together with internet of Things (IoT) and computer networks and signal processing and communication systems requires improved optimization techniques. Research focuses on few selected studies from 2023 to 2025 to conduct an extensive review of optimization methods used in these technological fields. The review groups selected studies according to their application sectors and their methodologies as well as their findings while noting important advantages and constraints along with practical results. The objective of this work is to provide a comprehensive analysis of existing optimization strategies, identifying their strengths and limitations to guide future advancements. The review discusses future optimization directions based on the study of emerging trends which include AI optimization principles and quantum computing capabilities and multi-objective optimization approaches. Research has identified major gaps in scalability and computational complexity along with the need for real-world testing and energy efficiency studies for electronic system optimization. Future work recommendations target these gaps to improve electronic system optimization.

关键词： Quantum computing Reviews Scalability Very large scale integration signal processing Energy efficiency Computer networks internet of Things Computational complexity Optimization

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EM2-YOLO: Lightweight Remote Sensing image Detection 5th

EM2-YOLO: Lightweight Remote Sensing Image Detection

引用

5th international conference on 3D Imaging Technologies—Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Liu, Xinyi Song, Yaolian Wang, Can School of Information Engineering and Automation Kunming University of Science and Technology Yunnan650500 China

ISBN: (纸本)9789819751808

Remote sensing photographs have a wealth of information, and object detection methods are crucial in this. On mobile and embedded platforms, deep learning-based target identification algorithms are challenging to implement. Additionally, the issues of delayed target detection and poor accuracy of small target detection for remote sensing photos must be resolved immediately. To address these issues, we provide EM2-YOLOv4, a simple method for recognizing remote sensing images. MobileNetv2 acts as the backbone network to reduce model complexity, while depth-separable convolution enhances detection speed. Additionally, we introduce a feature enhancement module and an Efficient Channel Attention (ECA) module to improve model accuracy. To address the issue of unequal assignment of positive and negative samples, we incorporate Focal Loss into the loss function. Experimental demonstrate that the model can perform detection at 45 frames per second with an average accuracy of 94.11%. The model has been significantly reduced, as seen by the fact that it is only 48 MB in size, but it still maintains good detection accuracy and accelerates detection. The model is ideal for usage on embedded devices because of this feature. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical remote sensing

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Self-Supervised Cross-Scale Nonlocal Attention Network for Blind image Super-Resolution

Self-Supervised Cross-Scale Nonlocal Attention Network for B...

引用

IEEE Internatonal conference on Mobile Adhoc and Sensor systems (MASS)

作者： Yinong Li Jing Yu Chuangbai Xiao Computer College Beijing University of Technology Beijing China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Most existing image super-resolution (SR) meth-ods commonly assume that the degradation kernel is fixed and known. Blind SR aims to handle various unknown degradation processes closer to real-world applications and more generalizations. We propose a self-supervised cross-scale nonlocal attention network for blind SR (CNSR) which jointly models a blur kernel estimation module (KEM) based on a regularization model and a high-resolution image reconstruction module (HRM) based on a deep neural network. The low-resolution (LR) image is used as the supervision signal, and the blur kernel and high-resolution image are estimated simultaneously by iterating the two modules alternately. In HRM, we introduce a cross-scale nonlocal correspondence aggregation module (CNCAM) that uses the cross-scale self-similarity of images to provide additional information for image reconstruction. Experimental results show that CNSR can effectively improve image reconstruction performance.

关键词： Degradation Superresolution Estimation Artificial neural networks Smart systems Kernel image reconstruction signal resolution

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Improving Deep image Matting Via Local Smoothness Assumption

Improving Deep Image Matting Via Local Smoothness Assumption

引用

2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Wang, Rui Xie, Jun Han, Jiacheng Qi, Dezhen Center for Applied Statistics and School of Statistics Renmin University of China Beijing100872 China Lenovo Research Pcie Lab Beijing China School of Optics and Photonics Beijing Institute of Technology Beijing China Xiamen University Fujian Provincial Key Laboratory for Soft Functional Materials Research Department of Physics Xiamen361005 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Natural image matting is a fundamental and challenging computer vision task. Conventionally, the problem is formulated as an underconstrained problem. Since the problem is ill-posed, further assumptions on the data distribution are required to make the problem well-posed. For classical matting methods, a commonly adopted assumption is the local smoothness assumption on foreground and background colors. However, the use of such assumptions was not systematically considered for deep learning based matting methods. In this work, we consider two local smoothness assumptions which can help improving deep image matting models. based on the local smoothness assumptions, we propose three techniques, i.e., training set refinement, color augmentation and backpropagating refinement, which can improve the performance of the deep image matting model significantly. We conduct experiments to examine the effectiveness of the proposed algorithm. The experimental results show that the proposed method has favorable performance compared with existing matting methods. © 2022 IEEE.

关键词： Deep learning

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Enhancing the Performance and Reliability of IoT systems Using Predictive Route Recovery Mechanism

Enhancing the Performance and Reliability of IoT Systems Usi...

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IEEE Region 10 international conference TENCON

作者： Mohammad M. Kadhum Information and Communications Technology - Security Systems & Networking School of Advanced Technology Algonquin College Ontario Canada

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

The rapid proliferation of the internet of Things (IoT) has brought numerous challenges related to the performance and reliability of these systems. Frequent link failures due to the dynamic nature of IoT environments, including device mobility and varying signal strengths, often disrupt communication. This paper presents a novel overhearing-based route recovery mechanism designed to predict potential link failures and maintain active routes by leveraging the overhearing capabilities of neighboring nodes. This proactive approach significantly reduces packet loss, enhances data transmission efficiency, and ensures robust network connectivity, thereby improving the overall performance and reliability of IoT systems. The proposed mechanism's efficacy is demonstrated through extensive simulations using the ns-3 network simulator.

关键词： Wireless communication Performance evaluation Wireless sensor networks Packet loss Reliability internet of Things Data communication IEEE Regions

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Asynchronous DRL and Trajectory Prediction based Vehicle Edge Computing Task Offloading

Asynchronous DRL and Trajectory Prediction Based Vehicle Edg...

引用

international Congress on image and signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Wenlong Liu Haifeng Sun Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

With the rapid expansion of the internet of Vehicles (IoVs), the complex data generated by in-vehicle devices has surged, challenging vehicle computational resources to meet real-time processing demands. To tackle this challenge, Vehicular Edge Computing (VEC) provides improved computational capabilities via roadside units (RSUs) and mobile vehicles. Nevertheless, the rapid movement of these vehicles restricts the communication with base stations, making task offloading a more complex endeavor. This study introduces a task offloading strategy that utilizes trajectory prediction to boost task completion rates by forecasting the base stations that a vehicle is likely to encounter. Furthermore, we propose an optimization method grounded in deep reinforcement learning, specifically A3C, to streamline the offloading process. Our simulation findings reveal a notable increase in task completion rates, coupled with shorter completion times.

关键词： Base stations signal processing algorithms signal processing Telematics Prediction algorithms Real-time systems Trajectory Delays Long short term memory Edge computing

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IoT-Enabled Intelligent Traffic Control System with Real-Time Congestion Management and Road Safety using Python and OpenCV

IoT-Enabled Intelligent Traffic Control System with Real-Tim...

引用

Artificial Intelligence and Quantum Computation-based Sensor Application (ICAIQSA), international conference on

作者： Giridhar Urkude Prof. Ankita Avthankar Department of Computer Science and Enginnering Symbiosis Institute of Technology Nagpur Nagpur Maharashtra

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

The internet of Things has become more significant in many fields. Traffic control and road safety are the most important areas for both the public's safety and the appropriate operation of vehicles on the road. These internet of Things (IoT) devices are designed to help humans with usage because human power is limited. As these data are used increasingly often to prevent traffic accidents, a reliability issue develops. In order to provide the highest level of safety possible for road safety and traffic management, we address in this paper “IoT-Enabled Intelligent Traffic Control System” aims to solve traffic congestion through an innovative approach. The proposed traffic management system through the internet of Things dynamically adjusts traffic signal lights based on real-time data. Optimizing signal timings enhances the traffic flow, reduces waiting and halt times, and improves overall road safety. The report outlines the system's design, implementation, and testing methodology, emphasizing the potential impact of IoT-driven automation in urban transportation management.

关键词： Transportation Traffic control Road safety Real-time systems Timing internet of Things Reliability Traffic congestion System analysis and design Testing

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Evaluation of Deep Learning Approaches for Air Quality Analysis using an image Dataset

Evaluation of Deep Learning Approaches for Air Quality Analy...

引用

Intelligent Cyber Physical systems and internet of Things (ICoICI), international conference on

作者： Rajanish Kumar Kaushal Himani Pandey G. Kalaiarasi Sujata Arya Devyani Rawat Natrayan L Department of Electrical Engineering Chandigarh University Mohali Punjab India Department of Applied Science and Humanities ITM (SLS) Baroda University Vadodara Gujarat India Department of Advanced Computer Science and Engineering Vignan's Foundation for Science Technology and Research (Deemed to be university) Guntur Andhra Pradesh India Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Air pollution has caused widespread concern, particularly in developing nations such as India and China. Despite the availability of expensive or inaccurate methods such as sensor-based or community network-based approaches, image-based air pollution evaluation still requires significant improvement. In response to this pressing issue, our research develops an accurate Deep Learning (DL) model for estimating air quality from environmental images. We employed Visual Geometry Group 19 (VGG-19) and Residual Network 50 (ResNet-50) as DL models. The air quality dataset was collected from the internet and grouped into three categories: Good, Moderate, and Severe, based on the Particulate Matter (PM) values. image pre-processing techniques such as resizing and rescaling were used. The DL models were evaluated using accuracy, loss, and time metrics. ResNet-50 was identified as the best model for identifying air quality based on the images. The achieved accuracy level by ResNet-50 is 97.87%, and it takes 45.02 seconds to analyze the test data count of 90 images. This shows the effectiveness of the ResNet-50 model in air quality prediction.

关键词： Deep learning Training Visualization Accuracy Atmospheric modeling Urban areas Web pages Air pollution Testing Residual neural networks

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ARTI: One New Adaptive Elliptical Weighting Model Combining with the Tikhonov-p-norm for image Reconstruction 1

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12th international conference on Broadband Communications, Networks, and systems, BROADNETS 2021

作者： Zhu, Chunhua Shi, Zhen Yang, Weidong Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Henan Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

To reconstruct the target-induced attenuation image keeping consistent with the observed measurement data, this paper explores the use of a new horizontal distance attenuation-based elliptical weighting model in building an attenuation image, where a horizontal distance attenuation factor and a vertical distance attenuation factor are introduced, respectively, which is able to clear the difference of the voxel weightings perpendicular to the line-of-sight (LOS) direction, as well as the difference of the voxel weightings parallel to the LOS direction. Compared with the existing model, the proposed model can additively reflect the occlusion effect of the radio frequency signal when the target is close to the transceiver nodes. Besides, the Tikhonov-p-norm regularization is incorporated into the image reconstruction, which makes full use of the sparse ability of the p-norm (0 © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image reconstruction

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U-Net-based COVID-19 CT image Semantic Segmentation: A Transfer Learning Approach 7

U-Net-based COVID-19 CT Image Semantic Segmentation: A Trans...

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7th international conference on image and signal Processing and their Applications, ISPA 2022

作者： Ferdi, Abdesselam Benierbah, Said Ferdi, Youcef Frères Mentouri Constantine 1 University Electronics Department Constantine Algeria National Higher School of Biotechnology Biotechnology Department Constantine Algeria

ISBN: (纸本)9781665480420

Deep learning (DL) algorithms are widely applied in many disciplines such as medical imaging, bioinformatics, and computer vision. DL models have been used in medical imaging to perform image segmentation, classification, and detection. During the outbreak of the COVID-19 pandemic, DL has been extensively used to develop COVID-19 screening systems. The reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard method for COVID-19 screening. However, DL has been proposed to detect patients infected with COVID-19 through radiological imaging in Chest X-rays and chest computed tomography (CT) images. This paper proposes transfer learning to train modified U-Net models to segment the COVID-19 chest CT images into two regions of lung infection (ground-glass and consolidation). The proposed modified U-Net models were constructed by replacing the encoder part with a pre-trained convolutional neural network (CNN) model. Three pre-trained CNN models, namely, EfficientNet-b0, EfficientNet-b1, and EfficientNet-b2 were used. The proposed models were evaluated on the COVID-19 CT images Segmentation dataset available in an open Kaggle challenge. The obtained results show that the proposed EfficientNet-b2_U-Net model yielded the highest FScore of 0.5666. © 2022 IEEE.

关键词： COVID-19

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