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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wang, Zhilu Lin, Haiyang Liu, Qi Zhang, Yonghong Liu, Xiaodong Nanjing University of Information Science and Technology School of Computer Science Jiangsu Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Nanjing China Nanjing University of Information Science and Technology School of Software Jiangsu Nanjing China Nanjing University of Information Science and Technology School of Automation Jiangsu Nanjing China Edinburgh Napier University School of Computing Edinburgh United Kingdom

ISBN: (纸本)9798350365658

With the rapid development of 5G and Internet of Things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. The massive amount of distributed data generated by these devices offers unprecedented opportunities for deep learning, especially in fields like computer vision and edge computing. However, this data often exists in isolated data silos, and due to its privacy-sensitive nature, it cannot be directly processed in a centralized manner. Additionally, the data heterogeneity and devices heterogeneity further exacer-bates the challenges in federated learning (FL). Moreover, with the implementation of data privacy protection regulations, user demand for privacy protection is growing significantly. Therefore, how to effectively utilize dispersed data for distributed collaborative learning while ensuring data privacy is a pressing issue that needs to be addressed in the field of FL. In response to these challenges, this work has conducted research on clustering and segmentation FL algorithms for heterogeneous resource-constrained devices. A lightweight homogeneous device clustering strategy has been designed, which incorporates a split learning (SL) mechanism to enhance the accuracy and training efficiency of FL models. This approach reduces the load on resource-constrained devices and improves privacy security. Consequently, this method can meet the dual requirements of privacy protection and computational efficiency in real-world scenarios. © 2024 IEEE.

关键词： Federated learning

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Application of Permanent Magnet Synchronous Motors in Electric Drive Systems

Application of Permanent Magnet Synchronous Motors in Electr...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Mehta, Deepak Kumar, Ritesh Shah, Pradeep Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Everlasting Magnet Synchronous vehicles (PMSMs) are becoming increasingly famous in electric-powered power structures due to their great efficiency and overall performance traits. PMSMs are synchronous motors that use everlasting magnets to supply torque. They provide easy and coffee-cost control answers and can be used for a spread of programs along with electric-powered motors, domestic appliances, and commercial automation. PMSMs are, in particular, appealing to electric pressure system programs due to their better electricity density, higher efficiency, better velocity variety, and less complicated manipulation than different synchronous vehicles. The precept in the back of the PMSM is that the rotor, which contains everlasting magnets, interacts with the rotating magnetic area created via the stator windings. The stator windings are energized via an outside strength digital supply, and the ensuing alternating cutting-edge creates a magnetic area, which in flip produces torque on the rotor. This torque causes the rotor to rotate and produce mechanical power. The torque of the PMSM can be managed through various amplitudes and segments of the stator modern-day. This adaptability makes PMSMs perfect for use in variable-speed power applications consisting of electric automobiles. © 2024 IEEE.

关键词： Everlasting Magnetic Synchronous Windings

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Semi-Supervised Adversarial Transfer Learning for Automated Skin Lesion Segmentation

Semi-Supervised Adversarial Transfer Learning for Automated ...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Bishnoi, Ashish Kannagi, A. Acharjya, Kalyan Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Semi-supervised adversarial transfer gaining knowledge of (SATL) has been proposed as a powerful method for automatic pores and skin lesion segmentation. This approach aims to transfer knowledge from a categorized supply area to an unlabeled target domain to enhance the segmentation accuracy. The approach uses a generative opposed community (GAN) to study a function area that's then used to switch the segmentation knowledge from the source to the target domain. Experiments have proven that SATL can enhance segmentation accuracy in the target domain by using as few as 2000 supply domain annotations. Usual, SATL provides a powerful method for automatic pores and skin lesion segmentation in domain names with limited amounts of labeled information and will probably revolutionize medical imaging diagnostics. © 2024 IEEE.

关键词： Adversarial generative imaging proposed Transfer

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Adversarial Transfer Learning for Automated Segmentation of Brain Tumors

Adversarial Transfer Learning for Automated Segmentation of ...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Acharjya, Kalyan Saxena, Vineet Kamalraj, R. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Adverse switch mastering for automated Segmentation of brain Tumors is a novel method of deep studying-based scientific photo segmentation that employs a generative opposed network to transfer learned expertise from a version trained on a goal dataset to any other version trained on a one-of-a-kind source dataset. The method allows the segmentation of mind tumors in a substantially quicker, more accurate, and more extraordinary steady style than conventional guide methods. It does this by leveraging the obtained expertise approximately the goal dataset from a trained model and reprojecting it onto any other educated community, thus ensuring that the version can analyze extra quickly and more appropriately from the source dataset. This technique may permit rapid and accurate segmentation of mind tumors, allowing for more dependable treatment plans and advanced affected person care... © 2024 IEEE.

关键词： Adversarial improved method Transfer treatment

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Adversarial Transfer Learning for Left Ventricle Segmentation in Cardiac CT Images

Adversarial Transfer Learning for Left Ventricle Segmentatio...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Alam, Intekhab Tripathi, Ramesh Chandra Raghavendra, R. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

adverse transfer learning for Left Ventricle Segmentation in Cardiac CT photos is an emerging method in clinical photograph analysis. It pursues to transfer the learned expertise from a source area to a goal area, that's, in any other case, steeply priced to annotate. That is achieved via schooling a segmentation version in the source area with adversarial education. The version then learns many functions which might be domain-agnostic and transferable to new responsibilities. At the target area, the version generalizes the features from the source and gives correct segmentation effects. In this paper, we gift experiments using opposed transfer mastering for left ventricle segmentation in cardiac CT pix. Our results show a significant improvement over using an everyday segmentation model trained on the goal domain. The outcomes are promising and inspire similar research on antagonistic switch getting to know in clinical image evaluation... © 2024 IEEE.

关键词： Adversarial analysis Learning promising Ventricle

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Deep Learning Using Convolutional Neural Nets for Image Classification

Deep Learning Using Convolutional Neural Nets for Image Clas...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Gupta, Rupal Jebaraj, N.R. Solomon Kumar, Ritesh Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Deep mastering the usage of convolutional neural Nets (CNNs) has been a popular technique for image categories. A CNN is designed to inversely clear up the mission today's representing input data in a significant way to classification tasks. It is composed of a cutting-edge sequence brand brand-new convolutions and pooling operations that reduce the enter to characteristic maps that constitute the shape of state-of-the-art enter. Given enough educational information, the convolution layers can learn how to extract meaningful functions from the input picture that allow the network to classify it. A common approach is to excellent track a pre-skilled CNN, including VGGnet or ResNet, that has already been skilled to understand objects in snapshots. CNNs can achieve accuracy for various picture-type responsibilities. Moreover, they are robust to quite a number of real-international situations, which include unique degrees of modern-day illumination, occlusions, and distortions within the photo. © 2024 IEEE.

关键词： Approach Characteristic Convolutional Distortions Illumination Significant

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Design and Violation Analysis of Bilateral Complementary Switches in Analog and Digital Circuits

Design and Violation Analysis of Bilateral Complementary Swi...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Joshi, Manish Saha, Ananya Singh, Shweta Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This technical summary examines the design and violation analysis of analog and virtual circuits with bilateral complementary switches. The primary consciousness is on examining the electric traits and operation of switches from the stand factors of overall gate fee and capacitance outcomes. To analyze the impact of switching parameters on circuit performance, a model is built to expect the time-domain waveform of an idealized switch. This version is then validated through experimental measurements of three special types of switches: PMOS, CMOS, and NMOS. The effects from the model and measurements are used to analyze the full gate charge, total capacitance, and rise/fall instances of switches. Evaluation between different forms of switches shows essential design implications for numerous forms of circuits. Sooner or later, violation evaluation is used to evaluate the effect of the switch's general gate rate and overall capacitance on circuit performance. © 2024 IEEE.

关键词： Capacitance Essential Implications Measurements Technical Violation

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Bioinspired Polarized Skylight Orientation Determination Artificial Neural Network

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Journal of Bionic Engineering 2023年第3期20卷 1141-1152页

作者： Huaju Liang Hongyang Bai Ke Hu Xinbo Lv School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing210094China Neuromorphic Computing Laboratory China Nanhu Academy of Electronics and Information TechnologyJiaxing314001China Guidance Radar General Department Xi’an Electronic Engineering Research InstituteXi’an710100China School of Automation Nanjing University of Science and TechnologyNanjing210094China

This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect’s encoding of polarization information and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.

关键词： Polarized light navigation Orientation determination Artificial neural network

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Efficient Convergence Analysis of Stochastic Gradient Descent for Large-Scale Optimization

Efficient Convergence Analysis of Stochastic Gradient Descen...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Saha, Ananya Yadav, Rakesh Kumar Dwivedi, Rakesh Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Stochastic gradient descent (SGD) is an optimization set of rules used to solve huge-scale optimization issues. It works by way of updating an approximate solution, primarily based on the gradient of the goal characteristic with admire to each parameter, in an iterative fashion. SGD is powerful in cases in which the full-gradient updates are not feasible because of the big length of the dataset, on the grounds that the simplest mini-batch of the facts is used for every update. In this paper, a brand new technique for analyzing the convergence homes of SGD is proposed. Particularly, the authors keep in mind the worst-case situation wherein the goal characteristic is composed of parts: a smooth element and a non-clean part, each with its personal Lipschitz constants. The authors then analyze SGD with a hard and fast step size and show that it may converge to the premier answer with a top-quality rate. The proposed approach is efficient in that it requires fewer reviews of the gradient than the prevailing convex optimization strategies. It is predicted that this new theoretical result will facilitate the improvement of extra green and dependable optimization strategies. © 2024 IEEE.

关键词： Composed Efficient Facilitate Gradient Iterative Stochastic Technique

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