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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Pan, Nianyi Sun, Jiacheng Zhou, Xinghong Li, Xiaomao School of Computer Engineering and Science Shanghai University Shanghai China School of Future Technology Shanghai University Shanghai China Research Institute of Usv Engineering School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798350385557

Unmanned aerial vehicle (UAV) detection of ice-covered transmission lines (ITL) is a crucial task for winter power grid maintenance. Instance segmentation offers robustness in handling complex scene variations in UAV images while providing instance-level information. However, the quality of instance segmentation heavily relies on precise pixel-level annotations, while predictions from low-cost annotations tend to be coarse. Therefore, this paper proposes a soft label mask optimization (SLMO) method to achieve high-quality segmentation results of ITL with only coarse annotations. Furthermore, to alleviate loss of feature details during multi-scale feature fusion, a pixel shuffle module (PSM) is introduced to replace the conventional interpolation sampling method. Combining these methods, this paper proposes a SLMO Network (SLMONet) based on the efficient SparseInst framework. Extensive experiments on our ITL datasets validate the effectiveness of SLMONet in segmenting ITL. © 2024 IEEE.

关键词： Electric lines

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UWB Radar Signal Kick Detection for Tailgate Unlocking Based on Spatio-Temporal Network 2

UWB Radar Signal Kick Detection for Tailgate Unlocking Based...

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2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Wang, Yuhan Yan, Shuang Fu, Yiling Shangguan, Tianyun Sun, Yuxuan Sun, Lin Hangzhou City University School of Computer and Computing Science Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Westlake University School of Engineering Hangzhou China

ISBN: (纸本)9798350307733

Inductive tailgate systems have significantly enhanced user convenience in vehicle operations. However, the performance of current systems can be hindered by environmental factors, leading to reduced recognition accuracy and durability over time. To address these issues, we propose a kick gesture recognition method based on ultra-wideband (UWB) signals, implemented through a spatial-temporal neural network. Our method starts with employing a Time-Range-Doppler method to extract kick gesture sequences that contain temporal and spatial features from preprocessed data. These sequences are then fed into a network containing both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) cells to predict the final results, where the CNN section is used to learn spatial features and the RNN section is used to learn temporal features. To verify the effectiveness of our approach, we conducted experiments in three typical parking environments: underground garages, overgrown lawns, and sloped terrains, collecting data from 12 participants for model training and testing. Experimental results demonstrated the feasibility of using UWB signals for wireless kick gesture recognition and confirmed the high accuracy of our proposed method. © 2024 IEEE.

关键词： Gesture recognition

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Low Light Image Enhancement: A Special Click 2

Low Light Image Enhancement: A Special Click

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Kapoor, Shalini Arora, Yojna Bansal, Nidhi Virdi, Khushwant Ismail, Firdous Sadaf M. Malik, Suraj School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Sharda University Uttar Pradesh Greater Noida India Haryana Faridabad India Department of Computer Science and Applications School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Nagpur Campus Pune India Department of Computer Science & Engineering IIMT University Uttar Pradesh Meerut India

ISBN: (纸本)9798331530389

Photography and images are an important and integral part of today's world, resulting in the development of multimedia and many other subjects comparable to image processing. The specifications of the camera and camera lens are just not enough hence image processing is always required. Low light photography, the problem gets even bigger because the camera lens fails to capture enough light and hence low light photography depends mostly upon image processing algorithms which help to reduce noise in the image, brighten the image for better overall visual quality and enlighten the hidden dark areas. Many efforts have been put into advancing image quality in little light, but none have given satisfactory results. This research report aims to analyses the techniques used for low-light image improvement and what advancements can be made to advance the image condition further. © 2025 IEEE.

关键词： Camera lenses

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Distributed Multi-hop Clustering Approach with Low Energy Consumption in WSN

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computer Systems Science & engineering 2023年第4期45卷 903-924页

作者： R.Nithya Roobaea Alroobaea Ahmed Binmahfoudh Zairi Ismael Rizman Department of Computer Science and Engineering Vivekananda College of Engineering for WomenTiruchengode637205Tamil NaduIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia School of Electrical Engineering College of EngineeringUniversiti Teknologi MARA23000DungunTerengganuMalaysia

The purpose of sensing the environment and geographical positions,device monitoring,and information gathering are accomplished using Wireless Sensor Network(WSN),which is a non-dependent device consisting of a distinct collection of Sensor Node(SN).Thus,a clustering based on Energy Efficient(EE),one of the most crucial processes performed in WSN with distinct environments,is *** order to efficiently manage energy allocation during sensing and communication,the present research on managing energy efficiency is performed on the basis of distributed *** of EE methods were incapable of supporting EE routing with MIN-EC in WSN in spite of the focus of EE methods on energy harvesting and minimum Energy Consumption(EC).The three stages of performance are proposed in this research *** the outset,during routing and Route Searching Time(RST)with fluctuating node density and PKTs,EC is reduced by the Hybrid Energy-based Multi-User Routing(HEMUR)model proposed in this *** efficiency and an ideal route for various SNs with distinct PKTs in WSN are obtained by this *** utilizing the Approximation Algorithm(AA),the Bregman Tensor Approximation Clustering(BTAC)is applied to improve the Route Path Selection(RPS)efficiency for Data Packet Transmission(DPT)at the Sink Node(SkN).The enhanced Network Throughput Rate(NTR)and low DPT Delay are provided by *** MAX the Clustering Efficiency(CE)and minimize the EC,the Energy Effective Distributed Multi-hop Clustering(GISEDC)method based on Generalized Iterative Scaling is *** Multi-User Routing(MUR)is used by the HEMUR model to enhance the EC by 20%during *** compared with other advanced techniques,the Average Energy Per Packet(AEPP)is enhanced by 39%with the application of proportional fairness with Boltzmann Distribution(BD).The Gaussian Fast Linear Combinations(GFLC)with AA are applied by BTAC method with an enhanced Communication Overhead(COH)for an increase in performance

关键词： Sensor node clustering routing energy consumption multi-path routing

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Deep Learning Tools for Covid-19 Pneumonia Classification

Deep Learning Tools for Covid-19 Pneumonia Classification

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Kanyangarara, Ngonidzashe Mathew Soumya, D.R. Sahana, Subrata Das, Sanjoy Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science Indira Gandhi National Tribal University RCM Manipur India

ISBN: (纸本)9789819986606

The outbreak of Covid-19 has triggered a worldwide problem, especially in Asia and America. The World Health Organization (WHO) declared the sickness a pandemic on March 20, 2020. It arrived in waves, and most countries worldwide have now experienced two waves and are on the approach of experiencing the third. The goal of this study is to build up and certify a computer-Aided Diagnosis (CADx) system for distinguishing between COVID-19-positive patients and non-COVID Patients people. Chest X-ray (CXR) images will be used to accomplish this. From public datasets which we got on GitHub 2295 CXR images were obtained which included 712 COVID-19 positive and 1583 normal. The proposed CADx system utilized a Conventional Neural Network (CNN) model for data argumentation and CNN was built, compiled, and trained with the help of TensorFlow and Keras. For the sake of appraisal, our datasets were separated into three categories: Train/Test and Validation. The three sets’ accuracy was evaluated and the results for Training, Validation, and Test were 97.77%, 97.81%, and 97.72% respectively. In the end, this study was able to create a precise computer-Aided Diagnosis system for the two categories of classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided diagnosis

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Cement Strength Prediction using Regression Techniques 10

Cement Strength Prediction using Regression Techniques

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Sharan, Bhagwati Goyal, Mayank Kumar Balde, Amadou Mouctar Greynoon, Ameen Mohamad Ahmad Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Amaravati India Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India

ISBN: (纸本)9789380544472

One of the primary structural materials in the development business is concrete. Compressive strength is the most frequently utilized metric when assessing the strength and nature of cement. This paper clarifies how to make use of technical regression (RT) methodology to decide the legitimate dose of the concrete mixture. Water, Cement, Fly Ash, Blast Furnace, Superplasticizer, Coarse, and Fine Aggregate, were used as input parameters, on the other hand, compressive strength was used as an output parameter. The produced models were evaluated using statistical parameters: Linear Regression, Decision Tree Regression, and Random Forest Regression. In contrast with other applicable models, it was found that the Gaussian process regression model based on the function of the nucleus of radial bases delivered relatively better results. The recommended models should get a good deal on saving money on materials, labor, and time while also improving accuracy. The proposed concrete should be more durable and accordingly more cost-effective. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Cements

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Class-Conditioned Transformation for Enhanced Robust Image Classification

Class-Conditioned Transformation for Enhanced Robust Image C...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Blau, Tsachi Ganz, Roy Baskin, Chaim Elad, Michael Bronstein, Alex M. Technion - Israel Institute of Technology Deparrment of Computer Science Israel School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel

ISBN: (纸本)9798331510831

Robust classification methods predominantly concen-trate on algorithms that address a specific threat model, resulting in ineffective defenses against other threat models. Real-world applications are exposed to this vulnerability, as malicious attackers might exploit alternative threat models. In this work, we propose a novel test-time threat model agnostic algorithm that enhances Adversarial-Trained (AT) models. Our method operates through COnditional image transformation and DIstance-based Prediction (CODIP) and includes two main steps: First, we transform the input image into each dataset class, where the input image might be either clean or attacked. Next, we make a prediction based on the shortest transformed distance. The conditional transformation utilizes the perceptually aligned gradients property possessed by AT models and, as a result, elimi-nates the need for additional models or additional training. Moreover, it allows users to choose the desired balance between clean and robust accuracy without training. The proposed method achieves state-of-the-art results demon-strated through extensive experiments on various models, AT methods, datasets, and attack types. Notably, applying CODIP leads to substantial robust accuracy improvement of up to +23%, +20%, +26%, and +22% on CIFAR10, CIFAR100, ImageNet and Flowers datasets, respectively. For more details, visit the project page. © 2025 IEEE.

关键词： Image classification

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Secure Adaptive Group Testing

Secure Adaptive Group Testing

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作者： Cohen, Alejandro Cohen, Asaf Gurewitz, Omer The Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel The School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer Sheva84105 Israel

Group Testing (GT) addresses the problem of identifying a small subset of defective items from a large population, by grouping items into as few test pools as possible. In Adaptive GT (AGT), outcomes of previous tests can influence the makeup of future tests. Using an information theoretic point of view, Aldridge 2012 showed that in the regime of a few defectives, adaptivity does not help much, as the number of tests required is essentially the same as for non-adaptive GT. Secure GT considers a scenario where there is an eavesdropper who may observe on average a fraction δ of the tests results, yet should not be able to infer the status of the items. In the non-adaptive scenario, the number of tests required is 1/(1 − δ) times the number of tests without the secrecy constraint. In this paper, we consider Secure Adaptive GT. Specifically, when during the makeup of the pools one has access to a private feedback link from the lab, of rate R f . We prove that the number of tests required for both correct reconstruction at the legitimate lab, with high probability, and negligible mutual information at the eavesdropper is 1/min{1, 1 − δ + R f } times the number of tests required with no secrecy constraint. Thus, unlike non-secure GT, where an adaptive algorithm has only a mild impact, under a security constraint it can significantly boost performance. A key insight is that not only the adaptive link should disregard the actual test results and simply send keys, these keys should be enhanced through a "secret sharing" scheme before usage. We derive sufficiency and necessity bounds that completely characterizes the Secure Adaptive GT capacity. Moreover, we consider additional models of Secure Adaptive GT, where we make a clear distinction between the lab performing the tests, and the doctor analyzing the results. Specifically, we consider curious but non-malicious, non-cooperating labs. Each lab gets a fraction δ of pool-tests to perform. Yet, we want to keep each lab ignor

关键词： Decoding

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The future is frozen:cryogenic CMOS for high-performance computing

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Chip 2024年第1期3卷 43-54页

作者： R.Saligram A.Raychowdhury Suman Datta School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaUSA

Low temperature complementary metal oxide semiconductor(CMOS)or cryogenic CMOS is a promising avenue for the continuation of Moore’s law while serving the needs of high performance *** temperature as a control“knob”to steepen the subthreshold slope behavior of CMOS devices,the supply voltage of operation can be reduced with no impact on operating *** the optimal threshold voltage engineering,the device ON current can be further enhanced,translating to higher *** this article,the experimentally calibrated data was adopted to tune the threshold voltage and investigated the power performance area of cryogenic CMOS at device,circuit and system *** also presented results from measurement and analysis of functional memory chips fabricated in 28 nm bulk CMOS and 22 nm fully depleted silicon on insulator(FDSOI)operating at cryogenic ***,the challenges and opportunities in the further development and deployment of such systems were discussed.

关键词： Cryogenic CMOS Design technology co-optimization High performance computing Parameter variation Threshold voltage engineering Cryogenic Memories Interconnects

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A deep neural network based feature fusion panoramic segmentation method

A deep neural network based feature fusion panoramic segment...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Fei, Hongyan Mokhtarzadeh, Amir Ali Zhou, Jiayu Geng, Junzuo Huaiyin Institute of Technology School of Computer and Software Engineering 223003 China Jiangsu Foreign Expert WorhShop Jiangsu Huaian223003 China Nanjing Institute of Technology School of Computer and Software Engineering 211167 China

Segmentation of complex and diverse scenes in the field of machine vision has the advantages of low cost and good robustness. However, in the real open intelligent driving scene, the segmented object will be affected by the change of proportion and illumination. The feature extraction of the interference algorithm causes some problems such as local information loss and feature background fusion in the process of execution. In this paper, deep neural network method is used for feature fusion panoramic segmentation. It focuses on the premise of lightweight, and improves the performance by improving the structure of semantic segmentation network layer and model calculation effect, and reducing the number of network parameters and calculations, so as to further improve the detection performance. At the same time, with the gradual deepening of deep learning network architecture, but most embedded devices can not bear a huge amount of computing workload. An attentional channel pruning based approach is proposed using a wide range of experimental and ablation studies to compare different data, including VOC2012 and ImageNet datasets. We show that the proposed method outperforms state of the art methods in terms of visual quality. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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