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International Conference on Computing and Networking technology (ICCNT)

作者： K. Yuvaraj Paramjit Baxi B Sivadharshini Sourav Rampal Priya Makhija Snehal R. Rathi Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This technical abstract discusses studies associated with the performance of self-sustaining communications systems. Independent communications systems are a wireless conversation gadget wherein all components are self-configuring and self-adjusting. This study makes a specialty of analyzing the overall performance of such systems with respect to robustness, reliability, energy performance, and value performance. The performance metrics are assessed by measuring the achievement costs of diverse transmission eventualities, where activities together with mobility, shadowing, and interference are taken into consideration. An evaluation of adaptive and static strategies is likewise made. The studies evaluate the overall performance of various conversation algorithms, which include channel-hopping and spread-spectrum, and present a proof-based strategy for selecting the quality parameters and protocols for self-sustaining conversation systems. Furthermore, a metric for evaluating the overall device strength performance is proposed. The research findings are anticipated to help improve the performance of independent communications structures and beautify their reliability and robustness.

关键词： Performance evaluation Wireless communication Protocols Transportation Oral communication Medical services Robustness Manufacturing Security Shadow mapping

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LGN-Net: Local-Global Normality Network for Video Anomaly Detection

SSRN

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SSRN 2023年

作者： Zhao, Mengyang Zeng, Xinhua Liu, Yang Liu, Jing Li, Di Hu, Xing Pang, Chengxin Academy for Engineering and Technology Fudan University No.220 Handan Road Shanghai200433 China School of Electronics and Information Engineering Shanghai University of Electric Power No.1851 Huchenghuan Road Shanghai201306 China School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology No.516 Jungong Road Shanghai200093 China College of Information Engineering Henan University of Science and Technology No.263 Kaiyuan Road Henan Luoyang471000 China

Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Existing unsupervised VAD methods tend to learn normality from training sets consisting of only normal videos and regard instances deviating from such normality as anomalies. However, they often consider only local or global normality in the temporal dimension. Some of them focus on learning local spatiotemporal representations from consecutive frames to enhance the representation for normal events. But powerful representation allows these methods to represent some anomalies and causes miss detection. In contrast, the other methods are devoted to memorizing prototypical normal patterns of whole training videos to weaken the generalization for anomalies, which also restricts them from representing diverse normal patterns and causes false alarm. To this end, we propose a two-branch model, Local-Global Normality Network (LGN-Net), to simultaneously learn local and global normality. Specifically, one branch learns the evolution regularities of appearance and motion from consecutive frames as local normality utilizing a spatiotemporal prediction network, while the other branch memorizes prototype features of the whole videos as global normality by a memory module. LGN-Net achieves a balance of representing normal and abnormal instances by fusing local and global normality. In addition, the fused normality enables LGN-Net to generalize to various scenes more than exploiting single normality. Experiments demonstrate the effectiveness and superior performance of our method. The code is available online: https://***/Myzhao1999/LGN-Net. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Analyzing the Robustness of Compact Neural Networks in Noisy Environments

Analyzing the Robustness of Compact Neural Networks in Noisy...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Dharmesh Dhabliya N. V. Balaji Anoop Dev Mohammad Faraz Idris Siddiqui Mithhil Arora Gautham Krishna Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Jamia Millia Islamia India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Department of Civil Engineering JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Compact neural networks are becoming increasingly popular because they operate effectively and need less computer power. They still struggle in loud situations. We suggest and evaluate three novel techniques to make compact neural networks more noise-resistant: noise-aware training, adversarial relationship-based training and testing, and noise filtering and knowledge distillation. Networks were subjected to controlled noise during noise-aware training to simulate noisy environments. Data shows that this methodology enhances accuracy, precision, and F1 scores significantly above typical training approaches. Adversarial Training and Evaluation employs adversarial instances. That makes the network more resistant to attacks from other networks. This method may make adversarial-vulnerable systems safer and more robust. Noise filtering and information distillation combine data preparation with information from larger, more reliable models. The two-step approach enhances accuracy while maintaining competitiveness and F1 scores. Our comparative study examines the benefits and downsides of each method to help you pick the best one for your application. Small neural networks are more robust using the provided strategies. Their data-driven responses to noise, bad actor assaults, and data faults make tiny neural networks more trustworthy in hectic real-world circumstances. The improvement of AI security and performance is facilitated by the usage of alternate data formats.

关键词： Training Resistance Knowledge engineering Accuracy Filtering Noise Neural networks Robustness Noise measurement Security

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Expression of Concern for: A Comprehensive Analysis of the Use of Machine Learning Techniques to Optimize the Information Retrieval Process

Expression of Concern for: A Comprehensive Analysis of the U...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： S. Hemelatha Kalanandhini. G Prasad B. Chaudhari Rajeev Sharma Jambi Ratna Raja Kumar Dipannita Mondal Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This explores using a system to gain knowledge of strategies that allow you to optimize the facts retrieval manner. Facts retrieval is a complicated technique; it is becoming even more complicated as advancements in generation have furnished tools to make the gathering, employing, and evaluating information less complicated and faster. Research techniques, including artificial intelligence, neural networks, and machine learning, can speed up records retrieval and improve outcomes' accuracy. This look examines the different machine learning techniques to optimize the facts retrieval process and the advantages and downsides of each technique. Furthermore, this study looks at how gadgetstudying algorithms improve a person's enjoyment of retrieval systems and how their use can be integrated into cutting-edge practices. Subsequently, methods for improving the performance of machine learning algorithms are discussed in addition to capacity destiny directions inside the use of gadget studying to optimize statistics retrieval.

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EG-Four$\mathbb {Q}$: An Embedded GPU-Based Efficient ECC Cryptography Accelerator for Edge Computing

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IEEE Transactions on Industrial Informatics 2022年第6期19卷 7291-7300页

作者： Jiankuo Dong Pinchang Zhang Kaisheng Sun Fu Xiao Fangyu Zheng Jingqiang Lin School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory of Information Security and Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Science and Technology of China Hefei China

With the continuous development of Industry 4.0 technology, the embedded devices in Industrial Internet of Things (IIoT) are showing explosive growth, and large-scale cyber attacks or related security incidents continue to sound the alarm bell of information security. IIoT has strict requirements on computing performance and energy consumption, which poses severe challenges to cryptographic algorithms, especially public key cryptographic algorithms with high computational complexity. Embedded graphic processing unit (GPU) devices, always as edge computing nodes or AI accelerators, are widely deployed in IIoT applications. In this article, we propose an embedded GPU-based Four $\mathbb {Q}$ (EG-Four $\mathbb {Q}$ ) elliptic curve public key cryptographic acceleration scheme. As far as we know, EG-Four $\mathbb {Q}$ is the first work to completely implement Four $\mathbb {Q}$ on the GPU platforms, including finite field operations, point arithmetic, and scalar multiplication. Relying only on 36-W power consumption, our scalar multiplication performance reaches 1717 kops/s with the latency of 2.38 ms. In terms of the energy-efficiency ratio, EG-Four $\mathbb {Q}$ has significant advantages over other platforms such as advanced RISC machines (ARM) CPU, Intel CPU, field programmable gate array (FPGA), and desktop GPUs. The throughput of EG-Four $\mathbb {Q}$ is 1.75 times that of the fastest elliptic curve cryptography implementation based on the same platform and even exceeds the performance of Intel top server CPU E5-2699v3 (18-core). Based on the embedded GPU Xavier, EG-Four $\mathbb {Q}$ can act as a cryptographic edge computing module or even a cloud cryptographic accelerator, providing more efficient elliptic curve cryptographic services for IIoT.

关键词： Graphics processing units Cryptography Arithmetic Performance evaluation Industrial Internet of Things Elliptic curve cryptography Edge computing

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Deep Learning for Flood Prediction: Identifying Vulnerable Zones and Assessing Infrastructure-Induced Risks

Deep Learning for Flood Prediction: Identifying Vulnerable Z...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Arhath Kumar Vivek Veeraiah S. Padmapriya M. Chitra Devi L. Leelavathy Ankur Gupta Dept. of MCA NMAM Institute of Technology NITTE (Deemed to be University) Karnataka India Dept. of Computer Science Sri Siddhartha Academy of Higher Education Tumkur Karnataka India Dept. of Computer Science SRM Trichy Arts and Science College Irungalur TN India Dept. of CSE Vaish College of Engineering Rohtak Haryana India

Many nations were still devastated by floods and similar disasters in recent years. Low-level residents and those near water resources like lakes, dams, rivers, and reservoirs are hardest impacted by floods every year. Improper planning in building and public construction (sewage or drainage systems) causes it. Floods are caused by excessive rain, thus these two causes are the most essential. Today, even mild rainfall causes flooding since there is no drainage or coastal area. This study seeks to identify flood-prone locations. It also gives information and determines flood damage risk.

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Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos

arXiv

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arXiv 2023年

作者： Lin, Junhao Dai, Qian Zhu, Lei Fu, Huazhu Wang, Qiong Li, Weibin Rao, Wenhao Huang, Xiaoyang Wang, Liansheng School of Informatics Xiamen University Xiamen China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Medicine Xiamen University Xiamen China

Breast lesion segmentation in ultrasound (US) videos is essential for diagnosing and treating axillary lymph node metastasis. However, the lack of a well-established and large-scale ultrasound video dataset with high-quality annotations has posed a persistent challenge for the research community. To overcome this issue, we meticulously curated a US video breast lesion segmentation dataset comprising 572 videos and 34,300 annotated frames, covering a wide range of realistic clinical scenarios. Furthermore, we propose a novel frequency and localization feature aggregation network (FLA-Net) that learns temporal features from the frequency domain and predicts additional lesion location positions to assist with breast lesion segmentation. We also devise a localization-based contrastive loss to reduce the lesion location distance between neighboring video frames within the same video and enlarge the location distances between frames from different ultrasound videos. Our experiments on our annotated dataset and two public video polyp segmentation datasets demonstrate that our proposed FLA-Net achieves state-of-the-art performance in breast lesion segmentation in US videos and video polyp segmentation while significantly reducing time and space complexity. Our model and dataset are available at https://***/jhlDet/FLA-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Location

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Based on Internet of Things Tracking of Hand Hygiene and Practices 2nd

Based on Internet of Things Tracking of Hand Hygiene and Pra...

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2nd International Conference on Mechanical and Energy Technologies , ICMET 2021

作者： Jayadeva, Sujay Mugaloremutt Singh, Krishanveer Karnan, Lokanayaki Franklin Telfer, L. Bharti, Anupama Dhiman, Viney Department of Health System Management Studies JSS Academy of Higher Education and Research Karnataka Mysuru India GLA University Mathura India Department of Computer Science St. Francis de Sales College Bengaluru India Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Sociology and Social Work Himachal Pradesh University Himachal Pradesh Shimla India Department of Cardiology PGIMER Chandigarh India

ISBN: (纸本)9789811901072

The study explores the tracking and practices essentiality in hospitals and health facilities, because related infections, such as higher risks and acceleration of illness, can have significant effects on health care. Human observers tracking and Practices have problems of their own, such as loss of observation concentration, accumulation of human mistake and expert deformation. In this investigation, the results of two internet-based techniques for controlling the hand hygiene of medical personnel during patient visits were carried out and evaluated. As base stations and smart phones, we employed ESP modules as mobile nodes and calculated the range to be located in the patient's room using Bluetooth RSSI values. We analyzed the RSSI in the proximity solution from a sensor host measured on different ESP nodes/module and used the premise that the sensor host is nearest to the ESP node which offers the maximum RSSI values. We utilized the RSSI value to measure the location between mobile nodes and every ESP node in the trilateration method and we applied the trilateration technique to identify the mobile node inside the room. Our tests have shown that 20% of the solution based on proximity was erroneous, whilst the 8% of the solution based on trilateration was erroneous. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smartphones

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A Dynamical Combination of Multiple Features-Based Approach for Classifying Agricultural Text

A Dynamical Combination of Multiple Features-Based Approach ...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： V Y John N. N. Wasatkar K. Yuvaraj Shubhansh Bansal A.S. Valarmathy Shitij Goyal School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Computer Engineering Vishwakarma Institute of Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

In the traditional text classification technique, values are read as letters, which destroys the original semantic illustration of numerical attributes. Completely mining deeper latent semantics features in agricultural literature requires a novel text classification technique that makes use of multivariate characteristic dynamic fusion. The global principal semantic components of the text were extracted using the Bi-Convolutional Neural Networks (CNNs) system using an attention mechanism, and the local semantic information was extracted using the Multiple Window Convolutional Neural Network. When classifying textual content based on phenotypic quantitative categories, the deep semantic portrayal of agricultural texts may be significantly enhanced by using the attention strategy to dynamically merge the obtained various semantic features.

关键词： Resistance Semantics Text categorization Neural networks Feature extraction Data models Information and communication technology Data mining Convolutional neural networks Kernel

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Single-Shot Single-Beam Coherent Raman Scattering Thermometry Based on Air Lasing

arXiv

引用

arXiv 2024年

作者： Lu, Xu Chen, Yewei Mazza, Francesco He, Siyi Li, Zihan Huang, Shunlin Wang, Quanjun Zhang, Ning Shen, Bo Wu, Yuzhu Yao, Jinping Cheng, Ya State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Faculty of Aerospace Engineering Delft University of Technology Kluyverweg 1 Delft2629 HS Netherlands Institute of Modern Optics Nankai University Tianjin300350 China School of Microelectronics Shanghai University Shanghai200444 China

Thermometric techniques with high accuracy, fast response speed and ease of implementation are desirable for the study of dynamic combustion environments, transient reacting flows, and non-equilibrium plasmas. Herein, single-shot single-beam coherent Raman scattering (SS-CRS) thermometry is developed, for the first time to our knowledge, by using air lasing as a probe. It’s proved that the air-lasing-assisted CRS signal has a high signal-to-noise ratio enabling single-shot measurements at a 1 kHz repetition rate. The SS-CRS thermometry consistently exhibits precision better than 2% at different temperatures, but the inaccuracy grows with the increase in temperature. The high detection precision, 1 kHz acquisition rate and easy-to-implement single-beam scheme are achieved thanks to the unique temporal, spectral and spatial characteristics of air lasing. This work opens a novel avenue for high-speed CRS thermometry, holding tremendous potential for fast diagnostics of transient reacting flows and plasmas. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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