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Experimental Evaluation of Transmission Control Method based on Received signal Strength for Spatio-Temporal Data Retention ∗

Experimental Evaluation of Transmission Control Method based...

引用

ieee Annual international Computer Software and Applications conference (COMPSAC)

作者： Renju Akashi Daiki Nobayashi Kazuya Tsukamoto Takeshi Ikenaga Myung Lee Kyushu Institute of Technology Fukuoka Japan City College of New York New York USA

With the development and spread of IoT technology, the number of devices connected to the internet is increasing. Some data generated by IoT devices include spatio-temporal data (STD) that depends on the location and time of data generation. Therefore, we have proposed the STD retention system (STD-RS) using vehicles as a network infrastructure for local production and consumption of STD. This paper proposes a transmission control method that can mitigate the fluctuation of RSS due to the influence of obstacles in the real environment and evaluates its effectiveness through experiments on actual devices.

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IoT based Energy Efficacious Smart Hygiene System 6

IoT Based Energy Efficacious Smart Hygiene System

引用

6th ieee international conference on signal Processing, Computing and Control, ISPCC 2021

作者： Patil, Tejal Rajendra Jayanthi, B Naga Reddy, Aditya Chenna Vellore Institute Of Technology Tamil Nadu Chennai India

ISBN: (纸本)9781665425520

Due to increase in population in India, the cleanliness in our nation is under risk. When it comes to the case of public washrooms, it is difficult to maintain proper hygiene. The main motto of the proposed work is to maintain cleanliness and proper hygiene in public washrooms by eliminating the man-power required in cleaning. In the existing system, there are no automated devices that are being used in public washrooms. The person who has used it, has to flush it. In our proposed system, public washrooms are maintained automatically. With Arduino as microcontroller to automate the process. To save energy, lights are turned on and off as the person enters and leaves the washroom. After the person leaves the washroom, the water flush will be automatically turned on for a pre-fixed duration of time. After the washroom is used for four times, the door will be closed and an arm with a cleaning brush attached to it is used to clean the washroom. While the arm is cleaning, a flush is enabled for using a chemical liquid. The status of the washroom is displayed on an LCD display that is fixed on the door. If the level of chemical liquid goes low, a message is sent to the authorities that a refill is required. internet-of-Things is used to automate and monitor the cleanliness of the washrooms. © 2021 ieee.

关键词： internet of things

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New Generative image Model for Variational Autoencoders based on Counts Partition 7

New Generative Image Model for Variational Autoencoders Base...

引用

7th ieee international conference on Information technology and Nanotechnology, ITNT 2021

作者： Antsiperov, Viacheslav Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences Signal Processing and Telecommunication Lab Moscow Russia

ISBN: (纸本)9781665432177

The article substantiates a generative image model based on a sample of random counts and its representation using compressed (coded) lattice partitioning. The basics of the model proposed is a concept of the ideal image. To avoid the problems with cumbersome descriptions, the ideal image is reduced to a sample of counts of a fixed (controlled) size. It is shown that the corresponding statistical description can be factorized into the product of the distributions of individual counts, which fits well with naive Bayesian algorithms and other machine learning approaches. Guided by this association in the image and likeness of the well-known K-means algorithm, the method of iterative partitioning - maximization of counts was synthesized. The main goal of the method is to calculate the maximum likelihood parameters of sample probability distribution and to use these parameters as a code for some intermediate representation of the input data. The method specific feature is the use of lattice neighbourhood trick for partitioning counts, that allows to control both the computation time and some characteristics of the resulting code. The final of the article is devoted to the algorithmic details of the proposed partition-maximization method are considered and several iterations related characteristics, including the convergence property, convergence criterion and asymptotic efficiency of the resulting estimates are discussed. Most of those characteristics are illustrated by the results of numerical simulations. © 2021 ieee.

关键词： K-means clustering

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Optimizing Urban Traffic with image Processing: A New Era of Management

Optimizing Urban Traffic with Image Processing: A New Era of...

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2025 international conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kumar, Alok Palati, Madhu Tripathi, Arijeet Sagar, Swapnil Seegi, Vishwa M. Bms Institute of Technology and Management Dept. of Eee Bangalore India

ISBN: (纸本)9798331538538

Urban traffic management is a crucial concern in today's cities due to increased vehicle density and the complexity of transport networks. Effective real-time vehicle tracking systems are critical for reducing traffic congestion, improving road safety, and optimizing traffic flow. The suggested system uses real-time video feeds from cameras strategically deployed throughout metropolitan traffic networks. YOLOv5 recognizes and localizes vehicles inside video frames, while Alex Net V3 classifies the observed vehicles as automobiles, busses, trucks, and motorcyclists. The system continuously refreshes traffic data, providing information about traffic density, flow patterns, and potential bottlenecks. This data is integrated into traffic management systems to enable adaptive signal control and dynamic route planning. This greatly increased the precision and efficiency of urban traffic management, providing a scalable solution to the mounting issues of urbanization. © 2025 ieee.

关键词： Traffic congestion

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image Forgery Detection Using Hybrid Model

Image Forgery Detection Using Hybrid Model

引用

Intelligent signal Processing and Effective Communication Technologies (INSPECT), ieee international conference on

作者： Priya Metri Sakshi Balla Shreya Pawar Atharva Sharma Prashant Sakunde Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The growing demand for trustworthy picture forgery detection techniques to maintain the integrity of visual content across a range of applications is addressed in this work. To improve picture authentication accuracy and efficiency, a user-friendly cascaded framework combining Support Vector Machines (SVM) and VGG 16 is presented. First, a highly effective supervised learning system called Support Vector Machines (SVM) rapidly filters photos by identifying overt alteration indi-cators. After passing this phase, images are examined and refined even more with the help of the VGG 16 deep learning model. When SVM and VGG16 are combined, their complimentary strengths are utilised, which improves accuracy and speeds up identification when compared to other approaches. According to benchmark datasets, the proposed method outperforms earlier methods with respect to accuracy and computational efficiency. This strategy may benefit many real-world applications- based on forensic verification, verification of media integrity, medical image authentication, and intellectual property protection-in the here-and-now solutions. This work enhances the confidence and trustworthiness of digital communications by providing an efficient, friendly user tool for the verification of visual content.

关键词： Support vector machines Visualization Accuracy Filters Forensics Supervised learning Authentication Intellectual property Media Forgery

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YOLOV9 Driven internet of Things Enabled Sustainable Solution for Intelligent Traffic Light Management System for Emergency Vehicles in Large Scale Urban Traffic

YOLOV9 Driven Internet of Things Enabled Sustainable Solutio...

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Machine Learning and Autonomous systems (ICMLAS), international conference on

作者： Vijay Ganesh P Sam Joshua V Saro Murugan L S. Mahalakshmi Department of Computer Science and Engineering Karpagam Academy of Higher Education (Deemed to be University) Coimbatore India Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education (Deemed to be University) Coimbatore India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Due to densely populated urban environment leads to huge traffic in peak hours, Intelligent traffic light management system becomes paramount for emergency vehicle transportation on leveraging the sensor technologies. However sensor data acquired from densely populated urban environment helps to process the traffic congestion based traffic density. Many researches has been carried out to enable intelligent transportation system using internet of things, artificial intelligence and communication technologies but still it requires sustainable solutions for intelligent transportation., traffic congestion management, traffic light controlling with respect to the detection of emergency vehicles like ambulance as it saves the life of the human being. In this paper, AI driven Intelligent of Things enabled sustainable solutions for intelligent traffic light management system for emergency vehicles in the large scale urban traffic. Initially sensor or camera deployed in the smart cities monitors the roads and its surroundings environments. Those acquired information is transmitted to the base station containing IoT servers. In IoT Server., video data is transformed into image frames and processed using YoloV9 based AI model. YoloV9 Model uses multiple component like backbone., neck and head for processing the image frame to recognize and tack the objects in each frame. Especially Backbone model employs convolution neural network for multi scale feature extraction and feature map generation on inclusion of the Generalized Efficient aggregation Network while neck component uses the path aggregation network for future fusion process and head component uses anchor box bounding box prediction method to detect and recognize the object of interest. On detect of the object of interest, distance and speed of the object is computed using gradient flow. Further model incorporates prediction approaches to detected emergency vehicle to estimate its speed and distance from traffic signal

关键词： Transportation Prototypes Predictive models Feature extraction Neck Performance analysis internet of Things Servers Artificial intelligence Traffic congestion

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Chameleon: An Adaptive System for Overlapping Keystroke signal Separation and Identification

Chameleon: An Adaptive System for Overlapping Keystroke Sign...

引用

international conference on Parallel and Distributed systems (ICPADS)

作者： Jiayi Zhao Yongzhi Huang Qipeng Xie Weizheng Wang Lu Wang Kaishun Wu Internet of Things (IoT)/ Hong Kong University of Science and Technology(Guangzhou) Guangzhou China Data Science and Analytics (DSA) Thrust Information Hub Hong Kong University of Science and Technology(Guangzhou) Guangzhou China Hong Kong University of Science and Technology Hong Kong China Computer Science Engineer City University of Hong Kong Hong Kong China Shenzhen University China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Keystroke dynamics has proven to be highly effective, with its applications expanding significantly over the years in areas such as preventing transaction fraud, account takeovers, and identity theft. Key-positioning and feature-learning methods are commonly used to identify keystroke signals. However, the existing methods face challenges in detecting overlapping keystrokes and environmentally changed signals. We propose a solution called Chameleon to address these limitations. Unlike previous signal separation and deep learning methods that are ineffective in keystroke signals and computationally demanding, Chameleon employs a low-computation Ranking Model to separate overlapping keystroke signals. Moreover, our experiments demonstrate that Chameleon separated signals can be recognized with an average accuracy of 92.69%, surpassing the commonly used FastICA method, which only reaches 25% accuracy. To account for environmental changes, we utilize the Fréchet Inception Distance (FID) as a guiding metric for model migration. Additionally, we introduce the Inductive Vector, which enables our key-identifying model to adapt to altered environmental conditions such as environment, phone location, and user variety. The Inductive Vector adjusts the model parameters based on the shift in FID. In scenarios with various phone locations, the Inductive Vector significantly improves recognition accuracy from 61% to 98%, outperforming the best existing keystroke recognition algorithm. In other dynamic environmental conditions, our approach achieves an average accuracy rate of 81.7%, which is at least 1.6 times better than the current state-of-the-art keystroke recognition algorithm.

关键词： Measurement Deep learning Adaptation models Accuracy Source separation Heuristic algorithms Identity theft Face recognition Keystroke dynamics Vectors

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An Enhanced Dynamic image Watermarking Framework based on Phase Congruency and Saliency

An Enhanced Dynamic Image Watermarking Framework based on Ph...

引用

2021 international conference in Advances in Power, signal, and Information technology, APSIT 2021

作者： Nayak, Manas Ranian Sahoo, Dinak Kumar Sahoo, Jyoti Prakash Satapathy, Lalit Mohan Dev, Prabhu Prasad School of Computer Engineering India Bhubaneswar Engineering College Dept. of Computer Science and Engineering India ITER Dept of CSIT India

ISBN: (纸本)9781665425063

Perceptual watermarking approaches achieve better image fidelity with the advantages of the Human Visual System (HVS). Saliency uses the advantages of this feature by identifying different visually perceptual regions of an image. Additionally, Phase congruency (PC) exploits those features of digital images by detecting the corner points. In the proposed method, the information hiding Capacity is generated from both bit depth maps created by comparing phase congruency results and saliency outcomes of an image that ascertains the low perceptually visible pixels (utilizing the Least Significant Bit (LSB) technique). The key idea is to ensure an additional layer of security using Symmetric Key Cryptography (SKC) and to hide information in a less observant area that is not easily perceptible inside the cover image. The experimental result analysis ensures better imperceptibility and robustness as it resists different attacks along with superior data hiding capacity. © 2021 ieee.

关键词： image watermarking

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Weight-based Mask For Domain Adaptation

Weight-Based Mask For Domain Adaptation

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Eunseop Lee Inhan Kim Daijin Kim Department of Computer Science and Engineering Pohang University of Science and Technology Korea

In computer vision, unsupervised domain adaptation (UDA) is an approach to transferring knowledge from a label-rich source domain to a fully-unlabeled target domain. Conventional UDA approaches have two problems. The first problem is that a class classifier can be biased to the source domain because it is trained using only source samples. The second is that previous approaches align image-level features regardless of foreground and background, although the classifier requires foreground features. To solve these problems, we introduce Weight-based Mask Network (WEMNet) composed of Domain Ignore Module (DIM) and Semantic Enhancement Module (SEM). DIM obtains domain-agnostic feature representations via the weight of the domain discriminator and predicts categories. In addition, SEM obtains class-related feature representations using the classifier weight and focuses on the foreground features for domain adaptation. Extensive experimental results reveal that the proposed WEMNet outperforms the competitive accuracy on representative UDA datasets.

关键词： Computer vision Semantics Computer architecture signal processing Benchmark testing Acoustics Speech processing

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Detecting Abnormal and Dangerous Activities Using Artificial Intelligence on The Edge for Smart City Application 15

Detecting Abnormal and Dangerous Activities Using Artificial...

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15th international conference on Advanced Computing and Applications (ACOMP)

作者： Nhat Nam Tran Huu Linh Mai Thanh Vo Minh Int Univ Sch Elect Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781665406390

Artificial Intelligence (AI) and internet of Things (IoT) technologies have developed rapidly in recent years. AI on the Edge technology combined with IoT technology are very potential for smart city applications, the security protection is one of the very important problem in smart city. This study proposes a solution for detecting abnormal and dangerous activities using AI on the edge which can be applied in smart city applications. This project aims at developing a system which can detect abnormal and dangerous activities using Deep learning model on the edge computer. The video signal from the camera will be processed by embedded computer Jetson Nano, which is implemented with deep learning models to detect some abnormal and dangerous activities such as human without facemask in the SARS-CoV-2 pandemic areas or man with gun and knife in the city public areas..., the information of detected abnormal activities will be sent to cloud server through the IoT system. YOLOv5 deep learning model is selected to implement in this system, thousands of abnormal activities have been collected to train the model. A prototype abnormal and dangerous activities detection system has been designed and implemented in practical testing areas, which has very high accuracy detection result. based on these initial results of the proposed solution we can develop some practical applications for smart city to detect and track different kinds of abnormal human activities in smart city for security issues.

关键词： Deep learning Smart cities image edge detection Computational modeling Prototypes Security internet of Things

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