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Autonomous Number Plate Detection System for Vehicle Identification and Tracking

Autonomous Number Plate Detection System for Vehicle Identif...

Recent Trends in Microelectronics, Automation, Computing and Communications systems (ICMACC), International Conference on

作者： Neha Sri Nareddy Abhinaya Polneni Rekha Baithi Pratyusha Kunchamwar Sunil Kumar Suvarna Durgabhavani Battu Naveena Budda Vikas Agarwal Premchander Munimanda AI IoT and Robotics Lab (AIR Lab) AGVs Group Autonomous Robotics Systems Limited Hyderabad India Department of Information Technology Malla Reddy Engineering College for Women Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta USA School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy.

关键词： Visualization Accuracy Law enforcement System performance Transportation Artificial neural networks Real-time systems Security License plate recognition Videos

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Task Offloading and Resource Allocation for SLAM Back-End Optimization: A Rewardless Active Inference Approach

Task Offloading and Resource Allocation for SLAM Back-End Op...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jingcheng Fang Ying He F. Richard Yu Jianqiang Li Victor C. Leung Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

With the increasingly sophisticated algorithms of simultaneous localization and mapping (SLAM), it is difficult for mobile terminals with limited resources to exploit the performance of SLAM algorithms fully. Traditional deep reinforcement learning (DRL)-based approaches have offloaded SLAM tasks to servers. However, existing solutions suffer from low data efficiency and poor generalization problems. This paper proposes a novel approach based on recent advances in rewardless active inference for SLAM back-end optimization. Specifically, the reward function is replaced with simple rewardless guidance in active inference. In addition, instead of simply considering the SLAM task as a whole, we delve into the sub-tasks of back-end optimization of SLAM for offloading and resource allocation. Simulation results show the superior performance of the proposed scheme.

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection

arXiv

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arXiv 2024年

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model’s generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL’s advancement in mitigating the effect of spurious correlation. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Secure-by-Construction Controller Synthesis via Control Barrier Functions

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IFAC-PapersOnLine 2023年第2期56卷 239-245页

作者： Bingzhuo Zhong Siyuan Liu Marco Caccamo Majid Zamani TUM School of Engineering and Design Technical University of Munich Germany Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science CU Boulder USA

In this paper, we propose a secure-by-construction scheme for synthesizing controllers to enforce safety and security properties simultaneously over control systems. As a key insight, we establish a bridge between the desired safety and security properties by leveraging notions of (augmented) control barrier functions. Based on these functions, we show that one can synthesize secure-by-construction controllers for control systems with continuous state and input sets. Additionally, we provide sum-of-square (SOS) conditions under which the desired (augmented) control barrier functions can be constructed. Finally, we demonstrate the applicability of our results on a case study.

关键词： Secure-by-Construction Controller Synthesis Safety Opacity Control Barrier Function Security

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Hemochromatosis Pathogenesis and Its Association with Liver Disease: An Analysis Through Machine Learning

Hemochromatosis Pathogenesis and Its Association with Liver ...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Isra Imtiaz Attique Ur Rehman Sabeen Javaid Tahir Muhammad Ali Azka Mir Mehedi Masud Deepak Kumar Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University for Science and Technology Kuwait Department of Computer Science Taif University Saudia Arabia Division of Research and Development Lovely Professional University Phagwara India KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

A significant risk factor for the development of liver disease is hemochromatosis, an inherited iron overload illness, but the complex pathophysiology behind this link is still unknown. To understand the intricate interactions between hemochromatosis and liver disease, this study uses a cutting-edge strategy that incorporates machine learning techniques. Promising prediction skills are demonstrated by machine learning models, which open up new possibilities for early detection and individualized intervention approaches. The results shed important light on the relationship between hemochromatosis and liver disease and improve our understanding of the pathogenesis of the disease. This study clarifies the pathophysiology of hemochromatosis and its subsequent connection to liver disease, highlighting the revolutionary power of machine learning in understanding intricate biological processes. This selected dataset contains 15 parameters and has 202 dated records with a target value of ‘Disease Name’ which has 5 different categories Type 1, 2, 2A, 2B,2C, 3, 4, and Type5. The experiment is performed using two classifiers including KNN and Naïve Bayes. The k-fold cross-validation technique is also used to improve the performance. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 98.00% using a K-NN classifier. Rapid miner platform is used for the application of the machine learning tools and techniques for this research These discoveries have consequences that go beyond clarifying the mechanisms behind disease; they also open the door to better clinical care and tailored treatment therapies for high-risk people.

关键词： Accuracy Liver diseases Pathogenesis Medical treatment Machine learning Predictive models Nearest neighbor methods Market research Iron Diseases

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A Robust Development of an Efficient Industrial Monitoring and Fault Identification Model using Internet of Things

A Robust Development of an Efficient Industrial Monitoring a...

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Big Data & Machine Learning (ICBDML), IEEE International Conference on

作者： Yahya Yaqoob Obaid Al Belushi P. Jasmin Dennis S. Deepa V. Arulkumar D. Kanchana Ragini Y P Department of Systems Engineering Military Technical College Sultanate of Oman Department of Electronics and Communication Engineering Vi Institute of Technology Chengalpattu Tamil Nadu Department of Computer Science and Business Systems RMD Engineering College Kavarapettai Tamil Nadu Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai Tamil Nadu Department of Computer Applications SRM Institute of Science and Technology Ramapuram Chennai Tamil Nadu Department of Biosciences Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374117

Internet of Things (IoT) is rapidly developing technologies and the Internet of Things (IoT) is a network of physical items or effects embedded with computer software, detectors, and network connectivity. The Internet of Things also addresses the issue of centralizing physical bias management online. We are leveraging the Internet of Things (IoT) idea to construct a system that will automatically monitor all artificial processes and issue warnings or expert comments when necessary. nowadays, gas leakage is a serious concern in the house and as well as vigilance. If the gas sense is indeed low, it's because we've been too careless, intolerant, or both to look for it. If the gas position is raised, however, calamity may ensue. In this research, the crucial concept is introduced to prevent this catastrophe from occurring. In this study, we propose an alternate approach to this impending calamity and design a system with integrated sensors, controllers, and some IoT-assisted software. The suggested method, known as the Internet of Things based Industrial Safety System (IoTISS), is compared against the more established Industrial Safety System (ISS) to see how well it performs. In this system, we are monitoring the detection of gas leakages with various warning characteristics as well as other devices are used for monitoring various aspects like humidity and temperature using DHT22 sensor, MQ2 gas monitoring sensor, fire sensor and NodeMCU ESP8266 WiFi module. NodeMCU ESP8266, which doubles as a WiFi module, will receive data from all of these types of sensors.

关键词： Temperature sensors Fault diagnosis Industries Sensor phenomena and characterization Sensor systems software Internet of Things

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AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning

arXiv

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arXiv 2024年

作者： Li, Beibei Zheng, Yiyuan Jin, Beihong Xiang, Tao Wang, Haobo Feng, Lei College of Computer Science Chongqing University Chongqing400044 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100044 China University of Chinese Academy of Sciences Beijing100044 China School of Software Technology Zhejiang University Hangzhou310027 China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

Partial-Label Learning (PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problem caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo, which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo. The code is available at https://***/libeibeics/AsyCo. © 2024, CC BY-NC-SA.

关键词： Phase locked loops

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Diagnosing Spinal Abnormalities Using Machine Learning: A Data-Driven Approach

Diagnosing Spinal Abnormalities Using Machine Learning: A Da...

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Artificial Intelligence (ICAI), International Conference on

作者： Zia Ul Islam Nasir Khalil Khan Momna Asghar School of Interdeciplanary Engineering & Sciences(SINES) National University of sciences & Technology (NUST) Isamabad Pakistan Department of Computer Software Engineering & Computer Science Pak-Austria Institute of Applied Science & Technology Haripur Pakistan Department of Electrical Engineering University of Azad Jammu & Kashmir Muzaffarabad A.K

Low back pain is a predominant condition which can affects people from different diaspora. The goal of this work is to use machine learning approach to forecast spinal abnormalities. Extratreesclassifier is utilized as a data preprocessing stage to choose the dataset's most prominent features. On a dataset of 310 samples, spinal anomalies are diagnosed using machine learning algorithms like the Support Vector Machine (SVM) and the multilayer perceptron (MLP). The purpose of this study is to determine the most crucial factors that produce backbone abnormalities and to predict them using supervised machine learning techniques. The classification of normal and abnormal spinal patients is investigated in terms of various aspects, including testing and training accuracy, precision, and recall. The observed accuracies for SVM and MLP with 80% training data are 92% and 90%, respectively. The result shows that these models can achieve high accuracy in predicting spinal abnormalities, with the SVM model performing the better. The result suggest that this approach has the potential to significantly improve the efficiency and accuracy of spinal abnormality diagnosis, leading to better patient outcomes.

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Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light

arXiv

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arXiv 2024年

作者： Song, Alexander Kottapalli, Sai Nikhilesh Murty Goyal, Rahul Schölkopf, Bernhard Fischer, Peer Max Planck Institute for Medical Research Heidelberg Germany Institute for Molecular Systems Engineering and Advanced Materials Universität Heidelberg Heidelberg Germany Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zürich Zürich Switzerland

Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. Our framework is designed for real-time, parallelized operations, leveraging 2D arrays of LEDs and photodetectors connected via independent analog electronics. We experimentally demonstrate this approach using a system with a three-layer network with two hidden layers and operate it to recognize images from the MNIST database with a recognition accuracy of 92% and classify classes from a nonlinear spiral data with 86% accuracy. By implementing multiple layers of a deep neural network simultaneously, our approach significantly reduces the number of read-ins and read-outs required and paves the way for scalable optical accelerators requiring ultra low energy. Copyright © 2024, The Authors. All rights reserved.

关键词： Multilayers

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Hypergraph-based Self-supervised Multi-channel Network for Drug-target Interaction Prediction

Hypergraph-based Self-supervised Multi-channel Network for D...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoting Zeng Yu Liang Peng Yang Weilin Chen Baiying Lei School of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Biomedical Engineering Shenzhen University Shenzhen China Institute of Biological Therapy Shenzhen University Medical School Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-target interaction (DTI) prediction is vital for drug discovery and repurposing. Hypergraph is utilized in DTI prediction for modeling higher-order relationships in biomedical networks. Although the strategies of modeling hypergraph-based drug-related interactions with multi-channel and utilizing self-supervised learning task to improve DTI prediction performance have been proven promising, current researches fail to effectively model feature interaction across different channels and fully exploit cross-channel information for self-supervised task. In this study, we propose a hypergraph-based self-supervised multi-channel interaction framework HSMI-DTI for DTI prediction. HSMI-DTI aims to extract hypergraph features effectively and model cross-channel correlations, leveraging hierarchical self-supervised learning to uncover the discover correlations between different channels. We compare HSMI-DTI with advanced baselines, and experiment results show our model outperforms existing methods, thereby optimizing DTI prediction performance.

关键词： Correlation Biological system modeling Self-supervised learning Predictive models Feature extraction Heterogeneous networks Drug discovery Bioinformatics Diffusion tensor imaging

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