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Efficient Spatio-Temporal Out-of-Distribution Detection for Autonomous systems 4

Efficient Spatio-Temporal Out-of-Distribution Detection for ...

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4th international conference on Intelligent Technology and embedded systems, ICITES 2024

作者： Ren, Jiankang Li, Simeng Li, Yong Yang, Xin Liu, Lin Li, Shengyu Dalian University of Technology School of Computer Science and Technology Dalian China Dalian University of Technology Key Laboratory of Social Computing and Cognitive Intelligence Dalian China Xinjiang Normal University School of Computer Science and Technology Xinjiang China

ISBN: (纸本)9798331506292

Learning-enabled components (LECs) are increasingly deployed in autonomous systems to provide adaptive and intelligent decision making in a complex dynamic environment. However, most LECs are typically data-driven, and they may exhibit erroneous predictions impacting system safety when there are some distribution shifts between real testing scenarios and training scenarios. In this paper, we introduce an efficient spatio-temporal Out-of-Distribution (OOD) detection scheme for time-series data to improve the detection performance and robustness over the state-of-the-art. Specifically, the optical flow method is employed to extract the spatio-temporal features in the high-dimensional time series data, and the extracted spatio-temporal optical flow features are encoded with the variational autoencoder to obtain input manifold for the OOD detection. Moreover, we proposed a martingale-based inductive conformal anomaly detection method to achieve lower bounded false alarms owing to the statistical foundation and robustness characteristic of martingale. The experimental evaluation demonstrates that our proposed approach significantly outperforms the existing methods in terms of detection capability and robustness. © 2024 ieee.

关键词： autonomous systems satety Out-of-Distribution detection time-series data

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Task-based methodology to characterise immersive user experience with multivariate data 31

Task-based methodology to characterise immersive user experi...

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ieee conference on Virtual reality and 3D User Interfaces (VR)

作者： Robert, Florent Wu, Hui-Yin Sassatelli, Lucile Winckler, Marco Univ Cote Azur CNRS Inria I3S Nice France Univ Cote Azur Inria Nice France Inst Univ France Paris France

ISBN: (纸本)9798350374025;9798350374032

Virtual reality (VR) technologies enable strong emotions compared to traditional media, stimulating the brain in ways comparable to real-life interactions. This makes VR systems promising for research and applications in training or rehabilitation, to imitate realistic situations. Nonetheless, the evaluation of the user experience in immersive environments is daunting, the richness of the media presents challenges to synchronise context with behavioural metrics in order to provide fine-grained personalised feedback or performance evaluation. The variety of scenarios and interaction modalities multiplies this difficulty of user understanding in face of lifelike training scenarios, complex interactions, and rich context. We propose a task-based methodology that provides fine-grained descriptions and analyses of the experiential user experience (UX) in VR that (1) aligns low-level tasks (i.e. take an object, go somewhere) with multivariate behaviour metrics: gaze, motion, skin conductance, (2) defines performance components (i.e., attention, decision, and efficiency) with baseline values to evaluate task performance, and (3) characterises task performance with multivariate user behaviour data. To illustrate our approach, we apply the task-based methodology to an existing dataset from a road crossing study in VR. We find that the task-based methodology allows us to better observe the experiential UX by highlighting fine-grained relations between behaviour profiles and task performance, opening pathways to personalised feedback and experiences in future VR applications.

关键词： virtual reality user experience multivariate data task modeling behaviour characterisation Human-centered computing-HCI theory,concepts and models Human-centered computing-User models computing methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality

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Research on Pseudo Measurement Generation Method Based on 1D-CNN and Transfer Learning for Medium-Voltage Distribution Networks 2

Research on Pseudo Measurement Generation Method Based on 1D...

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2nd international conference on Cyber-Energy systems and Intelligent Energy, ICCSIE 2024

作者： Sun, Zhiqing Lei, Shuya Yi, Xuan Zhang, Wense Wang, Yishen Ni, Xiabing Wang, Qifeng Liang, Xiao Huang, Yan State grid Hangzhou power supply company Zhejiang China State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Grid Advanced Computing and Applications Zhejiang China State Grid Smart Grid Research State Grid Laboratory of Grid Advanced Computing and Applications Zhejiang China Tsinghua University Sichuan Energy Internet Research Institute Chengdu China

ISBN: (纸本)9798350373318

With the increasing scale and complexity of new power systems, ensuring the safe and optimized operation of distribution networks becomes particularly important. Due to constraints such as economic factors, the comprehensive deployment of real-time measurement devices in mediumvoltage distribution networks faces significant challenges, resulting in insufficient observability of the distribution network. To address this issue, this paper proposes a method that combines one-dimensional convolutional neural networks (1D-CNN) with transfer learning for pseudo measurement generation. This method first constructs a 1D-CNN pseudo measurement generation model using a large amount of historical measurement data, and then trains and optimizes the 1D-CNN model through transfer learning to achieve pseudo measurement generation even under topological changes. The test results of actual engineering data demonstrate that the proposed method can generate high-frequency pseudo measurement data for nodes in medium-voltage distribution networks in real time, accurately, and efficiently. Additionally, the proposed method exhibits good adaptability when the distribution network topology changes. In this way, this paper provides a new technical path for the perception of the operational status of medium-voltage distribution networks and provides technical support for the safe and optimized operation of distribution networks. © 2024 ieee.

关键词： Power distribution networks

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Exploring Computational Linguistics Techniques for Enhanced Outing Planning: A Comprehensive Review 4

Exploring Computational Linguistics Techniques for Enhanced ...

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4th international conference on Advanced Research in computing, ICARC 2024

作者： Muthaliff, Faizan Sumanathilaka, T.G.D.K. School of Computing Informatics Institute of Technology Colombo06 Sri Lanka Swansea University Department of Computer Science Wales Swansea United Kingdom

ISBN: (纸本)9798350384864

In this day and age where convenience is king a new competitor in the form of chat-bots has appeared which requires users to only have prior knowledge on the language the chat-bot converses in. When it comes to planning outings there are a multitude of criteria to be wary of to determine the ideal solution. This review paper will address the common issues with extracting meaningful information from the user utilizing modern NLP techniques, the required criteria in addition to the user input such as, real time weather information and communicable diseases pertaining to the outing location, to determine an ideal suggestion and finally the method in which said ideal suggestion is determined after prioritizing criteria. The conclusion underscores that while NLP applications might not entirely replace existing menu-based systems for outing planning, they excel in certain scenarios. Notably, NLP's innate capacity to comprehend user preferences and context allows it to offer tailored outing suggestions. Furthermore, the application's unique feature of dynamically adapting to user requests without necessitating a return to the home page adds a distinctive advantage to its usability. © 2024 ieee.

关键词： Natural language processing systems

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Optimizing Convolutional Neural Networks for Plant Disease Detection on ARM-M Microcontrollers

Optimizing Convolutional Neural Networks for Plant Disease D...

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2024 ieee international conference on Agrosystem Engineering, Technology and applications, AGRETA 2024

作者： Nugroho, Hermawan Xan, Chew Jing Siang, Tay Fei Eswaran, Sivaraman University of Nottingham Malaysia Department of Electrical and Electronic Engineering Semenyih Malaysia Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing and Science Sarawak Malaysia Curtin University Malaysia Department of Electrical and Computer Engineering Miri Malaysia

ISBN: (纸本)9798350373660

Rice production in Malaysia is facing significant challenges due to plant diseases and environmental hazards, leading to a decline in the rice self-sufficiency ratio. To address these issues, this study explores the deployment of AI-driven plant disease detection models on ARM-M based microcontrollers, specifically the STM32H747I-DISCO Discovery kit. Utilizing Convolutional Neural Networks (CNNs) for image recognition, we aim to achieve real-time disease monitoring without relying on continuous internet connectivity. Our research evaluates five state-of-the-art models-FD-MobileNet, MobileNet V1, MobileNet V2, ResNet V1, and SqueezeNet V1-focusing on their suitability for resource-constrained environments. The results indicate that FD-MobileN et is the most resource-efficient model with the lowest number of Multiply-Accumulate operations (MACCs) and the shortest inference time, achieving an accuracy rate of 98.44%. However, SqueezeNet, despite having the highest accuracy at 99.22%, faces deployment challenges due to its large model size on the STM32H747I-DISCO board. This work highlights the potential of embedded AI applications in enhancing agricultural resilience and efficiency. © 2024 ieee.

关键词： ARM-M microcontroller CNNs embedded systems plant disease detection

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A Comprehensive Survey on Phytopathogen Surveillance with Modern Artificial Intelligence Practices 5

A Comprehensive Survey on Phytopathogen Surveillance with Mo...

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5th international conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Kaleeswari, G. Sundarrajan, R. Kalasalingam Academy of Research and Education Department of Computer Applications Tamilnadu Virudhunagar India Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Virudhunagar India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The most important aspect of modern agriculture is the detection of plant diseases with the goal of improving crop quality and output. This survey paper investigates modern approaches of deep learning algorithms, Explainable AI and Federated Learning to plant disease diagnosis. The background and broadening of plant disease detection are briefly reviewed at the outset of the study, with a focus on the importance of precise and effective identification techniques. The multivariate normal DL neural network (MNDLNN) classifier and Customized CNN are the most robust feature extraction and classification techniques among the examined technologies. Using Higher-Order Whitened Singular Value Decomposition (HOWSVD), complex data is processed in a way that highlights distinct patterns and features for easy data classification and also increases accuracy. The E-GreenNet generates disease classification features based on a MobileNetV3. The ResNet9 and Improved Vision Transformer models perform better when processing complicated visual data. Spectral Generative Adversarial Neural Network (DSGAN2) and Lite Multikernel Depthwise Convolutions architecture are suitable for real-time applications because they provide promising results with less computational overhead. These findings' implications point to a possible move toward disease detection systems that are easier to use and more effective, which would improve crop management and food security. The development of more affordable solutions for small scale farmers, the integration of embedded systems for real-time monitoring, and the improvement of these models for increased precision are some future directions. This survey highlights the transformative potential of combining deep learning with other techniques like Federated Learning in advancing agricultural practices. © 2024 ieee.

关键词： Generative adversarial networks

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A Point Cloud-Based Non-Intrusive Approach for Human Posture Classification by Utilizing 77 GHz FMCW Radar and Deep Learning Models

A Point Cloud-Based Non-Intrusive Approach for Human Posture...

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ieee international Symposium on Circuits and systems (ISCAS)

作者： Mahajan, Pranjal Chaudhary, Devansh Khan, Mujeev Khan, Mohammed Hammad Wajid, Mohd Srivastava, Abhishek IIIT Hyderabad Ctr VLSI & Embedded Syst Technol CVEST Hyderabad India ZHCET Aligarh Muslim Univ Dept Elect Engn Aligarh India

ISBN: (纸本)9798350330991;9798350331004

Human posture analysis has gained a significant research interest in the recent times. It helps in many applications such as gait analysis for detecting neurological disorders, fall detection of elderly people, and continuous monitoring of severely ill patients. Camera-based vision systems are commonly employed for detecting human postures;however, they cause concerns over the subjects' privacy. To address this challenge, we present a millimeter wave (mmWave) radar-based, truly non-contact, non-intrusive, and privacy-conscious posture detection and classification system in this research. The proposed system utilizes three-dimensional point cloud data of the subject to comprehensively classify body postures, capturing intricate real-time details. In this work, we also present a custom-designed Convolutional Neural Network (CNN) and its comparison with other models, which are conventionally used for posture classification. We also demonstrate the hardware implementation of the proposed system and present the measurement results using Texas Instruments' IWR1843BOOST radar module. The proposed CNN model achieves an accuracy of 97.10% while classifying standing, sitting, lying and bending postures.

关键词： Deep Learning (CNN) FMCW Radar mmWave point-cloud posture classification

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High Gain Dual Duty Stepup Triswitching Transformerless Converter for Stable High Voltage DC applications 6

High Gain Dual Duty Stepup Triswitching Transformerless Conv...

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6th ieee international conference on Recent Advances in Intelligent Computational systems, RAICS 2024

作者： Popat, Ruchit Sanjivbhai Saiyad, Junaid Yadav, Ankit Lalbahadur Mahla, Mayankbhai Nevajbhai Songara, Chandreshkumar Kanteelal Bhandari, Kamal Singh Gujarat India Gujarat India

ISBN: (纸本)9798350381689

This paper focuses on advancing high gain DC-DC converter for stable high voltage applications. The ATMEGA328P microcontroller is employed for precise voltage control, enabling real-time monitoring and adjustments to maintain a constant voltage level. The proposed approach integrates cutting edge power electronics with digital control, overcoming challenges in high-gain converter voltage stability while ensuring efficiency. The research explores potential applications in renewable energy systems, DC Micro-grids, and portable electronic devices with respect to the limitations of existing DC-DC converter topologies in terms of stable high voltage applications. Eventually, the best architecture is selected, and the boost converter topology with a feedback control loop is implemented. The paper delves into detailed design aspects, including component selection and algorithm development. Employing pulse width modulation, the implemented control algorithm efficiently drives the converter, achieving high voltage gains with low output voltage ripple and robust regulation. A 12/120V prototype circuit is developed and verified for performance validation. © 2024 ieee.

关键词： Feedback control

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An Ensembled real-time Hand-Gesture Recognition using CNN 15

An Ensembled Real-Time Hand-Gesture Recognition using CNN

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kavitha, M.N. Saranya, S.S. Prasad, M. Kaviyarasu, S. Ragunath, N. Rahul, P. Kongu Engineering College Department of Computer Science and Design Tamilnadu Erode India PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamilnadu Coimbatore India Controller of Examinations Tamilnadu Namakkal India Kongu Engineering College Bachelor of Science - Information Systems Tamilnadu Erode India

ISBN: (纸本)9798350370249

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems. © 2024 ieee.

关键词： Intelligent systems

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IO Virtualization for real time Automotive systems

IO Virtualization for Real Time Automotive Systems

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2022 ieee international conference on Consumer Electronics, ICCE 2022

作者： Sriramakrishnan, Govindarajan Mody, Mihir Shurtz, Gregory Fuoco, Chuck Chitnis, Kedar Devshatwar, Nikhil Steiss, Don Bergsagel, Jonathan Jones, Jason Y A, Prithvi Shankar Texas Instruments Incorporated Embedded Processor Division Bangalore India Texas Instruments Incorporated Embedded Processor Division Houston United States Texas Instruments Incorporated Embedded Processor Division Dallas United States

ISBN: (纸本)9781665441544

Virtualization is common in enterprise systems to isolate multiple server applications using the same underlying HW. In automotive systems virtualization is fast becoming table stakes to isolate safety applications like ADAS (Advanced Driver Assist System) and infotainment using the same underlying SOC. This presents unique challenges related to peripheral IO virtualization, not seen in enterprise systems, like deterministic latency to external memory ( © 2022 ieee.

关键词： Cameras

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