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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications X"

共 16620 条记录，以下是551-560 订阅

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3D-PSH: Lightweight 3D LiDAR Object Detection Using Adaptive Clustering and 3D Point Spatial Histograms 36

3D-PSH: Lightweight 3D LiDAR Object Detection Using Adaptive...

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36th IEEE International conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Baber, Junaid Aycard, Olivier GIPSA-LAB University of Grenoble Alpes France

ISBN: (纸本)9798331527235

The advent of 3D LiDAR technology has revolutionized object detection in applications such as autonomous driving, robotics, and advanced driver assistance systems. However, existing methods often require substantial computational resources, limiting their practicality for real-time applications on devices with constrained hardware capabilities. This paper presents an efficient and lightweight 3D LiDAR object detection framework, 3D-PSH, that combines adaptive clustering with 3D Point Spatial Histograms (3D-PSH) and classical classification techniques to address these challenges. Our framework begins with an adaptive clustering algorithm that segments the point cloud data into distinct clusters, representing potential objects. 3D Point Spatial Histograms (3D-PSH) are then computed from these clusters and subsequently quantized into a Bag of Visual Words (BoVW) to create a compact and informative representation. These representations are then classified using robust classical classification methods to identify object types, such as pedestrians and vehicles. This multi-step approach ensures a balance between computational efficiency and detection accuracy, making it suitable for real-time deployment. Extensive experiments on the KITTI dataset and our live sensor data demonstrate the effectiveness and efficiency of our proposed framework. The results indicate that our method achieves competitive accuracy while significantly reducing computational requirements compared to traditional approaches. This framework offers a practical solution for deploying 3D object detection in a wide range of applications, particularly where computational resources are limited. © 2024 IEEE.

关键词： advanced driver assistance systems

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A Cost Efficient Reversible QCA Multiplexer for Low Power applications 4

A Cost Efficient Reversible QCA Multiplexer for Low Power Ap...

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4th International conference on Artificial Intelligence and Signal Processing

作者： Aruna, Patthi Komanapalli, Gurumurthy VIT AP Univ Sch Elect Engn Amaravati Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Low-power architectures are increasingly significant across a spectrum of applications, ranging from the Internet of Things (IoT) and Quantum Computing. The Quantum dot cellular automata (QCA) is regarded as a promising platform for implementing digital circuits at the nanoscale owing to its less power consumption (PC) and great performance. The proposed design is a 4x1 multiplexer (MUx) using QCA technology. The MUx serves as a fundamental building block in digital systems, enabling the selection of one out of four input signals based on a control signal. Our proposed design leverages the unique properties of QCA, such as Coulombic interactions among quantum dots, to realize compact and efficient multiplexing functionality. We present a Controlled Operational Gate(COG) with detailed design methodology, layout optimization techniques, and performance analysis of the proposed 4x1 QCA 4x1 MUx using COG. Simulation results demonstrate the feasibility and effectiveness of our design in terms of area efficiency, latency, and quantum cost leading to low PC. The proposed 4x1 QCA MUx holds promise for realizing complex digital systems with improved performance and energy efficiency, paving the way for advanced nanoscale computing applications.

关键词： Quantum electronics QCA power consumption nanoscale computing reversibility

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Simultaneous Fault Diagnosis for sensor and Railway Point Machine for Autonomous Rail System 27

Simultaneous Fault Diagnosis for Sensor and Railway Point Ma...

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27th IEEE International conference on Intelligent Transportation systems, ITSC 2024

作者： Hu, xiaoxi Zhang, xiaohan Chen, Fei Liu, Zongyang Liu, Jin Tan, Lei Tang, Tao Polytechnic Institute Zhejiang University Hangzhou310015 China Wuhan University State Key Laboratory of Water Resources Engineering and Management Wuhan430072 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China Institute for Transport Studies University of Leeds LeedsLS2 9JT United Kingdom Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Municipal Engineering Research Institute Beijing100037 China

ISBN: (纸本)9798331505929

Since the safe and reliable behavior of a Railway Point Machine (RPM) is definitely pivotal for rail transportation, scholars and engineers have studied a considerable number of algorithms for RPM fault diagnosis via many kinds of sensor. However, few scholars have considered the possibility that sensors can also experience faults, which cannot be ignored in practical railway applications. To fill the gap, we propose an end-to-end deep learning network named Simultaneous Fault Diagnosis Network (SFDNet) for the simultaneous fault diagnosis for sensor and RPM. Our approach includes Information Aggregate and Update Module (IAUM) and Fault Diagnosis Module (FDM). The IAUM module leverages the concept of Spatial Attention Mechanism (SAM) and Channel Attention Mechanism (CAM) to effectively filter and aggregate information from diverse sensors. It enhances the salience of normal sensor observations while attenuating the influence of anomalous signals, thereby enabling the resultant features to more accurately capture the operational state of the RPMs. The features yielded by SAM are directly employed for sensor fault diagnosis. The FDM integrates Bidirectional Gated Recurrent Units (BiGRUs) and Time-series Feature Pyramid Network (TFPN) to respectively extract and integrate long-term dependencies and multi-scale features, enhancing fault diagnosis in RPM by capturing comprehensive temporal patterns and correlations. Lastly, ablation experiments and comparison studies shows the superiority of our SFDNet. © 2024 IEEE.

关键词： Railroads

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sensor and Microcontroller based 360 Degree Car Assistance System 4

Sensor and Microcontroller based 360 Degree Car Assistance S...

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4th International conference on Electronics and Sustainable Communication systems, ICESC 2023

作者： Jenit Elavarasi, J. Franklin, Abilash Nagarajan, Srideivanai Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

The 360 Degree Car Assistance System automatically applies emergency brakes when necessary and regulates a vehicle's speed based on the proximity of an object. Damages that may be made while driving will be avoided by this approach. The car's undercarriage is vulnerable to hit from the ground and vice versa, and the region is also hidden from the driver's view. Thus, in order to avoid this, a survey was conducted, and this project was built in such a manner that it would identify the item and alert the driver if there were any impediments beneath the car that are close to the Chassis and the Wheels. A prototype of the car is created and used in this project. The system uses an Ultrasonic sensor and AT Mega to detect obstructions, and it allows the execution of emergency braking in accordance with pre-burnt code in the AT Mega. Consequently, a cost-effective automatic collision avoidance system is suggested. In order to avoid and prevent accidents all automobile models can use the 360 Degree Car Assistance System that has been designed. © 2023 IEEE.

关键词： advanced driver assistance systems

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Intelligent manufacturing systems for on-line tool wear monitoring under cross conditions: a brief review 3

Intelligent manufacturing systems for on-line tool wear moni...

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3rd International conference on advanced Manufacturing Technology and Manufacturing systems, ICAMTMS 2024

作者： Ma, Kaile Wang, Guofeng Key Laboratory of Mechanism Theory and Equipment Design of Education School of Mechanical Engineering Tianjin University Tianjin China

ISBN: (纸本)9781510681804

Tool wear monitoring (TWM) based on data-driven methods is essential to ensure product quality and overall efficiency. Machine learning (ML) models, unlike physics-based approaches, are practical for online monitoring without interrupting the machining processes. Existing reviews on TWM mainly focus on a comprehensive perspective or partial aspects. This paper reviews the development of shallow machine learning (SML) and deep learning (DL) techniques for smart tool monitoring under cross conditions. The mainstream framework of online TWM is introduced. The intelligent tool monitoring, which is based on SML and DL, was reviewed and categorized into two tasks: tool condition classification and tool wear prediction. The advantages and disadvantages of SML and DL were subsequently discussed. Finally, the challenges of smart tool monitoring for practical engineering application including sensor selection, few samples, model reliability and online update were outlined. © 2024 SPIE.

关键词： Smart manufacturing

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Investigation on the Impact Of CQI Index in Performance Enhancement of 5G Downlink systems 10

Investigation on the Impact Of CQI Index in Performance Enha...

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10th International conference on advanced Computing and Communication systems, ICACCS 2024

作者： Deepak Athipan, A.M.B. Subha Dharshini, M. Ramesh, Parameswaran Shanmugapriya, R. Madras Institute of Technology Anna University Department of Information Technology Chennai India Madras Institute of Technology Anna University Department of Electronics Enigneering Chennai India

ISBN: (纸本)9798350384369

In recent years due to the introduction of 5G, diversified applications are increasing rapidly. Ensuring service specific Quality of Service (QoS) is essential. The objective of this is to investigate the channel behaviour on QoS. Signal Noise Ratio (SNR), Channel Quality Indicator (CQI) index on Bit Error Rate and Throughput on QPSK modulated data under Rayleigh Channel Model is investigated. Two 5G systems namely OFDMA and PD-NOMA are simulated in MATLAB. 2 x 2 5G systems are considered for analysis. From the results obtained, it is found that maximum average throughput achieved by UEs in the OFDMA system is 100% while PD-NOMA is 96.12%. The above is achieved at SNR of 30 dB. The developed research has various verticals of extensions in terms of channel model, MCS, differential traffic, UE mobility, etc., © 2024 IEEE.

关键词： MATLAB

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Detection of Leading CNN Models for AI Image Accuracy and Efficiency

Detection of Leading CNN Models for AI Image Accuracy and Ef...

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2024 International conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Nayim, Meshal Mohan, Vishnu Pandey, Trilok Nath Dash, Bibhuti Bhusan Dash, Bibek Bikram Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to be University School of Computer Applications Bhubaneswar India HealthWorksAI Bangalore India

ISBN: (纸本)9798350360660

This research explores the challenge of distinguishing between authentic and AI-generated images using advanced convolutional neural network (CNN) architectures. Leveraging the CIFAKE dataset, which includes a diverse array of AI-generated and real images, we evaluated the performance of three pre-trained models: ResNet50, EfficientNetB0, and DenseNet121. Our study involved training these models with images of 32x32 and resized 64x64 pixel resolutions to assess their classification accuracy, precision, recall, and computational efficiency. DenseNet121 emerged as the most effective model, achieving an accuracy of 98.49% on the test dataset, significantly outperforming the other models. This work highlights the potential of deep learning methodologies in addressing the growing need for reliable AI image detection, providing a robust foundation for future advancements and applications in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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Examining Multi-Input Pipelines for the Service Degradation Point 6

Examining Multi-Input Pipelines for the Service Degradation ...

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conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations applications VI

作者： Motorny, Sergey Glandon, S. Ross Cheng, Jing-Ru C. UICGS 6564 Loisdale Ct Springfield VA 22150 USA Bowhead Family Companies 6564 Loisdale Ct Springfield VA 22150 USA US Army Engn Res & Dev Ctr 3909 Halls Ferry Rd Vicksburg MS 39180 USA

ISBN: (纸本)9781510674219;9781510674202

sensor data from multiple sources provides valuable insights for data-driven decision making. sensor fusion is the process of combining data from multiple sensors to produce a more complete view than would be possible with a single sensor alone. To implement sensor fusion at the tactical edge, heterogeneous data sources are often combined. Various types of sensors, such as cameras, lidar, radar, magnetic, etc. are frequently blended for optimal decision making. Multi-domain operation (MDO) brings a unique set of challenges to the subdomain of sensor fusion. MDO software architects must consider such challenges as the need to operate in a resource-constrained, fast-paced, and contested environment. Software architects must focus on such design features as high data velocity, high data volume, scalability, and real-time processing. This work offers a blueprint for benchmarking a multi-input video processing pipeline in search of a service degradation point. We throttle the number of input video feeds while quantitatively measuring the pipeline's performance. We employ an established Nvidia DeepStream SDK framework used for building commercial computer vision and video analytics applications. We begin with a double input application performing object detection, semantic segmentation, and a tracker. We continue scaling up the number of video inputs meant to simulate pipeline load variability frequently present in the field. While objectively measuring the pipeline's execution parameters, we simulate the point of channel saturation and demonstrate the degradation of the quality of service. We postulate that it may be logical to apply such a benchmarking template in order to establish the range of limits and predict the anticipated pipeline processing times. We conclude with some of the potential future developments of the proposed template.

关键词： DeepStream benchmarking Nvidia Nsight systems Nvidia Nsight Compute MDO video pipelines

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Balancing Fairness and Accuracy for Predictive Models in Criminal Justice applications Using Multi-Objective Optimization Methods

Balancing Fairness and Accuracy for Predictive Models in Cri...

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2024 IEEE International conference on systems, Man, and Cybernetics, SMC 2024

作者： Qi, xiaowen Ma, Yujungrong Nakamura, Kiminori Bhattacharyya, Shuvra S. University of Maryland Department of Electrical and Computer Engineering Institute for Advanced Computer Studies College ParkMD20740 United States University of Mary-land Department of Criminology and Criminal Justice College ParkMD20740 United States

ISBN: (纸本)9781665410205

In the field of predictive modeling for criminal justice applications, the dual challenges of ensuring fairness and maintaining interpretability are crucial. This paper addresses these challenges by introducing a new approach to optimizing decision trees using evolutionary algorithms (EAs). Our approach focuses on refining decision trees to achieve a balance between accuracy and algorithmic fairness, a task complicated by potential bias present in historical data. By leveraging the principles of multi-objective optimization, our model systematically trades off prediction accuracy and fair-ness. The evolutionary process characterized by selection, crossover, and mutation is tailored to fit the decision tree structure, ensuring that model development is not only accurate but also promoting measurable fairness. Experimental results demonstrate the effectiveness of our approach in providing interpretable and fair predictive models that can be considered for high-stakes applications in criminal justice. More broadly, this research makes a significant contribution to the field of explainable machine learning, providing a powerful framework for engineering systems that are transparent, fair, and adaptable to different data environments. © 2024 IEEE.

关键词： Forestry

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Enhanced Robot State Estimation Using Physics-Informed Neural Networks and Multimodal Proprioceptive Data 17

Enhanced Robot State Estimation Using Physics-Informed Neura...

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conference on sensors and systems for Space applications xVII

作者： Liu, Yuqing Bao, Yajie Cheng, Peng Shen, Dan Chen, Genshe xu, Hao Univ Nevada 1664 N Virginia StMS 260 Reno NV 89557 USA Intelligent Fus Technol Inc Germantown MD USA

ISBN: (纸本)9781510674431;9781510674424

In this study, we introduce an innovative Robot State Estimation (RSE) methodology incorporating a learning-based contact estimation framework for legged robots, which obviates the need for external physical contact sensors. This approach integrates multimodal proprioceptive sensory data, employing a Physics-Informed Neural Network (PINN) in conjunction with an Unscented Kalman Filter (UKF) to enhance the state estimation process. The primary objective of this RSE technique is to calibrate the Inertial Measurement Unit (IMU) effectively and furnish a detailed representation of the robot's dynamic state. Our methodology exploits the PINN to mitigate IMU drift issues by imposing constraints on the loss function via Ordinary Differential Equations (ODEs). The advantages of utilizing a contact estimator based on proprioceptive sensory data are multifold. Unlike vision-based state estimators, our proprioceptive approach is immune to visual impairments such as obscured or ambiguous environments. Moreover, it circumvents the necessity for dedicated contact sensors-components not universally present on robotic platforms and challenging to integrate without substantial hardware modifications. The contact estimator within our network is trained to discern contact events across various terrains, thereby facilitating resilient proprioceptive odometry. This enables the contact-aided invariant Kalman Filter to produce precise odometric trajectories. Subsequently, the UKF algorithm estimates the robot's three-dimensional attitude, velocity, and position. Experimental validation of our proposed PINN-based method illustrates its capacity to assimilate data from multiple sensors, effectively reducing the influence of sensor biases by en forcing ODE constraints, all while preserving intrinsic sensor characteristics. When juxtaposed with the employment of the UKF algorithm in isolation, our integrated RSE model demonstrates superior performance in state estimation. This enhanced capabi

关键词： IMU calibration Robot state estimation deep learning Unscented Kalman filtering physics-informed neural network multi-sensor fusion

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