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检索条件"任意字段=IEEE Data Compression Conference"

共 16713 条记录，以下是111-120 订阅

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Preliminary Experiment on Point Cloud Collection Through 3D Mobile Crowdsensing

Preliminary Experiment on Point Cloud Collection Through 3D ...

引用

ieee International conference on Consumer Electronics (ICCE)

作者： Kaito Watanabe Takumi Miyoshi Taku Yamazaki Graduate School of Engineering and Science Shibaura Institute of Technology Saitama Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The utilization of digital twins is highly anticipated for the realization of smart cities, enabling efficient urban management using the internet of things (IoT) and artificial intelligence (AI). However, constructing and maintaining spatial digital twins requires large amounts of spatial information obtained using light detection and ranging (LiDAR). In this study, we develop a mobile crowdsensing system, in which participants capture partial point cloud with various sensing information of urban spaces. We conduct experiments using the proposed system to capture point cloud data and examine its characteristics to verify the effectiveness of real-world sensing. Additionally, we evaluate the performance of removing unreliable points from the captured point cloud using associated sensing information to enhance data quality.

关键词： Point cloud compression Three-dimensional displays Laser radar Crowdsensing Smart cities Filtering Sensors Digital twins Internet of Things Artificial intelligence

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Real-Time Recursive Point Cloud Prediction for Interpolating Anomalous Frames

Real-Time Recursive Point Cloud Prediction for Interpolating...

引用

ieee International conference on Consumer Electronics (ICCE)

作者： Yoshiki Yamaguchi Yuki Umemoto Takumi Miyoshi Taku Yamazaki Ryoichi Shinkuma College of Systems Engineering and Science Shibaura Institute of Technology Saitama Japan Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan College of Engineering Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Physical attacks that target light detection and ranging (LiDAR) sensors have become a significant threat to the security and reliability of LiDAR-based systems, particularly in autonomous driving and digital twin applications. These attacks selectively manipulate or remove point cloud data, thereby undermining the functionality of safety-critical systems. In this study, we propose a recursive point cloud prediction mechanism that anticipates future frames based on past data, enabling real-time interpolation of anomalous frames. The proposed mechanism predicts five future frames based on the preceding five frames and replaces compromised frames with interpolated predictions, thereby ensuring data continuity. Furthermore, we demonstrate the effectiveness of the proposed approach using a major dataset, showing that it can maintain data integrity even during physical attacks.

关键词： Point cloud compression Interpolation Laser radar data integrity Sensor systems and applications Real-time systems Digital twins Security Anomaly detection Autonomous vehicles

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A Memory-Efficient Edge Inference Accelerator with XOR-Based Model compression 23

A Memory-Efficient Edge Inference Accelerator with XOR-Based...

引用

Proceedings of the 60th Annual ACM/ieee Design Automation conference

作者： Hyunseung Lee Jihoon Hong Soosung Kim Seung Yul Lee Jae W. Lee Seoul National University Seoul Republic of Korea

ISBN: (纸本)9798350323481

Model compression is widely adopted for edge inference of neural networks (NNs) to minimize both costly DRAM accesses and memory footprints. Recently, XOR-based model compression has demonstrated promising results to maximize compression ratio and minimize accuracy drop. However, XOR-based decompression alone produces bit errors and requires auxiliary data for error correction. To minimize model size and hence DRAM traffic, we propose an enhanced decompression algorithm and a low-cost hardware accelerator for it. Since not all errors are equal, our algorithm selects only important errors to correct with no accuracy drop. Compared with the baseline XOR compression scheme correcting all errors, the compressed model size of ResNet-18 and VGG-16 is reduced by 23% and 27% respectively. We also present a low-cost hardware implementation of on-line XOR decompression and error-correction logic built on Gemmini, an open-source systolic array accelerator, at the cost of only a 0.39% and 0.46% increase in area and power.

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Joint Channel Estimation and Feedback in Multiple-Input Multiple-Output Systems by using Pilot Feedback Network with Adaptive compression Scheme

Joint Channel Estimation and Feedback in Multiple-Input Mult...

引用

Integrated Circuits and Communication Systems (ICICACS), ieee International conference on

作者： Haeedir Mohameed Vidyadevi G Biradar K. Murugan Sakthivel Subramaniam Thomas Koilraj Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Sona College of Technology salem India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Nowadays, Massive Multiple-Input Multiple-Output (MIMO) has become important in 5G which enhances channel capacity by developing large antenna arrays at the Base Station (BS). Traditional approaches had faced several challenges which include high pilot overhead and increased computational complexity. Therefore, this research proposes Pilot Feedback Network with Adaptive compression Scheme (PFnet with Adaptive compression Scheme) for Channel Estimation (CE) and feedback in MIMO systems. Initially, data is generated from COST 2100 model. Then, a massive MIMO system with OFDM subcarriers and N_t antennas are considered. For channel estimation, the Least Squares (LS) estimator with interpolation is employed because of its low complexity by making it suitable for scenarios where computational efficiency is prioritized. However, to enhance estimation accuracy, a deep learning-based Channel Estimation and Feedback Network (CEFnet) is integrated by improving the robustness of estimation. Unlike conventional deep learning-based CSI compression methods like CsiNet, PFnet directly compresses pilot signals rather than CSI by reducing overhead. The proposed Adaptive compression Scheme further optimizes feedback efficiency by dynamically adjusting the compression ratio based on the Signal-to-Noise Ratio (SNR). The proposed PFnet with Adaptive compression Scheme achieved better results in terms of Normalized Mean Square Error (NMSE) when compared with existing PFnet.

关键词： Adaptation models Adaptive systems Costs Transmitting antennas Channel estimation Receiving antennas Estimation Massive MIMO data models Signal to noise ratio

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Delta-ICM: Entropy Modeling with Delta Function for Learned Image compression

Delta-ICM: Entropy Modeling with Delta Function for Learned ...

引用

ieee International conference on Consumer Electronics (ICCE)

作者： Takahiro Shindo Taiju Watanabe Yui Tatsumi Hiroshi Watanabe Waseda University Tokyo Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Image Coding for Machines (ICM) is becoming more important as research in computer vision progresses. ICM is a vital research field that pursues the use of images for image recognition models, facilitating efficient image transmission and storage. Demand and required performance for recognition models are rapidly growing within consumers. To meet these needs, exchanging image data between consumer devices and cloud AI using ICM technology could be one possible solution. In ICM, various image compression methods have adopted Learned Image compression (LIC). LIC includes an entropy model for estimating the bitrate of latent features, and the design of this model significantly affects its performance. Typically, LIC methods assume that the distribution of latent features follows a normal distribution. This assumption is effective for compressing images intended for human vision. However, employing an entropy model based on normal distribution is inefficient in ICM due to the limitation of image parts that require precise decoding. To address this, we propose Delta-ICM, which uses a probability distribution based on a delta function. Assuming the delta distribution as a distribution of latent features reduces the entropy of image portions unnecessary for machines. We compress the remaining portions using an entropy model based on normal distribution, similar to existing methods. Delta-ICM selects between the entropy model based on the delta distribution and the one based on the normal distribution for each latent feature. Our method outperforms existing ICM methods in image compression performance aimed at machines.

关键词： Performance evaluation YOLO Image texture Image coding Image recognition Computational modeling Gaussian distribution Entropy Probability distribution Smart devices

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Towards System-Level Modeling of Broadband Sub-THz Amplifiers for Wireless Communication

Towards System-Level Modeling of Broadband Sub-THz Amplifier...

引用

ieee Topical conference on Power Amplifiers for Wireless and Radio Applications (PAWR)

作者： Simon Haussmann Dominik Wrana Lutfi Samara Tommaso Zugno Ingmar Kallfass Inst. of Robust Power Semiconductor Systems University of Stuttgart Stuttgart Germany Huawei Technologies Munich Research Center Munich Germany

ISBN: (数字)9798331507275

ISBN: (纸本)9798331507282

This paper reports on the initial system-level modeling of the non-linear behavior of an H-band power amplifier, looking towards ultra-broadband wireless applications. Based on small-signal as well as large-signal continuous-wave measurements, polynomial as well as Rapp models are extracted for a carrier frequency of 280 GHz, accounting for AM-AM and AM-PM distortion due to amplifier compression. System-level simulations are carried out to predict the signal degradation in terms of error vector magnitude for 1 GBd 64-QAM signals. Measurements obtained using a VNA-based cross-domain measurement platform demonstrate novel characterization capabilities under broadband-modulated conditions and confirm the correct modeling of the compression behavior. However, it is already indicated that models that do not account for the frequency dependency of its parameters are no longer suitable for accurate prediction under ultra-broadband signal conditions.

关键词： Wireless communication Frequency modulation Chirp modulation Power amplifiers Predictive models Polynomials data models Vectors Frequency measurement Distortion measurement

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An Approach for Effective CPR Trainings in Virtual Reality with Multimodal Feedback

An Approach for Effective CPR Trainings in Virtual Reality w...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Suyash A. Lal Chiranjoy Chattopadhyay Rahul Kumar Ray Computing and Data Sciences FLAME University Pune India

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Effective cardiopulmonary resuscitation (CPR) is a life-saving skill that demands precise control of chest compression depth and rate, which are challenging to master without expert guidance. To address this, we developed a Virtual Reality (VR)-based CPR training module that provides interactive real-time feedback to guide users toward performing compressions within the recommended parameters. The training system features three feedback mechanisms: a dynamic UI slider that visualizes compression force and rate, a color-coded chest overlay that transitions between green (ideal), yellow (insufficient), and red (excessive), and vibrotactile feedback delivered through a linear resonance actuator (LRA) mounted on the user’s hand. By combining visual and tactile feed-back(multimodal interaction), the module provides a comprehensive and intuitive learning experience, enabling users to correct errors in real-time and build muscle memory for effective CPR. This training module is aimed to improve skill acquisition and addresses the limitations of traditional mannequin-based training, such as resource dependency and limited interactivity.

关键词： Training Visualization Three-dimensional displays Tactile sensors Virtual reality User interfaces Muscles Real-time systems Resonance Haptic interfaces

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Multi-modal Dynamic Point Cloud Geometric compression Based on Bidirectional Recurrent Scene Flow*

Multi-modal Dynamic Point Cloud Geometric Compression Based ...

引用

International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fangzhe Nan Frederick Li Zhuoyue Wang Gary K.L. Tam Zhaoyi Jiang DongZheng DongZheng Bailin Yang School of Statistics and Mathematics Zhejiang Gongshang University School of Computer Science and Technolgy Zhejiang Gongshang University University of Durham University of California Berkeley Swansea University Universal Ubiquitous Technology Co. LTD

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning methods have recently shown significant promise in compressing the geometric features of point clouds. However, challenges arise when consecutive point clouds contain holes, resulting in incomplete information that complicates motion estimation. To our knowledge, most existing dynamic point cloud compression methods have largely overlooked this critical issue. Moreover, these methods typically employ a multi-scale single-pass approach for motion estimation, performing only one estimation at each scale. This limits accuracy and adversely impacts compression performance. To address these challenges, we propose a dynamic point cloud compression model called M2BR-DPCC (Multi-Modal Multi-Scale Bidirectional Recursion for Dynamic Point Cloud compression). Our method introduces two key innovations. First, we integrate both point cloud and image data as inputs, leveraging a multi-modal feature representation completion (MFRepC) approach to align information across modalities. This addresses the issue of missing data in point clouds by using complementary information from images. Second, we implement a multi-scale bidirectional recursive (MSBR) motion estimation method. This module iteratively refines motion flows in both forward and backward directions, progressively enhancing point cloud features and improving motion estimation accuracy. Experimental results on widely used datasets, including MVUB and 8iVFB, demonstrate the effectiveness of our approach. Compared to existing methods, M2BR-DPCC achieves superior performance, with an average BD-rate improvement of 95.23% over V-PCC, 12.92% over D-DPCC, and 16.16% over patchDPCC. These results underscore the potential of leveraging multi-modal data and bidirectional refinement for dynamic point cloud compression.

关键词： Point cloud compression Technological innovation Image coding Accuracy Limiting Motion estimation Dynamics Estimation Filling Speech processing

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Research on Simulation and Reconstruction Technology of Power Tower Collapse Accident Based on Three-Dimensional Point Cloud

Research on Simulation and Reconstruction Technology of Powe...

引用

Electronics, Energy Systems and Power Engineering (EESPE), ieee International conference on

作者： Guozhu Yang Enhui Wei Shirui Sun Chao Gao Pingkai Fang Maojie Tian State Grid Electric Power Space Technology Company Limited Beijing China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper studies a simulation and reconstruction technology of power tower collapse accident based on three-dimensional point cloud. First, the three-dimensional point cloud data of the power tower before and after the collapse is obtained by high-precision laser scanning and photogrammetry technology. The Voxel Grid algorithm is used to reduce the noise of the point cloud data to improve the data quality. Then a reconstruction system based on point cloud data is designed, which combines the physical simulation model with the geometric characteristics of the point cloud to accurately reconstruct the collapse process of the power tower. The mechanical properties of the tower structure, the dynamic changes of the collapse process, and the influence of environmental factors on the accident are considered in the simulation model. The simulation results show that the proposed technology can effectively simulate the whole process of power tower collapse and compare it with the actual accident data with an accuracy of more than 90%. Through data analysis, the key influencing factors of tower collapse are revealed, which provides a scientific basis for the safety assessment and accident prevention of the power system.

关键词： Point cloud compression Accuracy Three-dimensional displays data analysis Systematics Simulation Poles and towers Mechanical factors data models Accidents

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data Efficient PV based Indoor Event Detection 25

Data Efficient PV based Indoor Event Detection

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Proceedings of the ACM/ieee 16th International conference on Cyber-Physical Systems (with CPS-IoT Week 2025)

作者： Tushar Routh Jiechao Gao Bradford Campbell University of Virginia Charlottesville Virginia USA

ISBN: (纸本)9798400714986

In this paper, we argue that instead of indiscriminately transmitting every sensed value, data transmission should prioritize relevance, intelligence, and efficiency. As a case study, we investigate exit/entry event detection through the generation of signature voltage patterns from PV module connected to an IoT platform and placed near entrance. This detection technique facilitates automated appliance management (e.g., lighting and HVAC control) and improves indoor security (e.g., unauthorized entry detection) by leveraging existing devices for sensing purposes. To enhance data efficiency, we propose the implementation of an intelligent on-board event detection algorithm, prioritizing sampling points within specific timeframes based on their significance on classification accuracy and lossless compression technique to further squeeze necessary information for classification. In our testbed evaluation, our approach successfully achieved a 99.7% data compression rate, substantially reducing the number of required advertisements and extending operation within a fixed energy budget.

关键词： data Efficient Classification

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