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

共 16714 条记录，以下是111-120 订阅

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Compressing a Flow-Based Privacy Protection Model via a Novel Joint Distilling and Pruning Method

Compressing a Flow-Based Privacy Protection Model via a Nove...

引用

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

作者： Sissi Xiaoxiao Wu Zhicong Liang Zehong Huang School of EIE Shenzhen Univeristy

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

While reversible anonymization techniques have been shown to be effective in protecting facial information, most of these methods rely on large reversible generative models that are only suitable for running on cloud servers, which poses a huge challenge to placing such privacy-preserving models on camera terminals. This work proposes a novel compression method that combines knowledge distillation and model pruning for training compact reversible generative models with an encoder-decoder pair. The proposed compression method exploits a novel collaborative knowledge distillation component to transfer the teacher model’s knowledge to the student model by pairing a teacher encoder (decoder) with a student decoder (encoder). Sub-sequently, a correlation-based regularized pruning component is adopted to further remove redundant parameters in the manually designed student model. This set of schemes enables us to install an anonymization system that builds privacy for sensitive personal data on embedded devices. We deploy and evaluate our compression privacy-preserving system on an NVIDIA Jetson TX2 device, which runs in real time and effectively anonymizes facial images to preserve privacy.

关键词： Training data privacy Privacy Image coding Signal processing Information filtering Decoding Servers Speech processing Information integrity

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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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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...

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

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

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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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data Efficient PV based Indoor Event Detection 25

Data Efficient PV based Indoor Event Detection

引用

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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SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous data

SADDLe: Sharpness-Aware Decentralized Deep Learning with Het...

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ieee Workshop on Applications of Computer Vision (WACV)

作者： Sakshi Choudhary Sai Aparna Aketi Kaushik Roy Purdue University West Lafayette IN

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Decentralized training enables learning with distributed datasets generated at different locations without relying on a central server. In realistic scenarios, the data distribution across these sparsely connected learning agents can be significantly heterogeneous, leading to local model over-fitting and poor global model generalization. Another challenge is the high communication cost of training models in such a peer-to-peer fashion without any central coordination. In this paper, we jointly tackle these two-fold practical challenges by proposing SADDLe, a set of sharpness-aware de-centralized deep learning algorithms. SADDLe leverages Sharpness-Aware Minimization (SAM) to seek a flatter loss landscape during training, resulting in better model generalization as well as enhanced robustness to communication compression. We present two versions of our approach and demonstrate its effectiveness through extensive experiments on various Computer Vision datasets (CIFAR-10, CIFAR-100, Imagenette, and ImageNet), model architectures, and graph topologies. Our results show that SADDLe leads to 1-20% improvement in test accuracy as compared to existing techniques while incurring a minimal accuracy drop (~ 1%) in the presence of up to 4× compression.

关键词： Training Deep learning Computer vision Accuracy Image coding Computational modeling Robustness data models Topology Convergence

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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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CJST: CTC Compressor based Joint Speech and Text Training for Decoder-Only ASR

CJST: CTC Compressor based Joint Speech and Text Training fo...

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

作者： Wei Zhou Junteng Jia Leda Sari Jay Mahadeokar Ozlem Kalinli Meta AI

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

CTC compressor can be an effective approach to integrate audio encoders to decoder-only models, which has gained growing interest for different speech applications. In this work, we propose a novel CTC compressor based joint speech and text training (CJST) framework for decoder-only ASR. CJST matches speech and text modalities from both directions by exploring a simple modality adaptor and several features of the CTC compressor, including sequence compression, on-the-fly forced peaky alignment and CTC class embeddings. Experimental results on the Librispeech and TED-LIUM2 corpora show that the proposed CJST achieves an effective text injection without the need of duration handling, leading to the best performance for both in-domain and cross-domain scenarios. We also provide a comprehensive study on CTC compressor, covering various compression modes, edge case handling and behavior under both clean and noisy data conditions, which reveals the most robust setting to use CTC compressor for decoder-only models.

关键词： Training Adaptation models Signal processing data models Acoustics Noise measurement Speech processing

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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...

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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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