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

共 16771 条记录，以下是941-950 订阅

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A Proposal of a Neural Predictor of Residual Compressive Strength in an SCC Exposed to High Temperatures for Resilient Housing 7

A Proposal of a Neural Predictor of Residual Compressive Str...

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7th ieee International Humanitarian Technologies conference, IHTC 2024

作者： Scala, Armando La Rizzo, Fabio Carnimeo, Leonarda Chorro, Salvador Ivorra Foti, Dora Polytechnic University of Bari Department of Architecture Construction and Design Bari Italy Polytechnic University of Bari Department of Electrical and Information Engineering Bari Italy University of Alicante Deparment of Civil Engineering San Vicentedel Raspeig Spain

ISBN: (纸本)9798350354645

This paper reports a proposal of a neural predictor, which could usefully improve performance in predicting the compressive strength of SCC, when used for innovative housing. The predictor is intended for its ability to provide accurate predictions on structures where verification against high temperatures should be required, thus offering an interesting possibility for resilient housing *** detail, predictions of the mechanical properties of Self-Compacting Concretes (SCC) after exposure to high temperatures will be obtained by designing a neural predictor for residual compressive strengths of this type of concrete for resilient housing. All considered data refer to SCC samples heated up to 800°C and subjected to standard compression *** this work, the proposed neural predictor will be related to temperature and geometric ratio of each SCC *** interesting behavior in capturing the complex relationships between input variables and residual compressive strengths of SCCs will be shown by the proposed neural network. Results in terms of residual compressive strengths are reported. © 2024 ieee.

关键词： compression testing

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Message Passing Neural Network based Light Field Image compression 6

Message Passing Neural Network based Light Field Image Compr...

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6th ieee International conference on Multimedia Information Processing and Retrieval, MIPR 2023

作者： Bach, Nguyen Gia Tran, Chanh Minh Duc, Tho Nguyen Tan, Phan Xuan Kamioka, Eiji Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo135-8548 Japan

ISBN: (纸本)9798350307818

Light Field possesses the ability to capture comprehensive information about light distribution in space, making it an appealing technology for immersive media applications. However, due to the vast amount of data generated by the Light Field, there has been significant interest in developing methods for its compression. The use of a graph data structure that outlines the relationship between viewpoints within the Light Field has been found to be a suitable way to process this data. Additionally, the graph convolution network (GCN), which merges graph and neural network approaches, has shown great promise in effectively handling graph data. However, since GCN doesn't directly support edge features, it is unable to weigh the contribution of source key view features, aggregating at the target non-key view, in the graph representation of a Light Field data. In this work, a Light Field compression scheme based on Message Passing Neural Network (MPNN) is proposed to account for the edge features during the training and reconstruction of non-key views, which is found to perform well on a sparse down-sampling pattern of key views. Experimental results show a gain of about 1 dB in PSNR, compared with the previous GCN-based approach, and our proposal also outperformed other baselines at low bitrates, using an adaptive quantization parameters (QP) selection. © 2023 ieee.

关键词： Convolution

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Covert and Quantum-Safe Tunneling of Multi-Band Military-RF Communication Waveforms Through Non-Cooperative 5G Networks

Covert and Quantum-Safe Tunneling of Multi-Band Military-RF ...

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41st ieee Military Communications conference (ieee MILCOM) - Communications Supporting Military Operations in a Contested Environment

作者： Alwan, Elias Volakis, John Islam, Md Khadimul De Silva, Udara Madanayake, Arjuna Sanchez, Jose Angel Sklivanitis, George Pados, Dimitris A. Beckwith, Luke Azarderakhsh, Reza Muralkrishan, Madhuvanti Rastogi, Rishabh Hore, Aniruddha Burger, Eric W. Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA Florida Atlantic Univ Ctr Connected Auton & AI Boca Raton FL 33431 USA PQSecure Technol LLC Boca Raton FL USA Virginia Tech Commonwealth Cyber Initiat Arlington VA USA

ISBN: (纸本)9798350321814

We have built a prototype universal radio adapter which furnishes seamless and secure wireless communication through non-cooperative indigenous 5G networks for military and government users. The adapter consists of a waveformagnostic hardware add-on that tunnels DoD terrestrial and satellite data. The adapter uses secure protocols for crossconnecting military-grade wireless RF communications equipment using spectrum in the range from UHF to Ka-band. A 5G data transport channel replaces the captured spectrum for transporting information at the IQ-sample level. In a sense, we replace the antenna-air interface and wireless channel with a transparent 5G data network. A plurarity of legacy military systems can operate through modern 5G networks in a seamless way without any knowledge of the characteristics of military waveforms. The adapter incorporates AI/ML based methods for smart spectrum sensing and autonomous radio reconfiguration. This enables intelligent interconnection of a number of military radios through non-cooperative (potentially adversarial) 5G commercial cellular networks. The adapter is built on four technical pillars: 1) ultra-wideband apertures for multi-functional and flexible software-defined radios (SDRs) with agile, wideband, and dual-band tunable RF transceivers for FR1/FR2 bands;2) physical layer operation that involve device authentication via deep-learning based RF fingerprinting and compression of acquired IQ data;3) secure and reconfigurable cryptographic coprocessors employing the new quantum-safe algorithms selected by NIST to achieve authentication, key exchange, and encryption with focus on resource-constrained low size, weight, power, and cost (SWaP-C) devices;and 4) generative artificial intelligence and spread-spectrum steganography to hide DoD traffic passed through 5G networks and improve resiliency against real-time traffic analysis by nation-state carriers and intelligence agencies.

关键词： 5G security data hiding steganography PQC IQ signal compression AI fingerprinting

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Iris Image data Base Management Using Retinal Biometric data for Security Applications 1

Iris Image Data Base Management Using Retinal Biometric Data...

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1st International conference on Computing, Sciences and Communications, ICCSC 2024

作者： Sivasankari, K. Hanirex, D. Kerana Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350353648

IrisDB (Iris image data Base) is an innovative database system presented in this work, designed to optimize the processes of retinal biometric data storage and retrieval. IrisDB effectively tackles the distinct obstacles presented by the substantial file size and high dimensionality of iris images through the incorporation of state-of-the-art indexing methodologies, optimal compression algorithms, and resilient encryption systems. To optimize storage utilization and facilitate retrieval operations, the system implements a hybrid compression method and a multi-tiered indexing mechanism. Critical data is further protected by AES-256 encryption. According to the testing outcomes, IrisDB is the optimal solution for large-scale biometric databases due to its substantial reduction in storage requirements and enhancement in retrieval times when compared to current systems. IrisDB competency in effectively managing enrollment and verification procedures is demonstrated through its thorough exposition of its architecture and workflow. Iris biometric data is utilized by numerous identification and security applications;the scalable and secure architecture of IrisDB has the capacity to fundamentally transform this procedure. © 2024 ieee.

关键词： Indexing (of information)

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CSI compression Using Channel Charting 58

CSI Compression Using Channel Charting

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58th Asilomar conference on Signals, Systems and Computers, ACSSC 2024

作者： Chatelier, Baptiste Corlay, Vincent Crussière, Matthieu Le Magoarou, Luc Mitsubishi Electric R&d Centre Europe Rennes France Univ Rennes Insa Rennes Cnrs IETR-UMR 6164 Rennes France Bcom Rennes France

ISBN: (纸本)9798350354058

Reaping the benefits of multi-antenna communication systems in frequency division duplex (FDD) requires channel state information (CSI) reporting from mobile users to the base station (BS). Over the last decades, the amount of CSI to be collected has become very challenging owing to the dramatic increase of the number of antennas at BSs. To mitigate the overhead associated with CSI reporting, compressed CSI techniques have been proposed with the idea of recovering the original CSI at the BS from its compressed version sent by the mobile users. Channel charting is an unsupervised dimensionality reduction method that consists in building a radio-environment map from CSIs. Such a method can be considered in the context of the CSI compression problem, since a chart location is, by definition, a low-dimensional representation of the CSI. In this paper, the performance of channel charting for a task-based CSI compression application is studied. A comparison of the proposed method against baselines on realistic synthetic data is proposed, showing promising results. © 2024 ieee.

关键词： Frequency division multiplexing

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An innovation analysis of LZ77 and LZ78 compression Algorithms for data compression & Source Coding 15

An innovation analysis of LZ77 and LZ78 Compression Algorith...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Mishra, Abhinav Rongali, Aruna Sri Bewoor, Laxmi A. Raghavendra, R. Kavitha, M.S. Sunrise University Department of Computer Science & Engineering Alwar301028 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India University of The Cumberlands Department of Information Technology Emphasis/Technology United States Vishwakarma Institute of Technology Department of Computer Engineering Pune India University College of Computing Sciences And Information Technology Karnataka Bangalore India M. K. Engineering College Department of ECER. RSM Nagar Kavaraipettai601206 India

ISBN: (纸本)9798350370249

This paper evaluates innovation on two of the most widely used compression algorithms - the LZ77 and the LZ78 algorithms - for information compression and supply coding programs. A detailed assessment of these algorithms presents insights approximately their design philosophy, technical traits, and performance metrics. Additionally, the studies offer a complete overview of their packages and implementations. The paper investigates the prominent considerations for implementing compression algorithms and analyzes their relative blessings and downsides. An exploration of the constraints of every algorithm is likewise undertaken. Furthermore, the research explores distinctive design elements of the algorithms, such as data structure, compression engine, and set of rules optimization. Eventually, the paper assesses the algorithms by reading the exchange-offs between their performance and complexity. This study facilitates practitioners and developers to apprehend the results of using those algorithms for diverse compression and supply coding applications. It can assist practitioners in identifying the first-class compression set of rules for their unique application needs. This paper gives an innovative evaluation of the LZ77 and LZ78 compression Algorithms. These algorithms are algorithms designed for statistics compression and source coding. They're used to take a given series of characters (or bits) and encode them right into an area-green representation. The innovation evaluation presents a valuable perception of how the algorithms improve compression ratios and shop area. It also discusses the effect of positive parameters such as window length, repeat cycle, and buffer size on compression efficiency. ultimately, the paper will speak to future studies' challenges in this region and the capability packages of those algorithms. © 2024 ieee.

关键词： Algorithms Approximately Blessings Coding Considerations

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Use of compression Analytics to Detect Deception 10

Use of Compression Analytics to Detect Deception

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10th ieee International conference on Behavioural and Social Computing, BESC 2023

作者： Bauer, Travis Gribble, Lisa Clifford, Joshua M. Jones, Aaron Murchison, Nicole Wisniewski, Kyra Sandia National Labs AlbuquerqueNM United States

ISBN: (纸本)9798350395884

This paper describes the development and application of compression-based machine learning (ML) combined with part of speech (PoS) analysis to detect deception. Multiple data sets, including research associated with papermills, social media, and online reviews were used to test these methods. Our research is especially concerned with achieving reliable performance with minimal parameters and data preprocessing. The core idea draws from scientific literature suggesting that subtle, yet predictable linguistic cues can be indicative of intentionally deceptive writing. Part of speech parsing was chosen as a means to represent how the person was communicating as opposed to the content of the writing. The contribution of this paper beyond existing literature is demonstrating the robustness of the application of compression-based ML across a variety of data sets and using explainability to validate that the results are consistent with the underlying psychology. These methods have relatively few parameters and are easy to apply while still providing the best performance among the methods we tested. We present the results of studies over six data sets. In addition to the classification results, we also perform an explainability analysis that shows what parts of speech contributed the most to the performance. for the compression-base ML. These results are largely consistent between data sets and match the underlying psychology research. © 2023 ieee.

关键词： Machine learning

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Multi-Spectral Entropy Constrained Neural compression of Solar Imagery 22

Multi-Spectral Entropy Constrained Neural Compression of Sol...

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22nd ieee International conference on Machine Learning and Applications, ICMLA 2023

作者： Zafari, Ali Khoshkhahtinat, Atefeh Mehta, Piyush M. Nasrabadi, Nasser M. Thompson, Barbara J. Kirk, Michael S. F. Da Silva, Daniel West Virginia University Department of Computer Science & Electrical Engineering WV United States West Virginia University Department of Mechanical & Aerospace Engineering WV United States Nasa Goddard Space Flight Center MD United States

ISBN: (纸本)9798350345346

Missions studying the dynamic behaviour of the Sun are defined to capture multi-spectral images of the sun and transmit them to the ground station in a daily basis. To make transmission efficient and feasible, image compression systems need to be exploited. Recently successful end-to-end optimized neural network-based image compression systems have shown great potential to be used in an ad-hoc manner. In this work we have proposed a transformer-based multi-spectral neural image compressor to efficiently capture redundancies both intra/inter-wavelength. To unleash the locality of window-based self attention mechanism, we propose an inter-window aggregated token multi head self attention. Additionally to make the neural compressor autoencoder shift invariant, a randomly shifted window attention mechanism is used which makes the transformer blocks insensitive to translations in their input domain. We demonstrate that the proposed approach not only outperforms the conventional compression algorithms but also it is able to better decorrelates images along the multiple wavelengths compared to single spectral compression. © 2023 ieee.

关键词： inter-window token aggregation multi-spectral neural image compression shift invariant self-attention

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An Unified data compression Framework in an IoT Network: Towards Performance Improvement 17

An Unified Data Compression Framework in an IoT Network: Tow...

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17th ieee International conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Kumar, Roshan Pandey, Om Jee Hegde, Rajesh M. Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur India Department of Electronics Engineering Varanasi India

ISBN: (纸本)9798350307672

The Internet of Things (IoT) has fundamentally changed the way we interact worldwide, however, the sheer volume of data it generates is enormous. Effectively handling this extensive data is a critical task in IoT networks, as transmitting it over the network consumes significant energy. Therefore, the need for data compression at the sensor layer and gateways directly improves the data transmission latency and network bandwidth requirements, preserving considerable energy. In this study, we propose a novel and unified data compression method based on a predictive encoding scheme at the sensor layer and a lossy dimensional reduction framework at the gateway level. The framework involves different stages of data pre-processing, including smoothing, quantization, and encoding, and the final stage involves the utilization of dimension reduction techniques to compress the data as a consequence, we achieve an overall reduction in energy consumption over the network. The performance of the proposed methods is evaluated using real field sensory data. Here, the performance is evaluated in terms of the obtained compression factor and the energy consumed by the network. Finally, the obtained results demonstrate the effectiveness of the proposed method when compared to existing methods in the literature. © 2023 ieee.

关键词： Energy efficiency

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A Brief Survey on data Aggregation and data compression Models using Blockchain Model in Wireless Sensor Network

A Brief Survey on Data Aggregation and Data Compression Mode...

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2023 ieee International conference on Innovative data Communication Technologies and Application, ICIDCA 2023

作者： Sudheer, Bandlamudi Naga Sujatha, Kamepalli School of Computing and Informatics Vignan's Foundation for Science Technology & Research Guntur Andhra Pradesh Vadlamudi India

ISBN: (纸本)9798350397208

Most of the time, nodes in Wireless Sensor Networks (WSNs) are set up in an unmonitored, potentially dangerous setting. Battery life, storage space, processing power, and network connectivity are all severely lacking in these nodes. Therefore, it is difficult to protect a WSN without negatively impacting the efficiency of the network. The issues listed above can be effectively addressed with a well-designed plan that makes use of data Aggregation (DA) in conjunction with a robust security system In this work, we provide a thorough analysis of Secure data Aggregation (SDA) in WSNs, highlighting both the security benefits sought and the challenges currently faced by this approach. Both established and up-and-coming network topologies are covered, with comparisons drawn between them to highlight key differences in application landscapes and security provisions afforded by each. Compressive data gathering (CDG), which employs Compressive Sensing (CS), can significantly enhance energy effieiency with the advantages gained from data compression. The data collection performance and longevity of Internet of Things (IoT) based WSNs are found to be significantly improved by CS based data collecting techniques. However, in hostile and sophisticated network deployments, they encounter significant difficulties with key distribution and adversary attacks. This research explores how wireless sensor networks' security measures affect their ability to collect and analyze data. This research provides a brief survey on data aggregation and data compression using blockchain in WSN for secure data transmission. © 2023 ieee.

关键词： Compressed sensing

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