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Distributed average consensus with quantization refinement

Publications associées (29)

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Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art systems are still built upon th ...

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Towards JPEG AIC part 3: visual quality assessment of high to visually lossless image coding

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Due to the increasing number of pictures captured and stored every day by and on digital devices, lossy image compression has become inevitable to limit the needed storage requirement. As a consequence, these compression methods might introduce some visual ...

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Quantization for Distributed Processing and Learning of Structured Data

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In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

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A Low-Complexity LoRa Synchronization Algorithm Robust to Sampling Time Offsets

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LoRaWAN is nowadays one of the most popular protocols for low-power Internet of Things communications. Although its physical layer, namely LoRa, has been thoroughly studied in the literature, aspects related to the synchronization of LoRa receivers have re ...

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State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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Highly energy-efficient wireless sensor nodes are a prerequisite for a sustainable operation of the Internet of things. Therefore, classical approaches for system design based on digital signal processing are not a viable solution, but system design has to ...

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Latent Space Slicing for Enhanced Entropy Modeling in Learning-Based Point Cloud Geometry Compression

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The growing adoption of point clouds as an imaging modality has stimulated the search for efficient solutions for compression. Learning-based algorithms have been reporting increasingly better performance and are drawing the attention from the research com ...

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Convex Quantization Preserves Logconcavity

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A logconcave likelihood is as important to proper statistical inference as a convex cost function is important to variational optimization. Quantization is often disregarded when writing likelihood models, ignoring the limitations of the physical detectors ...

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An All-Digital 1.25 Mbps 5-Subcarrier OFDM Backscatter Uplink with Delta-Sigma Modulation for Improved Spurious-Free Dynamic Range

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This paper describes an all-digital backscatter modulation approach leveraging delta-sigma modulation (DSM) to improve the in-channel spectral characteristics of orthogonal frequency division multiplexed (OFDM) backscatter communication. We demonstrate thr ...

IEEE2022