Joint source-channel coding with feedback

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We prove global in time well-posedness for perturbations of the 2D stochastic Navier-Stokes equations partial derivative( t)u + u center dot del u = Delta u - del p + sigma + xi, u(0, center dot ) = u(0),div (u) = 0, driven by additive space-time white noi ...

Due to its high parallelism, belief propagation (BP)decoding is amenable to high-throughput applications and thusrepresents a promising solution for the ultra-high peak datarate required by future communication systems. To bridge theperformance gap compare ...

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

This thesis delves into the critical study of particle transport in matter, particularly emphasising its implications for machine protection at CERN's accelerator complex and facilities. To conduct studies of this nature, FLUKA and Geant4 stand out within ...

Quantum computing not only holds the potential to solve long-standing problems in quantum physics, but also to offer speed-ups across a broad spectrum of other fields. Access to a computational space that incorporates quantum effects, such as superposition ...

In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

Low-Density Parity-Check (LDPC) decoder is among the power hungry building blocks of wireless communication systems. Voltage scaling down to Near-Threshold (NT) voltages substantially improves energy efficiency, in theory up 10x. However, tuning the voltag ...

The use of point clouds as an imaging modality has been rapidly growing, motivating research on compression methods to enable efficient transmission and storage for many applications. While compression standards relying on conven- tional techniques such as ...

This paper presents a novel distributed approach for solving AC power flow (PF) problems. The optimization problem is reformulated into a distributed form using a communication structure corresponding to a hypergraph, by which complex relationships between ...

Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainable gains as well as their ability to discover relevant patterns. On the other ...

Joint source-channel coding with feedback

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Global existence for perturbations of the 2D stochastic Navier-Stokes equations with space-time white noise

High-Throughput and Flexible Belief Propagation List Decoder for Polar Codes

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

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Scalable Quantum Algorithms for Noisy Quantum Computers

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Low Power LDPC Decoding by Reliable Voltage Down-Scaling

Evaluating the effect of sparse convolutions on point cloud compression

Hypergraph-Based Fast Distributed AC Power Flow Optimization

Partition and Code: learning how to compress graphs