Concept

Opus Interactive Audio Codec

Publications associées (107)

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

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen, Yifei Shen, Yuqing Ren, Chuan Zhang

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 ...

Ieee-Inst Electrical Electronics Engineers Inc2024

SC-TPTP: An Extension of the TPTP Derivation Format for Sequent-Based Calculus

Simon Guilloud

Motivated by the transfer of proofs between proof systems, and in particular from first order automated theorem provers (ATPs) to interactive theorem provers (ITPs), we specify an extension of the TPTP derivation text format to describe proofs in first-ord ...

2024

Evaluation of the impact of lossy compression on event camera-based computer vision tasks

Touradj Ebrahimi, Davi Nachtigall Lazzarotto, Bowen Huang

In the field of image acquisition, Dynamic Vision Sensors (DVS) present an innovative methodology, capturing only the variations in pixel brightness instead of absolute values and thereby revealing unique features. Given that the primary deployment of DVS ...

2023

Systems and methods for digital information decoding and data storage in hybrid macromolecules

Aleksandra Radenovic, Matteo Dal Peraro, Chan Cao, Lucien Fabrice Krapp

Systems and methods for data storage and readout using hybrid nucleic acid- polymeric molecules are herein disclosed. A molecular data storage medium is presented comprising a header and a footer, each comprising or consisting of at least one unit of a fir ...

2022

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

Touradj Ebrahimi

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 ...

IEEE2022

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Flavio Ponzina

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

2022

A Sequence Repetition Node-Based Successive Cancellation List Decoder for 5G Polar Codes: Algorithm and Implementation

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen, Yifei Shen, Yuqing Ren, Chuan Zhang

Due to the low-latency and high-reliability require-ments of 5G, low-complexity node-based successive cancellation list (SCL) decoding has received considerable attention for use in 5G communications systems. By identifying special constituent codes in the ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

COBRA: Enhancing DNN Latency Prediction with Language Models trained on Source Code

Robin Adrien Zbinden

With the recent developments of Deep Learning, having an accurate and device specific latency prediction for Deep Neural Networks (DNNs) has become important for both the manual and automatic design of efficient DNNs. Directly predicting the latency of DNN ...

2022

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

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

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 ...

SPIE2022

Learnable filter-banks for CNN-based audio applications

Nicolas Aspert, Benjamin Ricaud, Helena Peic Tukuljac, Laurent Colbois

We investigate the design of a convolutional layer where kernels are parameterized functions. This layer aims at being the input layer of convolutional neural networks for audio applications or applications involving time-series. The kernels are defined as ...

2022

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