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Noiseless compressive sensing is a two-steps setting that allows for undersampling a sparse signal and then reconstructing it without loss of information. The LASSO algorithm, based on $\lone$ regularization, provides an efficient and…

Information Theory · Computer Science 2025-11-13 Damien Barbier , Carlo Lucibello , Luca Saglietti , Florent Krzakala , Lenka Zdeborová

Because of the vast volume of data being produced by today's scientific simulations, lossy compression allowing user-controlled information loss can significantly reduce the data size and the I/O burden. However, for large-scale cosmology…

Information Theory · Computer Science 2017-08-08 Dingewn Tao , Sheng Di , Zizhong Chen , Franck Cappello

We present a data compression and dimensionality reduction scheme for data fusion and aggregation applications to prevent data congestion and reduce energy consumption at network connecting points such as cluster heads and gateways. Our…

Networking and Internet Architecture · Computer Science 2014-08-14 Mohammad Abu Alsheikh , Puay Kai Poh , Shaowei Lin , Hwee-Pink Tan , Dusit Niyato

The increasing volume and velocity of science data necessitate the frequent movement of enormous data volumes as part of routine research activities. As a result, limited wide-area bandwidth often leads to bottlenecks in research progress.…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-07-12 Yuanjian Liu , Sheng Di , Kyle Chard , Ian Foster , Franck Cappello

We describe the detection, interpretation, and removal of the signal resulting from interactions of high energy particles with the \Planck\ High Frequency Instrument (HFI). There are two types of interactions: heating of the 0.1\,K…

Cosmology and Nongalactic Astrophysics · Physics 2023-08-07 Planck Collaboration , P. A. R. Ade , N. Aghanim , C. Armitage-Caplan , M. Arnaud , M. Ashdown , F. Atrio-Barandela , J. Aumont , C. Baccigalupi , A. J. Banday , R. B. Barreiro , E. Battaner , K. Benabed , A. Benoît , A. Benoit-Lévy , J. -P. Bernard , M. Bersanelli , P. Bielewicz , J. Bobin , J. J. Bock , J. R. Bond , J. Borrill , F. R. Bouchet , M. Bridges , M. Bucher , C. Burigana , J. -F. Cardoso , A. Catalano , A. Challinor , A. Chamballu , L. -Y Chiang , H. C. Chiang , P. R. Christensen , S. Church , D. L. Clements , S. Colombi , L. P. L. Colombo , F. Couchot , A. Coulais , B. P. Crill , A. Curto , F. Cuttaia , L. Danese , R. D. Davies , P. de Bernardis , A. de Rosa , G. de Zotti , J. Delabrouille , J. -M. Delouis , F. -X. Désert , J. M. Diego , H. Dole , S. Donzelli , O. Doré , M. Douspis , X. Dupac , G. Efstathiou , T. A. Enßlin , H. K. Eriksen , F. Finelli , O. Forni , M. Frailis , E. Franceschi , S. Galeotta , K. Ganga , M. Giard , D. Girard , Y. Giraud-Héraud , J. González-Nuevo , K. M. Górski , S. Gratton , A. Gregorio , A. Gruppuso , F. K. Hansen , D. Hanson , D. Harrison , S. Henrot-Versillé , C. Hernández-Monteagudo , D. Herranz , S. R. Hildebrandt , E. Hivon , M. Hobson , W. A. Holmes , A. Hornstrup , W. Hovest , K. M. Huffenberger , T. R. Jaffe , A. H. Jaffe , W. C. Jones , M. Juvela , E. Keihänen , R. Keskitalo , T. S. Kisner , R. Kneissl , J. Knoche , L. Knox , M. Kunz , H. Kurki-Suonio , G. Lagache , J. -M. Lamarre , A. Lasenby , R. J. Laureijs , C. R. Lawrence , R. Leonardi , C. Leroy , J. Lesgourgues , M. Liguori , P. B. Lilje , M. Linden-Vørnle , M. López-Caniego , P. M. Lubin , J. F. Macías-Pérez , N. Mandolesi , M. Maris , D. J. Marshall , P. G. Martin , E. Martínez-González , S. Masi , S. Matarrese , F. Matthai , P. Mazzotta , P. McGehee , A. Melchiorri , L. Mendes , A. Mennella , M. Migliaccio , A. Miniussi , S. Mitra , M. -A. Miville-Deschênes , A. Moneti , L. Montier , G. Morgante , D. Mortlock , S. Mottet , D. Munshi , J. A. Murphy , P. Naselsky , F. Nati , P. Natoli , C. B. Netterfield , H. U. Nørgaard-Nielsen , F. Noviello , D. Novikov , I. Novikov , S. Osborne , C. A. Oxborrow , F. Paci , L. Pagano , F. Pajot , D. Paoletti , F. Pasian , G. Patanchon , O. Perdereau , L. Perotto , F. Perrotta , F. Piacentini , M. Piat , E. Pierpaoli , D. Pietrobon , S. Plaszczynski , E. Pointecouteau , G. Polenta , N. Ponthieu , L. Popa , T. Poutanen , G. W. Pratt , G. Prézeau , S. Prunet , J. -L. Puget , J. P. Rachen , B. Racine , M. Reinecke , M. Remazeilles , C. Renault , S. Ricciardi , T. Riller , I. Ristorcelli , G. Rocha , C. Rosset , G. Roudier , B. Rusholme , L. Sanselme , D. Santos , A. Sauvé , G. Savini , E. P. S. Shellard , L. D. Spencer , J. -L. Starck , V. Stolyarov , R. Stompor , R. Sudiwala , F. Sureau , D. Sutton , A. -S. Suur-Uski , J. -F. Sygnet , J. A. Tauber , D. Tavagnacco , L. Terenzi , L. Toffolatti , M. Tomasi , M. Tristram , M. Tucci , G. Umana , L. Valenziano , J. Valiviita , B. Van Tent , P. Vielva , F. Villa , N. Vittorio , L. A. Wade , B. D. Wandelt , D. Yvon , A. Zacchei , A. Zonca

Compressed sensing is a signal processing method that acquires data directly in a compressed form. This allows one to make less measurements than what was considered necessary to record a signal, enabling faster or more precise measurement…

Statistical Mechanics · Physics 2012-08-20 Florent Krzakala , Marc Mézard , François Sausset , Yifan Sun , Lenka Zdeborová

Compressive sensing has been receiving a great deal of interest from researchers in many areas because of its ability in speeding up data acquisition. This framework allows fast signal acquisition and compression when signals are sparse in…

Information Theory · Computer Science 2020-03-17 Fatima Salahdine , Elias Ghribi , Naima Kaabouch

The number of IoT devices is expected to continue its dramatic growth in the coming years and, with it, a growth in the amount of data to be transmitted, processed and stored. Compression techniques that support analytics directly on the…

Data Structures and Algorithms · Computer Science 2023-08-29 Francesco Taurone , Daniel E. Lucani , Marcell Fehér , Qi Zhang

As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the…

Information Theory · Computer Science 2014-04-22 Zhilin Zhang , Bhaskar D. Rao , Tzyy-Ping Jung

Light-field fluorescence microscopy (LFM) is a powerful elegant compact method for long-term high-speed imaging of complex biological systems, such as neuron activities and rapid movements of organelles. LFM experiments typically generate…

Image and Video Processing · Electrical Eng. & Systems 2023-10-17 Changqing Su , Zihan Lin , You Zhou , Shuai Wang , Yuhan Gao , Chenggang Yan , Bo Xiong

Physics experiments produce enormous amount of raw data, counted in petabytes per day. Hence, there is large effort to reduce this amount, mainly by using some filters. The situation can be improved by additionally applying some data…

Information Theory · Computer Science 2015-11-04 Jarek Duda , Grzegorz Korcyl

Error-bounded lossy compression is one of the most effective techniques for scientific data reduction. However, the traditional trial-and-error approach used to configure lossy compressors for finding the optimal trade-off between…

Databases · Computer Science 2022-05-09 Sian Jin , Sheng Di , Jiannan Tian , Suren Byna , Dingwen Tao , Franck Cappello

Soft compression is a lossless image compression method, which is committed to eliminating coding redundancy and spatial redundancy at the same time by adopting locations and shapes of codebook to encode an image from the perspective of…

Information Theory · Computer Science 2020-12-14 Gangtao Xin , Pingyi Fan

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, through increased stations in the array and frequency span. Two primary problems encountered when processing the resultant…

Instrumentation and Methods for Astrophysics · Physics 2025-07-30 Richard Dodson , Alex Williamson , Qian Gong , Pascal Elahi , Andreas Wicenec , Maria J. Rioja , Jieyang Chen , Norbert Podhorszki , Scott Klasky , Martin Meyer

In this paper, we analyze the information theoretic lower bound on the necessary number of samples needed for recovering a sparse signal under different compressed sensing settings. We focus on the weighted graph model, a model-based…

Information Theory · Computer Science 2022-09-16 Adarsh Barik , Jean Honorio

We study two approaches to distributed compressed sensing for in-network data compression and signal reconstruction at a sink in a wireless sensor network where sensors are placed on a straight line. Communication to the sink is considered…

Information Theory · Computer Science 2015-04-01 Christopher Lindberg , Alexandre Graell i Amat , Henk Wymeersch

The industry of wearable remote health monitoring system keeps growing. In the diagnosis of cardiovascular disease, Electrocardiography~(ECG) waveform is one of the major tools which is thus widely taken as the monitoring objective. For the…

Information Theory · Computer Science 2017-04-07 Pengda Huang

Radio Frequency Interference (RFI) is emitted from various sources, terrestrial or orbital, and create a nuisance for ground-based 21cm experiments. In particular, single-dish 21cm intensity mapping experiments will be highly susceptible to…

We describe the BeyondPlanck project in terms of motivation, methodology and main products, and provide a guide to a set of companion papers that describe each result in fuller detail. We implement a complete end-to-end Bayesian analysis…

Entropy coding is widely used in typical learned image compression (LIC) that converts latents into a compact bitstream. However, entropy coding is typically sequential and becomes the coding latency bottleneck. To overcome it, we present…

Image and Video Processing · Electrical Eng. & Systems 2026-05-25 Hao Cao , Wenqi Guo , Zhijin Qin , Jungong Han