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Relevant bibliographies by topics / Compressione dati

Academic literature on the topic 'Compressione dati'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Compressione dati"

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Visocchi, M., M. Meglio, F. La Marca, and B. Cioni. "Esostosi ereditaria multipla e compressione cervicodorsale." Rivista di Neuroradiologia 9, no. 4 (August 1996): 501–3. http://dx.doi.org/10.1177/197140099600900424.

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L'esostosi multipla ereditaria o discondroplasia o condroplasia ereditaria deformante o aclasia diafisale è una malattia relativamente rara a carattere autosomico dominante a penetranza variabile. Nel presente studio, relativo ad un caso clinico giunto alla nostra osservazione, segnaliamo la peculiarità della localizzazione neurochirurgica (cervicodorsale) della malattia e suggeriamo, anche alla luce dei dati di letteratura, la strategia terapeutica più appropriata. La gravità della storia naturale, la possibilità di recidiva e la scarsa radiosensibilità, a nostro avviso stabiliscono la priorità dell'indicazione al trattamento chirurgico quanto più radicale possibile.

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Severoni, Cecilia. "La sicurezza dell'aviazione civile e i limiti alla libertà di circolazione: riflessioni a seguito della pandemia da COVID-19." RIVISTA ITALIANA DI DIRITTO DEL TURISMO, no. 30 (September 2020): 148–84. http://dx.doi.org/10.3280/dt2020-030011.

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Il quadro normativo attuale in tema di sicurezza dell'aviazione civile è stato modificato in modo sostanziale dopo l'attacco alle torri gemelle e dispone un insieme di norme puntuali e dettagliate. Occorre tuttavia trovare un compromesso ragionevole tra l'obiettivo indicato della prevenzione, indagine, accertamento e repressione degli atti di terrorismo ed altri reati gravi e quello della protezione della libertà di circolazione e dei dati personali nel rispetto della vita privata degli interessati. In questa ottica la compressione del diritto alla protezione dei dati personali deve rispondere a regole chiare e deve essere strettamente proporzionale all'obiettivo da conseguire. Analoghe riflessioni possono oggi essere ripetute in merito ad una più recente applicazione del principio contenuto nell'art. 16, primo comma, Cost. in tema di libertà di circolazione. Non sfugge, infatti, all'interprete l'analogia tra le restrizioni alla libertà di circolazione ed il diritto di muoversi liberamente derivante dalla normativa in materia di sicurezza dell'aviazione civile, e la più recente vicenda legata alla pandemia da Coronavirus che ha imposto una limitazione alla libertà di circolazione delle persone: si tratta nei due casi di un compromesso dettato da «motivi di sanità e sicurezza», circostanze accomunate dal legislatore costituzionale tra le ipotesi per le quali è possibile prevedere per legge una limitazione alla piena esplicazione dei diritti sopra ricordati.

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Cardinale, Antonio, and Francesco Paolo Calciano. "Insufficienza venosa cronica: epidemiologia, fisiopatologia e diagnosi." Cardiologia Ambulatoriale 29, no. 1 (May 30, 2021): 41–53. http://dx.doi.org/10.17473/1971-6818-2021-1-6.

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La medicina dovrebbe essere un gioco di squadra, oggi sempre declamato ma poco realizzato. Andare al di là dell'individualismo, valido in ogni disciplina, lo è anche in ambito angiologico. La gestione della insufficienza venosa cronica (IVC) coinvolge diversi specialisti, dal medico di medicina generale (MMG), all’angiologo, al cardiologo, al nutrizionista, al ginecologo, al chirurgo vascolare per citarne alcuni. La prevalenza dell’IVC è elevata: un italiano su due è colpito da questa malattia. L’età media di chi la contrae è in genere sopra i 50 anni, e sono le donne a esserne maggiormente colpite. I segni clinici sono correlati alle alterazioni fisiopatologiche. Il quadro clinico della IVC è caratterizzato da sintomi e segni legati all’ipertensione venosa, con alterazioni strutturali o funzionali delle vene. Vengono richiamati i punti fondamentali della fisiopatologia della IVC: ipertensione venosa passiva, stasi, aumento della permeabilità, disfunzione endoteliale, attivazione infiammatoria. La diagnosi di trombosi venosa profonda (TVP) è sottostimata. Il gold-standard diagnostico è l’eco-color-Doppler con l’utilizzo della ultrasonografia per compressione semplificata (CUS). Nell’era della digitalizzazione è nata l’esigenza di realizzare un database condiviso da tutti, con dati uniformi, per cui da alcuni anni è stata messa a punto la MEVec (mappa venosa emodinamica). Infine viene sottolineata l’importanza della tempestività nell'iniziare la terapia medica seguendo le linee guide emanate dalle società scientifiche per ridurre morbilità e mortalità associate e contrastare l’incidenza di sequele a distanza.

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Shevchuk, Yury Vladimirovich. "Memory-efficient sensor data compression." Program Systems: Theory and Applications 13, no. 2 (April 4, 2022): 35–63. http://dx.doi.org/10.25209/2079-3316-2022-13-2-35-63.

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We treat scalar data compression in sensor network nodes in streaming mode (compressing data points as they arrive, no pre-compression buffering). Several experimental algorithms based on linear predictive coding (LPC) combined with run length encoding (RLE) are considered. In entropy coding stage we evaluated (a) variable-length coding with dynamic prefixes generated with MTF-transform, (b) adaptive width binary coding, and (c) adaptive Golomb-Rice coding. We provide a comparison of known and experimental compression algorithms on 75 sensor data sources. Compression ratios achieved in the tests are about 1.5/4/1000000 (min/med/max), with compression context size about 10 bytes.

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P, Srividya. "Optimization of Lossless Compression Algorithms using Multithreading." Journal of Information Technology and Sciences 9, no. 1 (March 2, 2023): 36–42. http://dx.doi.org/10.46610/joits.2022.v09i01.005.

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The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usage has not only raised the utility but also the storage of text, audio and video files. These multimedia files demand more storage space as compared to traditional files. This has given rise to the requirement for an efficient compression algorithm. There is a considerable improvement in the computing performance of the machines due to the advent of the multi-core processor. However, this multi-core architecture is not used by compression algorithms. This paper shows the implementation of lossless compression algorithms namely the Lempel-Ziv-Markov Algorithm, BZip2 and ZLIB algorithms using the concept of multithreading. The results obtained prove that the ZLIB algorithm proves to be more efficient in terms of the time taken to compress and decompress the text. The comparison is done for both compressions without multithreading and compression with multi-threading.

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Xiao, Ling, Renfa Li, Juan Luo, and Zhu Xiao. "Energy-efficient recognition of human activity in body sensor networks via compressed classification." International Journal of Distributed Sensor Networks 12, no. 12 (December 2016): 155014771667966. http://dx.doi.org/10.1177/1550147716679668.

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Energy efficiency is an important challenge to broad deployment of wireless body sensor networks for long-term physical movement monitoring. Inspired by theories of sparse representation and compressed sensing, the power-aware compressive classification approach SRC-DRP (sparse representation–based classification with distributed random projection) for activity recognition is proposed, which integrates data compressing and classification. Random projection as a data compression tool is individually implemented on each sensor node to reduce the amount of data for transmission. Compressive classification can be applied directly on the compressed samples received from all nodes. This method was validated on the Wearable Action Recognition Dataset and implemented on embedded nodes for offline and online experiments. It is shown that our method reduces energy consumption by approximately 20% while maintaining an activity recognition accuracy of 88% at a compression ratio of 0.5.

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Ko, Yousun, Alex Chadwick, Daniel Bates, and Robert Mullins. "Lane Compression." ACM Transactions on Embedded Computing Systems 20, no. 2 (March 2021): 1–26. http://dx.doi.org/10.1145/3431815.

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This article presents Lane Compression, a lightweight lossless compression technique for machine learning that is based on a detailed study of the statistical properties of machine learning data. The proposed technique profiles machine learning data gathered ahead of run-time and partitions values bit-wise into different lanes with more distinctive statistical characteristics. Then the most appropriate compression technique is chosen for each lane out of a small number of low-cost compression techniques. Lane Compression’s compute and memory requirements are very low and yet it achieves a compression rate comparable to or better than Huffman coding. We evaluate and analyse Lane Compression on a wide range of machine learning networks for both inference and re-training. We also demonstrate the profiling prior to run-time and the ability to configure the hardware based on the profiling guarantee robust performance across different models and datasets. Hardware implementations are described and the scheme’s simplicity makes it suitable for compressing both on-chip and off-chip traffic.

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Saidhbi, Sheik. "An Intelligent Multimedia Data Encryption and Compression and Secure Data Transmission of Public Cloud." Asian Journal of Engineering and Applied Technology 8, no. 2 (May 5, 2019): 37–40. http://dx.doi.org/10.51983/ajeat-2019.8.2.1141.

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Data compression is a method of reducing the size of the data file so that the file should take less disk space for storage. Compression of a file depends upon encoding of file. In lossless data compression algorithm there is no data loss while compressing a file, therefore confidential data can be reproduce if it is compressed using lossless data compression. Compression reduces the redundancy and if a compressed file is encrypted it is having a better security and faster transfer rate across the network than encrypting and transferring uncompressed file. Most of the computer applications related to health are not secure and these applications exchange lot of confidential health data having different file formats like HL7, DICOM images and other audio, image, textual and video data formats etc. These types of confidential data need to be transmitted securely and stored efficiently. Therefore this paper proposes a learning compression- encryption model for identifying the files that should be compressed before encrypting and the files that should be encrypted without compressing them.

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Andreula, C., and I. Kambas. "Il dolore lombosacrale da ernie discali lombosacrali e patologia degenerativa correlata." Rivista di Neuroradiologia 15, no. 4 (August 2002): 421–30. http://dx.doi.org/10.1177/197140090201500411.

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La patogenesi del dolore lombo-sacrale è ancora motivo di discussione e potrebbe essere sostenuta non solo da fattori meccanici diretti di compressione del disco (protrusione o ernia) sul nervo con conseguente alterazione della guaina mielinica, ma anche da fattori meccanici indiretti generati da stasi venosa e conseguente ischemia delle radici particolarmente sensibili all'ipossia e da fattori infiammatori di tipo immunomediato e di tipo bioumorale legati al disco. La gestione del paziente lombosciatalgico affidata al chirurgo dopo il fallimento della terapia medica, conservativa e fisiatrica ha rivelato che nelle casistiche chirurgiche più equilibrate la percentuale di successo degli interventi per ernia del disco lombosacrale si aggira sul 95–98% a breve termine con un'incidenza di reale recidiva erniaria nel 2–6%, la percentuale di successo scende a distanza fino all' 80–85%, per la comparsa di sintomatologia legata al fallimento chirurgico (Failed Back Surgery Sindrome FBSS), caratterizzata da recidive e/o cicatrici ipertrofiche, con sintomi rilevanti nel 20%, e vera e propria FBSS nel 15%. Tali dati hanno indotto a ricercare sempre nuove tecniche microchirurgiche per ridurre tali risultati indesiderati e contemporaneamente sono state approntate tecniche di trattamento percutaneo secondo procedure intervenzionali (chemiodiscolisi con chimopapaina, con ossigeno-ozono, nucleoaspirazione secondo la tecnica di Onik …) per ridurre al minimo da un lato l' “invasività” chirurgica, e dall'altro le non rare complicazioni di natura infettiva correlate all'intervento. Tutte le tecniche percutanee sono atti medici poco invasivi, con tempi di ospedalizzazione brevi. Il loro approccio extra canale spinale elimina i rischi connessi all'atto chirurgico di cicatrice post-operatoria, spesso responsabile di recidiva di sintomatologia dolorosa. Hanno inoltre il vantaggio di essere ripetibili nello stesso paziente senza precludere in caso di insuccesso il ricorso alla chirurgia tradizionale. Le percentuali di successo riportate da numerose casistiche si aggirano sul 65–75% di risultati ottimi o buoni. Queste procedure interventistiche spinali agirebbero sulla genesi meccanica del dolore riducendo quantitativamente il materiale nucleare, ma non espleterebbero alcuna azione sulla componente infiammatoria di origine radicolare e/o gangliare, talvolta causa autonoma del dolore. Pertanto in corso di trattamento di chemiodiscolisi con miscela di ossigeno-ozono, si è proceduto all'aggiunta di infiltrazione periradicolare e periganglionare con ossigeno-ozono, steroidi e anestetici. Gli autori riportano la loro personale esperienza sull'utilizzo del trattamento di Chemiodiscolisi con nucleoptesi con ossigeno-ozono con infiltrazione periradicolare e periganglionare nelle ernie discali lombosacrali e patologia degenerativa correlata.

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Budiman, Gelar, Andriyan Bayu Suksmono, and Donny Danudirdjo. "Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding." Applied Sciences 10, no. 12 (June 24, 2020): 4338. http://dx.doi.org/10.3390/app10124338.

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We propose a novel data hiding method in an audio host with a compressive sampling technique. An over-complete dictionary represents a group of watermarks. Each row of the dictionary is a Hadamard sequence representing multiple bits of the watermark. Then, the singular values of the segment-based host audio in a diagonal matrix are multiplied by the over-complete dictionary, producing a lower size matrix. At the same time, we embed the watermark into the compressed audio. In the detector, we detect the watermark and reconstruct the audio. This proposed method offers not only hiding the information, but also compressing the audio host. The application of the proposed method is broadcast monitoring and biomedical signal recording. We can mark and secure the signal content by hiding the watermark inside the signal while we compress the signal for memory efficiency. We evaluate the performance in terms of payload, compression ratio, audio quality, and watermark quality. The proposed method can hide the data imperceptibly, in the range of 729–5292 bps, with a compression ratio 1.47–4.84, and a perfectly detected watermark.

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Dissertations / Theses on the topic "Compressione dati"

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Marconi, Chiara. "Tecniche di compressione senza perdita per dati unidimensionali e bidimensionali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5394/.

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Pizzolante, Raffaele. "Compression and protection of multidimensional data." Doctoral thesis, Universita degli studi di Salerno, 2015. http://hdl.handle.net/10556/1943.

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2013 - 2014
The main objective of this thesis is to explore and discuss novel techniques related to the compression and protection of multidimensional data (i.e., 3-D medical images, hyperspectral images, 3-D microscopy images and 5-D functional Magnetic Resonance Images). First, we outline a lossless compression scheme based on the predictive model, denoted as Medical Images Lossless Compression algorithm (MILC). MILC is characterized to provide a good trade-off between the compression performances and reduced usage of the hardware resources. Since in the medical and medical-related fields, the execution speed of an algorithm, could be a “critical” parameter, we investigate the parallelization of the compression strategy of the MILC algorithm, which is denoted as Parallel MILC. Parallel MILC can be executed on heterogeneous devices (i.e., CPUs, GPUs, etc.) and provides significant results in terms of speedup with respect to the MILC. This is followed by the important aspects related to the protection of two sensitive typologies of multidimensional data: 3-D medical images and 3-D microscopy images. Regarding the protection of 3-D medical images, we outline a novel hybrid approach, which allows for the efficient compression of 3-D medical images as well as the embedding of a digital watermark, at the same time. In relation to the protection of 3-D microscopy images, the simultaneous embedding of two watermarks is explained. It should be noted that 3-D microscopy images are often used in delicate tasks (i.e., forensic analysis, etc.). Subsequently, we review a novel predictive structure that is appropriate for the lossless compression of different typologies of multidimensional data... [edited by Author]
XIII n.s.

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Pesare, Stefano. "Sistemi di Backup e tecniche di conservazione dei dati digitali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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La tesi si occupa del problema della conservazione dei dati digitali, spesso sottovalutato. Le odierne tecniche e strategie di conservazione e archiviazione non possono garantire da sole la sicurezza dei dati nel tempo, ma solo se vengono utilizzate sinergicamente. Durante questo percorso capiremo cosa siano i dati digitali, le loro caratteristiche e problematiche inerenti la loro gestione, nonché le tecniche di conservazione e storage. Vedremo come si sono evolute le memorie di massa, dalle schede perforate fino alla nascita dei dischi a stato solido. Inoltre, verranno introdotti il Cloud Computing e il ventaglio di servizi che offre, compreso il Cloud Storage. Infine, si mostreranno gli algoritmi principali di compressione, utili nella gestione dei dati.

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Williams, Ross Neil. "Adaptive data compression." Adelaide, 1989. http://web4.library.adelaide.edu.au/theses/09PH/09phw7262.pdf.

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Steinruecken, Christian. "Lossless data compression." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709134.

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Lindsay, Robert A., and B. V. Cox. "UNIVERSAL DATA COMPRESSION." International Foundation for Telemetering, 1985. http://hdl.handle.net/10150/615552.

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International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, Nevada
Universal and adaptive data compression techniques have the capability to globally compress all types of data without loss of information but have the disadvantage of complexity and computation speed. Advances in hardware speed and the reduction of computational costs have made universal data compression feasible. Implementations of the Adaptive Huffman and Lempel-Ziv compression algorithms are evaluated for performance. Compression ratios versus run times for different size data files are graphically presented and discussed in the paper. Required adjustments needed for optimum performance of the algorithms relative to theoretical achievable limits will be outlined.

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Dušák, Petr. "Fractal application in data compression." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-201795.

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The mission of the Technology Transfer Programme Office is to increase impact on a society by transferring technologies developed by the European Space Agency. Method and Apparatus for compressing time series is a patented compression algorithm designed to be efficient as its purpose is to run on deep space probes or satellites. The algorithm is inspired by a method for fractal terrain generation, namely the midpoint displacement algorithm. This work introduces fractals, their application and modifying the patented algorithm, in order to achieve greater compression. The modification lies in modifying the displacement mechanism. The modified algorithm is capable of reducing data up to 25 %, compared to the patented algorithm. The modification made the algorithm less efficient. In large-scale test, performed on Rosetta spacecraft telemetry, the modified algorithm achieved around 5 % higher compression.

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Radhakrishnan, Radhika. "Genome data modeling and data compression." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447611.

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García, Sobrino Francisco Joaquín. "Sounder spectral data compression." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/663984.

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IASI (Infrared Atmospheric Sounding Interferometer) es un espectrómetro basado en la transformada de Fourier diseñado para medir radiación infrarroja emitida por La Tierra. A partir de estas mediciones se generan datos con una precisión y resolución espectral sin precedentes. Esta información es útil para obtener perfiles de temperatura y humedad, así como concentraciones de gases traza, que son esenciales para la comprensión y monitorización del clima y para realizar previsiones atmosféricas. La alta resolución espectral, espacial y temporal de los datos producidos por el instrumento implica generar productos con un tamaño considerablemente grande, lo que demanda el uso de técnicas de compresión eficientes para mejorar tanto las capacidades de transmisión como las de almacenamiento. En esta tesis se realiza un exhaustivo análisis de la compresión de datos IASI y se proporcionan recomendaciones para generar datos reconstruidos útiles para el usuario final. En este análisis se utilizan datos IASI transmitidos a las estaciones de recepción (productos IASI L0) y datos destinados a usuarios finales que son distribuidos a centros de predicción numérica y a la comunidad científica en general (productos IASI L1C). Para comprender mejor la naturaleza de los datos capturados por el instrumento se analizan las estadísiticas de la información y el rendimiento de varias técnicas de compresión en datos IASI L0. Se estudia la entropía de orden-0 y las entropías contextuales de orden-1, orden-2 y orden-3. Este estudio revela que el tamaño de los datos se podría reducir considerablemente explotando la entropía de orden-0. Ganancias más significativas se podrían conseguir si se utilizaran modelos contextuales. También se investiga el rendimiento de varias técnicas de compresión sin pérdida. Los resultados experimentales sugieren que se puede alcanzar un ratio de compresión de 2,6:1, lo que implica que sería posible transmitir más datos manteniendo la tasa de transmisión original o, como alternativa, la tasa de transmisión del instrumento se podría reducir. También se realiza un exhaustivo análisis de la compresión de datos IASI L1C donde se evalúa el rendimiento de varias transformadas espectrales y técnicas de compresión. La experimentación abarca compresión sin pérdida, compresión casi sin pérdida y compresión con pérdida sobre una amplia gama de productos IASI-A e IASI-B. Para compresión sin pérdida es posible conseguir ratios de compresión superiores a 2,5:1. Para compresión casi sin pérdida y compresión con pérdida se pueden alcanzar ratios de compresión mayores a la vez que se producen espectros reconstruidos de calidad. Aunque la compresión casi sin pérdida y la compresión con pérdida producen ratios de compresión altos, la utilidad de los espectros reconstruidos se puede ver comprometida ya que cierta información es eliminada durante la etapa de compresión. En consecuencia, se estudia el impacto de la compresión casi sin pérdida y la compresión con pérdida en aplicaciones de usuario final. Concretamente, se evalúa el impacto de la compresión en datos IASI L1C cuando algoritmos estadísticos se utilizan posteriormente para predecir información física a partir de los espectros reconstruidos. Los resultados experimentales muestran que el uso de espectros reconstruidos puede conseguir resultados de predicción competitivos, mejorando incluso los resultados que se obtienen cuando se utilizan datos sin comprimir. A partir del análisis previo se estudia el origen de los beneficios que produce la compresión obteniendo dos observaciones principales. Por un lado, la compresión produce eliminación de ruido y filtrado de la señal lo que beneficia a los métodos de predicción. Por otro lado, la compresión es una forma indirecta de producir regularización espectral y espacial entre píxeles vecinos lo que beneficia a algoritmos que trabajan a nivel de píxel.
The Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform Spectrometer implemented on the MetOp satellite series. The instrument is intended to measure infrared radiation emitted from the Earth. IASI produces data with unprecedented accuracy and spectral resolution. Notably, the sounder harvests spectral information to derive temperature and moisture profiles, as well as concentrations of trace gases, essential for the understanding of weather, for climate monitoring, and for atmospheric forecasts. The large spectral, spatial, and temporal resolution of the data collected by the instrument involves generating products with a considerably large size, about 16 Gigabytes per day by each of the IASI-A and IASI-B instruments currently operated. The amount of data produced by IASI demands efficient compression techniques to improve both the transmission and the storage capabilities. This thesis supplies a comprehensive analysis of IASI data compression and provides effective recommendations to produce useful reconstructed spectra. The study analyzes data at different processing stages. Specifically, we use data transmitted by the instrument to the reception stations (IASI L0 products) and end-user data disseminated to the Numerical Weather Prediction (NWP) centres and the scientific community (IASI L1C products). In order to better understand the nature of the data collected by the instrument, we analyze the information statistics and the compression performance of several coding strategies and techniques on IASI L0 data. The order-0 entropy and the order-1, order-2, and order-3 context-based entropies are analyzed in several IASI L0 products. This study reveals that the size of the data could be considerably reduced by exploiting the order-0 entropy. More significant gains could be achieved if contextual models were used. We also investigate the performance of several state-of-the-art lossless compression techniques. Experimental results suggest that a compression ratio of 2.6:1 can be achieved, which involves that more data could be transmitted at the original transmission rate or, alternatively, the transmission rate of the instrument could be further decreased. A comprehensive study of IASI L1C data compression is performed as well. Several state-of-the-art spectral transforms and compression techniques are evaluated on IASI L1C spectra. Extensive experiments, which embrace lossless, near-lossless, and lossy compression, are carried out over a wide range of IASI-A and IASI-B orbits. For lossless compression, compression ratios over 2.5:1 can be achieved. For near-lossless and lossy compression, higher compression ratios can be achieved, while producing useful reconstructed spectra. Even though near-lossless and lossy compression produce higher compression ratios compared to lossless compression, the usefulness of the reconstructed spectra may be compromised because some information is removed during the compression stage. Therefore, we investigate the impact of near-lossless and lossy compression on end-user applications. Specifically, the impact of compression on IASI L1C data is evaluated when statistical retrieval algorithms are later used to retrieve physical information. Experimental results reveal that the reconstructed spectra can enable competitive retrieval performance, improving the results achieved for the uncompressed data, even at high compression ratios. We extend the previous study to a real scenario, where spectra from different disjoint orbits are used in the retrieval stage. Experimental results suggest that the benefits produced by compression are still significant. We also investigate the origin of these benefits. On the one hand, results illustrate that compression performs signal filtering and denoising, which benefits the retrieval methods. On the other hand, compression is an indirect way to produce spectral and spatial regularization, which helps pixel-wise statistical algorithms.

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Du, Toit Benjamin David. "Data Compression and Quantization." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/79233.

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Data Compression Due to limitations in data storage and bandwidth, data of all types has often required compression. This need has spawned many different methods of compressing data. In certain situations the fidelity of the data can be compromised and unnecessary information can be discarded, while in other situations, the fidelity of the data is necessary for the data to be useful thereby requiring methods of reducing the data storage requirements without discarding any information. The theory of data compression has received much attention over the past half century, with some of the most important work done by Claude E. Shannon in the 1940’s and 1950’s and at present topics such as Information and Coding Theory, which encompass a wide variety of sciences, continue to make headway into the interesting and highly applicable topic of data compression. Quantization Quantization is a broad notion used in several fields especially in the sciences, including signal processing, quantum physics, computer science, geometry, music and others. The concept of quantization is related to the idea of grouping, dividing or approximating some physical quantity by a set of small discrete measurements. Data Quantization involves the discretization of data, or the approximation of large data sets by smaller data sets. This mini dissertation is a research dissertation that considers how data, which is of a statistical nature, can be quantized and compressed.
Dissertation (MSc)--University of Pretoria, 2014.
Statistics
MSc
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Books on the topic "Compressione dati"

1

Data compression: The complete reference. New York: Springer, 1998.

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Salomon, David. Data Compression. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9.

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Salomon, David. Data Compression. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8.

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Huang, Bormin, ed. Satellite Data Compression. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1183-3.

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Williams, Ross N. Adaptive Data Compression. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4046-5.

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6

Motta, Giovanni, Francesco Rizzo, and James A. Storer, eds. Hyperspectral Data Compression. Boston: Kluwer Academic Publishers, 2006. http://dx.doi.org/10.1007/0-387-28600-4.

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Huang, Bormin. Satellite data compression. New York, NY: Springer Science+Business Media, LLC, 2011.

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Giovanni, Motta, Rizzo Francesco, and Storer James A. 1953-, eds. Hyperspectral data compression. New York: Springer, 2005.

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Williams, Ross N. Adaptive Data Compression. Boston: Kluwer Academic Publishers, 1991.

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Ann, Lindskoog Kathryn, and Wynne Patrick ill, eds. Hans Brinker, or, The silver skates. Sisters, Or: Multnomah Books, 1993.

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Book chapters on the topic "Compressione dati"

1

Salomon, David. "Basic Techniques." In Data Compression, 1–19. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_1.

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Salomon, David. "Error Correcting Codes." In Data Compression, 337–48. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_10.

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Salomon, David. "Fourier Transform." In Data Compression, 349–53. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_11.

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Salomon, David. "Group 4 Codes Summary." In Data Compression, 355–56. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_12.

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Salomon, David. "Hashing." In Data Compression, 357–60. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_13.

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Salomon, David. "Interpolating Polynomials." In Data Compression, 361–66. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_14.

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Salomon, David. "Statistical Methods." In Data Compression, 21–100. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_2.

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Salomon, David. "Dictionary Methods." In Data Compression, 101–62. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_3.

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Salomon, David. "Image Compression." In Data Compression, 163–249. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_4.

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Salomon, David. "Other Methods." In Data Compression, 251–99. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_5.

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Conference papers on the topic "Compressione dati"

1

Marin, Jeison, Leonardo Betancur, and Henry Arguello. "Compression Ratio Design in Compressive Spectral Imaging." In 2016 Data Compression Conference (DCC). IEEE, 2016. http://dx.doi.org/10.1109/dcc.2016.81.

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2

Jahanian, Shahriar, and A. J. McPhate. "Approximate Residual Interface Compression in a Laminated Magnet." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0072.

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Abstract Present design of large laminated magnets are based on effective modulus of elasticity for the plate stack that is invariant with interplate compression. Experimental results indicates this is not a valid assumption. This analysis considers the specific design used for compact electron storage ring dipole magnet at the Center for Advanced Micro Devices (CAMD) at Louisiana State University. An iterative technique, using FEM is developed to approximate the effective modulus throughout the magnet structure. This particular magnet is constructed from 1424 steel plates (1.5mm × 610mm × 780mm) by first compressing a 45° 2.93m raduis curvilinear stack to a specified preload and then welding straight and curved straps to the exterior of the stack. Release of the preload allowsexpansion of the stack and forces stretch of the straps, the resultant interplate compression is considerably different from the initial preload and varies throughout the magnet. The analysis technique introduced in this paper is a simplified approach to the interplate compression modeling and consists of the supeposition of two simple analysis; one with straps attached, one without straps attached. An iterative approach is used to incorporate the dependence of effective compressive modulus on the interplate compression. An estimate of the average modulus is assumed for the entire stack and residual compression is calculated. From this residual compression and from experimental data, a distribution of compressive modulus throughout the magnet is computed. From this computed distribution, a new estimate for the compressive modulus is made for each element of the model and the analysis iterated. The net results are the compressive modulus distribution throughout the magnet ia a form suitable in subsequent dynamic analysis.

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3

Fowler, James E. "Compressive-Projection Principal Component Analysis for the Compression of Hyperspectral Signatures." In 2008 Data Compression Conference DCC. IEEE, 2008. http://dx.doi.org/10.1109/dcc.2008.26.

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Chenxi Tu, Eijiro Takeuchi, Chiyomi Miyajima, and Kazuya Takeda. "Compressing continuous point cloud data using image compression methods." In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2016. http://dx.doi.org/10.1109/itsc.2016.7795789.

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"Author Index." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.19.

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Sangho Yoon, Chee Sun Won, Kyungsuk Pyun, and R. M. Gray. "Image classification using GMM with context information and with a solution of singular covariance problem." In Data Compression Conference. IEEE, 2003. http://dx.doi.org/10.1109/dcc.2003.1194076.

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"Author index." In Data Compression Conference. IEEE, 2003. http://dx.doi.org/10.1109/dcc.2003.1194078.

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"Proceedings. DCC 2005. Data Compression Conference." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.28.

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"Table of Contents." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.84.

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"Title Page." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.89.

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Reports on the topic "Compressione dati"

1

Creusere, Charles D., and Jim Witham. Data Compression Project. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada370497.

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Gryder, R., and K. Hake. Survey of data compression techniques. Office of Scientific and Technical Information (OSTI), September 1991. http://dx.doi.org/10.2172/10107839.

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Gryder, R., and K. Hake. Survey of data compression techniques. Office of Scientific and Technical Information (OSTI), September 1991. http://dx.doi.org/10.2172/5926128.

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Duff, C. R. W. Data compression and computation speed. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/315270.

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Clark, D. Argon Excluder Foam Compression Data. Office of Scientific and Technical Information (OSTI), July 1991. http://dx.doi.org/10.2172/1031764.

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Pan, David. Efficient Data Compression Techniques for Weather Data. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada540395.

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Choi, Junho, and Mitchell R. Grunes. Lossless Data Compression of Packet Data Streams,. Fort Belvoir, VA: Defense Technical Information Center, February 1996. http://dx.doi.org/10.21236/ada304792.

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Horan, Shield. Data Compression Techniques to Reduce Bandwidth. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada399290.

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Senecal, Joshua G. Length-Limited Data Transformation and Compression. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/877882.

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Perkins, William W. Data Compression With Application to Geo-Location. Fort Belvoir, VA: Defense Technical Information Center, August 2010. http://dx.doi.org/10.21236/ada532376.

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