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Algorithms, Volume 4, Issue 3 (September 2011) – 3 articles , Pages 155-222

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Research

2677 KiB  
Article
Radio-Frequency Interference Detection and Mitigation Algorithms for Synthetic Aperture Radiometers
by Adriano Camps, Jerome Gourrion, Jose Miguel Tarongi, Mercedes Vall Llossera, Antonio Gutierrez, Jose Barbosa and Rita Castro
Algorithms 2011, 4(3), 155-182; https://doi.org/10.3390/a4030155 - 30 Aug 2011
Cited by 51 | Viewed by 9367
Abstract
The European Space Agency (ESA) successfully launched the Soil Moisture and Ocean Salinity (SMOS) mission in November 2, 2009. SMOS uses a new type of instrument, a synthetic aperture radiometer named MIRAS that provides full-polarimetric multi-angular L-band brightness temperatures, from which regular and [...] Read more.
The European Space Agency (ESA) successfully launched the Soil Moisture and Ocean Salinity (SMOS) mission in November 2, 2009. SMOS uses a new type of instrument, a synthetic aperture radiometer named MIRAS that provides full-polarimetric multi-angular L-band brightness temperatures, from which regular and global maps of Sea Surface Salinity (SSS) and Soil Moisture (SM) are generated. Although SMOS operates in a restricted band (1400–1427 MHz), radio-frequency interference (RFI) appears in SMOS imagery in many areas of the world, and it is an important issue to be addressed for quality SSS and SM retrievals. The impact on SMOS imagery of a sinusoidal RFI source is reviewed, and the problem is illustrated with actual RFI encountered by SMOS. Two RFI detection and mitigation algorithms are developed (dual-polarization and full-polarimetric modes), the performance of the second one has been quantitatively evaluated in terms of probability of detection and false alarm (using a synthetic test scene), and results presented using real dual-polarization and full-polarimetric SMOS imagery. Finally, a statistical analysis of more than 13,000 L1b snap-shots is presented and discussed. Full article
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167 KiB  
Article
Lempel–Ziv Data Compression on Parallel and Distributed Systems
by Sergio De Agostino
Algorithms 2011, 4(3), 183-199; https://doi.org/10.3390/a4030183 - 14 Sep 2011
Cited by 9 | Viewed by 8628
Abstract
We present a survey of results concerning Lempel–Ziv data compression on parallel and distributed systems, starting from the theoretical approach to parallel time complexity to conclude with the practical goal of designing distributed algorithms with low communication cost. Storer’s extension for image compression [...] Read more.
We present a survey of results concerning Lempel–Ziv data compression on parallel and distributed systems, starting from the theoretical approach to parallel time complexity to conclude with the practical goal of designing distributed algorithms with low communication cost. Storer’s extension for image compression is also discussed. Full article
(This article belongs to the Special Issue Data Compression, Communication and Processing)
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405 KiB  
Article
Approximating Frequent Items in Asynchronous Data Stream over a Sliding Window
by Hing-Fung Ting, Lap-Kei Lee, Ho-Leung Chan and Tak-Wah Lam
Algorithms 2011, 4(3), 200-222; https://doi.org/10.3390/a4030200 - 22 Sep 2011
Cited by 5 | Viewed by 7874
Abstract
In an asynchronous data stream, the data items may be out of order with respect to their original timestamps. This paper studies the space complexity required by a data structure to maintain such a data stream so that it can approximate the set [...] Read more.
In an asynchronous data stream, the data items may be out of order with respect to their original timestamps. This paper studies the space complexity required by a data structure to maintain such a data stream so that it can approximate the set of frequent items over a sliding time window with sufficient accuracy. Prior to our work, the best solution is given by Cormode et al. [1], who gave an O (1/ε log W log (εB/ log W) min {log W, 1/ε} log |U|)- space data structure that can approximate the frequent items within an ε error bound, where W and B are parameters of the sliding window, and U is the set of all possible item names. We gave a more space-efficient data structure that only requires O (1/ε log W log (εB/ logW) log log W) space. Full article
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