Compressive Sensing Based Candidate by Eva Lagunas (6 results)

Taschenbuch. Condition: Neu. Compressive Sensing Based Candidate Detector | Applications to Spectrum Sensing and Through-the-Wall Radar Imaging | Lagunas Eva | Taschenbuch | 184 S. | Englisch | 2016 | LAP Lambert Academic Publishing | EAN 9783330009509 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. …

Paperback. Condition: Brand New. 01 edition. 184 pages. 8.66x5.91x0.42 inches. In Stock.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Signal acquisition is a main topic in signal processing. The well-known Shannon-Nyquist theorem lies at the heart of any conventional analog to digital converters stating that any signal has to be sampled with a constant frequency which must be at least twice the highest frequency present in the signal in order to perfectly recover the signal. However, the Shannon-Nyquist theorem provides a worst-case rate bound for any bandlimited data. In this context, Compressive Sensing (CS) is a new framework in which data acquisition and data processing are merged. CS allows to compress the data while is sampled by exploiting the sparsity present in many common signals. Unlike majority of CS literature, the proposed PhD thesis surveys the CS theory applied to signal detection, estimation and classification, which not necessary requires perfect signal reconstruction or approximation. In particular, a novel CS-based detection technique which exploits prior information about some features of the signal is presented. The basic idea is to scan the domain where the signal is expected to lie with a candidate signal estimated from the known features. 184 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Eva LagunasEva Lagunas (S 09-M 13) received the M.Sc. and Ph.D. degrees in telecommunications engineering from UPC, Barcelona, Spain, in 2010 and 2014, respectively. In 2012, she held a visiting research appointment at the Center for.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Signal acquisition is a main topic in signal processing. The well-known Shannon-Nyquist theorem lies at the heart of any conventional analog to digital converters stating that any signal has to be sampled with a constant frequency which must be at least twice the highest frequency present in the signal in order to perfectly recover the signal. However, the Shannon-Nyquist theorem provides a worst-case rate bound for any bandlimited data. In this context, Compressive Sensing (CS) is a new framework in which data acquisition and data processing are merged. CS allows to compress the data while is sampled by exploiting the sparsity present in many common signals. Unlike majority of CS literature, the proposed PhD thesis surveys the CS theory applied to signal detection, estimation and classification, which not necessary requires perfect signal reconstruction or approximation. In particular, a novel CS-based detection technique which exploits prior information about some features of the signal is presented. The basic idea is to scan the domain where the signal is expected to lie with a candidate signal estimated from the known features.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Signal acquisition is a main topic in signal processing. The well-known Shannon-Nyquist theorem lies at the heart of any conventional analog to digital converters stating that any signal has to be sampled with a constant frequency which must be at least twice the highest frequency present in the signal in order to perfectly recover the signal. However, the Shannon-Nyquist theorem provides a worst-case rate bound for any bandlimited data. In this context, Compressive Sensing (CS) is a new framework in which data acquisition and data processing are merged. CS allows to compress the data while is sampled by exploiting the sparsity present in many common signals. Unlike majority of CS literature, the proposed PhD thesis surveys the CS theory applied to signal detection, estimation and classification, which not necessary requires perfect signal reconstruction or approximation. In particular, a novel CS-based detection technique which exploits prior information about some features of the signal is presented. The basic idea is to scan the domain where the signal is expected to lie with a candidate signal estimated from the known features.Books on Demand GmbH, Überseering 33, 22297 Hamburg 184 pp. Englisch.…