http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:8769 ℓ⁰ Norm based signal recovery is attractive in compressed sensing as it can facilitate exact recovery of sparse signal with very high probability. Unfortunately, direct ℓ⁰ norm minimization problem is NP-hard. This paper describes an approximate ℓ⁰ norm algorithm for sparse representation which preserves most of the advantages of ℓ⁰ norm. The algorithm shows attractive convergence properties, and provides remarkable performance improvement in noisy environment compared to other popular algorithms. The sparse representation algorithm presented is capable of very fast signal recovery, thereby reducing retrieval latency when handling high dimensional signal. 2013-03-26T06:04:07.260Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:8762 Multiple description coding (MDC) offers an elegant approach to data transmission over lossy packet-based networks. This paper proposes an MDC decoder for Compressed Sensing (CS) based MDC. Our decoder minimizes ℓ⁰ norm of the total variation of the image in a recursive manner, making it effective when different descriptions experience different time delays in the network. The proposed approach brings in a significant performance improvement in reconstruction accuracy and reconstruction time. 2013-03-26T06:02:02.055Z ]]>