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Compressive sensing is a new technique in signal processing which can recover a sparse signal vector via a much smaller of non-adaptive, linear measurements than the dimension of the signal vector. In this paper, we applied compressive sensing to a joint source compression-channel coding scheme. With analysis of the reconstruction error of the sparse(More)
An analysis of performance degradation due to limited precision of reference voltage for pipelined ADC (Analog to Digital Converter) is presented in this paper. For the MDAC (multiplying D/A converter) and subADC in pipelined ADC have very different requirements for the precision of reference voltage, an improved differential reference voltage source and(More)
Time delay as well as error accumulation makes transmission of high-quality streaming media over multi-hop wireless networks more challenging. Since conventional TCP/IP-based protocol drops and retransmits the whole packet once error(s) occur above Physical Layer, which leads to long time delay and low efficiency in transmission, a new protocol for(More)
Compressive sensing (CS) is a new method of sampling and compression which has great advantage over previous signal compression techniques. However, its compression ratio is relatively low compared with most of the current coding standards, which means a good quantization method is very important for CS. In this paper, a new method of non-uniform(More)
Mobile devices performing video coding and streaming over wireless communication networks are limited in energy supply. In this paper, a power-efficient fast cloud-based Compressive Sensing (CS) video communication system framework is proposed, which shifts the heavy computational burden from mobile devices to cloud thus the operational lifetime of the(More)
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