Compressed Sensing Based on Random Symmetric Bernoulli Matrix

The task of compressed sensing is to recover a sparse vector from a small number of linear and non-adaptive measurements, and the problem of finding a suitable measurement matrix is very important in this field. While most recent works focused on random matrices with entries drawn independently from certain probability distributions, in this paper we show that a partial random symmetric Bernoulli matrix whose entries are not independent, can be used to recover signal from observations successfully with high probability. The experimental results also show that the proposed matrix is a suitable measurement matrix.Comment: arXiv admin note: text overlap with arXiv:0902.4394 by other author

A New Temperature Independent Current Controlled Oscillator

[[abstract]]This paper describes a circuit, which generates a low temperature-dependent bias currents. In this paper, low temperature coefficient reference is presented. The circuit is firstly employed to generate a current reference with temperature compensation, then, supply current to the current controlled ring oscillator (CCO). Because of the oscillation frequency of CCO is proportional to temperature, the current is designed to be inversely proportional to temperature to compensate the temperature variation of CCO. There are four different oscillation frequency with temperature compensation has been completed, they are 10MHz, 20MHz, 30MHz and 40MHz respectively. The proposed circuit has been design by a 0.18um CMOS technology process and using computer simulation to evaluate the thermal drift of the reference current. The temperature coefficient of the proposed CCO is 24 ppm/℃ in the temperature range between -25 and 75℃ at 10MHz. In the 20MHz, 30MHz and 40MHz oscillation frequency, the temperature coefficients are 32 ppm/℃, 38 ppm/℃ and 34 ppm/℃ respectively.[[conferencetype]]國際[[conferencedate]]20111207~20111209[[iscallforpapers]]Y[[conferencelocation]]Chiang Mai, Thailan