A time-domain analog spatial compressed sensing encoder for neural recording applications is proposed. Owing to the advantage of MEMS technologies, the number of channels on a silicon neural probe array has doubled in 7.4 years, and therefore, a greater number of recording channels and higher density of front-end circuitry is required. Since neural signals such as action potential (AP) have wid...

As an emerging approach of signal processing, not only has compressed sensing (CS) successfully compressed and sampled signals with few measurements, but also has owned the capabilities of ensuring the exact recovery of signals. However, the above-mentioned properties are based on the (compressed) sensing matrices. Hence the construction of sensing matrices is the key problem. Compared with the...

Sensing devices including mobile phones and biomedical sensors generate massive amounts of spatio-temporal data. Compressive sensing (CS) can significantly reduce energy and resource consumption by shifting the complexity burden of encoding process to the decoder. CS reconstructs the compressed signals exactly with overwhelming probability when incoming data can be sparsely represented with a f...

In the last few years Compressed Sampling (CS) has been well used in the area of signal processing and image compression. Recently, CS has been earning a great interest in the area of wireless communication networks. CS exploits the sparsity of the signal processed for digital acquisition to reduce the number of measurement, which leads to reductions in the size, power consumption, processing t...

In a simulation of compressed sensing (CS), one must test whether the recovered solution x̂ is the true solution x, i.e., “exact recovery.” Most CS simulations employ one of two criteria: 1) the recovered support is the true support; or 2) the normalized squared error is less than . We analyze these exact recovery criteria independent of any recovery algorithm, but with respect to signal distrib...

Introduction: A novel theory, called Compressed Sensing (CS) [1, 2], has demonstrated that MR images can be successfully reconstructed from a small number of k-space measurements [3]. The practical impact and success of CS in imaging applications can be attributed to the fact that most signals of practical interest have sparse representations in a transform domain. While initial CS techniques a...

Advances in compressive sensing (CS) techniques and instrumentation have created a renewed interest in exploring new methods for data collection and post-processing [1,2]. Recently developed Temporal CS (TCS) techniques based on post-specimen, high-speed electrostatic beam deflectors effectively multiply the frame rate of commonly available TEM cameras by pre-compressing video data on the detec...

Introduction: The recently introduced Compressed Sensing (CS) theory has demonstrated that MR images can be reconstructed from a small number of k-space measurements [1-3]. The key assumption in CS MRI is that the image has a sparse representation in a predetermined basis. Selection of this sparsity basis is critically important in CS. In this work, we introduce a new sparse reconstruction fram...

The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices—especially in optics—have only started being addressed. This paper presents an implementation of compressive sensing...

There have been many matching pursuit algorithms (MPAs) which handle the sparse signal recovery problem a.k.a. compressed sensing (CS). In the MPAs, the correlation computation step has a dominant computational complexity. In this letter, we propose a new fast correlation computation method when we use some classes of partial unitary matrices as the sensing matrix. Those partial unitary matrice...