We address post-silicon timing characterization of the unique gate delays and their distributions on each manufactured IC. Our proposed approach is based upon the new theory of compressed sensing that enables efficient sampling and reconstruction of sparse signals using significantly fewer measurements than previously thought possible. The first step in performing timing measurements is to find...

This paper explores new approaches to active learning (AL) for semantic role labeling (SRL), focusing in particular on combining typical informativity-based sampling strategies with a novel measure of representativeness based on compressed dependency trees (CDTs). In essence, the compressed representation encodes the target predicate and the key dependents of the verb complex in the sentence. W...

Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio in mobile communication. Compressed sensing (CS) is introduced to transfer the sampling burden. The standard sparse signal recovery of CS does not consider the distortion in the analogue-to-information converter (AIC). To mitigate performance degeneration casued by the mismatch in least square distortionle...

Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). Recently, a spread spectrum compressed sensing MRI method modulates an image with a quadratic phase. It performs better than the conventional compressed sensing MRI with variable density sampling, since the coherence between the sensing and sparsity bases are reduced. However, spr...

We address the problem of parameter estimation of superimposed chirp signals in noise. The approach used here is a computationally modest implementation of a maximum likelihood (ML) technique. The ML technique for estimating the complex amplitudes, chirping rates and frequencies reduces to a separable optimization problem where the chirping rates and frequencies are determined by maximizing a c...

As the compressed sensing theory can offer a better performance than Nyquist sampling theorem when dealing with large amounts of data, it becomes very popular for image fusion and target recognition in image processing. In this paper, a new image fusion algorithm based on compressed sensing was proposed. By discrete cosine transform, it fused images through weighted coefficient, recovered the f...

The new emerging theory of compressive sampling demonstrates that by exploiting the structure of a signal, it is possible to sample a signal below the Nyquist rate—using random projections—and achieve perfect reconstruction. In this paper, we consider a special case of compressive sampling where the uncompressed signal is non-negative, and propose a number of sparse recovery algorithms—which ut...

It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements, possibly contaminated with noise. This technique known as “compressed sensing” or “compressive sampling” relies on properties of the sensing matrix such as the restricted isometry property. In this Note, we establish new results about the accuracy of the reconstruc...