Traditional signal representation and coding theory is focused on how to most efficiently represent and encode a signal with the goal of preserving it as best as possible. However, very often, the processing only concerns specific information in the signal and does not require conserving the signal itself. In this work we examine the problem of representing signals such that some function of th...

Approaches to signal representation and coding theory have traditionally focused on how to best represent signals using parsimonious representations that incur the lowest possible distortion. Classical examples include linear and nonlinear approximations, sparse representations and rate-distortion theory. Very often, however, the goal of processing is to extract specific information from the si...

In a gate level n-value compiled simulator, the time necessary to propagate a given signal through a gate is of fundamental importance for the speed of the system. This propagation time is minimized when the boolean equations that drive the simulator are minimized. These Boolean equations are generated from the truth tables for AND and OR gates. Therefore there is no unique best assignment for ...

We assessed the hypothesis that brain signal variability is a reflection of functional network reconfiguration during memory processing. In the present experiments, we use multiscale entropy to capture the variability of human electroencephalogram (EEG) while manipulating the knowledge representation associated with faces stored in memory. Across two experiments, we observed increased variabili...

Compressed sensing appears as a framework to solve underdetermined linear systems, or efficiently acquire sparse signals. Assuming sparsity in a certain given basis of either the signal or its approximation, the recovery can be completed from a few linear measurements. The problem here is not to find an adequate basis but to find the few non-zeros entries of a high-dimensional signal in this pa...

For digital representation of analog data the minimum mean-squared-error criterion is commonly used as a criterion for the basis of optimum quantizer design. In this paper we show that in some situations measures other than the minimum mean-squared-error may be more appropriate. For the signal representation problem, it is shown that the meanabsolute-error criterion has theoretical justificatio...

floorplanning is an important step in physical design of vlsi circuits. it is used to plan the positions of a set of circuit modules on a chip in order to optimize the circuit performance. however, modern floorplanning takes better care of providing extra options to place dedicated modules in the hierarchical designs to align circuit blocks one by one within certain bounding box for helping seq...

The problem of detecting transient signals in a noisy environment has been studied for decades. In the Statistical Signal Detection Theory the presence of a useful signal in a background noise is normally cast as a hypothesis testing, where under the null hypothesis no signal is present. If the statistics of signal to be detected is not perfectly known, usually no uniformly most powerful (UMP) ...

Seismic waves are non-stationary due to its propagation through the earth. Time-frequency transforms are suitable tools for analyzing non-stationary seismic signals. Spectral decomposition can reveal the non-stationary characteristics which cannot be easily observed in the time or frequency representation alone. Various types of spectral decomposition methods have been introduced by some resear...