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The GraSP toolbox aims at processing and visualizing graphs and graphs signal with ease. In the demo, we show those capabilities using several examples from the literature and from our own experiments.
The graph translation operator has been defined with good spectral properties in mind, and in particular with the end goal of being an isometric operator. Unfortunately, the resulting definitions do not provide good intuitions on a vertex-domain interpretation. In this paper, we show that this operator does have a vertex-domain interpretation as a diffusion operator using a polynomial approximation...
We propose an arbitrary order stable allpass filter structure for frequency transformation from Hertz to Bark scale. According to the proposed filter structure, the first order allpass filter is causal, but the second and higher order allpass filters are non-causal. We find that the accuracy of the transformation significantly improves when a second or higher order allpass filter is designed compared...
A robust algorithm to model the harmony structure of a music piece is proposed. The harmony structure is extracted directly from a music audio signal using a second-order statistic of chroma feature vectors. The method is experimentally shown to be robust against the degradation of chroma feature vectors due to noisy pitch estimation in our classical music opus identification evaluation. To analyze...
Sensor networks provide a convenient way of estimating diffuse fields, but deployment issues and inaccurate clock synchronization may introduce randomness in their behavior. Recently, asynchronous sensor networks (ASN) comprising uncoordinated and randomly placed nodes, have been shown to sustain quasi-stable communication. In order to conserve energy, it is desirable to reduce the number of samples...
In this paper, we present the sensor network-on-a-chip (SNOC) paradigm for designing robust and energy-efficient systems-on-a-chip (SOC). In this paradigm, computation in the presence of nanometer non-idealities such as process variations, leakage and noise is viewed as an estimation problem. Robust statistical signal processing theory is then employed to recover the performance of the system in the...
Algorithmic noise tolerance (ANT) techniques have reduced power consumption in many signal-processing applications, but they typically require an error-free estimator block and do not fully exploit the statistical nature of timing errors. By approaching certain computations as estimation problems, we draw inspiration from sensor networks and develop a novel power conservation scheme in which the main...
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