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— Mapping audio data to feature vectors for the classification , retrieval or identification tasks presents four principal challenges. The dimensionality of the input must be significantly reduced; the resulting features must be robust to likely distortions of the input; the features must be informative for the task at hand; and the feature extraction(More)
The Karhunen-Loeacuteve transform (KLT) is known to be optimal for high-rate transform coding of Gaussian vectors for both fixed-rate and variable-rate encoding. The KLT is also known to be suboptimal for some non-Gaussian models. This paper proves high-rate optimality of the KLT for variable-rate encoding of a broad class of non-Gaussian vectors: Gaussian(More)
—Wireless transfer of energy through directed radio frequency waves has the potential to realize perennially operating sensor nodes by replenishing the energy contained in the limited on-board battery. However, the high power energy transfer from energy transmitters (ETs) interferes with data communication, limiting the coexistence of these functions. This(More)
A key problem faced by audio identification, classification, and retrieval systems is the mapping of high-dimensional audio input data into informative lower-dimensional feature vectors. This paper explores an automatic dimensionality reduction algorithm called Distortion Discriminant Analysis (DDA). Each layer of DDA projects its input into directions(More)
—RF energy Harvesting (RFH) is emerging as a potential method for the proactive energy replenishment of next generation wireless networks. Unlike other harvesting techniques that depend on the environment, RFH can be predictable or on-demand, and as such it is better suited for supporting quality-of-service-based applications. However, RFH efficiency is(More)
—Batteries of field nodes in a wireless sensor network pose an upper limit on the network lifetime. Energy harvesting and harvesting aware medium access control protocols have the potential to provide uninterrupted network operation, as they aim to replenish the lost energy so that energy neutral operation of the energy harvesting nodes can be achieved. To(More)
In a variety of applications (including automatic target recognition) image classification algorithms operate on compressed image data. This paper explores the design of optimal transform coders and scalar quantizers using Chernoff bounds on probability of misclassification as the measure of classification accuracy. This design improves classification(More)
Principles behind lossless and lossy coding are usually considered related, yet distinct. In contrast, we show that the direct statements of the rate-distortion theorem and the lossless coding theorem are consequences of a common distortion-abstracted phenomenon. Significantly, we extend such distortion abstraction to a more general multiterminal framework,(More)
We derive new algorithms for approximating the rate regions for a family of source coding problems that includes lossy source coding, lossy source coding with uncoded side information at the receiver (the Wyner-Ziv problem), and an achievability bound for lossy source coding with coded side in formation at the receiver. The new algorithms generalize a(More)
Consider the two-terminal partial side information problem, where one source is decoded under a distortion measure, while the other acts as a helper. There are two well known inner bounds on the (convex) achievable region: (i) a bound due to Berger et al., and (ii) a suitable specialization of the general Berger-Tung bound. While the former bound admits a(More)