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In this paper, the importance of analytic phase of the speech signal in automatic speaker verification systems is demonstrated in the context of replay spoof attacks. In order to accurately detect the replay spoof attacks, effective feature representations of speech signals are required to capture the distortion introduced due to the intermediate playback/recording devices, which is convolutive in nature.

This paper explores the modeling and exploitation of semi-local similarity in natural images to address the ill-posed nature of image interpolation. Our approach distinguishes itself from prior approaches by direct and careful use of semi-local similar patches to interpolate each individual patch. Our work uses a simple, parallelizable algorithm without the need to solve complicated optimization problems. Experimental results demonstrate that our interpolated images achieve significantly higher objective and subjective quality compared with those from state-of-the-art algorithms.

The problem of impulse noise mitigation is considered when videos encoded using a SoftCast based Linear Video Coding scheme are transmitted using an OFDM scheme over a wideband channel prone to impulse noise A Fast Bayesian Matching Pursuit algorithm is employed for impulse noise mitigation This approach requires the provisioning of some OFDM subchannels to estimate the impulse noise locations and amplitudes Provisioned subchannels cannot be used to transmit data and lead to a decrease of the nominal decoded video quality at receivers in absence of impulse noise Using a phenomenological mod

Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, adaptivity and generalizability. Popular Deep Convolutional Neural Networks (DCNNs) have recently been utilized for nuclei segmentation, outperforming \textit{traditional} approaches that exploit color and texture features in combination with shallow classifiers or segmentation algorithms.

Secret key generation refers to the problem of generating a common secret key without revealing any information about it to an eavesdropper. All users observe correlated components of a common source and can further use a rate-limited public channel for discussion which is open to eavesdroppers. This paper studies the Turing computability of the secret key capacity with a single rate-limited public forward transmission. Turing computability provides fundamental performance limits for today’s digital computers.

In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost.

In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost.