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We propose a multiple initialization based spectral peak tracking (MISPT) technique for heart rate monitoring from
photoplethysmography (PPG) signal.MISPT is applied on the PPG signal after removing the motion artifact using an adaptive noise cancellation filter. MISPT yields several estimates of the heart rate trajectory from the spectrogram of the denoised PPG signal which are finally combined using a novel measure called
trajectory strength. Multiple initializations help in correcting

A fast intra mode decision scheme for HEVC screen content coding is proposed to reduce the complexity of intra mode decision search for each coding unit. A fast block matching scheme for intra block copy is proposed to reduce the number of blocks for 2-D search. 39% and 35% encoding time reduction for lossy and lossless encoding scenarios with negligible quality loss are achieved under the SCC common test condition.

We present an algorithm for clustering complex-valued unit length vectors on the unit hypersphere, which we call complex spherical k-mode clustering, as it can be viewed as a generalization of the spherical k-means algorithm to normalized complex-valued vectors. We show how the proposed algorithm can be derived from the Expectation Maximization algorithm for complex Watson mixture models and prove its applicability in a blind speech separation (BSS) task with real-world room impulse response measurements.

We present a neural network based approach to acoustic beamform- ing. The network is used to estimate spectral masks from which the Cross-Power Spectral Density matrices of speech and noise are estimated, which in turn are used to compute the beamformer co- efficients. The network training is independent of the number and the geometric configuration of the microphones. We further show that it is possible to train the network on clean speech only, avoid- ing the need for stereo data with separated speech and noise. Two types of networks are evaluated.

Visual tracking in unconstrained environments often involves following an object that exhibits a number of appearance changes from factors such as scale change, rotation, and illumination. Effective tracking requires adapting a tracker to the object’s changing appearance over time. When a target becomes occluded by other objects in the scene, a naive tracker may end up learning the appearance of the occluding object. Our work introduces a method of detecting occlusion by considering the color profile of the target to prevent inappropriate tracker updates while the target is occluded.

Differential privacy is a strong, cryptographically-motivated definition of privacy that has recently received a significant amount of research attention for its robustness to known attacks. The principal component analysis (PCA) algorithm is frequently used in signal processing, machine learning and statistics pipelines. In this paper, we propose a new algorithm for differentially-private computation of PCA and compare the performance empirically with some recent state-of-the-art algorithms on different data sets.

With developments in experimental connectomics producing wiring diagrams of many neuronal networks, there is emerging interest in theories to understand the relationship between structure and function. Efficiency of information flow in networks has been proposed as a key functional in characterizing cognition, and we have previously shown that information-theoretic limits on information flow are predictive of behavioral speed in the nematode Caenorhabditis elegans.

We propose a new algorithm for source localization on rigid surfaces, which allows one to convert daily objects into human-computer touch interfaces using surface-mounted vibration sensors. This is achieved via estimating the time-difference-of-arrivals (TDOA) of the signals across the sensors. In this work, we employ a smooth parametrized function to model the gradual noise-to-signal energy transition at each sensor. Specifically, the noise-to-signal transition is modeled by a four-parameter logistic function.

This work formulates song recommendation as a matrix completion problem that benefits from collaborative filter- ing through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using a rich combination of audio, meta-data and social features. As we demonstrate, our hybrid recom- mendation system is very versatile and incorporates several well-known methods while outperforming them. Particularly, we show on real-world data that our model overcomes w.r.t.