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Affective states classification has become an important part of the Human-Computer Interface (HCI) study. In recent years, studies of physiological signals, such as ECG, GSR and EEG on affective expression have shown very promising results. In this study, we carried out two experiments to better understand the neurological expression of emotions through the use of EEG signals. In particular, we carried out a subject- independent affective states classification study using narrow- band spectral power of the EEG signals.

An extended version of the paper has been submitted to IEEE Transactions on Signal Processing (TSP):
B. Mamandipoor, D. Ramasamy, U. Madhow, “Newtonized Orthogonal Matching Pursuit: Frequency Estimation over the Continuum,”arXiv preprint arXiv:1509.01942, 2015.

A MATLAB implementation of the algorithm can be found here:
https://bitbucket.org/wcslspectralestimation/continuous-frequency-estima...

A two-stage stochastic programming approach is pursued to optimally place and size photovoltaic (PV) inverters in a radial distribution network under solar irradiance and load uncertainties. First-stage variables include binary PV unit placement as well as continuous real and apparent power capacities of the inverters. Second-stage decisions comprise reactive power compensation, power flows, and nodal voltages, which are determined adaptively to the uncertainty. The objective is to minimize installation cost and expected thermal losses on the network.

In modern communication systems, a sample rate
conversion is necessary since often the system clock is fixed at
some specific rate. Such resampling is critical because there exists
a tight coupling between the data rates and sampling rates. It
is desirable to have a flexible, high performance, and resource
efficient resampler that can accommodate various required data
rates. To achieve these objectives, we present a novel multirate
resampling method based on graphics processing units (GPUs).

We develop a new multiple input multiple output (MIMO) transceiver architecture – Differential Beamspace MIMO (DB-MIMO) for high-dimensional MIMO systems. DB- MIMO enables linear quasi-coherent interference suppression between multiple spatially multiplexed data streams in con- junction with differential transmission. The differential nature of DB-MIMO is particularly attractive in emerging high- frequency systems, e.g. centimeter-wave (cmW) and millimeter- wave (mmW) systems, where the requirement of a phase- coherent local oscillator at the receiver can be challenging.

Removing noise from images usually results in smoothing of edges and areas with discontinuities. Such nonsmooth areas however play a significant role in the perception of image quality. This paper studies the restoration of these regions during denoising. We exploit the fact that the discontinuities in the pixel chromaticity in these regions are less abrupt than those in the pixel luminance. We derive a Bayesian method that estimates the parts of the latent image data in the nonsmooth areas that a denoiser erroneously removes.

The paper investigates a new framework for spectrum sharing between a MIMO-MC radar (MIMO radar using matrix completion) and a MIMO communication system, based on radar transmit precoding. The radar transmit precoder is jointly designed with the communication codewords so that the SINR at the radar receiver is maximized while meeting certain rate and power constraints at the communication system. By shaping the transmit beam, the proposed approach results in enhanced SINR at the receive antennas.