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The inherent dependencies among multiple physiological signals are crucial for multimodal emotion recognition, but have not been thoroughly exploited yet. This paper propose to use restricted Boltzmann machine (RBM) to model such dependencies.Specifically, the visible nodes of RBM represent EEG and peripheral physiological signals, and thus the connections between visible nodes and hidden nodes capture the intrinsic relations among multiple physiological signals. The RBM also generate new representation from multiple physiological signals.

The inherent dependencies among video content, personal characteristics, and perceptual emotion are crucial for personalized video emotion tagging, but have not been thoroughly exploited. To address this, we propose a novel topic model to capture such inherent dependencies. We assume that there are several potential human factors, or “topics,” that affect the personal characteristics and the personalized emotion responses to videos.

Shape Boltzmann machine (a type of Deep Boltzmann machine) is a powerful tool for shape modelling; however, has some drawbacks in representation of local shape parts. Disjunctive Normal Shape Model (DNSM) is a strong shape model that can effectively represent local parts of objects. In this paper, we propose a new shape model based on Shape Boltzmann Machine and Disjunctive Normal Shape Model which we call Disjunctive Normal Shape Boltzmann Machine (DNSBM).

The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) type I and type II error probabilities and (ii) mean squared error performance.

Power distribution grids are currently challenged by observability issues due to limited metering infrastructure. On the other hand, smart meter data, including local voltage magnitudes and power injections, are collected at grid nodes with renewable generation and demand response programs. A power flow-based approach using these data is put forth here to infer the unknown power injections at non-metered grid nodes.

The IEEE 802.11ad waveform can be used for automotive radar by exploiting the Golay complementary sequences in the preamble of a frame. The performance of radar, however, is limited by the preamble structure. In this paper, we propose an adaptive preamble design that permits a trade-off between radar parameters’ estimation accuracy and communication rate. To quantify this trade-off, we propose a minimum mean square error (MMSE) metric based on rate distortion theory.

Hybrid analog/digital precoding in the downlink of multiuser massive MIMO systems can reduce the number of RF chains hence reducing total cost and improving power efficiency. Having few RF chains, however, makes it difficult for a base station to acquire instantaneous channel state information across all antennas. We develop a hybrid technique that uses only long-term (slowly changing) channel statistics in computing the analog precoding matrix. The proposed analog precoder is designed to maximize signal-to-leakage-plusnoise ratio (SLNR) when combined with a baseband precoder.

Sidelobe suppression has always been an important part of crafting communications signals to keep interference with users of adjacent spectrum to a minimum. Systems based on the discrete Fourier transform, such as orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-division multiple access (SC-FDMA) are especially prone to out-of-band power leakage. Although many techniques have been proposed to suppress sidelobes in DFT-based systems, a satisfactory balance between computational complexity and out-of-band power leakage has remained elusive.