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This paper considers an optimal artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, two sorts of services are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers.

Color pixel encoding optimizes the conversion of linear physical values of light into integer values. The efficiency of such encoding methods depends on a trade-off between the bit-depth used and the visible distortion introduced by quantization. This efficiency for different color pixel encoding approaches has been evaluated in literature, without considering the fact that before transmission to the end-user, color encoded content needs to be compressed using a video codec.

Quaternion Fourier transforms (QFT) provide a powerful tool for the analysis of signals obtained from vector probes. Acoustic particle velocity is one such signal which can be measured with specially designed microphone arrays. This paper presents a time-frequency source separation method based on the short-time quaternion Fourier transform of acoustic particle velocity signals and the agglomerative k-plane clustering of the vector part of the resulting representation. Two example cases, one with a single and one with two interfering sources are presented.

The paper quantifies and compares the exact asymptotic performance of multiple measurement vector (MMV) and distributed sensing (DS) models. Both models assume multiple measurement instances y_k = A_kx_k + w_k; k = 1,2,...,K. The difference is that MMV involves identical measurement matrices whereas DS allows different matrices for different measurement instances. It has been recognized that DS works better than MMV empirically. However, the quantification of the performance difference is not available in the literature.

Although emotions play a major role in voice communication, the quality of their reproduction by low bit rate voice coders has never been investigated so far. This paper shows that the emotional state of a speaker can be identified automatically, with reasonable precision and accuracy, using conventional voice coding parameters (pitch, voicing, energy and LPC coefficients). It also shows that the performance of this identification degrades when these parameters are quantized, especially at lower rates (1200 bits/s).

SmartRoadSense is a collaborative project aimed at monitoring road surface conditions exploiting the sensors, i.e., accelerometers and GPS, commonly embedded in most mobile devices. Given a smartphone connected to the cabin of a car travelling on a road, the SmartRoadSense app analyzes the accelerometer signals and extracts a roughness index (based on a linear predictive coding analysis) characterizing the quality of the road.

Negative symptoms in schizophrenia are associated with significant
burden and functional impairment, especially speech
production. There are no robust
treatments for negative symptoms and one obstacle surrounding
its research is the lack of an objective measure. To this
end, we explore non-verbal speech cues as objective measures.
Specifically, we extract these cues while schizophrenic
patients are interviewed by psychologists. Our results suggest a
strong correlation between certain measures of the two rating
sets.

Negative symptoms in schizophrenia are associated with significant
burden and functional impairment, especially speech
production. In clinical practice today, there are no robust
treatments for negative symptoms and one obstacle surrounding
its research is the lack of an objective measure. To this
end, we explore non-verbal speech cues as objective measures.
Specifically, we extract these cues while schizophrenic
patients are interviewed by psychologists. Our results suggest a
strong correlation between certain measures of the two rating