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The IEEE Global Conference on Signal and Information Processing (GlobalSIP) is a flagship conference of the IEEE Signal Processing Society. GlobalSIP'15 will be held in Orlando, Florida, USA, December 14-16, 2015. The conference will focus on signal and information processing with an emphasis on up-and-coming signal processing themes. The conference will feature world-class speakers, tutorials, exhibits, and sessions consisting of poster or oral presentations. Outstanding papers will be selected for Best Paper Awards or Best Student Paper Awards; a paper is eligible for a best student paper award if the first author of the paper is a student. IEEE Signal Processing Society and National Science Foundation will provide travel grants to eligible students.

Heating, Ventilating and Air Conditioning units (HVAC) are a major electrical energy consumer in buildings. Monitoring of the operation and energy consumption of HVAC would increase the awareness of building owners and maintenance service providers of the condition and quality of performance of these units, enabling conditioned-based maintenance which would help achieving high efficiency in energy consumption.

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In [doi{10.1109/ICMEW.2014.6890711}], a~graph-based filtering of noisy images is performed by directly computing a projection of the image to be filtered onto a lower dimensional Krylov subspace of the graph Laplacian, constructed using non-negative graph weights determined by distances between image data corresponding to image pixels. We extend the construction of the graph Laplacian to the case, where some graph weights can be negative.

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In this paper, we present an automated system for robust biometric recognition based upon sparse representation and dictionary learning. In sparse representation, extracted features from the training data are used to develop a dictionary. Training data of real world applications are likely to be exposed to geometric transformations, which is a big challenge for designing of discriminative dictionaries. Classification is achieved by representing the extracted features of the test data as a linear combination of entries in the dictionary.

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The document presents the multi-linear algebra framework to the RFI excision research by proposing a multi-linear subspace estimation and projection (MLSEP) algorithm for efficient RFI excision in single-input multiple-output (SIMO) systems.

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Abstract — This paper proposes a low-complexity scheme for the integration of power-line communication (PLC) and visible-light communication (VLC) systems. Rather than decoding the PLC signal prior to transmission from an LED luminary, a simple all-analog PLC/VLC amplify-and-forward (AF) module is described. The incoming PLC signals, which occupy a band of 2-28 MHz, are frequency down-shifted prior to transmission to increase the usable bandwidth of the LEDs. The required DC bias is then added to make the signals compatible with intensity modulation/direct detection (IM/DD).

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Abstract — This paper proposes a low-complexity scheme for the integration of power-line communication (PLC) and visible-light communication (VLC) systems. Rather than decoding the PLC signal prior to transmission from an LED luminary, a simple all-analog PLC/VLC amplify-and-forward (AF) module is described. The incoming PLC signals, which occupy a band of 2-28 MHz, are frequency down-shifted prior to transmission to increase the usable bandwidth of the LEDs. The required DC bias is then added to make the signals compatible with intensity modulation/direct detection (IM/DD).

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The inter prediction decoding is one of the most time consuming modules in modern video decoders, which may significantly limit their real-time capabilities. To circumvent this issue, an efficient acceleration of the HEVC inter prediction decoding module is proposed, by offloading the involved workload to GPU devices. The proposed approach aims at efficiently exploiting the GPU resources by carefully managing the processing within the computational kernels, as well as by optimizing the usage of the complex GPU memory hierarchy.

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In this paper we consider the task of locating salient group-structured features in potentially high-dimensional images; the salient feature detection here is modeled as a Robust Principal Component Analysis problem, in which the aim is to locate groups of outlier columns embedded in an otherwise low rank matrix.

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