9,789 research outputs found

    Graph Spectral Image Processing

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    Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

    Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism

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    Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of the intervention in the therapy of autism. Therefore, it is essential to develop automatic SMM detection systems in a real world setting, taking care of strong inter-subject and intra-subject variability. Wireless accelerometer sensing technology can provide a valid infrastructure for real-time SMM detection, however such variability remains a problem also for machine learning methods, in particular whenever handcrafted features extracted from accelerometer signal are considered. Here, we propose to employ the deep learning paradigm in order to learn discriminating features from multi-sensor accelerometer signals. Our results provide preliminary evidence that feature learning and transfer learning embedded in the deep architecture achieve higher accurate SMM detectors in longitudinal scenarios.Comment: Presented at 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI), 2015, (http://arxiv.org/html/1605.04435), Report-no: MLINI/2015/1

    A Wideband Direct Data Domain Genetic Algorithm Beamforming

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    In this paper, a wideband direct data-domain genetic algorithm beamforming is presented. Received wideband signals are decomposed to a set of narrow sub-bands using fast Fourier transform. Each sub-band is transformed to a reference frequency using the steering vector transformation. So, narrowband approaches could be used for any of these sub-bands. Hence, the direct data-domain genetic algorithm beamforming can be used to form a single ‘hybrid’ beam pattern with sufficiently deep nulls in order to separate and reconstruct frequency components of the signal of interest efficiently. The proposed approach avoids most of drawbacks of already-existing statistical and gradient-based approaches since formation of a covariance matrix is not needed, and a genetic algorithm is used to solve the beamforming problem

    Exploring supportive and defensive communication behavior and psychological safety between supervisors and their subordinates

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    Master's Project (M.A.) University of Alaska Fairbanks, 2015This project explores the relationship between supportive and defensive communication behavior and psychological safety in the organizational setting. A paper and pencil survey measuring team psychological safety and supportive and defensive communication behaviors was administered to participants in the northwestern region of the United States. Supervisor use of supportive communication behavior was hypothesized to be positively correlated with employee psychological safety. Support was found for the hypothesis. This research sought to expand the scope of our understanding of psychological safety in an organizational setting while highlighting the benefits of using supportive communication behavior

    Cooperative Jamming for Secure Communications in MIMO Relay Networks

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    Secure communications can be impeded by eavesdroppers in conventional relay systems. This paper proposes cooperative jamming strategies for two-hop relay networks where the eavesdropper can wiretap the relay channels in both hops. In these approaches, the normally inactive nodes in the relay network can be used as cooperative jamming sources to confuse the eavesdropper. Linear precoding schemes are investigated for two scenarios where single or multiple data streams are transmitted via a decode-and-forward (DF) relay, under the assumption that global channel state information (CSI) is available. For the case of single data stream transmission, we derive closed-form jamming beamformers and the corresponding optimal power allocation. Generalized singular value decomposition (GSVD)-based secure relaying schemes are proposed for the transmission of multiple data streams. The optimal power allocation is found for the GSVD relaying scheme via geometric programming. Based on this result, a GSVD-based cooperative jamming scheme is proposed that shows significant improvement in terms of secrecy rate compared to the approach without jamming. Furthermore, the case involving an eavesdropper with unknown CSI is also investigated in this paper. Simulation results show that the secrecy rate is dramatically increased when inactive nodes in the relay network participate in cooperative jamming.Comment: 30 pages, 7 figures, to appear in IEEE Transactions on Signal Processin

    Coding Prony's method in MATLAB and applying it to biomedical signal filtering

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    Background:The response of many biomedical systems can be modelled using a linear combination of damped exponential functions. The approximation parameters, based on equally spaced samples, can be obtained using Prony's method and its variants (e.g. the matrix pencil method). This paper provides a tutorial on the main polynomial Prony and matrix pencil methods and their implementation in MATLAB and analyses how they perform with synthetic and multifocal visual-evoked potential (mfVEP) signals. This paper briefly describes the theoretical basis of four polynomial Prony approximation methods: classic, least squares (LS), total least squares (TLS) and matrix pencil method (MPM). In each of these cases, implementation uses general MATLAB functions. The features of the various options are tested by approximating a set of synthetic mathematical functions and evaluating filtering performance in the Prony domain when applied to mfVEP signals to improve diagnosis of patients with multiple sclerosis (MS). Results:The code implemented does not achieve 100%-correct signal approximation and, of the methods tested, LS and MPM perform best. When filtering mfVEP records in the Prony domain, the value of the area under the receiver-operating-characteristic (ROC) curve is 0.7055 compared with 0.6538 obtained with the usual filtering method used for this type of signal (discrete Fourier transform low-pass filter with a cut-off frequency of 35 Hz). Conclusions:This paper reviews Prony's method in relation to signal filtering and approximation, provides the MATLAB code needed to implement the classic, LS, TLS and MPM methods, and tests their performance in biomedical signal filtering and function approximation. It emphasizes the importance of improving the computational methods used to implement the various methods described above.Universidad de AlcaláSecretaría de Estado de Investigación, Desarrollo e Innovació

    Effect of disciplinary content of a simulation on open-ended problem-solving strategies

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    There is a long-standing debate about so-called generic vs. domain- or context-specific problem-solving skills and strategies. To investigate this issue, we developed a pair of structurally and functionally similar computer simulations and used them in an experimental study with undergraduate students from all fields of study. One, described to users as a teaching tool based on current entomology research, featured three species of ants whose tasks could change as a result of encounters with each other. The other, which we call a non-disciplinary simulation, featured three types of abstract moving shapes whose color could change after a collision. Users received no information about the latter, not even that it was a simulation. Both simulations, which students used in succession, allowed users to change the number and types of objects, and to move them freely on the screen. We told students that the problem was to describe and explain what occurred on the screen, and asked them to explain, while they were working with the simulation, what they observed, what they did and why. We conducted a first, quantitative analysis of various “surfac

    A matrix-pencil approach to blind separation of colored nonstationary signals

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    For many signal sources such as speech with distinct, nonwhite power spectral densities, second-order statistics of the received signal mixture can be exploited for signal separation. Without knowledge on noise correlation matrix, we propose a simple and yet effective signal extraction method for signal source separation under unknown temporally white noise. This new and unbiased signal extractor is derived from the matrix pencil formed between output autocorrelation matrices at different delays. Based on the matrix pencil, an ESPRIT-type algorithm is derived to get an optimal solution in least square sense. Our method is well suited for systems with colored sensor noises and for nonstationary signals. © 2000 IEEE.published_or_final_versio
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