413 research outputs found

    Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

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    The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

    Electroencephalograph (EEG) signal processing techniques for motor imagery Brain Computer interface systems

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    Brain-Computer Interface (BCI) system provides a channel for the brain to control external devices using electrical activities of the brain without using the peripheral nervous system. These BCI systems are being used in various medical applications, for example controlling a wheelchair and neuroprosthesis devices for the disabled, thereby assisting them in activities of daily living. People suffering from Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis and completely locked in are unable to perform any body movements because of the damage of the peripheral nervous system, but their cognitive function is still intact. BCIs operate external devices by acquiring brain signals and converting them to control commands to operate external devices. Motor-imagery (MI) based BCI systems, in particular, are based on the sensory-motor rhythms which are generated by the imagination of body limbs. These signals can be decoded as control commands in BCI application. Electroencephalogram (EEG) is commonly used for BCI applications because it is non-invasive. The main challenges of decoding the EEG signal are because it is non-stationary and has a low spatial resolution. The common spatial pattern algorithm is considered to be the most effective technique for discrimination of spatial filter but is easily affected by the presence of outliers. Therefore, a robust algorithm is required for extraction of discriminative features from the motor imagery EEG signals. This thesis mainly aims in developing robust spatial filtering criteria which are effective for classification of MI movements. We have proposed two approaches for the robust classification of MI movements. The first approach is for the classification of multiclass MI movements based on the thinICA (Independent Component Analysis) and mCSP (multiclass Common Spatial Pattern Filter) method. The observed results indicate that these approaches can be a step towards the development of robust feature extraction for MI-based BCI system. The main contribution of the thesis is the second criterion, which is based on Alpha- Beta logarithmic-determinant divergence for the classification of two class MI movements. A detailed study has been done by obtaining a link between the AB log det divergence and CSP criterion. We propose a scaling parameter to enable a similar way for selecting the respective filters like the CSP algorithm. Additionally, the optimization of the gradient of AB log-det divergence for this application was also performed. The Sub-ABLD (Subspace Alpha-Beta Log-Det divergence) algorithm is proposed for the discrimination of two class MI movements. The robustness of this algorithm is tested with both the simulated and real data from BCI competition dataset. Finally, the resulting performances of the proposed algorithms have been favorably compared with other existing algorithms

    Three more Decades in Array Signal Processing Research: An Optimization and Structure Exploitation Perspective

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    The signal processing community currently witnesses the emergence of sensor array processing and Direction-of-Arrival (DoA) estimation in various modern applications, such as automotive radar, mobile user and millimeter wave indoor localization, drone surveillance, as well as in new paradigms, such as joint sensing and communication in future wireless systems. This trend is further enhanced by technology leaps and availability of powerful and affordable multi-antenna hardware platforms. The history of advances in super resolution DoA estimation techniques is long, starting from the early parametric multi-source methods such as the computationally expensive maximum likelihood (ML) techniques to the early subspace-based techniques such as Pisarenko and MUSIC. Inspired by the seminal review paper Two Decades of Array Signal Processing Research: The Parametric Approach by Krim and Viberg published in the IEEE Signal Processing Magazine, we are looking back at another three decades in Array Signal Processing Research under the classical narrowband array processing model based on second order statistics. We revisit major trends in the field and retell the story of array signal processing from a modern optimization and structure exploitation perspective. In our overview, through prominent examples, we illustrate how different DoA estimation methods can be cast as optimization problems with side constraints originating from prior knowledge regarding the structure of the measurement system. Due to space limitations, our review of the DoA estimation research in the past three decades is by no means complete. For didactic reasons, we mainly focus on developments in the field that easily relate the traditional multi-source estimation criteria and choose simple illustrative examples.Comment: 16 pages, 8 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

    Scaling Multidimensional Inference for Big Structured Data

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    In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications [151]. In a world of increasing sensor modalities, cheaper storage, and more data oriented questions, we are quickly passing the limits of tractable computations using traditional statistical analysis methods. Methods which often show great results on simple data have difficulties processing complicated multidimensional data. Accuracy alone can no longer justify unwarranted memory use and computational complexity. Improving the scaling properties of these methods for multidimensional data is the only way to make these methods relevant. In this work we explore methods for improving the scaling properties of parametric and nonparametric models. Namely, we focus on the structure of the data to lower the complexity of a specific family of problems. The two types of structures considered in this work are distributive optimization with separable constraints (Chapters 2-3), and scaling Gaussian processes for multidimensional lattice input (Chapters 4-5). By improving the scaling of these methods, we can expand their use to a wide range of applications which were previously intractable open the door to new research questions
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