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In this paper, a new nonlinear subspace learning technique for class-specific data representation based on an optimized class representation is described. An iterative optimization scheme is formulated where both the optimal nonlinear data projection and the optimal class representation are determined at each optimization step. This approach is tested on human face and action recognition problems,...
Millimeter Wave (MMW) imaging systems are currently being used to detect hidden threats. Unfortunately the current performance of detection algorithms is very poor due to the presence of severe noise, the low resolution of MMW images and, in general, the poor quality of the acquired images. In this paper we present a new real time MMW threat detection algorithm based on a tailored de-noising, body...
Among many security threats to sensor networks, compromised sensing is particularly challenging due to the fact that it cannot be addressed by standard authentication approaches. We consider a clustered scenario for data aggregation in which an attacker injects a disturbance in sensor readings. Casting the problem in an estimation framework, we systematically apply the Generalized Likelihood Ratio...
We develop Bayesian learning algorithms for estimation of time-varying linear prediction (TVLP) coefficients of speech. Estimation of TVLP coefficients is a naturally underdeter-mined problem. We consider sparsity and subspace based approaches for dealing with the corresponding underde-termined system. Bayesian learning algorithms are developed to achieve better estimation performance. Expectation-maximization...
This paper details the state of the art, the design, development and deployment of the EGI Federated Cloud platform, an e-infrastructure offering scalable and flexible models of utilization to the European research community. While continuing support for the traditional High Throughput Computing model, the EGI Cloud Platform extends its reach to other models of utilization such as long-lived services...
A new stirring mode for reverberation chambers is proposed and analyzed using a cavity Green's function boundary element method algorithm. In contrast to the standard mode, parts of the stirrer are rotated clockwise while the other parts are moved counterclockwise. The performance of synchronized and interleaved modes is investigated by determining the number of independent stirrer positions and testing...
Health protection reference levels were established for electric and magnetic fields. However, the corresponding reference levels for voltages and currents in cables could not be established in the general case, since they strongly depend on each particular situation. In this paper, health protection reference levels for voltages and currents are obtained for the relevant case of typical domestic...
The standard median filter has only one tuning parameter — the width of the moving window on which it is based — and this has led to the development of a number of extremely useful extensons, including the recursive median filter, weighted median filters, and recursive weighted median filters. The Hampel filter is a member of the class of decision filters that, as we note here, may be viewed as another...
Adaptive differential pulse code modulation (ADPCM) has been standardized in ITU-T Recommendations G.726 and G.722 and is widely used in IP and cordless telephony. Although adaptive quantization and adaptive prediction is employed in ADPCM using a fixed scalar quantization codebook/lookup table, residual correlation of the quantizer input samples is yet observed. Exploiting source correlation, it...
In this work, we carry out a first exploration of the possibility of increasing the performance of Deep Neural Networks (DNNs) by applying diversity techniques to them. Since DNNs are usually very strong, weakening them can be important for this purpose. This paper includes experimental evidence of the effectiveness of binarizing multi-class problems to make beneficial the application of bagging to...
This work aims at recovering signals that are sparse on graphs. Compressed sensing offers techniques for signal recovery from a few linear measurements and graph Fourier analysis provides a signal representation on graph. In this paper, we leverage these two frameworks to introduce a new Lasso recovery algorithm on graphs. More precisely, we present a non-convex, non-smooth algorithm that outperforms...
In this paper we propose a segmentation of finite support sequences based on the even/odd decomposition of a signal. The objective is to ind a more compact representation of information. To this aim, the paper starts to generalize the even/odd decomposition by concentrating the energy on either the even or the odd part by optimally placing the centre of symmetry. Local symmetry intervals are thus...
Subspace learning techniques are among the most popular methods for face recognition. In this paper, we propose a novel face recognition technique for two dimensional subspace learning which is able to exploit the symmetry nature of human faces. We extent the Two Dimensional Clustering based Discriminant Analysis (2DCDA) by incorporating an appropriate symmetry regularizer into its objective function...
Template matching methods have been shown to offer bit-rate savings of up to 15% when used for in-loop prediction in compression. Yet the required nearest-template search process results in prohibitive complexity. Hence, in this paper we use approximate nearest neighbor search methods to successfully address this drawback of template matching methods. Our approach uses a template index that is updated...
In this work we present a new room impulse response simulation for spherical microphone arrays taking into account source directivity. We calculate the emission angle of the sound ray leaving the source based on the location of the image and the receiver using Allen and Berkley's image method. We provide an implementation of a room impulse response simulator for a spherical microphone array including...
Summary keywords are words that are used in the reference extracted summary, therefore can be used to discriminate between summary sentences from non-summary ones. Finding these words is important for the extractive summarization algorithms that measure the importance of a sentence based on the importance of its constituent words. This paper is focused on extracting summary keywords in the multi-party...
Linear prediction is a popular strategy employed in the analysis and representation of signals. In this paper, we propose a new linear prediction approach by considering the standard linear prediction in the context of graph signal processing, which has gained significant attention recently. We view the signal to be defined on the nodes of a graph with an adjacency matrix constructed using the coefficients...
This paper introduces a clustering-based unsupervised approach to the problem of drum transcription. The proposed method is based on a stack of multiple clustering and segmentation stages that progressively build up meaningful audio events, in a bottom-up fashion. At each level, the inherent redundancy of the repeating events guides the clustering of objects into more complex structures. Comparison...
This paper proposes to revisit the 2-D Variational Mode Decomposition (2-D-VMD) in order to separate the incident and reflected waves in experimental images of internal waves velocity field. 2-D-VMD aims at splitting an image into a sequence of oscillating components which are centered around specific spatial frequencies. In this work we develop a proximal algorithm with local convergence guarantees,...
We investigate the numerical solution of a special type of descriptor continuous-time Riccati equation which is key to several problems in robust control formulated for a general system. We give necessary and sufficient existence conditions together with computable formulas for both stabilizing and antistabilizing solutions in terms of an associated matrix pencil. Analytic formulas for computing normalized...
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