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We introduce a highly efficient online nonlinear regression algorithm. We process the data in a truly online manner such that no storage is needed, i.e., the data is discarded after used. For nonlinear modeling we use a hierarchical piecewise linear approach based on the notion of decision trees, where the regressor space is adaptively partitioned based directly on the performance. As the first time...
Image inpainting is the process of filling missing or fixing corrupted regions in a given image. The intensity values of the pixels in missing area are expected to be associated with the pixels in the surrounding area. Interpolation-based methods that can solve the problem with a high accuracy may become inefficient when the dimension of the data increases. We solve this problem by representing images...
It is desirable to support efficient lossless coding within video coding standards, which are primarily designed for lossy coding, with as little modification as possible. A simple approach is to skip transform and quantization, and directly entropy code the prediction residual, but this is inefficient for compression. A more efficient and popular approach is to process the residual block with DPCM...
The possibility of studying multiple objects at once for forensic analysis has paved the way to the development of multimedia phylogeny algorithms. Concerning video phylogeny, a fundamental step at the base of many applications is multiple video alignment. This is, given a pool of near-duplicate video sequences partially overlapping in the temporal domain, find the relative time delay between all...
We propose an online algorithm for supervised learning with strong performance guarantees under the empirical zero-one loss. The proposed method adaptively partitions the feature space in a hierarchical manner and generates a powerful finite combination of basic models. This provides algorithm to obtain a strong classification method which enables it to create a linear piecewise classifier model that...
To deal with large multimodal datasets, coupled canonical polyadic decompositions are used as an approximation model. In this paper, a joint compression scheme is introduced to reduce the dimensions of the dataset. Joint compression allows to solve the approximation problem in a compressed domain using standard coupled decomposition algorithms. Computational complexity required to obtain the coupled...
Recent years have witnessed the use of biometric recognition systems in increasing number of applications with the number of users growing at a steady pace. However, security and privacy problems have arisen from this upsurge of interest to biometric systems. Template protection methods solve such security and privacy problems where unpredictability is a crucial goal. Here, we study the unpredictability...
Recent variants of Distributed Denial-of-Service (DDoS) attacks leverage the flexibility of application-layer protocols to disguise malicious activities as normal traffic patterns, while concurrently overwhelming the target destination with a large request rate. New countermeasures are necessary, aimed at guaranteeing an early and reliable identification of the compromised network nodes (the botnet)...
In order to improve asset knowledge and avoid third part damages during road works, the localization of gas pipes in a non-destructive way has become a wide domain of research during these last years. Several devices have been developed in order to answer this problem. Acoustic, electromagnetic or RFID technologies are used to find pipes in the underground. Ground Penetrating Radar (GPR) is also used...
In communication systems, clean speech is often reproduced by loudspeakers and disturbed by local acoustical noise. Near-end listening enhancement (NELE) is a technique to enhance the speech intelligibility in environmental noise by adaptively preprocessing the speech based on a noise estimate. Conventional NELE-algorithms adaptively filter the speech by applying spectral gains which are determined...
In this paper, we propose a novel single-group multicast beamforming technique using non-orthogonal space-time block coding (STBC). In the system, a multi-antenna base station broadcasts its information to a large group of single-antenna users. We introduce a modified max-min fair beamforming optimization problem, which maximizes the worst user's modified Euclidean distance instead of the conventional...
A sparsity-aware least-mean mixed-norm (LMMN) adaptive filter algorithm is proposed for sparse channel estimation applications. The proposed algorithm is realized by incorporating a sum-log function constraint into the cost function of a LMMN which is a mixed norm controlled by a scalar-mixing parameter. As a result, a shrinkage is given to enhance the performance of the LMMN algorithm when the majority...
We propose a decentralized algorithm for weighted sum rate (WSR) maximization via large system analysis. The rate maximization problem is done via weighted sum mean-squared error (WSMSE) minimization. Decentralized processing relies on the exchange via a backhaul link of a low amount of information. The inter-cell interference terms couple the maximization problems at the different base stations (BS)s...
Joint detection and estimation is an important yet little-studied problem that arises in many signal processing applications. In this paper, a sequential and robust solution approach is presented. To design the test fulfilling constraints on the error probabilities and the quality of the estimate, the problem is converted into an unconstrained form and subsequently solved using Linear Programming...
We develop a new efficient method for designing unimodular waveforms with good auto- and cross-correlation properties for multiple-input multiple-output (MIMO) radar. Our waveform design scheme is conducted based on minimization of the integrated sidelobe level of designed waveforms, which is formulated as a quartic non-convex optimization problem. We start from simplifying the quartic optimization...
The interference between automotive radar sensors becomes a major issue with the increasing number of radars integrated in vehicles for comfort and safety functions. The state-of-the-art radars, typically operating with frequency modulated continuous wave (FMCW) modulation, can be regarded as narrowband interferers for a high bandwidth orthogonal frequency division multiplexing (OFDM) radar with comparably...
In this paper a novel efficient online possibilistic clustering algorithm suitable for hyperspectral image clustering is proposed. The algorithm is an online version of the recently proposed adaptive possibilistic c-means (APCM) algorithm and inherits its basic advantage, that is the ability to adapt the involved parameters during its execution in order to track variations during the clustering formation...
In this paper we address supervised learning problems where, instead of having a single annotator who provides the ground truth, multiple annotators, usually with varying degrees of expertise, provide conflicting labels for the same sample. Once Gaussian Process classification has been adapted to this problem we propose and describe how Variational Bayes inference can be used to, given the observed...
We propose a technique for detection of neovascularization near the optic disk due to diabetic retinopathy. Images of the retinal fundus are analyzed using a measure of angular spread of the Fourier power spectrum of the gradient magnitude of the original images using the horizontal and vertical Prewitt operators. The entropy of the angular spread of the Fourier power spectrum and spatial variance...
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