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Multicell coordinated beamforming, where multiple base stations (BSs) collaborate with each other in the beam-vectors design to mitigate the intercell interference (ICI), has been a subject of great attention recently. With perfect channel information available at the base stations, we first present a fast converging algorithm that solves the weighted sum rate maximization (WSRM) problem. Then, the...
Missing data theory has recently been used as a solution to noise robustness issue in Automatic Speech Recognition (ASR). Missing components of spectrogram can either be reconstructed, as carried out in Spectral Imputation, or simply ignored, as done in classifier modification. Most of the research has been focused on imputation because of the problems associated with classifier modification approaches...
Effectiveness in loop closing detection is crucial to increase accuracy in SLAM (Simultaneous Localization and Mapping) for mobile robots. The most representative approaches to visual loop closing detection are based on feature matching or BOW (Bag of Words), being slow and needing a lot of memory resources or a previously defined vocabulary, which complicates and delays the whole process. This paper...
Remote health monitoring BASNs promise substantive improvements in the quality of healthcare by providing access to diagnostically rich patient data in real-time. However, adoption is hindered by the threat of compromise of the diagnostic quality of the data by faults. Simultaneously, unresolved issues exist with the secure sharing of the sensitive medical data measured by automated BASNs, stemming...
In geometric constraint solving, the constraints are represented with an equation system F(U, X) = 0, where X denotes the unknowns and U denotes a set of parameters. The target solution for X is noted XT. A witness is a couple (UW, XW) such that F(UW, XW) = 0. The witness is not the target solution, but they share the same combinatorial features, even when the witness and the target lie on two distinct...
Video Copy Detection focuses on preventing illicit use of digital videos. Video copies are generated by applying different sorts of transformations on the original video content. To detect such transformed copies, the extraction of a transformation invariant feature descriptor is a requisite. Among the various existing transformations, flipping is the recently employed copy attacks. Hence, we propose...
This paper presents the application of Na??ve Bayesian classifier to automatic classification of webpage. The key point in this article is that massive empirical data derives from the real traffic data collected from the backbone network of certain province in China, and we apply cumulative probability to determine the optimal size of feature vector adaptively. It's proved that the adaptive method...
Network based recommendation systems leverage the topology of the underlying graph and the current user context to rank objects in the database. Random-walk based techniques, such as PageRank, encode the structure of the graph in the form of a transition matrix of a stochastic process from which the significances of the nodes in the graph are inferred. Personalized PageRank (PPR) techniques complement...
It is known that EDA tools produce results of different quality dependent on seemingly neutral details in the input. We bring further results in this direction, which show that the differences can impair any quantitative comparisons of the tools. To gain qualitative insight, we present a stochastic model of result quality based on Gaussian Mixtures. We show on three case studies how these models help...
Scene acquisition using RGB and Near Infra-Red (NIR) filters generates useful visual information about scene contents. But it induces significant intensity and textural changes between RGB and NIR images of the same scene. It becomes a challenging problem to perform interest point based image matching under such intensity and textural changes. To cope with this problem, a novel method for the description...
Centrality measures have many practical uses in network analysis, where closeness centrality is one of the original measures introduced by Freeman. Closeness centrality is a measure of how close a node is to all other nodes. Typically it is used as a measure of how fast information will spread from one node in a network to all other nodes, or, in a network planning situation which nodes are favorable...
We propose a novel method for joint probabilistic constrained robust beamforming and antenna selection used in cognitive radio (CR) networks. Assuming complex Gaussian distributed channel state information (CSI) errors, the Bernstein-type inequalities are used to transform the no closed-form probabilistic constrained into the deterministic forms. Moreover, l1-norm is introduced as the closest convex...
We propose a novel multiple model fitting method based on outlier insensitive evolutionary dynamics, fulfilling several important requirements. Our method automatically identifies a unspecified number of models and is robust to noise and outliers in the data. Furthermore, we are able to handle overlapping models, by allowing that data points are assigned to more than one model. This is implicitly...
We propose a fast-adapted subspace tracking algorithm for background subtraction in video surveillance. While background scenes are modelled as a linear combination of basis images, foreground scenes are regarded as a sparse image. Every time a video frame streams in, two alternating procedures are repeatedly done: basis images are updated by a recursive least square algorithm and foreground images...
In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. The second phase is responsible to extract the meta-features and train the meta-classifier. Five distinct sets of meta-features are proposed, each one corresponding to a different criterion...
Need for automatic video copy detection is increased with the recent technical developments in the internet technologies and video recording. Even though image-based techniques with bag-of-word kind of representations are accepted as the best solution because of robustness and speed, they discard the convenient geometric relation which exists among interest points. In this work, we propose a novel...
This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l2,1-norms regularization. To solve the proposed model, an...
Robust feature plays an important role in many vision based applications. This paper proposes a fast extreme illumination robust feature in affine space. It inherits the techniques of extreme point location and main orientation computation from SIFT (Scale Invariant Feature Transform) algorithm, and adopts the rotation and scale invariant circular binary pattern based histograms in the affine space...
The need for efficient tools to index and retrieve 3D content becomes even more acute. This paper presents a fully automatic 3D-object retrieval method. It consists of two main steps namely shape signature extraction to describe the shape of objects, and similarity computing to compute similarity between objects. In the first step (signature extraction), we use a shape descriptor called geodesic cords...
Clustering is an important unsupervised learning approach and widely used in pattern recognition, data mining and image processing, etc. Different from existing clustering algorithms based on partitioning within data, dominant sets clustering extracts clusters in a sequential fashion. Based on graph-theoretic concept of a cluster, dominant sets clustering can be accomplished with a game dynamics efficiently...
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