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In a cognitive radio ad hoc network, secondary users must cooperate in a decentralized way in order to determine the presence or absence of the primary user. In such a setting, malicious nodes deteriorate the cooperative spectrum sensing performance by reporting incorrect sensing information to the other nodes. We classify distributed cooperative spectrum sensing in cognitive radio ad hoc networks...
The present contribution deals with the statistical tool of Independent Component Analysis (ICA). The focus is on Fas-tICA, arguably the most popular algorithm in the domain of ICA. Despite its success, it is observed that FastICA occasionally yields outcomes that do not correspond to any solutions of ICA. These outcomes are called spurious solutions. In this work, we give a thorough and rigorous...
The desire to improve short-term predictions of wind speed and direction has motivated the development of a spatial covariance-based predictor in a complex valued multichannel structure. Wind speed and direction are modelled as the magnitude and phase of complex time series and measurements from multiple geographic locations are embedded in a complex vector which is then used as input to a multichannel...
Normalized Gram matrices formed from multiple vectors of sensor data, and functions of the eigenvalues of such matrices in particular, have a long history in connection with multiple-channel detection. The determinant and various other functions of the eigenvalues of these matrices arise as detection statistics in a variety of multichannel problems, and knowledge of their distributions under the H...
Information theoretic divergences are fundamental tools used to measure the difference between the information conveyed by two random processes. In this paper, we show that the Cauchy-Schwarz divergence between two Poisson point processes is the half the squared L2-distance between their respective intensity functions. Moreover, this can be evaluated in closed form when the intensities are Gaussian...
This contribution deals with the generalized symmetric FastICA algorithm in the domain of Independent Component Analysis (ICA). The generalized symmetric version of FastICA has the potential to achieve the optimal separation performance by allowing the usage of different nonlinearity functions in its parallel implementations of one-unit FastICA. In spite of this appealing property, a rigorous study...
Subspace clustering is a useful tool for analyzing large complex data, but in many relevant applications missing data are common. Existing theoretical analysis of this problem shows that subspace clustering from incomplete data is possible, but that analysis requires the number of samples (i.e., partially observed vectors) to be super-polynomial in the dimension d. Such huge sample sizes are unnecessary...
In this paper, it is shown that Local Zernike Moments which is used in object and face recognition applications succesfully, can also used for face-pair matching problem. In this study, instead of using feature vectors produced by LZM directly, we focussed on reducing the dimensions of feature vectors and increasing the performance. In the light of experimental results, a new method called L2ML-YZM...
The most widely used in the field of visual object recognition descriptive features are shape based features. Identify objects in the image, contour and region shape descriptors based on two main topics to be examined. In order to describe objects with lesser number of descriptors, linear or cubic curves are fitted to the contours of the objects. The end points of these finite length curves are usually...
In this paper, robustness of star identification and attitude estimation methods that are operating in Lost-in-Space (LIS) mode, under harsh noise conditions of the near space, are analyzed. Despite star extraction, identification and attitude estimation methods that are proposed as solutions for subproblems, there is a lack of study that investigates the effects of errors in the initial stages on...
In this paper, a low computational load based region covariance descriptor (RCD) approach has been proposed. The proposed method is based on using fewer feature vectors on construction of RCD. Number of feature vectors is reduced by utilizing the random pixel decimation pattern. By making use of the proposed method, fast covariance descriptor based object tracking has been carried out. As can be seen...
Glasses detection is one of attractive tasks in image processing since it increases the performance of face recognition systems. In this study, we aimed to detect the glasses on face images automatically. In order to do this, we trained a classifier with Labelled Faces in the Wild Home(LFW) dataset to decide whether a person wear glasses or not on face images. Before classification process, image...
Higher order acoustic sensors rely on the spatial gradients of the acoustic pressure field for achieving high directivity. Two practical methods for realizing higher order acoustic sensors using acoustic vector sensor arrays are presented. The first method relies on short linear arrays of closely spaced 2-D vector sensors. The second method is based on the particle velocity measurements obtained along...
High-quality word representations have been very successful in recent years at improving performance across a variety of NLP tasks. These word representations are the mappings of each word in the vocabulary to a real vector in the Euclidean space. Besides high performance on specific tasks, learned word representations have been shown to perform well on establishing linear relationships among words...
In this paper, a feature combining method which can be used in gender classification has been proposed. This method is based on examinating the importance of the pixel regions on face images. In this study, after the analysing commonly used three feature extraction methods (Local binary patterns, discrete cosine transform, histogram of oriented gradients) dimension reduction is achieved via eliminating...
The capability of avoid obstacles is the one of the key issues in autonomous search-and-rescue robots research area. In this study, the avoiding obstacles capability has been provided to the virtula robots in USARSim environment. The aim is finding the minimum movement when robot faces an obstacle in path. For obstacle avoidance we used an real time path planning method which is called Vector Field...
In this study, it is aimed to follow a visual route by an Unmanned Aerial Vehicle (UAV). The recognition of the predetermined line by using image processing algorithms and the process of following the route by using the method of Tangent Vector Field Guidance (TVFG) have been performed in indoor and outdoor experiments. UAV s following the correct route has been ensured by calculating the deflection...
Nowadays, image processing software has been improved progressively. Image modifications with unnoticed tricks are available via advanced software. There are many image tricky techniques and Copy-Move Forgery is the most extensive. It is called copy-move forgery when duplicated image region is pasted to another location in same image. In this study, image is divided into sub-blocks to detect tricky...
The performance of a face recognition system is negatively affected by the accessories used on the face. Like many methods, the recognition performance of the Common Vector Approach (CVA) [1] over occluded images is not at the desired level. In this work, we proposed an extension of the CVA, namely the Modular Common Vector Approach (M-CVA), which improves the recognition performance at the occluded...
We present a framework for Tensor-based subspace Tracking via Kronecker-structured projections (TeTraKron). TeTraKron allows to extend arbitrary matrix-based subspace tracking schemes to track the tensor-based subspace estimate. The latter can be computed via a structured projection applied to the matrix-based subspace estimate which enforces the multi-dimensional structure in a computationally efficient...
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