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This paper describes a Support Vector Machine (SVM)-based obstacle recognition system that can recognize both vehicles and pedestrians using bimodal vision. Different techniques were investigated in order to recognize the detected obstacles by the extraction of a compact and pertinent numeric signature from visible and infrared spectrum. A bi-objective optimization (using error classification rate...
This paper forms a part of a series of recent studies we have undertaken, where the problem of nonlinear signal modelling is examined. We assume that an observed "output" signal is derived from a Volterra filter that is driven by a Gaussian input. Both the filter parameters and the input signal are unknown and therefore the problem can be classified as blind or unsupervised in nature. In...
This paper considers the impulsive-smooth behavior of a switched system interconnection described by the first-order kernel representation. The switched interconnection consists of past and future trajectories which are switched at a certain instant by an external switching mechanism. We derive necessary and sufficient conditions for the concatenability of the past and future behaviors both in the...
In this paper, we consider the characteristics of the kernel adaptive filters for the mixture of linear and non-linear environments. We first consider employing a linear kernel as one of the kernels in multi-kernel adaptive filters. It is pointed out that the convergence characteristics of the filter corresponding to the linear kernel is affected by the selection of the other kernels. Then, we propose...
Many statistical learning tasks deal with data which are presented in high-dimensional spaces, and the 'curse of dimensionality' phenomenon is often an obstacle to the use of many methods for solving these tasks. To avoid this phenomenon, various dimensionality reduction algorithms are used as the first key step in solving these tasks. The algorithms transform original high-dimensional data into lower...
A new method for image sharpening via image warping is proposed. The idea of the method is to warp the uniform grid of the image in a way that the pixels around the blurred edge move closer to the edge according to its blurriness. The advantage of the proposed method is that instead of an accurate estimation of the blur kernel only approximate value of the edge blur level is required. Also, since...
Emotional Polarity Classification is an important task in Sentiment Analysis area. It is applied in many real problems such as reviews of consumer products and services, financial markets, and forensic analysis. The scientists from the areas of text mining and nature language processing have studied how to solve emotional polarity classification problem. They used a variety of methods, from simple...
Oncogene is a kind of inherent genes exists in humans' cells. It has been recognized as a genetic disease, if the cells activated, it can make a person carcinogenesis. So, the research of digging out the useful information from gene chip is very hot in modern society. The sample size is small, high dimension, nonlinear which causes the 'dimension disaster', so dimensionality reduction becomes the...
Point of interest (POI) categorization is the task of finding of categories of POIs within a document. Because the documents that possess POIs have clue words for identifying POI categories, the task can be solved as document classification. However, this approach misses two crucial factors for identifying the category of a POI. First, the approach pays no attention to onomastic information, even...
The overall method used for determining disparity in a stereo setup is a widely recognized framework consisting of four steps of cost space computation, cost aggregation, disparity selection, and post-processing. In this paper a cost aggregation approach for a typical local disparity estimation method is introduced. The method introduced is built on top of an existing method called Adaptive Support-Weight...
This paper makes two contributions to the problem of correcting non-Euclidean dissimilarities. Data of this sort arrise when there are negative Eigen values of the dissimilarity matrix, and can therefore not be embedded into a real-valued Euclidean space. Our first contribution is to show how the non-Euclidean artifacts can be rectified. This is achieved by applying Ricci flow to the embedding of...
System Identification has been developed, by and large, following the classical parametric approach. In this tutorial we shall discuss how Bayesian statistics and regularization theory can be employed to tackle the system identification problem from a nonparametric (or semi-parametric) point of view. The present paper provides an introduction to the use of Bayesian techniques for smoothness and sparseness,...
In the past decade, adaptive dynamic programming (ADP) has been widely used to realize online learning tracking control of dynamical systems, where neural networks with manually designed features are commonly used. In order to improve the generalization capability and learning efficiency of ADP, this paper presents a novel framework of ADP with sparse kernel machines by integrating kernel methods...
Solving a multiple-valued problem means to assign values to a given set of multiple-valued variables such that certain conditions are satisfied. The solution of a multiplevalued problem is a subset of vk v-valued tuples of the length k, where k is the number of variables and v is the number of their possible values. This paper compares several approaches which solve such problems. These approaches...
The present paper analyzes some previously unexplored aspects of motion estimation that are fundamental both for discrete block matching as well as for differential ‘optical flow’ approaches à la Lucas-Kanade. It aims at providing a complete estimation-theoretic approach that makes the assumptions about noisy observations of samples from a continuous signal of a certain class explicit. It turns out...
Modal sound synthesis is a useful method to interactively generate sounds for Virtual Environments. Forces acting on objects excite modes, which then have to be accumulated to generate the output sound. Due to the high audio sampling rate, algorithms using the CPU typically can handle only a few actively sounding objects. Additionally, force excitation should be applied at a high sampling rate. We...
New upper and lower bounds for the error probability over an erasure channel are provided, making use of Wei's generalized weights, hierarchy and spectra. In many situations the upper and lower bounds coincide and this allows us to improve the existing bounds. Results concerning MDS and AMDS codes are deduced from those bounds.
Survival Regression models play a vital role in analyzing time-to-event data in many practical applications ranging from engineering to economics to healthcare. These models are ideal for prediction in complex data problems where the response is a time-to-event variable. An event is defined as the occurrence of a specific event of interest such as a chronic health condition. Cox regression is one...
Clustering graphs annotated with feature vectors has recently gained much attention. The goal is to detect groups of vertices that are densely connected in the graph as well as similar with respect to their feature values. While early approaches treated all dimensions of the feature space as equally important, more advanced techniques consider the varying relevance of dimensions for different groups...
To improve efficiency of power amplifier (PA), linearity characteristics is often compromised when targeting lower power consumption (class B). Moreover, sophisticated PA efficiency improvement schemes such as envelope tracking tend to further boost the nonlinear characteristics of the PA. Digital pre-distortion (DPD) is a technique to improve the linearity of a power amplifier (PA) at expense of...
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