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Intrusion is an illegitimate event that can either be active or passive in a network. In this work, we propose an Intrusion Detection System (IDS) on the basis of Kernel Extreme Learning Machine (KELM) clubbed with Levenberg-Marquardt optimization technique. We incorporate KELM in this work, because of its efficiency in pattern recognition. Levenberg-Marquardt optimization technique is employed because...
Accurate surface estimation is critical for autonomous robot navigation on the rough terrain. In this paper, we present a new method for estimating the surface of an unknown arbitrarily shaped terrain from the range data. This problem is generally formulated as the estimation of a function whose zero-set corresponds to the surface. The range data are obtained from indigenous designed Laser range scanner...
In this paper, we propose an automated Euler's time-step adjustment scheme for diffeomorphic image registration using stationary velocity fields (SVFs). The proposed variational problem aims at bounding the inverse consistency error by adaptively adjusting the number of Euler's step required to realize the time integration. This particular formulation allows us to gain computationally since only relevant...
In this paper, a blind equalizer based on probability density function (pdf) fitting is proposed. It does not require any prior information about the transmission channel or the emitted constellation. We also investigate Automatic Modulation Classification (AMC) for Quadrature Amplitude Modulation (QAM) based on the pdf of the equalized signal. We propose three new approaches for AMC. The first employs...
A missing intensity restoration method via adaptive selection of perceptually optimized subspaces is presented in this paper. In order to realize adaptive and perceptually optimized restoration, the proposed method generates several subspaces of known textures optimized in terms of the structural similarity (SSIM) index. Furthermore, the SSIM-based missing intensity restoration is performed by a projection...
The particle Gibbs algorithm can be used for Bayesian parameter estimation in Markovian state space models. Sometimes the resulting Markov chains mix slowly when the component particle filter suffers from degeneracy. This effect can be somewhat alleviated using backward simulation. In this paper we show how a simple modification to this scheme, which we refer to as refreshed backward simulation, can...
Hermite functions are an effective tool for improving the resolution of the single-window spectrogram. In this paper, we analyze the Hermite functions in the ambiguity domain and show that the higher order terms can introduce undesirable cross-terms in the multiwindow spectrogram. The optimal number of Hermite functions depends on the location and spread of signal auto-terms in the ambiguity domain...
In this paper, the maximum entropy property of the discrete-time first-order stable spline kernel is studied. The advantages of studying this property in discrete-time domain instead of continuous-time domain are outlined. One of such advantages is that the differential entropy rate is well-defined for discrete-time stochastic processes. By formulating the maximum entropy problem for discrete-time...
We investigate the use of compactly supported kernels (CSKs) for the kernel normalized least mean square (KNLMS) algorithm proposed initially by Richard et al. in 2009. The use of CSKs yields sparse kernelized input vectors, offering an opportunity for complexity reduction. We propose a simple two-step method to compute the kernelized input vectors efficiently. In the first step, it computes an over-estimation...
The fuzzy c-means (FCM) algorithm is a very popular algorithm in the field of image segmentation because of its simplicity and less sensitivity to noise and it is widely used in the field of engineering disciplines. The FCM membership function can handle the overlapped clusters efficiently with predefined number of clusters, but this algorithm are unable to cluster non-linearly separable data as well...
The massive volume of video and image data, compels them to be stored in a distributed file system. To process the data stored in the distributed file system, Google proposed a programming model named MapReduce. Existing methods of processing images held in such a distributed file system, requires whole image or a substantial portion of the image to be streamed every time a filter is applied. In this...
This paper deals with hybrid dynamical systems with state constrains and target. We investigate the subset of initial positions from which there exists at least one run forever remaining in the constraint set — the hybrid viability kernel — or remaining in the constraint set until it reaches a given closed target in finite time — the hybrid capture basin. We present an algorithm for approximating...
The central object of interest of this paper are systems of linear constant coefficient ordinary differential equations of arbitrary order of the form s(d/dt)w = M(d/dt)f with G and M given, but otherwise arbitrary, polynomial matrices. In these equations w and f are vector-valued functions of which f is assumed to be given, while w is the solution to (1) that we are looking for. Alongside (1) we...
Collections of time-series data appear in a wide variety of contexts. To gain insight into the underlying phenomenon (that the data represents), one must analyze the time-series data. Analysis can quickly become challenging for very large data (~terabytes or more) sets, and it may be infeasible to scan the entire data-set on each query due to time limits or resource constraints. To avoid this problem,...
With heterogeneous computing on the rise, executing programs efficiently on different devices from a single source code has become increasingly important. OpenCL, having a bulk-synchronous programming model, has been proposed as a framework for writing such performance-portable programs. Execution order of work-items in a program is unconstrained except at barrier synchronization events, giving some...
The rapid development of modern technology has resulted in large amount of electronically available information in articles and patents. The search engines are programs that searches the documents in a database correspond to queries specified by the user. Searching by using keywords for millions of documents will not be precise and thus retrieve incomplete information. This makes it difficult for...
Credit risk assessment is a crucial process for financial institutions when granting commercial loans. However, the manual analysis of the overall condition of firms through customer due diligence reports is costly for both time and labor. This paper proposes a novel credit risk evaluation approach using GMKL model to automate the decision-making process. Sentiment indexes are generated by mining...
In this paper, we highlight a design of Gaussian kernels for online model selection by the multikernel adaptive filtering approach. In the typical multikernel adaptive filtering, the maximum value that each kernel function can take is one. This means that, if one employs multiple Gaussian kernels with multiple variances, the one with the largest variance would become dominant in the kernelized input...
Full use of the parallel computation capabilities of present and expected CPUs and CPUs require use of vector extensions. Yet many actors in data flow systems for digital signal processing have internal state (or, equivalently, an edge that loops from the actor back to itself) that impose serial dependencies between actor invocations that make vectorizing across actor invocations impossible. Ideally,...
In this paper, a Genetic algorithm (GA) based supporting vector machine classifier (GA-SVM) is proposed for lymph diseases diagnosis. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to six features using GA. In the second stage, a support vector machine with different kernel functions including linear, Quadratic and Gaussian was utilized as a classifier. The...
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