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The baseband Volterra series is a general approach to model nonlinear passband systems like radio frequency power amplifiers in equivalent baseband. In the present paper, we review the derivation of the baseband Volterra series using a compact vector notation and show that it only includes odd-order terms. After that, we present a new derivation which shows that by assuming modified basis functionals...
Wiener systems represent a linear time invariant (LTI) system followed by a static nonlinearity. The identification of these systems has been a research problem for a long time as it is not a trivial task. A new methodology for identifying Wiener systems is proposed in this paper. The proposed method is a combination of well known techniques, namely the Best Linear Approximation (BLA) from the system...
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification techniques. Any intrusion detection model developed has to provide maximum accuracy with minimal false alarms. Identifying the optimal feature subset for classification is an important task for improved classification. In this paper, consistency based feature selection...
Blind steganalysis is a method used to detect whether there is a hidden message in a media without having to know the steganography algorithm behind it. Digital image is converted into features using feature extraction algorithm subtractive pixel adjacency matrix. A model is built based on the resulting features using machine learning method support vector machine. The support vector machine method...
In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for geometric model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" model hypotheses. In the conceptual space, the distributions of inliers and...
The Propositional Satisfiability Problem (SAT) is one of the most fundamental NP-complete problems, and is central to many domains of computer science. Utilizing a massively parallel architecture on a Graphics Processing Unit (GPU) together with a conventional CPU on NVIDIA's Compute Unified Device Architecture (CUDA) platform, this work proposes an efficient scheme to implement one parallel Stochastic...
In this paper, we propose a blind restoration method that is insusceptible to noise. This method is used for point spread function estimation. In addition, we propose to use a filter that can remove noise. Experimental results show that our proposed method more accurately estimates a motion blur and a deblurring image compared with the conventional method.
Handwriting has been known to be a very strong identifying characteristic of an individual and can be considered a behavioural biometric trait. This has made hand writer identification an important area of research. In this paper, a novel offline writer identification system is proposed using ensemble of multi-scale local ternary pattern histogram features. Features are extracted at multiple scales...
Forecasting electricity price allows market participants to make informed and sound decisions. Selecting the best training variables is often involved in forecasting in order to obtain optimal prediction. Support Vector Regression (SVR) provides an effective method to fit data and find minimal risk slack variables around a fit line. The best fit depends on the selected input feature set and the tuning...
In this paper, we propose a novel inverse reinforcement learning algorithm with leveraged Gaussian processes that can learn from both positive and negative demonstrations. While most existing inverse reinforcement learning (IRL) methods suffer from the lack of information near low reward regions, the proposed method alleviates this issue by incorporating (negative) demonstrations of what not to do...
One of the most investigated methods to increase the accuracy of convolutional neural networks (CNN) is by increasing its depth. However, increasing the depth also increases the number of parameters, which makes convergence of back-propagation very slow and prone to overfitting. Convolutional networks with deep supervision (CNDS) add auxiliary branch to addresses the problem of slower convergence...
the noisy and complex nature of many biological signals such as the electroencephalogram (EEG) has long constituted a major challenge in terms of analysis and prediction for single and multivariate problems. Nonlinear signal modeling, despite its widespread applicability, often shows limited success whenever the signal is contaminated with noise or is time varying in nature. We herein introduce a...
Spectral clustering is a suitable technique to deal with problems involving unlabeled clusters and having a complex structure, being kernel-based approaches the most recommended ones. This work aims at demonstrating the relationship between a widely-recommended method, so-named kernel spectral clustering (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. Such...
Edge detection technique serves as a preprocessing step for many IMAGE PROCESSING algorithms such as image enhancement, image segmentation, tracking and image/video coding. The edge detection is the heart of all the stages in image processing and object recognition. EDGE DETECTION is a basic operation in image processing, which means the process to identify and locate sharp discontinuities in an image,...
It makes the haze removal in real-time by CUDA based on the atmospheric scattering model and temporal coherence algorithm. Firstly, a hierarchical search method based on four fork tree subdivision replaced the original algorithm to obtain the atmospheric light, and put the number of pixels as the number of parallel threads, which processes the required calculation of pixels, the intermediate results...
The Gauss-Kristoffel method for numerical solving the integral Lindley equation is considered in the article. The algorithm of the mean length of the path and the buffer size for the network node represented as the queuing system (QS) type G/G/1 has been worked out. The algorithm is implemented in Matlab system. The results of the algorithm and the previous known analytical results are compared.
In this paper finite element method for 3D DC resistivity modeling accelerated using multi-GPU (Graphics Processing Unit). Solution of the large system of linear equations is the most expensive computation in finite element method performed in GPUs to reduce the computational time. Conjugate gradient solver used to solve large system of linear equations. We developed kernel for conjugate gradient...
In this paper, a study of the parallel exploitation of a Support Vector Machine (SVM) classifier with a linear kernel running on a Massively Parallel Processor Array platform is exposed. This system joins 256 cores working in parallel and grouped in 16 different clusters. The main objective of the research has been to develop an optimal implementation of the SVM classifier on a MPPA platform whilst...
This paper present a nonlinear system identification based kernel methods, such as regularization networks, support vector regression and kernel principal component analysis. In this case, black-box models are used in a particular space named reproducing kernel Hilbert space (RKHS) which only considered the input/output signals of the nonlinear system. In this particular space, the model is a linear...
We present the simulation of the application of the Model-based Predictive Control (MPC) of the drum level in a Combined Cycle Plant in order to minimize the time for reaching the highest capacity of plant, around 225 MW. In contrast to others control techniques, our simulation yields that the MPC has shown capabilities as to reach its expected power in about 40 minutes before than PID, time which...
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