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This paper presents a biologically plausible model for the structural information processing in early vision. Our investigation on the frequency spectrum of natural images filtered by the retina shows that the DC component containing much redundancy information and the high frequency components containing much noisy information are reduced, while the middle and low frequency components containing...
This paper presents a novel Dynamic Self-Organised Multilayer Neural Network that can be used for prediction of noisy time series data. The proposed technique is based on the Immune Algorithm for financial time series prediction; combining the properties of both recurrent and self-organised neural networks. The network is derived to ensure that a unique equilibrium state can be achieved to overcome...
This paper presents an adaptive control strategy for wind energy conversion systems. The control scheme uses a B-spline artificial neural network for tuning controllers when the system is subjected to disturbances. Voltage-source converter is controlled in a synchronous orthogonal d-q frame by an adaptive PI controller. The B-spline neural network must be able to enhance the system performance through...
The aim of this work is to present a novel technique for the identification of lumped circuit models of general distributed apparatus and devices. It is based on the use of a double modified complex value neural network. The method is not oriented to a unique class of electromagnetic systems, but it gives a procedure for the complete validation of the approximated lumped model and the extraction of...
With the improvement of battery technology over the past two decades and automotive technology advances, more and more vehicle manufacturers have joined in the race to produce new generation of affordable, high-performance Electric Drive Vehicles (EDVs). Permanent Magnet Synchronous Motors (PMSMs) are at the top of AC motors in high performance drive systems for EDVs. Traditionally, a PMSM is controlled...
Bladder cancer is the fourth most common type of cancer in men and the ninth in women in United States. A recent approach for early bladder cancer detection is to mix human urine samples with some very small beads that are coated with special biochemical materials which can bind to tumor cells, but not to normal cells. By examining and analyzing bead images of urine samples under a microscope, patients...
Semantic role labeling (SRL) is a well known task in Natural Language Processing, consisting of identifying and labeling verbal arguments. It has been widely studied in English, but scarcely explored in other languages. In this paper, we employ a two-step convolutional neural architecture to label semantic arguments in Brazilian Portuguese texts, and avoid the use of external NLP tools. We achieve...
In this paper we outline a novel method for classifying ballistic as well as quasi-ballistic missiles using realtime neural network. Fast classification time plays a stellar role for early and prompt action in air-defense scenario. In-order to get the trajectory information of the missile we initially use simulated radar measurements and for final validation real-world radar track is used. Trajectories...
In this paper, a new fast neural model for testing massive volume of medical data is presented. The idea is to accelerate the process of detecting and classifying pediatric respiratory diseases by using neural networks. This is done by applying cross correlation between the input patterns and the input weights of neural networks in the frequency domain rather than time domain. Furthermore, such model...
Accurate prediction of traffic information such as flow, density, speed, and travel time is an important component for traffic control systems and optimizing vehicle operation. Prediction of an individual speed profile on an urban network is a challenging problem because traffic flow on urban routes is frequently interrupted and delayed by traffic lights, stop signs, and intersections. In this paper,...
ImageNet dataset [1] with more than 14M images and 21K classes makes the problem of visual classification more difficult to deal with. One of the most difficult tasks is to train a fast and accurate classifier on computers with limited memory resource. In this paper, we address this challenge by extending the state-of-the-art large scale classifier Power Mean SVM (PmSVM) proposed by Jianxin Wu [2]...
This paper addresses modeling and predicting vehicle fuel economy based on simple vehicle characteristics. The models are identified using a historical vehicle fuel economy data set. First, the use of least squares regression analysis is pursued, and a mathematical model is created that is capable of predicting highway fuel economy based on six vehicle characteristics: engine displacement volume,...
Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal verification. The objective of this paper is to present an empirical contribution towards the understanding of a threshold-based signature verification technique involving off-line Bangla (Bengali) signatures. Experiments on signature verification...
The present paper presents an intelligent system which determines the need for readmission in postoperative patients of ambulatory surgery, after being discharged. The decision made by the system depends on the answers given to a follow-up questionnaire, which includes questions related to the recovery state of the patient, and information about his/her medical records stored in a database. In order...
F-measure is a popular performance metric used in classification when the dataset is unbalanced. Optimizing this measure directly is often challenging since no closed form solution exists. Current algorithms use approximations to the F-measure and design classifiers using maximum margin or logistic regression framework. These algorithms are not scalable and the classifiers designed are not robust...
This article presents a new method for shape description suitable to be used as a solution to the retrieval problem in large image collections. The proposed approach, called Multiscale Symbolic Data Descriptor (MSDD) combines multiscale methods with Symbolic Data Analysis. The contour convexities and concavities at different scale levels are represented using a two-dimensional matrix from which we...
Traffic sign recognition has been a recurring application domain for visual objects detection. The public datasets have only recently reached large enough size and variety to enable proper empirical studies. We revisit the topic by showing how modern methods perform on two large detection and classification datasets (thousand of images, tens of categories) captured in Belgium and Germany. We show...
Since its introduction, kernel adaptive filtering (KAF) has attracted considerable attention in recent years. Its main advantages include universal nonlinear approximation using kernel methods, linearity with convex learning in the Reproducing Kernel Hilbert Space (RKHS), and online adaptation with moderate complexity. Among its applications, the kernel least mean square (KLMS) algorithm deserves...
Though treatment of the ventilated premature infant has experienced many advances over the past decades, determining the best time point for extubation of these infants remains challenging and the incidence of extubation failures largely unchanged. The objective was to provide clinicians with a decision-support tool to determine whether to extubate a mechanically ventilated premature infant by using...
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