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Paper currency recognition is an important concern for automation to improve our daily monetary activities. Such recognition system uses the banknote images to train a classifier for identification of unknown input notes. One of the basic problems of such system is high dimensional representation of the feature vector (more than 100 dimensions) of note images. Moreover, most of the traditional approaches...
Classification is a supervised learning technique typically uses two-thirds of the given annotated data set for training and the remaining for test. In this paper, we developed a frame work which uses less than one-third of the data set for training and tests the remaining two-thirds of the data and still gives results comparable to other classifiers. To achieve good classification accuracy with small...
In this paper, we proposed a biased support vector machine (Biased-SVM) with self-constructed Universum (termed as U-BSVM) to solve the PU learning problem. We first treat the PU problem as an imbalanced binary classification problem by labeling all the unlabeled inputs as negative with noise, then inspired by the Universum-SVM (U-SVM), introduce the Universum data set which is constructed from the...
Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental...
Over the past few decades, a considerable amount of literature has been published on shape classification. Since classification of well-segmented shapes has become easy to achieve, a number of recent studies have emphasized the importance of robustness to noise and deformations. So in this paper, we undertake the task of classifying similar & noisy binary shape images, using a biologically inspired...
This document describes a proof of concept for a new approach for next generation wheel alignment systems. We propose to measure the relevant angels with Kohonen self organizing networks from an image of a heavy precision camera, instead of a projecting system clamped on the wheel. This has the clear advantage, that we do not need to attach a specially designed clamp which holds on to a wheel with...
This paper introduces a new approach to dictionary-based source separation employing a learned non-linear metric. In contrast to existing parametric source separation systems, this model is able to utilize a rich dictionary of speech signals. In contrast to previous dictionary-based source separation systems, the system can utilize perceptually relevant non-linear features of the noisy and clean audio...
In this paper, we present the ATM (Awesome Translation Machine), which translates handwriting texts in English into Chinese, and then provides its pronunciations in both the two languages. Specifically, two types of the databases that contain characters and sentences for training the ATM are constructed. Various signal processing techniques are employed sequentially for processing and analyzing the...
In this paper, we propose a two-step approach for the super-resolution reconstruction of video sequences based on the degraded model. Firstly we use the sparse principal component analysis and the linear minimum mean square-error estimation method to remove the noises from the degraded video sequences. Secondly we adopt the Newton-Thiele's vector valued rational interpolation which is one of the nonlinear...
In this paper we extend our previous work on strategies for automatically constructing noise resilient SVM detectors from click through data for large scale concept-based image retrieval. First, search log data is used in conjunction with Information Retrieval (IR) models to score images with respect to each concept. The IR models evaluated in this work include Vector Space Models (VSM), BM25 and...
Facial gender recognition plays an important role in various industrial applications such as human-computer interaction and targeted advertising. Although several methods have been applied to facial gender recognition, it is still considered as a challenging problem. In this paper, a system based on optimal trade-off (OT) — Maximum average correlation height (MACH) filter is developed for facial gender...
Active robot learners take an active role in its own learning by asking queries to its human teachers when they receive new data. However, not every received input is useful for the robot, and asking for non-informative inputs or asking too many questions might produce a negative impact on how the human perceives the robot. We present a novelty detection system that enables a robot to ask questions...
One of the most challenging problems encountered when analyzing real-world gene expression datasets is high dimensionality (overabundance of features/attributes). This large number of features can lead to suboptimal classification performance and increased computation time. Feature selection, whereby only a subset of the original features are used for building a classification model, is the most commonly...
In this paper we are going to estimate the vehicular traffic density by using acoustic or sound signals. Here we will estimate three probable conditions of traffic that is heavy flow traffic (0-10km/h), medium flow (20-40km/h), and free flow (above 40km/h) traffic. Cumulative sound signals consist of various noises coming from various part of vehicles which includes rotational parts, vibrations in...
Side-channel analysis of cryptographic systems can allow for the recovery of secret information by an adversary even where the underlying algorithms have been shown to be provably secure. This is achieved by exploiting the unintentional leakages inherent in the underlying implementation of the algorithm in software or hardware. Within this field of research, a class of attacks known as profiling attacks,...
In this paper we propose the use of Support Vector Machine classifiers (SVM) and linear discriminant analysis (LDA) to determine the existence of magnetic flux leakage (MFL) in non-destructive testing (NDT for its acronym in English) performed on ferromagnetic sheets. These signals were provided by the Corporation for Research in Corrosion (CIC) and were acquired on a dyno. The signals are preprocessed...
This paper explores how noise can improve classification accuracy of motor imagery classification using an ensemble support vector machine (ESVM) classifier. We add white Gaussian noise to the EEG signals and use them with the original signal data set for the ESVM training process. The ESVM classifier uses coefficients of the discrete wavelet transform (DWT) and coefficients of the autoregressive...
We describe a novel appearance model with optimal combined features to produce the accurate vessel segmentation. It starts with investigating a set of multi-scale vessel features, followed by a weighed approach to optimally combine different features. Then the optimally combined features advantage the appearance model to reveal more detailed information of vessel. The novelty of the work lies in the...
Non-intrusive load monitoring (NILM) is an important topic in smart-grid and smart-home. Many energy disaggregation algorithms have been proposed to detect various individual appliances from one aggregated signal observation. However, few works studied the energy disaggregation of plug-in electric vehicle (EV) charging in the residential environment since EVs charging at home has emerged only recently...
An indoor positioning system based on Receive Signal Strength Indication(RSSI) from wireless access equipment has become very popular in recent years. This system is very useful in many applications such as tracking service for older people or customer inside living communities, mobile robot localization, logistics systems etc. While outdoor environment using Global Navigation Satellite System(GNSS)...
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