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Activity Recognition is important in order to facilitate elderly residents' and their caregivers' needs. This problem has been widely investigated using different methods including probabilistic and Markovian approaches. The focus of this paper is to perform activity recognition more accurately than existing approaches using non-intrusive sensors. We represent motion sensors of smart environments...
Speech recognition systems are either based on parametric approach or non-parametric approach. Parametric based systems such as HMMs have been the dominant technology for speech recognition in the past decade. Despite a lot of advancements and enhancements in the design of these systems: key problems such as long term temporal dependence, etc. Has not yet been solved. Recently due to availability...
Handwritten digits recognition has been an interesting area due to its applications in several fields. Recognition of bank account numbers and zip codes are a few examples. Handwritten digits recognition is not a trivial task due to presence of large variation in writing style in available data. In order to cope with this problem both features and classifier need to be efficient. In this research,...
Characterizing the soft error impacts is significant for a good trade off between design cost (e.g. Area and power) and reliability. In this paper, a heuristically mechanical model is proposed to quantify the soft error metric Architectural Vulnerability Factor (AVF) of storage structures (e.g. Register file and Cache) efficiently. This model not only considers the error spread among successive read...
Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of...
Energy disaggregation is to discover the energy consumption of individual appliances from their aggregated energy values. To solve the problem, most existing approaches rely on either appliances' signatures or their state transition patterns, both hard to obtain in practice. Aiming at developing a simple, universal model that works without depending on sophisticated machine learning techniques or...
Rhythm and intonation are important factors in the English sentence pronunciation evaluation. In this paper, the Mel Frequency Cepstrum Coefficient (MFCC) feature and Hidden Markov Model (HMM) algorithm are used to establish a model for speech recognition. Then it makes an evaluation of English sentence pronunciation focusing on rhythm and intonation, and gives feedbacks and recommendations about...
Classification is a technique in data mining for categorizing objects. Text Classification is re-challenged for classifying very short documents or text as shown in social media collection. This paper proposes a method to improve the performance of classification on short documents. In this work, we expand words in every document before the documents are classified We use TFIDF model, Hidden Markov...
Labelling maximization (F-max) is an unbiased metric for estimation of the quality of non-supervised classification (clustering) that promotes the clusters with a maximum value of feature F-measure. In this paper, we show that an adaptation of this metric within the supervised classification allows to perform a selection of features and to calculate for each of them a function of contrast. The method...
Computer assisted language learning (CALL) and, more specifically, computer assisted pronunciation training (CAPT) have received considerable attention in recent years. CAPT allows continuous feedback to the learner without requiring the sole attention of the teacher; it facilitates self study and encourages interactive use of the language in preference to rote learning. One of the important processes...
Online handwriting recognition has many applications and the recognition with high accuracy is essential. In this paper, we introduce a method for online handwriting Farsi character and number recognition using Hidden Markov Models (HMM). First we recognize handwriting direction then we get some statistical and formatting features. The letters are classified by means of these features and then we...
This paper describes an implementation of speech recognition that recognizes and suppresses ten (10) defined profane and vulgar Filipino words. The adapted speech recognition architecture was that of the Oregon Graduate Institute's (OGI) Center for Spoken Language and Learning (CSLU). It utilizes a hybrid Hidden Markov Model/ Artificial Neural Network (HMM/ANN) keyword spotting framework. The feature...
We propose two algorithms for power load disaggregation at low-sampling rates (greater than 1sec): a low-complexity, supervised approach based on Decision Trees and an unsupervised method based on Dynamic Time Warping. Both proposed algorithms share common pre-classification steps. We provide reproducible algorithmic description and benchmark the proposed methods with a state-of-the-art Hidden Markov...
The use of Electroencephalography (EEG) in the domain of Brain Computer Interface is a now common place. EEG for imagined speech reproduction and observation of brain response to audio stimuli are active areas of research. In this paper, we consider the case of imagined and mouthed non-audible speech recorded with EEG electrodes. We analyze different feature extraction techniques such as Mel Frequency...
In understanding the biological role of genes and gene products, the analysis of gene regulatory functions is important. Computational methods for running gene regulatory networks inference have its own limitations. For instance, Bayesian Network and Boolean Network are unable to model the cyclic relationship and the interaction uncertainties, which are important elements in the biological networks...
In speech recognition system, the Mel Frequency Cepstrum Coefficients (i.e. MFCC) feature extraction is an important process. It has also been wildly used in many applications. In this paper, we present the conventional MFCC feature extraction method and propose two novel versions of MFCC method that will combine the PCA technique and conventional MFCC feature extraction method. Finally, these three...
Wearable wireless devices and ubiquitous computing are expected to grow significantly in the upcoming years. Standard inputs such as a mouse and keyboard are not well suited for these more on-the-go style systems. Gestures are seen as an effective alternative to these classical input styles. In this paper we examine two recognition gesture algorithms that use an inertial sensor worn on the forearm...
We asset about the analysis of electrical appliance consumption signatures for the identification task. We apply Hidden Markov Models to appliance signatures for the identification of their category and of the most probable sequence of states. The electrical signatures are measured at low frequency (10−1 Hz) and are sourced from a specific database. We follow two predefined protocols for providing...
We present here a data mining approach for part-of-speech (POS) tagging, an important Natural language processing (NLP) classification task. We propose a semi-supervised associative classification method for POS tagging. Existing methods for building POS taggers require extensive domain and linguistic knowledge and resources. Our method uses a combination of a small POS tagged corpus and untagged...
Presently existing lightweight indoor/outdoor detection schemes on phones acquire accuracy by sensing variations of ambient physical environmental properties with inherent sensors on mobile phones, with which, however, the detection scheme cannot work well in some ambient environments, where the variations are not very observable. This detection scheme is with very high dependency on light. The I/O...
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