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In this paper we propose a blind source separation method to process the data acquired by an array of ion-selective electrodes in order to measure the ionic activity of different ions in an aqueous solution. While this problem has already been studied in the past, the method presented differs from the ones previously analyzed by approximating the mixing function by a second-degree polynomial, and...
In this paper, Legendre Ramanujan Sums transform(LRST) is proposed and derived by applying DFT to the complete generalized Legendre sequence (CGLS) matrices. The original matrix based Ramanujan Sums transform (RST) by truncating the Ramanujan Sums series is non-orthogonal and lack of fast algorithm, the proposed LRST has orthogonal property and O(Nlog2V) complexity fast algorithm. The LRST transform...
Dynamical systems describing a physical process with a dominant diffusion phenomenon require a large dimensional model due to their long memory. Without prior knowledge, it is however not straightforward to know if/whether one deals with a fractional order system or long memory effects. Since the parametric modeling of a fractional system is very involved, we tackle the question whether fractional...
This paper describes a review model for an on-line question and answer (Q&A) system whose objective is to personalize student's study review session in order to improve user conceptual proficiency and limit the number of necessary review questions based on individual and class performance data. The Question Review Model (QRM) structures study review for each user by prioritizing a list of critical...
A doubly weighted DFT-based feedback codebook design for systems using linear precoding scheme and orthogonal space-time block codes (OSTBCs) is proposed. The proposed codebook design employs a two-stage phase-amplitude weighting of the DFT codewords, which can be better to match the channel state information (CSI) than that using phase-weighting only [1]. With the same amount of feedback bits, say...
The use of open-source Python as opposed to traditional computing platforms such MATLAB, Mathematica, and C/C++, is becoming more and more noticeable as all forms of opensource software develop. The Python user community itself is very vibrant, but what really stands out for those of us in signals and systems, is what is happening in the numerical computing side of Python. This paper will describe...
As medical imaging facilities move towards film-less imaging technology, robust image compression systems are starting to play a key role. Conventional storage and transmission of large-scale raw medical image datasets can be very expensive and time-consuming. Recently, we proposed a memory-assisted lossless image compression algorithm based on Principal Component Analysis(PCA). In this paper, we...
In a sparse component analysis problem, under some non-strict conditions on sparsity of the sources, called k-SCA, we are able to estimate both mixing system (A) and sparse sources (5) uniquely. Based on k-SCA assumptions, if each column of source matrix has at most Nx−1 nonzero component, where Nx is the number of sensors, observed signal lies on a hyperplane spanned by active columns of the mixing...
In this paper, the problem of proportional covariance matrices estimation for random Gaussian complex vectors is investigated. The maximum likelihood estimates of the matrix and the scale factors are derived, and their statistical performances are studied, through bias, consistency and asymptotic distribution. It is also shown that the problem treated here generalizes the covariance estimation problem...
Meta learning uses information from base learners (e.g. classifiers or estimators) as well as information about the learning problem to improve upon the performance of a single base learner. For example, the Bayes error rate of a given feature space, if known, can be used to aid in choosing a classifier, as well as in feature selection and model selection for the base classifiers and the meta classifier...
We present a reduced dimensionality, information rich (RDIR) visual representation for scene information that distills the most distinguishing elements in an image, enabling scene classification by humans and computers under reduced dimensionality conditions. The representation utilizes the Gist model [1] to convey scene information in low bandwidth conditions, exhibiting enhanced classification performance...
A mapping system based on an artificial neural network was designed, trained, and tested to map Arabic acoustic parameters to their corresponding articulatory features. The main objective of the study was to find the correlation between these two different types of features. To train and test the system, an in-house database was created for all 29 Arabic alphabets as carrier words for our intended...
We consider a model for temperature-emissivity separation (TES) in hyperspectral image processing. The emissivity is modulated by both the black body function and the atmospheric downwelling. The interaction has made it difficult to extract both temperature and emissivity, since offsets in one can be compensated by the other. Working with only a single wavelength component, we propose here a model...
High frequency percussive ventilation (HFPV) is an advanced ventilatory strategy which has proven very effective in patients with acute respiratory failure. The airway pressure measured by HFPV ventilator represents the sum of the endotracheal tube pressure drop and the tracheal pressure dissipated to inflate a lung. The estimation of the difference between the peak airway and tracheal pressure AP...
We propose a speaker emotional state classification method that employs inference-based Bayesian networks to learn posterior density of emotional speech sequentially. We aim to alleviate difficulty in detecting medium-term states where the required monitoring time is longer compared to short-term emotional states that makes temporal content representation harder. Our inference algorithm takes advantage...
Acoustic event detection in surveillance scenarios is an important but difficult problem. Realistic systems are struggling with noisy recording conditions. In this work, we propose to use Gabor filterbank features to detect target events in different noisy background scenes. These features capture spectro-temporal modulation frequencies in the signal, which makes them suited for the detection of non-stationary...
In the analysis of Electroencephalograms (EEG), notably in their graphical modeling, the estimation of the spectral matrix and associated variables is of central importance. Often, when adjusting for the bandwidth of the spectral matrix estimate, singularity issues arise and information derived from the inverse spectral matrix is intractable. This requires the use of regularization methods, which...
In this paper we propose a new design method for oversampled perfect reconstruction (PR) Discrete Fourier Transform (DFT) transmultiplexers (TMUXs). The resulting multicarrier modulation (MCM) systems are characterized by their minimal dimension, i.e., they involve a minimal number of Givens rotations. Our design method is applicable for system parameters that have never been reached before and also...
Studies show that many people have difficulties in understanding dialogue in movies when watching TV, especially hard-of-hearing listeners or in adverse listening environments. In order to overcome this problem, we propose an efficient methodology to enhance the speech component of a stereo signal. The method is designed with low computational complexity in mind, and consists of first extracting a...
An efficient estimation of the State of Charge (SoC) of a battery is a challenging issue in the electric vehicle domain. The battery behavior depends on its chemistry and uncontrolled usage conditions, making it very difficult to estimate the SoC. This paper introduces a new model for SoC estimation given instantaneous measurements of current and voltage using a Switching Markov State-Space Model...
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