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Covariance sketching has been recently introduced as an effective strategy to reduce the data dimensionality without sacrificing the ability to reconstruct second-order statistics of the data. In this paper, we propose a novel covariance sketching scheme with reduced complexity for spatial-temporal data, whose covariance matrices satisfy the Kronecker product expansion model recently introduced by...
We investigate the problem of finding the real-valued vectors h, of size L, and x, of size P, from M independent measurements ym = 〈am, h〉〈bm, x〉, where am and bm are known random vectors. Recovery of the unknowns entails solving a set of bilinear equations, a challenging problem encountered in signal processing tasks such as blind deconvolution for channel equalization or image deblurring. Inspired...
The autoregressive models (AR) and moving-average models (MA) are regularly used in signal processing. Previous works have been done on dissimilarity measures between AR models by using a Riemannian distance, the Jeffrey's divergence (JD) and the spectral distances such as the Itakura-Saito divergence. In this paper, we compare the Rao distance and the JD for MA models and more particularly in the...
Correlation size together with Lyapunov exponents estimated from both electroencephalography (EEG) and electromyography (EMG) signals, are the crucial variables in the classification of mental tasks using an artificial neural network (ANN) classifier for patients suffering from neurological disorders/diseases. The above parameters vary according to the status of the patient, for example: depending...
Sparse reconstruction algorithms aim to retrieve high-dimensional sparse signals from a limited number of measurements. A common example is LASSO or Basis Pursuit where sparsity is enforced using an ℓ1-penalty together with a cost function ‖y — Hx‖22. For random design matrices H, a sharp phase transition boundary separates the ‘good’ parameter region where error-free recovery of a sufficiently sparse...
In this paper we study the application of Matrix Completion in topic detection and classification in Twitter. The proposed method first employs Joint Complexity to perform topic detection based on score matrices. Based on the spatial correlation of tweets and the spatial characteristics of the score matrices, we apply a novel framework which extends the Matrix Completion to build dynamically complete...
In diagnosis and treatment planning of brain tumors, characterisation and localization of tissue plays an important role. Blind source separation techniques are generally employed to extract the tissue-specific profiles and its corresponding distribution from the multi-parametric MRI. A 3-dimensional tensor is constructed from in-vivo multi-parametric MRI of high grade glioma patients. Constrained...
Interest in risk measurement for spot price has increased since the worldwide deregulation and liberalization of electricity started in the early 90's. This paper is focused on quantifying risk for the Swedish spot price. Our analysis is based on a generalized autoregressive conditional heteroskedastic (GARCH) process with skewed exponential power innovations to model the stochastic component of the...
In this paper some results on Schur transform are reviewed to address the problem of one-dimensional discrete phase retrieval. The goal is to provide a test whether a sequence of input magnitude data gives a solution to one-dimensional discrete phase retrieval problem. It has been previously shown that this issue is related to the nonnegativity of trigonometric polynomials. The proposed method is...
In this paper, we consider the problem of estimating an unknown random scalar observed by two modalities. We study two scenarios using mutual information and mean square error. In the first scenario, we consider that the noise correlation is known and examine its impact on the information content of two modalities. In the second scenario we quantify the information loss when the considered value of...
A periodic sequence is defined as a perfect periodic sequence for a certain nonlinear filter if the cross-correlation between any two of the filter basis functions, estimated over a period, is zero. Using a perfect periodic sequence as input signal, an unknown nonlinear system can be efficiently identified with the cross-correlation method. Moreover, the basis functions that guarantee the most compact...
Obssesive-compulsive disorder (OCD) is a serious mental illness that affects the overall quality of the patients' daily lives. Accurate diagnosis of this disorder is a primary step towards effective treatment. Diagnosing OCD is a lengthy procedure that involves interviews, symptom rating scales and behavioral observation as well as the experience of a clinician. Discovering signal processing and network...
Cross-modal hashing has received more and more attention because of its fast query speed and low storage cost. In this paper, we propose a flexible yet simple cross-modal hashing method to deal with the problem of cross-modal retrieval. The proposed method consists of two steps. In the first phase, we use a kernel canonical correlation analysis method named Anchor kernel canonical correlation analysis...
In order to achieve the time domain load signals of CNC lathe during the cutting process, a new signal processing method based on improved Neighbor Block was proposed according to the character of noise in this paper. First, statistical variance smoothing method was utilized to remove the singular points. Then, denoising method of Neighbor Block was chosen. Finally, feasibility of wavelet basis was...
The quality and freshness of a fish sample is mainly affected due to the handling and storage conditions during the post harvesting period. The retention time and storage medium are the two main factors affecting the fish quality. This paper presents an image processing based method for automatic and efficient segmentation of gills from the fish sample image which can be used for fish freshness validation...
The purpose of this research is to show that the spatio-temporal analysis on surface Electromyographic (sEMG) signals that originally confirmed existence of a standing wave Central Pattern Generator (CPG) along the spine are reproducible under less than ideal conditions and despite evolution of the entrainment technique, different hardware and data collection protocol. This analysis reveals a coherence...
In the last few years, numerous new methods have been proposed to overcome the complexity of the signals generated by complex machines and those generated by faults. Monitoring and fault diagnosis methods based on signal processing have proved effective in fault identification. The present paper introduces the theory of wavelet transforms coefficients (WTC) processes and autocorrelation as powerful...
In wireless communication systems, spoofing attack significantly impact the information security, for this attack is not hard to launch with little effort. Although traditional cryptographic authentication can be utilized for node identification, it is not desirable in some low power requirements application scenarios such as Wireless Sensor Networks (WSNs). In this paper, we formulate this problem...
In this paper we introduce and check a new description of polarimetric parameters that are asymptotically invariant to the group of all monotonous transformations of input signals. In particular, this can give a possibility to estimate pure correlation between polarimetric signals independently on statistical characteristics of initial data. This copula-based approach is applied to signal processing...
Polyphonic signal analysing process becomes the basic topic of signal analysis currently. Furthermore, analysis on the signal of music is also becomes the part of this topic. The main chosen instrument is sasando. As the matter of fact, this is not only about its characteristic sound but also because of the need for the preservation of this musical instrument which is about getting to be abandoned...
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