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The paper deals with fusion of physiological and GPS data acquired during cycling and their analysis using general methods of multichannel signal processing. Experimental data were acquired during 17 identical cycling routes each about 12 km long including more then 1100 segments of the length 60 s recorded with the varying sampling period. The proposed algorithm includes their initial analysis, de-nosing...
Given the importance of an accurate wind speed forecasting for efficient utilization of wind farms, and the volatile nature of wind speed data including its non-linear and uncertain nature, the wind speed forecasting has remained an active field of research. In this study, the non-linearity of wind speed is tackled using artificial neural network and its uncertainty by wavelet transform. To avoid...
The usage of locating systems in sports (e.g. soccer) elevates match and training analysis to a new level. By tracking players and balls during matches or training, the performance of players can be analyzed, the training can be adapted and new strategies can be developed. The radio-based RedFIR system equips players and the ball with miniaturized transmitters, while antennas distributed around the...
In this paper, we developed a mathematical model for a single-hop relay-based communication channel. Assuming the transmitter-to-relay and receiver-to-relay channels are non-line-of-sight flat fading channels, we show that the real and imaginary components of the combined single channel have Laplace probability density functions. We, therefore, develop a complex Laplace autoregressive process (AR)...
In this paper, we show a convenient way of generating a Laplace process of a desired autocorrelation. Our approach is based upon the fact that the real or imaginary component of the product of two independent complex Gaussian random variables has a Laplace marginal probability density function (pdf). We, therefore, generate a Laplace process by multiplying two independent complex Gaussian autoregressive...
In this paper, we propose to address the moving average (MA) parameters estimation issue based only on noisy observations and without any knowledge on the variance of the additive stationary white Gaussian measurement noise. For this purpose, the MA process is approximated by a high-order AR process and its parameters are estimated by using an errors-in-variables (EIV) approach, which also makes it...
Autoregressive (AR) models are used in various applications, from speech processing to radar signal analysis. In this paper, our purpose is to extract different model subsets from a set of two or more AR models. The approach operates with the following steps: firstly the matrix composed of dissimilarity measures between AR-model pairs are created. This can be based on the symmetric Itakura divergence,...
We extend the concept of stationary temporal signals to stationary graph signals. Doing so, we introduce the concept of strict sense stationary and wide sense stationary graph signals as a statistical invariance through an isometric graph translation operator. Using these definitions, we propose a spectral characterisation of WSS graph signals allowing to study stationarity using only the spectral...
The method of instrumental variables has been successfully applied to pseudolinear estimation for angle-of-arrival target motion analysis (TMA). The objective of instrumental variables is to modify the normal equations of a biased least-squares estimator to make it asymptotically unbiased. The instrumental variable (IV) matrix, used in the modified normal equations, is required to be strongly correlated...
Detecting the presence of one or multiple signals with unknown spatial signature can be addressed by testing the structure of the observation covariance matrix. The problem can be typically formulated as a sphericity test, which checks whether the spatial covariance matrix is proportional to the identity (white noise), or as a correlation test, which checks whether this matrix has a diagonal structure...
Previous device identification studies on the iris sensors of the CASIA-Iris V4 database using PRNU fingerprints showed high variations regarding the differentiability of the sensors. These variations may have been caused by the usage of multiple sensors of the same model for the image acquisition. Since no speciic documentation on this exists we investigate the presence of multiple image sensors...
We propose a classification method to distinguish between normal and abnormal respiration by considering the correlation of the observation frequencies of adventitious sounds between auscultation points. This method is based on the fact that adventitious sounds are frequently observed in lung sounds from multiple points. We use the product of the correlation score and the abnormality score, which...
We consider the identification of nonlinear filters using periodic sequences. Perfect periodic sequences have already been proposed for this purpose. A periodic sequence is called perfect for a nonlinear filter if it causes the basis functions to be orthogonal and the autocorrelation matrix to be diagonal. In this paper, we introduce for the same purpose the quasi-perfect periodic sequences. We define...
This paper presents human stress pattern for alpha and beta waves obtained from EEG Power Spectrum analysis. The EEG stress evaluation conducted between human Cohen's PSS-10 Stress Questionnaires with EEG Power Spectrum. The EEG recording had been carried out among 86 volunteers as soon as they finished answering the stress questionnaires. The scores from questionnaires were computed immediately right...
This study employed the correlation coefficients analysis of time sequence, spectrogram, and auto-regression model of signals which were the lung sounds of subject end, and distant end where the sound was transmitted by mobile-to-mobile communication. The results presented that the correlation coefficients of time sequence, spectrogram, 4-order coefficients of auto-regression model were 0.36, 0.69,...
The purpose of this chapter is to provide guidance on how to estimate the correlation functions and the spectral density functions that are needed to interpret test data. It provides insight into what is important to know when processing test data and what are the important issues that should be considered before the estimating these functions is started. All the steps involved in proper signal processing...
Due to a wider range of services in future heterogeneous cellular network (HCN), packet transmission delay becomes a more essential performance metric for system design. In this paper, we propose an analytical framework to derive the delay performance of HCN in terms of local delay, by modeling the locations of base stations (BSs) in HCN as superimposed of independent Poisson point processes. In the...
Article deals with synthesis and analysis of detector of fractal signal at the background of non-correlated Gaussian noise. Model of signal is fractional Brownian motion (fBm), which describes many types of signal particularly flicker-noise. Expression for likelihood ratio is given being based on fBm models in time and spectral fields. This expression is a sufficient statistics for many problems of...
In this paper we study cache-enabled small cell networks (SCNs) with local regularly requested content sampling to take into account local user interests for the cache decisions. We consider Zipf-like local content popularity with variables indicating the correlation level of user interests in the same region. Based on stochastic spatial models for the small cell base station (SCBS) and user distribution,...
Aim of this work was to evaluate the effectiveness of a recently introduced ultrasound (US) parameter for the estimation of bone mineral density (BMD) of the lumbar spine, when extensively used in a clinical context to investigate adult women of variable body mass index (BMI). A total of 414 female patients (aged 51–60 years) underwent a spinal dual X-ray absorptiometry (DXA) and an abdominal echographic...
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