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Resting State-fMRI represents an emerging and powerful tool to explore brain functional connectivity (FC) changes associated with neurologic disorders. Compared to activation/task-related fMRI, Resting State fMRI has the advantages such as (i) Blood oxygen level dependent (BOLD) fMRI signals are self-generated and independent of subject's performance during the task and (ii) a single dataset is sufficient...
In this paper, the problem of single-channel blind source separation (SCBSS) of a mixture of two co-frequency phase-shift keying (PSK) signals with unknown carrier frequency offsets (CFOs) is investigated. Two SCBSS algorithms which are robust to CFOs are proposed to perform separation of the mixture signals. In the first algorithm, the phase changes of the received signals caused by CFOs are tracked...
This paper describes the use of a space usage determination algorithm for teaching signal processing and machine learning concepts to undergraduate electrical engineering and computer science students. An Android device transmits a high-frequency signal in an unknown space. The device determines the reflective properties of this unknown space by analyzing the received signal. Based on the features...
In this paper, several methods based on signal processing on graphs are proposed to improve the performance of credit card fraud detection. The proposed methods consist of a variant of the classic iterative amplitude adjusted Fourier transform (IAAFT) and two methods that we have called iterative surrogate signals on graph algorithms (ISSG). The objective is to generate surrogate samples from the...
To better characterize movement-related neurophysiological change, the authors propose to measure not only neural activity through the electroencephalogram (EEG) but also cerebral blood flow (CBF) using a new technology, near-infrared diffuse correlation spectroscopy (DCS). A preliminary trial is described, in which EEG, DCS, and exerted force were simultaneously recorded during a cue-triggered hand...
We consider the problem of covariance estimation with projected or missing data, and in particular the application to spatial channel covariance estimation in a multi-user Massive MIMO wireless communication system with arbitrary (possibly time-varying and/or non-orthogonal) pilot sequences. We introduce batch and online estimators based on the expectation-maximization (EM) approach, and provide sufficient...
This paper presents a new adaptation of Zadoff-Chu sequences for the purpose of range estimation and movement tracking. The proposed method uses Zadoff-Chu sequences utilizing a wideband ultrasonic signal to estimate the range between two devices with very high accuracy and high update rate. This range estimation method is based on time of flight (TOF) estimation using cyclic cross correlation. The...
Clustering of multimodal data according to their information content is considered in this paper. Statistical correlations present in data that contain similar information are exploited to perform the clustering task. Specifically, multiset canonical correlation analysis is equipped with norm-one regularization mechanisms to identify clusters within different types of data that share the same information...
A novel solution of the inverse Frobenius-Perron problem for constructing semi-Markov chaotic maps with prescribed statistical properties is presented. The proposed solution uses recursive Markov state disaggregation to construct an ergodic map with a piecewise constant invariant density function that approximates an arbitrary probability distribution over a compact interval. The solution is novel...
Brain functional connectivity measured by functional magnetic resonance imaging was shown to be influenced by preprocessing procedures. We aim to describe this influence separately for different preprocessing factors and in 20 different most used preprocessing pipelines. We evaluate the effects of slice-timing correction and physiological noise filtering by RETROICOR, diverse levels of motion correction,...
Gait analysis is important in diagnosing and quantifying the severity of Parkinson's disease. Different motion tracking systems such as inertial measurement units (IMU) are widely used to detect gait parameters associated with the severity of Parkinson's disease. Although these systems are accurate enough to measure different gait parameters, they utilize a predefined model of human gait to measure...
The NASA dual-frequency, dual-polarization, Doppler radar (D3R) is a weather radar operating at 13.91 GHz (Ku-band) and 35.56 GHz (Ka-band). The operational range of the D3R is 40 km, this relatively short operating range, along with the high sensitivity of D3R, are susceptible to increased observation of range ambiguous echoes, also referred to as “second-trip echoes”. In this work, by leveraging...
LTE has several advantages to be investigated as transmitter of opportunity for passive radar in Germany: low carrier frequencies (i.e. low free space attenuation), high bandwidth (i.e. high range resolution) and wide area network (i.e. large availability). Therefore the signal correlation behavior is investigated. Additionally, a signal processing method is proposed for passive radar using LTE. The...
Compressive sensing (CS) is a viable source of an innovative 5G system, what's more, it's an effective technology to deal with the data redundancy problem of massive machine-to-machine communication (MMC), since it enables the recovery of sparse and approximately sparse signals with significantly fewer samples than demanded by Nyquist-Shannon sampling theory. Interference in signal will lead a series...
In practical asynchronous physical-layer network coding (PNC) systems, the symbols from multiple transmitters to a common receiver may be misaligned. The good performance of an asynchronous PNC decoder hinges on accurate estimation of the symbol misalignment. This paper puts forth an optimal symbol misalignment estimator that considerably improves the estimation accuracy over prior schemes. Our scheme...
Multi-agent systems (MAS) communicate over a wireless network to coordinate their actions and to report their mission status. Connectivity and system-level performance can be improved by channel gain prediction. We present a distributed Gaussian process regression (GPR) framework for channel prediction in terms of the received power in MAS. The framework combines a Bayesian committee machine with...
In this paper, we shall develop a generic channel estimation framework based on the convex formulation for dense cloud radio access networks (Cloud-RAN). Due to the training resource constraint and the large number of transmit antennas, the pilot length is smaller than the antenna number, and thus channel estimation becomes an ill-posed inverse problem. By observing that the wireless channel possesses...
The main goal of the paper is to present an analysis of the possibility of using faster-than-nyquist signal processing method to improve performance of navigation system. First the faster-than-nyquist concept is introduced and its pros and cons are described. After that the correlation signal processing in radio-nav systems is explained and one typical examle is presented. Demonstrated is the TDOA...
Autonomous drone is the Unmanned Aerial Vehicle (UAV) that did not have human control when operated. But if the drone encounter problem and cannot continue the mission, it has to land and wait for recover. It need to analyze the safety of landing area. This paper proposes the method to analyze the landing area of the drone using cross-correlation of the 3D stereoscopic image. The output of cross-correlation...
Functional connectivity is the stochastic association or the dependency of two or more distinct brain regions. It is primarily used for finding patterns that are validated through statistical methods, in the context of brain connectivity. Quantification of functional connectivity is usually performed using Pearson's correlation coefficient (PCC). Many Functional magnetic resonance imaging (fMRI) studies...
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