Search results for "Spectral Density"

Random Tensor Theory: Extending Random Matrix Theory to Mixtures of Random Product States

2012

We consider a problem in random matrix theory that is inspired by quantum information theory: determining the largest eigenvalue of a sum of p random product states in $${(\mathbb {C}^d)^{\otimes k}}$$ , where k and p/d k are fixed while d → ∞. When k = 1, the Marcenko-Pastur law determines (up to small corrections) not only the largest eigenvalue ( $${(1+\sqrt{p/d^k})^2}$$ ) but the smallest eigenvalue $${(\min(0,1-\sqrt{p/d^k})^2)}$$ and the spectral density in between. We use the method of moments to show that for k > 1 the largest eigenvalue is still approximately $${(1+\sqrt{p/d^k})^2}$$ and the spectral density approaches that of the Marcenko-Pastur law, generalizing the random matrix…

Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network

2019

Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machinelearning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density es…

A 3D Non-Stationary Cluster Channel Model for Human Activity Recognition

2019

Author's accepted manuscript. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper proposes a three-dimensional (3D) non- stationary fixed-to-fixed indoor channel simulator model for human activity recognition. The channel model enables the formulation of temporal variations of the received signal caused by a moving human. The moving human is modelled by …

On the estimation of the fatigue cycle distribution from spectral density data

1999

This paper deals with the fatigue life prediction of components and structures subjected to random fatigue, i.e. to cyclic loading whose amplitude varies in an essentially random manner. In particular, this study concentrates on the general problem of directly relating fatigue cycle distribution to the power spectral density (PSD) by means of closed-form expressions that avoid expensive digital simulations of the stress process. At present, all the methods proposed to achieve this objective are based on the use of a single parameter of the PSD. In this work, by numerical simulations and theoretical considerations, it is shown that the statistical distribution of fatigue cycles depends on f…

ES1D: A Deep Network for EEG-Based Subject Identification

2017

Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…

Selection of the Optimal Algorithm for Real-Time Estimation of Beta Band Power during DBS Surgeries in Patients with Parkinson's Disease

2017

Deep Brain Stimulation (DBS) is a surgical procedure for the treatment of motor disorders in patients with Parkinson’s Disease (PD). DBS involves the application of controlled electrical stimuli to a given brain structure. The implantation of the electrodes for DBS is performed by a minimally invasive stereotactic surgery where neuroimaging and microelectrode recordings (MER) are used to locate the target brain structure. The Subthalamic Nucleus (STN) is often chosen for the implantation of stimulation electrodes in DBS therapy. During the surgery, an intraoperative validation is performed to locate the dorsolateral region of STN. Patients with PD reveal a high power in the β band (frequenc…

Effects of a 21 days space flight on the mechanical performance and the EMG power spectrum of the leg muscles

2002

Force and EMG power spectrum during eccentric and concentric actions

2000

This study was designed to examine the force and activation levels of elbow flexor muscles during preactivated eccentric, concentric and isometric actions.Force, average EMG (aEMG), and the EMG power spectrum were investigated at different constant movement velocities (1 rad x s(-1), 2 rad x s(-1), 3 rad x s(-1), and 4 rad x s(-1)) at different joint angles.Average force at a 110 degree elbow angle was lower and aEMG was higher in concentric actions as compared with eccentric and isometric actions. At a 55 degree elbow angle, there was no difference in aEMG, or it was slightly higher in eccentric actions. MF was higher in the concentric as compared with eccentric actions at the three fastes…

Covariation of spectral and nonlinear EEG measures with alpha biofeedback.

2002

Item does not contain fulltext This study investigated how different spectral and nonlinear EEG measures covaried with alpha power during auditory alpha biofeedback training, performed by 13 healthy subjects. We found a significant positive correlation of alpha power with the largest Lyapunov-exponent, pointing to an increased dynamical instability of the EEG accompanying alpha enhancement. Alpha power amplification, moreover, was significantly correlated with a decrease of spectral entropy within the alpha range. This outcome reflects a sharpening of the alpha peak during biofeedback training. The fact that the sharpening effect clearly preceded the increase of alpha amplitude could be exp…

Why Have Many of the Brightest Radio-loud Blazars Not Been Detected in Gamma-Rays by Fermi?

2015

We use the complete MOJAVE 1.5 Jy sample of active galactic nuclei (AGNs) to examine the gamma-ray detection statistics of the brightest radio-loud blazars in the northern sky. We find that 23% of these AGNs were not detected above 0.1 GeV by the Fermi-LAT during the four-year 3FGL catalog period partly because of an instrumental selection effect and partly due to their lower Doppler boosting factors. Blazars with synchrotron peaks in their spectral energy distributions located below 10^(13.4) Hz also tend to have high-energy peaks that lie below the 0.1 GeV threshold of the LAT, and are thus less likely to be detected by Fermi. The non-detected AGNs in the 1.5 Jy sample also have significa…