scholarly journals Multiscale Adaptive Gabor Expansion (MAGE): Improved Detection of Transient Oscillatory Burst Amplitude and Phase

2018 ◽  
Author(s):  
Ryan T. Canolty ◽  
Thilo Womelsdorf
Keyword(s):  
Matching Pursuit ◽  
Field Potential ◽  
Accurate Estimate ◽  
Time Frequency ◽  
Frequency Scale ◽  
Wide Range ◽  
Analytic Expressions ◽  
Computationally Intensive ◽  
Gabor Expansion ◽  
Elementary Signal

AbstractSince Denis Gabor’s pioneering paper on the discrete Gabor Expansion (Gabor, 1946), time-frequency signal analysis has proven to be an important tool for many fields. In neurophysiology, time-frequency analysis has often been used to characterize and describe transient bursts in local field potential data. However, these transient bursts have a wide range of variable durations, suggesting that a time-frequency-scale dictionary composed of elementary signal “atoms” may prove useful to more accurately match recorded bursts. While overcomplete multiscale dictionaries are useful, generating a sparse code over such dictionaries is a difficult computational problem. Existing adaptive algorithms for discovering a sparse description are slow and computationally intensive. Here we describe the Multiscale Adaptive Gabor Expansion (MAGE), which uses an implicit dictionary of parametric time-frequency-scale Gabor atoms to perform fast parameter reassignment to accelerate discovery of a sparse decomposition. Using analytic expressions together with numerical computation, MAGE is a greedy pursuit algorithm similar to Matching Pursuit, restricted to a dictionary of multiscale Gaussian-envelope Gabor atoms. MAGE parameter reassignment is robust in the presence of moderate noise. By expressing a unknown signal as a weighted sum of Gabor atoms, MAGE permits a more accurate estimate of the amplitude and phase of transient bursts than existing methods.

scholarly journals Low Complexity FFT Factorization for CS Reconstruction

2020 ◽  
Vol 9 (3) ◽  
pp. 438-442
Keyword(s):  
Real Time ◽  
High Speed ◽  
Performance Metrics ◽  
Matching Pursuit ◽  
Matrix Multiplication ◽  
Low Complexity ◽  
Orthogonal Matching Pursuit ◽  
Reconstruction Algorithms ◽  
Wide Range ◽  
Computationally Intensive

In this paper is presented a novel area efficient Fast Fourier transform (FFT) for real-time compressive sensing (CS) reconstruction. Among various methodologies used for CS reconstruction algorithms, Greedy-based orthogonal matching pursuit (OMP) approach provides better solution in terms of accurate implementation with complex computations overhead. Several computationally intensive arithmetic operations like complex matrix multiplication are required to formulate correlative vectors making this algorithm highly complex and power consuming hardware implementation. Computational complexity becomes very important especially in complex FFT models to meet different operational standards and system requirements. In general, for real time applications, FFT transforms are required for high speed computations as well as with least possible complexity overhead in order to support wide range of applications. This paper presents an hardware efficient FFT computation technique with twiddle factor normalization for correlation optimization in orthogonal matching pursuit (OMP). Experimental results are provided to validate the performance metrics of the proposed normalization techniques against complexity and energy related issues. The proposed method is verified by FPGA synthesizer, and validated with appropriate currently available comparative analyzes.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

scholarly journals RANSAC algorithm for instantaneous frequency estimation and reconstruction of frequency-modulated undersampled signals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Igor Djurović
Keyword(s):  
Instantaneous Frequency ◽  
Matching Pursuit ◽  
Frequency Estimation ◽  
Consensus Algorithm ◽  
Multidimensional Space ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency ◽  
Polynomial Phase ◽  
Fm Signals ◽  
Fifth Order

AbstractFrequency modulated (FM) signals sampled below the Nyquist rate or with missing samples (nowadays part of wider compressive sensing (CS) framework) are considered. Recently proposed matching pursuit and greedy techniques are inefficient for signals with several phase parameters since they require a search over multidimensional space. An alternative is proposed here based on the random samples consensus algorithm (RANSAC) applied to the instantaneous frequency (IF) estimates obtained from the time-frequency (TF) representation of recordings (undersampled or signal with missing samples). The O’Shea refinement strategy is employed to refine results. The proposed technique is tested against third- and fifth-order polynomial phase signals (PPS) and also for signals corrupted by noise.

scholarly journals A Novel Ultrasonic TOF Ranging System Using AlN Based PMUTs

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 284
Author(s):  
Yihsiang Chiu ◽  
Chen Wang ◽  
Dan Gong ◽  
Nan Li ◽  
Shenglin Ma ◽  
...  
Keyword(s):  
Clock Cycle ◽  
High Accuracy ◽  
Ultrasonic Waves ◽  
Oxide Semiconductor ◽  
Average Error ◽  
Cmos Process ◽  
Clock Frequency ◽  
Time Frequency ◽  
Range Finding ◽  
Wide Range

This paper presents a high-accuracy complementary metal oxide semiconductor (CMOS) driven ultrasonic ranging system based on air coupled aluminum nitride (AlN) based piezoelectric micromachined ultrasonic transducers (PMUTs) using time of flight (TOF). The mode shape and the time-frequency characteristics of PMUTs are simulated and analyzed. Two pieces of PMUTs with a frequency of 97 kHz and 96 kHz are applied. One is used to transmit and the other is used to receive ultrasonic waves. The Time to Digital Converter circuit (TDC), correlating the clock frequency with sound velocity, is utilized for range finding via TOF calculated from the system clock cycle. An application specific integrated circuit (ASIC) chip is designed and fabricated on a 0.18 μm CMOS process to acquire data from the PMUT. Compared to state of the art, the developed ranging system features a wide range and high accuracy, which allows to measure the range of 50 cm with an average error of 0.63 mm. AlN based PMUT is a promising candidate for an integrated portable ranging system.

HEAVY TRAFFIC APPROXIMATIONS FOR A SYSTEM OF INFINITE SERVERS WITH LOAD BALANCING

1999 ◽  
Vol 13 (3) ◽  
pp. 251-273 ◽  
Author(s):  
Philip J. Fleming ◽  
Burton Simon
Keyword(s):  
State Space ◽  
Heavy Traffic ◽  
Joint Probability ◽  
Mass Function ◽  
Accurate Estimate ◽  
The State ◽  
Probability Mass Function ◽  
Traffic Loads ◽  
Wide Range ◽  
Heavy Traffic Approximations

We consider an exponential queueing system with multiple stations, each of which has an infinite number of servers and a dedicated arrival stream of jobs. In addition, there is an arrival stream of jobs that choose a station based on the state of the system. In this paper we describe two heavy traffic approximations for the stationary joint probability mass function of the number of busy servers at each station. One of the approximations involves state-space collapse and is accurate for large traffic loads. The state-space in the second approximation does not collapse. It provides an accurate estimate of the stationary behavior of the system over a wide range of traffic loads.

scholarly journals ‘Awake delta’ and theta-rhythmic hippocampal network modes during intermittent locomotor behaviors in the rat

2019 ◽  
Author(s):  
Nathan W. Schultheiss ◽  
Maximillian Schlecht ◽  
Maanasa Jayachandran ◽  
Deborah R. Brooks ◽  
Jennifer L. McGlothan ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Field Potential ◽  
Small Sample ◽  
Network Activity ◽  
Time Frequency ◽  
Theta Power ◽  
Frequency Decomposition ◽  
Speed Modulation ◽  
Delta Band ◽  
Locomotor Behaviors

AbstractDelta-frequency network activity is commonly associated with sleep or behavioral disengagement accompanied by a dearth of cortical spiking, but delta in awake behaving animals is not well understood. We show that hippocampal (HC) synchronization in the delta frequency band (1-4 Hz) is related to animals’ locomotor behavior using a detailed analysis of simultaneous head- and body-tracking data. In contrast to running-speed modulation of the theta rhythm (6-10 Hz, a critical mechanism in navigation models), we observed that strong delta synchronization occurred when animals were stationary or moving slowly and while theta and fast gamma (55-120 Hz) were weak. We next combined time-frequency decomposition of the local field potential with hierarchical clustering algorithms to categorize momentary estimations of the power spectral density (PSD) into putative modes of HC activity. Delta and theta power measures from these modes were notably orthogonal, and theta and delta coherences between HC recording sites were monotonically related to theta-delta ratios across modes. Next, we focused on bouts of precisely-defined running and stationary behavior. Extraction of delta and theta power density estimates for each instance of these bout types confirmed the orthogonality between frequency bands seen across modes. We found that delta-band and theta-band coherence within HC, and in a small sample, between HC and medial prefrontal cortex (mPFC), mirrored delta and theta components of the PSD. Delta-band synchronization often developed rapidly when animals paused briefly between runs, as well as appearing throughout longer stationary bouts. Taken together, our findings suggest that delta-dominated network modes (and corresponding mPFC-HC couplings) represent functionally-distinct circuit dynamics that are temporally and behaviorally interspersed amongst theta-dominated modes during navigation. As such these modes of mPFC-HC circuit dynamics could play a fundamental role in coordinating encoding and retrieval mechanisms or decision-making processes at a timescale that segments event sequences within behavioral episodes.

scholarly journals The wavelet transforms technique in the computer-aided system for controlling the quarry unmanned vehicles

2021 ◽  
Vol 315 ◽  
pp. 03022
Author(s):  
Ivan Chicherin ◽  
Boris Fedosenkov ◽  
Dmitriy Dubinkin ◽  
Wang Zhenbo
Keyword(s):  
Wavelet Transforms ◽  
Matching Pursuit ◽  
Effective Means ◽  
Unmanned Vehicles ◽  
Open Pit ◽  
Open Pit Mining ◽  
Frequency Distributions ◽  
One Dimensional ◽  
Time Frequency ◽  
Computer Aided

Introduction. Purpose of the work. Within the framework of the computer-aided system, a technology has been formed for the method of controlling the current trajectories (CTs) of unmanned vehicles (UMVs) when they move along routes in a quarry in open pit mining. The purpose of the presented studies is to analyze the application of a wavelet transforms technique to the problem of routing unmanned vehicles when they move along routes within open pit roads. Methodology. The results of modeling certain one-dimensional signals corresponding to the UMV current trajectories when they deviate to the left / right from a nominal axial trajectory (NAT), as well as their time-frequency representations in a wavelet medium are presented. An algorithm of the procedure for displaying scalar UMV CT control signals in a complex medium of time-frequency wavelet transforms has been developed and described. Such a transformation allows for a functionally transparent and information-capacious monitoring of the UMV movement and efficiently manage the processes of trajectory routing dump trucks in an open pit. Research results, analysis. The processes of modifying the UMV movement current trajectories under the control of the computer-aided system are generated using wavelet transforms methods. They are based on algorithms for projecting the trajectory signals with a time-dependent frequency (chirp signals) onto a set of wavelet functions as part of a wavelet thesaurus (wavelet dictionary), executing certain wavelet matching pursuit procedures, and displaying the CT scalar signals in a specific multidimensional medium of Cohen’s class time-frequency distributions. The simulation results in the form of the current trajectory (CT-) signals waveforms and their three-dimensional time-frequency representations as Wigner maps showing the UMV movement in a start-stop mode, as well as the signals of formed continuous deviation trajectories when they leave to the left and to the right from the NAT, are presented. An algorithm for the formation of 3D-representations of UMV current trajectory one-dimensional signals is presented. Conclusion. The conclusion is made that the mathematical technique of wavelet transforms is the most expedient and effective means for computer-aided monitoring and controlling the dynamics of UMV movement along routes within open pit roads.

scholarly journals Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

2021 ◽  
Vol 7 ◽  
pp. e638
Author(s):  
Md Nahidul Islam ◽  
Norizam Sulaiman ◽  
Fahmid Al Farid ◽  
Jia Uddin ◽  
Salem A. Alyami ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Network Architecture ◽  
Auditory Evoked Potential ◽  
Human Communication ◽  
Time Frequency ◽  
The Neural Network ◽  
Cortex Area ◽  
Wide Range ◽  
Functional Reliability ◽  
Fully Connected

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis.

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