Multirate signal processing

Manolakis, Dimitris G.; Ingle, Vinay K.

doi:10.1017/cbo9780511835261.013

Cited by 5 publications

(5 citation statements)

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“…The degree of entrainment was measured by the residual power of the mean time-dependent firing rate at 238–242Hz. Power spectra were calculated using the Welch-Bartlett method [41] , with sampling frequency of 1KHz, window length 512 ms, with an overlap of 256ms. Other window lengths and overlaps yielded similar results.…”

Section: Methodsmentioning

confidence: 99%

Coding Efficiency of Fly Motion Processing Is Set by Firing Rate, Not Firing Precision

2010

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To comprehend the principles underlying sensory information processing, it is important to understand how the nervous system deals with various sources of perturbation. Here, we analyze how the representation of motion information in the fly's nervous system changes with temperature and luminance. Although these two environmental variables have a considerable impact on the fly's nervous system, they do not impede the fly to behave suitably over a wide range of conditions. We recorded responses from a motion-sensitive neuron, the H1-cell, to a time-varying stimulus at many different combinations of temperature and luminance. We found that the mean firing rate, but not firing precision, changes with temperature, while both were affected by mean luminance. Because we also found that information rate and coding efficiency are mainly set by the mean firing rate, our results suggest that, in the face of environmental perturbations, the coding efficiency is improved by an increase in the mean firing rate, rather than by an increased firing precision.

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Section: Methodsmentioning

confidence: 99%

Coding Efficiency of Fly Motion Processing Is Set by Firing Rate, Not Firing Precision

2010

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“…The amplitude of the first component normally will be small, whereas the sixth will be large since the periodic pattern consists of all six breathing cycles. The FS model is given by [16]:…”

Section: B Fourier Series (Fs) Modeling Of Db Signalsmentioning

confidence: 99%

A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization During Paced Deep Breathing

2019

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Autonomic function during deep breathing (DB) is normally scored based on the assumption that the heart rate is synchronized with the breathing. We have observed individuals with subtle arrhythmias during DB, where an autonomic function cannot be evaluated. This paper presents a novel method for analyzing cardiorespiratory synchronization: feature-based analysis of the similarity between heart rate and respiration using the principles of hyperdimensional computing. Heart rate and respiration signals were modeled using Fourier series analysis. Three feature variables were derived and mapped to binary vectors in a high-dimensional space. Using both synthesized data and recordings from patients/healthy subjects, the similarity between the feature vectors was assessed using Hamming distance (high-dimensional space), Euclidean distance (original space), and with a coherence-based index. Methods were evaluated via the classification of the similarity indices into three groups. The distance-based methods achieved good separation of signals into classes with different degrees of cardiorespiratory synchronization, also providing identification of patients with low cardiorespiratory synchronization but high values of conventional DB scores. Moreover, binary high-dimensional vectors allowed an additional analysis of the obtained Hamming distance. Feature-based similarity analysis using hyperdimensional computing is capable of identifying signals with low cardiorespiratory synchronization during DB due to arrhythmias. Vector-based similarity analysis could be applied to other types of feature variables than based on spectral analysis. The proposed methods for robustly assessing cardiorespiratory synchronization during DB facilitate the identification of individuals where the evaluation of the autonomic function is problematic or even impossible, thus increasing the correctness of the conventional DB scores. INDEX TERMS Deep breathing test, deep breathing index, similarity analysis, heart rate variability, hyperdimensional computing.

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“…As for the second type of methods, such as: ACE [30], GLRT [25] and JSD [37]. They make use of probabilistic methods to model the target spectrum, and determine what is the probability of this being a given pixel.…”

Section: Materials Identification Using Spectral Informationmentioning

confidence: 99%

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

Freitas

Silva

Almeida

et al. 2017

J Intell Robot Syst

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This work address hyperspectral imaging systems use for maritime target detection using unmanned aerial vehicles. Specifically, by working in the creation of a hyperspectral real-time data processing system pipeline. We develop a boresight calibration method that allows to calibrate the position of the navigation sensor related to the camera imaging sensor, and improve substantially the accuracy of the target geo-reference. We also develop an unsupervised method for segmenting targets (boats) from their dominant background in real-time. We evaluated the performance of our proposed system for target detection in real-time with UAV flight data and present detection results comparing favorably our approach against other state-ofthe-art method.

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Multirate signal processing

Cited by 5 publications

References 0 publications

Coding Efficiency of Fly Motion Processing Is Set by Firing Rate, Not Firing Precision

Coding Efficiency of Fly Motion Processing Is Set by Firing Rate, Not Firing Precision

A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization During Paced Deep Breathing

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

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