949 research outputs found

    Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings

    Full text link
    Efficient and accurate detection of subtle motion generated from small objects in noisy environments, as needed for vital sign monitoring, is challenging, but can be substantially improved with magnification. We developed a complex Gabor filter-based decomposition method to amplify phases at different spatial wavelength levels to magnify motion and extract 1D motion signals for fundamental frequency estimation. The phase-based complex Gabor filter outputs are processed and then used to train machine learning models that predict respiration and heart rate with greater accuracy. We show that our proposed technique performs better than the conventional temporal FFT-based method in clinical settings, such as sleep laboratories and emergency departments, as well for a variety of human postures.Comment: Accepted in IEEE Sensors 202

    Recording electrical activity from the brain of behaving octopus

    Get PDF
    : Octopuses, which are among the most intelligent invertebrates,1,2,3,4 have no skeleton and eight flexible arms whose sensory and motor activities are at once autonomous and coordinated by a complex central nervous system.5,6,7,8 The octopus brain contains a very large number of neurons, organized into numerous distinct lobes, the functions of which have been proposed based largely on the results of lesioning experiments.9,10,11,12,13 In other species, linking brain activity to behavior is done by implanting electrodes and directly correlating electrical activity with observed animal behavior. However, because the octopus lacks any hard structure to which recording equipment can be anchored, and because it uses its eight flexible arms to remove any foreign object attached to the outside of its body, in vivo recording of electrical activity from untethered, behaving octopuses has thus far not been possible. Here, we describe a novel technique for inserting a portable data logger into the octopus and implanting electrodes into the vertical lobe system, such that brain activity can be recorded for up to 12 h from unanesthetized, untethered octopuses and can be synchronized with simultaneous video recordings of behavior. In the brain activity, we identified several distinct patterns that appeared consistently in all animals. While some resemble activity patterns in mammalian neural tissue, others, such as episodes of 2 Hz, large amplitude oscillations, have not been reported. By providing an experimental platform for recording brain activity in behaving octopuses, our study is a critical step toward understanding how the brain controls behavior in these remarkable animals

    An Efficient Eulerian Video Magnification Technique for Micro-biology Applications

    Get PDF
    The micro-biology videos often contain motions of particles which are not visible to naked eye. Therefore an efficient motion magnification technique is required to magnify these motions. A time efficient eulerian video magnification technique for micro-biological applications is proposed. The proposed technique utilizes the concept of time and spatial uniformity to reduce the computational complexity. Simulation results reveal that the proposed scheme is almost four times efficient and more accurate as compared to state of art video magnification technique

    Mean Oriented Riesz Features for Micro Expression Classification

    Get PDF
    Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second. This kind of facial expressions usually occurs in high stake situations and is considered to reflect a human's real intent. There has been some interest in micro-expression analysis, however, a great majority of the methods are based on classically established computer vision methods such as local binary patterns, histogram of gradients and optical flow. A novel methodology for micro-expression recognition using the Riesz pyramid, a multi-scale steerable Hilbert transform is presented. In fact, an image sequence is transformed with this tool, then the image phase variations are extracted and filtered as proxies for motion. Furthermore, the dominant orientation constancy from the Riesz transform is exploited to average the micro-expression sequence into an image pair. Based on that, the Mean Oriented Riesz Feature description is introduced. Finally the performance of our methods are tested in two spontaneous micro-expressions databases and compared to state-of-the-art methods

    Eulerian Video Magnification Adaptation for Live Cell Microscopy Analysis

    Get PDF
    In this paper an adaptation of the Eulerian Video Magnification technique is described for use with .TIFF files produced by a photo-conversion time lapse protocol for live cell microscopy, specifically for research into Acquired Immune Deficiency Syndrome. The tracking and characterisation of a protein found in Human Immunodeficiency Virus, to determine its dynamics and pathways is a key determinant in understanding the protein’s function. The aim of this algorithm is to process an image sequence in the temporal direction with the result being that changes in fluorescence for particular pixel locations, or regions of interest, are tracked and filtered thereby removing noise which is inherent with these types of images. This reduction in noise produced overall clearer results that will aid in further analysis of the live cells. In addition to this, this implementation attempts to adapt the existing EVM algorithm to aid in the analysis of photo-conversion experiments. The algorithm will decompose images into a multi-scale representation, and filter images in the temporal domain, recompose the image with amplifications applied to exaggerate particular motions in the images sequence. This paper also investigates the applicability of this magnification, to determine if it is practical in the situation of tracking protein dynamics. Modification of captured data is to be kept at a minimum to reduce the possibility of misinterpretation of the data

    Fractal Analysis of Microstructural and Fractograpghic Images for Evaluation of Materials

    Get PDF
    Materials have hierarchically organized complex structures at different length scales. Quantitative description of material behaviour is dependent on four fundamental length scales [1], which are of concern to materials scientists. These are (1) nano scale, 1-103 nm, (2)micro scale, 1-10 3 ÎĽm, (3) macro scale, 1-103mm, and (4) global size scale, 1-106 m. While the nano scale corresponds to, often, highly ordered atomic structures, the global size scale relates geophysical phenomena and large man made engineering structures. Micro scale and macro scale correspond to size of material samples used in laboratories, for designing and for fabrication of miniature to small machineries

    Quality Assurance of Lightweight Structures via Phase-based Motion Estimation

    Get PDF
    In recent years, lightweight structures have become mature and adopted in various applications. The importance of quality assurance cannot be overemphasized hence extensive research has been conducted to assess the quality of lightweight structures. This study investigates a novel process that exploits motion magnification to investigate the damage characteristics of lightweight mission-critical parts. The goal is to assure the structural integrity of 3D printed structures and composite structures by determining the inherent defects present in the part by exploiting their vibration characteristics. The minuscule vibration of the structure was recorded with the aid of a high-speed digital camera, and the motion was estimated by applying a phase-based algorithm. The spectral information was compared with the results obtained by a laser displacement sensor for validation. Then, the video-based results were used to perform damage identification by comparing the extracted information with that of a baseline. The resonance frequencies and the corresponding operational mode shapes of the test structure was obtained using the motion magnification algorithm by applying a bandpass filter around selected resonant frequencies. The resonance frequency and operational mode shape are quantified to compare the damaged structure with the baseline. The damage characteristics depending on the location and depth of damages were experimentally explored and numerically analyzed. Overall, this study provides an accurate, easily available and fast approach in structural health monitoring, utilizing video-based vibration analysis. It is envisioned that this study will provide a foundation for both commercial and non-commercial purposes exploiting the straightforward and low-cost implementation of video-based method

    Data Augmentation for Deep-Learning-Based Electroencephalography

    Get PDF
    Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average. Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis

    Data Augmentation for Deep-Learning-Based Electroencephalography

    Get PDF
    Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average. Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis
    • …
    corecore