Browsing by Subject "Signal processing"

Now showing 1 - 7 of 7
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    Advancing profiling sensors with a wireless approach
    (2013-11-20) Galvis, Alejandro; Russomanno, David J.; Li, Feng; Rizkalla, Maher E.; King, Brian
    In general, profiling sensors are low-cost crude imagers that typically utilize a sparse detector array, whereas traditional cameras employ a dense focal-plane array. Profiling sensors are of particular interest in applications that require classification of a sensed object into broad categories, such as human, animal, or vehicle. However, profiling sensors have many other applications in which reliable classification of a crude silhouette or profile produced by the sensor is of value. The notion of a profiling sensor was first realized by a Near-Infrared (N-IR), retro-reflective prototype consisting of a vertical column of sparse detectors. Alternative arrangements of detectors have been implemented in which a subset of the detectors have been offset from the vertical column and placed at arbitrary locations along the anticipated path of the objects of interest. All prior work with the N-IR, retro-reflective profiling sensors has consisted of wired detectors. This thesis surveys prior work and advances this work with a wireless profiling sensor prototype in which each detector is a wireless sensor node and the aggregation of these nodes comprises a profiling sensor’s field of view. In this novel approach, a base station pre-processes the data collected from the sensor nodes, including data realignment, prior to its classification through a back-propagation neural network. Such a wireless detector configuration advances deployment options for N-IR, retro-reflective profiling sensors.
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    Analysis of the Bioelectric Impedance of the Tissue-Electrode Interface Using a Novel Full-Spectrum Approach
    (2014-01-15) Sempsrott, David Robert; Yoshida, Ken; Salama, Paul; Berbari, Edward J.
    Non-invasive surface recording of bioelectric potentials continues to be an essential tool in a variety of research and medical diagnostic procedures. However, the integrity of these recordings, and hence the reliability of subsequent analysis, diagnosis, or recommendations based on the recordings, can be significantly compromised when various types of noise are allowed to penetrate the recording circuit and contaminate the signals. In particular, for bioelectric phenomena in which the amplitude of the biosignal is relatively low, such as muscle activity (typically on the order of millivolts) or neural traffic (microvolts), external noise may substantially contaminate or even completely overwhelm the signal. In such circumstances, the tissue-electrode interface is typically the primary point of signal contamination since its impedance is relatively high compared to the rest of the recording circuit. Therefore, in the recording of low-amplitude biological signals, it is of paramount importance to minimize the impedance of the tissue-electrode interface in order to consistently obtain low-noise recordings. The aims of the current work were (1) to complete the development of a set of tools for rapid, simple, and reliable full-spectrum characterization and analytical modeling of the complex impedance of the tissue-electrode interface, and (2) to characterize the interfacial impedance and signal-to-noise ratio (SNR) at the surface of the skin across a variety of preparation methods and determine a factor or set of factors that contribute most effectively to the reduction of tissue-electrode impedance and noise contamination during recording. Specifically, we desired to test an initial hypothesis that surface abrasion is the principal determining factor in skin preparation to achieve consistently low-impedance, low-noise recordings. During the course of this master’s study, (1) a system with portable, battery-powered hardware and robust acquisition/analysis software for broadband impedance characterization has been achieved, and (2) the effects of skin preparation methods on the impedance of the tissue-electrode interface and the SNR of surface electromyographic recordings have been systematically quantified and compared in human subjects. We found our hypothesis to be strongly supported by the results: the degree of surface abrasion was the only factor that could be correlated to significant differences in either the interfacial impedance or the SNR. Given these findings, we believe that abrasion holds the key to consistently obtaining a low-impedance contact interface and high-quality recordings and should thus be considered an essential component of proper skin preparation prior to attachment of electrodes for recording of small bioelectric surface potentials.
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    Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data
    (Springer, 2019-05-10) Fadel, William F.; Urbanek, Jacek K.; Albertson, Steven R.; Li, Xiaochun; Chomistek, Andrea K.; Harezlak, Jaroslaw; Biostatistics, School of Public Health
    Wearable accelerometers provide an objective measure of human physical activity. They record high frequency unlabeled three-dimensional time series data. We extract meaningful features from the raw accelerometry data and based on them develop and evaluate a classification method for the detection of walking and its sub-classes, i.e. level walking, descending stairs and ascending stairs. Our methodology is tested on a sample of 32 middle-aged subjects for whom we extracted features based on the Fourier and wavelet transforms. We build subject-specific and group-level classification models utilizing a tree-based methodology. We evaluate the effects of sensor location and tuning parameters on the classification accuracy of the tree models. In the group-level classification setting, we propose a robust feature inter-subject normalization and evaluate its performance compared to unnormalized data. The overall classification accuracy for the three activities at the subject-specific level was on average 87.6%, with the ankle-worn accelerometers showing the best performance with an average accuracy 90.5%. At the group-level, the average overall classification accuracy for the three activities using the normalized features was 80.2% compared to 72.3% for the unnormalized features. In summary, a framework is provided for better use and feature extraction from raw accelerometry data to differentiate among different walking modalities as well as considerations for study design.
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    EFFICIENT AND SECURE IMAGE AND VIDEO PROCESSING AND TRANSMISSION IN WIRELESS SENSOR NETWORKS
    (2010) Assegie, Samuel; King, Brian; Salama, Paul; Rizkalla, Maher
    Sensor nodes forming a network and using wireless communications are highly useful in a variety of applications including battle field (military) surveillance, building security, medical and health services, environmental monitoring in harsh conditions, for scientific investigations on other planets, etc. But these wireless sensors are resource constricted: limited power supply, bandwidth for communication, processing speed, and memory space. One possible way of achieve maximum utilization of those constrained resource is applying signal processing and compressing the sensor readings. Usually, processing data consumes much less power than transmitting data in wireless medium, so it is effective to apply data compression by trading computation for communication before transmitting data for reducing total power consumption by a sensor node. However the existing state of the art compression algorithms are not suitable for wireless sensor nodes due to their limited resource. Therefore there is a need to design signal processing (compression) algorithms considering the resource constraint of wireless sensors. In our work, we designed a lightweight codec system aiming surveillance as a target application. In designing the codec system, we have proposed new design ideas and also tweak the existing encoding algorithms to fit the target application. Also during data transmission among sensors and between sensors and base station, the data has to be secured. We have addressed some security issues by assessing the security of wavelet tree shuffling as the only security mechanism.
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    Electric utility planning methods for the design of one shot stability controls
    (2012-12) Naghsh Nilchi, Maryam; Rovnyak, Steven; Chen, Yaobin; Du, Yingzi, 1975-
    Reliability of the wide-area power system is becoming a greater concern as the power grid is growing. Delivering electric power from the most economical source through fewest and shortest transmission lines to customers frequently increases the stress on the system and prevents it from maintaining its stability. Events like loss of transmission equipment and phase to ground faults can force the system to cross its stability limits by causing the generators to lose their synchronism. Therefore, a helpful solution is detection of these dynamic events and prediction of instability. Decision Trees (DTs) were used as a pattern recognition tool in this thesis. Based on training data, DT generated rules for detecting event, predicting loss of synchronism, and selecting stabilizing control. To evaluate the accuracy of these rules, they were applied to testing data sets. To train DTs of this thesis, direct system measurements like generator rotor angles and bus voltage angles as well as calculated indices such as the rate of change of bus angles, the Integral Square Bus Angle (ISBA) and the gradient of ISBA were used. The initial method of this thesis included a response based DT only for instability prediction. In this method, time and location of the events were unknown and the one shot control was applied when the instability was predicted. The control applied was in the form of fast power changes on four different buses. Further, an event detection DT was combined with the instability prediction such that the data samples of each case was checked with event detection DT rules. In cases that an event was detected, control was applied upon prediction of instability. Later in the research, it was investigated that different control cases could behave differently in terms of the number of cases they stabilize. Therefore, a third DT was trained to select between two different control cases to improve the effectiveness of the methodology. It was learned through internship at Midwest Independent Transmission Operators (MISO) that post-event steady-state analysis is necessary for better understanding the effect of the faults on the power system. Hence, this study was included in this research.
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    A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data
    (Frontiers, 2016) Li, Shanshan; Chen, Shaojie; Yue, Chen; Caffo, Brian; Department of Biostatistics, School of Public Health
    Independent Component analysis (ICA) is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks.
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    A Pilot Study for Algorithmic Diction Detection for Use by Singers and Vocal Teachers
    (Studio Musica Press, 2021-01) Rathi, Bhawna; Hsu, Timothy Y.; Music and Arts Technology, School of Engineering and Technology
    This paper introduces an algorithmic signal processing method to quantify vocal dic-tion using audio files that can potentially assist singers and teachers. Clear diction and pronunciation in singing is important for a variety of reasons and should be ex-ercised alongside the development of voice. In order to convey a clear verbal mes-sage, strong diction is needed. To accomplish this goal of diction detection, the in-terpretation of the consonants is of prime significance. The proposed algorithm works with features such as zero crossing rate, spectral spread, spectral flux and spectral centroid. In this paper, we offer a proposed framework and algorithm of dic-tion detection using modern applicable audio features and extraction techniques. Fu-ture approach for analysis of diction is also defined.