Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 46 for “"Signal Classification"”.
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Wavelet packet based transient signal classification
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.
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EEG signal classification for wheelchair control application
… Interface (BCI) requires generating control signals for external device by analyzing and processing the internal brain signal. Person with severe impairment or spinal cord injury has loss of ability to do anything. This project about the EEG signals classification for wheelchair control …
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Acoustic Emission Signal Classification for Gearbox Failure Detection
… fatigue crack growth were observed from the AE signals acquired from the result of the optimal number of clusters in a data set. Previous researches have determined the number of clusters by visually inspecting the AE plots from number of iterations. This research is focused on finding the …
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Adversarial RFML: Evading Deep Learning Enabled Signal Classification
… radio resources, as well as detect and classify signals. While there are numerous advantages to RFML, this thesis answers the question "is it secure?" DNNs have been shown, in other applications such as Computer Vision (CV), to be vulnerable to what are known as adversarial evasion attacks, which …
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Neural Fuzzy Techniques in Vehicle Acoustic Signal Classification
Vehicle acoustic signals have long been considered as unwanted traffic noise. In this research acoustic signals generated by each vehicle will be used to detect its presence and classify its type. Circular arrays of microphones were designed and built to detect desired signals and suppress unwanted …
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Approaches to Multiple-source Localization and Signal Classification
… For the second technique, the process of signal classification is considered as another approach to the data association problem. Environments in which each signal possesses unique features can be exploited to separate signals at each sensor by their characteristics, which mitigates the …
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Secure Machine Learning Based RF Signal Classification for Wireless Systems
… can identify the underlying waveform of an RF signal based on the in-phase/quadrature (I/Q) samples without decoding them. Our research starts with DNN designs in the context of spectrum sharing, focusing on Wi-Fi, LTE-LAA, and 5G NR-U systems that coexist over the unlicensed 5 GHz bands. …
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Surface electromyography signal classification using SFDN+DNN for hand gesture recognition
… to classify the Surface Electromyography (SEMG) signals for hand movement recognition is presented and compared to the other approaches in the literature. EMG or muscle’s cells electrical activity are the electrical signals that are carried from the brain to the muscles through the spinal cord. …
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GNSS pre-correlation waveform feature extraction methods for multipath-afflicted signal classification
… novel algorithms for GNSS multipath environment classification on the receiver Digital Signal Processing (DSP) stage, but at an earlier processing point than it is used usually; at the generation of the digitized samples of the RF signal. Towards this direction, a detailed study behind the theory …
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Wideband Spectrum Sensing and Signal Classification for Autonomous Self-Learning Cognitive Radios
… CR architecture is based on a sequence of signal processing and machine learning techniques that enable the Radiobot to sense a wide frequency band and act autonomously by learning from past experience. To achieve its goals, the proposed CR is equipped with the following functionalities: 1) …
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Condition Classification in Underground Pipes Based on Acoustical Characteristics. Acoustical characteristics are used to classify the structural and operational conditions in underground pipes with advanced signal classification methods
… diagnosis and musical modelling. Audio based classification and research has been mainly focusing on speech recognition and music retrieval, but few applications have attempted to use acoustic characteristics for underground pipe condition classification. Traditional CCTV inspection methods …
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PASSIVE RADAR TRACK CLUSTERING: HIGHER FIDELITY OF TARGET IDENTIFICATION AND CLASSIFICATION OF UNLABELED TRACKS
Effective radar signal classification is critical for naval electronic warfare systems like the SLQ-32, but data scarcity limits machine learning applications. This research evaluates semi-supervised learning (SSL) techniques to leverage unlabeled data for improved classification. Using a NIST …
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Analysis of digital communication signals and extraction of parameters
The signal classification performance of four types of Electronics Support Measure (ESM) Communications detection systems is compared from the standpoint of the unintended receiver (interceptor). Typical digital communication signals considered include Binary Phase Shift Keying (BPSK), Quadrature …
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Frequency-modulated continuous-wave radar processing fundamentals
… be discussed, followed by a look at the multiple signal classification (MUSIC) approach for angle estimation. Finally, we present the results of a simulated FMCW radar system with ideal targets for a variety of configurable system parameters.
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Applications of Sensor Fusion to Classification, Localization and Mapping
… sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoor localization and mapping based on smartphone sensors. Automatic Modulation Classification is a key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military applications. Although …
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Evaluation of Joint AOA and DOA Estimation Algorithms Using the Antenna Array Systems
… Location (PL) algorithms, such as MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques ESPRIT algorithms. Since using delay of arrival information can improve AOA estimates and classical PL algorithms do not incorporate Delay of …
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Optimizing electrocardiogram analysis for efficient heart condition diagnosis
… for efficient real-time Electrocardiography signal classification. This method uses a maximum of six leads instead of the traditional 12-lead approach, leading to significant reductions in sampling time (93.67%), data size at the data acquisition device (50%), and signal processing time …
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Performance Comparison Between Music And Esprit Algorithms For Direction Estimation Of Arrival Signals
… and compares the performance of Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) for the estimation of Direction of Arrival (DOA) of incoming signals to the smart antenna. The comparison of these two algorithms was …
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Acoustic Modeling and Feature Selection for Speech Recognition
… estimation of LPC coefficients, MUSIC (Multiple Signal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) are used to improve the accuracy of formant estimation. Furthermore, a mixture of nonlinear dynamic systems is developed to improve the …
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Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications
… feature extractors, feature selectors, and classification algorithms. In this work, we explore the different classification algorithms currently used in electroencephalographic (EEG) signal classification and assess their performance on intracranial EEG (iEEG) data. We first discuss the …
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