Scaled reassigned spectrograms applied to linear transducer signals

This study evaluates the applicability of scaled reassigned spectrograms (ReSTS) on ultrasound radio frequency data obtained with a clinical linear array ultrasound transducer. The ReSTS's ability to resolve axially closely spaced objects in a phantom is compared to the classical cross-correlation method with respect to the ability to resolve closely spaced objects as individual reflectors using ultrasound pulses with different lengths. The results show that the axial resolution achieved with the ReSTS was superior to the cross-correlation method when the reflected pulses from two objects overlap. A novel B-mode imaging method, facilitating higher image resolution for distinct reflectors, is proposed

Time-frequency methods for coherent spectroscopy

Time-frequency decomposition techniques, borrowed from the signal-processing field, have been adapted and applied to the analysis of 2D oscillating signals. While the Fourier-analysis techniques available so far are able to interpret the information content of the oscillating signal only in terms of its frequency components, the time-frequency transforms (TFT) proposed in this work can instead provide simultaneously frequency and time resolution, unveiling the dynamics of the relevant beating components, and supplying a valuable help in their interpretation. In order to fully exploit the potentiality of this method, several TFTs have been tested in the analysis of sample 2D data. Possible artifacts and sources of misinterpretation have been identified and discussed

Advanced Processing of Multispectral Satellite Data for Detecting and Learning Knowledge-based Features of Planetary Surface Anomalies

abstract: The marked increase in the inflow of remotely sensed data from satellites have trans- formed the Earth and Space Sciences to a data rich domain creating a rich repository for domain experts to analyze. These observations shed light on a diverse array of disciplines ranging from monitoring Earth system components to planetary explo- ration by highlighting the expected trend and patterns in the data. However, the complexity of these patterns from local to global scales, coupled with the volume of this ever-growing repository necessitates advanced techniques to sequentially process the datasets to determine the underlying trends. Such techniques essentially model the observations to learn characteristic parameters of data-generating processes and highlight anomalous planetary surface observations to help domain scientists for making informed decisions. The primary challenge in defining such models arises due to the spatio-temporal variability of these processes. This dissertation introduces models of multispectral satellite observations that sequentially learn the expected trend from the data by extracting salient features of planetary surface observations. The main objectives are to learn the temporal variability for modeling dynamic processes and to build representations of features of interest that is learned over the lifespan of an instrument. The estimated model parameters are then exploited in detecting anomalies due to changes in land surface reflectance as well as novelties in planetary surface landforms. A model switching approach is proposed that allows the selection of the best matched representation given the observations that is designed to account for rate of time-variability in land surface. The estimated parameters are exploited to design a change detector, analyze the separability of change events, and form an expert-guided representation of planetary landforms for prioritizing the retrieval of scientifically relevant observations with both onboard and post-downlink applications.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Time-frequency component analyzer

Cataloged from PDF version of article.In this thesis, a new algorithm, time–frequency component analyzer (TFCA), is proposed to analyze composite signals, whose components have compact time–frequency supports. Examples of this type of signals include biological, acoustic, seismic, speech, radar and sonar signals. By conducting its time–frequency analysis in an adaptively chosen warped fractional domain the method provides time–frequency distributions which are as sharp as the Wigner distribution, while suppressing the undesirable interference terms present in the Wigner distribution. Being almost fully automated, TFCA does not require any a priori information on the analyzed signal. By making use of recently developed fast Wigner slice computation algorithm, directionally smoothed Wigner distribution algorithm and fractional domain incision algorithm in the warped fractional domain, the method provides an overall time-frequency representation of the composite signals. It also provides time–frequency representations corresponding to the individual signal components constituting the composite signal. Since, TFCA based analysis enables the extraction of the identified components from the composite signals, it allows detailed post processing of the extracted signal components and their corresponding time–frequency distributions, as well.Özdemir, Ahmet KemalPh.D

Auto-encoder based deep learning for surface electromyography signal processing

© 2018 Advances in Science, Technology and Engineering Systems. All Rights Reserved. Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning, which depends on training the system on recognising and picking the best features that match the application. The main concept of feature learning is to create a model that is expected to be able to learn the best features without any human intervention instead of recourse the traditional methods for feature extraction or reduction and avoid dealing with feature extraction that depends on researcher experience. In this paper, Auto-Encoder will be utilised as a feature learning algorithm to practice the recommended model to excerpt the useful features from the surface electromyography signal. Deep learning method will be suggested by using Auto-Encoder to learn features. Wavelet Packet, Spectrogram, and Wavelet will be employed to represent the surface electromyography signal in our recommended model. Then, the newly represented bio-signal will be fed to stacked autoencoder (2 stages) to learn features and finally, the behaviour of the proposed algorithm will be estimated by hiring different classifiers such as Extreme Learning Machine, Support Vector Machine, and SoftMax Layer. The Rectified Linear Unit (ReLU) will be created as an activation function for extreme learning machine classifier besides existing functions such as sigmoid and radial basis function. ReLU will show a better classification ability than sigmoid and Radial basis function (RBF) for wavelet, Wavelet scale 5 and wavelet packet signal representations implemented techniques. ReLU will illustrate better classification ability, as an activation function, than sigmoid and poorer than RBF for spectrogram signal representation. Both confidence interval and Analysis of Variance will be estimated for different classifiers. Classifier fusion layer will be implemented to glean the classifier which will progress the best accuracies' values for both testing and training to develop the results. Classifier fusion layer brought an encouraging value for both accuracies either training or testing ones