Commingled composites are stronger than those manufactured with the standard manufacturing method. The objective of the thesis is to answer why that is, to investigate the microstructure of the composites, to model composite strength and compare experimental values with theoretical for composites having poly lactic acid (PLA) as matrix material and composites having poly propylene (PP) as matrix material. X - Ray micro - computed tomography was used to investigate the micro structure of the composites. Input from X - Ray micro - computed tomography was used to show that commingled PLA composites are stronger than standard PLA composites because the fibers are longer and more of them are orientated closer to the loading direction. Composites having PP as matrix material have lower strength than composites with PLA as matrix material. The strength for these composites is pretty much the same regardless of manufacturing method but still a little higher for commingled PP composites. Theoretical strength is modelled with the modified rule of mixture and correlates well with experimental values, having an R2 value of 0.95 for average composite strength.

The technique of non-redundant masking (NRM) transforms a conventional telescope into an interferometric array. In practice, this provides a much better constrained point-spread function than a filled aperture and thus higher resolution than traditional imaging methods. Here, we describe an NRM data reduction pipeline. We discuss strategies for NRM observations regarding dithering patterns and calibrator selection. We describe relevant image calibrations and use example Large Binocular Telescope data sets to show their effects on the scatter in the Fourier measurements. We also describe the various ways to calculate Fourier quantities, and discuss different calibration strategies. We present the results of image reconstructions from simulated observations where we adjust prior images, weighting schemes, and error bar estimation. We compare two imaging algorithms and discuss implications for reconstructing images from real observations. Finally, we explore how the current state of the art compares to next-generation Extremely Large Telescopes.

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No description available.

Tire-pavement interaction noise is one of the dominant sources of vehicle noise, and one of the most significant sources of urban noise pollution. One critical generation mechanism of tire-pavement interaction noise is tire tread excitation. The tire tread contributes to the tire-pavement interaction noise mainly through two mechanisms: (1) tread block impact, and (2) the compression and expansion of the air in the tread groove at the contact patch. The tread pattern is the critical part of the tire design since it can be easily modified. Hence, the main focus of this study is to quantify the tread pattern contribution in total tire-pavement interaction noise. To achieve this goal, the noise produced by the tread pattern is separated from the total tire-pavement interaction noise. Since the tread pattern excitation is periodic with tire rotation, the noise produced by the tread is assumed to be related to the tire rotation. Hence, the order domain synchronous averaging method is used in this study to separate and quantify the tread pattern contribution to the total tire-pavement interaction noise. The experiment has been carried out using an On-Board-Sound-Intensity (OBSI) system. Five tires were tested including the Standard Reference Test Tire (SRTT). Compared to the conventional OBSI system, an optical sensor was added to the system to monitor the tire rotation. The once per revolution signal provided by the optical sensor is used to identify the noise signals associate to each revolution. In addition to the averaging method using optical signals, other data processing techniques have been investigated for separating the tread-pattern noise without utilizing the once per revolution signal. These techniques are autocorrelation analysis, a frequency domain filter, principal component analysis, and independent component analysis. In the tread-pattern noise generation, the tread profile is the most important input parameter. To characterize the tread profile, the tread pattern spectral content and air volume velocity spectral content for all the five tires are computed. Then, the tread pattern spectrum and the air volume velocity spectrum are both correlated with the separated tread-pattern noise by visual inspection of the spectra shape. / Master of Science

International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, Nevada / Digital processing techniques and related algorithms for receiving and processing space vehicle downlink signals are discussed. The combination of low minimum signal to noise density (C/No), large signal dynamic range, unknown time of arrival, and high space vehicle dynamics that is characteristic of some of these downlink signals results in a difficult acquisition problem. A method for rapid acquisition is described which employs a Fast Fourier Transform (FFT). Also discussed are digital techniques for precise measurement of space vehicle range and range rate using a digitally synthesized number controlled oscillator (NCO).

In the field of ergonomics, biomechanics, sports and rehabilitation muscle fatigue is regarded as an important aspect since muscle fatigue is considered to be one of the main reasons for musculoskeletal disorders. Classical signal processing techniques used to understand muscle behavior are mainly based on spectral based parameters estimation, and mostly applied during static contraction and the signal must be stationary within the analysis window; otherwise, the resulting spectrum will make little physical sense. Furthermore, the shape and size of the analysis window also directly affect the spectral estimation. But fatigue analysis in dynamic conditions is of utmost requirement because of its daily life applicability. It is really difficult to consistently find the muscle fatigue during dynamic contraction due to the inherent non-stationary nature and associated noise in the signal along with complex physiological changes in muscles. Nowadays, in addition to linear signal processing, different non-linear signal processing techniques are adopted to find out the consistent and robust indicator for muscle fatigue under dynamic condition considering the high degree of non-linearity (caused by functional interference between different muscles, changes of signal sources and paths to recording electrodes, variable electrode interface etc.) in the signal. In this work, various linear and nonlinear-non-stationary signal processing methods, applied on surface EMG signal for muscular fatigue analysis under dynamic contraction are studied. In present study, surface EMG (sEMG) signals are recorded from Biceps Brachii muscles from eight (N=8) physically active college students during dynamic lifting 7 kg load at the rate of 20 lifts/min till they become fatigue. EMG data is processed in two ways -1. taking the whole EMG response and 2. breaking into three ranges of contraction (0-45)o, (45-90)o and >90o, to study better response region. It is observed that in spectral estimation techniques auto-regressive (AR) based spectral estimation technique gives better frequency resolution than periodogram for small epochs, as AR is based on parametric estimation. Both the previous methods provide only the frequency information in the signal. In order to estimate the time varying nature of frequency content in a signal various time-frequency signal processing techniques are used like – Short Time-Fourier Transform (STFT), Smoothed pseudo Wigner-Ville (SPWD), Choi-William distribution (CWD), Continuous Wavelet Transform (CWT), Huang-Hilbert Transform (HHT) and Recurrence Quantification Analysis (RQA) are used. The last two techniques are used by considering the EMG signal as non-linear and non-stationary signals. Among these techniques, STFT is the simplest time-frequency analysis technique. But tradeoff between time and frequency resolution is the major constraint in STFT, therefore, a window length of 256 samples are considered in this study. In order to tackle time-frequency resolution problem different Cohen-class distribution techniques are used like SPWD and CWD, where the result is severely affected by the presence of interference terms which make its interpretation really difficult. Different adaptive filters are used in SPWD and CWD to suppress these interference terms during analysis. Among these time-frequency analysis techniques continuous wavelet transform provides the most accurate results in comparison to other time-frequency analysis techniques. Similar result is obtained in present study. This fatigue response is further improved using non-linear and non-stationary techniques like HHT and RQA. HHT shows less variation in frequency response than CWT analysis result. Percentage of determinism calculated using recurrence quantification analysis method is found to be more sensitive than mean frequency estimation. Therefore, non-linear and non-stationary signal processing techniques are to be better indicator of muscle fatigue during dynamic contraction.

Protein sequencing has produced overwhelming amount of protein sequences, especially in the last decade. Nevertheless, the majority of the proteins' functional and structural classes are still unknown, and experimental methods currently used to determine these properties are very expensive, laborious and time consuming. Therefore, automated computational methods are urgently required to accurately and reliably predict functional and structural classes of the proteins. Several bioinformatics methods have been developed to determine such properties of the proteins directly from their sequence information. Such methods that involve signal processing methods have recently become popular in the bioinformatics area and been investigated for the analysis of DNA and protein sequences and shown to be useful and generally help better characterise the sequences. However, there are various technical issues that need to be addressed in order to overcome problems associated with the signal processing methods for the analysis of the proteins sequences. Amino acid indices that are used to transform the protein sequences into signals have various applications and can represent diverse features of the protein sequences and amino acids. As the majority of indices have similar features, this project proposes a new set of computationally derived indices that better represent the original group of indices. A study is also carried out that resulted in finding a unique and universal set of best discriminating amino acid indices for the characterisation of allergenic proteins. This analysis extracts features directly from the protein sequences by using Discrete Fourier Transform (DFT) to build a classification model based on Support Vector Machines (SVM) for the allergenic proteins. The proposed predictive model yields a higher and more reliable accuracy than those of the existing methods. A new method is proposed for performing a multiple sequence alignment. For this method, DFT-based method is used to construct a new distance matrix in combination with multiple amino acid indices that were used to encode protein sequences into numerical sequences. Additionally, a new type of substitution matrix is proposed where the physicochemical similarities between any given amino acids is calculated. These similarities were calculated based on the 25 amino acids indices selected, where each one represents a unique biological protein feature. The proposed multiple sequence alignment method yields a better and more reliable alignment than the existing methods. In order to evaluate complex information that is generated as a result of DFT, Complex Informational Spectrum Analysis (CISA) is developed and presented. As the results show, when protein classes present similarities or differences according to the Common Frequency Peak (CFP) in specific amino acid indices, then it is probable that these classes are related to the protein feature that the specific amino acid represents. By using only the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient, as biologically related features can appear individually either in the real or the imaginary spectrum. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Upon identification of a new protein, it is important to single out amino acid responsible for the structural and functional classification of the protein, as well as the amino acids contributing to the protein's specific biological characterisation. In this work, a novel approach is presented to identify and quantify the relationship between individual amino acids and the protein. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Characterisation and identification problem of the Influenza A virus protein sequences is tackled through a Subgroup Discovery (SD) algorithm, which can provide ancillary knowledge to the experts. The main objective of the case study was to derive interpretable knowledge for the influenza A virus problem and to consequently better describe the relationships between subtypes of this virus. Finally, by using DFT-based sequence-driven features a Support Vector Machine (SVM)-based classification model was built and tested, that yields higher predictive accuracy than that of SD. The methods developed and presented in this study yield promising results and can be easily applied to proteomic fields.

As computers have become more and more advanced, with even the most basic computer capable of tasks almost unimaginable only a decade ago, researchers and developers are focusing on improving the way that computers interact with people in their everyday lives. A core goal, therefore, is to develop a computer system which can understand and react appropriately to natural human behavior. A key requirement for such a system is the ability to automatically, and in real time, recognises human facial expressions. In addition, this must be successfully achieved regardless of the inherent differences in human faces or variations in lighting and other external conditions. The focus of this research was to develop such a system by evaluating and then utilizing the most appropriate of the many image processing techniques currently available, and, where appropriate, developing new methodologies and algorithms. The first key step in the system is to recognise a human face with acceptable levels of misses and false positives. This research analysed and evaluated a number of different face detection techniques, before developing a novel algorithm which combined phase congruency and template matching techniques. This novel algorithm provides key advantages over existing techniques because it can detect faces rotated to any angle, and it works in real time. Existing techniques could only recognise faces which were rotated less than 10 degrees (in either direction) and most could not work in real time due to excessive computational power requirements. The next step for the system is to enhance and extract the facial features. To successfully achieve the stated goal, the enhancement and extraction of the facial features must reduce the number of facial dimensions to ensure the system can operate in real time, as well as providing sufficient clear and detailed features to allow the facial expressions to be accurately recognised. This part of the system was successfully completed by developing a novel algorithm based on the existing Contrast Limited Adaptive Histogram Equalization technique which quickly and accurately represents facial features, and developing another novel algorithm which reduces the number of feature dimensions by combining radon transformation and fast Fourier transformation techniques, ensuring real time operation is possible. The final step for the system is to use the information provided by the first two steps to accurately recognise facial expressions. This is achieved using an SVM trained using a database including both real and computer generated facial images with various facial expressions. The system developed during this research can be utilised in a number of ways, and, most significantly, has the potential to revolutionise future interactions between humans and computers by assisting these reactions to become natural and intuitive. In addition, individual components of the system also have significant potential, with, for example, the algorithms which allow the recognition of an object regardless of its rotation under consideration as part of a project aiming to achieve non-invasive detection of early stage cancer cells.

Σκοπός της παρούσας Διπλωματικής Εργασίας είναι η ανάπτυξη και εφαρμογή τεχνικών ψηφιακής επεξεργασίας εικόνων για τον εντοπισμό του οφθαλμού και τον υπολογισμό συγκεκριμένων παραμέτρων που συνδέονται με την κατάσταση του χρήστη. Συγκεκριμένα, χρησιμοποιήθηκε ένα ολοκληρωμένο Σύστημα Εντοπισμού Οφθαλμού που περιλαμβάνει τα υποσυστήματα της CMOS κάμερα, της μεταφοράς δεδομένων – εικόνων, της ψηφιοποίησης των δεδομένων, και τέλος το υποσύστημα της επεξεργασίας εικόνων οφθαλμού και του υπολογισμού παραμέτρων. Στα πλαίσια του τελευταίου αυτού υποσυστήματος αναπτύχθηκαν δύο μεθοδολογίες που βασίστηκαν στην εφαρμογή αλγορίθμων ψηφιακής επεξεργασίας εικόνων. Η πρώτη μεθοδολογία βασίστηκε στον υπολογισμό της μέσης φωτεινότητας για την άνω και την κάτω περιοχή του οφθαλμού. Η χρονική μεταβολή των δύο τιμών της φωτεινότητας χρησιμοποιήθηκε για την εξαγωγή πληροφοριών για την κατάσταση του οφθαλμού (ανοιχτός ή κλειστός). Η δεύτερη μεθοδολογία στηρίχτηκε σε ένα συνδυασμό τεχνικών ψηφιακής επεξεργασίας εικόνων. Η επεξεργασία κάθε εικόνας της ακολουθίας video περιλαμβάνει τέσσερα βασικά βήματα: (α) ευθυγράμμιση της εικόνας σε σχέση με ένα κοινό σύστημα αναφοράς, (β) εφαρμογή δύο φίλτρων για την ανίχνευση των κορυφών και των κοιλάδων της εικόνας, (γ) σύντηξη των δύο φιλτραρισμένων εικόνων που προκύπτουν και (δ) μετατροπή της εικόνας σύντηξης σε δυαδική με εφαρμογή κατάλληλου κατωφλίου. Η καταμέτρηση των λευκών εικονοστοιχείων της δυαδικής εικόνας στην περιοχή του οφθαλμού καθορίζει την κατάσταση του οφθαλμού (ανοικτός ή κλειστός). Τέλος, και μέσω του λογισμικού, υπολογίζονται οι σχετικές παράμετροι της κατάστασης του οφθαλμού όπως ο αριθμός ανοιγο-κλεισίματος οφθαλμού, η διάρκεια κάθε ανοιγο-κλεισίματος οφθαλμού και οι χρονικές αποστάσεις μεταξύ των προσδιορισμένων ανοιγο-κλεισιμάτων σε μια αλληλουχία συλλεγμένων εικόνων. / The scope of the thesis was the development and application of digital image processing techniques in order to detect human eye in video sequences and determine parameters related to the user’s state. Specifically, an integrated Eye-Tracking System was used in order to obtain the necessary image frames for further processing. The System consists of four modules, the CMOS camera module, the transfer module, the digitization module and the software module. The software module was based on the application of image processing techniques to detect the eye and calculate specific parameters. Two image processing techniques were developed and tested throughout this thesis. The first method was based on the calculations of the mean brightness of the upper and lower eye region for each frame of the video sequence. The temporal variation of this mean value provided useful information for the eye state (open/closed). The second method was based on a combination of various image processing techniques. The processing of each video frame comprises of four basic steps: a) registration of the image in relation to the first frame of the video sequence, b) filtering in order to detect the peaks and valleys of the image being processed, c) fusion of the filtered images, and d) binarization of the fused image by thresholding. The calculation of the number of white pixels in the eye region of the binary image indicates the state of the eye (open/closed) and allows the determination of the blink parameters related to the user’s state (vigilance/somnolence). The parameters being measured throughout this thesis were the number of eye blinks, the blink duration and the blink interval.