Master of Science / Department of Electrical and Computer Engineering / Dwight D. Day / Balasubramaniam Natarajan / This thesis presents an automated pavement crack detection and classification system via image processing and pattern recognition algorithms. Pavement crack detection is important to the Departments of Transportation around the country as it is directly related to maintenance of pavement quality. Manual inspection and analysis of pavement distress is the prevalent method for monitoring pavement quality. However, inspecting miles of highway sections and analyzing each is a cumbersome and time consuming process. Hence, there has been research into automating the system of crack detection. In this thesis, an automated crack detection and classification algorithm is presented. The algorithm is built around the statistical tool of Principal Component Analysis (PCA). The application of PCA on images yields the primary features of cracks based on which, cracked images are distinguished from non-cracked ones. The algorithm consists of three levels of classification: a) pixel-level b) subimage (32 X 32 pixels) level and c) image level. Initially, at the lowermost level, pixels are classified as cracked/non-cracked using adaptive thresholding. Then the classified pixels are grouped into subimages, for reducing processing complexity. Following the grouping process, the classification of subimages is validated based on the decision of a Bayes classifier. Finally, image level classification is performed based on a subimage profile generated for the image. Following this stage, the cracks are further classified as sealed/unsealed depending on the number of sealed and unsealed subimages. This classification is based on the Fourier transform of each subimage. The proposed algorithm detects cracks aligned in longitudinal as well as transverse directions with respect to the wheel path with high accuracy. The algorithm can also be extended to detect block cracks, which comprise of a pattern of cracks in both alignments.

The work presented in this thesis has demonstrated that the use of elemental profiles for the quality control of herbal medicines can be applied to multiple stages of processing. A single method was developed for the elemental analysis of a variety of St John’s Wort (Hypericum perforatum) preparations using Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES). The optimised method consisted of using 5 ml of nitric acid and microwave digestion reaching temperatures of 185⁰C. Using NIST Polish tea (NIST INCT-TL- 1) the method was found to be accurate and the matrix effect from selected St John’s Wort (SJW) preparations was found to be ≤22%. The optimised method was then used to determine the elemental profiles for a larger number of SJW preparations (raw herbs=22, tablets=20 and capsules=12). Specifically, the method was used to determine the typical concentrations of 25 elements (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, In, Mg, Mn, Mo, Ni, Pb, Pt, Sb, Se, Sr, V, Y and Zn) for each form of SJW which ranged from not detected to 200 mg/g. To further interpret the element profiles, Principal Component Analysis (PCA) was carried out. This showed that different forms of SJW could be differentiated based on their elemental profile and the SJW ingredient used (i.e. extract or raw herb) identified. The differences in the profiles were likely due to two factors: (1) the addition of bulking agents and (2) solvent extraction. In order to further understand how the elemental profile changes when producing the extract from the raw plant, eight SJW herb samples were extracted with four solvents (100% water, 60% ethanol, 80% ethanol and 100% ethanol) and analysed for their element content. The results showed that the transfer of elements from the raw herb to an extract was solvent and metal dependent. Generally the highest concentrations of an element were extracted with 100% water, which decreased as the concentration of ethanol increased. However, the transfer efficiency for the element Cu was highest with 60% ethanol. The solvents utilised in industry (60% and 80% ethanol) were found to preconcentrate some elements; Cu (+119%), Mg (+93%), Ni (+183%) and Zn (+12%) were found to preconcentrate in 60 %v/v ethanol extracts and Cu (+5%) and Ni (+30%). PCA of the elemental profiles of the four types of extract showed that differentiation was observed between the different solvents and as the ethanol concentration increased, the extracts became more standardised. Analysis of the bioactive compounds rutin, hyperoside, quercetin, hyperforin and adhyperforin followed by subsequent Correlation Analysis (CA) displayed relationships between the elemental profiles and the molecular profiles. For example strong correlations were seen between hyperoside and Cr as well as Quercetin and Fe. This shows potential for tuning elemental extractions for metal-bioactive compounds for increased bioactivity and bioavailability; however further work in needed in this area.

Η παρούσα μεταπτυχιακή εργασία ασχολείται με τη μελέτη, το σχεδιασμό και την υλοποίηση ενός συστήματος αναγνώρισης προσώπων σε ψηφιακές εικόνες. Για την υλοποίηση αυτή θα χρησιμοποιήσουμε τεχνικές του τομέα της Υπολογιστικής Νοημοσύνης όπως τα τεχνητά νευρωνικά δίκτυα και οι μηχανές υποστήριξης διανυσμάτων. / This thesis deals with the study, design and implement a face recognition system for digital images. for this implementation will use techniques in the field of Computational Intelligence such as artificial neural networks and support vector machines.

As adults age, they lose the ability to produce maximal power and speed of movement. Success in daily living is often dependent upon power and speed. Thus these age-related decrements in performance can reduce physical independence and quality of life. An active lifestyle in older adulthood is associated with more successful aging. The purpose of this research program was to define the link between habitual activity and performance, specifically in regard to activities requiring power and speed. The hypothesis was that active older adults, compared to sedentary older adults, would be characterized by greater power production in maximal- and submaximal-effort tasks. Grouping older adults by activity level, coactivation was associated with activity level. Functional tasks are performed with a range of power requirements. Coactivation was used to distinguish groups in a maximal power task (Study 1) and submaximal power tasks (Study 2). In Study 1, the young adults demonstrated a greater maximal power than the older adults. While maximal power was not different between the older active and sedentary groups, the groups did differ on how they created maximal power. The active older adults produced a greater coactivation in the lower leg muscles compared to the older sedentary adults. In Study 2, the active older adults responded to different speeds during a submaximal power task with greater coactivation in the muscles of the lower leg at slow speeds compared with the sedentary older adults. Both older adults groups increased coactivation in the thigh muscles at high speeds. The sedentary older adults responded to speed with increased coactivation in the lower leg at fast speeds. The active older adults increased proximal thigh coactivation, EMG index, at the fastest speed compared with the sedentary older adults. Both older adult groups showed muscle activation adaptation to the change in task demands. The results of this dissertation increase our understanding about the link between physical activity and performance. Age-related differences in coactivation were observed during both maximal and submaximal tasks. Activity-related differences were observed suggesting the active older adults have a greater capability to adjust muscle activity to meet the challenges of community living. / text

隨著醫療技術的進步、環境衛生的改善與人類追求健康生活型態的趨勢，全世界人類死亡率不斷逐年地下降中。但死亡率的下降不僅影響政府的社會福利政策，也影響到壽險公司對於未來的不確定性。例如在年金商品定價上，如果使用不適當的死亡率預測將會導致保險公司在未來現金流量上的不穩定，進而影響到公司的財務健全度。因此用來預估死亡率的模型便扮演著相當重要的角色。本研究首先透過Reduction Factor圖形觀察台灣、日本、美國、加拿大、英國與法國的歷年死亡率變動，之後再使用廣為人使用的Lee-Carter模型與其改善方法主成分分析方法(Principal Component Analysis, PCA)預估未來死亡率，最後再比較兩種方法在預測死亡率的表現。再透過計算年金商品與壽險商品的純保費部份，了解忽略死亡率變動趨勢所可能產生的影響。最後利用上述年金商品與壽險商品對於死亡率帶來的影響，討論保險公司在上述情形之下可以採取的最佳自然避險策略。

The study of facial expressions in humans dates back to the 19th century and the study of the emotions that these facial expressions portray dates back even further. It is a natural part of non-verbal communication for humans to pass across messages using facial expressions either consciously or subconsciously, it is also routine for other humans to recognize these facial expressions and understand or deduce the underlying emotions which they represent. Over two decades ago and following technological advances, particularly in the area of image processing, research began into the use of machines for the recognition of facial expressions from images with the aim of inferring the corresponding emotion. Given a previously unknown test sample, the supervised learning problem is to accurately determine the facial expression class to which the test sample belongs using the knowledge of the known class memberships of each image from a set of training images. The solution to this problem building an effective classifier to recognize the facial expression is hinged on the availability of representative training data. To date, much of the research in the area of Facial Expression Recognition (FER) is still based on posed (acted) facial expression databases, which are often exaggerated and therefore not representative of real life affective displays, as such there is a need for more publically accessible spontaneous databases that are well labelled. This thesis therefore reports on the development of the newly collected Loughborough University Spontaneous Expression Database (LUSED); designed to bolster the development of new recognition systems and to provide a benchmark for researchers to compare results with more natural expression classes than most existing databases. To collect the database, an experiment was set up where volunteers were discretely videotaped while they watched a selection of emotion inducing video clips. The utility of the new LUSED dataset is validated using both traditional and more recent pattern recognition techniques; (1) baseline results are presented using the combination of Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA) and their kernel variants Kernel Principal Component Analysis (KPCA), Kernel Fisher Discriminant Analysis (KFDA) with a Nearest Neighbour-based classifier. These results are compared to the performance of an existing natural expression database Natural Visible and Infrared Expression (NVIE) database. A scheme for the recognition of encrypted facial expression images is also presented. (2) Benchmark results are presented by combining PCA, FLDA, KPCA and KFDA with a Sparse Representation-based Classifier (SRC). A maximum accuracy of 68% was obtained recognizing five expression classes, which is comparatively better than the known maximum for a natural database; around 70% (from recognizing only three classes) obtained from NVIE.

Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.

Fungal infection and mycotoxin contamination in agricultural products are a serious food safety issue. The detection of fungal infection and mycotoxin contamination in food products should be in a rapid way. A Near-infrared (NIR) hyperspectral imaging system was used to detect fungal infection in 2013 crop year canola, wheat, and barley at different periods after inoculation and different concentration levels of ochratoxin A in wheat and barley. Artificially fungal infected (Fungi: Aspergillus glaucus, Penicillium spp.) kernels of canola, wheat and barley, were subjected to single kernel imaging after 2, 4, 6, 8, and 10 weeks post inoculation in the NIR region from 1000 to 1600 nm at 61 evenly distributed wavelengths at 10 nm intervals. The acquired image data were in the three-dimensional hypercube forms, and these were transformed into two-dimensional data. The two-dimensional data were subjected to principal component analysis to identify significant wavelengths based on the highest principal component factor loadings. Wavelengths 1100, 1130, 1250, and 1300 nm were identified as significant for detection of fungal infection in canola kernels, wavelengths 1280, 1300, and 1350 nm were identified as significant for detection of fungal infection in wheat kernels, and wavelengths 1260, 1310, and 1360 nm were identified as significant for detection of fungal infection in barley kernels. The linear, quadratic and Mahalanobis statistical discriminant classifiers differentiated healthy canola kernels with > 95% and fungal infected canola kernels with > 90% classification accuracy. All the three classifiers discriminated healthy wheat and barley kernels with > 90% and fungal infected wheat and barley kernels with > 80% classification accuracy. The wavelengths 1300, 1350, and 1480 nm were identified as significant for detection of ochratoxin A contaminated wheat kernels, and wavelengths 1310, 1360, 1480 nm were identified as significant for detection of ochratoxin A contaminated barley kernels. All the three statistical classifiers differentiated healthy wheat and barley kernels and ochratoxin A contaminated wheat and barley kernels with a classification accuracy of 100%. The classifiers were able to discriminate between different durations of fungal infections in canola, wheat, and barley kernels with classification accuracy of more than 80% at initial periods (2 weeks) of fungal infection and 100% at the later periods of fungal infection. Different concentration levels of ochratoxin A contamination in wheat and barley kernels were discriminated with a classification accuracy of > 98% at ochratoxin A concentration level of ≤ 72 ppb in wheat kernels and ≤ 140 ppb in barley kernels and with 100% classification accuracy at higher concentration levels. / May 2016

Random projection method is one of the important tools for the dimensionality reduction of data which can be made efficient with strong error guarantees. In this thesis, we focus on linear transforms of high dimensional data to the low dimensional space satisfying the Johnson-Lindenstrauss lemma. In addition, we also prove some theoretical results relating to the projections that are of interest when applying them in practical applications. We show how the technique can be applied to synthetic data with probabilistic guarantee on the pairwise distance. The connection between dimensionality reduction and compressed sensing is also discussed.

Thesis advisor: Rudolph Hon / This study characterizes the current state of water quality of surface streams and rivers in the eastern New England region. A set of water quality data for nine rivers, part of the USGS National Water-Quality Assessment (NAWQA) Program was statistically evaluated to identify natural and anthropogenic persistent influential factors on water quality in surface waters. Binary analysis and multivariate analysis, mainly Principal Component Analysis (PCA) and Factor Analysis (FA) were applied to determine the least number of independent relationships among multiple chemical components in the data set. Statistical results show that in eight of the nine rivers included in this study, four principal components can explain about 80% of the total variance of the original data. The most significant contributing factors can be identified with: (1) chemical weathering; (2) road salt applications; (3) nutrient cycling; and (4) agricultural/waste water. / Thesis (MS) — Boston College, 2009. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Geology and Geophysics.