Alzheimer's disease (AD) is developing into the single greatest healthcare challenge in the coming decades. The development of early and effective treatments that can prevent the pathological damage responsible for AD-related dementia is of utmost priority for healthcare authorities. The role of the APOE-ε4 genotype, which has been shown to increase an individual's risk of developing AD, is of central interest to this goal. Understanding the mechanism by which possession of this gene modulates brain function, leading to a predisposition towards AD is an active area of research. Functional connectivity (FC) is an excellent candidate for linking APOE-related differences in brain function to sites of AD pathology. Magnetoencephalography (MEG) is a neuroimaging tool that can provide a unique insight into the electrophysiology underpinning resting-state networks (RSNs) - whose dysfunction is postulated to lead to a predisposition to AD. This thesis presents a range of methods for measuring functional connectivity in MEG data. We first develop a set of novel adaptations for preprocessing MEG data and performing source reconstruction using a beamformer (chapter 3). We then develop a range of analyses for measuring FC through correlations in the slow envelope oscillations of band-limited source-space MEG data (chapter 4). We investigate the optimum time scales for detecting FC. We then develop methods for extracting single networks (using seed-based correlation) and multiple networks (using ICA). We proceed to develop a group-statistical framework for detecting spatial differences in RSNs and present a preliminary finding for APOE-genotype-dependent differences in RSNs (chapter 5). We also develop a statistical framework for quantifying task-locked temporal differences in functional networks during task-positive experiments (chapter 6). Finally, we demonstrate a data-driven parcellation and network analysis pipeline that includes a novel correction for signal leakage between parcels. We use this framework to show evidence of stationary cross-frequency FC (chapter 7).

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.

Our thesis work focuses on Frequency-domain Blind Source Separation (BSS) in which the received mixed signals are converted into the frequency domain and Independent Component Analysis (ICA) is applied to instantaneous mixtures at each frequency bin. Computational complexity is also reduced by using this method. We also investigate the famous problem associated with Frequency-Domain Blind Source Separation using ICA referred to as the Permutation and Scaling ambiguities, using methods proposed by some researchers. This is our main target in this project; to solve the permutation and scaling ambiguities in real time applications / Gozie: modebelu2001@yahoo.com Anayo: ezeudea@yahoo.com

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

<p>All products will eventually end up as waste, which in a sustainable society has to be handled in an efficient and environment friendly way. This report focuses on waste fractions meant for combustion, often difficult to characterize. However, more homogeneous fractions that are treated biologically are also discussed.</p><p>The study concerns the region of Borås, Sweden, where the waste plant Sobacken has provided a good starting point. On this site, fuel to the Energy-from-Waste plant of Borås Energi is prepared and the biological waste is treated through anaerobic digestion.</p><p>One important part of the study has been to collect experience-based knowledge from the technical staff at Sobacken and Borås Energi. This information was compiled into an overview of wanted and unwanted fractions to the preparation plant and the boilers respectively. The purpose of this overview is to complement existing delivery terms and thereby facilitate an increased quality of the fuel from the suppliers.</p><p>A significant element of the analysis has been to characterize the content of the industrial waste sent to Sobacken for combustion. Chemical analyses of the prepared fuel as well as the conducted waste component analysis indicate a heterogeneous composition of the waste. A heterogeneous fuel often results in an uneven combustion, leading to higher emissions and an unwanted variation in the energy production.</p><p>Through the waste component analysis, a comprehensive picture of the waste composition was attained. Materials non-valid for delivery mostly consisted of wet domestic waste (biodegradable materials), but some hazardous waste was also found. The results of the waste component analysis were communicated to the involved suppliers and this has already resulted in a considerable reduction of the amount of biodegradable waste in the deliveries of industrial waste.</p> / <p>Förr eller senare blir alla produkter avfall, som i ett uthålligt samhälle måste hanteras på ett resurssnålt och miljövänligt sätt. Det här arbetet är främst inriktat mot de svåridentifierade fraktionerna som är ämnade för förbränning, men berör även de mer homogena fraktionerna som behandlas biologiskt genom rötning.</p><p>Arbetet har utförts i Boråsregionen där Sobackens avfallsanläggning, med beredning av avfallsbränsle till Borås Energis två FB-pannor och rötkammare, har utgjort en naturlig utgångspunkt.</p><p>Ett stort inslag i arbetet var sammanställning av erfarenhetsbaserade kunskaper hos driftteknikerna på beredningsanläggningen, liksom hos Anders Johnsson på Borås Energi. På detta sätt erhölls viktig information om både bra och dåliga fraktioner, för såväl avfallskross som för pannor. Dessa fakta har bland annat använts för att sätta samman en översikt över önskade respektive oönskade fraktioner. Översikten är tänkt som komplement till befintliga leveransregler, i syfte att förenkla för avfallsleverantörerna.</p><p>Stor vikt har lagts vid att försöka kartlägga sammansättningen av det verksamhetsavfall som kommer in till Sobackens beredningsanläggning. Både kemiska analyser av bränsleprov och utförd plockanalys visar på en heterogen sammansättning i avfallet. Ett heterogent bränsle brinner i många fall ojämt, vilket resulterar i högre emissionsnivåer samt en icke-önskvärd variation i energiproduktion.</p><p>I och med plockanalysen erhölls en övergripande bild av förbränningsavfallets sammansättning. Det icke leveransgilla materialet som påträffades utgjordes främst av blött hushållsavfall (biologiskt nedbrytbart material), men även av en del elektronik påträffades. Efter avslutad analys kommunicerades erhållna resultat med aktuella leverantörer, vilket hittills har resulterat i en betydande minskning av biologiskt nedbrytbart material i verksamhetsavfallet.</p>

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。

Singing voice separation (SVS) can be defined as the process of extracting the vocal element from a given song recording. The impetus for research in this area is mainly that of facilitating certain important applications of music information retrieval (MIR) such as lyrics recognition, singer identification, and melody extraction. To date, the research in the field of SVS has been relatively limited, and mainly focused on the extraction of vocals from monophonic sources. The general approach in this scenario has been one of considering SVS as a blind source separation (BSS) problem. Given the inherent diversity of music, such an approach is motivated by the quest for a generic solution. However, it does not allow the exploitation of prior information, regarding the way in which commercial music is produced. To this end, investigations are conducted into effective methods for unsupervised separation of singing voice from stereophonic studio recordings. The work involves extensive literature review of existing methods that relate to SVS, as well as commercial approaches. Following the identification of shortcomings of the conventional methods, two novel approaches are developed for the purpose of SVS. These approaches, termed SEMANICS and SEMANTICS draw their motivation from statistical as well as spectral properties of the target signal and focus on the separation of voice in the frequency domain. In addition, a third method, named Hybrid SEMANTICS, is introduced that addresses time‐, as well as frequency‐domain separation. As there is lack of a concrete standardised music database that includes a large number of songs, a dataset is created using conventional stereophonic mixing methods. Using this database, and based on widely adopted objective metrics, the effectiveness of the proposed methods has been evaluated through thorough experimental investigations.

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