Towards Automation and Human Assessment of Objective Skin Quantification

The goal of this study is to provide an objective criterion for computerised skin quality assessment. Humans have been impacted by a variety of face features. Utilising eye-tracking technology assists to get a better understanding of human visual behaviour, this research examined the influence of face characteristics on the quantification of skin evaluation and age estimation. The results revealed that when facial features are apparent, individuals do well in age estimation. Also, this research attempts to examine the performance and perception of machine learning algorithms for various skin attributes. Comparison of the traditional machine learning technique to deep learning approaches. Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) were used to evaluate classification algorithms, with CNNs outperforming SVM. The primary difficulty in training deep learning algorithms is the need of large-scale dataset. This thesis proposed two high-resolution face datasets to address the requirement of face images for research community to study face and skin quality. Additionally, the study of machine-generated skin patches using Generative Adversarial Networks (GANs) is conducted. Dermatologists confirmed the machine-generated images by evaluating the fake and real images. Only 38% accurately predicted the real from fake correctly. Lastly, the performance of human perception and machine algorithm is compared using the heat-map from the eye-tracking experiment and the machine learning prediction on age estimation. The finding indicates that both humans and machines predict in a similar manner

Dimensionality Reduction via Matrix Factorization for Predictive Modeling from Large, Sparse Behavioral Data

Matrix factorization is a popular technique for engineering features for use in predictive models; it is viewed as a key part of the predictive analytics process and is used in many different domain areas. The purpose of this paper is to investigate matrix-factorization-based dimensionality reduction as a design artifact in predictive analytics. With the rise in availability of large amounts of sparse behavioral data, this investigation comes at a time when traditional techniques must be reevaluated. Our contribution is based on two lines of inquiry: we survey the literature on dimensionality reduction in predictive analytics, and we undertake an experimental evaluation comparing using dimensionality reduction versus not using dimensionality reduction for predictive modeling from large, sparse behavioral data. Our survey of the dimensionality reduction literature reveals that, despite mixed empirical evidence as to the benefit of computing dimensionality reduction, it is frequently applied in predictive modeling research and application without either comparing to a model built using the full feature set or utilizing state-of-the-art predictive modeling techniques for complexity control. This presents a concern, as the survey reveals complexity control as one of the main reasons for employing dimensionality reduction. This lack of comparison is troubling in light of our empirical results. We experimentally evaluate the e cacy of dimensionality reduction in the context of a collection of predictive modeling problems from a large-scale published study. We find that utilizing dimensionality reduction improves predictive performance only under certain, rather narrow, conditions. Specifically, under default regularization (complexity control)settings dimensionality reduction helps for the more di cult predictive problems (where the predictive performance of a model built using the original feature set is relatively lower), but it actually decreases the performance on the easier problems. More surprisingly, employing state-of-the-art methods for selecting regularization parameters actually eliminates any advantage that dimensionality reduction has! Since the value of building accurate predictive models for business analytics applications has been well-established, the resulting guidelines for the application of dimensionality reduction should lead to better research and managerial decisions.NYU Stern School of Busines

Aerospace Medicine and Biology: A continuing bibliography with indexes

This bibliography lists 223 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1975

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

UV-excited SnO2SnO_{2} nanowire based electronic nose

Unsere Atemluft ist täglichen Schwankungen ausgesetzt und die Marktnachfrage nach Sensoren, die die Luftqualität messen können, steigt rapide an. Ein großer Teil dieser Nachfrage kann mit Metall-Oxid Gas Sensoren bedient werden. Diese Art von Gassensoren hat jedoch einige Nachteile im Bezug auf Genauigkeit, Langzeitstabilität, Leistungsaufnahme und Selektivität. Auch fehlen großvolumige Anwendungsbeispiele auf dem Markt, die Metall-Oxid (MOX) Gassensoren einsetzen und dabei alle Systemanforderungen erfüllen. Diese Arbeit stellt die neueste Entwicklung der "KArlsruhe MIkro NAse", einer im Rahmen der EU Horizon 2020 Initiative namens SMOKESENSE entwickelten elektrischen Nase, vor und vergleicht diese mit dem aktuellen Stand der Technik für Metalloxid-Gassensoren. Es wird gezeigt, dass durch UV-Anregung der $SnO_{2}$-Nanodrähte ein geringerer Stromverbrauch sowie eine minimierte Siloxan-Kontaminierung im Vergleich zu klassischen MOX-Sensoren erzielt wird. Zudem lässt sich mittels Aerosol-Jet-Druck eine vereinfachte und kostengünstigere Herstellung der Sensoren realisieren. Um die Massenproduktionstauglichkeit für eine Anwendung als intelligenter Feuersensor sicherzustellen, wird der Wachstumsprozess der Nanodrähte optimiert. Außerdem wird ein neuartiges chemisches FET-ähnliches Sensorkonzept namens Chem-FET vorgestellt, das im Vergleich zu UV-KAMINA ein verbessertes Signal-Rausch-Verhältnis und eine schnellere Reaktionszeit bietet. Eine überwachte Lernmethode des Maschinellen Lernens basierend auf einer linearen Diskriminanzfunktion wird verwendet, um verschiedene Zielgerüche zu klassifizieren. In einer Anwendung als Feuersensor erwiesen sich die entwickelten Sensorprototypen als konkurrenzfähig. Zusätzlich werden Möglichkeiten aufgezeigt, das Sensorprinzip als Plattform für andere Anwendungsarten verwenden zu können. Während mit den vorgestellten Methoden die Leistung des Gesamtsystems optimiert werden konne, bleibt als Ausblick Verbesserungsbedarf in Bereichen, wie z. B. der Charakterisierung von Gerüchen und der Testmethodik für die Anwendung in hohen Stückzahlen

Diatermiasavun analysointiin perustuva leikkauksenaikainen tervekudosmarginaalin tunnistava mittausjärjestelmä

In this thesis, a method and system capable of intraoperative cancer margin detection, with potential to improve the current methodology, is introduced, tested and validated. The system is based on diathermy smoke analysis by differential ion mobility spectrometry (DMS). Three large measurement sets with different objectives were executed. The first measurement set concentrated on validating the function of a novel filtration device, which is an essential part of the full system proposed in this thesis, and was patented during the thesis work. In the second measurement set, a proof-of-concept study with porcine tissues was conducted to elucidate, whether healthy tissue identification with the system is possible. The third measurement set was a pilot test with two types of clinical human brain tumor samples, with the aim to achieve actual reliable cancer identification with the proposed system. Regarding the objectives, all the three measurement sets were successful. Based on the results, we state that the patented filtration solution works with a high efficiency without compromising tissue identification, and that cancer detection based on the ion mobility spectrometer analysis of tissue smoke can be achieved with our system

On the development of intelligent medical systems for pre-operative anaesthesia assessment

This thesis describes the research and development of a decision support tool for determining a medical patient's suitability for surgical anaesthesia. At present, there is a change in the way that patients are clinically assessedp rior to surgery. The pre-operative assessment, usually conducted by a qualified anaesthetist, is being more frequently performed by nursing grade staff. The pre-operative assessmenet xists to minimise the risk of surgical complications for the patient. Nursing grade staff are often not as experienced as qualified anaesthetists, and thus are not as well suited to the role of performing the pre-operative assessment. This research project used data collected during pre-operative assessments to develop a decision support tool that would assist the nurse (or anaesthetist) in determining whether a patient is suitable for surgical anaesthesia. The three main objectives are: firstly, to research and develop an automated intelligent systems technique for classifying heart and lung sounds and hence identifying cardio-respiratory pathology. Secondly, to research and develop an automated intelligent systems technique for assessing the patient's blood oxygen level and pulse waveform. Finally, to develop a decision support tool that would combine the assessmentsa bove in forming a decision as to whether the patient is suitable for surgical anaesthesia. Clinical data were collected from hospital outpatient departments and recorded alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds were collected using an electronic stethoscope. Using this data two ensembles of artificial neural networks were trained to classify the different heart and lung sounds into different pathology groups. Classification accuracies up to 99.77% for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate between normal and abnormal pulse waveforms. A discrimination accuracy of 98% has been obtained from the system. A fuzzy inference system was generated to classify the patient's blood oxygen level as being either an inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia. When tested the system successfully classified 100% of the test dataset. A decision support tool, applying the genetic programming evolutionary technique to a fuzzy classification system was created. The decision support tool combined the results from the heart sound, lung sound and pulse oximetry classifiers in determining whether a patient was suitable for surgical anaesthesia. The evolved fuzzy system attained a classification accuracy of 91.79%. The principal conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic programming, and fuzzy inference systems, can be successfully applied to the creation of medical decision support tools.EThOS - Electronic Theses Online ServiceMedicdirect.co.uk Ltd.GBUnited Kingdo

Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 74-82.Dynamic textures are moving image sequences that exhibit stationary characteristics in time such as fire, smoke, volatile organic compound (VOC) plumes, waves, etc. Most surveillance applications already have motion detection and recognition capability, but dynamic texture detection algorithms are not integral part of these applications. In this thesis, image processing based algorithms for detection of specific dynamic textures are developed. Our methods can be developed in practical surveillance applications to detect VOC leaks, fire and smoke. The method developed for VOC emission detection in infrared videos uses a change detection algorithm to find the rising VOC plume. The rising characteristic of the plume is detected using a hidden Markov model (HMM). The dark regions that are formed on the leaking equipment are found using a background subtraction algorithm. Another method is developed based on an active learning algorithm that is used to detect wild fires at night and close range flames. The active learning algorithm is based on the Least-Mean-Square (LMS) method. Decisions from the sub-algorithms, each of which characterize a certain property of the texture to be detected, are combined using the LMS algorithm to reach a final decision. Another image processing method is developed to detect fire and smoke from moving camera video sequences. The global motion of the camera is compensated by finding an affine transformation between the frames using optical flow and RANSAC. Three frame change detection methods with motion compensation are used for fire detection with a moving camera. A background subtraction algorithm with global motion estimation is developed for smoke detection.Günay, OsmanM.S

Research and technology, 1992

Selected research and technology activities at Ames Research Center, including the Moffett Field site and the Dryden Flight Research Facility, are summarized. These activities exemplify the Center's varied and productive research efforts for 1992