Modern radar technology relies heavily on digital signal processing. As radar technology pushes the boundaries of miniaturization, computational systems must be developed to support the processing demand. One particular application for small radar technology is in modern drone systems. Many drone applications are currently inhibited by safety concerns of autonomous vehicles navigating shared airspace. Research in radar based Detect and Avoid (DAA) attempts to address these concerns by using radar to detect nearby aircraft and choosing an alternative flight path. Implementation of radar on small Unmanned Air Vehicles (UAV), however, requires a lightweight and power efficient design. Likewise, the radar processing system must also be small and efficient.This thesis presents the design of the processing system for a small Frequency Modulated Continuous Wave (FMCW) phased array radar. The radar and processing is designed to be light-weight and low-power in order to fly onboard a UAV less than 25 kg in weight. The radar algorithms for this design include a parallelized Fast Fourier Transform (FFT), cross correlation, and beamforming. Target detection algorithms are also implemented. All of the computation is performed in real-time on a Xilinx Zynq 7010 System on Chip (SoC) processor utilizing both FPGA and CPU resources.The radar system (excluding antennas) has dimensions of 2.25 x 4 x 1.5 in3, weighs 120 g, and consumes 8 W of power of which the processing system occupies 2.6 W. The processing system performs over 652 million arithmetic operations per second and is capable of performing the full processing in real-time. The radar has also been tested in several scenarios both airborne on small UAVs as well as on the ground. Small UAVs have been detected to ranges of 350 m and larger aircraft up to 800 m. This thesis will describe the radar design architecture, the custom designed radar hardware, the FPGA based processing implementations, and conclude with an evaluation of the system's effectiveness and performance.

Objective: Insulin-like growth factor-I (IGF-I) has been suggested to be a prognostic marker for the development of cancer and, more recently, cardiovascular disease. These diseases are closely linked to obesity, but reports of the association of IGF-I with measures of obesity are divergent. In this study, we assessed the association of age-dependent IGF-I standard deviation scores with body mass index (BMI) and intra-abdominal fat accumulation in a large population. Design: A cross-sectional, epidemiological study. Methods: IGF-I levels were measured with an automated chemiluminescence assay system in 6282 patients from the DETECT study. Weight, height, and waist and hip circumference were measured according to the written instructions. Standard deviation scores (SDS), correcting IGF-I levels for age, were calculated and were used for further analyses. Results: An inverse U-shaped association of IGF-I SDS with BMI, waist circumference, and the ratio of waist circumference to height was found. BMI was positively associated with IGF-I SDS in normal weight subjects, and negatively associated in obese subjects. The highest mean IGF-I SDS were seen at a BMI of 22.5–25 kg/m2 in men (+0.08), and at a BMI of 27.5–30 kg/m2 in women (+0.21). Multiple linear regression models, controlling for different diseases, medications and risk conditions, revealed a significant negative association of BMI with IGF-I SDS. BMI contributed most to the additional explained variance to the other health conditions. Conclusions: IGF-I standard deviation scores are decreased in obesity and underweight subjects. These interactions should be taken into account when analyzing the association of IGF-I with diseases and risk conditions.

Objective: IGF1 is associated with metabolic parameters and involved in glucose metabolism. Low-IGF1 has been implicated in the etiology of glucose intolerance and subjects with pathological causes of either low- or high-IGF1 are at risk of diabetes. We hypothesized that both low- and high-IGF1 levels increase the risk of diabetes and aimed to assess the role of IGF1 in the risk of developing diabetes in a large prospective study. Design: An analysis of two prospective cohort studies, the DETECT study and SHIP. Methods: We measured IGF1 levels in 7777 nondiabetic subjects and assessed incident diabetes mellitus during follow-up. Results: There were 464 cases of incident diabetes during 32 229 person-years (time of follow-up in the DETECT study and SHIP: 4.5 and 5 years respectively). There was no heterogeneity between both studies (P>0.4). The hazard ratios (HRs) of incident diabetes in subjects with IGF1 levels below the 10th or above the 90th age- and sex-specific percentile, compared to subjects with intermediate IGF1 levels, were 1.44 (95% confidence interval (CI) 1.07–1.94) and 1.55 (95% CI 1.06–2.06) respectively, after multiple adjustment. After further adjustment for metabolic parameters, the HR for low-IGF1 became insignificant. Analysis of IGF1 quintiles revealed a U-shaped association of IGF1 with risk of diabetes. Results remained similar after exclusion of patients with onset of new diabetes within 1 year or with borderline glucose or HbA1c levels at baseline. Conclusions: Subjects with low- or high-IGF1 level are at increased risk of developing diabetes.

The problem of object detection deals with determining whether an instance of a given class of object is present or not. There are robust, supervised learning based algorithms available for object detection in an image. These image object detectors (image-based object detectors) use characteristics learnt from the training samples to find object and non-object regions. The characteristics used are such that the detectors work under a variety of conditions and hence are very robust. Object detection in video can be performed by using such a detector on each frame of the video sequence. This approach checks for presence of an object around each pixel, at different scales. Such a frame-based approach completely ignores the temporal continuity inherent in the video. The detector declares presence of the object independent of what has happened in the past frames. Also, various visual cues such as motion and color, which give hints about the location of the object, are not used. The current work is aimed at building a generic framework for using a supervised learning based image object detector for video that exploits temporal continuity and the presence of various visual cues. We use temporal continuity and visual cues to speed up the detection and improve detection accuracy by considering past detection results. We propose a generic framework, based on Experiential Sampling [1], which considers temporal continuity and visual cues to focus on a relevant subset of each frame. We determine some key positions in each frame, called attention samples, and object detection is performed only at scales with these positions as centers. These key positions are statistical samples from a density function that is estimated based on various visual cues, past experience and temporal continuity. This density estimation is modeled as a Bayesian Filtering problem and is carried out using Sequential Monte Carlo methods (also known as Particle Filtering), where a density is represented by a weighted sample set. The experiential sampling framework is inspired by Neisser’s perceptual cycle [2] and Itti-Koch’s static visual attention model[3]. In this work, we first use Basic Experiential Sampling as presented in[1]for object detection in video and show its limitations. To overcome these limitations, we extend the framework to effectively combine top-down and bottom-up visual attention phenomena. We use learning based detector’s response, which is a top-down cue, along with visual cues to improve attention estimate. To effectively handle multiple objects, we maintain a minimum number of attention samples per object. We propose to use motion as an alert cue to reduce the delay in detecting new objects entering the field of view. We use an inhibition map to avoid revisiting already attended regions. Finally, we improve detection accuracy by using a particle filter based detection scheme [4], also known as Track Before Detect (TBD). In this scheme, we compute likelihood of presence of the object based on current and past frame data. This likelihood is shown to be approximately equal to the product of average sample weights over past frames. Our framework results in a significant reduction in overall computation required by the object detector, with an improvement in accuracy while retaining its robustness. This enables the use of learning based image object detectors in real time video applications which otherwise are computationally expensive. We demonstrate the usefulness of this framework for frontal face detection in video. We use Viola-Jones’ frontal face detector[5] and color and motion visual cues. We show results for various cases such as sequences with single object, multiple objects, distracting background, moving camera, changing illumination, objects entering/exiting the frame, crossing objects, objects with pose variation and sequences with scene change. The main contributions of the thesis are i) We give an experiential sampling formulation for object detection in video. Many concepts like attention point and attention density which are vague in[1] are precisely defined. ii) We combine detector’s response along with visual cues to estimate attention. This is inspired by a combination of top-down and bottom-up attention maps in visual attention models. To the best of our knowledge, this is used for the first time for object detection in video. iii) In case of multiple objects, we highlight the problem with sample based density representation and solve by maintaining a minimum number of attention samples per object. iv) For objects first detected by the learning based detector, we propose to use a TBD scheme for their subsequent detections along with the learning based detector. This improves accuracy compared to using the learning based detector alone. This thesis is organized as follows . Chapter 1: In this chapter we present a brief survey of related work and define our problem. . Chapter 2: We present an overview of biological models that have motivated our work. . Chapter 3: We give the experiential sampling formulation as in previous work [1], show results and discuss its limitations. . Chapter 4: In this chapter, which is on Enhanced Experiential Sampling, we suggest enhancements to overcome limitations of basic experiential sampling. We propose track-before-detect scheme to improve detection accuracy. . Chapter 5: We conclude the thesis and give possible directions for future work in this area. . Appendix A: A description of video database used in this thesis. . Appendix B: A list of commonly used abbreviations and notations.

大抵面對一連串戰敗、不平等條約的陰霾，英法日德美各國殖民勢力的入侵，民初上海因為歷史造成地域血緣上的混雜性，掙扎於「華╱洋」夾縫內，注定要以邁向「現代化」作為自我建構身分和翻轉形象的策略。彼時一波波新政策、新思維和新文化景觀衝擊著上海，民初上海的文化歷史舞台，上演著政治性、戰鬥性和革命性等直截的激烈論爭，以及文學性、哲學性和藝術性等間接的表述詰疑。民初上海，因為其獨特的身世背景――租界空間、華洋文化、都市治理、建築形構和住民型態等――已構成一個豐富的文本；如今，再加上歷來研究者建立在這個文本上的批評研究――懷舊記憶、商品消費、新聞產製、建築美學和社會空間實踐等――使它構成一個更為多義和可解讀的龐大文本。 程小青（1893—1976）創作的《霍桑探案》，作為民初新興的偵探文類，交會著地理學、都市學、社會學、新聞學、犯罪學、心理學、科學和醫學等多方視域，確實值得加以探勘。本論文雖立基在前人對於上海學的研究基礎上，卻針對「偵探――文本――都市文化」之間的緊密關係，作一個更為清晰的分層處理。從《霍桑探案》泰半取材自上海市井民間的文化故事與社會平日的案件觀之，其以小博大之視野，呼應了近現代中國政潮起伏、社會局勢凌替，和民生人心動盪之圖景，則不容小覷。是而，本論文將聚焦於程小青的偵探小說，對其上海文化圖景之內蘊與意涵，作層層之釐析。論文主體分別由地理空間層面、文化記憶層面、傳媒消費層面和醫學科學層面，逐次地展開「街道的表情學」、「日常生活的實踐」、「跨時空之旅」和「從偵察路線圖探討民初上海的醫病關係」層層深入的剖析。 以地理空間層面言，《霍桑探案》不以考究學理和艱深詞彙的地理學介紹為目的，倒是反過來鑑照了民初上海的地理空間，鮮活地捕捉每個街道的表情──從馬路上的黃包車、馬車、汽車和電車，到街坊里弄的舊石庫門、新石庫門和洋房，均豐富了民初上海立體多層的歷史記憶。 就文化記憶層面言，《霍桑探案》透過「偵探」的日常生活以實踐一種與市民經常性、韻律性和對話式的互動關係，從而對老上海文化記憶與摩登上海文化風情進行建構。在建構過程中，「小食挑子、老虎灶、孵茶館」和「舞廳、戲院、西餐廳」不啻成為兩組強大的反差和對比，霍桑和包朗將第二組概念視為歧出，力圖拉近與第一組概念的距離，似乎想以此贖救上海文化逐漸掉失、歪斜、墮落的「本」。 在傳媒消費層面上，以《霍桑探案》作為探討的個案，文本中所展現的「新聞圖景」和「偵探遊戲」，所蘊涵著脈絡化的意義，可從傳媒和消費兩個層面來辨析。以傳媒角度言，新聞報紙不僅被挪用、編織、進入小說的偵探版圖，又成為偵探偵查時不可或缺的一個重要依據。 由醫學科學層面言，綜合「敘事層面」、「地理景觀」、「身體空間」三層面探討的「偵察路線圖」，可歸納出《霍桑探案》不只勾勒偵探個人的「探案歷程」，還過渡到對社會「醫／病關係」的想像。 本論文以文學研究為主，偵探理論和文化研究為輔，分為地理空間、文化記憶、傳媒消費和醫學科學等層面論述之，以此勾勒出一幅嶄新的民初上海文化圖景，豐富了偵探小說、上海都市和文化研究的區塊。

Esse trabalho está baseado na investigação dos detectores de falhas aplicando classificadores de classe única. As falhas a serem detectadas são relativas ao estado de funcionamento de cada componente do circuito, especificamente de suas tolerâncias (falha paramétrica). Usando a função de transferência de cada um dos circuitos são gerados e analisados os sinais de saída com os componentes dentro e fora da tolerância. Uma função degrau é aplicada à entrada do circuito, o sinal de saída desse circuito passa por uma função diferenciadora e um filtro. O sinal de saída do filtro passa por um processo de redução de atributos e finalmente, o sinal segue simultaneamente para os classificadores multiclasse e classe única. Na análise são empregados ferramentas de reconhecimento de padrões e de classificação de classe única. Os classficadores multiclasse são capazes de classificar o sinal de saída do circuito em uma das classes de falha para o qual foram treinados. Eles apresentam um bom desempenho quando as classes de falha não possuem superposição e quando eles não são apresentados a classes de falhas para os quais não foram treinados. Comitê de classificadores de classe única podem classificar o sinal de saída em uma ou mais classes de falha e também podem classificá-lo em nenhuma classe. Eles apresentam desempenho comparável ao classificador multiclasse, mas também são capazes detectar casos de sobreposição de classes de falhas e indicar situações de falhas para os quais não foram treinados (falhas desconhecidas). Os resultados obtidos nesse trabalho mostraram que os classificadores de classe única, além de ser compatível com o desempenho do classificador multiclasse quando não há sobreposição, também detectou todas as sobreposições existentes sugerindo as possíveis falhas. / This work deals with the application of one class classifiers in fault detection. The faults to be detected are related parametric faults. The transfer function of each circuit was generated and the outputs signals with the components in and out of tolerance were analyzed. Pattern recognition and one class classifications tools are employed to perform the analysis. The multiclass classifiers are able to classify the circuit output signal in one of the trained classes. They present a good performance when the fault classes do not overlap or when they are not presented to fault classes that were not presented in the training. The one class classifier committee may classify the output signal in one or more fault classes and may also classify them in none of the trained class faults. They present comparable performance to multiclass classifiers, but also are able to detect overlapping fault classes and show fault situations that were no present in the training (unknown faults).

Esse trabalho está baseado na investigação dos detectores de falhas aplicando classificadores de classe única. As falhas a serem detectadas são relativas ao estado de funcionamento de cada componente do circuito, especificamente de suas tolerâncias (falha paramétrica). Usando a função de transferência de cada um dos circuitos são gerados e analisados os sinais de saída com os componentes dentro e fora da tolerância. Uma função degrau é aplicada à entrada do circuito, o sinal de saída desse circuito passa por uma função diferenciadora e um filtro. O sinal de saída do filtro passa por um processo de redução de atributos e finalmente, o sinal segue simultaneamente para os classificadores multiclasse e classe única. Na análise são empregados ferramentas de reconhecimento de padrões e de classificação de classe única. Os classficadores multiclasse são capazes de classificar o sinal de saída do circuito em uma das classes de falha para o qual foram treinados. Eles apresentam um bom desempenho quando as classes de falha não possuem superposição e quando eles não são apresentados a classes de falhas para os quais não foram treinados. Comitê de classificadores de classe única podem classificar o sinal de saída em uma ou mais classes de falha e também podem classificá-lo em nenhuma classe. Eles apresentam desempenho comparável ao classificador multiclasse, mas também são capazes detectar casos de sobreposição de classes de falhas e indicar situações de falhas para os quais não foram treinados (falhas desconhecidas). Os resultados obtidos nesse trabalho mostraram que os classificadores de classe única, além de ser compatível com o desempenho do classificador multiclasse quando não há sobreposição, também detectou todas as sobreposições existentes sugerindo as possíveis falhas. / This work deals with the application of one class classifiers in fault detection. The faults to be detected are related parametric faults. The transfer function of each circuit was generated and the outputs signals with the components in and out of tolerance were analyzed. Pattern recognition and one class classifications tools are employed to perform the analysis. The multiclass classifiers are able to classify the circuit output signal in one of the trained classes. They present a good performance when the fault classes do not overlap or when they are not presented to fault classes that were not presented in the training. The one class classifier committee may classify the output signal in one or more fault classes and may also classify them in none of the trained class faults. They present comparable performance to multiclass classifiers, but also are able to detect overlapping fault classes and show fault situations that were no present in the training (unknown faults).

The problem of face detection and recognition within a given database has become one of the important problems in computer vision. A simple approach for Face Detection in video is to run a learning based face detector every frame. But such an approach is computationally expensive and completely ignores the temporal continuity present in videos. Moreover the search space can be reduced by utilizing visual cues extracted based on the relevant task at hand(top down approach). Once detection is done next step is to perform a face recognition based on the available database. But the faces detected from face detect or output is neither aligned nor well cropped and is prone to scale change. We call such faces as free form faces. But the current existing algorithms on face recognition assume faces to be properly aligned and cropped, and having the same scale as the faces in the database, which is highly constrained. In this thesis, we propose an integrated detect-track framework for Multiview face detection in videos. We overcome the limitations of the frame based approaches, by utilizing the temporal continuity present in videos and also incorporating the top down information of the task. We model the problem based on the concept from Experiential sampling [2]. This consists of determining certain key positions which are relevant to the task(face detection). These key positions are referred to as attention samples and Multiview face detection is performed only at these locations. These statistical samples are estimated based on the visual cues, past experience and the temporal continuity and is modeled as a Bayesian filtering problem, which is solved using Particle Filters. In order to detect all views we use a tracker integrated with the detector and come out with a novel track termination algorithm using the concepts from Track Before Detect(TBD)[26]. Such an approach is computationally efficient and also results in lower false positive rate. We provide experiments showing the efficiency of the integrated detect-track approach over the multiview face detector approach without a tracker. For free form face recognition we propose to use the concept of Principal Geodesic Analysis(PGA) of the Covariance descriptors obtained from Gabor filters. This is similar to Principal Component Analysis in Euclidean spaces (Covariance descriptors lie on a Riemannian manifold). Such a descriptor is robust to alignment and scaling problems and also are of lower dimensions. We also employ sparse modeling technique for Face recognition task using these Covariance descriptor which are dimensionally reduced by transforming them on to a tangent space, which we call PGA feature. Further, we improve upon the recognition results of linear sparse modeling, by non-linear mapping of the PGA features by employing “Kernel Trick” for these sparse models. We show that the Kernelized sparse models using the PGA features are indeed very efficient for free form face recognition by testing on two standard databases namely AR and YaleB database.

Bakgrund: Sepsis är ett akut sjukdomstillstånd med varierande symtom och är därför svårt att upptäcka. Tillståndet innebär lidande för patienten och mortaliteten bland drabbade är hög. Snabb identifiering och behandling är av betydelse för patientens hälsa och överlevnad. Sjuksköterskan har ansvar för omvårdnaden, såväl för beroende som oberoende omvårdnadsåtgärder. För att möjliggöra god och säker vård ska sjuksköterskan arbeta förebyggande med patientsäkerhetsarbete. Syfte: Syftet var att undersöka faktorer i sjuksköterskans arbete som är viktiga för att tidigt upptäcka och behandla patienter med sepsis. Metod: Artiklar med kvantitativ design har sammanställts till en litteraturöversikt och analyserats med deduktiv ansats. I urval och datainsamling har Polit och Becks niostegsmodell används och totalt gick 11 artiklar vidare från kvalitetsgranskning till resultat. Resultat: Tre faktorer hittades; Utbildning, screeningverktyg samt riktlinjer för behandling vid sepsis. Utbildning ledde till ökad kunskap och ökade sjuksköterskans förmåga att identifiera sepsis. Vid användning av riktlinjer ökade följsamheten till behandlingsåtgärder och tiden till behandling minskade. Screeningverktyg och riktlinjer för behandling minskade mortaliteten. Slutsats: Sjuksköterskans är central i arbetet för tidig upptäckt och behandling, då sjuksköterskan screenade patienter för sepsis, initierade till och utförde behandlingsåtgärder. För att säker vård ska möjliggöras vid sepsis krävs såväl kunskap, screeningverktyg samt riktlinjer för behandling. / Background: Sepsis is a critical condition with varying symptoms that makes it difficult to detect. The mortality is high and the condition causes the patient suffering. Early identification and rapid treatment is important for the patient's health and survival. The nurse is responsible for nursing care, both independent interventions and ordered by physicians.  The nurse needs to work preventing regards patients and providers safety to provide good care. Aim: To survey factors for early detection and treatment in the nurse's work with patients having sepsis. Method:  A literature overview with quantitative articles and a deductive approach. For selection and collection of data Polit and Becks nine-way model was used. A total of 11 articles was quality reviewed and included to the result.  Results: Three factors were found; Education, screening tools and guidelines for treatment of sepsis. Education led to increased knowledge and increased the nurse's ability to identify sepsis. When using guidelines, adherence to treatment measures increased and time to treatment decreased. Furthermore, screening tools and treatment guidelines were found to reduce mortality. Conclusion: The nurse is central in the work of early detection and treatment. The nurse screened patients for sepsis, initiated and fulfilled treatment measures. Knowledge, screening tools and guidelines for treatment are required to enable safe care in the case of sepsis.

This thesis deals with moving objects detection in video sequences. The principal aim of such detection is to detect and locate motion in the image, separate individual objects, and track these objects. Subsequently, to eliminate shadows, the paper introduces method of motion detection based on Local Binary Patterns together with differential method above the HSV color space. The proposed method provides rapid and accurate movement detection in video sequences.