The aim of this thesis is a diagnosis of ventricular tachycardias, fibrillations and flutters from electrocardiogram. These disturbances of heart rate are ranked among the life threatening arrhytmias. This work presents own method of the automatic detection, which is created for the ECG holter monitoring system. The proposed algorithm is based on the detection in the spectral domain, which is supported by the detection in the time domain. The results show the discrimination of arrhytmias from the normal sinus rhythm and the discrimination from the noise. The method is tested with ECG records from the The AHA Database (American Heart Association) and from The MIT-BIH Malignant Ventricular Arrhythmia Database.

Contact is one of the most challenging nonlinearities to solve in solid mechanics. In traditional linear finite element analysis, the contact surface is only C^0 continuous, as a result, the normal to the contact surface is not continuous. The normal contact force is directed along the normal in the direction of the contact surface, and therefore, the contact force is discontinuous. This issue is tackled in linear finite element analysis using various surface smoothing techniques, however, a better solution is to use isogeometric analysis where the solution space is spanned by smooth spline basis functions. Unfortunately, spline-based isogeometric contact analysis still has limited applicability to industrial computer aided design (CAD) representations. Building analysis suitable mesh from the industrial CAD representations has been a major bottleneck of the computer aided engineering workflow. One promising alternative field of study, intended to address this challenge, is called the immersed finite element method. In this method, the original CAD domain is immersed in a rectilinear grid called the background mesh. This cuts down the model preparation and the mesh generation time from the original CAD domain, but the method suffers from limited accuracy issues. In this dissertation, the original CAD domain is immersed in an envelope domain which can be of arbitrary topological and geometric complexity and can approximate none, some or all of the features of the original CAD domain. Therefore, the method, called the flex representation method, is much more flexible than the traditional immersed finite element method. Within the framework of the flex representation method, a robust and accurate contact search algorithm is developed, that efficiently computes the collision points between the contacting surfaces in a discrete setting. With this information at hand, a penalty based formulation is derived to enforce the contact constraint weakly for multibody and self-contact problems. In addition, the contact algorithm is used to solve various proof-of-concept academic problems and some real world industrial problems to demonstrate the validity and robustness of the algorithms.

The McMaster incident detection algorithm is being tested on-line within the Burlington freeway traffic management system (FTMS) as an alternative to the existing California-type algorithm currently in place. This paper represents the most recent and comprehensive evaluation of the McMaster algorithm's performance to date. In the past, the algorithm has been tested using single lane detectors for the northbound lanes only. This evaluation uses data from lanes 1 and 2 for each of the 13 northbound and 13 southbound detector stations. The data was collected during a 60-day period beginning on November 15, 1990 and ending January 13, 1991. Detection rate, mean time-lag to detection and false alarm rate are used to evaluate the performance of the algorithm. As well, those factors such as winter precipitation, which influenced the performance of the algorithm are also examined. To improve the algorithm's detection rate and lower its false alarm rate, it is reccomended that the persistence check used to declare an incident be increased by 30-seconds from 2 to 3 periods. / Thesis / Candidate in Philosophy

No description available.

Pitcher is a prototype which makes it easier for inexperienced guitar players to tune their guitars without any prior knowledge required. This thesis will explore how the construction varies between the usage of DC and a stepper motor, how reliable the tuner is and how long it takes to tune the guitar. The tuner will capture sound with a microphone and calculate the current frequency of the string with YIN autocorrelation. Based on the frequency a control system regulator is used to determine the speed and direction of a motor which turns the tuning peg, this is repeated until the string is in tune. 30 tests were conducted from different starting frequencies, and the time it took for the tuner to find the right pitch and the string’s corresponding frequency was measured. Some of the measurements were a couple of Hz off pitch, and only about half of the frequencies measured belonged to the interval where there is no noticeable dierence of the pitch, therefore the tuner could not be considered reliable. The time it takes to tune the guitar is dependent on how far off pitch the string is andthe dierence in time does not depend linearly with the starting frequency, it increases faster the further off pitch the string is.The tuner is portable and to apply the tuner to the guitar it is held and placed on the tuning peg with one hand as the other hand is plucking the string. / Den automatiska gitarrstämmaren, Pitcher, är en prototyp som möjliggör för oerfarna gitarranvändare utan förkunskaper att stämma en gitarr. Den här avhandlingen kommer att undersöka hur konstruktionen skiljer sig åt vidanvändning utav en stegmotor respektive en likströmsmotor, hur lång tid det tar att stämma gitarren samt hur tillförlitligprototypen är. Stämmaren avläser ljudsignaler med en mikrofon och beräknar sedan frekvensen av strängen med hjälp av YIN autokorrelation. Den beräknade frekvensen behandlas i en regulator som avgör vilken hastighet och i vilken riktningmotorn ska rotera stämskruven. Detta repeteras tills korrekt frekvens erhålls. 30 test gjordes då gitarren stämdes från olika startfrekvenser där tiden att stämma strängen respektive dess frekvens mättes. Några mätningar hade en frekvens som avvek flera Hz från korrekt frekvens, och cirka hälften av frekvenserna från alla mätningar tillhörde frekvensintervallet där ingen skillnad kan höras på tonen, därför kan gitarrstämmaren ej anses vara tillförlitlig. Tidendet tar att stämma en sträng är beroende på hur ostämd den är och skillnaden i tid beror inte linjärt av startfrekvens, utan den ökar snabbare desto mer ostämd gitarren är. Stämmaren är portabel och för att applicera den på gitarren placeras munstycket på stämskruven medan den andra handen slår an strängen.

Objectives: To detect and distinguish oral malignant and non-malignant lesions from clinical photographs using YOLO v8 deep learning algorithm. Methods: This is a diagnostic study conducted using clinical images of oral cavity lesions. The 427 clinical images of the oral cavity were extracted from a publicly available dataset repository specifically Kaggle and Mendeley data repositories. The datasets obtained were then categorized into normal, abnormal (non-malignant), and malignant oral lesions by two independent oral pathologists using Roboflow Annotation Software. The images collected were first set to a resolution of 640 x 640 pixels and then randomly split into 3 sets: training, validation, and testing – 70:20:10, respectively. Finally, the image classification analysis was performed using the YOLO V8 classification algorithm at 20 epochs to classify and distinguish between malignant lesions, non-malignant lesions, and normal tissue. The performance of the algorithm was assessed using the following parameters accuracy, precision, sensitivity, and specificity. Results: After training and validation with 20 epochs, our oral cancer image classification algorithm showed maximum performance at 15 epochs. Based on the generated normalized confusion matrix, the sensitivity of our algorithm in classifying normal images, non-malignant images, and malignant images was 71%, 47%, and 54%, respectively. The specificity of our algorithm in classifying normal images, non-malignant, and malignant images were 86%, 65%, and 72%. The precision of our algorithm in classifying normal images, non-malignant images, and malignant images was 73%, 62%, and 35%, respectively. The overall accuracy of our oral cancer image classification algorithm was 55%. On a test set, our algorithm gave an overall 96% accuracy in detecting malignant lesions. Conclusion: Our object classification algorithm showed a promising application in distinguishing between malignant, non-malignant, and normal tissue. Further studies and continued research will observe increasing emphasis on the use of artificial intelligence to enhance understanding of early detection of oral cancer and pre-cancerous lesions. Keywords: Normal, Non-malignant, Malignant lesions, Image classification, Roboflow annotation software, YOLO v8 object/image classification algorithm. / Oral Biology

A battery-powered hydroacoustic modem concept has been developed to enable underwater communication. These modems face challenges due to limited computing capacity and battery life. To save power, the modem can go into sleep mode and wake up only when it receives important information, which requires efficient detection algorithms. In the marine environment, interfering factors such as noise, Doppler effects, reflections and signal absorption can affect signal quality and the performance of detection algorithms. The performance of two detection algorithms, one non-coherent and one coherent, was evaluated. For the non-coherent detection algorithm, four different codewords were evaluated and showed that codeword 1 performed best and detected at the lowest SNR. The varying performance between different codewords may be due to the suppression of certain frequencies in different channels. Simulations with different thresholds showed that a threshold of 0 dB gave the best detection performance. The algorithm performed sufficiently in all tested channels and could handle a Doppler velocity of 2 m/s. Its performance decreased at 5 m/s, which was within acceptable limits. For the coherent detection algorithm, a threshold of 0.05 was the best. It met the requirement to handle Doppler velocities of 2 m/s in all channels except one, a channel recorded on Hawaii named KAU2, possibly due to higher time delays in this channel. To reduce the number of replicas in the Doppler bank, an approximately 4 dB higher SNR was required for effective detection, with an increase of 8 dB for Doppler velocities of 5 m/s. A comparison between the two algorithms shows that the choice of the best method depends on the specific conditions and requirements of the particular use case. The coherent detection algorithm is generally preferred because it has high performance in the case of noisy signals, but it fails to detect in some channels due to their conditions. In scenarios where channel conditions vary and it is important to be able to detect in different conditions, even if it means slightly worse noise performance, the non-coherent detection algorithm is more suitable. Also, the coherent detection algorithm may be more suitable for scenarios where faster symbol rate and easier decoding are critical, while the non-coherent detection algorithm may be beneficial in situations where more detailed information from the detection is more important than speed.

The fiber reinforced polymer (FRP) bars have been widely used in pre-stressing applications and reinforcing of the civil structures. High strength-to-weight ratio and high resistance to the corrosion make the FRP bars a good replacement for steel reinforcing bars in civil engineering applications. According to the CAN/CSA-S806-12 standard, the maximum recommended stress in FRP bars under service loads should not exceed 25% and 65% of the ultimate strength for glass FRP (GFRP) and carbon FRP (CFRP), respectively. These stress values are set to prevent creep failure in FRP bars. However, for in-service applications, there are few physical indicators that these values have been reached or exceeded. In this work analysis of acoustic emission (AE) signals is used. Two new techniques based on pattern recognition and frequency entropy of the isolated acoustic emission (AE) signal are presented for monitoring damage progression and prediction of failure in FRPs. / May 2017

Ces travaux de recherche visaient à développer un système d'aide à la mobilité pour les personnes ayant une déficience visuelle (VIP ‘Visually Impaired Person’) appelé ‘Smart Environment Explorer Stick (SEES)’. Le but particulier de cette recherche était de développer de nouveaux algorithmes pour améliorer la précision de la détection de la présence de poteaux de la canne SEE-stick en utilisant la méthode de calcul de distance et la recherche de paires de lignes verticales basées sur l'optimisation de la technique de détection de contour de Canny. Désormais, l'algorithme de détection des poteaux est appelé l’algorithme YuRHoS. Le SEES développé comme système de support d'aide à la mobilité VIP a été intégré avec succès à plusieurs dispositifs tels que le serveur distant dénommé iSEE, le serveur local embarqué dénommé SEE-phone et la canne intelligente dénommée SEE-stick. Les performances de SEE-stick ont été améliorées grâce à l'algorithme YuRHoS qui permet de discriminer avec précision les objets (obstacles) en forme de poteau parmi les objets détectés. La comparaison des résultats de détection des poteaux avec ceux des autres algorithmes a conclu que l'algorithme YuRHoS était plus efficace et précis. Le lieu et la couleur des poteaux de test d’évaluation étaient deux des facteurs les plus importants qui influaient sur la capacité du SEE-stick à détecter leur présence. Le niveau de précision de SEE-stick est optimal lorsque le test d’évaluation est effectué à l'extérieur et que les poteaux sont de couleur argentée. Les statistiques montrent que la performance de l'algorithme YuRHoS à l'intérieur était 0,085 fois moins bonne qu'à l'extérieur. De plus, la détection de la présence de poteaux de couleur argentée est 11 fois meilleure que celle de poteaux de couleur noir. / This research aimed to develop a technology system of mobility aid for Visually Impaired Person (VIP) called Smart Environment Explorer Stick (SEES).Particular purpose of this research was developing new algorithm in improving accuracy of SEE-stick for pole detection using distance calculation method and vertical line pair search based on Canny edge detection optimization and Hough transform. Henceforth, the pole detection algorithm was named as YuRHoS algorithm.The developed SEES as supporting system of VIP mobility aid had been successfully integrated several devices such as global remote server (iSEE), embedded local server (SEE-phone) and smart stick (SEE-stick). Performance of SEE-stick could be improved through YuRHoS algorithm, which was able to fix the accuracy of SEE-stick in detecting pole. Test comparison of pole detection results among others algorithm concluded that YuRHoS algorithm had better accuracy in pole detection.Two most significant factors affecting SEE-stick ability in detecting pole was test location and pole color. Level of accuracy of SEE-stick would be optimum once the test location was performed outdoor and pole color was silver. Statistics result shown that YuRHoS algorithm performance indoor was 0.085 times worse than outdoor. Meanwhile, silver-pole-color as object detection could increase YuRHoS algorithm performance as much as 11 times better compare to black-pole-color.

[pt] Com o desenvolvimento tecnológico no setor de telecomunicações, o espectro radioelétrico está quase totalmente ocupado com um grande número de múltiplas atribuições para os muitos serviços sem fio de aplicação comercial e, também, não comercial, tais como defesa, controle de tráfego aéreo e exploração científica. O espectro eletromagnético é um recurso natural precioso e escasso, por isso, importantes esforços estão sendo direcionados para o desenvolvimento de rádios cognitivos, com capacidade de sensoriar o uso do espectro e utilizar frequências momentaneamente disponíveis de forma oportunista. O rastreamento e a utilização de intervalos espectrais, ou white spaces, através da tecnologia de rádios cognitivos, permitirá aumentar a eficiência de uso do espectro com a introdução de novos serviços de telecomunicações a serem explorados por usuários secundários, obrigados a não interferir ou a provocar interferência muito limitada nos usuários primários. O objetivo geral deste trabalho é avaliar os principais algoritmos de detecção dos intervalos espectrais (Detector de Energia, Detecção do Valor Absoluto de Covariância, Sensoriamento de Covariância Espectral) por meio de simulações com dados experimentais obtidos em campanhas de medições e testes em laboratório. Os algoritmos foram testados para avaliar o seu desempenho em termos de probabilidade de detecção dada uma probabilidade de falso alarme requerida, complexidade computacional e robustez quanto a relações sinal-a-ruído baixas. Os dados experimentais utilizados provêm de campanhas de medidas realizadas em ambiente urbano na faixa de 3.5 GHz. / [en] With the technological development of the telecommunications industry, the radio spectrum is almost fully occupied with a large number of multiple assignments for wireless services for both commercial and non-commercial applications, such as defense, air traffic control and scientific exploration. The electromagnetic spectrum is a precious and scarce natural resource. Therefore, a considerable effort is being directed at the development of cognitive radios, capable of sensoring the spectrum and using momentarily available frequency bands in an opportunistic way. The tracking and using of these spectral intervals, or white spaces, using cognitive radio technology will enhance the efficiency of the spectrum use and allow the introduction of new telecommunications services to be exploited by secondary users, obliged not to interfere or produce very limited interference to primary users. The aim of this study is to evaluate the main algorithms for detection of spectral intervals (Energy Detector, Detection of Covariance Absolute Value, Spectral Covariance Sensing) through simulations with experimental data obtained in field measurements campaigns. The algorithms were tested to evaluate their performance in terms of detection probability given a required false alarm probability, computational complexity and robustness in low signal-to-noise conditions. The experimental data used comes from the measurements campaigns in urban environments at the 3.5 GHz band.