This paper addresses the problem of detecting K-complexes in sleep EEG. The study of sleep has become very essential to diagnose the brain disorders and analysis of brain activities. Since Kcomplex can have a wide variety of shapes it is very difficult to detect the K-complexes manually. In this paper, I present an automatic method for K-complexes detection based wavelet transform,TKEO and method for classification using feedforward multilayer neural network designed in Matlab. Detection performance reached the value approx. from 52,9 to 83,6 %.

This paper represents the most well-known compression methods, which have been published. A Compression of ECG signal is important primarily for space saving in memory cards or efficiency improvement of data transfer. An application of wavelet transform for compression is a worldwide discussed topic and this is the reason why the paper focuses in this direction. Gained wavelet coefficients might be firstly quantized and then compressed using suitable method. There are many options for a selection of wavelet and a degree of decomposition, which will be tested from the point of view of the most efficient compression of ECG signal.

Práce se zaměřuje na efektivní výpočet dvourozměrné diskrétní vlnkové transformace. Současné metody jsou v práci rozšířeny v několika směrech a to tak, aby spočetly tuto transformaci v jediném průchodu, a to případně víceúrovňově, použitím kompaktního jádra. Tohle jádro dále může být vhodně přeorganizováno za účelem minimalizace užití některých prostředků. Představený přístup krásně zapadá do běžně používaných rozšíření SIMD, využívá hierarchii cache pamětí moderních procesorů a je vhodný k paralelnímu výpočtu. Prezentovaný přístup je nakonec začleněn do kompresního řetězce formátu JPEG 2000, ve kterém se ukázal být zásadně rychlejší než široce používané implementace.

This thesis is concerned with the removal of artefacts typical for JPEG im- age compression. First, we describe the mathematical formulation of the JPEG format and the problem of artefact removal. We then formulate the problem as an optimization problem, where the minimized functional is obtained via Bayes' theorem and complex wavelets. We describe proximal operators and algorithms and apply them to the minimization of the given functional. The final algorithm is implemented in MATLAB and tested on several test problems. 1

With the rapid development of power systems, locating and classifying faults is critical to the continuity and reliability of the transmission system. In this thesis, a traveling-wave based technique for fault location and classification on high voltage and extremely high-voltage transmission lines is proposed. The traveling-wave based protection has the advantage of fast response and not being affected by power swing and CTs saturation. In this thesis, the transient characteristics of single line to ground fault (which can be divided into solid fault and arcing fault) and lightning disturbance are extracted by using Clarke transformation and wavelet transformation. The differences among recorded traveling wave arrival times are used to calculate the fault location, and the wavelet energy at different frequency bands is utilized to distinguish between lightning and different kinds of fault. A criterion is proposed according to the energy ratio. The proposed scheme can identify different faults correctly and quickly. In addition, the influence of busbar capacitance, current transformer and coupling capacitor voltage transformer are considered. The simulation of a transmission system has been made in ATP/EMTP, and the calculations have been made in MATLAB. / Med den snabba utvecklingen av kraftsystem är lokalisering och klassificering av fel avgörande för kontinuiteten och tillförlitligheten hos överföringssystem. I denna avhandling föreslås en vågrörelse-baserad teknik för fellokalisering och klassificering av kraftledningar för högspänning och extremt hög spänning. Vågrörelsebaserat skydd har fördelen av snabb respons och att det inte påverkas av kraft fluktuationer och strömtransformsmättnad. I denna avhandling tas momentana egenskaperna av jord till ledningsfel (vilket kan delas in i stumt jordfel och ljusbågefel) och blixtstörning fram med hjälp av Clarke transformation och wavelet transformation. Skillnaderna mellan de uppmätta vågrörelsernas ankomsttider används för att beräkna fellokalisering och wavelet energin vid olika frekvensband, vilket används för att skilja mellan blixt och olika sorters fel. Ett kriterium föreslås enligt energiförhållandet. Det föreslagna systemet kan identifiera olika sorters fel korrekt och snabbt. Dessutom övervägs påverkan av strömskenans kapacitans, strömtransformator och kopplingskondensatorspänningsomvandlare. Simuleringen av transmissionssystem har gjorts med ATP/EMTP, och beräkningarna är gjorda med MATLAB.

Cardiovascular diseases are a leading cause of death globally. Early diagnosis and treatment is of prime importance to prevent or mitigate health complications. Electrocardiogram (ECG) is a standard test modality used for early diagnosis of arrhythmias. The standard ECG uses 12 leads (i.e., 12 different views of the electrical activity of the heart). However, it is not always possible to perform a standard 12-lead ECG, for instance, in certain emergency situations. Such devices used in emergency situations are able to measure only a subset of leads. Although it is a simpler way of recording ECG, it comes at the cost of losing some information. The project presented in this thesis applies three different models based on canonical correlation analysis (CCA) to perform transfer learning from 12-lead ECGs to improve performance when only a subset of leads is available. The models used were linear canonical correlation analysis, deep canonical correlation analysis (DCCA) and deep canonically correlated bidirectional long short-term memory networks (DCC-BiLSTMs). These models are compared to each other using different configurations to study their performance on ECG data. Linear canonical correlation analysis performed better than its more complex variants, DCCA and DCC-BiLSTMs. With this method, it was possible to improve performance on ECG classification when using two, three, four and six leads in a computationally efficient way. / Hjärt- och kärlsjukdomar är den främsta dödsorsaken i världen. Tidig diagnos och behandling är av största vikt för att förhindra ytterligare och allvarliga hälsoproblem. Elektrokardiogram (EKG) är den standardmetod som används för tidig diagnos av arytmier. Standardförfarandet inom EKG använder sig av 12 avledningar (dvs. 12 olika vyer av hjärtats elektriska aktivitet). Det är dock inte alltid möjligt att utföra ett standard-EKG med 12 ledningar, vilket t.ex. förekommer i vissa nödsituationer. I dessa fall kan utrustning som gör det möjligt att ta fram ett 12-ledars EKG inte vara tillgänglig av flera olika skäl, och därför används andra apparater som kan mäta endast en delmängd av ledningarna för tidig diagnostik. Även om det är ett enklare sätt att utföra ett EKG, innebär det att man förlorar en del information. I det projekt som presenteras i detta dokument används tre olika modeller baserade på kanonisk korrelationsanalys (CCA) för att utföra överföringsinlärning från 12-ledars EKG för att förbättra prestanda när endast en delmängd av avledningar används. De modeller som användes var linjär kanonisk korrelationsanalys, djup kanonisk korrelationsanalys (DCCA) och djupa kanoniskt korrelerade bidirektionella långtidsminnesnätverk (DCCBiLSTMs). Dessa modeller jämförs med varandra med hjälp av olika konfigurationer för att studera deras prestanda på EKG-data. Linjär kanonisk korrelationsanalys presterade bättre än dess mer komplexa varianter, DCCA och DCC-BiLSTMs. Med denna metod var det möjligt att förbättra prestandan för klassificering av EKG när man använder två, tre, fyra och sex ledningar på ett beräkningseffektivt sätt.

De nos jours, la compression de l'image médicale est un processus essentiel dans les systèmes de cybersanté. Compresser des images médicales de haute qualité est une exigence vitale pour éviter de mal diagnostiquer les examens médicaux par les radiologues. WAAVES est un algorithme de compression d'images médicales prometteur basé sur la transformée en ondelettes discrètes (DWT) qui permet d'obtenir une performance de compression élevée par rapport à l'état de la technique. Les principaux objectifs de ce travail sont d'améliorer la qualité d'image lors de la compression à l'aide de WAAVES et de fournir une architecture DWT haute vitesse pour la compression d'image sur des systèmes embarqués. En ce qui concerne l'amélioration de la qualité, les systèmes de nombres logarithmiques (LNS) ont été explorés pour être utilisés comme une alternative à l'arithmétique linéaire dans les calculs de DWT. Une nouvelle bibliothèque LNS a été développée et validée pour réaliser le DWT logarithmique. En outre, une nouvelle méthode de quantification appelée (LNS-Q) basée sur l'arithmétique logarithmique a été proposée. Un nouveau schéma de compression (LNS-WAAVES) basé sur l'intégration de la méthode Hybrid-DWT et de la méthode LNS-Q avec WAAVES a été développé. Hybrid-DWT combine les avantages des domaines logarithmique et linéaire conduisant à l'amélioration de la qualité d'image et du taux de compression. Les résultats montrent que LNS-WAAVES est capable d'obtenir une amélioration de la qualité d'un pourcentage de 8% et de 34% par rapport aux WAAVES en fonction des paramètres de configuration de compression et des modalités d'image. Pour la compression sur les systèmes embarqués, le défi majeur consistait à concevoir une architecture 2D DWT qui permet d'obtenir un débit de 100 trames full HD. Une nouvelle architecture unifiée de calcul 2D DWT a été proposée. Cette nouvelle architecture effectue à la fois des transformations horizontale et verticale simultanément et élimine le problème des accès de pixel d'image en colonne à partir de la RAM DDR hors-puce. Tous ces facteurs ont conduit à une réduction de la largeur de bande DDR RAM requise de plus de 2X. Le concept proposé utilise des tampons de ligne à 4 ports conduisant à quatre opérations en parallèle pipeline: la DWT verticale, la transformée DWT horizontale et les opérations de lecture / écriture vers la mémoire externe. L'architecture proposée a seulement 1/8 de cycles par pixel (CPP) lui permettant de traiter plus de 100fps Full HD et est considérée comme une solution prometteuse pour le futur traitement vidéo 4K et 8K. Enfin, l'architecture développée est hautement évolutive, surperforme l'état de l'art des travaux connexes existants, et est actuellement déployé dans un prototype médical EEG vidéo. / Nowadays, medical image compression is an essential process in eHealth systems. Compressing medical images in high quality is a vital demand to avoid misdiagnosing medical exams by radiologists. WAAVES is a promising medical images compression algorithm based on the discrete wavelet transform (DWT) that achieves a high compression performance compared to the state of the art. The main aims of this work are to enhance image quality when compressing using WAAVES and to provide a high-speed DWT architecture for image compression on embedded systems. Regarding the quality improvement, the logarithmic number systems (LNS) was explored to be used as an alternative to the linear arithmetic in DWT computations. A new LNS library was developed and validated to realize the logarithmic DWT. In addition, a new quantization method called (LNS-Q) based on logarithmic arithmetic was proposed. A novel compression scheme (LNS-WAAVES) based on integrating the Hybrid-DWT and the LNS-Q method with WAAVES was developed. Hybrid-DWT combines the advantages of both the logarithmic and the linear domains leading to enhancement of the image quality and the compression ratio. The results showed that LNS-WAAVES is able to achieve an improvement in the quality by a percentage of 8% and up to 34% compared to WAAVES depending on the compression configuration parameters and the image modalities. For compression on embedded systems, the major challenge was to design a 2D DWT architecture that achieves a throughput of 100 full HD frame/s. A novel unified 2D DWT computation architecture was proposed. This new architecture performs both horizontal and vertical transform simultaneously and eliminates the problem of column-wise image pixel accesses to/from the off-chip DDR RAM. All of these factors have led to a reduction of the required off-chip DDR RAM bandwidth by more than 2X. The proposed concept uses 4-port line buffers leading to pipelined parallel four operations: the vertical DWT, the horizontal DWT transform, and the read/write operations to the external memory. The proposed architecture has only 1/8 cycles per pixel (CPP) enabling it to process more than 100fps Full HD and it is considered a promising solution for future 4K and 8K video processing. Finally, the developed architecture is highly scalable, outperforms the state of the art existing related work, and currently is deployed in a video EEG medical prototype.

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