This thesis focuses on searching for CpG islands of DNA sequences based on analysis of DNA spectrograms. The first part is theoretical and deals with the significance CpG island, and a description of the algorithms that are used or have been proposed for their search. The theoretical basis were implemented two algorithms based on the analysis of DNA spectrogram. One is based on the assumption that the region CpG islands has a higher content of guanine and cytosine than the region outside the CpG island and the other on the assumption of a higher frequency of occurrence of CG dinucleotides in the CpG island. The algorithms are implemented through MATLAB programming interface. For evaluation usefulness and effectiveness of solutions, results achieved on the selected DNA sequences implemented algorithms are compared with the results achieved by search engines CpG islands, which are freely available on the internet.

Samma ljudupptagning har genomförts i fyra olika samplingsfrekvenser. Det som undersökts är vad som skiljer dessa åt i fråga om frekvensinnehåll, stereoinnehåll och signalstyrka samt huruvida det går att höra någon skillnad mellan dessa. Syftet är att visa på vilken samplingsfrekvens som lämpar sig bäst då slutresultatet lyssnas på via Spotify. Mätningar inom den digitala domänen visar på att inspelning i 44.1 kHz är att föredra då slutresultatet är ämnat för denna medieplattform. Resultat från genomfört lyssningstest pekar inte på någon tydlig trend åt något håll. Analys med spektrogram visar på vissa skillnader då inspelningarna i WAV-format komprimeras till formatet Ogg-Vorbis.

This thesis focuses on searching for CpG islands of DNA sequences based on analysis of DNA spectrograms. The first part is theoretical and deals with the significance CpG island, and a description of the algorithms that are used or have been proposed for their search. The theoretical basis were implemented two algorithms based on the analysis of DNA spectrogram. One is based on the assumption that the region CpG islands has a higher content of guanine and cytosine than the region outside the CpG island and the other on the assumption of a higher frequency of occurrence of CG dinucleotides in the CpG island. The algorithms are implemented through MATLAB programming interface. For evaluation usefulness and effectiveness of solutions, results achieved on the selected DNA sequences implemented algorithms are compared with the results achieved by search engines CpG islands, which are freely available on the internet.

Background: Arrhythmias are a collection of heart conditions that affect almost half of the world’s population and accounted for roughly 32.1% of all deaths in 2015. More importantly, early detection of arrhythmia through electrocardiogram analysis can prevent up to 90% of deaths. Neural networks are a modern and increasingly popular tool of choice for classifying arrhythmias hidden within ECG-data. In the pursuit of achieving increased classification accuracy, some of these neural networks can become quite complex which can result in overfitting. To combat this phenomena, a technique called regularization is typically used. Thesis’ Problem Statement: Practically all of today’s research on utilizing neural networks for arrhythmia detection incorporates some form of regularization. However, most of this research has chosen not to focus on, and experiment with, regularization. In this thesis we measured and compared different regularization techniques in order to improve arrhythmia classification accuracy. Objectives: The main objective of this thesis is to expand upon a baseline neural network model by incorporating various regularization techniques and compare how these new models perform in relation to the baseline model. The regularization techniques used are L1, L2, L1 + L2, and Dropout. Methods: The study used quantitative experimentation in order to gather metrics from all of the models. Information regarding related works and relevant scientific articles were collected from Summon and Google Scholar. Results: The study shows that Dropout generally produces the best results, on average improving performance across all parameters and metrics. The Dropout model with a regularization parameter of 0.1 performed particularly well. Conclusions: The study concludes that there are multiple models which can be considered to have the greatest positive impact on the baseline model. Depending on how much one values the consequences of False Negatives vs. False Positives, there are multiple candidates which can be considered to be the best model. For example, is it worth choosing a model which misses 11 people suffering from arrhythmia but simultaneously catches 1651 mistakenly classified arrhythmia cases? / Bakgrund: Arytmier är en samling hjärt-kärlsjukdomar som drabbar nästan hälften av världens befolkning och stod för ungefär 32,1% av alla dödsfall 2015. 90% av dödsfallen som arytmi orsakar kan förhindras om arytmin identifieras tidigare. Neurala nätverk har blivit ett populärt verktyg för att detektera arytmi baserat på ECG-data. I strävan på att uppnå bättre klassificeringsnogrannhet kan dessa nätverk råka ut för problemet ’overfitting’. Overfitting kan dock förebyggas med regulariseringstekniker. Problemställning: Praktiskt taget all forskning som utnyttjar neurala nätverk för att klassifiera arytmi innehåller någon form av regularisering. Dock har majoriteten av denna forsknings inte valt att fokusera och experimentera med regularisering. I den här avhandlingen kommer vi att testa olika regulariseringstekniker för att jämföra hur de förbättrar grundmodellens arytmiklassificeringsförmåga. Mål: Huvudmålet med denna avhandling är att modifiera ett neuralt nätverk som utnyttjar transfer learning för att klassificera arytmi baserat på två-dimensionell ECG-data. Grundmodellen utökades med olika regulariseringstekniker i mån om att jämföra dessa och därmed komma fram till vilken teknik som har störst positiv påverkan. De tekniker som jämfördes är L1, L2, L1 + L2, och Dropout. Metod: Kvantitativa experiment användes för att samla in data kring teknikernas olika prestationer och denna data analyserades och presenterades sedan. En litteraturstudie genomfördes med hjälp av Summon och Google Scholar för att hitta information från relevanta artiklar. Resultat: Forskningen tyder på att generellt sett presterar Dropout bättre än de andra teknikerna. Dropout med parametern 0.1 förbättrade mätvärderna mest. Slutsatser: I specifikt denna kontext presterade Dropout(0.1) bäst. Dock anser vi att falska negativ och falska positiv inte är ekvivalenta. Vissa modeller presterar bättre än andra beroende på hur mycket dessa variabler värderas, och därmed är den bästa modellen subjektiv. Är det till exempel värt att låta 11 personer dö om det innebär att 1651 personer inte kommer att vidare testas i onödan?

This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method designed is based on the analysis of module and phase spectrograms of the modulated signals. Their histograms are used for the examination of the spectrogram properties. They provide information on the count of carrier frequencies in the signal, which is used for the FSK and MSK recognition, and on the count of phase states on which the BPSK, QPSK, and QAM-16 are classified. The spectrograms in that the characteristic attributes of the modulations are visible are obtained with the segment length equal to the symbol length. It was found that it is possible to correctly recognize the modulation with the known symbol length at the signal-to-noise ratio at least 0 dB. That is why it is necessary to detect the symbol length prior to the spectrogram calculation. Four methods were designed for this purpose: autocorrelation function, cepstrum analysis, wavelet transform, and LPC coefficients. These methods were algorithmized and analyzed with signals disturbed by the white Gaussian noise, phase noise and with signals passed through a multipass fading channel. The method of detection by means of cepstrum analysis proved the most suitable and reliable. Finally the new method for digital modulation recognition was verified with signals passed through a channel with properties close to the real one.

Various methods of DNA sequences modifications for frequency analysis and basic characteristics of DNA are described in the theoretical part of this thesis. Tricolor spectrograms, created by short time Fourier transform help us to recognize some characteristic patterns in DNA sequences. Practical part of this work deals with developed programme which generates spectrograms and analyse them. Last part deals with the analysis of selected sequences of C. elegans genome. Some patterns are related to data of public databases such as NCBI. Various patterns are explained from the biological nature, which relates to chromosome structure and protein coding regions. Another well recognised patterns, tandem repetitions composed of satellites, microsatellites and minisatelites are described by spectrograms as well.

This thesis discusses about possibilities of construction colour DNA spectrograms and about patterns which can be detected there. Spectrograms as tools of spectral analysis give us a simultaneous view of the local frequency throughout the nucleotide sequence. They are suitable for gene identification or gene regions identification, determination of global character about whole chromosomes and also give us a chance for the discovery of yet unknown regions of potential significance. For purpose of this kind of DNA analysis is possible to use digital signal processing methods. We can apply them on only after conversion of DNA sequence to numerical representation. Selection of correct numerical representation affects how well will be reflected biological features in numerical record which we need for another use in digital signal analysis.

This work focuses on speech recognition by computer, pattern recognition stages and problems. Also there is a goal to create a speech recognition tool. At the beginning, there is a general overview of the audio signal and language concepts. The subsequent presentation of the essential tasks of speech recognition, introduction to matrix algebra, which is used to described algorithm. Information is provided on what basis and how features are extracted. For this work often is used LPC. This algorithm is one of the most popular extracting features of speech signal, so it is reviewed in this paper, as well as its modification WLPC. The following text of the speech recognition gives theory of extracted features use. Section „Acoustic modeling“ describes the recognition of speech units and one of the most commonly used acoustic modeling technologies – Hidden Markov Models and the next section „Speech modeling“ describes the language modeling, which purpose is to correct data referring to dictionaries and speech structure. The rest of the text is focused on speach recognition using specrtogram and implementation of speach recognition system. After that, there were executed experiments, that where used to define quality of speech recognition. / Šiame darbe gilinamasi i kalbos atpažinima kompiuteriu, atpažinimo etapus, problemas, o veliau meginama sukurti kalbos atpažinimo iranki. Pradžioje, bendrai apžvelgiama garso signalo, kalbos savokos. Veliau pateikiamos kalbos atpažinimo esminiai uždaviniai, supažindinama su matricu algebra, kuri naudojama aprašytuose algoritmuose. Pateikiama informacija kuo remiantis ir kaip išskiriami požymiai. Šiam darbui dažnai naudojamas LPC. Šis algoritmas yra vienas iš populiariausiu išskiriant kalbos signalo požymius, todel jis šiame darbe yra apžvelgtas, kaip ir jo modifikacija WLPC. Toliau tekste pateikiama kalbos atpažinimo teorija, apie išskirtu požymiu panaudojima. Skyriuje „Akustinis modeliavimas“, aprašomas kalbos vienetu atpažinimas ir vienas iš dažniausiai naudojamu akustinio modeliavimo technologiju - pasleptieji Markov’o modeliai, sekantis skyrius „Kalbos modeliavimas“, aprašo kalbos modeliavima, skirta jau turimiems duomenims sutvarkyti, remiantis žodynais ir analizuojamos kalbos struktura. Likusioje teksto dalyje koncentruojamasi ties kalbos atpažinimu panaudojant spektrograma ir kalbos atpažinimo sistemos igyvendinimu. Po to atlikti eksperimentai, kuriais buvo tiriama pateikto algoritmo atpažinimo kokybe.

Modern society has seen an increase in automation using AI in a variety of applications. To keep up with recent development, it is therefore logical to investigate the application of AI programs to military tasks. The great advantage with automation lies in the possible increase in efficiency and possible relocation of resources of personnel to other tasks. Therefore, this study aims to evaluate the use of Convolutional Neural Networks (CNN) in classification of communication and radar emitters based on collected Electronic Support Measures (ESM) data and to estimate to what extent human analysts could be replaced. The evaluation was performed by applying the concept of military Utility as a framework for evaluation with the addition of Technology Readiness Level (TRL) to survey how far the technology has developed. Data was collected using two methods: Firstly, through a literature review of research done on the application of CNNs in classifying information such as spectrograms and images. Secondly, by interviewing a subject matter expert from SAAB, who mainly helped estimate the TRL of the technology’s components. The study found that CNN appears suitable to apply on the proposed task and that the program could potentially replace human analysts to a great extent, at least when doing routine classifications. Full automation seems unlikely as analysts would be required with more challenging classifications, especially those outside the range of the training data used in teaching the CNN. Finally, challenges involved with deep learning programs inherent structure, demands and application to military tasks are discussed and subjects for future research are proposed. / Det moderna samhället har sett en ökad automatisering med AI i en mängd olika applikationer och för att hålla jämna steg med den senaste utvecklingen är det därför logiskt att undersöka tillämpningen av AI-program på militära uppgifter. Den stora fördelen med automatisering ligger i den möjliga ökningen av effektivitet och möjlig flytt av personalresurser till andra uppgifter. Därför syftar denna studie till att utvärdera användningen av convolutional neural networks (CNN) vid klassificering av kommunikations- och radarsändare baserat på insamlade data från elektronisk stödverksamhet (sv. ES motsvara eng. ESM) och att uppskatta i vilken utsträckning mänskliga analytiker kan ersättas. Utvärderingen genomfördes genom att använda konceptet militär nytta som ett ramverk för utvärdering med tillägg av technology readiness level (TRL) för att kartlägga hur långt tekniken har utvecklats. Data samlades in med två metoder: För det första genom en litteraturöversikt av forskning som gjorts om tillämpningen av CNN för att klassificera information såsom spektrogram och bilder. För det andra genom att intervjua en ämnesexpert från SAAB, som främst hjälpte till att uppskatta TRL för teknikens komponenter. Studien fann att CNN verkar lämplig att använda till den föreslagna uppgiften och att programmet potentiellt skulle kunna ersätta mänskliga analytiker i stor utsträckning, åtminstone for rutinklassificeringar. En fullständig automatisering verkar osannolik eftersom analytiker skulle krävas med mer utmanande klassificeringar, särskilt de som ligger utanför utbildningsdata som används för att lära upp programmet. Slutligen diskuteras utmaningar kopplade till djup-inlärningsprogrammens struktur, krav och tillämpning på militära uppgifter samt att ämnen för framtida forskning föreslås.

Title: Spectral Analysis of Electromyography Data of Power Lifters Objectives: The aim of this thesis is a description of muscle activity and its measu- rement using electromyography (EMG), description of parameters of EMG signal and their relationship to neuromuscular physiology. The aim of the practical part is an implementation of spectral analysis of EMG data of power lifters when performing 4 series of 5 split-squats carrying loading in one hand. Methods: The spectral analysis of EMG data of 35 athletes is presented. The athletes performed 4 series of 5 split-squats with one sided loading. The loading was 25% and 50% of their body mass and the carrying position was homolateral and contralateral to stance leg. Muscles chosen for measurement were vastus medialis obliquus, vastus lateralis obliquus, gluteus medius and biceps femoris of both sides. The implementation of digital signal proces- sing algorithm was done using Matlab and its Signal Processing Toolbox. The spectral analysis was implemented using discrete Fourier transform with sliding 256-sample window size and 32-sample window increment. The resulting spectrogram was divided into parts based on smoothed EMG. Median frequency for each split-squat was calculated. For the statistical processing we used median frequency differencies...