In this thesis, three different neural network architectures are investigated to detect the action of a shot within a football game using video data. The first architecture uses con- ventional convolution and pooling layers as feature extraction. It acts as a baseline and gives insight into the challenges faced during shot detection. The second architecture uses a pre-trained feature extractor. The last architecture uses three-dimensional convolution. All these networks are trained using short video clips extracted from football game video streams. Apart from investigating network architectures, different sampling methods are evaluated as well. This thesis shows that amongst the three evaluated methods, the ap- proach using MobileNetV2 as a feature extractor works best. However, when applying the networks to a video stream there are a multitude of challenges, such as false positives and incorrect annotations that inhibit the potential of detecting shots.

<p dir="ltr">Impaired cerebral vasoregulation is one of the most common post-ischemic stroke effects. Diagnosis and prevention of this condition is often invasive, costly and in-effective. This impairment restricts the cerebral blood vessels to properly regulate blood flow, which is very important for normal brain functioning. Developing accurate, non-invasive and efficient methods to detect this condition aids in better stroke diagnosis and prevention. </p><p dir="ltr">The aim of this thesis is to develop deep learning techniques for the purpose of detection of cerebral vasoregulation impairments by analyzing physiological signals. This research employs various Deep learning techniques like Convolution Neural Networks (CNN), MobileNet, and Long-Short-Term Memory (LSTM) to determine variety of physiological signals from the PhysioNet database like Electrocardio-gram (ECG), Transcranial Doppler (TCD), Electromyogram (EMG), and Blood Pressure(BP) as stroke or non-stroke subjects. The effectiveness of these algorithms is demonstrated by a classification accuracy of 90\% for the combination of ECG and EMG signals. </p><p dir="ltr">Furthermore, this research explores the importance of analyzing dynamic physiological activities in determining the impairment. The dynamic activities include Sit-stand, Sit-stand-balance, Head-up-tilt, and Walk dataset from the PhysioNet website. CNN and MobileNetV3 are employed in classification purposes of these signals, attempting to identify cerebral health. The accuracy of the model and robustness of these methods is greatly enhanced when multiple signals are integrated. </p><p dir="ltr">Overall, this study highlights the potential of deep multimodal physiological learning in the development of precision brain medicine further enhancing stroke diagnosis. The results pave the way for the development of advanced diagnostic tools to determine cerebral health. </p>

The 2019 novel coronavirus has been proven to present several unique features on chest X-rays and CT-scans that distinguish it from imaging of other pulmonary diseases such as bacterial pneumonia and viral pneumonia unrelated to COVID-19. However, the key characteristics of a COVID-19 infection have been proven challenging to detect with the human eye. The aim of this project is to explore if it is possible to distinguish a patient with COVID-19 from a patient who is not suffering from the disease from posteroanterior chest X-ray images using synthetic minority over-sampling and transfer learning. Furthermore, the report will also present the mechanics of COVID-19, the used dataset and models and the validity of the results.

Táto práca sa zaoberá využitím aproximovaných obvodov v neurónových sieťach so zámerom prínosu energetických úspor. K tejto téme už existujú štúdie, avšak väčšina z nich bola príliš špecifická k aplikácii alebo bola demonštrovaná v malom rozsahu. Pre dodatočné preskúmanie možností sme preto skrz netriviálne modifikácie open-source frameworku TensorFlow vytvorili platformu umožňujúcu simulovať používanie approximovaných obvodov na populárnych a robustných neurónových sieťach ako Inception alebo MobileNet. Bodom záujmu bolo nahradenie väčšiny výpočtovo náročných častí konvolučných neurónových sietí, ktorými sú konkrétne operácie násobenia v konvolučnách vrstvách. Experimentálne sme ukázali a porovnávali rozličné varianty a aj napriek tomu, že sme postupovali bez preučenia siete sa nám podarilo získať zaujímavé výsledky. Napríklad pri architektúre Inception v4 sme získali takmer 8% úspor, pričom nedošlo k žiadnemu poklesu presnosti. Táto úspora vie rozhodne nájsť uplatnenie v mobilných zariadeniach alebo pri veľkých neurónových sieťach s enormnými výpočtovými nárokmi.

This thesis focuses on the possibility of using current methods in the field of computer vision to re-identify graffiti tags. The work examines the possibility of using convolutional neural networks to re-identify graffiti tags, which are the most common type of graffiti. The work experimented with various models of convolutional neural networks, the most suitable of which was MobileNet using the triplet loss function, which managed to achieve a mAP of 36.02%.

Convolutional Neural Network is a deep learning algorithm that brings revolutionary impact on computer vision area. One of its applications is image classification. However, problem exists in this algorithm that it involves huge number of operations and parameters, which limits its possibility in time and resource restricted embedded applications. MobileNet, a neural network that uses separable convolutional layers instead of standard convolutional layers, largely reduces computational consumption compared to traditional CNN models. By implementing MobileNet on FPGA, image classification problems could be largely accelerated. In this thesis, we have designed an accelerator block for MobileNet. We have implemented a simplified MobileNet on Xilinx UltraScale+ Zu104 FPGA board with 64 accelerators. We use the implemented MobileNet to solve a gesture classification problem. The implemented design works under 100MHz frequency. It shows a 28.4x speed up than CPU (Intel(R) Pentium(R) CPU G4560 @ 3.50GHz), and a 6.5x speed up than GPU (NVIDIA GeForce 940MX 1.004GHz). Besides, it is a power efficient design. Its power consumption is 4.07w. The accuracy reaches 43% in gesture classification. / CNN-Nätverk är en djupinlärning algoritm som ger revolutionerande inverkan på datorvision, till exempel, bildklassificering. Det finns emellertid problem i denna algoritm att det innebär ett stort antal operationer och parametrar, vilket begränsar möjligheten i tidsbegränsade och resursbegränsade inbäddade applikationer. MobileNet, ett neuralt nätverk som använder separerbara convolution lager i stället för standard convolution lager, minskar i stor utsträckning beräkningsmängder än traditionella CNN-modeller. Genom att implementera MobileNet på FPGA kan problem med bildklassificering accelereras i stor utsträckning. Vi har utformat ett acceleratorblock för MobileNet. Vi har implementerat ett förenklat MobileNet på Xilinx UltraScale + Zu104 FPGA-kort med 64 acceleratorer. Vi använder det implementerade MobileNet för att lösa ett gestklassificeringsproblem. Implementerade designen fungerar under 100MHzfrekvens. Den visar en hastighet på 28,4x än CPU (Intel (R) Pentium (R) CPU G4560 @ 3,50 GHz) och en 6,5x snabbare hastighet än GPU (NVIDIA GeForce 940MX 1,004GHz). Det är en energieffektiv design. Strömförbrukningen är 4,07w. Noggrannheten når 43% i gestklassificering.

Modern state-of-the-art object detection models are based on convolutional neural networks and can be divided into single-shot detectors and two-stage detectors. Two-stage detectors exhibit impressive detection performance but their complex pipelines make them slow. Single-shot detectors are not as accurate as two-stage detectors, but are faster and can be used for real-time object detection. Despite the fact that single-shot detectors are faster, a large number of calculations are still required to produce a prediction that not many embedded devices are capable of doing in a reasonable time. Therefore, it is natural to ask if single-shot detectors could become faster even. Pruning is a technique to reduce the size of neural networks. The main idea behind network pruning is that some model parameters are redundant and do not contribute to the final output. By removing those redundant parameters, fewer computations are needed to produce predictions, which may lead to a faster inference and since the parameters are redundant, the model accuracy should not be affected. This thesis investigates two approaches for pruning the SSD-MobileNet- V2 single-shot detector. The first approach prunes the single-shot detector by a large portion and retrains the remaining parameters only once. In the other approach, a smaller portion is pruned, but pruning and retraining are done in an iterative fashion, where pruning and retraining constitute one iteration. Beyond comparing two pruning approaches, the thesis also studies the tradeoff between model accuracy and inference speed that pruning induces. The results from the experiments suggest that the iterative pruning approach preserves the accuracy of the original model better than the other approach where pruning and finetuning are performed once. For all four pruning levels that the two approaches are compared iterative pruning yields more accurate results. In addition, an inference evaluation indicates that iterative pruning is a good compression method for SSD-MobileNet-V2, finding models that both are faster and more accurate than the original model. The thesis findings could be used to guide future pruning research on SSD-MobileNet- V2, but also on other single-shot detectors such as RetinaNet and the YOLO models. / Moderna modeller för objektsdetektering bygger på konvolutionella neurala nätverk och kan delas in i ensteg- och tvåstegsdetektorer. Tvåstegsdetektorer uppvisar imponerande detektionsprestanda, men deras komplexa pipelines gör dem långsamma. Enstegsdetektorer uppvisar oftast inte lika bra detektionsprestanda som tvåstegsdetektorer, men de är snabbare och kan användas för objektdetektering i realtid. Trots att enstegsdetektorer är snabbare krävs det fortfarande ett stort antal beräkningar för att få fram en prediktering, vilket inte många inbyggda enheter kan göra på rimlig tid. Därför är det naturligt att fråga sig om enstegsdetektorer kan bli ännu snabbare. Nätverksbeskärning är en teknik för att minska storleken på neurala nätverk. Huvudtanken bakom nätverksbeskärning är att vissa modellparametrar är överflödiga och inte bidrar till det slutliga resultatet. Genom att ta bort dessa överflödiga parametrar krävs färre beräkningar för att producera en prediktering, vilket kan leda till att nätverket blir snabbare och eftersom parametrarna är överflödiga bör modellens detektionsprestanda inte påverkas. I den här masteruppsatsen undersöks två metoder för att beskära enstegsdetektorn SSD-MobileNet-V2. Det första tillvägagångssättet går ut på att en stor del av detektorn vikter beskärs och att de återstående parametrarna endast finjusteras en gång. I det andra tillvägagångssättet beskärs en mindre del, men beskärning och finjustering sker på ett iterativt sätt, där beskärning och finjustering utgör en iteration. Förutom att jämföra två metoder för beskärning studeras i masteruppsatsen också den kompromiss mellan modellens detektionsprestanda och inferenshastighet som beskärningen medför. Resultaten från experimenten tyder på att den iterativa beskärningsmetoden bevarar den ursprungliga modellens detektionsprestanda bättre än den andra metoden där beskärning och finjustering utförs en gång. För alla fyra beskärningsnivåer som de två metoderna jämförs ger iterativ beskärning mer exakta resultat. Dessutom visar en hastighetsutvärdering att iterativ beskärning är en bra komprimeringsmetod för SSD-MobileNet-V2, eftersom modeller som både snabbare och mer exakta än den ursprungliga modellen går att hitta. Masteruppsatsens resultat kan användas för att vägleda framtida forskning om beskärning av SSD-MobileNet-V2, men även av andra enstegsdetektorer, t.ex. RetinaNet och YOLO-modellerna.