This thesis considers methods for extracting blood volume pulse (BVP) representations from video of the human face. Whereas most previous systems have been concerned with estimating vital signs such as average heart rate, this thesis addresses the more difficult problem of recovering BVP signal morphology. We present a new approach that is inspired by temporal encoder-decoder architectures that have been used for audio signal separation. As input, this system accepts a temporal sequence of RGB (red, green, blue) values that have been spatially averaged over a small portion of the face. The output of the system is a temporal sequence that approximates a BVP signal. In order to reduce noise in the recovered signal, a separate processing step extracts individual pulses and performs normalization and outlier removal. After these steps, individual pulse shapes have been extracted that are sufficiently distinct to support biometric authentication. Our findings demonstrate the effectiveness of our approach in extracting BVP signal morphology from facial videos, which presents exciting opportunities for further research in this area. The source code is available at https://github.com/Adleof/CVPM-2023-Temporal-Encoder-Decoder-iPPG / Master of Science / This thesis considers methods for extracting blood volume pulse (BVP) representations from video of the human face. We present a new approach that is inspired by the method that has been used for audio signal separation. The output of our system is an approximation of the BVP signal of the person in the video. Our method can extract a signal that is sufficiently distinct to support biometric authentication. Our findings demonstrate the effectiveness of our approach in extracting BVP signal morphology from facial videos, which presents exciting opportunities for further research in this area.

In order to meet higher performance demands on brushless motor controllers, motor feedback signals must meet correspondingly higher standards. Brushless motor controllers require angular feedback for motor commutation, and generally for one or more of the following: torque, velocity or position regulation. These feedback categories impose different requirements on the control system. In many brushless motor applications, sinusoidal encoders have significant advantages over square-wave encoders. Signal processing techniques for sinusoidal encoder feedback signals are considered in the context of a brushless motor positioning system. In particular, a method is presented for correcting gain and offset measurement errors based on ellipse-fitting techniques. / Master of Science

This master’s thesis deals with calibration of incremental encoders. Introduces basic principles and terms in the field of rotary encoders and its calibration. The first part describes angular displacement sensors. Especially it focuses on optoelectrical incremental encoders. It also includes market research of very high-accuracy encoders. The second part contains description of precision calibration techniques with uncertainty to the thousandth of an arc-sec level. In the last part, the development of a calibration stage is presented and consequently the error map of an encoder is obtained.

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Halmstad University is currently equipped with an Echo-free chamber to perform EMC testing, but no reverberation chamber. The construction of a paddle stirrer to be utilized in Halmstad University would drastically reduce the time required to perform EMC testing, since reverberation chambers are more efficient than Echo-free chambers for these kind of tests. The goal of this project was to design and develop a paddle stirrer structure and a control system able to rotate the stirrer to specific repeatable absolute angles with an accuracy of a tenth of a degree,holding a mass up to 70kg and rotating a mass up to 20 kg distributed on a 1x1m surface. To achieve this goal the system has been designed using a metal base structure, bearings to hold the lower shaft in its axes reducing its friction, a stepper motor connected to the gears to increase its holding torque, a magnetic rotary encoder and a control system with a double feedback from interrupts and from the encoder to improve the accuracy and reliability of the system. The resultis a completely working prototype, which fulfils all the requirements except for the speed. The target speed has not been achieved due to the insufficient holding torque of the available stepper motor. / Halmstad Högskola är för närvarande utrustad med en Ekofri kammare för att utföra EMCtestning, men ingen modväxlande kammare. Konstruktionen av en paddle-omrörare för användning i Halmstad Högskola skulle drastiskt minska tiden som krävs för att utföra EMC-testning, eftersom modväxlande kammaren är effektivare än en ekofri kammare för dessa typer av tester.Målet med detta projekt var att designa och utveckla en paddle-omrörare, både strukturen samt ett styrsystem som kan rotera den till specifika repeterbara absoluta vinklar med en noggrannhet av en tiondel av en grad, hantera en massa upp till 70 kg och rotera en massa upp till 20 kg fördelad på en 1x1m yta. För att uppnå detta mål har systemet konstruerats med en basstruktur gjord av metall, lager har placerats i axeln för att reducera friktion, en stegmotor är anslutentill kugghjul för att öka vridmomentet, en magnetisk roterande sensor och ett styrsystem med en dubbel återkoppling från interrupts och från sensorn för att förbättra systemets noggrannhet och tillförlitlighet. Resultatet är en helt fungerande prototyp som uppfyller alla krav, förutom hastigheten. Målhastigheten har inte uppnåtts på grund av otillräckligt vridmoment hos stegmotorn.

Excessive resource allocation in telecommunications networks can be prevented by forecasting the resource demand when dimensioning the networks and then allocating the necessary resources accordingly, which is an ongoing effort to achieve a more sustainable development. In this work, traffic data from cloud environments that host deployed virtualized network functions (VNFs) of an IP Multimedia Subsystem (IMS) has been collected along with the computational resource consumption of the VNFs. A supervised learning approach was adopted to address the forecasting problem by considering encoder-decoder networks. These networks were applied to forecast future resource consumption of the VNFs by regarding the problem as a time series forecasting problem, and recasting it as a sequence-to-sequence (seq2seq) problem. Different encoder-decoder network architectures were then utilized to forecast the resource consumption. The encoder-decoder networks were compared against a widely deployed classical time series forecasting model that served as a baseline model. The results show that while the considered encoder-decoder models failed to outperform the baseline model in overall Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), the forecasting capabilities were more resilient to degradation over time. This suggests that the encoder-decoder networks are more appropriate for long-term forecasting, which is in agreement with related literature. Furthermore, the encoder-decoder models achieved competitive performance when compared to the baseline, despite being treated with limited hyperparameter-tuning and the absence of more sophisticated functionality such as attention. This work has shown that there is indeed potential for deep learning applications in forecasting of cloud resource consumption. / Överflödig allokering av resurser i telekommunikationsnätverk kan förhindras genom att prognosera resursbehoven vid dimensionering av dessa nätverk. Detta görs i syfte att bidra till en mer hållbar utveckling. Infor  detta  projekt har  trafikdata från molnmiljon som hyser aktiva virtuella komponenter (VNFs) till ett  IP Multimedia Subsystem (IMS) samlats in tillsammans med resursförbrukningen  av dessa komponenter. Detta examensarbete avhandlar hur effektivt övervakad maskininlärning i form av encoder-decoder natverk kan användas för att prognosera resursbehovet hos ovan nämnda VNFs. Encoder-decoder nätverken appliceras genom att betrakta den samlade datan som en tidsserie. Problemet med att förutspå utvecklingen av tidsserien formuleras sedan som ett sequence-to-sequence (seq2seq) problem. I detta arbete användes en samling encoder-decoder nätverk med olika arkitekturer for att prognosera resursförbrukningen och dessa jämfördes med en populär modell hämtad från klassisk tidsserieanalys. Resultaten visar att encoder- decoder nätverken misslyckades med att överträffa den klassiska tidsseriemodellen med avseende på Root Mean Squared Error (RMSE) och Mean Absolute Error (MAE). Dock visade encoder-decoder nätverken en betydlig motståndskraft mot prestandaförfall över tid i jämförelse med den klassiska tidsseriemodellen. Detta indikerar att encoder-decoder nätverk är lämpliga för prognosering över en längre tidshorisont. Utöver detta visade encoder-decoder nätverken en konkurrenskraftig förmåga att förutspå det korrekta resursbehovet, trots en begränsad justering av disponeringsparametrarna och utan mer sofistikerad funktionalitet implementerad som exempelvis attention.

The key objective of this project was to quantitatively and qualitatively assess the performance of a sentence embedding model, Universal Sentence Encoder (USE), and a word embedding model, word2vec, at the task of topic modelling. The first step in the process was data collection. The data used for the project was podcast descriptions available at Spotify, and the topics associated with them. Following this, the data was used to generate description vectors and topic vectors using the embedding models, which were then used to assign topics to descriptions. The results from this study led to the conclusion that embedding models are well suited to this task, and that overall the USE outperforms the word2vec models. / Det huvudsakliga syftet med det i denna uppsats rapporterade projektet är att kvantitativt och kvalitativt utvärdera och jämföra hur väl Universal Sentence Encoder USE, ett semantiskt vektorrum för meningar, och word2vec, ett semantiskt vektorrum för ord, fungerar för att modellera ämnesinnehåll i text. Projektet har som träningsdata använt skriftliga sammanfattningar och ämnesetiketter för podd-episoder som gjorts tillgängliga av Spotify. De skriftliga sammanfattningarna har använts för att generera både vektorer för de enskilda podd-episoderna och för de ämnen de behandlar. De båda ansatsernas vektorer har sedan utvärderats genom att de använts för att tilldela ämnen till beskrivningar ur en testmängd. Resultaten har sedan jämförts och leder både till den allmänna slutsatsen att semantiska vektorrum är väl lämpade för den här sortens uppgifter, och att USE totalt sett överträffar word2vec-modellerna.

In the ever-changing landscape of the fishing industry, demands for automating specific processes are increasing substantially. Predicting future events eliminates much of the existing communication latency between fishing vessels and their customers and makes real-time analysis of onboard catch possible for the fishing industry. Further, machine learning models, may reduce the number of human resources necessary for the numerous processes that may be automated. In this document, we focus on weight estimation of three different species of fish. Namely, we want to estimate the fish weight given its specie through datadriven techniques. Due to the high complexity of image data, the overhead expenses of collecting images at sea, and the complexities of fish features, we consider a dimensionality reduction on the inputs to reduce the curse of dimensionality and increase interpretability. We will study the viability of modeling fish weights from lower-dimensional feature vectors and the conjunction of lower-dimensional feature vectors and algorithmically obtained features. We found that modeling the residuals with latent representations of a simple power model fitted on length features resulted in a significant difference in the weight estimates for two types of fish and a decrease in Root Mean Squared Error (rMSE) and Mean Absolute Percentage Error (MAPE) scores in favour of the estimations utilizing latent representations. / I fiskeindustrins ständigt föränderliga landskap ökar kraven på att automatisera specifika processer väsentligt. Att förutsäga framtida händelser eliminerar mycket av den befintliga kommunikationsfördröjningen mellan fiskefartyg och deras kunder och möjliggör analys i realtid av ombordfångst för fiskeindustrin. Vidare kan det minska antalet personalresurser som krävs för de många processer som kan automatiseras. I detta dokument studerar vi två olika beslutsproblem relaterade till att sortera fisk av tre olika arter. Vi vill nämligen bestämma fiskvikten och dess art genom datadrivna tekniker. På grund av bilddatas höga komplexitet, de allmänna kostnaderna för att samla bilder till sjöss och komplexiteten hos fiskegenskaper, anser vi att en dimensionalitetsminskning av särdragen minskar problemet relaterat till dimensionsexplosion och ökar tolkbarheten. Vi kommer att studera lämpligheten av modellering av fiskvikter och arter från lägre dimensionella särdragsvektorer samt kombinationen av dessa med algoritmiskt erhållna funktioner. Vi fann att modellering av residual med latenta representationer av en enkel potensfunktionsmodell som är anpassad till fisklängder resulterade i en signifikant skillnad i viktuppskattningarna för två typer av fisk och en minskning av rMSE och MAPE poäng.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Don M. Gruenbacher / Future technologies such as cognitive radio require flexible and reliable hardware architectures that can be easily configured and adapted to varying coding parameters. The objective of this work is to develop a flexible hardware encoder and decoder for low-density parity-check (LDPC) codes. The design methodologies used for the implementation of a LDPC encoder and decoder are flexible in terms of parity-check matrix, code rate and code length. All these designs are implemented on a programmable chip and tested. Encoder implementations of LDPC codes are optimized for area due to their high complexity. Such designs usually have relatively low data rate. Two new encoder designs are developed that achieve much higher data rates of up to 844 Mbps while requiring more area for implementation. Using structured LDPC codes decreases the encoding complexity and provides design flexibility. The architecture for an encoder is presented that adheres to the structured LDPC codes defined in the IEEE 802.16e standard. A single encoder design is also developed that accommodates different code lengths and code rates and does not require re-synthesis of the design in order to change the encoding parameters. The flexible encoder design for structured LDPC codes is also implemented on a custom chip. The maximum coded data rate of the structured encoder is up to 844 Mbps and for a given code rate its value is independent of the code length. An LDPC decoder is designed and its design methodology is generic. It is applicable to both structured and any randomly generated LDPC codes. The coded data rate of the decoder increases with the increase in the code length. The number of decoding iterations used for the decoding process plays an important role in determining the decoder performance and latency. This design validates the estimated codeword after every iteration and stops the decoding process when the correct codeword is estimated which saves power consumption. For a given parity-check matrix and signal-to-noise ratio, a procedure to find an optimum value of the maximum number of decoding iterations is presented that considers the affects of power, delay, and error performance.