Under de senaste åren har stora datamängder blivit allt vanligare för beslutsfattande och analys. Maskininlärning och matematiska beräkningar är två avgörande metoder som används för detta. Dessa beräkningar kan dock vara tidskrävande, och de kräver högpresterande datorer som är utmanande att skala upp. Raspberry Pi är en liten, kraftfull och billig dator som lämpar sig för parallella beräkningar. Kubernetes är en öppen källkodsplattform för att hantera containerbaserade applikationer som tillåter automatisk skalning av mjukvaruapplikationer. Genom att kombinera Raspberry Pi med Kubernetes kan ett kostnadseffektivt och skalbart system för matematiska beräkningar och maskininlärning skapas. I denna studie undersöks möjligheten att bygga ett kostnadseffektivt och skalbart system för matematiska beräkningar och maskininlärning med hjälp av Raspberry Pi och Kubernetes. Det kommer att göras teoretisk forskning kring Kubernetes och Raspberry Pi, designa ett system för matematiska beräkningar och maskininlärning, implementera systemet genom att installera och konfigurera Kubernetes på flera Raspberry Pi:s, mäta och utvärdera systemets prestanda och skalbarhet samt presentera studiens resultat. Resultatet visade att användningen av Raspberry Pi i kombination med Kubernetes för att utföra matematiska beräkningar är både kostnadseffektiv och skalbar. När det gäller prestanda kunde systemet hantera intensiva beräkningsuppgifter på ett tillfredsställande sätt, vilket visar sin potential som en lösning för storskalig dataanalys. Förbättringar i systemdesign och mjukvaruoptimering kan ytterligare öka effektiviteten och prestanda / In the recent years, large data sets have become more often used for decision-making and analysis. Machine learning and mathematical calculations are two crucial methods employed for this. However, these computations may be time-consuming, and they require highperformance computers that are challenging to scale up. Raspberry Pi is a small, powerful, and cheap computer suitable for parallel calculations. Kubernetes is an open-source platform for managing container-based applications that allows automatic scaling of software applications. By combining Raspberry Pi with Kubernetes, a cost-effective and scalable system for mathematical calculations and machine learning can be created. In this study, the possibility of building a cost-effective and scalable system for mathematical calculations and machine learning using Raspberry Pi and Kubernetes is investigated. There will be theoretical research on Kubernetes and Raspberry Pi, design a system for mathematical calculations and machine learning, implement the system by installing and configuring Kubernetes on multiple Raspberry Pi's, measure and evaluate the system's performance and scalability, and present the study's results. The result showed that the use of Raspberry Pi in combination with Kubernetes to perform mathematical calculations is both cost-effective and scalable. In terms of performance, the system was able to handle intensive computational tasks satisfactorily, demonstrating its potential as a solution for large-scale data analysis. Improvements in system design and software optimization can further increase efficiency and performance.

[pt] Dores no pé e joelho estão relacionadas com patologias ortopédicas e lesões nos membros inferiores. Desde a corrida de rua até o treinamento funcional CrossFit, estas dores e lesões estão correlacionadas com a distribuição iregular da pressão plantar e o posicionamento inadequado do joelho durante a prática física de longo prazo, e podem levar a lesões ortopédicas graves se o padrão de movimento não for corrigido. Portanto, o monitoramento da distribuição da pressão plantar do pé e das características espaciais e temporais das irregularidades no posicionamento dos pés e joelhos são de extrema importância para a prevenção de lesões. Este trabalho propõe uma plataforma, composta de uma rede de sensores vestíveis e um classificador de Reconhecimento de Atividade Humana (HAR), para fornecer feedback em tempo real de exercícios funcionais, visando auxiliar educadores físicos a reduzir a probabilidade de lesões durante o treinamento. Realizamos um experimento com 12 voluntários diversos para construir um classificador HAR com aproximadamente de 87 porcento de precisão geral na classificação, e um segundo experimento para validar nosso modelo de avaliação física. Por fim, realizamos uma entrevista semi estruturada para avaliar questões de usabilidade e experiência do usuário da plataforma proposta.Visando uma pesquisa replicável, fornecemos informações completas sobre o hardware e o código fonte do sistema, e disponibilizamos o conjunto de dados do experimento. / [en] Foot and knee pain fave been associated with numerous orthopedic pathologies and injuries of the lower limbs. From street running to CrossFitTM functional training, these common pains and injuries correlate highly with unevenly distributed plantar pressure and knee positioning during long-term physical practice and can lead to severe orthopedic injuries if the movement pattern is not amended. Therefore, the monitoring of foot plantar pressure distribution and the spatial and temporal characteristics of foot and knee positioning abnomalities is of utmost importance for injury prevention. This work proposes a platform, composed af an lot wearable body sensor network and a Human Activity Recognition (HAR), to provide realtime feedback of functional exercises, aiming to enhace physical educators capability to mitigate the probability of injuries during training. We conducted an experiment with 12 diverse volunteers to build a HAR classifier that achieved about 87 percent overall classification accuracy, and a second experiment to validate our physical evaluation model. Finally, we performed a semi-structured interview to evaluate usability and user experience issues regarding the proposed platform. Aiming at a replicable research, we provide full hardware information, system source code and a public domain dataset.

This project examines cyber threats and their impact on individuals and organizations. The thesis focuses on a thorough literature review and uses surveys for primary data collection. The quantitative method was chosen to gather numeric data on these threats. The methodology classifies digital threats and analyzes survey results about these threats. It also gathers data on the perceived difficulty of these threats and compares general beliefs with expert opinions and statistical data from literature. Surveys targeted individuals aged 20-45 with a university degree, obtaining 86 responses. Interviews with five security professionals followed a standardized format, aiding in a comparative analysis with the survey data. The questions addressed several cyber threats, including phishing, ransomware, insecure passwords, malware, traffic sniffing, and denial of service. Notably, many respondents lacked a clear understanding of the significance of insecure passwords and traffic sniffing. By using quantitative methods and integrating survey results with expert opinions and literature findings, this study deepens the understanding of cyber threats. The results spotlight misconceptions and knowledge gaps about cyber threats, underscoring the need for better cybersecurity awareness and education. / Detta projekt undersöker cyberhot och deras påverkan på individer och organisationer. Avhandlingen fokuserar på en grundlig litteraturgranskning och använder enkäter för primär datainsamling. Den kvantitativa metoden valdes för att samla numeriska data om dessa hot. Metodiken klassificerar digitala hot och analyserar enkätresultat om dessa hot. Den samlar också in data om den upplevda svårigheten av dessa hot och jämför allmänna uppfattningar med expertåsikter och statistiska data från litteratur. Enkäter riktade sig till individer i åldern 20-45 med en universitetsexamen, och gav 86 svar. Intervjuer med fem säkerhetsprofessionella följde ett standardiserat format, vilket underlättade en jämförande analys med enkätdata. Frågorna behandlade flera cyberhot, inklusive phishing, ransomware, osäkra lösenord, skadlig programvara, trafikavlyssning och denial of service. Framför allt saknade många svarande en tydlig förståelse för betydelsen av osäkra lösenord och trafikavlyssning. Genom att använda kvantitativa metoder och integrera enkätresultat med expertutlåtanden och litteraturfynd fördjupar denna studie förståelsen för cyberhot. Resultaten belyser missuppfattningar och kunskapsluckor om cyberhot, vilket understryker behovet av bättre medvetenhet och utbildning inom cybersäkerhet.

<p dir="ltr">With the ability to connect to networks and send and receive data, Internet of Things (IoT) devices involve associated security risks and threats, for a given environment. These threats are even more of a concern in a Smart Home network, where there is a lack of a dedicated security IT team, unlike a corporate environment. While efficient user interface(UI) and ease of use is at the front and center of IoT devices within Smart Home which enables its wider adoption, often security and privacy have been an afterthought and haven’t kept pace when needed. Therefore, a unsafe possibility exists where malicious actors could exploit vulnerable devices in a domestic home environment.</p><p dir="ltr">This thesis involves a detailed study of the cybersecurity for a Smart Home and also examines the various types of cyberthreats encountered, such as DDoS, Man-In-Middle, Ransomware, etc. that IoT devices face. Given, IoT devices are commonplace in most home automation scenarios, its crucially important to detect intrusions and unauthorized access. Privacy issues are also involved making this an even more pertinent topic. Towards this, various state of the art industry standard tools, such as Nmap, Nessus, Metasploit, etc. were used to gather data on a Smart Home environment to analyze their impacts to detect security vulnerabilities and risks to a Smart Home. Results from the research indicated various vulnerabilities, such as open ports, password vulnerabilities, SSL certificate anomalies and others that exist in many cases, and how precautions when taken in timely manner can help alleviate and bring down those risks.</p><p dir="ltr">Also, an IoT monitoring dashboard was developed based on open-source tools, which helps visualize threats and emphasize the importance of monitoring. The IoT dashboard showed how to raise alerts and alarms based on specific threat conditions or events. In addition, currently available cybersecurity regulations, standards, and guidelines were also examined that can help safeguard against threats to commonly used IoT devices in a Smart Home. It is hoped that the research carried out in this dissertation can help maintain safe and secure Smart Homes and provide direction for future work in the area of Smart Home Cybersecurity.</p>