The waste industry is an important industry for both society and the climate, and where technological development makes waste sorting more efficient. The purpose of this project has been to identify new areas of use for the algorithm, called Spiral Code, which was developed by Professor Josef Bigun at Halmstad University. The technology is unique in its structure and function, where the code can be read regardless of how large a part of it is exposed during the moment of detection. The project's goal is to investigate whether it is possible to implement the Spiral Code at the company Envac in order to make their waste management more efficient. Conducted tests show that the Spiral Code can locate and identify the spirals in the messy environment that prevails during waste sorting and where today's method of optical color reading does not enable the identification of the origin of the waste. Furthermore, completed tests show that it is practically feasible to implement the Spiral Code on Envac's plant system. In order to get the best efficiency, some adaptations is recommended to be carried out, such as implementation of the Spiral code in Envac's camera and image analysis system, control and adaptation of the camera's location and improvement of lighting at the same to ensure correct detection. / Avfallsbranschen är en viktig bransch för både samhället och klimatet och där den tekniska utvecklingen möjliggör en effektivisering av avfallssorteringen. Syfte med detta projekt har varit att identifiera nya användningsområden för den algoritm, benämnd Spiralkod och som är utvecklad av professor Josef Bigun vid Högskolan i Halmstad. Teknologin är unik i sin uppbyggnad samt funktion, där koden är möjlig att läsas av oberoende av hur stor del av den som exponeras under avläsningsögonblicket. Projektets mål är att undersöka om det är möjligt att implementera Spiralkoden hos företaget Envac för att på så sätt effektivisera deras avfallshantering. Genomförda tester visar att Spiralkoden kan lokalisera och identifiera spiralerna i den rörig miljö som råder vid avfallssortering och där dagens metod med optisk färgavläsning inte möjliggör identifiering av avfallets ursprung. Vidare visar genomförda tester att det är praktiskt genomförbart att kunna implementera Spiralkoden på Envacs anläggningssystem. För att få ut bäst effektivitet bör en del anpassningar genomföras, som exempel implementering av Spiralkoden i Envacs kamera-och bildanalyssystem, kontroll och anpassning av kamerans placering samt

Context: In the field of computer science and communication systems, cloud computing plays animportant role in Information and Technology industry, it allows users to start from small and increase resources when there is a demand. AWS (Amazon Web Services) and GCP (Google cloud Platform) are two different cloud platform providers. Many organizations are still relying onstructured databases like MySQL etc. Structured databases cannot handle huge requests and data efficiently when number of requests and data increase. To overcome this problem, the organizations shift to NoSQL unstructured databases like Apache cassandra, Mongo DB etc. Conclusions: From the literature review, I have gained knowledge regarding the cloud computing, problems existed in cloud, which leads to setup this research in evaluating the performance of cassandra on AWS and GCP. The conclusion from the experiment is that as the thread count increases throughput and latency has increased gradually till thread count 600 in both the clouds. By comparing both the clouds throughput values, AWS scales up compare to GCP. GCP scales up, when compared to AWS in terms of latency.  Keywords: Apache Cassandra, AWS, Google Cloud Platform, Cassandra Stress, Throughput, Latency

A problem that exhibition halls have is the balance between having good indoor air quality andminimizing energy waste due to the naturally slow decrease of CO2 concentration, which causes Heat-ing, Ventilation and Air-Conditioning systems to keep ventilating empty halls when occupants have leftthe vicinity. Several studies have been made on the topic of CO2 prediction and occupancy predictionbased on CO2 for smaller spaces such as offices and schools. However, few studies have been madefor bigger venues where a larger group of people gather. An online machine learning model using theRiver library was developed to tackle this problem by predicting the CO2 ahead of time. Five datasetswere used for training and predicting, three with real data and two with simulated data. The resultsfrom this model was compared with three already developed traditional models in order to evaluate theperformance of an online machine learning model compared to traditional models. The online machinelearning model was successful in predicting CO2 one hour ahead of time considerably faster than thetraditional models, achieving a r2 score of up to 0.95.

I och med samhällets ökande digitalisering stiger behoven för att kunna fastställa att säkerheten hos datornätverk ligger på en adekvat nivå. När det gäller informations- säkerhet fastnar fokus lätt på enskilda enheter men idag består i regel de flesta orga- nisationer av större datanätverk där information finns åtkomlig på ett flertal sätt. Denna rapport försöker utröna huruvida det går att kvantifierbart påvisa effektivite- ten hos de säkerhetsåtgärder som nätverkstekniker implementerar i syfte att höja säkerheten i en organisations datornätverk. För att mäta säkerhetsförbättringar kombineras en penetrationstestningsstandard med en hotmodelleringsmetod som sedan appliceras på ett nätverk i en laborations- miljö. I ett första skede appliceras dessa på ett sårbart nätverk för att ge en insikt om det ursprungliga säkerhetsläget. Därefter implementeras ett urval av skyddsåtgärder på det sårbara datornätverket vilka baserat på vilka säkerhetsbrister som har upp- täckts. I ett nästa steg appliceras både penetrationstester och hotmodellering återi- gen på samma sätt som tidigare och därefter jämförs resultaten från före och efter att sårbarheterna har åtgärdats. När resultaten jämförs påvisas de säkerhetshöjande åtgärdernas effekt. Tack vare kombinationen av både penetrationstester och hotmodellering tydliggörs även re- sultaten både bredare och mer djupgående än vad användandet av enbart en av me- toderna hade visat på. Dessa resultat visar att metoden med applikation av kombinerade penetrationstester och hotmodelleringar, både före och efter att säkerhetsluckor har åtgärdats, kan an-vändas som ett kvalitetsintyg för säkerhetsarbeten riktade mot datornätverk. / Following the increasing digitalization of society there is a growing need to ensure that the security of computer networks is at an adequate level. When it comes to net- work security, focus tend to fall on individual devices, but nowadays most organiza- tions consist of large computer networks where information is accessible in several different ways. This thesis attempts to determine whether it is possible to ensure the effectiveness of the security measures implemented by network engineers to improve an organisation’s security stance. In order to measure security, a combination of a penetration testing standard and threat modelling is applied to a network in a laboratory environment. First, these are applied to a vulnerable network. Then, a selection of protection measures are imple- mented on the vulnerable network based on the results from the methodology. In a next step, both penetration testing and threat modelling are reapplied. The com- bined results, before and after the vulnerabilities have been addressed, can then be compared to each other. When comparing the results, the impact of the security improving measures becomes clear. Due to the use of both penetration testing and threat modelling the results are further clarified compared to what the use of only one of the methods would have shown. These results shows that the method of combining penetration testing with threat modelling in two stages, both before and after security measures have been imple- mented, can be used as a quality certificate for security work directed at computernetworks.

This thesis describes a method to generate new trajectories based on historic positiondata for a given geographical area. The thesis uses AIS-data from fishing boats to first describe a method that uses DBSCAN and OPTICS algorithms to cluster the data into clustersbased on routes where the boats travel and areas where the boats fish.Here bayesian optimization has been utilized to search for parameters for the clusteringalgorithms. In this given scenario it was shown DBSCAN is better in all fields, but it hasmany points where OPTICS has the potential to become better if it was modified a bit.This is followed by a method describing how to take the clusters and build a nodenetwork that then can be traversed using a path finding algorithm combined with internalrules to generate new routes that can be used in simulations to give a realistic enoughsituation picture. Finally a method to evaluate these generated routes are described andused to compare the routes to each other

Detta kandidatarbete gjordes tillsammans med Rollco AB med mål att digitalisera deras beställningssystem för specialbeställda kulskruvar. Det befintliga systemet för att specialbeställa kulskruvar var föråldrat, innehöll många brister och var inte integrerat mot deras befintliga betalsystem.   Arbetets mål var därför att digitalisera det befintliga systemet och presentera ett “proof of concept” för hur ett eventuellt system kan se ut i framtiden. För att skapa konceptet användes det CAD-liknande programmet DynaMaker där kod skrevs i programmeringsspråket TypeScript för att skapa 3D-modeller och logik i systemet.   Arbetet resulterade i ett skalbart system där användaren fritt kan skapa sin kulskruv efter personlig preferens. Användaren kan sedan ladda ner en ritning i PDF-format samt en CAD-modell av kulskruven för att sedan gå vidare till Rollcos betalsystem Litium. Resultatet av arbetet är en stabil grund där Rollco har möjlighet att bygga vidare systemet i framtiden med fler funktionaliteter. / This bachelor's thesis was done together with Rollco AB with the aim of digitizing their ordering system for custom ordered ball screws. The existing system for custom ordered ball screws was outdated, contained many shortcomings and was not integrated with their existing payment system. The aim of the thesis was therefore to digitize the existing system and present a "proof of concept" for what a possible system might look like in the future. To create the concept, the CAD-like program DynaMaker was used in conjunction with TypeScript. This programming language was used  to create 3D-models and create the logic behind the system. The work resulted in a scalable system where the user is free to create a ball screw according to personal preferences. The user is then able to download a drawing in PDF format as well as a CAD model of the ball screw and then proceed to Rollco's payment system Lithium. The work resulted in a stable foundation where Rollco has the opportunity to expand the system in the future with more functionalities.

IO-Link is a constantly growing market, bringing more interested parties who want to know if it is a market worth investing in. While deciding if the market is worth investing in is for the companies to decide, this thesis aims to shed some light on what it takes to get started with IO-Link development which could help that decision. This work presents the development of a proof-of-concept IO-Link master stack, which is then evaluated technically and financially. The proof-of-concept stack was then evaluated by testing it with a commercially available IO-Link Device (KT6101). This resulted in a functional proof-of-concept master stack with the physical, data link and application layer implemented, with ISDU support. The master stack supports cyclic Process Data exchange and acyclic On-request Data exchange, which is the minimum needed for IO-Link operations with a fully implemented IO-Link Device. The financial evaluation provides insights about the cost of developing the stack in Sweden, and the varying costs stemming from the place where the development occurs, since the host company has a global scope. Information from this work is a tool for stakeholders and decision makers in regard of the financial viability of the project, while the technical evaluation of the proof-of-concept is positive.

Unmanned aerial vehicles (UAVs) with high-resolution cameras are common in today’s society. Industries, such as the forestry industry, use drones to get a fast overview of tree populations. More advanced sensors, such as near-infrared light or depth data, can increase the amount of information that UAV images provide, providing information about the forest, such as; tree quantity or forest health. However, the fast-expanding field of deep learning could help expand the information acquired using only RGB cameras. Three deep learning models, FasterR-CNN, RetinaNet, and YOLOR were compared to investigate this. It was also investigated if initializing the models using transfer learning from the MS COCO dataset could increase the performance of the models. The dataset used was Swedish Forest Agency (2021): Forest Damages-Spruce Bark Beetle 1.0 National Forest Data Lab and drone images provided by IT-Bolaget Per & Per. The deep learning models were to detect five different tree species; spruce, pine, birch, aspen, and others. The results show potential for the usage of deep learning to detect tree species in images from UAVs. / Obemannade drönare med högupplösta kameror är vanliga i dagens samhälle. Branscher, så som skogsindustrin, kan använda sig av sådana drönare för att få en snabb översikt över ett skogsområde.Mer avancerade sensorer, som använder nära-infrarött ljus eller djupdata, kan öka mängden information som drönarna kan samla in, information såsom; trädmängd eller data om skogens hälsa. Det snabbt växande området djup-maskinlärning kan dock hjälpa till att utöka informationen som kan extraheras vid användning av endast RGB-kameror. Tre modeller för djupinlärning, Faster R-CNN, RetinaNet och YOLOR, jämfördes för att undersöka detta. Det undersöktes också om initiering med för-tränade vikter, med överföringsinlärning från datasetet MS COCO, skulle kunna öka modellernas prestanda. Datasetet som användes var Skogsstyrelsen (2021): Skogsskador-Granbarkborre1.0 Nationell Forest Data Lab samt drönarbilder tillhandahållna av IT-Bolaget Per & Per. Det tredjupinlärnings-modellerna skulle detektera fem olika trädarter: gran, tall, björk, asp, och övrigt.Resultaten visar potential för användning av djupinlärning för att upptäcka trädarter i bilder från drönare.

This thesis aims to produce a similarity metric for tracks of the genre: Electronic Dance Music, by taking a high-dimensional data representation of each track and then project it to a low-dimensional embedded space (2D and 3D) by applying two Dimensionality Reduction (DR) techniques called t-distributed stochastic neighbor embedding (t-SNE) and Pairwise Controlled Manifold Approximation (PaCMAP). A content-based approach is taken to identify similarity, which is defined as the distances between points in the embedded space. This work strives to explore the connection between the extractable content and the feel of a track. Features are extracted from every track over a 30 second window with Digital Signal Processing tools. Three evaluation methods were conducted with the purpose of establishing ground truth in the data. The first evaluation method established expected similarity sub clusters and tuned the DR techniques until the expected clusters appeared in the visualisations of the embedded space. The second evaluation method attempted to generate new tracks with a controlled level of separation by applying various distortion techniques with increasing magnitude to copies of a track. The third evaluation method introduces a data set with annotated scores on valence and arousal values of music snippets which was used to train estimators that was used to estimate the feeling of tracks and to perform classification. Lastly, a similarity metric was computed based on distances in the embedded space. Findings suggest that certain contextual groups such as remixes and tracks by the same artist, can be identified with this metric and that tracks with small distortions (similar tracks) are located more closely in the embedded space than tracks with large distortions.

Anomaly detection in computer systems operating within complex environments,such as cyber-physical systems (CPS), has become increasingly popularduring these last years due to useful insights this process can provide aboutcomputer systems’ health conditions against known reference nominal states.As performance anomalies lead degraded service delivery, and, eventually,system-wide failures, promptly detecting such anomalies may trigger timelyrecovery responses. In this thesis, Process Mining, a discipline aiming at connectingdata science with process science, is broadly explored and employedfor detecting performance anomalies in complex computer systems, proposinga methodology for connecting event data to high-level process models forvalidating functional and non-functional requirements, evaluating system performances,and detecting anomalies. The proposed methodology is appliedto the industry-relevant European Rail Traffic Management System/EuropeanTrain Control System (ERTMS/ETCS) case-study. Experimental results sampledfrom an ERTMS/ETCS system Demonstrator implementing one of thescenarios the standard prescribe have shown Process Mining allows characterizingnominal system performances and detect deviations from such nominalconditions, opening the opportunity to apply recovery routines for steeringsystem performances to acceptable levels.