Manufacturers can develop next-generation production and service for their customers by the data gathered and analyzed from customers’ usage conditions. In this research, the operating condition of articular haulers is collected and analyzed through machine learning algorithms to predict the type of operational topographies and road surface. To achieve that, elevation data and satellite images, which were gathered from Microsoft Azure Maps, are used as data sources to identify the topography and road surface on which machines operated. In the end, two machine learning models are trained with machines’ inclination records and road roughness records, respectively, to classify the topography and road surface. For the topography classifier, the topography is categorized into four terrain labels, including "Low Hills", "Mountains", "Plains", and "Tablelands & High Hills". The road surface is classified into "Paved" and "Unpaved". A Convolutional Neural Network (CNN) image classification model is built for labeling satellite images instead of labeling manually. The results indicate that the prediction for topography labels "Plains" and "Tablelands & High Hills" has superior performance, which accounts for the majority of the raw dataset; on the contrary, the road surface classifier still needs further improvement in the future. In addition, an analysis and discussion regarding the imbalanced dataset are included, and it shows the limited effect on an extremely imbalanced dataset. Finally, the conclusion and future work are given.

With the increasing popularity of blockchain - the cryptocurrency technology, the decentralized potential of the Blockchain technique is driving a new wave across the manufacturing industry. This paper introduce how to use the blockchain technique as a tool for solving supply chain related tasks in manufacture industry, and drive quantum leaps in efficiency, agility and innovation comparing with traditional centralized management system. This paper introduces the blockchain technique with its value properties and the requirement of this technique from manufacture industry. It also presents a clear blockchain architecture based on manufacture industry supply chain management mechanism describing its characteristics, unique consensus algorithms, smart contracts, network, scalability, databases. The paper also gives out a practical supply chain Dapp upon this architecture. / I och med det ökande intresset för kryptovaluta-teknologin Blockchain, går decentraliseringen av Blockchain-tekniken som en ny våg över tillverkningsindustrin. Denna uppsats syftar till att introducera hur tekniken av blockchain kan användas som ett verktyg för att lösa problem relaterade till leverantörskedjan i tillverkningen. Den belyser även vilka fördelar tekniken har gällande effektivitet, flexibilitet och förnyelse jämfört med traditionella centraliserade styrningssystem. Arbetet presenterar fördelarna med blockchain och hur industrin är i behov av denna teknik. Uppsatsen presenterar även en tydlig blockchain konstruerad struktur baserad på tillverkningskedjans mekanism som består av unika algoritmer, nätverk och databaser. Ett praktiskt exempel på en decentraliserad applikation baserat på denna struktur ges även.

Projection mapping, a subtopic of augmented reality, displays computer-generated light visualizations from projectors onto the real environment. A challenge for projection mapping in performing interactive arts is dynamic body movements. Accuracy and speed are key components for an immersive application of body projection mapping and dependent on scanning and processing time. This thesis presents a novel technique to achieve real-time body projection mapping utilizing a state of the art body tracking device, Microsoft’s Azure Kinect DK, by using an array of trackers for error minimization and movement prediction. The device's Sensor and Bodytracking SDKs allow multiple device synchronization. We combine our tracking results from this feature with motion prediction to provide an accurate approximation for body joint tracking. Using the new joint approximations and the depth information from the Kinect, we create a silhouette and map textures and animations to it before projecting it back onto the user. Our implementation of gesture detection provides interaction between the user and the projected images. Our results decreased the lag time created from the devices, code, and projector to create a realistic real-time body projection mapping. Our end goal was to display it in an art show. This thesis was presented at Burning Man 2019 and Delfines de San Carlos 2020 as interactive art installations.

Práce se zabývá popisem tvorby mobilního systému pro monitoring zákaznické spokojenosti a sběr zpětné vazby od návštěvníků v restauracích s názvem Huerate. Komplexně jsou popsané všechny fáze vývoje systému. První část práce se zabývá analýzou existujících řešení a stavem na trhu. Následně jsou na základně komunikace s majiteli restaurací sestaveny požadavky na systém. Nakonec se práce věnuje samotnému návrhu systému, jeho implementaci a nasazení v restauracích. Systém Huerate běží jako webová aplikace a je dostupný na adrese http://huerate.cz.

The thesis is focused on a determination of heparin and chondroitin sulfate, using flow injection analysis with spectrofluorimetric detection. The determination is based on the interaction of negatively charged heparin, chondroitin sulfate resp., with a cationic dye (azure B or phenosafranine) which is manifested by the decrease in fluorescence intensity of the dye in its emission maximum. The optimal conditions for the determination in static mode were found, and calibration dependencies were measured. The conditions of FIA were optimized and following parameters were established: the volume of dispensed sample of 100 ml, the length of the reaction coil 60 cm, the flow rate 0.7 ml min-1 , the concentration of azure B 1.6×10-5 mol dm-3 , the concentration of phenosafranine 3.5×10-5 mol dm-3 . For the determination of heparin using azure B it was found: LOD = 0.023 IU ml-1 , LOQ = 0.186 IU ml-1 , and linear dynamic range 0.19-1.43 IU ml-1 . For the determination of heparin using phenosafranine it was found: LOD = 0.102 IU ml-1 , LOQ = 0.192 IU ml-1 , and linear dynamic range 0.19-1.79 IU ml-1 . For the determination of chondroitin sulfate using azure B it was found: LOD = 0.58 mg dm-3 , LOQ = 2.37 mg dm-3 , and linear dynamic range 2.37-8.32 mg dm-3 . The developed determination was applied to the...

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Cloud computing has been given a great deal of attention during recent years. Almost all the technology market leaders and leading hosting service providers (like IBM, Microsoft and Verizon) have entered into the Cloud market as Cloud Providers. Cloud computing promises to provide highly available, secure, low cost, agile and highly scalable solution to the consumers. Scania is a global company and one of the world’s leading heavy vehicle manufacturers with 35,000+ employees. All the large organizations such as Scania, aim to constantly update themselves with the latest technology in order to meet their business requirements but, these organizations must always be convinced that there is a strong reason(s) to implement new technology. This research provides the method and criteria in relation to initiating Cloud computing. A number of Scania’s specific business requirements that it is possible to map to the Cloud are addressed in this thesis. The methodology of research is split in two parts. Firstly, the identification of business cases at Scania and their requirements with the Cloud and Secondly, the evaluation and comparison of the functionalities and capabilities of different vendors. The accumulated data is then compared and suitable vendors, according to those business requirements are suggested. This thesis also shares the experience of moving on premise applications to the Cloud. These are Scania specific applications which are currently being hosted in-house. The research also addresses the possibilities of portability between the Cloud providers. Although there is no standardization in relation to Cloud computing, some initiatives such as OpenStack are available and its current position and some application and data migration tools are also discussed. The thesis concludes with a general discussion, recommendations in relation to adapting Cloud computing and selecting the Cloud provider. This recommendation applies to every organization including Scania.

With the advent of cloud computing, resizable scalable infrastructures for data processing is now available to everyone. Software platforms and frameworks that support data intensive distributed applications such as Amazon Web Services and Apache Hadoop enable users to the necessary tools and infrastructure to work with thousands of scalable computers and process terabytes of data. However writing scalable applications that are run on top of these distributed frameworks is still a demanding and challenging task. The thesis aimed to advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large data sets, collectively known as “big data”. The term “big-data” in this thesis refers to large, diverse, complex, longitudinal and/or distributed data sets generated from instruments, sensors, internet transactions, email, social networks, twitter streams, and/or all digital sources available today and in the future. We introduced architectures and concepts for implementing a cloud-based infrastructure for analyzing large volume of semi-structured and unstructured data. We built and evaluated an application prototype for collecting, organizing, processing, visualizing and analyzing data from the retail industry gathered from indoor navigation systems and social networks (Twitter, Facebook etc). Our finding was that developing large scale data analysis platform is often quite complex when there is an expectation that the processed data will grow continuously in future. The architecture varies depend on requirements. If we want to make a data warehouse and analyze the data afterwards (batch processing) the best choices will be Hadoop clusters and Pig or Hive. This architecture has been proven in Facebook and Yahoo for years. On the other hand, if the application involves real-time data analytics then the recommendation will be Hadoop clusters with Storm which has been successfully used in Twitter. After evaluating the developed prototype we introduced a new architecture which will be able to handle large scale batch and real-time data. We also proposed an upgrade of the existing prototype to handle real-time indoor navigation data.

The usage of cloud computing in order to move away from local servers and infrastructure have grown enormously the last decade. The ability to quickly scale capacity of servers and their resources at once when needed is something that can both be a price saver for companies and help them deliver high end products that will function correctly at all times even under heavy load to their customers. To meet todays challenges, one of the strategic directions of Attentec, a software company located in Linköping, is to examine the world of cloud computing in order to deliver robust and scalable applications to their customers. This thesis investigates the usage of cloud services in order to deploy scalable applications which can adapt to usage peaks within minutes.

This thesis investigates the prerequisites needed for the Swedish real estate company Fabege to create useful machine learning models for classification and prediction of error reports from tenants. These error reports are regarding cold indoor climates and bad indoor air quality. By analyzing the available data, that consists of error reporting data, weather data and indoor climate data, the thesis investigates the different correlations between the sensor data and the error reports. By using an algorithm called decision jungle, two machine learning models have been trained in Microsoft Azure Machine Learning Studio. The main model, trained on error reporting data and weather data, shows the possibilities to classify data instances as a part of different error reporting classes. The model proves that it is possible to predict the emergence of future error reports of different classes with an average accuracy of 78%. The complementary model, trained on a small but more richly annotated dataset consisting of one year of indoor sensor data as well as the above-mentioned data, shows that there is a possibility to improve the main model by using indoor climate data. The thesis has shown that for Fabege to expand and improve these models, the amount of data collected from the indoor sensors needs to be largely increased. Fabege also needs to improve the quality of the error reporting data, which could be achieved by improving the error reporting form used by the tenants.