While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; we have also shown that this application greatly reduces the time a reseller would need to spend on trading activities, since the bulk of market research is now done automatically with the help of the learned model.

In this master thesis, we implement a two-step anomaly detection mechanism for non-recurrent traffic congestions with data collected from public transport buses in Stockholm. We investigate the use of machine learning to model time series data with LSTMs and evaluate the results with a baseline prediction model. The anomaly detection algorithm embodies both collective and contextual expressivity, meaning it is capable of findingcollections of delayed buses and also takes the temporality of the data into account. Results show that the anomaly detection performance benefits from the lower prediction errors produced by the LSTM network. The intersection rule significantly decreases the number of false positives while maintaining the true positive rate at a sufficient level. The performance of the anomaly detection algorithm has been found to depend on the road segment it is applied to, some segments have been identified to be particularly hard whereas other have been identified to be easier than others. The performance of the best performing setup of the anomaly detection mechanism had a true positive rate of 84.3 % and a true negative rate of 96.0 %. / I den här masteruppsatsen implementerar vi en tvåstegsalgoritm för avvikelsedetektering för icke återkommande trafikstockningar. Data är insamlad från kollektivtrafikbussarna i Stockholm. Vi undersöker användningen av maskininlärning för att modellerna tidsseriedata med hjälp av LSTM-nätverk och evaluerar sedan dessa resultat med en grundmodell. Avvikelsedetekteringsalgoritmen inkluderar både kollektiv och kontextuell uttrycksfullhet, vilket innebär att kollektiva förseningar kan hittas och att även temporaliteten hos datan beaktas. Resultaten visar att prestandan hos avvikelsedetekteringen förbättras av mindre prediktionsfel genererade av LSTM-nätverket i jämförelse med grundmodellen. En regel för avvikelser baserad på snittet av två andra regler reducerar märkbart antalet falska positiva medan den höll kvar antalet sanna positiva på en tillräckligt hög nivå. Prestandan hos avvikelsedetekteringsalgoritmen har setts bero av vilken vägsträcka den tillämpas på, där några vägsträckor är svårare medan andra är lättare för avvikelsedetekteringen. Den bästa varianten av algoritmen hittade 84.3 % av alla avvikelser och 96.0 % av all avvikelsefri data blev markerad som normal data.

Water stored in snow is a critical contribution to the world’s available freshwater supply and is fundamental to the sustenance of natural ecosystems, agriculture and human societies. The importance of snow for the natural environment and for many socio-economic sectors in several mid‐ to high‐latitude mountain regions around the world, leads scientists to continuously develop new approaches to monitor and study snow and its properties. The need to develop new monitoring methods arises from the limitations of in situ measurements, which are pointwise, only possible in accessible and safe locations and do not allow for a continuous monitoring of the evolution of the snowpack and its characteristics. These limitations have been overcome by the increasingly used methods of remote monitoring with space-borne sensors that allow monitoring the wide spatial and temporal variability of the snowpack. Snow models, based on modeling the physical processes that occur in the snowpack, are an alternative to remote sensing for studying snow characteristics. However, from literature it is evident that both remote sensing and snow models suffer from limitations as well as have significant strengths that it would be worth jointly exploiting to achieve improved snow products. Accordingly, the main objective of this thesis is the development of novel methods for the estimation of snow parameters by exploiting the different properties of remote sensing and snow model data. In particular, the following specific novel contributions are presented in this thesis: i. A novel data fusion technique for improving the snow cover mapping. The proposed method is based on the exploitation of the snow cover maps derived from the AMUNDSEN snow model and the MODIS product together with their quality layer in a decision level fusion approach by mean of a machine learning technique, namely the Support Vector Machine (SVM). ii. A new approach has been developed for improving the snow water equivalent (SWE) product obtained from AMUNDSEN model simulations. The proposed method exploits some auxiliary information from optical remote sensing and from topographic characteristics of the study area in a new approach that differs from the classical data assimilation approaches and is based on the estimation of AMUNDSEN error with respect to the ground data through a k-NN algorithm. The new product has been validated with ground measurement data and by a comparison with MODIS snow cover maps. In a second step, the contribution of information derived from X-band SAR imagery acquired by COSMO-SkyMed constellation has been evaluated, by exploiting simulations from a theoretical model to enlarge the dataset.

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Net Neutrality implicates that all data being transmitted online is treated equal by Internet Service Providers. In 2016, the public debate regarding Net Neutrality in Sweden started growing as two major Mobile Network Operators were investigated by the Swedish Post and Telecom Authority for violation of European Union Net Neutrality regulations. Several studies have been conducted regarding Net Neutrality, most of them written in a legal, financial or technological perspective. This study takes another direction, aimed at understanding the consumer’s point of view regarding Net Neutrality. This study investigates whether or not consumers are aware of the subject and if so, how they value it. To measure this, an online survey was constructed, containing a total of 12 questions and statements. 77 people participated in the survey and out of these, 10 people participated in qualitative follow-up interviews. The interviews were semi-structured and individually designed according to each participant’s answers in the survey. This was done in order to gain a deeper understanding of the consumer’s reasoning while answering the survey. The results show that consumers lack knowledge regarding Net Neutrality. A major part of the consumers had not heard of the term or did not know the meaning of it, making it hard to determine whether or not the consumers value NN. However, when given a more concrete example of the implications of Internet Traffic Management from ISPs, the participants had a better understanding of what kind of implications NN could have on their Internet usage. They valued the implications of Net Neutrality, even though they did not know the theory of the term itself. The study also revealed that consumers have a big confidence in National Regulatory Authorities when it comes to looking after the openness of the Internet. Therefore, it is likely that National Regulatory Authorities must inform and educate consumers in the matter of Net Neutrality for them to value it and see its long-term implications. / Nätneutralitet innebär kortfattat att all data som skickas över Internet ska behandlas likvärdigt utav Internetleverantörer (ISP). Under 2016 växte debatten kring nätneutralitet i Sverige då två stycken mobiloperatörer utreddes utav Post- och Telestyrelsen. Båda dessa mobiloperatörer lanserade kampanjer till sina kunder som ansågs strida mot EU:s förordning 2015/2120 rörande nätneutralitet. Ett antal studier har redan gjorts på ämnet nätneutralitet, dock har de flesta haft en infallsvinkel där man tittat på juridiska, finansiella eller tekniska perspektiv. Den här studien har en annan infallsvinkel och riktar sig istället mot konsumenters syn på nätneutralitet. Den ämnar undersöka huruvida konsumenter känner till begreppet nätneutralitet och om de gör det, hur värderar de konceptet? För att undersöka detta konstruerades en online-enkät, innehållandes 12 frågor. 77 personer deltog i enkäten och utav dessa så deltog 10 personer i uppföljande, kvalitativa intervjuer. Intervjuerna var semi-strukturerade och individuella med frågor baserade på individens svar i enkäten. Dessa intervjuer var till för att ge en fördjupad förståelse av konsumenternas syn på nätneutralitet och deras resonemang kring svaren under enkäten. Resultaten visar att konsumenter, deltagande i den här studien, har låg kunskap kring nätneutralitet. Majoriteten utav deltagarna hade inte hört termen eller kände inte till dess mening, vilket gjorde det svårt att dra några slutsatser kring huruvida konsumenterna värderar konceptet. Men när konsumenterna fick ett mer konkret exempel på hur Internetleverantörers datahantering påverkar kundernas Internetanvändande så tycktes konsumenterna förstå vilka implikationer nätneutralitet kan ha på deras eget Internetanvändande. De tycktes således värdera innebörden av nätneutralitet, även om de inte förstod teorin kring konceptet. Studien påvisade också att konsumenter har en stor tilltro till vederbörande myndighet, Post- och Telestyrelsen här i Sverige, när det gäller att se efter Internets öppenhet och mångfald. Det är därför troligt att Post- och Telestyrelsen kommer att behöva informera och utbilda konsumenter rörande nätneutralitet för att få konsumenter att se värdet av och de långsiktiga implikationerna utav det.

With the increasing number of wireless devices and the phenomenal amount of data that is being generated by them, there is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. However, managing the large-scale multi-dimensional data to maintain the efficiency and scalability of the ML algorithms has obviously been a challenge. Tensors provide a useful framework to represent multi-dimensional data in an integrated manner by preserving relationships in data across different dimensions. This thesis studies two new applications of tensors to ML for wireless communications where the tensor structure of the concerned data is exploited in novel ways. The first contribution of this thesis is a tensor learning-based low-complexity precoder codebook design technique for a full-dimension multiple-input multiple-output (FD-MIMO) system with a uniform planar antenna (UPA) array at the transmitter (Tx) whose channel distribution is available through a dataset. Represented as a tensor, the FD-MIMO channel is further decomposed using a tensor decomposition technique to obtain an optimal precoder which is a function of Kronecker-Product (KP) of two low-dimensional precoders, each corresponding to the horizontal and vertical dimensions of the FD-MIMO channel. From the design perspective, we have made contributions in deriving a criterion for optimal product precoder codebooks using the obtained low-dimensional precoders. We show that this product codebook design problem is an unsupervised clustering problem on a Cartesian Product Grassmann Manifold (CPM), where the optimal cluster centroids form the desired codebook. We further simplify this clustering problem to a $K$-means algorithm on the low-dimensional factor Grassmann manifolds (GMs) of the CPM which correspond to the horizontal and vertical dimensions of the UPA, thus significantly reducing the complexity of precoder codebook construction when compared to the existing codebook learning techniques. The second contribution of this thesis is a tensor-based bandwidth-efficient gradient communication technique for federated learning (FL) with convolutional neural networks (CNNs). Concisely, FL is a decentralized ML approach that allows to jointly train an ML model at the server using the data generated by the distributed users coordinated by a server, by sharing only the local gradients with the server and not the raw data. Here, we focus on efficient compression and reconstruction of convolutional gradients at the users and the server, respectively. To reduce the gradient communication overhead, we compress the sparse gradients at the users to obtain their low-dimensional estimates using compressive sensing (CS)-based technique and transmit to the server for joint training of the CNN. We exploit a natural tensor structure offered by the convolutional gradients to demonstrate the correlation of a gradient element with its neighbors. We propose a novel prior for the convolutional gradients that captures the described spatial consistency along with its sparse nature in an appropriate way. We further propose a novel Bayesian reconstruction algorithm based on the Generalized Approximate Message Passing (GAMP) framework that exploits this prior information about the gradients. Through the numerical simulations, we demonstrate that the developed gradient reconstruction method improves the convergence of the CNN model. / Master of Science / The increase in the number of wireless and mobile devices have led to the generation of massive amounts of multi-modal data at the users in various real-world applications including wireless communications. This has led to an increasing interest in machine learning (ML)-based data-driven techniques for communication system design. The native setting of ML is {em centralized} where all the data is available on a single device. However, the distributed nature of the users and their data has also motivated the development of distributed ML techniques. Since the success of ML techniques is grounded in their data-based nature, there is a need to maintain the efficiency and scalability of the algorithms to manage the large-scale data. Tensors are multi-dimensional arrays that provide an integrated way of representing multi-modal data. Tensor algebra and tensor decompositions have enabled the extension of several classical ML techniques to tensors-based ML techniques in various application domains such as computer vision, data-mining, image processing, and wireless communications. Tensors-based ML techniques have shown to improve the performance of the ML models because of their ability to leverage the underlying structural information in the data. In this thesis, we present two new applications of tensors to ML for wireless applications and show how the tensor structure of the concerned data can be exploited and incorporated in different ways. The first contribution is a tensor learning-based precoder codebook design technique for full-dimension multiple-input multiple-output (FD-MIMO) systems where we develop a scheme for designing low-complexity product precoder codebooks by identifying and leveraging a tensor representation of the FD-MIMO channel. The second contribution is a tensor-based gradient communication scheme for a decentralized ML technique known as federated learning (FL) with convolutional neural networks (CNNs), where we design a novel bandwidth-efficient gradient compression-reconstruction algorithm that leverages a tensor structure of the convolutional gradients. The numerical simulations in both applications demonstrate that exploiting the underlying tensor structure in the data provides significant gains in their respective performance criteria.

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The contents of this thesis are the introduction to mesh grids, the method of dispatch control of these grids and the description of technology used in mesh grids distribution nodes in the city of Brno. This thesis also describes low voltage switchgears used in these types of grids. The development of dispatch control in ECD company is also mentioned. The development describes the grid dispatch control methods prior to implementing the OMS system and the changes which followed after the implementation. The process improvements of the new system resulting from this thesis can be found in this thesis as well. The improvements are incorporated in the OMS system and are used for more efficient dispatch control of the low voltage mesh grids. There is a more detailed analysis of the mesh grid Brno – Bohunice in the practical part of this thesis. The practical part contains analyses of mesh grids stabilized conditions measurements and analyses of mesh grids fault conditions measurements. The analyses assess the power load in transformers, minimal and maximal phase current and maximal power load at the time they were observed. The analysis of the measured data is accompanied by the assessment of differences in current phase measurements and differences in voltage phase and combined measurements. The result, based on the analysis and collection of the data, is a proposition of adding switchgears for the support of the dispatch control of the mesh grid Brno – Bohunice. In the summary of the thesis there is a comparison of the result of the factual and theoretical analysis. An experimental model of the mesh grid Brno – Bohunice was created in PS CAD software as a part of the theoretical analysis.

Characterizing the uncertainty in rainfall using stochastic models has been a challenging area of research in the field of operational hydrology for about half a century. Simulated sequences drawn from such models find use in a variety of hydrological applications. Traditionally, parametric models are used for simulating rainfall. But the parametric models are not parsimonious and have uncertainties associated with identification of model form, normalizing transformation, and parameter estimation. None of the models in vogue have gained universal acceptability among practising engineers. This may either be due to lack of confidence in the existing models, or the inability to adopt models proposed in literature because of their complexity or both. In the present study, a new nonparametric Matched Block Bootstrap (MABB) model is proposed for stochastic simulation of rainfall at daily time scale. It is based on conditional matching of blocks formed from the historical rainfall data using a set of predictors (conditioning variables) proposed for matching the blocks. The efficiency of the developed model is demonstrated through application to rainfall data from India, Australia, and USA. The performance of MABB is compared with two non-parametric rainfall simulation models, k-NN and ROG-RAG, for a site in Melbourne, Australia. The results showed that MABB model is a feasible alternative to ROG-RAG and k-NN models for simulating daily rainfall sequences for hydrologic applications. Further it is found that MABB and ROG-RAG models outperform k-NN model. The proposed MABB model preserved the summary statistics of rainfall and fraction of wet days at daily, monthly, seasonal and annual scales. It could also provide reasonable performance in simulating spell statistics. The MABB is parsimonious and requires less computational effort than ROG-RAG model. It reproduces probability density function (marginal distribution) fairly well due to its data driven nature. Results obtained for sites in India and U.S.A. show that the model is robust and promising.

No description available.