This thesis deals with classification of different types of time courses of ECG signals. Main objective was to recognize the normal cycles and several forms of arrhythmia and to classify the exact types of them. Classification has been done with usage of algorithms of Neural Networks in Matlab program, with its add-on (Neural Network Toolbox). The result of this thesis is application, which makes possible to load an ECG signal, pre-process it and classify its each cycle into five classes. Percentage results of this classification are in the conclusion of this thesis.

Research questions:  How can the Aspvik model be useful for the traditionally functioning water and sewage industry? What are the main challenges for regime actors to implement systems similar to those derived from the Aspvik model? Purpose: The purpose of the study is two-fold. Firstly, we intend to investigate system development and entrepreneurship from below in a local project in the water and sewage industry and its business model. Secondly, the study aims to provide an understanding of how the organization and the development process differ between the local Aspvik project and the existing regime within the water and sewage industry. Method: The study was of a qualitative nature based on an abductive approach. The theoretical framework was based on scientific articles and publications as well as books. The empirical data collection consisted of the collection of primary and secondary data, of which primary data was obtained through a total of 13 semi-structured interviews and secondary data via web pages, public print (laws and regulations) and other sources. A thematic analysis was performed based on the theoretical framework and the empirical data. Conclusion: The Aspvik model can generate usefulness as it contributes by providing a businesslike approach with transparency and visualization of what opportunities the industry actors have and what it may cost. With transparency, the current problems and bottlenecks in the industry became more evident. There are major environmental benefits in increasing the rate of development of the municipal water and sewage grid. It is mainly the Water Services Act, section 6 in particular, which is the main challenge for achieving a more efficient expansion of the water and sewage grid, both in terms of resources and time. It may require organizational change at the regime level, this in order to handle several projects simultaneously, or to provide increased support for private initiatives similar to the Aspvik project. / Problemställning: Hur kan Aspviksmodellen generera nytta för den traditionellt fungerande VA-branschen? Vilka är de huvudsakliga utmaningarna för regimaktörer att implementera system liknande det som härrör från Aspviksmodellen? Syfte: Studiens syfte är tvådelat. För det första ämnar vi undersöka systemuppbyggnad och entreprenörskap från grunden i ett lokalt VA- projekt och dess affärsmodell. För det andra syftar studien till att ge förståelse för hur organisationen och VA-utbyggnadsprocessen skiljer sig mellan det lokala VA-projektet och den befintliga regimen. Metod: Studien var av kvalitativ karaktär med utgångspunkt i en abduktiv ansats. Den teoretiska referensramen var baserad på vetenskapliga publikationer och böcker samt studentlitteratur. Den empiriska datainsamlingen utgjordes av insamling av primära och sekundära data, varav primärdata erhållits genom totalt 13 semistrukturerade intervjuer och sekundärdata via webbsidor, offentligt tryck (lagar och förordningar) och övriga källor. Utifrån den teoretiska referensramen och den empiriska data genomfördes en tematisk analys. Slutsats: Aspviksmodellen kan generera nytta då den bidrar med att tillhandahålla en affärsmässig strategi med ökad transparens och visualisering över vilka möjligheter branschaktörer har och vad det kan kosta. Med transparens blev rådande problem och flaskhalsar i branschen mer tydliga. Det finns stora miljömässiga fördelar med att öka utbyggnadstakten av det kommunala VA-nätet. Från regimens synvinkel är det huvudsakligen vattentjänstlagen i sig, 6 § i synnerhet som utgör en utmaning mot en effektivare utbyggnad av VA-nätet, både ekonomiskt och tidsmässigt. För att hantera det krävs någon organisatorisk förändring på regimnivå för att kunna hantera flera projekt samtidigt, alternativt att från regimens sida ge ett ökat stöd till privata initiativ i samma slag som Aspviksprojektet.

The need for a robust model for predicting the value of condominiums and houses are becoming more apparent as further evidence of systematic errors in existing models are presented. Traditional valuation methods fail to produce good predictions of condominium sales prices and systematic patterns in the errors linked to for example the repeat sales methodology and the hedonic pricing model have been pointed out by papers referenced in this thesis. This inability can lead to monetary problems for individuals and in worst-case economic crises for whole societies. In this master thesis paper we present how a predictive model constructed from a multilayer perceptron can predict the price of a condominium in the centre of Stockholm using objective data from sources publicly available. The value produced by the model is enriched with a predictive interval using the Inductive Conformal Prediction algorithm to give a clear view of the quality of the prediction. In addition, the Multilayer Perceptron is compared with the commonly used Support Vector Regression algorithm to underline the hallmark of neural networks handling of a broad spectrum of features. The features used to construct the Multilayer Perceptron model are gathered from multiple “Open Data” sources and includes data as: 5,990 apartment sales prices from 2011- 2013, interest rates for condominium loans from two major banks, national election results from 2010, geographic information and nineteen local features. Several well-known techniques of improving performance of Multilayer Perceptrons are applied and evaluated. A Genetic Algorithm is deployed to facilitate the process of determine appropriate parameters used by the backpropagation algorithm. Finally, we conclude that the model created as a Multilayer Perceptron using backpropagation can produce good predictions and outperforms the results from the Support Vector Regression models and the studies in the referenced papers. / Behovet av en robust modell för att förutsäga värdet på bostadsrättslägenheter och hus blir allt mer uppenbart alt eftersom ytterligare bevis på systematiska fel i befintliga modeller läggs fram. I artiklar refererade i denna avhandling påvisas systematiska fel i de estimat som görs av metoder som bygger på priser från repetitiv försäljning och hedoniska prismodeller. Detta tillkortakommandet kan leda till monetära problem för individer och i värsta fall ekonomisk kris för hela samhällen. I detta examensarbete påvisar vi att en prediktiv modell konstruerad utifrån en “Multilayer Perceptron” kan estimera priset på en bostadsrättslägenhet i centrala Stockholm baserad på allmänt tillgängligt data (“Öppen Data”). Modellens resultat har utökats med ett prediktivt intervall beräknat utifrån “Inductive Conformal Prediction”- algoritmen som ger en klar bild över estimatets tillförlitlighet. Utöver detta jämförs “Multilayer Perceptron”-algoritmen med en annan vanlig algoritm för maskinlärande, den så kallade “Support Vector Regression” för att påvisa neurala nätverks kvalité och förmåga att hantera dataset med många variabler. De variabler som används för att konstruera “Multilayer Perceptron”-modellen är sammanställda utifrån allmänt tillgängliga öppna datakällor och innehåller information så som: priser från 5990 sålda lägenheter under perioden 2011- 2013, ränteläget för bostadsrättslån från två av de stora bankerna, valresultat från riksdagsvalet 2010, geografisk information och nitton lokala särdrag. Ett flertal välkända förbättringar för “Multilayer Perceptron”-algoritmen har applicerats och evaluerats. En genetisk algoritm har använts för att stödja processen att hitta lämpliga parametrar till “Backpropagation”-algoritmen. I detta arbete drar vi slutsatsen att modellen kan producera goda förutsägelser med en modell konstruerad utifrån ett neuralt nätverk av typen “Multilayer Perceptron” beräknad med “backpropagation”, och därmed utklassar de resultat som levereras av Support Vector Regression modellen och de studier som refererats i denna avhandling

<div> The Unmanned Aerial Vehicles (UAV) technology has evolved exponentially in recent years. Smaller and less expensive devices allow a world of new applications in different areas, but as this progress can be beneficial, the use of UAVs with malicious intentions also poses a threat. UAVs can carry weapons or explosives and access restricted zones passing undetected, representing a real threat for civilians and institutions. Acoustic detection in combination with machine learning models emerges as a viable solution since, despite its limitations related with environmental noise, it has provided promising results on classifying UAV sounds, it is adaptable to multiple environments, and especially, it can be a cost-effective solution, something much needed in the counter UAV market with high projections for the coming years. The problem addressed by this project is the need for a real-world adaptable solution which can show that an array of acoustic sensors can be implemented for the detection and localization of UAVs with minimal cost and competitive performance.<br><br></div><div> In this research, a low-cost acoustic detection system that can detect, in real time, about the presence and direction of arrival of a UAV approaching a target was engineered and validated. The model developed includes an array of acoustic sensors remotely connected to a central server, which uses the sound signals to estimate the direction of arrival of the UAV. This model works with a single microphone per node which calculates the position based on the acoustic intensity change produced by the UAV, reducing the implementation costs and being able to work asynchronously. The development of the project included collecting data from UAVs flying both indoors and outdoors, and a performance analysis under realistic conditions. <br><br></div><div> The results demonstrated that the solution provides real time UAV detection and localization information to protect a target from an attacking UAV, and that it can be applied in real world scenarios. </div><div><br></div>

The development of machine learning research has provided statistical innovations and further developments within the field of time series analysis. This study seeks to investigate two different approaches on artificial neural network models based on different learning techniques, and answering how well the neural network approach compares with a basic autoregressive approach, as well as how the artificial neural network models compare to each other. The models were compared and analyzed in regards to the univariate forecast accuracy on 20 randomly drawn time series from two different time frequencies from the M4 competition dataset. Forecasting was made dependent on one time lag (t-1) and forecasted three and six steps ahead respectively. The artificial neural network models outperformed the baseline Autoregressive model, showing notably lower mean average percentage error overall. The Multilayered perceptron models performed better than the Long short-term memory model overall, whereas the Long short-term memory model showed improvement on longer prediction time dimensions. As the training were done univariately  on a limited set of time steps, it is believed that the one layered-approach gave a good enough approximation on the data, whereas the added layer couldn’t fully utilize its strengths of processing power. Likewise, the Long short-term memory model couldn’t fully demonstrate the advantagements of recurrent learning. Using the same dataset, further studies could be made with another approach to data processing. Implementing an unsupervised approach of clustering the data before analysis, the same models could be tested with multivariate analysis on models trained on multiple time series simultaneously.

As the complexity of deep learning model increases, the transparency of the systems does the opposite. It may be hard to understand the predictions a deep learning model makes, but even harder to understand why these predictions are made. Using eXplainable AI (XAI), we can gain greater knowledge of how the model operates and how the input in which the model receives can change its predictions. In this thesis, we apply Integrated Gradients (IG), an XAI method primarily used on image data and on datasets containing tabular and time-series data. We also evaluate how the results of IG differ from various types of models and how the change of baseline can change the outcome. In these results, we observe that IG can be applied to both sequenced and nonsequenced data, with varying results. We can see that the gradient baseline does not affect the results of IG on models such as RNN, LSTM, and GRU, where the data contains time series, as much as it does for models like MLP with nonsequenced data. To confirm this, we also applied IG to SVM models, which gave the results that the choice of gradient baseline has a significant impact on the results of IG.

No description available.

Ambient temperature poses a significant challenge to the performance of mobile phones, impacting their internal thermal flow and increasing the likelihood of overheating, leading to a compromised user experience. The knowledge about the ambient temperature in mobile phones is crucial as it assists engineers in correlating external factors with internal factors that might affect the mobile's performance under various conditions. Notably, these devices lack dedicated sensors to measure ambient temperature independently, underscoring the need for innovative solutions to estimate it accurately.      In response to this challenge, our research investigates the feasibility of estimating ambient temperature using machine-learning algorithms based on data from internal thermal sensors in Sony mobile phones.  Through comprehensive data collection and analysis, custom datasets were constructed to simulate different use-case scenarios, including CPU workloads, camera operation, and GPU tasks. These scenarios introduced varying levels of thermal disturbance, providing a robust basis for evaluating model performance. Feature engineering played a pivotal role in ensuring that the models could effectively interpret the internal thermal dynamics and correlate them with the ambient temperature. The results demonstrate that while simpler models like Linear Regression offer computational efficiency, they fall short in scenarios with complex thermal patterns. In contrast, deep learning models, particularly those incorporating time series analysis, showed superior accuracy and robustness. The Attention-LSTM model, in particular, excelled in generalizing across diverse and novel thermal conditions, although its complexity poses challenges for on-device deployment. This research underscores the importance of selecting appropriate sensors and incorporating a wide range of training scenarios to enhance model performance. It also highlights the potential of advanced machine learning techniques in providing advance solutions for ambient temperature estimation, thereby contributing to more effective thermal management in mobile devices.

Transportations role in society is increasingly important and today it has a prominent role in business, citizens lives as well as in the world economy. The increasing globalization and urbanization puts significant pressure on the existing transport system, with increasing demand for high-speed travel. However, this comes with implications on the environment, and the environmental concerns constitutes one of the biggest pressures in transport. And as the contemporary modes are bound by their technologies, enabling marginal rather than radical improvements, a possible window of opportunity for new radical technologies to enter the market can emerge. One new technology emerging within transportation today is called hyperloop, a technology that could prove to meet demand for faster, cheaper, safer and more environmentally efficient transportation. However, the technology is still in an early stage of development and hence surrounded by major uncertainties. Further, the nature of the technology necessitates overcoming several obstacles before it can reach commercial practice. And this together with a limited knowledge of the concept in Sweden makes it difficult to predict if hyperloop can become a viable transport alternative on the Swedish market. Which condensed lays the foundation to the purpose of this paper: "To give an overarching understanding of the Swedish transport market dynamics, together with a comprehensive evaluation of the hyperloop concept. And hence contribute to more inclusive knowledge and understanding of hyperloop’s viability in the Swedish context." Since the phenomenon has not been comprehensively studied previously, the elected research design is that of an exploratory case study, with an inductive, qualitative approach. To address the purpose, a literary review of the theoretical field was conducted. Looking in to previous research on disruptive innovation, diffusion of innovations, technical transitions, transformational pressure as well as window of opportunity. The empirical material gathered during the research process was derived from two main channels. Firstly, an extensive review of scientific articles about the hyperloop technology was conducted, providing insights on the technology and its surroundings. This was complemented by qualitative interviews to obtain material on the dynamics of the Swedish transport market as well as for understanding hyperloop in the Swedish context. The empirical study was further accompanied by a review of news articles and websites to map the most recent progress in the hyperloop development. By analyzing the empirical material through three frameworks; Characteristics of Diffusion, the Multi-Level Perspective (MLP) and Technology Readiness Level (TRL), interesting findings and conclusions were drawn. These together points towards that hyperloop, if the technology reaches its predicted performance, will have significant relative advantages and observable effects in the relation to the contemporary modes of transportation. Further, a noticeable window of opportunity, sprung from capacity shortages and pressure towards environmental sustainability, seems to exist on the Swedish market. A window which could be capitalized upon and justify hyperloop in the Swedish context. The current state of the technology does however come with implications as it so far is insufficient to decrease uncertainty amongst the potential adopters. Factors that likely will prolong the adoption of the technology in Sweden relates to the relative complexity of the system, its limited compatibility with existing practices and the low maturity of the technology. Hence, the hyperloop companies must prove the concept feasible and increase the maturity to gain sufficient acceptance and recognition. This paper contributes to the academic community by assessing the compatibility of hyperloop on the Swedish market, as well as if hyperloop could become a viable alternative transport solution in Sweden. It provides insight to specific perspectives of the Swedish market, its requirements and the demand for alternative transport solutions. Hence, this paper is considered to make both an analytical contribution in terms of evaluating the viability of disruptive technologies. And an empirical contribution by shedding light on new important insights for the potential diffusion of hyperloop. Insights that are significant for hyperloop actors as well as for dominant actors on the Swedish transport market. / Transporters roll i samhället blir allt viktigare och de har idag en framträdande roll inom näringsliv, medborgares liv samt världsekonomin. Den ökande globaliseringen och urbaniseringen sätter dock ett betydande tryck på det existerande transportsystemet, med ökande efterfrågan för höghastighetsalternativ. Detta medför implikationer för miljön, och oron kring transporters miljöpåverkan är ett av de största bekymren för transportsektorn. Eftersom de existerande transportalternativen är bundna av sin teknik, vilket begränsar dem till inkrementella snarare än radikala förbättringar, kan en möjlighet för nya transportsätt att komma in på marknaden öppna sig. En kommande ny teknik som utvecklas inom transport idag kallas hyperloop, en teknik som kan visa sig möta efterfrågan för snabbare, billigare, säkrare och mer miljösmarta transporter. Tekniken är dock i ett tidigt utvecklingsskede och är därav omgärdad av stora osäkerheter. Vidare kräver teknikens natur att flertalet hinder kommer att behöva överkommas innan tekniken kan nå kommersiellt bruk. Detta tillsammans med den begränsade kunskap som finns kring konceptet i Sverige gör det svårt att förutspå om hyperloop kan bli ett möjligt transportalternativ på den svenska marknaden. Kondenserat ligger detta till grund för syftet med den här uppsatsen: "Att ge en övergripande förståelse av dynamiken på den svenska transportmarknaden, tillsammans med en djupgående utvärdering av hyperloop konceptet. Och därav bidra till en mer inkluderande kunskap och förståelse kring hyperloops möjligheter i den svenska kontexten." Eftersom detta fenomen inte tidigare har studerats i större utsträckning valdes en forskningsdesign i form av en undersökande fallstudie med ett induktivt, kvalitativt tillvägagångssätt. För att adressera syftet gjordes en litterär översyn av det teoretiska fältet. Med inblickar i tidigare forskning kring disruptiv teknik, diffusion av innovation, tekniska övergångar, transformationstryck samt möjlighetsfönster. Det empiriska materialet till studien samlades in genom två kanaler i huvudsak. Först, genom en djupdykning i tidigare forskning och vetenskapliga artiklar relaterade till hyperlooptekniken, för att generera insikter kring tekniken och dess omgivning. Detta kompletteras med kvalitativa intervjuer för att erhålla material om dynamiken på den svenska transportmarknaden samt för att ge en förståelse av hyperloop i den svenska kontexten. Den empiriska studien kompletterades ytterligare med en översyn av nyhetsartiklar och webbplatser för att kartlägga de senaste framstegen i hyperlooputvecklingen. Genom att analysera det empiriska materialet med hjälp av tre ramverk; Egenskaper för Spridning av Innovation, Perspektiv i Multipla Nivåer (MLP) och Teknisk Mogenhetsnivå (TRL), kunde flertalet intressanta upptäckter och slutsatser dras. Vilka tillsammans pekar mot att hyperloop, om tekniken lyckas uppnå den predikterade prestandan, kommer att ha betydande relativa fördelar och synliga effekter i förhållande till dagens transportsätt. Vidare kan ett märkbart möjlighetsfönster, sprunget ur kapacitetsbrist och tryck mot miljömässig hållbarhet, identifieras på den svenska marknaden. Detta fönster skulle kunna kapitaliseras på och motivera hyperloop i den svenska kontexten. Teknologins nuvarande tillstånd har emellertid konsekvenser, eftersom den hittills inte är tillräcklig för att minska osäkerheten hos potentiella adopterare. Faktorer som sannolikt kommer att förlänga processen att adoptera tekniken i Sverige härstammar från systemets relativa komplexitet, dess begränsade kompatibilitet med befintliga metoder samt teknikens låga mogenhet. Därav är det essentiellt för hyperloopbolagen att bevisa konceptet möjligt och öka mogenheten för att få tillräcklig acceptans och erkännande. Detta arbete bidrar till det akademiska samhället genom att bedöma kompatibiliteten mellan hyperloop och den svenska marknaden, samt om hyperloop kan bli ett genomförbart transportalternativ i Sverige. Arbetet bidrar med insikter i specifika perspektiv på den svenska marknaden, dess krav samt efterfrågan för alternativa transportlösningar. Därav kan denna uppsats anses utgöra både ett analytiskt bidrag genom dess utvärdering av genomförbarheten av disruptiv teknik. Samt ett empiriskt bidrag genom att belysa viktiga insikter för den potentiella spridningen av hyperloop. Insikter som är viktiga för såväl hyperloopaktörer som de dominanta aktörerna på den svenska transportmarknaden.

In this dissertation, generalized additive neural networks (GANNs) and multilayer perceptrons (MLPs) are studied and compared as prediction techniques. MLPs are the most widely used type of artificial neural network (ANN), but are considered black boxes with regard to interpretability. There is currently no simple a priori method to determine the number of hidden neurons in each of the hidden layers of ANNs. Guidelines exist that are either heuristic or based on simulations that are derived from limited experiments. A modified version of the neural network construction with cross–validation samples (N2C2S) algorithm is therefore implemented and utilized to construct good MLP models. This algorithm enables the comparison with GANN models. GANNs are a relatively new type of ANN, based on the generalized additive model. The architecture of a GANN is less complex compared to MLPs and results can be interpreted with a graphical method, called the partial residual plot. A GANN consists of an input layer where each of the input nodes has its own MLP with one hidden layer. Originally, GANNs were constructed by interpreting partial residual plots. This method is time consuming and subjective, which may lead to the creation of suboptimal models. Consequently, an automated construction algorithm for GANNs was created and implemented in the SAS R statistical language. This system was called AutoGANN and is used to create good GANN models. A number of experiments are conducted on five publicly available data sets to gain insight into the similarities and differences between GANN and MLP models. The data sets include regression and classification tasks. In–sample model selection with the SBC model selection criterion and out–of–sample model selection with the average validation error as model selection criterion are performed. The models created are compared in terms of predictive accuracy, model complexity, comprehensibility, ease of construction and utility. The results show that the choice of model is highly dependent on the problem, as no single model always outperforms the other in terms of predictive accuracy. GANNs may be suggested for problems where interpretability of the results is important. The time taken to construct good MLP models by the modified N2C2S algorithm may be shorter than the time to build good GANN models by the automated construction algorithm / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2011.