Recommender systems (RS) have become increasingly popular in many web applications for eliminating online information overload and making personalized suggestions to users. In recent years, user personality has been recognized as valuable info to build more personalized recommender systems. However, the existing personality-based recommender systems has mainly focused on revealing the impact of personality on the user's preference over a single item or an attribute, which may ignore the impact of personality on users' perceptions of recommender systems when multiple recommendations are returned at the same time. In addition, they have mostly relied on personality quiz to explicitly acquire users' personality, which unavoidably demands user efforts. From users' perspective, they may be unwilling to answer the quiz for the sake of saving efforts or protecting their privacy. The application of existing personality-based recommender systems will thus be limited in real life.;In this thesis, we aim at 1) incorporating personality into top-N (N > 1) recommendations, with emphases on personalizing recommendation diversity and improving the recommendation interface design, 2) deriving users' personality from their implicit behavior for augmenting the existing recommender systems.;Specifically, we first develop a generalized, dynamic diversity adjusting approach based on user personality with the goal of achieving personalized diversity tailored to individual users' intrinsic needs. In particular, personality is integrated into a greedy re-ranking process, by which we select the item that can best balance accuracy and personalized diversity at each step, and then produce the final recommendation list. In this approach, personality is both used to estimate each user's diversity preference and to alleviate the cold-start problem of collaborative filtering recommendations. The experimental results demonstrate that our personalized diversity-oriented approach significantly outperforms related methods (including both non-diversity-oriented and diversity-oriented methods) in terms of both accuracy and diversity metrics, especially in the cold-start setting.;In addition to the algorithm development, designing diversity-oriented interface has been proven helpful to augment users' perception of recommendation diversity. However, little work has been done to identify the impact of users' personality on their preference for different types of recommendation interfaces (e.g., the diversity-oriented interface and the non-diversity-oriented interface). In order to fill the gap, we conduct a within-subject user study. We concretely compare a diversity-oriented organization-based recommendation interface with the standard ranked list interface covering three product domains with different investment levels and users' purchase experiences (i.e., mobile phone, hotel and movie). We find that users' perceptions of different recommendation interface are influenced by the product types. More notably, we identify the important role of users' personality in influencing their preference for recommendation interfaces. For instance, introverted users tend to reuse the organization-based interface in the future than the standard ranked list. The results can hence be constructive for improving existing recommendation interface design by considering users' personality.;Although personality has been proven effective at enhancing the multiple recommendations, the effort of explicitly acquiring users' personality traits via psychological questionnaire is unavoidably high, which may impede the application of personality-based recommenders in real life. We hence propose a generalized method to derive users' personality from their implicit behavior and further improve the existing recommender systems. A preliminary experiment has been conducted in movie domain. More specifically, we first identify a set of behavioral features through experimental validation, and develop inference model based on Gaussian Process to unify these features for determining users' big-five personality traits. We then test the model in a collaborative filtering based recommending framework on two real-life movie datasets, which demonstrates that our implicit personality based recommending algorithm significantly outperforms related methods in terms of both rating prediction and ranking accuracy. The experimental results point out an effective solution to boost the applicability of personality-based recommender systems in online environment.

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This thesis extends previous work on navigational aidingof groups of aircraft, primarily intended for the fighter SAAB JAS 39 Gripen,as long as an aircraft gets GPS signals, it is easy to estimate position, but theGPS is relatively easy to jam, rendering alternative methods of positioning necessary.To use internal sensors measuring accelerations and angular velocities is agood replacement on short terms, but gives a drift in positioning over longer timeperiods. To resolve these issues, we review different possibilities to improve navigation performance bycombining measurement data from different aircraft using a consensus filter.We show that the performance canbe improved by using measurements of distance and angles to other aircraft withinthe group in a distributed filter.The filter is implemented in Matlab and evaluated in different scenarios, and this Extended Kalman-Consensus Filter (EKCF) is compared to a previously proposed solution using an Extended Kalman Filter (EKF). / Det här examensarbetet vidareutvecklar en befintlig algorithm för navigeringsstöttningav grupper av flygplan, främst inriktat på SAAB JAS 39 Gripen. Genomatt kombinera mätdata från olika flygplan kommer vi gå igenom hur man kanförbättra prestanda genom applicering av consensusfilter. Så länge ett plan harGPS-signal är positionering enkelt. Dock är den relativt lätt att störa ut, vilketgör alternativa lösningar för positionering nödvändiga. Att använda interna sensorersom mäter accelerationer och vinkelhastigheter fungerar utmärkt på kortsikt, men ger en drift över en längre tidsperiod.För att lösa de här problemen utvärderar vi olika möjligheter att förbättra navigationsprestandangenom att kombinera mätdata från olika flygplan med hjälpav ett consensusfilter. Vi visar att prestandan kan förbättras genom att användadistans- och vinkelmätningar inom gruppen med distribuerade filter. Filtret ärimplementerat i Matlab med olika scenarier och jämför Extended Kalman-ConsensusFilter (EKCF) med den föregående lösningen med ett Extended Kalman Filter (EKF).

Mobile devices with wireless networking capabilities are used in a wide range of environments. Geolocation information increases the value of the data generated by a device and is vital in the development of a wide range of applications from autonomous vehicles to the Internet of things. Systems that generate signals specifically for geolocation have become widely adopted but, due to fundamental constraints, lack coverage and accuracy in complex urban and indoor environments. In addition to this, the reliance on a single signal source is not desirable in many applications that value the integrity of the geolocation estimate. A direction of research aiming to improve geolocation in indoor and urban environments measures signals of opportunity in order to generate a more robust estimate. While this approach improves signal availability, the unpredictable nature of these variable and uncontrolled signals leads to poor geolocation estimates, which are typically not suitable for use in many applications. This project aims to improve on the accuracy, resilience and integrity of a geolocation estimate obtained from signal of opportunity measurements in indoor and urban environments while reducing hardware requirements. This has been achieved by efficiently coupling signals of opportunity within the radio environment with other system signals, such as those from an inertial measurement unit. Research has been carried out to optimise the coupling of these data sources resulting in techniques to allow the identification and removal of key error drivers from both the radio environment and other system sensors. This thesis proposes a specifically designed extended Kalman filter to improve on the signal coupling. The filter aims to optimise the accuracy of radio environment measurements while also providing the ability to identify signal error sources in urban and indoor environments, leading to both greater accuracy and resilience of the geo-location estimate. Further, the proposed extended Kalman filter may use the radio environment as a source of geolocation data. The ability of the filter to recognise and mitigate leading radio environment error sources such as multipath and interference allowed the design of filters to obtain detailed and accurate signal strength and time of arrival information. The thesis also presents a thorough set of simulation and modelling experiments to investigate and optimise the efficiency of the proposed solutions in a range of environments. Validation testing confirmed that in the urban and indoor environments, the average error of geo-location estimates has been reduced from 10 m to 3 m without improvement to the hardware surrounding infrastructure. The improvements presented in this thesis allow networked devices to improve the value of their data by incorporating the context that comes from increased geolocation accuracy and resilience. In turn, this allows the development of a wide range of new location based applications for mobile devises in indoor and urban environments.

With the increase in health consciousness, noninvasive body monitoring has aroused interest among researchers. As one of the most important pieces of physiological information, researchers have remotely estimated heart rates from facial videos in recent years. Although progress has been made over the past few years, there are still some limitations, such as the increase in processing time with accuracy and the lack of comprehensive and challenging datasets for use and comparison. Recently, it was shown that heart rate information can be extracted from facial videos by spatial decomposition and temporal filtering. Inspired by this, a new framework is introduced in this thesis for remotely estimating the heart rate under realistic conditions by combining spatial and temporal filtering and a convolutional neural network. Our proposed approach exhibits better performance compared with that of the benchmark on the MMSE-HR dataset in terms of both the average heart rate estimation and short-term heart rate estimation. High consistency in short-term heart rate estimation is observed between our method and the ground truth.

High speed real time video processing puts a lot of demand on hardware and Field Programmable Gate Arrays (FPGA) are becoming more popular for this. What makes them interesting in this field is their inherent concurrency which make them ideal for high speed applications. Higher demands for energy efficient solutions require the designer to have knowledge on how different implementations on the FPGA effects the power consumption. Therefore, a study on power consumption for image filtering with FPGA was conducted.   Two image filtering algorithms are implemented on a FPGA with the goal of reducing the power consumption for real time image filtering by optimising the implementations on the FPGA.   To reduce the power consumption three main areas where examined: optimizing the algorithm, using the different hardware capabilities that come with FPGAs and working with different clock speeds.   The different approaches were simulated in a power estimator to evaluate the effects on the power consumption before implementing them on a FPGA and measuring the results.   In this project it was determined that lowering the frequency and utilizing the resources to the full extent can have a positive impact on the power consumption. The results were too small for the accuracy of the amperemeter used to be able to make any conclusions. Larger systems with multiple FPGAs might show more noticeable power savings. More knowledge in Hardware Description Language (HDL) programming and resource managing could lead to even lower power consumption.

In this thesis, we have investigated several computational methods on data assimilation for air quality prediction, especially on the characteristic of sparse matrix and the underlying information of gradient in the concentration of pollutant species. In the first part, we have studied the ensemble Kalman filter (EnKF) for chemical species simulation in air quality forecast data assimilation. The main contribution of this paper is to study the sparse data observations and make use of the matrix structure of the Kalman filter updated equations to design an algorithm to compute the analysis of chemical species in the air quality forecast system efficiently. The proposed method can also handle the combined observations from multiple species together. We have applied the proposed method and tested its performance for real air quality data assimilation. Numerical examples have demonstrated the efficiency of the proposed computational method for Kalman filter update, and the effectiveness of the proposed method for NO2, NO, CO, SO2, O3, PM2.5 and PM10 in air quality data assimilation. Within the third part, we have set up an automatic workflow to connect the management system of the chemical transport model - CMAQ with our proposed data assimilation methods. The setup has successfully integrated the data assimilation into the management system and shown that the accuracy of the prediction has risen to a new level. This technique has transformed the system into a real-time and high-precision system. When the new observations are available, the predictions can then be estimated almost instantaneously. Then the agencies are able to make the decisions and respond to the situations immediately. In this way, citizens are able to protect themselves effectively. Meanwhile, it allows the mathematical algorithm to be industrialized implying that the improvements on data assimilation have directly positive effects on the developments of the environment, the human health and the society. Therefore, this has become an inspiring indication to encourage us to study, achieve and even devote more research into this promising method.

This article summarizes the ideas behind a few programs we developed for spatial data analysis in EViews and MATLAB. They allow the user to check for spatial autocorrelation using Moran's I and provide a spatial filtering procedure based on the Gi statistic by Getis and Ord (1992). We have also implemented graphical tools like Moran Scatterplots for the detection of outliers or local spatial clusters.

By now, people are accustomed to getting some personalized recommendations when they are finding movies to watch, music to listen, and so on. All of these recommendations come from recommender systems, and can aid the process of the decision making to avoid the problem of "information overload". Over the years, there has been much work done both in industry and academia on developing new approaches for recommender systems. However, there are still some hurdles in adapting recommender systems to a broader range of real-life applications. In the e-commerce environment especially with the so called high-risk products (also called high-cost or high-involvement products, such as digital cameras, computers, and cars), because a user does not buy the high-risk product very often, it is normal that s/he is not able to rate many products. For the same reason, the current buyer is often a new user because s/he would not afford to buy the same kind of high-risk product before. The traditional recommender techniques (such as user-based collaborative filtering and content-based methods) can thus not be effectively applicable in this environment, because they largely assume that the users have prior experiences with products. Thus, the "data sparsity" and "new users" are two typical challenging issues that the classical recommender systems cannot well address in high-risk product domains. In some recommender systems, a new user will be asked to indicate his/her preferences on some aspects in order to address the so called cold-start problem via collecting some preferences. Such collected preferences are usually not complete due to unfamilaring with the product domain, which are called partial preferences.;In this thesis, we propose to leverage some auxiliary data of online reviewers' opinions, so as to enrich the partial preferences. With the objective of developing more effective recommender systems for high-risk products in e-commerce, in our work, we have exerted to derive reviewers' preferences from the textual reviews they posted. Then, these recovered preferences are leveraged to estimate and supplement a new buyer's preference with which the product recommendation is produced. Firstly, we propose a novel clustering method based on Latent Class Regression model (LCRM), which is able to consider both the overall ratings and feature-level opinion values (as extracted from textual reviews) to infer individual reviewers' weight feature preferences, that represent the weights the user places on different product features. Secondly, we propose a method to estimate reviewers' value preferences (i.e., the user's preferences on the product's attribute values) by matching their review opinions to the corresponding attributes' static specifications. Thirdly, we investigate how to combine weight preferences and value preferences to model user preferences based on Multi-Attribute Utility Theory (MAUT) with the purpose of providing higher quality product recommendations. Particularly, it was shown from our experimental studies that the incorporation of review information can significantly enhance the recommendation accuracy, relative to those without considering reviews. As the practical implication, our proposed solutions can be usefully plugged into an online system to be adopted in real-ecommerce sites.

Rotating elements, including gears, are one of the most problematic elements in machinery. It is not preferable to monitor their condition visually considering time and money is required to take apart the machine to observe the parts. Monitoring of gears is important because the failure of such elements can cause major damage to machinery. A few non-invasive methods are proposed, however vibration analysis is, so far, the most efficient way to monitor the condition of the gear. Vibrations are caused by the continuous contact between the two rotating gears. When a fault occurs, the signal is modified in different ways depending on the type of fault - distributed or local. Many fault detection methods are effective for one type of fault or the other. In this thesis, several methods are proposed with the objective of finding an efficient method for both types of faults. The calculus enhanced energy operator (CEEO), previously designed for bearing fault detection, is proposed here for the first time on gears. Two other methods, the EO123 and EO23, are derived based on the original energy operator. The proposed methods are filter free, simple and can handle a certain level of noise and interference. With the exception of low rotational frequencies of the gears, it can be concluded from simulated and experimentally-obtained signals that the CEEO method can handle noise better than the other proposed methods and that the EO23 method can handle interference better than the others. Different conditions determine the effectiveness of the methods.