High performance faster-than-nyquist signaling

AbstractIn a wireless broadband context, multi-path dispersive channels can severely affectdata communication of Mobile Terminals (MTs) uplink.Single Carrier withFrequency-Domain Equalization (SC-FDE) has been proposed to deal with highlydispersive channels for the uplink of broadband wireless systems. However, currentsystems rely on older assumptions of the Nyquist theorem and assume that a systemneeds a minimum bandwidth 2Wper MT. Faster-Than-Nyquist (FTN) assumesthat it is possible to employ a bandwidth as low as 0.802 of the original Nyquistbandwidth with minimum loss - despite this, the current literature has only proposedcomplex receivers for a simple characterization of the wireless channel. Furthermore,the uplink of SC-FDE can be severely affected by a deep-fade and or poor channelconditions; to cope with such difficulties Diversity Combining (DC) Hybrid ARQ(H-ARQ) is a viable technique, since it combines the several packet copies sent bya MT to create reliable packet symbols at the receiver.In this thesis we consider the use of FTN signaling for the uplink of broadbandwireless systems employing SC-FDE based on the Iterative Block with DecisionFeedback Equalization (IB-DFE) receiver with a simple scheduled access HybridAutomatic Repeat reQuest (H-ARQ) specially designed taking into account thecharacteristics of FTN signals. This approach achieves a better performance thanNyquist signaling by taking advantage of the additional bandwidth employed of aroot-raised cosine pulse for additional diversity.Alongside a Packet Error Rate (PER) analytical model, simulation results show that this receiver presents a better performance when compared with a regular system,with higher system throughputs and a lower Energy per Useful Packet (EPUP)

Qualitative Assessment of Effective Gamification Design Processes Using Motivators to Identify Game Mechanics

This research focuses on the study and qualitative assessment of the relationships between motivators and game mechanics per the ratings of expert gamification consultants. By taking this approach, it is intended that during the design phase of a gamified system, decisions can be made about the design of the system based on the motivators of each of the profiles. These motivators can be determined from the information provided by the potential players themselves. The research presented starts from a previous analysis in which, based on the three most used gamification frameworks and through a card sorting technique that allows the user to organize and classify the content, a set of mechanics are determined. In the present study, each of the mechanics is analyzed, and a more precise motive is decided. As a result, a higher level of personalization is achieved and, consequently, approximates a higher level of gamification effectiveness. The main conclusions are implemented in the development of the Game4City 3.0 project, which addresses gamified and interactive strategies to visualize urban environments in 3D at an educational and social level

Localização automática de objectos em sequências de imagens

Dissertação de mestrado em Informática.A detecção e seguimento de objectos tem uma grande variedade de aplicações em visão por computador. Embora tenha sido alvo de anos de investigação, continua a ser um tópico em aberto. Continua a ser ainda hoje um grande desafio a obtenção de uma abordagem que inclua simultaneamente flexibilidade e precisão, principalmente quando se trata de ambiente aberto. O objectivo desta dissertação é o desenvolvimento de uma metodologia que permita a localização de objectos genéricos e uma outra de localização de objectos conhecidos (sinais de trânsito), em sequências de imagens em ambiente aberto, sendo, nesta última, efectuado também o seu reconhecimento. No caso da primeira metodologia o objectivo proposto é concretizado com a indicação do objecto de interesse, através da sua selecção, numa primeira imagem, sendo o seu seguimento efectuado, numa primeira fase, recorrendo a uma aproximação grosseira à posição do objecto, utilizando informação de cor (característica interna), seguida de uma aproximação refinada, utilizando informação de forma (característica externa). No caso da segunda metodologia, a localização (detecção e seguimento) do objecto é realizada com base na informação de cor, através da segmentação de cor (azul e vermelha) no espaço cor HSI, e na forma, através das assinaturas de contorno. Finalmente é utilizada uma base de dados constituída pelas imagens dos objectos que se pretende reconhecer para identificar o objecto. Para determinar a viabilidade das metodologias propostas, foram efectuados vários testes dos quais se obtiveram, para a metodologia de localização de um objecto genérico, resultados aceitáveis, tendo em conta, por um lado, a não utilização de informação específica sobre o objecto, e por outro lado a complexidade contida nas sequências de imagens testadas, obtidas de ambiente aberto. A segunda metodologia, que corresponde à localização automática de objectos, obteve bons resultados, apesar dos testes terem sido direccionados para a sinalização rodoviária e restringida à localização de quatro formas e duas cores em concreto. A metodologia foi submetida, tal como no caso anterior, a cenas em ambiente aberto, mais concretamente 172 imagens, das quais se observaram 238 sinais de trânsito em condições de serem localizados, e dos quais resultaram 90,3% detectados correctamente por cor e forma e destes 82,8% foram reconhecidos correctamente, apesar do algoritmo utilizado nesta fase de reconhecimento ter sido aplicado apenas como abordagem inicial. Os resultados obtidos das metodologias desenvolvidas são encorajadores e um forte incentivo para continuar a apostar no seu melhoramento.Object detection and tracking has a wide range of applications in computer vision. Although it as been studied for many years, it remains an open research problem. A flexible and accurate approach is still a great challenge today, specially in outdoor environments. The objective of this thesis is the development of a methodology able to track generic objects and another able to localize known objects (traffic signs) and their recognition, in outdoor environment image sequences. The proposed objective concerning the first methodology is achieved by selecting the object of interest in a first frame, and the tracking performed, in a first step, by a coarse approach to the object position, using color information (internal feature), followed by a refined approach, using shape information (external feature). In the second methodology, the object localization (detection and tracking) is based on color information, through color segmentation (blue and red) in HSI color space, and shape, through contour signatures. Object identification is performed using a database filled with the objects images to recognize. Several tests were performed to determine the proposed methodologies effectiveness, obtaining acceptable results in the generic object localization methodology, taking into account, on one hand, the non utilization of any specific information about the object, and the other hand, the tested outdoor environment image sequences complexity. The second methodology, corresponding to the automatic object localization, obtained good results, although the tests were directed to traffic signs and restricted to four shapes and two colors. The methodology was submitted, as in the previous case, to outdoor environment scenes, more specifically 172 images, from which 238 localizable traffic signs were spotted. In this test 90.3% color and shape were correctly detected and from these 82.8% were correctly recognized, although the algorithm used in this recognition phase is only an initial approach. The developed methodologies results are encouraging and a strong incentive for future improvements

Enhancing Recommendations in Specialist Search Through Semantic-based Techniques and Multiple Resources

Information resources abound on the Internet, but mining these resources is a non-trivial task. Such abundance has raised the need to enhance services provided to users, such as recommendations. The purpose of this work is to explore how better recommendations can be provided to specialists in specific domains such as bioinformatics by introducing semantic techniques that reason through different resources and using specialist search techniques. Such techniques exploit semantic relations and hidden associations that occur as a result of the information overlapping among various concepts in multiple bioinformatics resources such as ontologies, websites and corpora. Thus, this work introduces a new method that reasons over different bioinformatics resources and then discovers and exploits different relations and information that may not exist in the original resources. Such relations may be discovered as a consequence of the information overlapping, such as the sibling and semantic similarity relations, to enhance the accuracy of the recommendations provided on bioinformatics content (e.g. articles). In addition, this research introduces a set of semantic rules that are able to extract different semantic information and relations inferred among various bioinformatics resources. This project introduces these semantic-based methods as part of a recommendation service within a content-based system. Moreover, it uses specialists' interests to enhance the provided recommendations by employing a method that is collecting user data implicitly. Then, it represents the data as adaptive ontological user profiles for each user based on his/her preferences, which contributes to more accurate recommendations provided to each specialist in the field of bioinformatics