Design gráfico e o livro infantil

Dissertação de Mestrado para obtenção do grau de Mestre em Design de Comunicação, apresentada na Universidade de Lisboa - Faculdade de Arquitectura.

Design inclusivo no espaço urbano

Dissertação de Mestrado para obtenção do grau de Mestre em Design de Produto, apresentada na Universidade de Lisboa - Faculdade de Arquitectura.

Adaptive lighting design as a holistic approach to public lighting

Dissertação para obtenção do grau de Doutor em Design, apresentada na Universidade de Lisboa - Faculdade de Arquitetura.

A Comparison of the Effects of Classroom and Multi-User Virtual Environments on the Perceived Speaking Anxiety of Adult Post-Secondary English Language Learners

The population of English Language Learners (ELLs) globally has been increasing substantially every year. In the United States alone, adult ELLs are the fastest growing portion of learners in adult education programs (Yang, 2005). There is a significant need to improve the teaching of English to ELLs in the United States and other English-speaking dominant countries. However, for many ELLs, speaking, especially to Native English Speakers (NESs), causes considerable language anxiety, which in turn plays a vital role in hindering their language development and academic progress (Pichette, 2009; Woodrow, 2006). Task-based Language Teaching (TBLT), such as simulation activities, has long been viewed as an effective approach for second-language development. The current advances in technology and rapid emergence of Multi-User Virtual Environments (MUVEs) have provided an opportunity for educators to consider conducting simulations online for ELLs to practice speaking English to NESs. Yet to date, empirical research on the effects of MUVEs on ELLs’ language development and speaking is limited (Garcia-Ruiz, Edwards, & Aquino-Santos, 2007). This study used a true experimental treatment control group repeated measures design to compare the perceived speaking anxiety levels (as measured by an anxiety scale administered per simulation activity) of 11 ELLs (5 in the control group, 6 in the experimental group) when speaking to Native English Speakers (NESs) during 10 simulation activities. Simulations in the control group were done face-to-face, while those in the experimental group were done in the MUVE of Second Life. The results of the repeated measures ANOVA revealed after the Huynh-Feldt epsilon correction, demonstrated for both groups a significant decrease in anxiety levels over time from the first simulation to the tenth and final simulation. When comparing the two groups, the results revealed a statistically significant difference, with the experimental group demonstrating a greater anxiety reduction. These results suggests that language instructors should consider including face-to-face and MUVE simulations with ELLs paired with NESs as part of their language instruction. Future investigations should investigate the use of other multi-user virtual environments and/or measure other dimensions of the ELL/NES interactions.

Fast and efficient user pairing and power allocation algorithm for non-orthogonal multiple access in cellular networks

Non-orthogonal multiple access (NOMA) is emerging as a promising multiple access technology for the fifth generation cellular networks to address the fast growing mobile data traffic. It applies superposition coding in transmitters, allowing simultaneous allocation of the same frequency resource to multiple intra-cell users. Successive interference cancellation is used at the receivers to cancel intra-cell interference. User pairing and power allocation (UPPA) is a key design aspect of NOMA. Existing UPPA algorithms are mainly based on exhaustive search method with extensive computation complexity, which can severely affect the NOMA performance. A fast proportional fairness (PF) scheduling based UPPA algorithm is proposed to address the problem. The novel idea is to form user pairs around the users with the highest PF metrics with pre-configured fixed power allocation. Systemlevel simulation results show that the proposed algorithm is significantly faster (seven times faster for the scenario with 20 users) with a negligible throughput loss than the existing exhaustive search algorithm.