Performance of direct-oversampling correlator-type receivers in chaos-based DS-CDMA systems over frequency non-selective fading channels

In this paper, we present a study on the performance of direct-oversampling correlator-type receivers in chaos-based direct-sequence code division multiple access systems over frequency non-selective fading channels. At the input, the received signal is sampled at a sampling rate higher than the chip rate. This oversampling step is used to precisely determine the delayed-signal components from multipath fading channels, which can be combined together by a correlator for the sake of increasing the SNR at its output. The main advantage of using direct-oversampling correlator-type receivers is not only their low energy consumption due to their simple structure, but also their ability to exploit the non-selective fading characteristic of multipath channels to improve the overall system performance in scenarios with limited data speeds and low energy requirements, such as low-rate wireless personal area networks. Mathematical models in discrete-time domain for the conventional transmitting side with multiple access operation, the generalized non-selective Rayleigh fading channel, and the proposed receiver are provided and described. A rough theoretical bit-error-rate (BER) expression is first derived by means of Gaussian approximation. We then define the main component in the expression and build its probability mass function through numerical computation. The final BER estimation is carried out by integrating the rough expression over possible discrete values of the PFM. In order to validate our findings, PC simulation is performed and simulated performance is compared with the corresponding estimated one. Obtained results show that the system performance get better with the increment of the number of paths in the channel.Peer ReviewedPostprint (author's final draft

Export intensity of foreign subsidiaries of multinational enterprises: the role of trade finance availability

We examine the relationship between the role of trade finance availability and the export intensity of foreign subsidiaries of multinational enterprises (MNEs). In developing our hypotheses, we draw upon insights derived from “new” internalisation theory (international business literature) and international trade finance (international economics literature). We empirically test these hypotheses using survey data compiled from subsidiary managers in six ASEAN countries, supplemented with host-country level data. We conceptualise, empirically test, and establish that the subsidiary-level capability in combining and utilising internal and external debts is an important subsidiary-specific advantage to support export intensity. We find that subsidiaries employ intra-firm loans from MNE internal capital markets and, to some extent, bank loans from external financial institutions to boost their export intensity. Subsidiaries may have concerns about foreign exchange risks, but the use of appropriate foreign exchange risk management is positively associated with export intensity. We discuss the implications of our findings for theory and practice

Emerging country MNEs and the role of home countries: separating fact from irrational expectations

This paper takes a look at the research on Emerging country multinational enterprises (EMNEs) over the last 25 years, and argues that growth in EMNE activity over the last 10 years continues to be dominated by Asian Newly Industrialised Countries (NICs), and to a lesser extent by Brazil, Russia, India and China (the BRICS). Instead of focusing on the success stories, we ask: Why have so few emerging home countries failed to fulfil their potential as significant outward investors, and converged (at least) with the NICs? Many of the EMNEs from the non-NICs continue to reflect limited O advantages, and unless they are able to upgrade their firm-specific assets, this trend is likely to continue. We propose that - in line with extant IB theory - the extent and intensity of EMNE activity is a function of their O advantages, which in turn are largely a function of their home country L advantages. We also call into question the soundness of the idea that EMNEs are able to utilise asset-seeking foreign direct investment (FDI) to build up their O advantages. Such asset-augmentation presumes that the firms have non-location-bound firm-specific assets that have the potential to be upgraded and augmented.FDI, Foreign Investment, MNEs, eclectic paradigm, asset-seeking, knowledge flows, emerging markets

Local responsiveness strategy of foreign subsidiaries of Chinese multinationals: the impacts of relational-assets, market-seeking FDI, and host country institutional environments

We build upon the theoretical framework of firm-specific advantages (FSAs) and country-specific advantages (CSAs) to examine the determinants of the local responsiveness strategy of foreign subsidiaries of Chinese multinational enterprises (MNEs). Specifically, we focus on relational assets (R-assets is seen as a unique type of Chinese MNEs’ FSA), the market-seeking foreign direct investment (FDI) and host country institutional environments as drivers of the local responsiveness strategy. We empirically test our hypotheses using a survey data of the foreign subsidiaries of Chinese firms together with other secondary data sources. We find that both Chinese MNEs' R-assets and the market-seeking oriented FDI are positively related to subsidiaries' local responsiveness strategy in accommodating local customer needs, government policies, market conditions, and competitive intensity. Moreover, the impact of R-assets in motivating the local responsiveness strategy is stronger in a host country with a weak and underdeveloped institutional environment. While the evidence confirms the existence of the R-assets in influencing subsidiary level strategy, it also casts doubt on such relations-based firm resources in advanced host countries with highly developed institutions

A novel approach to security enhancement of chaotic DSSS systems

In this paper, we propose a novel approach to the enhancement of physical layer security for chaotic direct-sequence spread-spectrum (DSSS) communication systems. The main idea behind our proposal is to vary the symbol period according to the behavior of the chaotic spreading sequence. As a result, the symbol period and the spreading sequence vary chaotically at the same time. This simultaneous variation aims at protecting DSSS-based communication systems from the blind estimation attacks in the detection of the symbol period. Discrete-time models for spreading and despreading schemes are presented and analyzed. Multiple access performance of the proposed technique in the presence of additional white Gaussian noise (AWGN) is determined by computer simulations. The increase in security at the physical layer is also evaluated by numerical results. Obtained results show that our proposed technique can protect the system against attacks based on the detection of the symbol period, even if the intruder has full information on the used chaotic sequence.Peer ReviewedPostprint (author's final draft

Anomaly Handling in Visual Analytics

Visual analytics is an emerging field which uses visual techniques to interact with users in the analytical reasoning process. Users can choose the most appropriate representation that conveys the important content of their data by acting upon different visual displays. The data itself has many features of interest, including clusters, trends (commonalities) and anomalies. Most visualization techniques currently focus on the discovery of trends and other relations, where uncommon phenomena are treated as outliers and are either removed from the datasets or de-emphasized on the visual displays. Much less work has been done on the visual analysis of outliers, or anomalies. In this thesis, I will introduce a method to identify the different levels of “outlierness†by using interactive selection and other approaches to process outliers after detection. In one approach, the values of these outliers will be estimated from the values of their k-Nearest Neighbors and replaced to increase the consistency of the whole dataset. Other approaches will leave users with the choice of removing the outliers from the graphs or highlighting the unusual patterns on the graphs if points of interest lie in these anomalous regions. I will develop and test these anomaly handling methods within the XMDV Tool

Analysing and forecasting tourism demand in Vietnam with artificial neural networks

Mestrado APNORVietnam has experienced a tourism boom over the last decade with more than 18 million international tourists in 2019, compared to 1.5 million twenty-five years ago. Tourist spending has translated into rising employment and income for the tourism sector, making it the key driver to the socio-economic development of the country. Facing the COVID-19 pandemic, Vietnam´s tourism has suffered extreme economic losses. However, the number of international tourists is expected to reach the pre-pandemic levels in the next few years after the COVID-19 pandemic subsides. Forecasting tourism demand plays an essential role in predicting future economic development. Accurate predictions of tourism volume would facilitate decision-makers and managers to optimize resource allocation as well as to balance environmental and economic aspects. Various methods to predict tourism demand have been introduced over the years. One of the most prominent approaches is Artificial Neural Network (ANN) thanks to its capability to handle highly volatile and non-linear data. Given the significance of tourism to the economy, a precise forecast of tourism demand would help to foresee the potential economic growth of Vietnam. First, the research aims to analyse Vietnam´s tourism sector with a special focus on international tourists. Next, several ANN architectures are experimented with the datasets from 2008 to 2020, to predict the monthly number of international tourists traveling to Vietnam including COVID-19 lockdown periods. The results showed that with the correct selection of ANN architectures and data from the previous 12 months, the best ANN models can forecast the number of international tourists for next month with a MAPE between 7.9% and 9.2%. As the method proves its forecasting accuracy, it would serve as a valuable tool for Vietnam´s policymakers and firm managers to make better investment and strategic decisions to promote tourism after the COVID-19 situation.O Vietname conheceu um boom turístico na última década com mais de 18 milhões de turistas internacionais em 2019, em comparação com 1,5 milhões há vinte e cinco anos. As despesas turísticas traduziram-se num aumento do emprego e de receitas no sector do turismo, tornando-o no principal motor do desenvolvimento socioeconómico do país. Perante a pandemia da COVID-19, o turismo no Vietname sofreu perdas económicas extremas. Porém, espera-se que o número de turistas internacionais, pós pandemia da COVID-19, atinja os níveis pré-pandémicos nos próximos anos. A previsão da procura turística desempenha um papel essencial na previsão do desenvolvimento económico futuro. Previsões precisas facilitariam os decisores e gestores a otimizar a afetação de recursos, bem como o equilíbrio entre os aspetos ambientais e económicos. Vários métodos para prever a procura turística têm sido introduzidos ao longo dos anos. Uma das abordagens mais proeminentes assenta na metodologia das Redes Neuronais Artificiais (ANN) dada a sua capacidade de lidar com dados voláteis e não lineares. Dada a importância do turismo para a economia, uma previsão precisa da procura turística ajudaria a prever o crescimento económico potencial do Vietname. Em primeiro lugar, a investigação tem por objetivo analisar o sector turístico do Vietname com especial incidência nos turistas internacionais. Em seguida, várias arquiteturas de ANN são experimentadas com um conjunto de dados de 2008 a 2020, para prever o número mensal de turistas internacionais que se deslocam ao Vietname, incluindo os períodos de confinamento relacionados com a COVID-19. Os resultados mostraram, com a correta seleção de arquiteturas ANN e dados dos 12 meses anteriores, os melhores modelos ANN podem prever o número de turistas internacionais para o próximo mês com uma MAPE entre 7,9% e 9,2%. Como o método evidenciou a sua precisão de previsão, o mesmo pode servir como uma ferramenta valiosa para os decisores políticos e gestores de empresas do Vietname, pois irá permitir fazer melhores investimentos e tomarem decisões estratégicas para promover o turismo pós situação da COVID-19

The Exploitation Of A Nonrenewable Resource Under Imperfect Competition

In this thesis, we investigate some problems concerning the exploitation of a nonrenewable resource under imperfect competition. The approach adopted here is that of partial equilibrium. The thesis consists of five chapters, with each chapter dealing with a specific topic.;In Chapter One, we study the pattern of resource extraction exected of a dominant firm facing competition from a naive competitive fringe. In Chapter Two, we build a model of monopoly resource extraction under stochastic entry with consumers as potential entrants. In Chapter Three, we build a model of simultaneous resource extraction and exploration under monopoly. Our model of exploration is spatially oriented and deals directly with location uncertainty. In Chapter Four, we present a game-theoretical model to study the phenomenon of information spillover encountered in the exploration for a nonrenewable resource. Chapter Five deals with a differential game of resource extraction under oligopoly. Here, we present a rigorous proof of the existence of a Cournot-Nash equilibrium. When the market demand curve satisfies a suitable assumption, we show that this equilibrium is unique and provide an algorithm to find it