Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition

Charcoal power: The political violence of non-fossil fuel in Uganda

The politics of global energy are subject to increasing academic interest. Most work on energy politics focuses on oil, based upon a normative vision of a global energy modernity of fossil fuels and a transition to renewables. In most African countries, however, the primary source of energy is not oil, but woodfuel. Charcoal is of particular importance due to its centrality to urbanization: charcoal is the primary energy source for up to 80% of urban Africa, and its consumption is expected to continue increasing with expanding urbanization. Despite this centrality, the politics of charcoal remain largely unexplored. This article explores how political power shapes charcoal production and how charcoal as an energy source shapes political power through an in-depth study of charcoal extraction in northern Uganda. It argues that charcoal production, and its particular destructiveness, should be understood as a continuation of the violence of the 1986-2006 war between the Lord’s Resistance Army and the Ugandan government. Based on several months of fieldwork in Northern Uganda, the article draws a distinction between the politics of small-scale household production and of large-scale industrial production. By focusing on the political violence of industrial charcoal production, we argue that orthodox academic and policy narratives about the charcoal industry in Africa can be qualified, and new questions can be raised concerning broader narratives of energy modernity and global energy politics

Data-driven time-frequency analysis of multivariate data

Empirical Mode Decomposition (EMD) is a data-driven method for the decomposition and time-frequency analysis of real world nonstationary signals. Its main advantages over other time-frequency methods are its locality, data-driven nature, multiresolution-based decomposition, higher time-frequency resolution and its ability to capture oscillation of any type (nonharmonic signals). These properties have made EMD a viable tool for real world nonstationary data analysis. Recent advances in sensor and data acquisition technologies have brought to light new classes of signals containing typically several data channels. Currently, such signals are almost invariably processed channel-wise, which is suboptimal. It is, therefore, imperative to design multivariate extensions of the existing nonlinear and nonstationary analysis algorithms as they are expected to give more insight into the dynamics and the interdependence between multiple channels of such signals. To this end, this thesis presents multivariate extensions of the empirical mode de- composition algorithm and illustrates their advantages with regards to multivariate non- stationary data analysis. Some important properties of such extensions are also explored, including their ability to exhibit wavelet-like dyadic filter bank structures for white Gaussian noise (WGN), and their capacity to align similar oscillatory modes from multiple data channels. Owing to the generality of the proposed methods, an improved multi- variate EMD-based algorithm is introduced which solves some inherent problems in the original EMD algorithm. Finally, to demonstrate the potential of the proposed methods, simulations on the fusion of multiple real world signals (wind, images and inertial body motion data) support the analysis

Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm

Evapotranspiration, as a combination of evaporation and transpiration of water vapour, is a primary component of global hydrological cycles. It accounts for significant loss of soil moisture from the earth to the atmosphere. Reliable methods to monitor and forecast evapotranspiration are required for decision-making. Reference evapotranspiration, denoted as ET , is a major parameter that is useful in quantifying soil moisture in a cropping system. This article aims to design a multi-stage deep learning hybrid Long Short-Term Memory (LSTM) predictive model that is coupled with Multivariate Empirical Mode Decomposition (MEMD) and Boruta-Random Forest (Boruta) algorithms to forecast ET in the drought-prone regions ( i.e ., Gatton, Fordsdale, Cairns) of Queensland, Australia. Daily data extracted from NASA’s Goddard Online Interactive Visualization and Analysis Infrastructure (GIOVANNI) and Scientific Information for Land Owners (SILO) repositories over 2003–2011 are used to build the proposed multi-stage deep learning hybrid model, i.e ., MEMD-Boruta-LSTM, and the model’s performance is compared against competitive benchmark models such as hybrid MEMD-Boruta-DNN, MEMD-Boruta-DT, and a standalone LSTM, DNN and DT model. The test MEMD-Boruta-LSTM hybrid model attained the lowest Relative Root Mean Square Error (≤17%), Absolute Percentage Bias (≤12.5%)and the highest Kling-Gupta Efficiency (≥0.89%) relative to benchmark models for all study sites. The proposed multi-stage deep hybrid MEMD-Boruta-LSTM model also outperformed all other benchmark models in terms of predictive efficacy, demonstrating its usefulness in the forecasting of the daily ET dataset. This MEMD-Boruta-LSTM hybrid model could therefore be employed in practical environments such as irrigation management systems to estimate evapotranspiration or to forecast ET

Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-made Objects

We present a novel freehand sketch beautification method, which takes as input a freely drawn sketch of a man-made object and automatically beautifies it both geometrically and structurally. Beautifying a sketch is challenging because of its highly abstract and heavily diverse drawing manner. Existing methods are usually confined to the distribution of their limited training samples and thus cannot beautify freely drawn sketches with rich variations. To address this challenge, we adopt a divide-and-combine strategy. Specifically, we first parse an input sketch into semantic components, beautify individual components by a learned part beautification module based on part-level implicit manifolds, and then reassemble the beautified components through a structure beautification module. With this strategy, our method can go beyond the training samples and handle novel freehand sketches. We demonstrate the effectiveness of our system with extensive experiments and a perceptive study.Comment: 13 figure

New signal processing and machine learning methods for EEG data analysis of patients with Alzheimer's disease

Les malalties neurodegeneratives són un conjunt de malalties que afecten al cervell. Aquestes malalties estan relacionades amb la pèrdua progressiva de l'estructura o la funció de les neurones, incloent-hi la mort d'aquestes. La malaltia de l'Alzheimer és una de les malalties neurodegeneratives més comunes. Actualment, no es coneix cap cura per a l'Alzheimer, però es creu que hi ha un grup de medicaments que el que fan és retardar-ne els principals símptomes. Aquests s'han de prendre en les primeres fases de la malaltia ja que sinó no tenen efecte. Per tant, el diagnòstic precoç de la malaltia de l'Alzheimer és un factor clau. En aquesta tesis doctoral s'han estudiat diferents aspectes relacionats amb la neurociència per investigar diferents eines que permetin realitzar un diagnòstic precoç de la malaltia en qüestió. Per fer-ho, s'han treballat diferents aspectes com el preprocessament de dades, l'extracció de característiques, la selecció de característiques i la seva posterior classificació.Neurodegenerative diseases are a group of disorders that affect the brain. These diseases are related with changes in the brain that lead to loss of brain structure or loss of neurons, including the dead of some neurons. Alzheimer's disease (AD) is one of the most well-known neurodegenerative diseases. Nowadays there is no cure for this disease. However, there are some medicaments that may delay the symptoms if they are used during the first stages of the disease, otherwise they have no effect. Therefore early diagnose is presented as a key factor. This PhD thesis works different aspects related with neuroscience, in order to develop new methods for the early diagnose of AD. Different aspects have been investigated, such as signal preprocessing, feature extraction, feature selection and its classification

Oil exploration and production in Sub-Saharan Africa, 1990-present: Trends and developments

The exploration and production of oil and gas continue to be vigorously pursued by African states and international corporations—both large and small. However, with unpredictable fluctuations in oil prices it becomes more difficult to exploit these resources in ways which accrue net benefits to both the state and its citizens. The oil and gas industry in Africa continues to grow and attract new investment, especially from China and India. Despite the lower price of oil, exploration and production activities continue to be carried out. At the same time, the possibilities for oil and gas to be a blessing narrow. Natural resource-based development has always been a difficult objective for any state. The question now may be whether embracing oil and gas is socially responsible: as renewable energy becomes more cost-effective and societies transition into a post-carbon world, the prospects for African states to make good use of carbon resources are waning. In exploring the closing window for petro-development in Africa, this paper uses a comparative cross-regional analysis of trends and developments to highlight how weak legal frameworks and a lack of institutional capacity pose major challenges for the continent\u27s states in managing their natural resources

E-sustainability 2022 | Atas do Seminário Doutoral do Doutoramento em Sustentabilidade Social e Desenvolvimento

Este e-book é o resultado das contribuições apresentadas pelos doutorandos de dois doutoramentos: Doutoramento em Sustentabilidade Social e Desenvolvimento, da Universidade Aberta; Programa de Pós-Graduação em Desenvolvimento sustentável da Universidade de Brasília., durante os dois dias e foi organizado em quatro grandes tópicos: i) Sociedade e Desenvolvimento local; ii) Ambiente, iii) Organizações e Governança e iv) Agroecologia. Apresentam-se de seguida as contribuições de cada tópico em forma de resumo alargado. A diversidade dos temas apresentados reflete não só a riqueza, complexidade e multidimensionalidade associadas ao conceito de sustentabilidade como a diversidade geográfica das comunicações.info:eu-repo/semantics/publishedVersio

Oil Wealth and Development in Uganda and Beyond

Large quantities of oil were discovered in the Albertine Rift Valley in Western Uganda in 2006. The sound management of these oil resources and revenues is undoubtedly one of the key public policy challenges for Uganda as it is for other African countries with large oil and/or gas endowments. With oil expected to start flowing in 2021, the current book analyses how this East African country is preparing for the challenge of effectively, efficiently, and transparently managing its oil sector and resources. Adopting a multidisciplinary, comprehensive, and comparative approach, the book identifies a broad scope of issues that need to be addressed in order for Uganda to realise the full potential of its oil wealth for national economic transformation. Predominantly grounded in local scholarship and including chapters drawing on the experiences of Nigeria, Ghana, and Kenya, the book blazes a trail on governance of African oil in an era of emerging producers. Oil Wealth and Development in Uganda and Beyond will be of great interest to social scientists and economic and social policy makers in oil-producing countries. It is suitable for course adoption across such disciplines as International/Global Affairs, Political Economy, Geography, Environmental Studies, Economics, Energy Studies, Development, Politics, Peace, Security and African Studies. Contributors: Badru Bukenya (Makerere University), Moses Isabirye (Busitema University), Wilson Bahati Kazi (Uganda Revenue Authority), Corti Paul Lakuma (Economic Policy Research Centre), Joseph Mawejje (Economic Policy Research Centre), Pamela Mbabazi (Uganda National Planning Authority), Martin Muhangi (independent researcher), Roberts Muriisa (Mbarara University of Science and Technology), Chris Byaruhanga Musiime (independent researcher), Germano Mwabu (University of Nairobi), Jackson A. Mwakali (Makerere University), Tom Owang (Mbarara University of Science and Technology), Joseph Oloka-Onyango (Makerere University), Peter Quartey (University of Ghana), Peter Wandera (Transparency International Uganda), Kathleen Brophy (Transparency International Uganda), Jaqueline Nakaiza (independent researcher), Babra Beyeza (independent researcher), Jackson Byaruhanga (Bank of Uganda), Emmanuel Abbey (University of Ghana)

Wearable in-ear pulse oximetry: theory and applications

Wearable health technology, most commonly in the form of the smart watch, is employed by millions of users worldwide. These devices generally exploit photoplethysmography (PPG), the non-invasive use of light to measure blood volume, in order to track physiological metrics such as pulse and respiration. Moreover, PPG is commonly used in hospitals in the form of pulse oximetry, which measures light absorbance by the blood at different wavelengths of light to estimate blood oxygen levels (SpO2). This thesis aims to demonstrate that despite its widespread usage over many decades, this sensor still possesses a wealth of untapped value. Through a combination of advanced signal processing and harnessing the ear as a location for wearable sensing, this thesis introduces several novel high impact applications of in-ear pulse oximetry and photoplethysmography. The aims of this thesis are accomplished through a three pronged approach: rapid detection of hypoxia, tracking of cognitive workload and fatigue, and detection of respiratory disease. By means of the simultaneous recording of in-ear and finger pulse oximetry at rest and during breath hold tests, it was found that in-ear SpO2 responds on average 12.4 seconds faster than the finger SpO2. This is likely due in part to the ear being in close proximity to the brain, making it a priority for oxygenation and thus making wearable in-ear SpO2 a good proxy for core blood oxygen. Next, the low latency of in-ear SpO2 was further exploited in the novel application of classifying cognitive workload. It was found that in-ear pulse oximetry was able to robustly detect tiny decreases in blood oxygen during increased cognitive workload, likely caused by increased brain metabolism. This thesis demonstrates that in-ear SpO2 can be used to accurately distinguish between different levels of an N-back memory task, representing different levels of mental effort. This concept was further validated through its application to gaming and then extended to the detection of driver related fatigue. It was found that features derived from SpO2 and PPG were predictive of absolute steering wheel angle, which acts as a proxy for fatigue. The strength of in-ear PPG for the monitoring of respiration was investigated with respect to the finger, with the conclusion that in-ear PPG exhibits far stronger respiration induced intensity variations and pulse amplitude variations than the finger. All three respiratory modes were harnessed through multivariate empirical mode decomposition (MEMD) to produce spirometry-like respiratory waveforms from PPG. It was discovered that these PPG derived respiratory waveforms can be used to detect obstruction to breathing, both through a novel apparatus for the simulation of breathing disorders and through the classification of chronic obstructive pulmonary disease (COPD) in the real world. This thesis establishes in-ear pulse oximetry as a wearable technology with the potential for immense societal impact, with applications from the classification of cognitive workload and the prediction of driver fatigue, through to the detection of chronic obstructive pulmonary disease. The experiments and analysis in this thesis conclusively demonstrate that widely used pulse oximetry and photoplethysmography possess a wealth of untapped value, in essence teaching the old PPG sensor new tricks.Open Acces