Efficient Learning in Heterogeneous Internet of Things Ecosystems

The Internet of Things (IoT) is a growing network of heterogeneous devices, combining various sensing and computing nodes at different scales, which creates a large volume of data. Many IoT applications use machine learning (ML) algorithms to analyze the data. The high computational complexity of ML workloads poses significant computational challenges to IoT computing platforms, which tend to be less-powerful and resource-constrained devices. Transmitting such large volumes of data to the cloud also have various issues such as scalability, security and privacy. In this dissertation, we propose efficient solutions to perform the ML tasks while decreasing power consumption and improving performance. We first leverage the heterogeneous and interconnected nature of the IoT systems, where IoT applications run on many different architectures (e.g., X86 server or ARM-based edge device) while communicating with each other. We present a cross-platform power and performance prediction technique for intelligent task allocation. The proposed technique estimates the time-variant energy consumption with only 7% error across completely different architectures, enabling the intelligent task allocation that saves the energy consumption of 16.5% for state-of-the-art ML workloads.We next show how to further advance the learning procedures towards real-time and online processing by distributing such learning tasks onto the hierarchy of IoT devices. Our solution leverages brain-inspired high-dimensional (HD) computing to derive a new class oflearning algorithms that can easily run on IoT devices, while providing high accuracy comparable to the state-of-the-arts. We present that the HD-based learning algorithms can cover various real-world problems from conventional classification to other cognitive tasks beyond classical MLs such as DNA pattern matching. We demonstrate that the HD-based learning can enable secure, collaborative learning by efficiently distributing a large volume of learning tasks into heterogeneous computing nodes. We have implemented the proposed learning solution on various platforms while offering superior computing efficiency. For example, our solution achieves 486×and 7× performance improvements for each of the training and inference phases on a low-power ARM processor, as compared to state-of-the-art deep learning

Mapping and monitoring forest remnants : a multiscale analysis of spatio-temporal data

KEYWORDS : Landsat, time series, machine learning, semideciduous Atlantic forest, Brazil, wavelet transforms, classification, change detectionForests play a major role in important global matters such as carbon cycle, climate change, and biodiversity. Besides, forests also influence soil and water dynamics with major consequences for ecological relations and decision-making. One basic requirement to quantify and model these processes is the availability of accurate maps of forest cover. Data acquisition and analysis at appropriate scales is the keystone to achieve the mapping accuracy needed for development and reliable use of ecological models.The current and upcoming production of high-resolution data sets plus the ever-increasing time series that have been collected since the seventieth must be effectively explored. Missing values and distortions further complicate the analysis of this data set. Thus, integration and proper analysis is of utmost importance for environmental research. New conceptual models in environmental sciences, like the perception of multiple scales, require the development of effective implementation techniques.This thesis presents new methodologies to map and monitor forests on large, highly fragmented areas with complex land use patterns. The use of temporal information is extensively explored to distinguish natural forests from other land cover types that are spectrally similar. In chapter 4, novel schemes based on multiscale wavelet analysis are introduced, which enabled an effective preprocessing of long time series of Landsat data and improved its applicability on environmental assessment.In chapter 5, the produced time series as well as other information on spectral and spatial characteristics were used to classify forested areas in an experiment relating a number of combinations of attribute features. Feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz . maximum likelihood, univariate and multivariate decision trees, and neural networks. The results showed that maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest in the study area.In chapter 6, a multiscale approach to digital change detection was developed to deal with multisensor and noisy remotely sensed images. Changes were extracted according to size classes minimising the effects of geometric and radiometric misregistration.Finally, in chapter 7, an automated procedure for GIS updating based on feature extraction, segmentation and classification was developed to monitor the remnants of semideciduos Atlantic forest. The procedure showed significant improvements over post classification comparison and direct multidate classification based on artificial neural networks.</p

Application of optimization techniques to solve overcurrent relay coordination.

Masters Degree. University of KwaZulu- Natal, Durban.Distribution systems continues to grow and becoming more complex with increasing operational challenges such as protection miscoordination. Initially, conventional methods were favoured to optimize protection coordination; however, the implementation process is laborious and time-consuming. “Therefore, recent studies have adopted the utilisation of particle swarm optimization and genetic algorithms to solve overcurrent relay coordination problems and maximise system selectivity and operational speed. Particle swarm optimization and genetic algorithms are evolutionary algorithms that at times suffer from premature convergence due to poor selection of control parameters. Consequently, this thesis aims to present a comprehensive sensitivity analysis to evaluate the effect of the discrete control parameters on the performance of particle swarm optimizer and genetic algorithms, alternatively on the behaviour of overcurrent relays. The main objectives of this research work also include modelling and simulation of distribution system protection scheme, employment of evolutionary algorithms with control parameters that perform efficiently and effectively to maximise protection coordination between relays, optimize relay operating time and maintain the stipulate coordination time interval, and lastly, to outline future recommendations. The distribution network understudy was modelled and simulated on a real-time digital simulator to validate protection settings, and the verification of evolutionary algorithms performance was displayed on Matlab/Simulink. An extensive parametric sensitivity analysis was conducted to understand the impact of the individual control parameters and their respective influence on the performance of evolutionary algorithms. The findings indicate that particle swarm optimization is more sensitive to inertia weight and swarm size while the number of iterations has minimal effect. The results also depict that genetic algorithms’ performance is mostly influenced by crossover probability, mutation probability, and population size. Sensitivity analysis results were verified by comparing the performance of particle swarm optimizer with genetic algorithms, which demonstrated that particle swarm optimization performs efficiently and robustly in solving the considered problem, especially in terms of convergence speed. Furthermore, overcurrent relays were more sensitive, selective, and the operational speed was reduced for particle swarm optimizer compared to other algorithms. The optimal protection coordination achieved using particle swarm optimization showed superiority of the algorithm, its ability to circumvent premature convergence, consistency, and” efficiency

Earth resources, a continuing bibliography with indexes

This bibliography lists 541 reports, articles and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Nowe metody przetwarzania losowo próbkowanych wielowymiarowych eksperymentów NMR

The topic of this dissertation is a new algorithm for processing of sparsely sampled data sets from multidimensional nuclear magnetic resonance (NMR) experiments. NMR remains one of the major experimental technique for studying biological macromolecules. However, increasing size of investigated objects poses a challenge for NMR due to rapidly decreasing sensitivity and increasing signal crowding. The first chapter focuses on recent advances in sensitivity enhancements and summarises a few solutions for resolution of spectral overlap. Subsequently, one describes the crucial and limiting problem of signal sampling in multidimensional NMR, which, up to recently, has impeded the widespread use of high-dimensional NMR methods. Major fast acquisition and non-uniform sampling (NUS) approaches are presented. The particular emphasis was put on detailed discussion of competetive approaches to processing of data from NUS experiments. In chapter 3 the new iterative algorithm is proposed for artefact suppression in high-resolution NMR spectra. The detailed description of its design and implementation is given, and followed by comparison with selected processing methods. The efficacy of the algorithm is demonstrated on model synthetic and experimental data. The last chapter of the thesis shows various applications of the proposed method to existing and new four- and five-dimensional NMR experiments. The algorithm is proven most beneficial in challenging applications including spectra for assignment of sidechain resonances in protein and nucleic acids, NOESY spectra for structural analysis, and cross-correlated relaxation measurements for proteins. // Niniejsza praca jest poświecona nowej metodzie przetwarzania danych pochodzących z oszczędnie próbkowanych wielowymiarowych eksperymentów jądrowego rezonansu magnetycznego (ang. Nuclear Magnetic Resonance, NMR). Technika ta jest, obok krystalografii rentgenowskiej, główną eksperymentalną metodą badawczą pozwalającą na określenie struktury i dynamiki makromolekuł o znaczeniu biologicznym. Jednakże NMR napotyka dwie istotne przeszkody w odniesieniu do dużych biomolekuł, a mianowicie gwałtownie pogarszającą się czułość oraz krytyczne zatłoczenie sygnałów w widmach. W rozdziale pierwszym przedstawiono ostatnie osiagnięcia w poprawie czułości technik NMR oraz rozwiązania służące podniesieniu rozdzielczości widm. Następnie opisano kluczowy problem próbkowania wielowymiarowych sygnałów NMR, który do niedawna uniemożliwiał wykorzystanie pełnego potencjału tych technik do rozdzielenia sygnałów. Omówiono pokrótce współczesne podejścia do szybkiej akwizycji i oszczędnego próbkowania sygnałów NMR (ang. non-uniform sampling, NUS). Szczególny nacisk położono na porównanie i dyskusje wad i zalet stosowanych obecnie metod przetwarzania sygnałów niejednorodnie próbkowanych. W rozdziale 3-cim opisano nowy iteracyjny algorytm oparty o transformacje Fouriera, usuwający artefakty oszczędnego próbkowania w wysokorozdzielczych widmach NMR. Szczegółowo omówiono schemat algorytmu oraz jego programową implementację. Rozdział uzupełnia porównanie wyników algorytmu oraz wybranych metod przetwarzania na wysymulowanych oraz modelowych danych eksperymentalnych. W ostatnim rozdziale pracy zademonstrowano użyteczność nowej metody do literaturowych oraz nowych cztero- i pieciowymiarowych eksperymentów NMR. Wśród proponowanych zastosowań wymienić można widma do przypisania sygnałów w łańcuchach bocznych aminokwasów (w białkach) i pierścieniach rybozy (w kwasach rybonukleinowych), widma NOESY służące określeniu struktury trójwymiarowej biomolekuł, oraz pomiary szybkości relaksacji skorelowanej w łańcuchach głównych białek

Some theoretical contributions to the evaluation and assessment of finite mixture models with applications

This dissertation develops theory and methodology for the evaluation and assessment of finite mixture models. New methods for simulating finite mixture models satisfying a pre-specified level of complexity defined through the notion of pairwise overlap, are developed. Corresponding software is publicly available at CRAN. This dissertation also develops methodology for assessing significance in finite mixture models with applications to model-based unsupervised and semi-supervised clustering frameworks. The dissertation concludes with an application of finite mixture models to two-dimensional gel electrophoresis

The recovery of ant communities in regenerating tropical forests

Tropische Regenwälder sind die artenreichsten aller terrestrischen Ökosysteme. Die rasch fortschreitende Zerstörung dieser Wälder stellt eine grosse Bedrohung für die biologische Vielfalt der Erde dar. Jedoch ist die Zerstörung des Waldes selten von Dauer — die landwirtschaftlichen Erträge der umgewandelten Flächen lassen nach, die Nutzung wird aufgegeben und die natürliche Waldregeneration setzt ein. Entscheidend für die Zukunft der Biodiversität in tropischen Wäldern ist, in wie weit diese Sekundärwälder als Ersatzhabitate für Waldarten fungieren und ähnliche Ökosystemfunktionen wie Primärwälder ausüben können. Das Wissen über den Wert von Sekundärwäldern für den Schutz von Biodiversität und Ökosystemfunktionen tropischer Wälder ist jedoch bislang unzureichend. Ziel der vorliegenden Arbeit ist es, einen Beitrag zur Beurteilung tropischer Sekundärwälder für den Schutz von Biodiversität und Ökosystemfunktionen zu leisten. In meiner Arbeit nutzte ich Ameisen (Hymenoptera: Formicidae) — eine dominante, artenreiche und funktionell wichtige Invertebratengruppe — als Modelorganismen. Alle Untersuchungen wurden im Naturreservat Rio Cachoeira im Küstenregenwald Brasiliens (Mata Atlântica) durchgeführt. Ich besammelte die Ameisengemeinschaft auf insgesamt 27 Untersuchungesflächen, die einen Gradienten der natürlichen Waldsukzession umfassten. Zusätzlich beprobte ich zum Vergleich die Ameisenfauna von Weiden und Primärwäldern. Nach Aufgabe der landwirtschaftlichen Nutzung erholte sich die Diversität der Ameisen in Sekundärwäldern in Bezug auf Artenreichtum und Zusammensetzung der Gemeinschaften nur langsam. Die Verteilung der Ameisentaxa entlang des Sukzessionsgradienten folgte einem verschachtelten Muster: Taxa in jüngeren Sukzessionsstadien waren eine Teilmenge der Taxa, welche auch in älteren Sukzessionsstadien lebten. Die Bodenbedingungen hatten nur einen geringen Einfluss auf das generelle Muster der Regeneration. Allgemein war der Artenreichtum in Wäldern mit vernässten Böden geringer als in Wäldern mit Böden ohne hydromorphe Merkmale. Die hypogäische Ameisengemeinschaft regenerierte langsamer als die epigäische Ameisengemeinschaft. Darüber hinaus weisen Schätzungen von 50 bis zu mehrere hundert Jahre für eine vollständige Regeneration, auf einen wesentlich längeren Zeitraum hin als bisher angenommen wurde. Die funtionelle Diversität der Ameisengemeinschaft war eng gekoppelt an ihre Arten-Diversität. Da Artengemeinschaften von Ameisen eine relativ geringe funktionelle Redundanz aufwiesen, resultierte die verrringerte Arten-Diversität in Sekundärwäldern in eine proportional verringerte funktionelle Diversität. Seltene, jedoch zugleich funktionell einzigartige Arten, fehlten in der Regel in Sekundärwäldern. Diese Ergebnisse weisen darauf hin, daß Sekundärwälder im Vergleich zu Primärwäldern lediglich eine reduziert Ökosystemfunktion ausüben können. Das Wissen über die Mechanismen nach denen sich lokale Artengemeinschaften aus dem regionalen Artenpool zusammensetzen, ist eine wertvolle wissenschaftliche Basis für den Schutz von Biodiversität. Günstige Resourcenverfügbarkeit und Habitatbedingungen übten einen stärkeren Einfluss auf die Zusammensetzung der Ameisengemeinschaften aus als interaktive Prozesse, wie etwa Konkurrenz zwischen den Arten. Trotz ausgeprägter Unterschiede in Artenreichtum und Zusammensetzung lokaler Ameisengemeinschaften, variierte das Muster der Besetzung des Nischen-Raums entlang des Sukzessionsgradienten nur wenig. In einem Feld-Experiment untersuchte ich die Köderpräferenzen von Ameisen (Protein- im Vergleich zu Kohlenhydrat-Ködern) in Sekundärwäldern und Primärwäldern. In Sekundär-wäldern präferierten Ameisen Protein-Köder, während sie in Primärwäldern Kohlenhydrat-Köder bevorzugten. Dies weist auf eine Verschiebung der stoichiometrischen Gleichgwichte zwischen Sekundär- und Primärwäldern hin, was die funktionellen Interaktionen in diesen Habitaten beeinflussen könnte. Zusammenfassend lässt sich feststellen, daß Sekundärwälder nur für einen relativ geringen Teil typischer Waldarten als Ersatzhabitate fungieren können. Die Struktur lokaler Ameisengemeinschaften ist in erster Linie ein Produkt der lokalen Umweltbedingungen. Der gleichzeitige Verlust von Artendiversität und funktioneller Diversität in Sekundärwäldern gibt keinen Anlass zur Hoffnung, daß die Auswirkungen des Artenverlustes in Sekundärwäldern durch funktionelle Redundanz der Ameisengemeinschaften abgemildert werden könnte. Folglich können Sekundärwälder nur einen reduzierten Teil der ökosystemaren Dienste von Primärwäldern bereitstellen. Diese Ergebnisse unterstreichen daher die kritische Bedeutung von Primärwäldern für den Schutz von Biodiversität und Ökosystemfunktionen in der tropischen Waldlandschaft

Ensemble classifiers for land cover mapping

This study presents experimental investigations on supervised ensemble classification for land cover classification. Despite the arrays of classifiers available in machine learning to create an ensemble, knowing and understanding the correct classifier to use for a particular dataset remains a major challenge. The ensemble method increases classification accuracy by consulting experts taking final decision in the classification process. This study generated various land cover maps, using image classification. This is to authenticate the number of classifiers that should be used for creating an ensemble. The study exploits feature selection techniques to create diversity in ensemble classification. Landsat imagery of Kampala (the capital of Uganda, East Africa), AVIRIS hyperspectral dataset of Indian pine of Indiana and Support Vector Machines were used to carry out the investigation. The research reveals that the superiority of different classification approaches employed depends on the datasets used. In addition, the pre-processing stage and the strategy used during the designing phase of each classifier is very essential. The results obtained from the experiments conducted showed that, there is no significant benefit in using many base classifiers for decision making in ensemble classification. The research outcome also reveals how to design better ensemble using feature selection approach for land cover mapping. The study also reports the experimental comparison of generalized support vector machines, random forests, C4.5, neural network and bagging classifiers for land cover classification of hyperspectral images. These classifiers are among the state-of-the-art supervised machine learning methods for solving complex pattern recognition problems. The pixel purity index was used to obtain the endmembers from the Indiana pine and Washington DC mall hyperspectral image datasets. Generalized reduced gradient optimization algorithm was used to estimate fractional abundance in the image dataset thereafter obtained numeric values for land cover classification. The fractional abundance of each pixel was obtained using the spectral signature values of the endmembers and pixel values of class labels. As the results of the experiments, the classifiers show promising results. Using Indiana pine and Washington DC mall hyperspectral datasets, experimental comparison of all the classifiers’ performances reveals that random forests outperforms the other classifiers and it is computational effective. The study makes a positive contribution to the problem of classifying land cover hyperspectral images by exploring the use of generalized reduced gradient method and five supervised classifiers. The accuracy comparison of these classifiers is valuable for decision makers to consider tradeoffs in method accuracy versus complexity. The results from the research has attracted nine publications which include, six international and one local conference papers, one published in Computing Research Repository (CoRR), one Journal paper submitted and one Springer book chapter, Abe et al., 2012 obtained a merit award based on the reviewer reports and the score reports of the conference committee members during the conference period

a Berlin case study

Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt...thesi