Adaptive learning for event modeling and pattern classification

It is crucial to detect, characterize and model events of interest in a new propulsion system. As technology advances, the amount of data being generated increases significantly with respect to time. This increase substantially strains our ability to interpret the data at an equivalent rate. It demands efficient methodologies and algorithms in the development of automated event modeling and pattern recognition to detect and characterize events of interest and correlate them to the system performance. The fact that the information required to properly evaluate system performance and health is seldom known in advance further exacerbates this issue. Event modeling and detection is essentially a discovery problem and involves the use of techniques in the pattern classification domain, specifically the use of cluster analysis if a prior information is unknown. In this dissertation, a framework of Adaptive Learning for Event Modeling and Characterization (ALEC) system is proposed to deal with this problem. Within this framework, a wavelet-based hierarchical fuzzy clustering approach which integrates several advanced technologies and overcomes the disadvantages of traditional clustering algorithms is developed to make the implementation of the system effective and computationally efficient. In another separate but related research, a generalized multi-dimensional Gaussian membership function is constructed and formulated to make the fuzzy classification of blade engine damage modes among a group of engines containing historical flight data after Principal Component Analysis (PCA) is applied to reduce the excessive dimensionality. This approach can be effectively used to deal with classification of patterns with overlapping structures in which some patterns fall into more than one classes or categories

Object Tracking in Video Images based on Image Segmentation and Pattern Matching

The moving object tracking in video pictures [1] has attracted a great deal of interest in computer vision. For object recognition, navigation systems and surveillance systems [10], object tracking is an indispensable first-step. We propose a novel algorithm for object tracking in video pictures, based on image segmentation and pattern matching [1]. With the image segmentation, we can detect all objects in images no matter whether they are moving or not. Using image segmentation results of successive frames, we exploit pattern matching in a simple feature space for tracking of the objects. Consequently, the proposed algorithm can be applied to multiple moving and still objects even in the case of a moving camera. We describe the algorithm in detail and perform simulation experiments on object tracking which verify the tracking algorithm‘s efficiency. VLSI implementation of the proposed algorithm is possible. The conventional approach to object tracking is based on the difference between the current image and the background image. However, algorithms based on the difference image cannot simultaneously detect still objects. Furthermore, they cannot be applied to the case of a moving camera. Algorithms including the camera motion information have been proposed previously, but, they still contain problems in separating the information from the background. The proposed algorithm, consisting of four stages i.e. image segmentation, feature extraction as well as object tracking and motion vector determination [12]. Here Image Segmentation is done in 3 ways and the efficiency of the tracking is compared in these three ways, the segmentation techniques used are ―Fuzzy C means clustering using Particle Swarm Optimization [5],[6],[17]”, ”Otsu’s global thresholding [16]”, ”Histogram based thresholding by manual threshold selection”, after image segmentation the features of each object are taken and Pattern Matching [10],[11],[20] algorithm is run on consecutive frames of video sequence, so that the pattern of extracted features is matched in the next frame , the motion of the object from reference frame to present frame is calculated in both X and Y directions, the mask is moved in the image accordingly, hence the moving object in the video sequences will be tracked

Vulnerability and resilience of competing land-based livelihoods in south eastern Zimbabwe

Key words: vulnerability; resilience; livelihood; drought; Great Limpopo Transfrontier Conservation Area; south eastern Zimbabwe. Vulnerability and resilience have emerged as powerful analytical concepts in the study of socio-ecological systems. In this research these concepts are used to enhance our understanding of heterogeneous rural livelihoods in a semi-arid area on the western border of protected wildlife areas in Zimbabwe’s southeast lowveld. The purpose of this thesis is to develop a methodological approach that helps understanding the vulnerability of rural livelihoods to change and relate this to adaptive mechanisms employed by people to cope with the resulting change. Although most households in the study area keep livestock, practice arable farming, and receive remittances, they differ in terms of their dependency on cattle, cropping, and non-farm and off-farm activities, especially in years of drought. Households most dependent on livestock – the cattle-based livelihood type – generally cope with hazards by selling cattle. Households of the crop-based livelihood type strive to spread the risk of crop failure by cropping across the landscape, ranging from flood plains to uplands on the interfluves. Households of the non-farm livelihood type rely for their survival on paid employment outside the study area, mostly of households’ members working in South Africa. Fuzzy Cognitive Mapping (FCM) was used to assess the vulnerability of the three livelihood types to different hazards. The vulnerability analysis shows that policies relating to the permeability and/or enforcement of protected area boundaries can strongly aggravate the effects of other external influences, such as drought or climate change. To cope with drought-induced fodder shortages, people of cattle-based households have recently started to use Neorautanenia amboensis (Schinz). This tuber shrub, locally known as Zhombwe, is now saving many cattle from death in periods of drought, thus reducing livestock keeping households vulnerability to drought. This thesis shows the anthelmintic properties of Zhombwe; its distribution in the field was quantified. Crop experiments explored adaptive strategies which can be used by the households of the crop-based livelihood type to increase food self-sufficiency. Results show that by making better use of different landscape units in the area food production can be increased, both in good and bad rainfall years. By applying a method like FCM and by analysing quantitatively different options for increasing the resilience of the local households, this thesis shows that it is key to take into account the heterogeneity of rural households in an area, as adaption options differ strongly between them. </p

Social Conflict on the Seas: Links Between Overfishing-Induced Marine Fish Stock Declines and Forced Labor Slavery

Despite media attention detailing labor abuses in fisheries, social-ecological systems research has largely failed to consider whether fish stock declines could be contributing to increases in forced labor slavery. Empirical fisheries data suggests, though not a ubiquitous response to declining stocks, many vessels will fish longer, farther from shore, and deeper in waters to maintain yields. This effort intensification increases production costs, and Brashares et al. (2014), consistent with slavery theory, posited cheap and/or unpaid labor as an approach to offset increasing costs and continue harvesting fish species at a rate otherwise cost-prohibitive. Using fuzzy cognitive mapping - a participatory, semi-quantitative systems modeling technique that uses participants\u27 knowledge to define complex system dynamics including fuzzy causality (causality represented as a matter of degree on a spectrum rather than certainty) - this study tested the hypothesis by interviewing stakeholders from global slavery hotspots. Data was obtained through semi-structured, qualitative interviews (n = 44) that included a cognitive mapping activity. An iterative, systematic, and inductive thematic content analysis condensed each map into major variables. Using structural models derived from graph theory, each cognitive map was converted into an adjacency matrix. From the matrix, influence metrics were calculated to elicit further information about each graph\u27s structure and group like maps. ANOVAs and independent sample t-tests to test for map structure differences across demographic variables were statistically insignificant. As such, using vector-matrix operations, all 44 maps were aggregated into one cumulative, consensus map. This consensus map was then used to refine the posited theory and execute case scenario analyses to assess the value of forced labor slavery changes in proposed case scenario simulations. Broadly, participants identified forced labor slavery as a distal outcome of marine fish stock declines, describing a process wherein declines intensify effort - increasing production costs. These increasing costs then incentivize the use of forced labor in response to narrowing profit margins, ultimately normalizing the use of forced labor as an economically rational decision. Case scenario analyses suggested if overfishing is not addressed, and marine stocks continue to decline, forced labor slavery in the fishing sector will continue to increase. Additionally, increases in forced labor slavery may increase stock declines. Proposed policy interventions to mitigate overfishing could reduce labor abuses in the sector. Therefore, the framework produced by the consensus map should guide more wide-scale, empirical testing of the relationship between fish stock declines and forced labor slavery and identify points-of-intervention for policy and fisheries management practices to mitigate social-ecological injustices in the fishing sector

From Ecosystem Services to Ecosystem Benefits: Unpacking the Links Between Ecosystems and Human Well-Being in Agricultural Communities in Costa Rica

This dissertation presents an exploration of the links between ecosystem services and human well-being in resource-dependent communities in diverse agricultural regions in Costa Rica. As such, this dissertation considers the key roles played by environmental management and environmental governance. In broad terms, the question that this dissertation examines is: How does the management of ecosystem services derived from agriculture impact human well-being in resource-dependent communities in Costa Rica? This dissertation has taken as a point of departure the framework proposed by the Millennium Ecosystem Assessment and has applied it to the examination of communities that are particularly vulnerable to environmental change. The focus on well-being brings to the forefront questions about the distribution of the benefits derived from ecosystems and highlights the perceptions of ecosystem-users. Three manuscripts make up this dissertation: The first manuscript uses a participatory method (photovoice) to elicit narratives about the ecosystems that impact the well-being of residents in the pineapple community of Volcán in South-Pacific Costa Rica. The manuscript offers a community-level perspective on the ecosystem services that contribute to the well-being of agricultural communities. The second manuscript focuses on how access and power relations affect the benefits experienced by Indigenous farmers in the Bribri Territory who produce plantains for sale in the national and international markets. The manuscript identifies how access to the means of production is gained, controlled and maintained within the social-ecological system of plantain agriculture. It also identifies the mechanisms that gatekeepers employ to exercise their power. The manuscript concludes with possible leverage points that could be used to challenge existing power relations and improve human well-being in the Bribri Indigenous Territory. The third manuscript presents three community-level assessments of well-being from agricultural regions on the Caribbean side of Costa Rica that have different environmental management systems ranging from large-scale monocrop banana plantations in Matina to agroforestry in the Bribri Indigenous Territory. The analysis investigates the ways in which different systems of resource extraction shape well-being at the local level. In brief, the dissertation offers insights for improving the theoretical and empirical understandings of how changes in ecosystems affect human well-being in resource-dependent communities. It also offers suggestions to render the ecosystem services framework more relevant to guide environmental management at the micro-scale and in the context of poverty alleviation

Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps

As extension of Fuzzy Cognitive Maps are now introduced the Neutrosophic Cognitive Map

Online Multi-Stage Deep Architectures for Feature Extraction and Object Recognition

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. Large datasets with high-dimensional features complicate the implementation of visual architectures in memory constrained environments. This dissertation constructs online learning replacements for the components within a multi-stage architecture and demonstrates that the proposed replacements (namely fuzzy competitive clustering, an incremental covariance estimator, and multi-layer neural network) can offer performance competitive with their offline batch counterparts while providing a reduced memory footprint. The online nature of this solution allows for the development of a method for adjusting parameters within the architecture via stochastic gradient descent. Testing over multiple datasets shows the potential benefits of this methodology when appropriate priors on the initial parameters are unknown. Alternatives to batch based decompositions for a whitening preprocessing stage which take advantage of natural image statistics and allow simple dictionary learners to work well in the problem domain are also explored. Expansions of the architecture using additional pooling statistics and multiple layers are presented and indicate that larger codebook sizes are not the only step forward to higher classification accuracies. Experimental results from these expansions further indicate the important role of sparsity and appropriate encodings within multi-stage visual feature extraction architectures

Sustainability, Digital Transformation and Fintech: The New Challenges of the Banking Industry

In the current competitive scenario, the banking industry must contend with multiple challenges tied to regulations, legacy systems, disruptive models/technologies, new competitors, and a restive customer base, while simultaneously pursuing new strategies for sustainable growth. Banking institutions that can address these emerging challenges and opportunities to effectively balance long-term goals with short-term performance pressures could be aptly rewarded. This book comprises a selection of papers addressing some of these relevant issues concerning the current challenges and opportunities for international banking institutions. Papers in this collection focus on the digital transformation of the banking industry and its effect on sustainability, the emergence of new competitors such as FinTech companies, the role of mobile banking in the industry, the connections between sustainability and financial performance, and other general sustainability and corporate social responsibility (CSR) topics related to the banking industry. The book is a Special Issue of the MDPI journal Sustainability, which has been sponsored by the Santander Financial Institute (SANFI), a Spanish research and training institution created as a collaboration between Santander Bank and the University of Cantabria. SANFI works to identify, develop, support, and promote knowledge, study, talent, and innovation in the financial sector