Incremental Principal Component Analysis Based Outliers Detection Methods for Spatiotemporal Data Streams

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams

Towards an autonomous vision-based unmanned aerial system againstwildlife poachers

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing.Peer Reviewe

Towards an autonomous vision-based unmanned aerial system against wildlife poachers.

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $ 213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries

Using blind image filtering for images from TEM microscopes

Předložená práce se zabývá problematikou slepé filtrace obrazů z transmisního elektronového mikroskopu. V úvodu práce je uveden popis transmisního elektronového mikroskopu. Navazující část popisuje mechanismy interakce elektronů se zkoumaným vzorkem a z toho vyplývající zobrazovací techniky elektronové mikroskopie. Poslední kapitola teoretické části práce zahrnuje popis vybraných metod slepé filtrace obrazu zejména s využitím dekompozice obrazu na charakteristické složky. Taktéž je zde uveden výčet metod pro zhodnocení úspěšnosti filtrace. V praktické části jsou popsány aplikované metody slepé filtrace obrazů a výsledky filtrování. Jednotlivé metody jsou mezi sebou porovnány. Získané výsledky a využitelnost aplikovaných metod jsou zhodnoceny v diskuzi.This work deals with the blind filtration of the images from the transmission electron microscope. At the beginning of this work there is a basic description of the transmission electron microscope. Following part describes the mechanisms of electron interactions with the observed specimen. Description of basic electron microscopy imaging techniques is included. The last chapter of the theoretical part includes the description of several chosen blind image filtration techniques, especially those using the decomposition of the image into characteristic components. It also contains a summary of methods for evaluation the filtration effectiveness. The practical part focuses on a description of applied blind filtering methods and brings the results of the filtration. Individual methods are compared. In conclusion, the obtained results and usability of the applied methods are discussed.

Exploration, Registration, and Analysis of High-Throughput 3D Microscopy Data from the Knife-Edge Scanning Microscope

Advances in high-throughput, high-volume microscopy techniques have enabled the acquisition of extremely detailed anatomical structures on human or animal organs. The Knife-Edge Scanning Microscope (KESM) is one of the first instruments to produce sub-micrometer resolution ( ~1 µm^(3)) data from whole small animal brains. We successfully imaged, using the KESM, entire mouse brains stained with Golgi (neuronal morphology), India ink (vascular network), and Nissl (soma distribution). Our data sets fill the gap of most existing data sets which have only partial organ coverage or have orders of magnitude lower resolution. However, even though we have such unprecedented data sets, we still do not have a suitable informatics platform to visualize and quantitatively analyze the data sets. This dissertation is designed to address three key gaps: (1) due to the large volume (several tera voxels) and the multiscale nature, visualization alone is a huge challenge, let alone quantitative connectivity analysis; (2) the size of the uncompressed KESM data exceeds a few terabytes and to compare and combine with other data sets from different imaging modalities, the KESM data must be registered to a standard coordinate space; and (3) quantitative analysis that seeks to count every neuron in our massive, growing, and sparsely labeled data is a serious challenge. The goals of my dissertation are as follows: (1) develop an online neuro-informatics framework for efficient visualization and analysis of the multiscale KESM data sets, (2) develop a robust landmark-based 3D registration method for mapping the KESM Nissl-stained entire mouse data into the Waxholm Space (a canonical coordinate system for the mouse brain), and (3) develop a scalable, incremental learning algorithm for cell detection in high-resolution KESM Nissl data. For the web-based neuroinformatics framework, I prepared multi-scale data sets at different zoom levels from the original data sets. And then I extended Google Maps API to develop atlas features such as scale bars, panel browsing, and transparent overlay for 3D rendering. Next, I adapted the OpenLayers API, which is a free mapping and layering API supporting similar functionality as the Google Maps API. Furthermore, I prepared multi-scale data sets in vector-graphics to improve page loading time by reducing the file size. To better appreciate the full 3D morphology of the objects embedded in the data volumes, I developed a WebGL-based approach that complements the web-based framework for interactive viewing. For the registration work, I adapted and customized a stable 2D rigid deformation method to map our data sets to the Waxholm Space. For the analysis of neuronal distribution, I designed and implemented a scalable, effective quantitative analysis method using supervised learning. I utilized Principal Components Analysis (PCA) in a supervised manner and implemented the algorithm using MapReduce parallelization. I expect my frameworks to enable effective exploration and analysis of our KESM data sets. In addition, I expect my approaches to be broadly applicable to the analysis of other high-throughput medical imaging data

Groupwise non-rigid registration for automatic construction of appearance models of the human craniofacial complex for analysis, synthesis and simulation

Finally, a novel application of 3D appearance modelling is proposed: a faster than real-time algorithm for statistically constrained quasi-mechanical simulation. Experiments demonstrate superior realism, achieved in the proposed method by employing statistical appearance models to drive the simulation, in comparison with the comparable state-of-the-art quasi-mechanical approaches.EThOS - Electronic Theses Online ServiceGBUnited Kingdo