Studies on image compression and image reconstruction

During this six month period our works concentrated on three, somewhat different areas. We looked at and developed a number of error concealment schemes for use in a variety of video coding environments. This work is described in an accompanying (draft) Masters thesis. In the thesis we describe application of this techniques to the MPEG video coding scheme. We felt that the unique frame ordering approach used in the MPEG scheme would be a challenge to any error concealment/error recovery technique. We continued with our work in the vector quantization area. We have also developed a new type of vector quantizer, which we call a scan predictive vector quantization. The scan predictive VQ was tested on data processed at Goddard to approximate Landsat 7 HRMSI resolution and compared favorably with existing VQ techniques. A paper describing this work is included. The third area is concerned more with reconstruction than compression. While there is a variety of efficient lossless image compression schemes, they all have a common property that they use past data to encode future data. This is done either via taking differences, context modeling, or by building dictionaries. When encoding large images, this common property becomes a common flaw. When the user wishes to decode just a portion of the image, the requirement that the past history be available forces the decoding of a significantly larger portion of the image than desired by the user. Even with intelligent partitioning of the image dataset, the number of pixels decoded may be four times the number of pixels requested. We have developed an adaptive scanning strategy which can be used with any lossless compression scheme and which lowers the additional number of pixels to be decoded to about 7 percent of the number of pixels requested! A paper describing these results is included

Coverage problems in mobile sensing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (p. 177-183).Sensor-networks can today measure physical phenomena at spatial and temporal scales that were not achievable earlier, and have shown promise in monitoring the environment, structures, agricultural fields and so on. A key challenge in sensor-networks is the coordination of four actions across the network: measurement (sensing), communication, motion and computation. The term coverage is applied to the central question of how well a sensor-network senses some phenomenon to make inferences. More formally, a coverage problem involves finding an arrangement of sensors that optimizes a coverage metric. In this thesis we examine coverage in the context of three sensing modalities. The literature on the topic has thus far focused largely on coverage problems with the first modality: static event-detection sensors, which detect purely binary events in their immediate vicinity based on thresholds. However, coverage problems for sensors which measure physical quantities like temperature, pressure, chemical concentrations, light intensity and so on in a network configuration have received limited attention in the literature. We refer to this second modality of sensors as estimation sensors; local estimates from such sensors can be used to reconstruct a field. Third, there has been recent interest in deploying sensors on mobile platforms. Mobility has the effect of increasing the effectiveness of sensing actions. We further classify sensor mobility into incidental and intentional motion. Incidentally mobile sensors move passively under the influence of the environment, for instance, a floating sensor drifting in the sea. We define intentional mobility as the ability to control the location and trajectory of the sensor, for example by mounting it on a mobile robot. We build our analysis on a series of cases. We first analyze coverage and connectivity of a network of floating sensors in rivers using simulations and experimental data, and give guidelines for sensor-network design. Second, we examine intentional mobility and detection sensors.(cont.) We examine the problem of covering indoor and outdoor pathways with reconfigurable camera sensor-networks. We propose and validate an empirical model for detection behavior of cameras. We propose a distributed algorithm for reconfiguring locations of cameras to maximize detection performance. Finally, we examine more general strategies for the placement of estimation sensors and ask when and where to take samples in order to estimate an unknown spatiotemporal field with tolerable estimation errors. We discuss various classes of error-tolerant sensor arrangements for trigonometric polynomial fields.by Ajay A. Deshpande.Ph.D

Nanometer-precision electron-beam lithography with applications in integrated optics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 179-185).Scanning electron-beam lithography (SEBL) provides sub-10-nm resolution and arbitrary-pattern generation; however, SEBL's pattern-placement accuracy remains inadequate for future integrated-circuits and integrated-optical devices. Environmental disturbances, system imperfections, charging, and a variety of other factors contribute to pattern-placement inaccuracy. To overcome these limitations, spatial-phase locked electron-beam lithography (SPLEBL) monitors the beam location with respect to a reference grid on the substrate. Phase detection of the periodic grid signal provides feedback control of the beam position to within a fraction of the period. Using this technique we exposed patterns globally locked to a fiducial grid and reduced local field-stitching errors to a < 1.3 nm. Spatial-phase locking is particularly important for integrated-optical devices that require pattern-placement accuracy within a fraction of the wavelength of light. As an example, Bragg-grating based optical filters were fabricated in silicon-on-insulator waveguides using SPLEBL. The filters were designed to reflect a narrow-range of wavelengths within the communications band near 1550-nm. We patterned the devices in a single lithography step by placing the gratings in the waveguide sidewalls. This design allows apodization of the filter response by lithographically varying the grating depth. Measured transmission spectra show greatly reduced sidelobe levels for apodized devices compared to devices with uniform gratings.by Jeffrey Todd Hastings.Ph.D

Vector Quantization Techniques for Approximate Nearest Neighbor Search on Large-Scale Datasets

The technological developments of the last twenty years are leading the world to a new era. The invention of the internet, mobile phones and smart devices are resulting in an exponential increase in data. As the data is growing every day, finding similar patterns or matching samples to a query is no longer a simple task because of its computational costs and storage limitations. Special signal processing techniques are required in order to handle the growth in data, as simply adding more and more computers cannot keep up.Nearest neighbor search, or similarity search, proximity search or near item search is the problem of finding an item that is nearest or most similar to a query according to a distance or similarity measure. When the reference set is very large, or the distance or similarity calculation is complex, performing the nearest neighbor search can be computationally demanding. Considering today’s ever-growing datasets, where the cardinality of samples also keep increasing, a growing interest towards approximate methods has emerged in the research community.Vector Quantization for Approximate Nearest Neighbor Search (VQ for ANN) has proven to be one of the most efficient and successful methods targeting the aforementioned problem. It proposes to compress vectors into binary strings and approximate the distances between vectors using look-up tables. With this approach, the approximation of distances is very fast, while the storage space requirement of the dataset is minimized thanks to the extreme compression levels. The distance approximation performance of VQ for ANN has been shown to be sufficiently well for retrieval and classification tasks demonstrating that VQ for ANN techniques can be a good replacement for exact distance calculation methods.This thesis contributes to VQ for ANN literature by proposing five advanced techniques, which aim to provide fast and efficient approximate nearest neighbor search on very large-scale datasets. The proposed methods can be divided into two groups. The first group consists of two techniques, which propose to introduce subspace clustering to VQ for ANN. These methods are shown to give the state-of-the-art performance according to tests on prevalent large-scale benchmarks. The second group consists of three methods, which propose improvements on residual vector quantization. These methods are also shown to outperform their predecessors. Apart from these, a sixth contribution in this thesis is a demonstration of VQ for ANN in an application of image classification on large-scale datasets. It is shown that a k-NN classifier based on VQ for ANN performs on par with the k-NN classifiers, but requires much less storage space and computations

Techniky segmentace obrazu v prostředí HPC a jejich aplikace.

Import 03/11/2016An image decomposition into regions of interests, which are commonly called segments, is an integral part of modern techniques for classifying and processing a piece of information which is decoded from discrete image functions. Typically, the segments are interesting objects in an image-foreground scene, and they are apart from an insignificant background. The goal of this diploma thesis is to study at least two different techniques for image segmentation and their massively parallel implementation for HPC platforms, and test them on real-world examples. One of the techniques will be based on a spectral clustering method. The implemented techniques will be imported into PERMON Toolbox specific modules. PERMON Toolbox is developed by IT4Innovations National Supercomputing Center in Ostrava in cooperation with the Department of Applied Mathematics at VŠB-TUO.Rozdělení obrazu na oblasti zájmu (segmenty), které typicky představují významné prvky v popředí zachycené scény, oddělené od nevýznamného pozadí, je nedílnou součástí moderních technik pro klasifikaci a zpracování informací dekódovaných z diskrétních obrazových funkcí. Cílem této diplomové práce je nastudování nejméně dvou různých technik pro segmentaci obrazu, z nichž jedna bude založena na metodě spektrálního shlukování, jejich masivně paralelní implementace pro HPC platformy a otestování na několika reálných úlohách. Implementované techniky budou začleněny do specifických modulů PERMON Toolboxu, který je vyvíjen v Národním superpočítačovém centru IT4Innovations v Ostravě ve spolupráci s Katedrou aplikované matematiky VŠB-TUO.470 - Katedra aplikované matematikyvýborn

Self-organising maps : statistical analysis, treatment and applications.

This thesis presents some substantial theoretical analyses and optimal treatments of Kohonen's self-organising map (SOM) algorithm, and explores the practical application potential of the algorithm for vector quantisation, pattern classification, and image processing. It consists of two major parts. In the first part, the SOM algorithm is investigated and analysed from a statistical viewpoint. The proof of its universal convergence for any dimensionality is obtained using a novel and extended form of the Central Limit Theorem. Its feature space is shown to be an approximate multivariate Gaussian process, which will eventually converge and form a mapping, which minimises the mean-square distortion between the feature and input spaces. The diminishing effect of the initial states and implicit effects of the learning rate and neighbourhood function on its convergence and ordering are analysed and discussed. Distinct and meaningful definitions, and associated measures, of its ordering are presented in relation to map's fault-tolerance. The SOM algorithm is further enhanced by incorporating a proposed constraint, or Bayesian modification, in order to achieve optimal vector quantisation or pattern classification. The second part of this thesis addresses the task of unsupervised texture-image segmentation by means of SOM networks and model-based descriptions. A brief review of texture analysis in terms of definitions, perceptions, and approaches is given. Markov random field model-based approaches are discussed in detail. Arising from this a hierarchical self-organised segmentation structure, which consists of a local MRF parameter estimator, a SOM network, and a simple voting layer, is proposed and is shown, by theoretical analysis and practical experiment, to achieve a maximum likelihood or maximum a posteriori segmentation. A fast, simple, but efficient boundary relaxation algorithm is proposed as a post-processor to further refine the resulting segmentation. The class number validation problem in a fully unsupervised segmentation is approached by a classical, simple, and on-line minimum mean-square-error method. Experimental results indicate that this method is very efficient for texture segmentation problems. The thesis concludes with some suggestions for further work on SOM neural networks

Acoustic emission monitoring of propulsion systems : a laboratory study on a small gas turbine

The motivation of the work is to investigate a new, non-intrusive condition monitoring system for gas turbines with capabilities for earlier identification of any changes and the possibility of locating the source of the faults. This thesis documents experimental research conducted on a laboratory-scale gas turbine to assess the monitoring capabilities of Acoustic Emission (AE). In particular it focuses on understanding the AE behaviour of gas turbines under various normal and faulty running conditions. A series of tests was performed with the turbine running normally, either idling or with load. Two abnormal running configurations were also instrumented in which the impeller was either prevented from rotation or removed entirely. With the help of demodulated resonance analysis and an ANN it was possible to identify two types of AE; a background broadband source which is associated with gas flow and flow resistance, and a set of spectral frequency peaks which are associated with reverberation in the exhaust and coupling between the alternator and the turbine. A second series of experiments was carried out with an impeller which had been damaged by removal of the tips of some of the blades (two damaged blades and four damaged blades). The results show the potential capability of AE to identify gas turbine blade faults. The AE records showed two obvious indicators of blade faults, the first being that the energy in the AE signals becomes much higher and is distinctly periodic at higher speeds, and the second being the appearance of particular pulse patterns which can be characterized in the demodulated frequency domain