High-Definition Video Distribution in 802.11g Home Wireless Networks

Multimedia distribution through wireless networks in the home environment presents a number of advantages which have fueled the interest of industry in recent years, such as simple connectivity and data delivery to a variety of devices. Together with High-Definition (HD) contents, multimedia wireless networks have been proposed for several applications, such as IPTV and Digital TV distribution for multiple devices in the home environment. For these scenarios, we propose a multicast distribution system for High-Definition video over 802.11 wireless networks based on rate-limited packet retransmission. We develop a limited rate ARQ system that retransmits packets according to the importance of their content (prioritization scheme) and according to their delay limitations (delay control). The performance of our proposed ARQ system is evaluated and compared with a similarly rate-limited ARQ algorithm. The results show a higher packet recovery rate and improvements in video quality for our proposed system.

Development of a 3-Dimensional Mathematical Collision Risk Model Based on Recorded Aircraft Trajectories to Estimate the Safety Level in High Density En-Route Airspaces

Como en todos los medios de transporte, la seguridad en los viajes en avión es de primordial importancia. Con los aumentos de tráfico aéreo previstos en Europa para la próxima década, es evidente que el riesgo de accidentes necesita ser evaluado y monitorizado cuidadosamente de forma continúa. La Tesis presente tiene como objetivo el desarrollo de un modelo de riesgo de colisión exhaustivo como método para evaluar el nivel de seguridad en ruta del espacio aéreo europeo, considerando todos los factores de influencia. La mayor limitación en el desarrollo de metodologías y herramientas de monitorización adecuadas para evaluar el nivel de seguridad en espacios de ruta europeos, donde los controladores aéreos monitorizan el tráfico aéreo mediante la vigilancia radar y proporcionan instrucciones tácticas a las aeronaves, reside en la estimación del riesgo operacional. Hoy en día, la estimación del riesgo operacional está basada normalmente en reportes de incidentes proporcionados por el proveedor de servicios de navegación aérea (ANSP). Esta Tesis propone un nuevo e innovador enfoque para evaluar el nivel de seguridad basado exclusivamente en el procesamiento y análisis trazas radar. La metodología propuesta ha sido diseñada para complementar la información recogida en las bases de datos de accidentes e incidentes, mediante la provisión de información robusta de los factores de tráfico aéreo y métricas de seguridad inferidas del análisis automático en profundidad de todos los eventos de proximidad. La metodología 3-D CRM se ha implementado en un prototipo desarrollado en MATLAB © para analizar automáticamente las trazas radar y planes de vuelo registrados por los Sistemas de Procesamiento de Datos Radar (RDP) e identificar y analizar todos los eventos de proximidad (conflictos, conflictos potenciales y colisiones potenciales) en un periodo de tiempo y volumen del espacio aéreo. Actualmente, el prototipo 3-D CRM está siendo adaptado e integrado en la herramienta de monitorización de prestaciones de Aena (PERSEO) para complementar las bases de accidentes e incidentes ATM y mejorar la monitorización y proporcionar evidencias de los niveles de seguridad. ABSTRACT As with all forms of transport, the safety of air travel is of paramount importance. With the projected increases in European air traffic in the next decade and beyond, it is clear that the risk of accidents needs to be assessed and carefully monitored on a continuing basis. The present thesis is aimed at the development of a comprehensive collision risk model as a method of assessing the European en-route risk, due to all causes and across all dimensions within the airspace. The major constraint in developing appropriate monitoring methodologies and tools to assess the level of safety in en-route airspaces where controllers monitor air traffic by means of radar surveillance and provide aircraft with tactical instructions lies in the estimation of the operational risk. The operational risk estimate normally relies on incident reports provided by the air navigation service providers (ANSPs). This thesis proposes a new and innovative approach to assessing aircraft safety level based exclusively upon the process and analysis of radar tracks. The proposed methodology has been designed to complement the information collected in the accident and incident databases, thereby providing robust information on air traffic factors and safety metrics inferred from the in depth assessment of proximate events. The 3-D CRM methodology is implemented in a prototype tool in MATLAB © in order to automatically analyze recorded aircraft tracks and flight plan data from the Radar Data Processing systems (RDP) and identify and analyze all proximate events (conflicts, potential conflicts and potential collisions) within a time span and a given volume of airspace. Currently, the 3D-CRM prototype is been adapted and integrated in AENA’S Performance Monitoring Tool (PERSEO) to complement the information provided by the ATM accident and incident databases and to enhance monitoring and providing evidence of levels of safety.

Volume, Surface, Connectivity and Size Distribution of Soil Pore Space in CT Images: Comparison of Samples at Different Depths from Nearby Natural and Tillage Areas

The study of soil structure, i.e., the pores, is of vital importance in different fields of science and technology. Total pore volume (porosity), pore surface, pore connectivity and pore size distribution are some (probably the most important) of the geometric measurements of pore space. The technology of X-ray computed tomography allows us to obtain 3D images of the inside of a soil sample enabling study of the pores without disturbing the samples. In this work we performed a set of geometrical measures, some of them from mathematical morphology, to assess and quantify any possible difference that tillage may have caused on the soil. We compared samples from tilled soil with samples from a soil with natural vegetation taken in a very close area. Our results show that the main differences between these two groups of samples are total surface area and pore connectivity per unit pore volume.

A unified theory of carcinogenesis based on order–disorder transitions in DNA structure as studied in the human ovary and breast

Fourier transform-infrared/statistics models demonstrate that the malignant transformation of morphologically normal human ovarian and breast tissues involves the creation of a high degree of structural modification (disorder) in DNA, before restoration of order in distant metastases. Order–disorder transitions were revealed by methods including principal components analysis of infrared spectra in which DNA samples were represented by points in two-dimensional space. Differences between the geometric sizes of clusters of points and between their locations revealed the magnitude of the order–disorder transitions. Infrared spectra provided evidence for the types of structural changes involved. Normal ovarian DNAs formed a tight cluster comparable to that of normal human blood leukocytes. The DNAs of ovarian primary carcinomas, including those that had given rise to metastases, had a high degree of disorder, whereas the DNAs of distant metastases from ovarian carcinomas were relatively ordered. However, the spectra of the metastases were more diverse than those of normal ovarian DNAs in regions assigned to base vibrations, implying increased genetic changes. DNAs of normal female breasts were substantially disordered (e.g., compared with the human blood leukocytes) as were those of the primary carcinomas, whether or not they had metastasized. The DNAs of distant breast cancer metastases were relatively ordered. These findings evoke a unified theory of carcinogenesis in which the creation of disorder in the DNA structure is an obligatory process followed by the selection of ordered, mutated DNA forms that ultimately give rise to metastases.

Methods of visualization and analysis of cardiac depolarization in the three dimensional space

The master thesis presents methods for intellectual analysis and visualization 3D EKG in order to increase the efficiency of ECG analysis by extracting additional data. Visualization is presented as part of the signal analysis tasks considered imaging techniques and their mathematical description. Have been developed algorithms for calculating and visualizing the signal attributes are described using mathematical methods and tools for mining signal. The model of patterns searching for comparison purposes of accuracy of methods was constructed, problems of a clustering and classification of data are solved, the program of visualization of data is also developed. This approach gives the largest accuracy in a task of the intellectual analysis that is confirmed in this work. Considered visualization and analysis techniques are also applicable to the multi-dimensional signals of a different kind.

Methods of Visualization and Analysis of Cardiac Depolarization in the Three Dimensional Space

Research analysis of electrocardiograms (ECG) today is carried out mostly using time depending signals of different leads shown in the graphs. Definition of ECG parameters is performed by qualified personnel, and requiring particular skills. To support decoding the cardiac depolarization phase of ECG there are methods to analyze space-time convolution charts in three dimensions where the heartbeat is described by the trajectory of its electrical vector. Based on this, it can be assumed that all available options of the classical ECG analysis of this time segment can be obtained using this technique. Investigated ECG visualization techniques in three dimensions combined with quantitative methods giving additional features of cardiac depolarization and allow a better exploitation of the information content of the given ECG signals.

(Table 2) Fauna Harpacticoida and its distribution in the Kandalaksha Bay (White Sea) in July 1998 and 1999

Dependence of the faunal composition and species structure of the White Sea littoral Harpacticoida on sediment properties was studied. Three groups of species could be distinguished according to their relationship with sediment properties: (1) species typical of silty sediments, (2) species preferring sediments with high gravel content, and (3) species inhabiting well-sorted washed sands. Vertical distribution of crustaceans within sediments of different types was studied. Vertical migrations of harpacticoids (3) during the tidal cycle were described. Data on interannual variability of harpacticoid fauna are presented.

An investigation of contaminant distribution in subsurface marine wood waste at Mill-A South Terminal, Port of Everett, Washington

This project investigates the correlation between contaminants and the wood waste present in marine sediments off the shore of the Port of Everett in the former Weyerhaeuser Mill-A pulp mill site. The investigation includes the results of two field studies, which tested contaminant levels in 22 boreholes as well as several surface samples. The contaminants include heavy metals and wood waste byproducts. These results, along with 14 other bore logs, provide the framework for a three-dimensional site model, interpolating the full extent of the depositional units and organic and inorganic chemicals found at Mill-A. The sediments of interest are divided into five depositional units defined by the percent wood content and type of wood: native material (<5% wood), intermediate (<30% wood), sawdust (<30% wood), woodchips (<30% wood), and poorly sorted sands with silt (SM-SP) (0% wood). The contaminants include arsenic, 2,4-dimethylphenol, and total organic carbon. Three-dimensional modeling software, RockWorks, interpolated the discrete borehole data of sediment and contaminants assuming horizontal continuity between sampling locations. The sediment distribution was calculated within concentration ranges for each contaminant of concern. The lowest detection limits, the screening levels, and the cleanup levels defined these ranges. Total organic carbon served as a proxy to estimate the quantity of wood waste in the sediment. As a known byproduct of wood decomposition, 2,4-dimethylphenol was expected to be more prevalent in the depositional units with more wood waste. Finally, arsenic was a proxy for other contaminants to determine if contaminants at Mill-A are dominant in sediments with high percentages of wood waste. The volumetric distribution established that high levels of total organic carbon are present in the sediment with higher percentages of wood waste. This correlation was stronger in the decomposing sawdust-rich sediment than the woodchip-rich sediment. The 2,4-dimethylphenol concentrations above cleanup standards were dominant in the sawdust-rich, intermediate and native sediments. Concentrations of 2,4-dimethylphenol below cleanup levels characterized the native sediment. The distribution of arsenic showed no statistically significant correlation to wood content in sediment. These results do not support the hypothesis of contaminant-rich wood waste, as many of the high concentrations of contaminants were not in the wood-rich sediments. This suggests that the contaminants are more distributed among all depositional units at Mill-A rather than focused within sediments with a high percent of wood waste. Understanding the distribution of potentially toxic compounds with wood waste is important for restoring the Puget Sound waterways to a more habitable environment. Future studies should include new data to validate these results and to limit the uncertainty of the extent of contaminants. Future studies may also find motive in looking for a correlation between contaminants and grain size based on previous studies linking these characteristics. These investigations will benefit the current cleanup effort as well as future cleanup efforts at similarly contaminated waterways.

A new method for identification of protein (sub)families in a set of proteins based on hydropathy distribution in proteins

Structural similarity among proteins is reflected in the distribution of hydropathicity along the amino acids in the protein sequence. Similarities in the hydropathy distributions are obvious for homologous proteins within a protein family. They also were observed for proteins with related structures, even when sequence similarities were undetectable. Here we present a novel method that employs the hydropathy distribution in proteins for identification of (sub)families in a set of (homologous) proteins. We represent proteins as points in a generalized hydropathy space, represented by vectors of specifically defined features. The features are derived from hydropathy of the individual amino acids. Projection of this space onto principal axes reveals groups of proteins with related hydropathy distributions. The groups identified correspond well to families of structurally and functionally related proteins. We found that this method accurately identifies protein families in a set of proteins, or subfamilies in a set of homologous proteins. Our results show that protein families can be identified by the analysis of hydropathy distribution, without the need for sequence alignment. (C) 2005 Wiley-Liss, Inc.

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

Resource partitioning in sympatric delphinids: Space use and habitat preferences of Australian snubfin and Indo-Pacific humpback dolphins

1. Many species of delphinids co-occur in space and time. However, little is known of their ecological interactions and the underlying mechanisms that mediate their coexistence. 2. Snubfin Orcaella heinsohni, and Indo-Pacific humpback dolphins Sousa chinensis, live in sympatry throughout most of their range in Australian waters. I conducted boat-based surveys in Cleveland Bay, north-east Queensland, to collect data on the space and habitat use of both species. Using Geographic Information Systems, kernel methods and Euclidean distances I investigated interspecific differences in their space use patterns, behaviour and habitat preferences. 3. Core areas of use (50% kernel range) for both species were located close to river mouths and modified habitat such as dredged channels and breakwaters close to the Port of Townsville. Foraging and travelling activities were the dominant behavioural activities of snubfin and humpback dolphins within and outside their core areas. 4. Their representative ranges (95% kernel range) overlapped considerably, with shared areas showing strong concordance in the space use by both species. Nevertheless, snubfin dolphins preferred slightly shallower (1-2 m) waters than humpback dolphins (2-5 m). Additionally, shallow areas with seagrass ranked high in the habitat preferences of snubfin dolphins, whereas humpback dolphins favoured dredged channels. 5. Slight differences in habitat preferences appear to be one of the principal factors maintaining the coexistence of snubfin and humpback dolphins. I suggest diet partitioning and interspecific aggression as the major forces determining habitat selection in these sympatric species.

Estimation of uncertainty in three-dimensional coordinate measurement by comparison with calibrated points

This paper details a method of estimating the uncertainty of dimensional measurement for a three-dimensional coordinate measurement machine. An experimental procedure was developed to compare three-dimensional coordinate measurements with calibrated reference points. The reference standard used to calibrate these reference points was a fringe counting interferometer with a multilateration-like technique employed to establish three-dimensional coordinates. This is an extension of the established technique of comparing measured lengths with calibrated lengths. Specifically a distributed coordinate measurement device was tested which consisted of a network of Rotary-Laser Automatic Theodolites (R-LATs), this system is known commercially as indoor GPS (iGPS). The method was found to be practical and was used to estimate that the uncertainty of measurement for the basic iGPS system is approximately 1 mm at a 95% confidence level throughout a measurement volume of approximately 10 m × 10 m × 1.5 m. © 2010 IOP Publishing Ltd.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.