A neural network for mining large volumes of time series data

Efficiently mining large volumes of time series data is amongst the most challenging problems that are fundamental in many fields such as industrial process monitoring, medical data analysis and business forecasting. This paper discusses a high-performance neural network for mining large time series data set and some practical issues on time series data mining. Examples of how this technology is used to search the engine data within a major UK eScience Grid project (DAME) for supporting the maintenance of Rolls-Royce aero-engine are presented

Vehicle Keypoint Detection and Fine-Grained Classification using Deep Learning

Los sistemas de detección de puntos clave en vehículos y de clasificación por marca y modelo han visto como sus capacidades evolucionaban a un ritmo nunca antes visto, pasando de rendimientos pobres a resultados increíbles en cuestión de unos años. La irrupción de las redes neuronales convolucionales y la disponibilidad de datos y sistemas de procesamiento cada vez más potentes han permitido que, mediante el uso de modelos cada vez más complejos, estos y muchos otros problemas sean afrontados y resueltos con enfoques muy diversos. Esta tesis se centra en el problema de detección de puntos clave y clasificación a nivel de marca y modelo de vehículos con un enfoque basado en aprendizaje profundo. Tras el análisis de los conjuntos datos existentes para afrontar ambas tareas se ha optado por crear tres bases de datos específicas. La primera, orientada a la detección de puntos clave en vehículos, es una mejora y extensión del famoso conjunto de datos PASCAL3D+, reetiquetando parte del mismo y añadiendo nuevos keypoints e imágenes para aportar mayor variabilidad. La segunda, se trata de un conjunto de prueba de clasificación de vehículos por marca y modelo basado en The PREVENTION dataset, una base de datos de predicción de trayectoria de vehículos en entornos de circulación real. Por último, un conjunto de datos cruzados (Cross-dataset) compuesto por las marcas y modelos comunes de tres de las principales bases de datos de clasificación de vehículos, CompCars, VMMR-db y Frontal-103. El sistema de detección de puntos clave se basa en un método de detección de pose en humanos que mediante el uso de redes neuronales convolucionales y capas de-convolucionales genera, a partir de una imagen de entrada, un mapa de calor por cada punto clave. La red ha sido modificada para ajustarse al problema de detección de puntos clave en vehículos obteniendo resultados que mejoran el estado del arte sin hacer uso de complejas arquitecturas o metodologías. Adicionalmente se ha analizado la idoneidad de los puntos clave de PASCAL3D+, validando la propuesta de nuevos puntos clave como una mejor alternativa. El sistema de clasificación de vehículos por marca y modelo se basa en el uso de redes preentrenadas en el famoso conjunto de datos ImageNet y adaptadas al problema de clasificación de vehículos. Uno de los problemas detectados en el estado del arte es la saturación de los resultados en las bases de datos existentes que, por otra parte, se encuentran sesgadas, limitando la capacidad de generalización de los modelos entrenados con ellas. Se han usado múltiples técnicas de aprendizaje y ponderación de los datos para tratar de aliviar el impacto del sesgo de los conjuntos de datos. Para poder evaluar la capacidad de generalización en situaciones reales de los modelos entrenados, se ha hecho uso del conjunto de pruebas derivado del PREVENTION dataset. Adicionalmente, se ha hecho uso del Cross-dataset para evaluar la complejidad de las bases de datos existentes y las capacidades de generalización de los modelos entrenados con ellas. Se demuestra que, sin hacer uso de complejas arquitecturas, se pueden obtener resultados competitivos y la necesidad de un conjunto de datos que refleje de manera adecuada el mundo real para poder afrontar adecuadamente el problema de clasificación de vehículos.Vehicle keypoint detection and fine-grained classification systems have seen their capabilities evolve at an unprecedented rate, from poor performance to incredible results in a matter of a few years. The advent of convolutional neural networks and the availability of large amounts of data and progress in computational capabilities have allowed these and many other problems to be tackled and solved with very different approaches using increasingly complex models. This thesis focuses on the problems of keypoint detection and fine-grained classification of vehicles with a deep learning approach. After the analysis of the existing datasets to tackle both tasks, three new datasets have been built. The first one, oriented to the detection of keypoints in vehicles, is an improvement and extension of the famous PASCAL3D+ dataset, re-labelling part of it and adding new keypoints and images to provide more variability. The second is a vehicle make and model classification test set based on the PREVENTION dataset, a realworld driving scenario vehicle trajectory prediction dataset. Finally, a cross-dataset composed of common makes and models from three major vehicle classification databases, CompCars, VMMR-db and Frontal-103. The keypoint detection system is based on a human pose detection method that by using convolutional neural networks and deconvolutional layers generates, from an input image, a heat map for each keypoint. The network has been modified to fit the problem of keypoint detection in vehicles obtaining results that improve the state of the art without using complex architectures or methodologies. Additionally, the suitability of the PASCAL3D+ keypoints has been analysed, validating the proposal of new keypoints as a better alternative. The vehicle make and model classification system is based on the use of ImageNet pre-trained networks and fine-tuned for the vehicle classification problem. One of the problems detected in the state of the art is the saturation of the results in the existing datasets, which, moreover, are biased, limiting the generalisation capacity of the models trained with them. Multiple data learning and weighting techniques have been used to try to alleviate the impact of dataset bias. In order to assess the generalisation capabilities of the trained models in real situations, the PREVENTION test set has been used. Additionally, the cross-dataset has been used to evaluate the complexity of the existing datasets and the generalisation capabilities of the models trained with them. It is shown that competitive results can be achieved without the use of complex architectures and that a high quality dataset that adequately reflects the real world is needed in order to properly address the vehicle classification problem

The 1991/92 graduate student researchers program, including the underrepresented minority focus component

The Graduate Student Research Program (GSRP) was expanded in 1987 to include the Underrepresented Minority Focus Component (UMFC). This program was designed to increase minority participation in graduate study and research, and ultimately, in space science and aerospace technology careers. This booklet presents the areas of research activities at NASA facilities for the GSRP and summarizes and presents the objectives of the UMFC

Characterizing motor control signals in the spinal cord

The main goal of this project is to develop a rodent model to study the central command signals generated in the brain and spinal cord for the control of motor function in the forearms. The nature of the central command signal has been debated for many decades with only limited progress. This thesis presents a project that investigated this problem using novel techniques. Rats are instrumented to record the control signals in their spinal cord while they are performing lever press task they are trained in. A haptic interface and wireless neural data amplifier system simultaneously collects dynamic and neural data. Isometric force is predicted from force signal using a combination of time-frequency analysis, Principle component analysis and linear filters. Neural-force mapping obtained at one location are subsequently applied to isometric data recorded at other locations. Prediction errors exhibited negative relationship with the isometric position at upper half of movement range. This suggests the presence of restorative forces which are consistent with positional feedback at spinal level. The animal also appears to become unstable in the lower half of their movement ranges, likely caused by a transition from bipedal to quadruped posture. The presence of local feedback and ability for animals to plan postures that are unstable in absence of external forces suggest that descending signal is a reference trajectory planned using internal models. This has important consequences in design of neuroprosthetic actuators: Inverse dynamic models of patient limbs and local positional feedbacks can improve their performance

C-9 and Other Microgravity Simulations

This document represents a summary of medical and scientific evaluations conducted aboard the C-9 or other NASA-sponsored aircraft from June 30, 2006, to June 30, 2007. Included is a general overview of investigations manifested and coordinated by the Human Adaptation and Countermeasures Office. A collection of brief reports that describe tests conducted aboard the NASA-sponsored aircraft follows the overview. Principal investigators and test engineers contributed significantly to the content of the report, describing their particular experiment or hardware evaluation. Although this document follows general guidelines, each report format may vary to accommodate differences in experiment design and procedures. This document concludes with an appendix that provides background information about the Reduced Gravity Program

Toward Global Localization of Unmanned Aircraft Systems using Overhead Image Registration with Deep Learning Convolutional Neural Networks

Global localization, in which an unmanned aircraft system (UAS) estimates its unknown current location without access to its take-off location or other locational data from its flight path, is a challenging problem. This research brings together aspects from the remote sensing, geoinformatics, and machine learning disciplines by framing the global localization problem as a geospatial image registration problem in which overhead aerial and satellite imagery serve as a proxy for UAS imagery. A literature review is conducted covering the use of deep learning convolutional neural networks (DLCNN) with global localization and other related geospatial imagery applications. Differences between geospatial imagery taken from the overhead perspective and terrestrial imagery are discussed, as well as difficulties in using geospatial overhead imagery for image registration due to a lack of suitable machine learning datasets. Geospatial analysis is conducted to identify suitable areas for future UAS imagery collection. One of these areas, Jerusalem northeast (JNE) is selected as the area of interest (AOI) for this research. Multi-modal, multi-temporal, and multi-resolution geospatial overhead imagery is aggregated from a variety of publicly available sources and processed to create a controlled image dataset called Jerusalem northeast rural controlled imagery (JNE RCI). JNE RCI is tested with handcrafted feature-based methods SURF and SIFT and a non-handcrafted feature-based pre-trained fine-tuned VGG-16 DLCNN on coarse-grained image registration. Both handcrafted and non-handcrafted feature based methods had difficulty with the coarse-grained registration process. The format of JNE RCI is determined to be unsuitable for the coarse-grained registration process with DLCNNs and the process to create a new supervised machine learning dataset, Jerusalem northeast machine learning (JNE ML) is covered in detail. A multi-resolution grid based approach is used, where each grid cell ID is treated as the supervised training label for that respective resolution. Pre-trained fine-tuned VGG-16 DLCNNs, two custom architecture two-channel DLCNNs, and a custom chain DLCNN are trained on JNE ML for each spatial resolution of subimages in the dataset. All DLCNNs used could more accurately coarsely register the JNE ML subimages compared to the pre-trained fine-tuned VGG-16 DLCNN on JNE RCI. This shows the process for creating JNE ML is valid and is suitable for using machine learning with the coarse-grained registration problem. All custom architecture two-channel DLCNNs and the custom chain DLCNN were able to more accurately coarsely register the JNE ML subimages compared to the fine-tuned pre-trained VGG-16 approach. Both the two-channel custom DLCNNs and the chain DLCNN were able to generalize well to new imagery that these networks had not previously trained on. Through the contributions of this research, a foundation is laid for future work to be conducted on the UAS global localization problem within the rural forested JNE AOI

The 1993/1994 NASA Graduate Student Researchers Program

The NASA Graduate Student Researchers Program (GSRP) attempts to reach a culturally diverse group of promising U.S. graduate students whose research interests are compatible with NASA's programs in space science and aerospace technology. Each year we select approximately 100 new awardees based on competitive evaluation of their academic qualifications, their proposed research plan and/or plan of study, and their planned utilization of NASA research facilities. Fellowships of up to $22,000 are awarded for one year and are renewable, based on satisfactory progress, for a total of three years. Approximately 300 graduate students are, thus, supported by this program at any one time. Students may apply any time during their graduate career or prior to receiving their baccalaureate degree. An applicant must be sponsored by his/her graduate department chair or faculty advisor; this book discusses the GSRP in great detail

Design and optimisation of a low cost Cognitive Mesh Network

Wireless Mesh Networks (WMNs) have been touted as the most promising wireless technology in providing high-bandwidth Internet access to rural, remote and under-served areas, with relatively lower investment cost as compared to traditional access networks. WMNs structurally comprise of mesh routers and mesh clients. Furthermore, WMNs have an envisaged ability to provide a heterogeneous network system that integrates wireless technologies such as IEEE 802.22 WRAN, IEEE 802.16 WiMAX, IEEE 802.11 Wi-Fi, Blue-tooth etc. The recent proliferation of new devices on the market such as smart phones and, tablets, and the growing number of resource hungry applications has placed a serious strain on spectrum availability which gives rise to the spectrum scarcity problem. The spectrum scarcity problem essentially results in increased spectrum prices that hamper the growth and efficient performance of WMNs as well as subsequent transformation of WMN into the envisaged next generation networks. Recent developments in TV white space communications technology and the emergence of Cognitive radio devices that facilitate Dynamic Spectrum Access (DSA) have provided an opportunity to mitigate the spectrum scarcity problem. To solve the scarcity problem, this thesis reconsiders the classical Network Engineering (NE) and Traffic Engineering (TE) problems to objectively design a low cost Cognitive Mesh network that promotes efficient resources utilization and thereby achieve better Quality of Service (QoS) levels