Efficient query processing on large spatial databases A performance study

Processing of spatial queries has been studied extensively in the literature. In most cases, it is accomplished by indexing spatial data using spatial access methods. Spatial indexes, such as those based on the Quadtree, are important in spatial databases for efficient execution of queries involving spatial constraints and objects. In this paper, we study a recent balanced disk-based index structure for point data, called xBR + -tree, that belongs to the Quadtree family and hierarchically decomposes space in a regular manner. For the most common spatial queries, like Point Location, Window, Distance Range, Nearest Neighbor and Distance-based Join, the R-tree family is a very popular choice of spatial index, due to its excellent query performance. For this reason, we compare the performance of the xBR + -tree with respect to the R ∗ -tree and the R + -tree for tree building and processing the most studied spatial queries. To perform this comparison, we utilize existing algorithms and present new ones. We demonstrate through extensive experimental performance results (I/O efficiency and execution time), based on medium and large real and synthetic datasets, that the xBR + -tree is a big winner in execution time in all cases and a winner in I/O in most cases

An Efficient Algorithm for Bulk-Loading xBR+ -trees

A major part of the interface to a database is made up of the queries that can be addressed to this database and answered (processed) in an efficient way, contributing to the quality of the developed software. Efficiently processed spatial queries constitute a fundamental part of the interface to spatial databases due to the wide area of applications that may address such queries, like geographical information systems (GIS), location-based services, computer visualization, automated mapping, facilities management, etc. Another important capability of the interface to a spatial database is to offer the creation of efficient index structures to speed up spatial query processing. The xBR + -tree is a balanced disk-resident quadtree-based index structure for point data, which is very efficient for processing such queries. Bulk-loading refers to the process of creating an index from scratch, when the dataset to be indexed is available beforehand, instead of creating the index gradually (and more slowly), when the dataset elements are inserted one-by-one. In this paper, we present an algorithm for bulk-loading xBR + -trees for big datasets residing on disk, using a limited amount of main memory. The resulting tree is not only built fast, but exhibits high performance in processing a broad range of spatial queries, where one or two datasets are involved. To justify these characteristics, using real and artificial datasets of various cardinalities, first, we present an experimental comparison of this algorithm vs. a previous version of the same algorithm and STR, a popular algorithm of bulk-loading R-trees, regarding tree creation time and the characteristics of the trees created, and second, we experimentally compare the query efficiency of bulk-loaded xBR + -trees vs. bulk-loaded R-trees, regarding I/O and execution time. Thus, this paper contributes to the implementation of spatial database interfaces and the efficient storage organization for big spatial data management

Bulk Insertions into xBR+ -trees

Bulk insertion refers to the process of updating an existing index by inserting a large batch of new data, treating the items of this batch as a whole and not by inserting these items one-by-one. Bulk insertion is related to bulk loading, which refers to the process of creating a non-existing index from scratch, when the dataset to be indexed is available beforehand. The xBR + -tree is a balanced, disk-resident, Quadtree-based index for point data, which is very efficient for processing spatial queries. In this paper, we present the first algorithm for bulk insertion into xBR+ -trees. This algorithm incorporates extensions of techniques that we have recently developed for bulk loading xBR+ -trees. Moreover, using real and artificial datasets of various cardinalities, we present an experimental comparison of this algorithm vs. inserting items one-by-one for updating xBR+ -trees, regarding performance (I/O and execution time) and the characteristics of the resulting trees. We also present experimental results regarding the query-processing efficiency of xBR+ -trees built by bulk insertions vs. xBR+ -trees built by inserting items one-by-one

Return Predictability and the Implied Intertemporal Hedging Demands for Stocks and Bonds: International Evidence

We investigate return predictability and the implied intertemporal hedging demands for stocks and bonds in the U.S., Australia, Canada, France, Germany, Italy, and U.K. We first estimate predictive regression models for domestic bill, stock, and bond returns in each country, where returns depend on the nominal bill yield, dividend yield, and term spread. Employing the recently developed methodology of Campbell, Chan, and Viceira (2003), we calculate the implied optimal asset demands, including their myopic and intertemporal hedging components, for domestic bills, stocks, and bonds for an investor with an infinite horizon and Epstein-Zin-Weil utility in each country. We find that return predictability generates sizable positive intertemporal hedging demands for domestic stocks in the U.S. and U.K., while the intertemporal hedging demands for domestic stocks are decidedly smaller in Australia, Canada, and Germany and essentially zero in France and Italy. The intertemporal hedging demands for domestic bonds are negative and reasonably large in magnitude in the U.S., France, Germany, and Italy, while they are considerably smaller in magnitude in Australia, Canada, and the U.K. We also calculate optimal asset demands for an investor in the U.S. who, in addition to domestic bills, stocks, and bonds, has access to foreign stocks and bonds. We continue to find a sizable positive intertemporal hedging demand for U.S. stocks, and an important positive intertemporal hedging demand for U.K. stocks emerges. In another exercise, we find that investors in Australia, Canada, France, Germany, Italy, and the U.K. who have access to U.S. stocks and bonds all display sizable positive intertemporal hedging demands for U.S. stocks. Overall, we discover interesting similarities and differences in the implied intertemporal hedging demands for stocks and bonds across countries, and our results indicate that return predictability implies especially strong intertemporal hedging demands for U.S. and U.K. stocks

Efficient visual grasping alignment for cylinders

Monocular information from a gripper-mounted camera is used to servo the robot gripper to grasp a cylinder. The fundamental concept for rapid pose estimation is to reduce the amount of information that needs to be processed during each vision update interval. The grasping procedure is divided into four phases: learn, recognition, alignment, and approach. In the learn phase, a cylinder is placed in the gripper and the pose estimate is stored and later used as the servo target. This is performed once as a calibration step. The recognition phase verifies the presence of a cylinder in the camera field of view. An initial pose estimate is computed and uncluttered scan regions are selected. The radius of the cylinder is estimated by moving the robot a fixed distance toward the cylinder and observing the change in the image. The alignment phase processes only the scan regions obtained previously. Rapid pose estimates are used to align the robot with the cylinder at a fixed distance from it. The relative motion of the cylinder is used to generate an extrapolated pose-based trajectory for the robot controller. The approach phase guides the robot gripper to a grasping position. The cylinder can be grasped with a minimal reaction force and torque when only rough global pose information is initially available

Development of a 3G Authentication Based Mobile Access of Health Records: A Mobile Telemedicine Application

As our country progresses in its aim to be a developed country by the Year 2020, the field of Information and Communications Technology or ICT is fast becoming the forerunner for the vision. The Internet is used in almost all aspects of life. As for the communications sector, according to Global Mobile Subscriber Database December 2002 report, there are 8,814,700 mobile subscribers in Malaysia with an Annual Growth of 16.6%. Withthe adoption of 3G-communication technology in the coming years, compelling high speed services, reaching up to 2 Mb/s together with improved security features would soon be possible. Through these years in the mobile industry, the health sector has always been neglected. Reason being, the technology could not support the application and it is not so much of a revenue generating business compared to mobile games or sports news. With globalization where the society is always on the move across borders, together with degrading environment conditions and the need for time, instant health services are becoming crucial. Looking into these conditions of mobile adoption and health status, the author intends to develop a solution for a mobile telemedicine application. Kevin Hung (2003) defines telemedicine as the utilization of telecommunication technology for medical diagnosis, treatment and patient care. Thus, the main aim of this project was to develop an application that could be used for medical purposes. This project integrates the latest mobile telecommunication technologies together with medical services with the idea of providing a highly secured personalize medical system and database query as mobile handsets are becoming a necessity to individuals. This would make updating and retrieving medical health records hassle free, anytime and anywhere. This project has also laid the groundwork for future expansion by incorporating the basic audio and video streaming features. This report accounts for all the concepts, design works and results of the mobile telemedicine application that has been developed successfully

The principles and practice of the Xylophone Bar Magnetometer

PhD ThesisThis thesis reports on work undertaken to analyse, design, optimise, and fabricate a high-Quality factor mechanical resonant magnetometer, based on a Xylophone Bar Resonator (XBR). The principle of operation is based on the use of nodal supports to mechanically isolate a transverse beam vibrating in its fundamental mode. A control model is developed for the device, incorporating the effect of electromechanical parametric amplification. The device response and performance is shown to be strongly dependent on the Q factor of the sense element. The need for a quantitative model of XBR dynamics in order to design an optimal XBR is thus established. Using a Rayleigh-Ritz based approach, a model of the modal dynamics of an XBR is developed for the first time. In order to examine the efficacy of the nodal supports, a new model for support loss for resonators with two supports is developed and presented. Analytical models for other sources of dissipation are adapted for the first time to the XBR case. Combining these developments with a system level model allows for the development of a quantitative predictor of the fundamental and electronic noise limits on performance for an XBR. The model is solved over the operational range of geometric parameters, yielding optimisation criteria for the geometry. Corresponding predictions for the force and magnetic field sensitivity are presented. Based on the results, an optimised XBR design is exhibited for a macroscopic metal flexural XBM to be fabricated via Wire EDM. The fabricated devices are characterised, constituting the first demonstration of a macroscopic flexural XBR. The resulting Q factors and sensitivities are shown to be in agreement with the predictions. Fruitful directions for further work are suggested throughout the thesis and summarised in the conclusions. The original contribution to knowledge made by the thesis can be summarised as the development of an original and detailed theory of the principles of XBR optimisation for high Q, and demonstration of an operational macroscopic flexural XBM for the first time

Trajectory Optimization for Cellular-Enabled UAV with Connectivity and Battery Constraints

In this paper, we address the problem of path planning for a cellular-enabled UAV with connectivity and battery constraints. The UAV's mission is to deliver a payload from an initial point to a final point as soon as possible, while maintaining connectivity with a BS and adhering to the battery constraint. The UAV's battery can be replaced by a fully charged battery at a charging station, which may take some time depending on waiting time. Our key contribution lies in proposing an algorithm that efficiently computes an optimal UAV path in polynomial time. We achieve this by transforming the problem into an equivalent two-level shortest path finding problem over weighted graphs and leveraging graph theoretic approaches. In more detail, we first find an optimal path and speed to travel between each pair of charging stations without replacing the battery, and then find the optimal order of visiting charging stations. To demonstrate the effectiveness of our approach, we compare it with previously proposed algorithms and show that our algorithm outperforms those in terms of both computational complexity and performance. Furthermore, we propose another algorithm that computes the maximum payload weight that the UAV can deliver under the connectivity and battery constraints.Comment: This article was presented in part at the IEEE Vehicular Technology Conference (VTC) 2023-Fal