Capturing Data Uncertainty in High-Volume Stream Processing

We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous random variables. For such data, our system employs an approach grounded in probability and statistical theory to capture data uncertainty and integrates this approach into high-volume stream processing. The first component of our system captures uncertainty of raw data streams from sensing devices. Since such raw streams can be highly noisy and may not carry sufficient information for query processing, our system employs probabilistic models of the data generation process and stream-speed inference to transform raw data into a desired format with an uncertainty metric. The second component captures uncertainty as data propagates through query operators. To efficiently quantify result uncertainty of a query operator, we explore a variety of techniques based on probability and statistical theory to compute the result distribution at stream speed. We are currently working with a group of scientists to evaluate our system using traces collected from the domains of (and eventually in the real systems for) hazardous weather monitoring and object tracking and monitoring.Comment: CIDR 200

The design and development of multi-agent based RFID middleware system for data and devices management

Thesis (D. Tech. (Electrical Engineering)) - Central University of technology, Free State, 2012Radio frequency identification technology (RFID) has emerged as a key technology for automatic identification and promises to revolutionize business processes. While RFID technology adoption is improving rapidly, reliable and widespread deployment of this technology still faces many significant challenges. The key deployment challenges include how to use the simple, unreliable raw data generated by RFID deployments to make business decisions; and how to manage a large number of deployed RFID devices. In this thesis, a multi-agent based RFID middleware which addresses some of the RFID data and device management challenges was developed. The middleware developed abstracts the auto-identification applications from physical RFID device specific details and provides necessary services such as device management, data cleaning, event generation, query capabilities and event persistence. The use of software agent technology offers a more scalable and distributed system architecture for the proposed middleware. As part of a multi-agent system, application-independent domain ontology for RFID devices was developed. This ontology can be used or extended in any application interested with RFID domain ontology. In order to address the event processing tasks within the proposed middleware system, a temporal-based RFID data model which considers both applications’ temporal and spatial granules in the data model itself for efficient event processing was developed. The developed data model extends the conventional Entity-Relationship constructs by adding a time attribute to the model. By maintaining the history of events and state changes, the data model captures the fundamental RFID application logic within the data model. Hence, this new data model supports efficient generation of application level events, updating, querying and analysis of both recent and historical events. As part of the RFID middleware, an adaptive sliding-window based data cleaning scheme for reducing missed readings from RFID data streams (called WSTD) was also developed. The WSTD scheme models the unreliability of the RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. The WSTD scheme is capable of efficiently coping with both environmental variations and tag dynamics by automatically and continuously adapting its cleaning window size, based on observed readings

People Detection and Tracking with Kinect for Mobile Platforms

Human detection is a key ability for robot applications that operate in environments where people are present, or in situation where those applications are requested to interact with them. It’s the case for social robots like aids for the rehabilitation of inmates in hospitals, assistance in office, guides for museum tours. In this thesis we will investigate on how we can make use of the new Microsoft’s gaming sensor, the Kinect, to address the issues of real-time people detection and tracking, since the sensor has been built in order to detect people and track their movements. We developed a system that is able of detecting and tracking people in near real-time both on fixed environments and mobile platforms. We tested four different classifiers on different situations. The best classifier showed very good detection and tracking results whereas, because of some segmentation problems, the performances of the complete system have been subjected to a lowering with respect to the theoretical ones. We developed also a method for getting rid of some of these segmentation problems and it showed some improvements for the complete system together with some drawbacks that affected the theoretical results. However the complete system works good and with a frame rate of 2 fps on average. Most of the computational load is due again to the segmentation module, so an improvement of this module would lead to both improvements on the real-time performances and on the detection result

Survey on video anomaly detection in dynamic scenes with moving cameras

The increasing popularity of compact and inexpensive cameras, e.g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras. However, existing reviews primarily concentrate on Video Anomaly Detection (VAD) methods assuming static cameras. The VAD literature with moving cameras remains fragmented, lacking comprehensive reviews to date. To address this gap, we endeavor to present the first comprehensive survey on Moving Camera Video Anomaly Detection (MC-VAD). We delve into the research papers related to MC-VAD, critically assessing their limitations and highlighting associated challenges. Our exploration encompasses three application domains: security, urban transportation, and marine environments, which in turn cover six specific tasks. We compile an extensive list of 25 publicly-available datasets spanning four distinct environments: underwater, water surface, ground, and aerial. We summarize the types of anomalies these datasets correspond to or contain, and present five main categories of approaches for detecting such anomalies. Lastly, we identify future research directions and discuss novel contributions that could advance the field of MC-VAD. With this survey, we aim to offer a valuable reference for researchers and practitioners striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair

3D visualization of in-flight recorded data.

Human being can easily acquire information by showing the object than reading the description of it. Our brain stores images that the eyes are seeing and by the brain mapping, people can analyze information by imagination in the brain. This is the reason why visualization is important and powerful. It helps people remember the scene later. Visualization transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations (Flurchick, 2001). As a consequence, many computer scientists and programmers take their time to build better visualization of the data for users. For the flight data from an aircraft, it is better to understand data in 3D computer graphics rather than to look at mere numbers. The flight data consists of several fields such as elapsed time, latitude, longitude, altitude, ground speed, roll angle, pitch angle, heading, wind speed, and so on. With these data variables, filtering is the first process for visualization in order to gather important information. The collection of processed data is transformed to 3D graphics form to be rendered by generating Keyhole Mark-up Language (KML) files in the system. KML is an XML grammar and file format for modeling and storing geographic features such as points, lines, images, polygons, and models for display in Google Earth or Google Maps. Like HTML, KML has a tag-based structure with names and attributes used for specific display purposes. In the present work, new approaches to visualize flight using Google Earth are developed. Because of the limitation of the Google Earth API, the Great Circle Distance calculation and trigonometric functions are implemented to handle the position, angles of roll and pitch, and a range of the camera positions to generate several points of view. Currently, visual representation of flight data depends on 2D graphics although an aircraft flies in a 3D space. The graphical interface allows flight analysts to create ground traces in 2D, and flight ribbons and flight paths with altitude in 3D. Additionally, by incorporating weather information, fog and clouds can also be generated as part of the animation effects. With 3D stereoscopic technique, a realistic visual representation of the flights is realized

Outdoor navigation of mobile robots

AGVs in the manufacturing industry currently constitute the largest application area for mobile robots. Other applications have been gradually emerging, including various transporting tasks in demanding environments, such as mines or harbours. Most of the new potential applications require a free-ranging navigation system, which means that the path of a robot is no longer bound to follow a buried inductive cable. Moreover, changing the route of a robot or taking a new working area into use must be as effective as possible. These requirements set new challenges for the navigation systems of mobile robots. One of the basic methods of building a free ranging navigation system is to combine dead reckoning navigation with the detection of beacons at known locations. This approach is the backbone of the navigation systems in this study. The study describes research and development work in the area of mobile robotics including the applications in forestry, agriculture, mining, and transportation in a factory yard. The focus is on describing navigation sensors and methods for position and heading estimation by fusing dead reckoning and beacon detection information. A Kalman filter is typically used here for sensor fusion. Both cases of using either artificial or natural beacons have been covered. Artificial beacons used in the research and development projects include specially designed flat objects to be detected using a camera as the detection sensor, GPS satellite positioning system, and passive transponders buried in the ground along the route of a robot. The walls in a mine tunnel have been used as natural beacons. In this case, special attention has been paid to map building and using the map for positioning. The main contribution of the study is in describing the structure of a working navigation system, including positioning and position control. The navigation system for mining application, in particular, contains some unique features that provide an easy-to-use procedure for taking new production areas into use and making it possible to drive a heavy mining machine autonomously at speed comparable to an experienced human driver.reviewe