Experiences in Multi-domain Management Service Development

The developers of management systems and the management services that operate over them will be faced with increasing complexity as services are developed for the open service market. This paper presents experiences in the development of management services that span several administrative domains and which are therefore representative of the complexities of the open service market. The work described involved the development of TMN based management systems that provided management services in support of multimedia teleservices operating over broadband networks

Smart lighting in multipurpose outdoor environments: energy efficient solution using network of cooperating objects

The first applications for smart environments targeted wellscoped spaces and appliances. These applications were strong drivers to advance Wireless Sensor Networks (WSN) and the Internet of Things (IoT). With the evolution of the technological base, more complex environments became the new targets. The concept of cooperating objects (CO) enables further advancement of IoT and helps to grasp the multiple aspects of these environments. This paper describes smart lighting application for the multipurpose outdoor environment at the university campus area implemented following the new paradigm. The application is aiming efficient use of energy and future integration with associated industrial systems

First results in terrain mapping for a roving planetary explorer

To perform planetary exploration without human supervision, a complete autonomous rover must be able to model its environment while exploring its surroundings. Researchers present a new algorithm to construct a geometric terrain representation from a single range image. The form of the representation is an elevation map that includes uncertainty, unknown areas, and local features. By virtue of working in spherical-polar space, the algorithm is independent of the desired map resolution and the orientation of the sensor, unlike other algorithms that work in Cartesian space. They also describe new methods to evaluate regions of the constructed elevation maps to support legged locomotion over rough terrain

Discovering user mobility and activity in smart lighting environments

"Smart lighting" environments seek to improve energy efficiency, human productivity and health by combining sensors, controls, and Internet-enabled lights with emerging “Internet-of-Things” technology. Interesting and potentially impactful applications involve adaptive lighting that responds to individual occupants' location, mobility and activity. In this dissertation, we focus on the recognition of user mobility and activity using sensing modalities and analytical techniques. This dissertation encompasses prior work using body-worn inertial sensors in one study, followed by smart-lighting inspired infrastructure sensors deployed with lights. The first approach employs wearable inertial sensors and body area networks that monitor human activities with a user's smart devices. Real-time algorithms are developed to (1) estimate angles of excess forward lean to prevent risk of falls, (2) identify functional activities, including postures, locomotion, and transitions, and (3) capture gait parameters. Two human activity datasets are collected from 10 healthy young adults and 297 elder subjects, respectively, for laboratory validation and real-world evaluation. Results show that these algorithms can identify all functional activities accurately with a sensitivity of 98.96% on the 10-subject dataset, and can detect walking activities and gait parameters consistently with high test-retest reliability (p-value < 0.001) on the 297-subject dataset. The second approach leverages pervasive "smart lighting" infrastructure to track human location and predict activities. A use case oriented design methodology is considered to guide the design of sensor operation parameters for localization performance metrics from a system perspective. Integrating a network of low-resolution time-of-flight sensors in ceiling fixtures, a recursive 3D location estimation formulation is established that links a physical indoor space to an analytical simulation framework. Based on indoor location information, a label-free clustering-based method is developed to learn user behaviors and activity patterns. Location datasets are collected when users are performing unconstrained and uninstructed activities in the smart lighting testbed under different layout configurations. Results show that the activity recognition performance measured in terms of CCR ranges from approximately 90% to 100% throughout a wide range of spatio-temporal resolutions on these location datasets, insensitive to the reconfiguration of environment layout and the presence of multiple users.2017-02-17T00:00:00

Remote sensing information sciences research group

Research conducted under this grant was used to extend and expand existing remote sensing activities at the University of California, Santa Barbara in the areas of georeferenced information systems, matching assisted information extraction from image data and large spatial data bases, artificial intelligence, and vegetation analysis and modeling. The research thrusts during the past year are summarized. The projects are discussed in some detail

A perception and manipulation system for collecting rock samples

An important part of a planetary exploration mission is to collect and analyze surface samples. As part of the Carnegie Mellon University Ambler Project, researchers are investigating techniques for collecting samples using a robot arm and a range sensor. The aim of this work is to make the sample collection operation fully autonomous. Described here are the components of the experimental system, including a perception module that extracts objects of interest from range images and produces models of their shapes, and a manipulation module that enables the system to pick up the objects identified by the perception module. The system was tested on a small testbed using natural terrain

Gaussian mixture model classifiers for detection and tracking in UAV video streams.

Masters Degree. University of KwaZulu-Natal, Durban.Manual visual surveillance systems are subject to a high degree of human-error and operator fatigue. The automation of such systems often employs detectors, trackers and classifiers as fundamental building blocks. Detection, tracking and classification are especially useful and challenging in Unmanned Aerial Vehicle (UAV) based surveillance systems. Previous solutions have addressed challenges via complex classification methods. This dissertation proposes less complex Gaussian Mixture Model (GMM) based classifiers that can simplify the process; where data is represented as a reduced set of model parameters, and classification is performed in the low dimensionality parameter-space. The specification and adoption of GMM based classifiers on the UAV visual tracking feature space formed the principal contribution of the work. This methodology can be generalised to other feature spaces. This dissertation presents two main contributions in the form of submissions to ISI accredited journals. In the first paper, objectives are demonstrated with a vehicle detector incorporating a two stage GMM classifier, applied to a single feature space, namely Histogram of Oriented Gradients (HoG). While the second paper demonstrates objectives with a vehicle tracker using colour histograms (in RGB and HSV), with Gaussian Mixture Model (GMM) classifiers and a Kalman filter. The proposed works are comparable to related works with testing performed on benchmark datasets. In the tracking domain for such platforms, tracking alone is insufficient. Adaptive detection and classification can assist in search space reduction, building of knowledge priors and improved target representations. Results show that the proposed approach improves performance and robustness. Findings also indicate potential further enhancements such as a multi-mode tracker with global and local tracking based on a combination of both papers

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world