Human behavioural analysis with self-organizing map for ambient assisted living

This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints

Operational experience, improvements, and performance of the CDF Run II silicon vertex detector

The Collider Detector at Fermilab (CDF) pursues a broad physics program at Fermilab's Tevatron collider. Between Run II commissioning in early 2001 and the end of operations in September 2011, the Tevatron delivered 12 fb-1 of integrated luminosity of p-pbar collisions at sqrt(s)=1.96 TeV. Many physics analyses undertaken by CDF require heavy flavor tagging with large charged particle tracking acceptance. To realize these goals, in 2001 CDF installed eight layers of silicon microstrip detectors around its interaction region. These detectors were designed for 2--5 years of operation, radiation doses up to 2 Mrad (0.02 Gy), and were expected to be replaced in 2004. The sensors were not replaced, and the Tevatron run was extended for several years beyond its design, exposing the sensors and electronics to much higher radiation doses than anticipated. In this paper we describe the operational challenges encountered over the past 10 years of running the CDF silicon detectors, the preventive measures undertaken, and the improvements made along the way to ensure their optimal performance for collecting high quality physics data. In addition, we describe the quantities and methods used to monitor radiation damage in the sensors for optimal performance and summarize the detector performance quantities important to CDF's physics program, including vertex resolution, heavy flavor tagging, and silicon vertex trigger performance.Comment: Preprint accepted for publication in Nuclear Instruments and Methods A (07/31/2013

Ambient Intelligence As The Bridge To The Future of Pervasive Computing

One prediction about this future of pervasive technology is that people will carry the tools needed to interface with technological resources sprinkled through out the environment. A problem with this vision is the dark side of the network effect: early adopters will end up carrying around interfaces for technology that largely does not yet exist, and building managers will question the value of installing technology with features that almost no one will be able to use. An intermediate solution is that certain buildings with specific needs for efficiency or security (such as hospitals) may become smart, with technology insinuated into particular spaces. Since many, or even most of the people in these spaces will not have the technology to interface directly with the new pervasive resources, we must think of the interaction idiom as initially being closer to the notion of smart environments. These environments will have to sense, interpret, and facilitate the actions of the inhabitants, possibly with very little help from technology attached to the people involved, or even their cooperation. We survey a body of work on perceptual tools for smart buildings, built on the sensor network model, and focused on the idea that statistical methods and population dynamics can provide valuable information even in situations where detection of individual instances of behavior may be difficult to detect. These are some of the tools which will fuel the building optimization applications that will justify the efforts of early adopters to build smart buildings studded with pervasive technology

Feature-Based Localization Using Fixed Ultrasonic Transducers

We describe an approach for mobile robot localization based on geometric features extracted from ultrasonic data. As is well known, a single sonar measurement using a standard POLAROIDTM sensor, though yielding relatively accurate information regarding the range of a reflective surface patch, provides scant information about the location in azimuth or elevation of that patch. This lack of sufficiently precise localization of the reflective patch hampers any attempt at data association, clustering of multiple measurements or subsequent classification and inference. In previous work [15, 16] we proposed a multi-stage approach to clustering which aggregates sonic data accumulated from arbitrary transducer locations in a sequential fashion. It is computationally tractable and efficient despite the inherent exponential nature of clustering, and is robust in the face of noise in the measurements. It therefore lends itself to applications where the transducers are fixed relative to the mobile platform, where remaining stationary during a scan is both impractical and infeasible, and where deadreckoning errors can be substantial. In the current work we apply this feature extraction algorithm to the problem of localization in a partially known environment. Feature-based localization boasts advantages in robustness and speed over several other approaches. We limit the set of extracted features to planar surfaces. We describe an approach for establishing correspondences between extracted and map features. Once such correspondences have been established, a least squares approach to mobile robot pose estimation is delineated. It is shown that once correspondence has been found, the pose estimation may be performed in time linear in the number of extracted features. The decoupling of the correspondence matching and estimation stages is shown to offer advantages in speed and precision. Since the clustering algorithm aggregates sonic data accumulated from arbitrary transducer locations, there are no constraints on the trajectory to be followed for localization except that sufficiently large portions of features be ensonified to allow clustering. Preliminary experiments indicate the usefulness of the approach, especially for accurate estimation of orientation