Towards dynamic context discovery and composition

Context-awareness has been identified as a key characteristic for pervasive computing systems. As a variety of context-aware environments begin to flourish, pervasive applications shall have to interact different environments well. In this paper we propose extensions to the Strathclyde Context Infrastructure that gives context-aware applications the potential to adapt to unfamiliar environments transparently. We present a vision of a context discovery technique based on automated semantic reasoning about context information and services. The technique will offer higher levels of scalability and of interoperability with new context environments that cannot be achieved with current methods

Situation determination with distributed context histories

Determining the situation within an environment is a key goal of smart environment research. A significant challenge in situation determination is reasoning about openended groups of people and devices that a smart environment may contain. Contemporary solutions are often tailored to the specific environment. In this position paper, we present a novel general situation determination framework, that by viewing people and tools as playing roles in a situation, can easily adapt recognition to incorporate the dynamic structure of a situation over time

Towards ad-hoc situation determination

Toolkits such as PlaceLab [1] have been successful in making location information freely available for use in experimental ubiquitous computing applications. As users' expectations of ubiquitous computing applications grow, we envisage a need for tools that can deliver a much richer set of contextual information. The high-level situation of the current environment is a key contextual element, and this position paper focuses on a method to provide this information for an ad-hoc group of people and devices. The contributions of this paper are i) a demonstration of how information retrieval (IR) techniques can be applied to situation determination in context-aware systems, ii) a proposal of a novel approach to situation determination that combines these adapted IR techniques with a process of cooperative interaction, and iii) a report of preliminary results. The approach offers a high level of utility and accuracy, with a greater level of automation than other contemporary approaches

A general purpose programming framework for ubiquitous computing environments

It is important to note that the need to support ad-hoc and potentially mobile arrangements of devices in ubiquitous environments does not fit well within the traditional client/server architecture. We believe peer-to-peer communication offers a preferable alternative due to its decentralised nature, removing dependence on individual nodes. However, this choice adds to the complexity of the developers task. In this paper, we describe a two-tiered approach to address this problem: A lower tier employing peer-to-peer interactions for managing the network infrastructure and an upper tier providing a mobile agent based programming framework. The result is a general purpose framework for developing ubiquitous applications and services, where the underlying complexity is hidden from the developer. This paper discusses our on-going work; presenting our design decisions, features supported by our framework, and some of the challenges still to be addressed in a complex programming environment

Situation determination with reusable situation specifications

Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long training periods before the system can be used. In both cases, the situations are tailored to the specific environment, and are therefore not transferable to other environments. Furthermore, situations are recognised at a low-level of granularity, which limits the scope of situation-aware applications. This paper presents a novel approach to situation determination that attempts to overcome these issues by providing a reusable library of general situation specications that can be easily extended to create new speficic situations, and immediately deployed without the need of an environment expert. A proposed architecture of an accompanying situation determination middleware is provided, as well as an analysis of a prototype implementation

A self-managing infrastructure for ad-hoc situation determination

Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long training periods before the system can be used. This paper presents a novel approach to situation determination that attempts to overcome these issues by providing a reusable library of general situation specifications that can be easily extended to create new specific situations, and immediately deployed without the need of an environment expert. The architecture of an accompanying situation determination infrastructure is provided, which autonomously optimises and repairs itself in reaction to changes or failures in the environment

Data processing method for a weak, moving telemetry signal

Method of processing data from a spacecraft, where the carrier has a low signal-to-noise ratio and wide unpredictable frequency shifts, consists of analogue recording of the noisy signal along with a high-frequency tone that is used as a clock to trigger a digitizer

Methods of editing cloud and atmospheric layer affected pixels from satellite data

The location and migration of cloud, land and water features were examined in spectral space (reflective VIS vs. emissive IR). Daytime HCMM data showed two distinct types of cloud affected pixels in the south Texas test area. High altitude cirrus and/or cirrostratus and "subvisible cirrus" (SCi) reflected the same or only slightly more than land features. In the emissive band, the digital counts ranged from 1 to over 75 and overlapped land features. Pixels consisting of cumulus clouds, or of mixed cumulus and landscape, clustered in a different area of spectral space than the high altitude cloud pixels. Cumulus affected pixels were more reflective than land and water pixels. In August the high altitude clouds and SCi were more emissive than similar clouds were in July. Four-channel TIROS-N data were examined with the objective of developing a multispectral screening technique for removing SCi contaminated data

Biological vulnerability to depression: Linked structural and functional brain network findings

Background: Patients in recovery following episodes of major depressive disorder (MDD) remain highly vulnerable to future recurrence. Although psychological determinants of this risk are well established, little is known about associated biological mechanisms. Recent work has implicated the default mode network (DMN) in this vulnerability but specific hypotheses remain untested within the high risk, recovered state of MDD. Aims: To test the hypothesis that there is excessive DMN functional connectivity during task performance within recovered-state MDD and to test for connected DMN cortical gyrification abnormalities. Method: A multimodal structural and functional magnetic resonance imaging (fMRI) study, including task-based functional connectivity and cortical folding analysis, comparing 20 recoveredstate patients with MDD with 20 matched healthy controls. Results: The MDD group showed significant task-based DMN hyperconnectivity, associated with hypogyrification of key DMN regions (bilateral precuneus). Conclusions: This is the first evidence of connected structural and functional DMN abnormalities in recovered-state MDD, supporting recent hypotheses on biological-level vulnerability

An Optimal UAV Deployment Algorithm for Bridging Communication

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In recent years, Unmanned Aerial Vehicles (UAVs) have attracted the attention of both the military and civilians because of their deployment in situations where part of the communication infrastructure is destroyed due to bomb blast, earthquake, flood, military operations or landslides. Also UAVs can be used in operations such as search and rescue, surveillance, forest fire monitoring, and border patrolling. Deployment of a UAV in a position where it can provide maximum coverage and high throughput is one of the vital problem that needs to be addressed. In this paper, we have proposed an optimal UAV deployment algorithm (OUDA) in order to bridge communication between two static nodes on the ground. In the proposed algorithm the UAV deploys to a position where it can provide the best communication facilities to both the nodes based on the received signal strength (RSS), and distance between nodes and UAV. Simulation results showed that the algorithm provides maximum throughput and low bit error rate (BER) once the UAV is fixed to an optimal position