100 research outputs found
SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks
Some mobile sensor network applications require the sensor nodes to transfer
their trajectories to a data sink. This paper proposes an adaptive trajectory
(lossy) compression algorithm based on compressive sensing. The algorithm has
two innovative elements. First, we propose a method to compute a deterministic
projection matrix from a learnt dictionary. Second, we propose a method for the
mobile nodes to adaptively predict the number of projections needed based on
the speed of the mobile nodes. Extensive evaluation of the proposed algorithm
using 6 datasets shows that our proposed algorithm can achieve sub-metre
accuracy. In addition, our method of computing projection matrices outperforms
two existing methods. Finally, comparison of our algorithm against a
state-of-the-art trajectory compression algorithm show that our algorithm can
reduce the error by 10-60 cm for the same compression ratio
A mask-based approach for the geometric calibration of thermal-infrared cameras
Accurate and efficient thermal-infrared (IR) camera calibration is important for advancing computer vision research within the thermal modality. This paper presents an approach for geometrically calibrating individual and multiple cameras in both the thermal and visible modalities. The proposed technique can be used to correct for lens distortion and to simultaneously reference both visible and thermal-IR cameras to a single coordinate frame. The most popular existing approach for the geometric calibration of thermal cameras uses a printed chessboard heated by a flood lamp and is comparatively inaccurate and difficult to execute. Additionally, software toolkits provided for calibration either are unsuitable for this task or require substantial manual intervention. A new geometric mask with high thermal contrast and not requiring a flood lamp is presented as an alternative calibration pattern. Calibration points on the pattern are then accurately located using a clustering-based algorithm which utilizes the maximally stable extremal region detector. This algorithm is integrated into an automatic end-to-end system for calibrating single or multiple cameras. The evaluation shows that using the proposed mask achieves a mean reprojection error up to 78% lower than that using a heated chessboard. The effectiveness of the approach is further demonstrated by using it to calibrate two multiple-camera multiple-modality setups. Source code and binaries for the developed software are provided on the project Web site
An exploration of feature detector performance in the thermal-infrared modality
Thermal-infrared images have superior statistical properties compared with visible-spectrum images in many low-light or no-light scenarios. However, a detailed understanding of feature detector performance in the thermal modality lags behind that of the visible modality. To address this, the first comprehensive study on feature detector performance on thermal-infrared images is conducted. A dataset is presented which explores a total of ten different environments with a range of statistical properties. An investigation is conducted into the effects of several digital and physical image transformations on detector repeatability in these environments. The effect of non-uniformity noise, unique to the thermal modality, is analyzed. The accumulation of sensor non-uniformities beyond the minimum possible level was found to have only a small negative effect. A limiting of feature counts was found to improve the repeatability performance of several detectors. Most other image transformations had predictable effects on feature stability. The best-performing detector varied considerably depending on the nature of the scene and the test
Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model
The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal specie
Springbrook: Challenges in developing a long-term, rainforest wireless sensor network
We describe the design, development and learnings from the first phase of a rainforest ecological sensor network at Springbrook - part of a World Heritage precinct in South East Queensland. This first phase is part of a major initiative to develop the capability to provide reliable, long-term monitoring of rainforest ecosystems. We focus in particular on our analysis around energy and communication challenges which need to be solved to allow for reliable, long-term deployments in these types of environments
Nano-scale reservoir computing
This work describes preliminary steps towards nano-scale reservoir computing
using quantum dots. Our research has focused on the development of an
accumulator-based sensing system that reacts to changes in the environment, as
well as the development of a software simulation. The investigated systems
generate nonlinear responses to inputs that make them suitable for a physical
implementation of a neural network. This development will enable
miniaturisation of the neurons to the molecular level, leading to a range of
applications including monitoring of changes in materials or structures. The
system is based around the optical properties of quantum dots. The paper will
report on experimental work on systems using Cadmium Selenide (CdSe) quantum
dots and on the various methods to render the systems sensitive to pH, redox
potential or specific ion concentration. Once the quantum dot-based systems are
rendered sensitive to these triggers they can provide a distributed array that
can monitor and transmit information on changes within the material.Comment: 8 pages, 9 figures, accepted for publication in Nano Communication
Networks, http://www.journals.elsevier.com/nano-communication-networks/. An
earlier version was presented at the 3rd IEEE International Workshop on
Molecular and Nanoscale Communications (IEEE MoNaCom 2013
Decentralized Monitoring of Moving Objects in a Transportation Network Augmented with Checkpoints
This paper examines efficient and decentralized monitoring of objects moving in a transportation network. Previous work in moving object monitoring has focused primarily on centralized information systems, like moving object databases and geographic information systems. In contrast, in this paper monitoring is in-network, requiring no centralized control and allowing for substantial spatial constraints to the movement of information. The transportation network is assumed to be augmented with fixed checkpoints that can detect passing mobile objects. This assumption is motivated by many practical applications, from traffic management in vehicle ad hoc networks to habitat monitoring by tracking animal movements. In this context, this paper proposes and evaluates a family of efficient decentralized algorithms for capturing, storing and querying the movements of objects. The algorithms differ in the restrictions they make on the communication and sensing constraints to the mobile nodes and the fixed checkpoints. The performance of the algorithms is evaluated and compared with respect to their scalability (in terms of communication and space complexity), and their latency (the time between when a movement event occurs, and when all interested nodes are updated with records about that event). The conclusions identify three key principles for efficient decentralized monitoring of objects moving past checkpoints: structuring computation around neighboring checkpoints; taking advantage of mobility diffusion and separating the generation and querying of movement informatio
Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing
Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle
Wildfire impact : natural experiment reveals differential short-term changes in soil microbial communities
A wildfire which overran a sensor network site provided an opportunity (a natural experiment) to monitor short-term post-fire impacts (immediate and up to three months post-fire) in remnant eucalypt woodland and managed pasture plots. The magnitude of fire-induced changes in soil properties and soil microbial communities was determined by comparing (1) variation in fire-adapted eucalypt woodland vs. pasture grassland at the burnt site; (2) variation at the burnt woodland-pasture sites with variation at two unburnt woodland-pasture sites in the same locality; and (3) temporal variation pre- and post-fire. In the eucalypt woodland, soil ammonium, pH and ROC content increased post-fire, while in the pasture soil, soil nitrate increased post-fire and became the dominant soluble N pool. However, apart from distinct changes in N pools, the magnitude of change in most soil properties was small when compared to the unburnt sites. At the burnt site, bacterial and fungal community structure showed significant temporal shifts between pre- and post-fire periods which were associated with changes in soil nutrients, especially N pools. In contrast, microbial communities at the unburnt sites showed little temporal change over the same period. Bacterial community composition at the burnt site also changed dramatically post-fire in terms of abundance and diversity, with positive impacts on abundance of phyla such as Actinobacteria, Proteobacteria and Firmicutes. Large and rapid changes in soil bacterial community composition occurred in the fire-adapted woodland plot compared to the pasture soil, which may be a reflection of differences in vegetation composition and fuel loading. Given the rapid yet differential response in contrasting land uses, identification of key soil bacterial groups may be useful in assessing recovery of fire-adapted ecosystems, especially as wildfire frequency is predicted to increase with global climate change
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