USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people

Distributive target tracking in sensor networks with a markov random field model

Abstract-Tracking in sensor networks has shown great potentials in many real world surveillance and emergency system. Due to the distributive nature and unpredictable topology structure of the randomly distributed sensor network, a good tracking algorithm must be able to aggregate large amounts of data from various unknown sources. In this paper, a distributive tracking algorithm is developed using a Markov random field (MRF) model to solve this problem. The Markov random field (MRF) utilizes probability distribution and conditional independency to identify the most relevant data from the less important data. The algorithm converts the randomly distributed network into a regularly distributed topology structure using cliques. This makes tracking in the randomly distributed network topology simple and more predictable. Simulation demonstrate that the algorithm performs well for various sensor field setting, and for various target sizes

Determination of free cyclosporine A with a LC-MS/MS method: Application to C2 monitoring in rabbits

Cyclosporine A (CsA) is a cyclic peptide widely used as an immunosuppressant. Therapeutic drug monitoring (TDM) of CsA is becoming mandatory for transplant patients who received CsA therapy in the routine clinical practice because of large individual variability, dose-related toxicity and the risk of acute rejection. In this study, a rapid, sensitive, and selective LC-MS/MS method was developed and validated for the quantitative analysis of free CsA (fCsA), a better indicator for the prediction of efficacy and safety of CsA-based therapy. Following ultrafiltration for fCsA, chromatographic separation was performed on an Agilent Zorbax SB-C18 column (100 mm x 2.1 mm, 3.5 μm ) with acetonitrile and 0.1 % ammonium hydroxide in water (85:15, v/v) as the mobile phases. The compounds were quantified by positive electrospray ionization tandem mass spectrometry. Selectivity, linearity, accuracy, precision, recovery, and stability were evaluated during method validation. The validated method was applied to a single blood concentration measurement 2 h after CsA administration (C2) measurement study of fCsA after an oral administration of a single 15 mg/kg intravenous dose of CsA to six rabbits.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Two-tier target tracking framework in distributed sensor networks

Copyright © 2014 Inderscience Enterprises Ltd. A distributive two-tier tracking framework is developed to track mobile targets in sensor networks. The algorithm utilises the efficiency of binary sensor reading to ensure fast isolation of the target, and then it uses a small cluster of sensors around the target to compute accurate target location. The framework has two tiers, in tier-one, binary sensor reading and Viterbi algorithm are used, it is a fast and distributed way of roughly locate the target. In tier-two, a detailed localisation using maximum likelihood estimation is done within a small cluster of sensors to compute the accurate location of the target. By dividing the framework into two tiers, scaling the size of the network only affects the computation cost in tier one, where binary data are used. The computation cost in tier 2 remains unchanged. The framework is first implemented in a chained-form network with sensors deployed in a regular pattern, it is then extended to other regularly deployed network in full 2D space. The two-tier tracking framework is implemented using ultrasonic sensor network in a real-world hallway-room environment in our lab

Distributive target tracking in sensor networks with a Markov random field model

Tracking in sensor networks has shown great potentials in many real world surveillance and emergency system. Due to the distributive nature and unpredictable topology structure of the randomly distributed sensor network, a good tracking algorithm must be able to aggregate large amounts of data from various unknown sources. In this paper, a distributive tracking algorithm is developed using a Markov random field (MRF) model to solve this problem. The Markov random field (MRF) utilizes probability distribution and conditional independency to identify the most relevant data from the less important data. The algorithm converts the randomly distributed network into a regularly distributed topology structure using cliques. This makes tracking in the randomly distributed network topology simple and more predictable. Simulation demonstrate that the algorithm performs well for various sensor field setting, and for various target sizes. © 2009 IEEE

Target tracking in sensor networks using statistical graphical models

Recent advancement in sensor networks provides a platform for applications that requires in-network data fusion and parallel algorithms. However, processing data in parallel while propagating at low latency is very challenging. Also, implementation of these algorithms is limited by various constraints including energy, computation costs and complex network topology. In this paper, a statistical graphical model based algorithm is developed for in-network processing, which can be applied to tracking problems in the sensor networks. This algorithm represents the complex topology of a sensor network with a simple clique tree. It further utilizes the message passing algorithms to effectively make accurate inferences about the target location. The simulation shows that the algorithm can accurately track the target in a large scale random distributed sensor field with low complexity and low cost. The algorithm is also proved to be robust, as the simulation random disabled some sensors during the tracking phase. © 2008 IEEE

Near optimal two-tier target tracking in sensor networks

A distributed two-tier near optimal algorithm is proposed for target tracking in sensor networks. Tier one is a multiple hypothesis tracking (MHT) algorithm where the Viterbi algorithm is used. In this tier, only binary data is used to obtain a rough region around the target. Tier two improves the accuracy of the MHT decision by localized maximum likelihood. This reduces the computational complexity and the communication costs between sensors over the global maximum likelihood approach. It also results in higher sensor power efficiency, hence longer service time of the tracking network. This two-tier system is a distributed near optimal tracking algorithm. The localized maximum likelihood tracking can tolerate errors made by the Viterbi algorithm in tier one, hence the overall algorithm is robust. ©2007 IEEE

Ship Intrusion Collision Risk Model Based on a Dynamic Elliptical Domain

To improve navigation safety in maritime environments, a key step is to reduce the influence of human factors on the risk assessment of ship collisions by automating the decision-making process as much as possible. This paper optimizes a dynamic elliptical ship domain based on Automatic Identification System (AIS) data, combines the relative motion between ships in different encounter situations and the level of ship intrusion in the domain, and proposes a ship intrusion collision risk (SICR) model. The simulation results show that the optimized ship domain meets the visualization requirements, and the intrusion model has good collision risk perception ability, which can be used as the evaluation standard of ship collision risk: when the SICR is 0.5–0.6, the ship can establish a collaborative collision avoidance decision-making relationship with other ships, and the action ship can take effective collision avoidance action at the best time when the SICR is between 0.3 and 0.5. The SICR model can give navigators a more accurate and rapid perception of navigation risks, enabling timely maneuvering decisions, and improving navigation safety

Collision Risk Index Calculation Based on an Improved Ship Domain Model

The traditional ship collision risk index model based on the distance at the closest point of approach (DCPA) and the time to the closest point of approach (TCPA) is insufficient for estimating ship collision risk and planning collision avoidance operations. This paper constructs an elliptical, dynamic ship domain that changes with ship speed and maneuverability parameters to overcome subjective human factors. Based on the constructed domain model, the concept of the ship domain proximity factor is introduced to improve the ship collision risk model based on DCPA and TCPA, and a risk calculation function model that considers the safety of ship navigation is constructed. The numerical calculation of the improved collision risk index calculation model confirms that the enhanced model has a higher rate of identification of risk between ships. The model is more compatible with the requirements of ship navigation decision-making and can provide theoretical support and a technical basis for research on ship collision avoidance decision-making

Lithography Hotspot Detection Method Based on Transfer Learning Using Pre-Trained Deep Convolutional Neural Network

As the designed feature size of integrated circuits (ICs) continues to shrink, the lithographic printability of the design has become one of the important issues in IC design and manufacturing. There are patterns that cause lithography hotspots in the IC layout. Hotspot detection affects the turn-around time and the yield of IC manufacturing. The precision and F1 score of available machine-learning-based hotspot-detection methods are still insufficient. In this paper, a lithography hotspot detection method based on transfer learning using pre-trained deep convolutional neural network is proposed. The proposed method uses the VGG13 network trained with the ImageNet dataset as the pre-trained model. In order to obtain a model suitable for hotspot detection, the pre-trained model is trained with some down-sampled layout pattern data and takes cross entropy as the loss function. ICCAD 2012 benchmark suite is used for model training and model verification. The proposed method performs well in accuracy, recall, precision, and F1 score. There is significant improvement in the precision and F1 score. The results show that updating the weights of partial convolutional layers has little effect on the results of this method