Adaptive Bayesian networks for video processing

ABSTRACT Due to its static nature, the inference capability of Bayesian Networks (BNs) oflen deteriorates when the basis of input data varies, especially in video processing applications where the environment often changes constantly. This paper presents an adaptive BN where the network parameters are adjusted in accordance to input variations. An efficient re-training method is introduced for updating the parameters and the proposed network is applied to shadow removal in video sequence processing with quantitative results demonstrating the significance of adapting the network with environmental changes

Application of data fusion techniques and technologies for wearable health monitoring

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market

Verifying safety messages using relative-time and zone priority in vehicular ad hoc networks

Gordon, SD ORCiD: 0000-0003-4090-1199In high-density road networks, with each vehicle broadcasting multiple messages per second, the arrival rate of safety messages can easily exceed the rate at which digital signatures can be verified. Since not all messages can be verified, algorithms for selecting which messages to verify are required to ensure that each vehicle receives appropriate awareness about neighbouring vehicles. This paper presents a novel scheme to select important safety messages for verification in vehicular ad hoc networks (VANETs). The proposed scheme uses location and direction of the sender, as well as proximity and relative-time between vehicles, to reduce the number of irrelevant messages verified (i.e., messages from vehicles that are unlikely to cause an accident). Compared with other existing schemes, the analysis results show that the proposed scheme can verify messages from nearby vehicles with lower inter-message delay and reduced packet loss and thus provides high level of awareness of the nearby vehicles. © 2018 by the authors. Licensee MDPI, Basel, Switzerland

Verifying safety messages using relative-time and zone priority in vehicular ad hoc networks

In high-density road networks, with each vehicle broadcasting multiple messages per second, the arrival rate of safety messages can easily exceed the rate at which digital signatures can be verified. Since not all messages can be verified, algorithms for selecting which messages to verify are required to ensure that each vehicle receives appropriate awareness about neighbouring vehicles. This paper presents a novel scheme to select important safety messages for verification in vehicular ad hoc networks (VANETs). The proposed scheme uses location and direction of the sender, as well as proximity and relative-time between vehicles, to reduce the number of irrelevant messages verified (i.e., messages from vehicles that are unlikely to cause an accident). Compared with other existing schemes, the analysis results show that the proposed scheme can verify messages from nearby vehicles with lower inter-message delay and reduced packet loss and thus provides high level of awareness of the nearby vehicles. © 2018 by the authors. Licensee MDPI, Basel, Switzerland

Safety message verification using history-based relative-time zone priority scheme

Safety message verification plays an important role in securing vehicular ad hoc networks (VANETs). As safety messages are broadcasted several times per second in a highly dense network, message arrival rate can easily exceed the verification rate of safety messages at a vehicle. As a result, an algorithm is needed for selecting and prioritizing relevant messages from received messages to increase the awareness of vehicles in the vicinity. This paper presents the history-based relative-time zone (HRTZ) priority scheme for selecting and verifying relevant received safety messages. HRTZ is an enhanced version of our previously proposed relative-time zone (RTZ) priority scheme. HRTZ achieves higher awareness of nearby vehicles and works in different road configurations. To increase awareness of neighboring vehicles, the average velocity of neighboring vehicles in the range of communication is used to determine the range of the danger zone and other zones. The messages are ranked based on the zone of transmitting vehicles, road configuration (with/without a barrier) and transmitting vehicle location and direction, and relative time between transmitting and receiving vehicles. Only the most up-to-date message from each vehicle is kept in the receiver's buffer. As a result, each neighboring vehicle has only the most recent safety message in the buffer at any time. The simulation results show that HRTZ achieves a higher rate of verified messages with low delay for nearby vehicles and achieves higher awareness for vehicles in the vicinity, when compared to RTZ and other existing schemes

Real-Time Pervasive Monitoring for Postoperative Care

Abstract—Post surgical care is an important part of the surgical recovery process. With the introduction of minimally invasive surgery (MIS), the recovery time of patients has been shortened significantly. This has led to a shift of postoperative care from hospital to home environment. To prevent the occurrence of adverse events, the care of these patients is mainly relied on routine visits by home-care nurses. This type of episodic examination can only capture a snapshot of the overall recovery process, and many early signs of potential complication can go undetected. The development of Body Sensor Networks (BSNs) has enabled the use of miniaturised wireless sensors for continuous monitoring of postoperative patients. This paper examines the potential of processing-on-node algorithms for further reducing the wireless bandwidth, and therefore the overall power consumption of the sensors. The accuracy and robustness of the technique are demonstrated with lab experiments and a preliminary clinical case study