A set of image preprocessing approaches are developed for processing plankton images reconstructed from digital holograms. First, a threshold-based algorithm of image segmentation is proposed and applied to extract the regions of plankton from the original digital images. To improve the performance of image segmentation, an appropriate filter is adopted to reduce the background noise from the image and the image gray level is adjusted to enhance the image contrast. Second, we develop a novel and efficient edge detection method purposefully for the binary images. Third, we propose and use a simple chain-code-based algorithm to eliminate the single-pixel branches along the shape boundary, which will help boundary tracing work stably. Then, an algorithm is improved and applied to trace the boundaries of the plankton regions. This algorithm is optimized based on the relationship between two consecutive chain-codes such that it is fast on implementation. Finally, break points of the shape boundary are efficiently detected based on chain-codes and the boundary is represented compactly by a polygon comprised of those points. After images are preprocessed by these approaches, some redundant information of shape is reduced that will accelerate the running speeds of further image processing and aid identification and classification of plankton at species level. We analyze the accuracy and efficiency of our algorithms. The results show that our algorithm of image segmentation has a good performance in accuracy. Our edge detection method also outperforms the commonly used edge detection methods in terms of localization performance and the running time.

Wireless sensor actuator networks are becoming a solution for control applications. Reliable data transmission and real time constraints are the most significant challenges. Control applications will have some Quality of Service (QoS) requirements from the sensor network, such as minimum delay and guaranteed delivery of packets. We investigate variable sampling method to mitigate the effects of time delays in wireless networked control systems using an observer based control system model. Our focus for variable sampling methodology is to determine the appropriate neural network topology for delay prediction and also investigate the impact of additional inputs to the neural network such as network packet loss rate and throughput. The major contribution of this work is the use of typical obtainable delay series for training the neural network. Most studies have used random generated numbers, which are not a correct representation of delays actually experienced in a wireless network. Our results here shows that adequate prediction of the time delay series using the observer based variable sampling is able to compensate for delays in the communication loop and influences the performance of the control system model.

In distributed computing systems, if an elected leader node fails, the other nodes of the system need to elect another leader. The bully algorithm is a classical approach for electing a leader in a synchronous distributed computing system. This paper presents an enhancement of the bully algorithm, requiring less time complexity and minimum message passing. This significant gain has been achieved by introducing node sets and tie breaker time. The latter provides a possible solution to simultaneous elections initiated by different nodes. In comparison with the classical algorithm and its existing modifications, this proposal generates minimum messages, stops redundant elections, and maintains fault-tolerant behaviour of the system.

Underwater Wireless Sensor Networks (UWSN) share the common challenges of terrestrial Wireless Sensor Network (WSN), however they are significantly different from terrestrial WSN. Mainly, because acoustic wireless communication is the main physical layer technology in UWSN. Acoustic communication offers longer range, but has limitations due to low speed of sound, high error probability, limited bandwidth capacity, node mobility and 3-dimensional network architecture. Most of the ground based WSN are static, however the UWSN condition keeps on changing due to water current and channel impairment. Therefore the UWSN must be able to dynamically reconfigure itself. The sensor nodes must be able to reroute their communication if the network configuration changes. In this paper we address a fundamental Networking layer issue by developing a dynamically reconfigurable routing protocol. It is a multi-hop datagram routing scheme which will offer reliable underwater wireless communication by dynamically rerouting the data, when network configuration changes.