Particle Swarm Optimisation for Edge Detection in Noisy Images

<p>Detection of continuous and connected edges is very important in many applications, such as detecting oil slicks in remote sensing and detecting cancers in medical images. The detection of such edges is a hard problem particularly in noisy images and most edge detection algorithms suffer from producing broken and thick edges in such images. The main goal of this thesis is to reduce broken edges by proposing an optimisation model and a solution method in order to detect edges in noisy images. This thesis suggests a newapproach in the framework of particle swarm optimisation (PSO) to overcome noise and reduce broken edges through exploring a large area and extracting the global structure of the edges. A fitness function is developed based on the possibility score of a curve being fitted on an edge and the curvature cost of the curve with two constraints. Unlike traditional algorithms, the new method can detect edges with greater continuity in noisy images. Furthermore, a new truncation method within PSO is proposed to truncate the real values of particle positions to integers in order to increase the diversity of the particles. This thesis also proposes a local thresholding technique for the PSObased edge detection algorithm to overcome the problem of detection of edges in noisy images with illuminated areas. The local thresholding technique is proposed based on themain idea of the Sauvola-Pietkinenmethod which is a way of binarisation of illuminated images. It is observed that the new local thresholding can improve the performance of the PSO-based edge detectors in the illuminated noisy images. Since the performance of using static topologies in various applications and in various versions of PSO is different , the performance of six different static topologies (fully connected, ring, star, tree-based, von Neumann and toroidal topologies)within threewell-known versions of PSO (Canonical PSO, Bare Bones PSO and Fully Informed PSO) are also investigated in the PSO-based edge detector. It is found that different topologies have different effects on the accuracy of the PSO-based edge detector. This thesis also proposes a novel dynamic topology called spatial random meaningful topology (SRMT) which is an adoptation version of a gradually increasing directed neighbourhood (GIDN). The new dynamic topology uses spatial meaningful information to compute the neighbourhood probability of each particle to be a neighbour of other particles. It uses this probability to randomly select the neighbours of each particle at each iteration of PSO. The results show that the performance of the proposed method is higher than that of other topologies in noisy images in terms of the localisation accuracy of edge detection.</p>

Particle Swarm Optimisation for Edge Detection in Noisy Images

<p>Detection of continuous and connected edges is very important in many applications, such as detecting oil slicks in remote sensing and detecting cancers in medical images. The detection of such edges is a hard problem particularly in noisy images and most edge detection algorithms suffer from producing broken and thick edges in such images. The main goal of this thesis is to reduce broken edges by proposing an optimisation model and a solution method in order to detect edges in noisy images. This thesis suggests a newapproach in the framework of particle swarm optimisation (PSO) to overcome noise and reduce broken edges through exploring a large area and extracting the global structure of the edges. A fitness function is developed based on the possibility score of a curve being fitted on an edge and the curvature cost of the curve with two constraints. Unlike traditional algorithms, the new method can detect edges with greater continuity in noisy images. Furthermore, a new truncation method within PSO is proposed to truncate the real values of particle positions to integers in order to increase the diversity of the particles. This thesis also proposes a local thresholding technique for the PSObased edge detection algorithm to overcome the problem of detection of edges in noisy images with illuminated areas. The local thresholding technique is proposed based on themain idea of the Sauvola-Pietkinenmethod which is a way of binarisation of illuminated images. It is observed that the new local thresholding can improve the performance of the PSO-based edge detectors in the illuminated noisy images. Since the performance of using static topologies in various applications and in various versions of PSO is different , the performance of six different static topologies (fully connected, ring, star, tree-based, von Neumann and toroidal topologies)within threewell-known versions of PSO (Canonical PSO, Bare Bones PSO and Fully Informed PSO) are also investigated in the PSO-based edge detector. It is found that different topologies have different effects on the accuracy of the PSO-based edge detector. This thesis also proposes a novel dynamic topology called spatial random meaningful topology (SRMT) which is an adoptation version of a gradually increasing directed neighbourhood (GIDN). The new dynamic topology uses spatial meaningful information to compute the neighbourhood probability of each particle to be a neighbour of other particles. It uses this probability to randomly select the neighbours of each particle at each iteration of PSO. The results show that the performance of the proposed method is higher than that of other topologies in noisy images in terms of the localisation accuracy of edge detection.</p>

A Hierarchical Restoration Mechanism for Distribution Networks Considering Multiple Faults

Service restoration of distribution networks in contingency situations is one of the highly investigated and challenging problems. In the conventional service restoration method, utilities reconfigure the topological structure of the distribution networks to supply the consumer load demands. However, the advancements in renewable distributed generations define a new dimension for developing service restoration methodologies. This paper proposes a hierarchical service restoration mechanism for distribution networks in the presence of distributed generations and multiple faults. The service restoration problem is modeled as a complicated and hierarchical program. The objectives are to achieve the maximization of loads restored with minimization of switch operations while simultaneously satisfying grid operational constraints and ensuring a radial operation configuration. We present the service restoration mechanism, which includes the dynamic topology analysis, matching isolated islands with renewable distributed generations, network reconfiguration, and network optimization. A new code scheme that avoids feasible solutions is applied to generate candidate solutions to reduce the computational burden. We evaluate the proposed mechanism on the IEEE 33 and 69 systems and report on the collected results under multitype fault cases. The results demonstrate the importance of the available renewable distributed generations in the proposed mechanism. Moreover, simulation results verify that the proposed mechanism can obtain reasonable service restoration plans to achieve the maximization of loads restored and minimization of switching operations under different faults.

A Dynamic Topology Construction Algorithm for Self-Organizing Wireless Sensor Networks

<div>Self-organizing protocols and algorithms require knowledge of the underlying topology of the network. The topology can be represented by a graph or an adjacency matrix. In most practical cases, establishing the topology prior to a deployment is not possible because the exact placement of nodes and the existence of a reliable link between any two individual nodes cannot guaranteed. Therefore, this task has to be carried out after deployment. If the network is stand-alone and certain aspects are fixed (such as the identity of the base station, the size of the network, etc.), the task is achievable. If, however, the network has to interact with other systems -- such as Unmanned Aerial Vehicles (UAVs) or mobile robots -- whose operation is affected by environmental factors, the task can be difficult to achieve. In this paper we propose a dynamic topology construction algorithm, assuming that the network is a part of a joint deployment and does not have a fixed based station.</div>

A Dynamic Topology Construction Algorithm for Self-Organizing Wireless Sensor Networks

<div>Self-organizing protocols and algorithms require knowledge of the underlying topology of the network. The topology can be represented by a graph or an adjacency matrix. In most practical cases, establishing the topology prior to a deployment is not possible because the exact placement of nodes and the existence of a reliable link between any two individual nodes cannot guaranteed. Therefore, this task has to be carried out after deployment. If the network is stand-alone and certain aspects are fixed (such as the identity of the base station, the size of the network, etc.), the task is achievable. If, however, the network has to interact with other systems -- such as Unmanned Aerial Vehicles (UAVs) or mobile robots -- whose operation is affected by environmental factors, the task can be difficult to achieve. In this paper we propose a dynamic topology construction algorithm, assuming that the network is a part of a joint deployment and does not have a fixed based station.</div>

A Trust-Integrated RPL Protocol to Detect Blackhole Attack in Internet of Things

Internet of things (IoT) offers communication between user-to-machine and machine-to-machine. Due to their inherent characteristics of open medium, very dynamic topology, lack of infrastructure and lack of centralized management authority, IoT present serious vulnerabilities to security attacks. The routing protocol for low-power and lossy networks (RPL) does not have an inherent mechanism to detect routing attacks. Popular among these IoT attacks is blackhole attack. An attacker can exploit the routing system of RPL to launch blackhole attack against an IoT network. To secure IoT networks from blackhole attack, trust-integrated RPL protocol (TRPL) is proposed and implemented. The trust system is embedded in the RPL protocol to detect and isolate a blackhole attack while optimizing network performance. The trust is calculated from successful interaction between two nodes. The calculated trust value is considered in parent selection. TRPL demonstrates its superior performance over the standard RPL protocol and existing techniques in the detection and isolation of blackhole attacks.

Dynamic Topology Optimization of Long-Span Continuum Structures

Herein, to improve the dynamic performance of continuum structures, their fundamental frequency is optimized using the topology optimization method. This helps to obtain the best material distribution in the design space and increases the fundamental frequency of the structure higher than the disturbance frequency. Using the variable density method, the dynamic topology optimization model of a long-span continuum structure is built based on the density interpolation model of a solid isotropic material with penalization (SIMP). The goal of this optimization is to maximize the first-order eigenvalue, and the optimization constraint is that the total volume of the structure is smaller than the given value. To improve the efficiency and accuracy of the model, sensitivity filtering is adopted to avoid numerical instability during calculation. Moreover, the optimization criterion method is used to iteratively solve the optimization results. Finally, the structural topology optimization method is implemented on the long-span single beam of a bridge crane at a construction site. The results show that the natural frequency of the structure is increased and the modal characteristics are improved, which lays the foundation for further optimization and dynamic-response analysis.