Midrange exploration exploitation searching particle swarm optimization in dynamic environment

Conventional Particle Swarm Optimization was introduced as an optimization technique for real problems such as scheduling, tracking, and traveling salesman. However, conventional Particle Swarm Optimization still has limitations in finding the optimal solution in a dynamic environment. Therefore, we proposed a new enhancement method of conventional Particle Swarm Optimization called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO). The main objective of this improvement is to enhance the searching ability of poor particles in finding the best solution in dynamic problems. In MEESPSO, we still applied the basic process in conventional Particle Swarm Optimization such as initialization of particle location, population evolution, and updating particle location. However, we added some enhancement processes in MEESPSO such as updating the location of new poor particles based on the average value of the particle minimum fitness and maximum fitness. To see the performance of the proposed method, we compare the proposed method with three existing methods such as Conventional Particle Swarm Optimization, Differential Evaluation Particle Swarm Optimization, and Global Best Local Neighborhood Particle Swarm Optimization. Based on the experimental result of 50 datasets show that MEESPSO can find the quality solution in term of number of particle and iteration, consistency, convergence, optimum value, and error rate

Video Tracking System Using Midrange Exploration Exploitation Searching-Particle Swarm Optimization (MEESPSO) in handling occlusion and similar appearance due to crowded environment

Detecting the correct object plays a key role in generating an accurate and precise object tracking result. In addition, the usage of conventional method still brings the limitation in term of the accuracy and precision of the detected object. Besides, the process of object tracking in an individual frame is also challenging due to the problems such as occlusion, crowded environment, and similar appearance Therefore, a Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) algorithm with color-shape feature pattern matching methods was introducing to address the problem of the similar appearance or color that comes close to target object in crowded environment, and the presence of occlusion problem cause motion of the crowded object or the camera views. The proposed method is tested by using the MOT16-11 benchmark video dataset. This benchmark video faced the challenges such as partial occlusion, fully occlusion and similar appearance due to crowded environment in the video scene. The experiment has shown that the tracking performance of the proposed method has increased more than 92.69% accuracy and 94.67% precisio

Development of images segmentation using image thresholder and batch processing technique on the blood smears

Image segmentation is an important part of image processing, and one of the most common approaches is threshold segmentation. A new segmentation technique with each pixel in the image has its own threshold is developed in response to the fact that standard threshold-based segmentation algorithms only establish one or many thresholds, making it difficult to extract the complex information in an image. This work employs image segmentation tools to examine images of thin blood smears data set. The goal is to explore options for a noniterative-based and automated system for detecting parasites in blood smears. This can be achieved by detecting the presence of a parasite in thin blood smears and quantifying the portion of red blood cells in the sample that are infected. First, we try segmenting the individual red blood cells from the background using the color thresholder. Next, we clean up the obtained cell mask and examine cell properties using the image region analyzer function, which allows quickly filling in region holes and filtering out regions based on their properties such as area dimensions or eccentricity. Then quickly gauge and specify the expected diameter range of the cells in pixels and indicate that the circles are dark relative to the background. Finally, we've combined the code for finding circles matching image histograms and the parasite threshold detection logic into a single function to quickly examine the performance of this function on the other images using the image batch processing technique. The proposed detection function labels the detected cells with blue circles the parasites are marked in red and the infected cells are highlighted in green. The proposed algorithm has appropriately compensated for the variability in image quality

Internet of Things: An Implementation and Its Challenges in Malaysia

to date, the Internet becomes one of the technologies that is rapidly evolving and changing. It has become trending over the world. The Internet of Thing is a mechanism composed of devices, sensors, networks, cloud storage, and application. Each device able to communicate with another device over the Internet to share the information and accomplished some objectives. IoT is known as one of the new future technologies and were gaining attention from various fields over the countries. Malaysia is one of the countries in the planning stage to increase the development of IoT, which is equivalent to other countries with the emerging IoT applications development. However, it was not easy to develop IoT devices due to some issues and challenges in implementing IOT devices. This paper addresses the major concern and challenges in IOT and the solution how to overcome these issues. The future trends and applications of IoT were also briefly discussed in this paper for gaining more in-depth knowledge about the IOT technology

An improved robust watermarking scheme using flexible scaling factor

Digital watermarking is needed to avoid piracy, forgery and illegal distribution from unauthorized people. The watermarking scheme is used to protect the ownership and copyright information in the multimedia data. A scaling factor plays an important role for balancing between invisibility and robustness for embedding watermark. However, the usage of a scaling factor may not be suitable for different selected blocks and image inputs. Flexible scaling factor is an alternative solution to obtain high robustness and invisibility in image watermarking. This research proposed a flexible scaling factor for DCT coefficients based on the image content itself. This research analyses the selected DCT coefficients against average coefficients on its block to obtain flexible scaling factor. The proposed scheme produced high invisibility with SSIM and PSNR values of 0.991 and 45dB, respectively. The proposed watermarking scheme also achieved strong resistant against noised image, filtered image and compressed image

An enhancement particle-based method for dynamic object tracking

Camera tracking systems have become a common requirement in today‘s society. The availability of high quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. Generally, it is not easy to track human behavior in an environment with a large view. This study aims to address three problems associated with object tracking. The first problem to be considered in this study is to improve the accuracy of object detection for multiple targets in nonlinear motion and during the occlusions occurs. Secondly, to track the precise location of object in relative size. The third problem to be considered is a to improve the processing time for the process of object detection and tracking. Thus, to address the accuracy of object detection, we proposed a new method of dynamic template matching using Global best Local Neighborhood in Particle Swarm Optimization (GbLN-PSO). In this study, feature-based approach using a GbLN-PSO algorithm will be applied to search the minimum value of dynamic template matching process. Furthermore, a model-based particle filter is used to address the problem of tracking objects precisely. This method is able to predict the precise location of object movement in the 2-D image. The combination of these two new proposed solutions, consequently, will improve the processing time in detecting the object with precision location. The proposed method has been tested with an experimental module using several sets of video data provided by the Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) and two other video streams of UBC hockey and Malaysian football games. The experiment has shown that the accuracy of tracking performance has increased up to 25% compared to others reported work in the scientific literature

Internet of Things: An Implementation and Its Challenges in Malaysia

To date, the Internet becomes one of the technologies that is rapidly evolving and changing. It has become trending over the world. The Internet of Thing is a mechanism composed of devices, sensors, networks, cloud storage, and application. Each device able to communicate with another device over the Internet to share the information and accomplished some objectives. IoT is known as one of the new future technologies and were gaining attention from various fields over the countries. Malaysia is one of the countries in the planning stage to increase the development of IoT, which is equivalent to other countries with the emerging IoT applications development. However, it was not easy to develop IoT devices due to some issues and challenges in implementing IOT devices. This paper addresses the major concern and challenges in IOT and the solution how to overcome these issues. The future trends and applications of IoT were also briefly discussed in this paper for gaining more in-depth knowledge about the IOT technology

A mobile camera tracking system using GbLN-PSO with an adaptive window

The availability of high quality and inexpensive video camera, as well as the increasing need for automated video analysis is leading towards a great deal of interest in numerous applications. However the video tracking systems is still having many open problems. Thus, some of research activities in a video tracking system are still being explored. Generally, most of the researchers are used a static camera in order to track an object motion. However, the use of a static camera system for detecting and tracking the motion of an object is only capable for capturing a limited view. Therefore, to overcome the above mentioned problem in a large view space, researcher may use several cameras to capture images. Thus, the cost will increases with the number of cameras. To overcome the cost increment a mobile camera is employed with the ability to track the wide field of view in an environment. Conversely, mobile camera technologies for tracking applications have faced several problems; simultaneous motion (when an object and camera are concurrently movable), distinguishing objects in occlusion, and dynamic changes in the background during data capture. In this study we propose a new method of Global best Local Neighborhood Oriented Particle Swarm Optimization (GbLN-PSO) to address these problems. The advantages of tracking using GbLN-PSO are demonstrated in experiments for intelligent human and vehicle tracking systems in comparison to a conventional method. The comparative study of the method is provided to evaluate its capabilities at the end of this paper

Global best Local Neighbourhood in Particle Swarm Optimization for Big Data Environment

The conventional Particle Swarm Optimization (PSO) still has weaknesses in finding optimal solutions especially in a dynamic environment. Therefore, in this paper we proposed a Global best Local Neighborhood in particle swarm optimization in order to solve the optimum solution in dynamic environment. Based on the experimental results of 50 datasets, show that GbLN-PSO has the ability to find the quality solution in dynamic environment