Grafika Jati

Simulation of intelligent Unmanned Aerial Vehicle (UAV) For military surveillance

Nowadays, Unmanned Aerial Vehicle (UAV) is an important technology for military and security application. Various missions can be done using UAV such as surveillance in unknown areas, forestry conservation, and spying enemy territory. Application which is developed in this research has a purpose to simulate condition in war zone for spying the enemy. Platform used in the experiment is Parrot AR. Drone ver.2.0, an mini quadrotor which was developed by Parrot SA. This quadrotor controlled by Robot Operating System (ROS) framework. The quadrotor will search and recognize some objects and locate their location. Many algorithms were used to do the mission. To recognize object Adaboost Classifier and Pinhole Algorithm were used. The result shows that average error for all scenario is only 0.24 meters.

Autonomous quadcopter swarm robots for object localization and tracking

A swarm Unmanned Aerial Vehicle (UAV) or quad copter robot for object localization and tracking has been developed. The robot is potentially utilized for military purpose, i.e. doing patrol continuously especially in frontier area. In other words, the UAV is proposed to carry out patrol and exploration by exploring coverage area, find, localize and track suspicious objects. The swarm robots are equipped with Modified Particle Swarm Optimization (PSO) Algorithm for intelligent feature. PSO is an optimization algorithm where each agent of swarm will use its individual perception (local base) and community perception (global base). This swarm quad copter system was implemented using Robot Operating System (ROS) Framework. Experiment was conducted with 3 quadcopter agents and one object as the target. Two main scenarios have been exercised, i.e. a scenario with steady target and another one with moving target. Experimental result shows that Modified PSO implemented in this system has better performance compared to fully random based moving algorithm for object localization and tracking.

Human Sperm tracking using Particle Swarm Optimization combined with Smoothing Stochastic sampling on low frame rate video

In this paper, we present a technique for visual tracking in the field of Human Sperm motion. Application of sperm cell tracking is mainly important in Intracytoplasmic Sperm Injection (ICSI), a medical procedure that has enabled the In Vitro Fertilization (IVF) of a single sperm which is injected directly into an egg. In this paper, we consider the problem of tracking single object in video sequences of human sperms and a newly developed Smoothing Stochastic Approximate Monte Carlo (SSAMC) based tracker enhanced by Particle Swarm Optimization (PSO). The problem for this research is that the motility or movement of Human Sperm is fast and unpredictable. In addition, each and every sperms have closely similar size and shape. To solve this problem, we used PSO for searching algorithm (finding the best target) in a Search Window, it can reduce the search space in every each consecutive frame. The measurement results of the proposed method are then compared with the manual measurements done by experts. The experiment results were conducted on both open video data and our own video data. Experiment results showed that the proposed method can handle our specific problem in human sperm cell tracking, and give us a better result as compared to our previous tracker, which used geometric transition dynamic model and without any enhancement by PSO.

Design DDoS attack detector using NTOPNG

Distributed Denial of Service (DDoS) is one kind of attacks using multiple computers. An attacker would act as a fake service requester that drains resources in computer target. This makes the target cannot serve the real request service. Thus we need to develop DDoS detector system. The proposed system consists of traffic capture, packet analyzer, and packet displayer. The system utilizes Ntopng as main traffic analyzer. Detector system has to meet good standard in accuracy, sensitivity, and reliability. We evaluate the system using one of dangerous DDoS tool named Slowloris. The system can detect attacks and provide alerts to detector user. The system also can process all incoming packets with a small margin of error (0.76%).

ECG signal compression by predictive coding and Set Partitioning in Hierarchical Trees (SPIHT)

In this paper we present a method for multi-lead ECG signal compression using Predictive Coding combined with Set Partitioning In Hierarchical Trees (SPIHT). We utilize linear prediction between the beats to exploit the high correlation among those beats. This method can optimize the redundancy between adjacent samples and adjacent beats. Predictive coding is the next step after beat reordering step. The purpose of using predictive coding is to minimize amplitude variance of 2D ECG array so the compression error can be minimize. The experiments from selected records from MIT-BIH arrhythmia database shows that the proposed method is more efficient for ECG signal compression compared with original SPIHT and relatively have lower distortion with the same compression ratios compared to the other wavelet transformation techniques.

Accurate visual tracking by combining Bayesian and evolutionary optimization framework

Visual tracking is the process of locating, identifying, and determining of an object within video frames. From a Bayesian perspective, this is done by estimating the posterior density function. On the other hand, evolutionary optimization perspective would like to generate and select sufficiently optimize solution using two major components: diversification and intensification. This research will develop visual tracking algorithm using a Bayesian approach with evolutionary optimization in order to perform accurate tracking. The main idea is to combine Particle Markov Chain Monte Carlo (Particle-MCMC) as representation of Bayesian approach, with evolutionary optimization that is Particle Swarm Optimization (PSO) in each video frame. The visual tracking is regulated by Particle-MCMC filter algorithm and PSO will work within this filter to get more accurate tracking. Based on the dataset groundtruth, we found the accuracy of tracking can be increased considerably comparing to our previous research.

Multi-sperm tracking using Hungarian Kalman Filter on low frame rate video

One factor of human sperm health is sperm motility. Motility is the ability of sperm to move. Sperm with healthy motility move forward promptly, not inactive and not moving in circles. In this paper, we would like to analyse sperm motility by considering the problem of multi object tracking in video sequences of human sperms. The challenges in multi-sperm tracking are many human sperms have fast and unpredictable movement In addition, the sperm have similar size and shape comparing by each others. To solve this problem, we used sperm detection in each video sequence to get the position of sperms. In the same time, the estimated sperm position is calculated based on previous tracking by using Kalman Filter. Finally the positions of detected sperms are compared to estimation results by using Hungarian assignment method. In this way, the trajectory of each sperm can be conclude. This paper analyze sperm motility qualitatively based on the resulted sperm trajectories. The experiment results were conducted on both open video data and our own low-frame-rate video data. The experiment results shows that the proposed method can handle the challenges in multi sperm tracking, create their trajectory and then analyze their behaviors.

Dimensionality reduction using deep belief network in big data case study: Hyperspectral image classification

The high dimensionality in big data need a heavy computation when the analysis needed. This research proposed a dimensionality reduction using deep belief network (DBN). We used hyperspectral images as case study. The hyperspectral image is a high dimensional image. Some researched have been proposed to reduce hyperspectral image dimension such as using LDA and PCA in spectral-spatial hyperspectral image classification. This paper proposed a dimensionality reduction using deep belief network (DBN) for hyperspectral image classification. In proposed framework, we use two DBNs. First DBN used to reduce the dimension of spectral bands and the second DBN used to extract spectral-spatial feature and as classifier. We used Indian Pines data set that consist of 16 classes and we compared DBN and PCA performance. The result indicates that by using DBN as dimensionality reduction method performed better than PCA in hyperspectral image classification.

Big data compression using spiht in Hadoop: A case study in multi-lead ECG signals

Compression still become main concern in big data framework. The performance of big data depend on speed of data transfer. Compressed data can speed up transfer data between network. It also save more space for storage. Several compression method is provide by Hadoop as a most common big data framework. That method mostly for general purpose. But the performance still have to optimize especially for Biomedical record like ECG data. We propose Set Partitioning in Hierarchical Tree (SPIHT) for big data compression with study case ECG signal data. In this paper compression will run in Hadoop Framework. The proposed method has stages such as input signal, map input signal, spiht coding, and reduce bit-stream. The compression produce compressed data for intermediate (Map) output and final (reduce) output. The experiment using ECG data to measure compression performance. The proposed method gets Percentage Root-mean-square difference (PRD) is about 1.0. Compare to existing method, the proposed method get better Compression Ratio (CR) with competitive longer compression time. So proposed method gets better performance compare to other method especially for ECG dataset.

Implementation of grid mapped robot planning algorithm in a continuous map for fire fighting robot

Fire-fighting robot is still one of the fields in robotic competitions held these days. This paper is aimed to see the implementation of the Markov Decision Planning (MDP) problem in a fire-fighting robots navigation. The MDP algorithm evolves planning of the actions the robot should take according to the policy. This planning is mapped into a grid map. Yet in the implementation, this planning is applied in a continuous map. Using a fire-fighting robot the succession of this planning implementation is undertaken. The result shows that the implementation of grid mapped in a continuous map yields significant impacts that lead the MDP to be able to solve the limitation of wall following algorithm. This algorithm is also applied in the real autonomous mobile robot.