M. Anwar Ma'sum

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.

Adaptive traffic signal control system using camera sensor and embedded system

Adaptive traffic signal control system is needed to avoid traffic congestion that has many disadvantages. This paper presents an adaptive traffic signal control system using camera as an input sensor that providing real-time traffic data. Principal Component Analysis (PCA) is used to analyze and to classify object on video frame for detecting vehicles. Distributed Constraint Satisfaction Problem (DCSP) method determine the duration of each traffic signal, based on counted number of vehicles at each lane. The system is implemented in embedded systems using BeagleBoard™.

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.

Enhanced adaptive traffic signal control system using camera sensor and embedded system

Traffic plays an important role in social stability and community development. Without an appropriate traffic signal control system, the possibility of traffic congestion will be very high and causes various negative impacts. The traffic signal control system with video camera sensor is implemented in embedded systems using BeagleBoard-xM. The system uses Viola-Jones method and Haar Training in detecting the vehicle object from a video frame. Then, Euclidean distance and kalman filter methods are used in tracking the vehicle. The ability of kalman filter in predicting the next position of the object is a very important feature for multi-object tracking. The number of counted vehicles in each lane at the intersection then will be processed using Fuzzy Logic to determine optimal cycle time and split time.

Particle swarm optimation based 2-dimensional randomized hough transform for fetal head biometry detection and approximation in ultrasound imaging

One of the most profound use of ultrasound imaging is to generate the image of fetal during pregnancy. This paper will describe an ellipse detection approach to automatically detect and approximate the head size of the fetal. The method was developed using the Hough Transform techniques that have been modified and optimized by Particle Swarm Optimization (PSO). Experiments of the method are tested on synthetic and real ellipse image dataset. For real images, the detection was applied on 2D ultrasonography images to perform fetal head measurement to approximate the Head Circumference (HC) and Biparietal Diameter (BPD). Experiment result showed that the proposed method can perform ellipse detection in synthetic dataset with satisfactory result for noisy images with noise density up to 0.4 and able to perform the fetal head detection for real images with an averate hit rate of 0.654. This proposed method can also perform detection on images that have high degree of noise or incomplete ellipse images generated from the fetal objects.

Developing smart telehealth system in Indonesia: Progress and challenge

Indonesia is developing country with high population. There are more than 200 million residents living in the country. As a developing country, Indonesia has several health problems. First, Indonesia has a high value of mortality caused by heart and cardio vascular diseases. One of the major cause is the lack of medical checkup especially for heart monitoring. It is caused by limited number of medical instrumentation e.g. ECG in hospital and public health center. The supporting factor is the small number of cardiologist in Indonesia. There are 365 cardiologists across the country, which is a very small number compared to the 200 million of Indonesia population. Furthermore, they are not distributed evenly in all provinces, but only centered in Jakarta and other capital cities. Therefore, it is difficult for residents to get appropriate heart monitoring. Second, the mortality rate of mother and baby during delivery of the baby in Indonesia is also high. One way to solve this problem is to devise a system where the health clinics in rural areas can perform fetal biometry detection before consulting the results to the expert physicians from other areas. The proposed system will be equipped with algorithms for automatic fetal detection and biometry measurement. By the end of this development, we have several results, the first is a classifier to automatic heartbeat disease prediction with accuracy more than 95%, the second is compression method based on wavelet decompositon, and the third is detection and approximation a fetus in an ultrasound image with hit rate more than 93%.

Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm

Vehicle speed estimation using Closed Circuit Television (CCTV) is one of the interesting issues in the field of computer vision. Various approaches are used to perform automation in vehicle speed estimation using CCTV. In this study, the use of Gaussian Mixture Model (GMM) for vehicle detection has been improved with the hole-filling method (HF). The speed estimation of the vehicles with various scenarios has been done, and gives the best estimation with the deviation of 7.63 Km/hr. GMM fusion with hole-filling algorithm combined with Pinhole models have shown the best results compared with results using other scenarios.

Intelligent K-Means clustering for expressed genes identification linked to malignancy of human colorectal carcinoma

Cancer is one kind of deadly disease. Colorectal carcinoma is one type of cancer which is difficult to detect in its early stage. It has dangerous malignancy in its advance stage. Identify gene expressed and cancer linked to phenotype is an effort to identify and analyze correlation of genes and clinical phenotype (metastasis). In this paper Intelligent K-Means is used to cluster genes expression. It is a non parametric clustering that more powerful and more stable than GMM clustering which is used in previous research. After getting clusters of genes, then correlation ratio is used to identify whether genes in a cluster has a correlation with clinical metastasis. As the result in this paper, genes in cluster C and cluster E have correlation with normal-cancer tissue metastasis and distant metastasis. But, there is no cluster of genes has correlation with lymph node metastasis.

Fetal head segmentation based on Gaussian elliptical path optimize by flower pollination algorithm and cuckoo search

Number of maternal and infant mortality in Indonesia is high. This problem can be minimized by monitoring the fetal condition via ultrasound image. In addition, Indonesia have small number of obstetrics and gynecology compare to number of its population. Moreover, it is centralized in urban areas, so it is hard to monitor the condition of every babies in Indonesia. In order to resolve this problem, we have built fetal head monitoring system. Part of the system is to segment the fetal head in ultrasound image. In this paper, we examine nature optimization such as bat algorithm, cuckoo search, and flower pollination algorithm for optimizing Gaussian elliptical path for automatic fetal head segmentation. Experiment results shows that nature optimization Based Gaussian elliptical path (DoGEII-FPA and DoGEII-CS) has a minimum error compared to Gaussian elliptical path (DoGEll) which is optimized by Nelder-Mead. Interestingly, DoGEll-FPA and DoGEll-CS perform well from DoGEll-NM in different image.

Design of intelligent k-means based on spark for big data clustering

The growth of data has bring us to the big data generation where the amount of data cannot be computed using conventional environment. There are a lot of computational environment that had been developed to compute big data, one of them is Hadoop that has Distributed File System and MapReduce framework. Spark is newly framework that can be combined with Hadoop and run on top of it. In this paper, we design intelligent k-means based on Spark for big data clustering. Our design is using batch of data instead using original Resilient Distributed Dataset (RDD). We compare our design with the implementation that using original RDD of data. Result of experiment shows that implementation using batch of data is faster than the implementation using original RDD.