Ibrahim, Sutrisno Warsono
Komunitas Ilmuwan dan Profesional Muslim Indonesia

Published : 2 Documents
Articles

Found 2 Documents
Search

A comprehensive review on intelligent surveillance systems Ibrahim, Sutrisno Warsono
Communications in Science and Technology Vol 1 No 1 (2016)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.1.1.2016.7

Abstract

Intelligent surveillance system (ISS) has received growing attention due to the increasing demand on security and safety. ISS is able to automatically analyze image, video, audio or other type of surveillance data without or with limited human intervention. The recent developments in sensor devices, computer vision, and machine learning have an important role in enabling such intelligent system. This paper aims to provide general overview of intelligent surveillance system and discuss some possible sensor modalities and their fusion scenarios such as visible camera (CCTV), infrared camera, thermal camera and radar. This paper also discusses main processing steps in ISS: background-foreground segmentation, object detection and classification, tracking, and behavioral analysis.
Electroencephalography (EEG)-based epileptic seizure prediction using entropy and K-nearest neighbor (KNN) Ibrahim, Sutrisno Warsono; Djemal, Ridha; Alsuwailem, Abdullah; Gannouni, Sofien
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.44

Abstract

Epilepsy is known as a brain disorder characterized by recurrent seizures. The development a system that able to predict seizure before its coming has several benefits such as allowing early treatment or even preventing the seizure. In this article, we propose a seizure prediction algorithm based on extracting Shannon entropy from electroencephalography (EEG) signals.  K-nearest neighbor (KNN) method is used to continuously monitor the EEG signals by comparing with normal and pre-seizure baselines to predict the upcoming seizure. Both baselines are continuously updated based on the most recent prediction result using distance-based method. Our proposed algorithm is able to predict correctly 42 from 55 seizures (76 %), tested using up to 570 hours EEG taken from MIT dataset. With its simplicity and fast processing time, the proposed algorithm is suitable to be implemented in embedded system or mobile application that has limited processing resources.