Carlos Oppus

Bio: Carlos Oppus is an academic researcher from Ateneo de Manila University. The author has contributed to research in topics: Arduino & Wireless sensor network. The author has an hindex of 9, co-authored 42 publications receiving 306 citations.

A mobile disaster management system using the android technology

Abstract: The Philippines is one of the countries in the world vulnerable to natural hazards because of its geographic location. It also lacks an efficient disaster management system that will help in times of need. One common scenario during disasters is that the activity of rescue and relief is not well-coordinated. For this reason, there is a need for a system that will help in the efficient provision of rescue and relief to disaster-affected areas. Since the use of smart phones is gaining interest in people, the disaster management system was implemented as a smart phone application using Google's Android operating system. The disaster management system Android application known as MyDisasterDroid determines the optimum route along different geographical locations that the volunteers and rescuers need to take in order to serve the most number of people and provide maximum coverage of the area in the shortest possible time. Genetic algorithm was applied for optimization and different parameters were varied to determine the most optimum route.

Image edge detection using ant colony optimization

Abstract: Ant colony optimization (ACO) is a population-based metaheuristic that mimics the foraging behavior of ants to find approximate solutions to difficult optimization problems. It can be used to find good solutions to combinatorial optimization problems that can be transformed into the problem of finding good paths through a weighted construction graph. In this paper, an edge detection technique that is based on ACO is presented. The proposed method establishes a pheromone matrix that represents the edge information at each pixel based on the routes formed by the ants dispatched on the image. The movement of the ants is guided by the local variation in the image's intensity values. The proposed ACO-based edge detection method takes advantage of the improvements introduced in ant colony system, one of the main extensions to the original ant system. Experimental results show the success of the technique in extracting edges from a digital image.

Post-disaster rescue facility: Human detection and geolocation using aerial drones

Abstract: The research focuses on the implementation of a human detection and geolocation system using aerial drones to complement search and rescue. The study includes the characterization of the human detection system for thermal and optical imagery by determining the frame accuracy, true and false positive rates. Geolocation was achieved using triangulated-adjusted GPS data and integration of Google Maps.

A study on ocular and facial muscle artifacts in EEG signals for BCI applications

Abstract: This work aims to analyze the EEG signals produced by facial gestures and eye movements called artifacts. Although these signals are considered contaminants in EEG signals used for medical diagnosis, these are observed in order to consider the possibility of using them as inputs for certain applications. As such, the project aims to observe distinct signal patterns in the EEG signals acquired for certain facial gestures as a preliminary work to facial gesture detection. Using the Emotiv Epoc Neuroheadset, the cross correlation between pairs of 14 channels for six facial gestures and their frequency response are compared. These facial gestures are blink, left wink, right wink, raise brow, smile, and clench. Particular channel pairs are found to be highly correlated for certain facial gestures and can be used as possible means of detecting these gestures. In the frequency domain, only the gestures smile and clench registered a distinctive frequency response among the other gestures. Moreover, the Emotiv Epoc neuroheadset paired with the Arduino Duemilanove board was found to be an effective tool as a controller for household appliances. Also, the neuroheadset was useful in developing an extended communication platform. As such not only does it prove to be a viable device for developing systems in aiding the physically-challenged, but also provides a glimpse of the potential advances in the field of Brain-Computer Interfaces.

User-oriented finger-gesture glove controller with hand movement virtualization using flex sensors and a digital accelerometer

Abstract: A low-cost wireless glove controller that detects finger gestures was developed using makeshift flex sensors and a digital accelerometer. The performance of the makeshift flex sensors was compared to that of commercially available ones. A system using Arduino, Bluetooth, and Processing was developed to allow the user to specify desired finger gestures for controlling a variety of robotic devices. A simple mobile robot (mobot) was used to demonstrate the capabilities of the glove in controlling devices. A 3D virtual environment was also created for the virtualization of the user's hand movements detected by the glove controller. Inside this virtual environment, the user-defined finger gestures also allowed the user to control elements inside the virtual environment which can branch out to various applications including rehabilitation and body-oriented gaming.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Signaling over cellular networks to reduce the Wi-Fi energy consumption of mobile devices

Abstract: Techniques for increasing the battery life on a mobile device by decreasing the energy consumption of the mobile device's wireless fidelity (Wi-Fi) interface are described. In one embodiment, the mobile device's Wi-Fi interface is automatically disabled when the device is not engaged. When the device receives a wake up call from a server via its Cellular interface, the Wi-Fi interface is enabled if the device answers the wake up call and the Wi-Fi interface is available. Using its Wi-Fi interface, the mobile device then connects to an IP-based network via a Wi-Fi access point.

Life Signs Detector Using a Drone in Disaster Zones

Abstract: In the aftermath of a disaster, such as earthquake, flood, or avalanche, ground search for survivors is usually hampered by unstable surfaces and difficult terrain. Drones now play an important role in these situations, allowing rescuers to locate survivors and allocate resources to saving those who can be helped. The aim of this study was to explore the utility of a drone equipped for human life detection with a novel computer vision system. The proposed system uses image sequences captured by a drone camera to remotely detect the cardiopulmonary motion caused by periodic chest movement of survivors. The results of eight human subjects and one mannequin in different poses shows that motion detection on the body surface of the survivors is likely to be useful to detect life signs without any physical contact. The results presented in this study may lead to a new approach to life detection and remote life sensing assessment of survivors.

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives.

Abstract: A fascinating challenge in the field of human-robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human-machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human-robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

Review on MANET Based Communication for Search and Rescue Operations

Abstract: Mobile ad hoc networks (MANETs) are defined as a collection of wireless mobile nodes that are characterized as independent, dynamic, self-adaptive and infrastructure less. This mobility and dynamic in nature of MANET urge for new set of networking strategies to be implemented so as to provide efficient communication during natural disaster. Mobile crowd management is yet an emerging new exploration for search and rescue (SAR) operations. The phase of catastrophic management has communication to play a vital role. In MANETs, the end nodes communicate through the TCP/IP protocol. The layers of TCP/IP model change frequently due to mobility and resource overhead. This makes routing in MANETs one of the challenging research area. This review provides an overview of MANET based routing protocols employed for communication during SAR operations. The main purpose of this survey is to provide taxonomy on communication strategies of MANETs during disaster and emergency situation. Furthermore, this paper examines the existing MANET based SAR techniques and the significant contribution made by researches in the past few years based upon routing protocol, communication techniques, type of infrastructure and phase of disaster management.