ICTIIA 2022 Conference Papers are now published on IEEE Xplore. Congratulations again to four of our students for these remarkable works:
1. Phoebe Bintoro
Portable Image Analyzer for Indicators with OpenCV
P. Bintoro, N. Timothy, A. A. Salim and E. Steven, "Portable Image Analyzer for Indicators with OpenCV," Conference Paper, 1st International Conference on Technology Innovation and Its Applications (ICTIIA), IEEE Xplore, pp. 1-6, DOI: 10.1109/ICTIIA54654.2022.9935895. (2022).
IEEE Xplore Link:
Abstract
Some diagnostic tools rely on human eyesight to determine their results such as urine dipsticks or pH strips. Ideally, in these cases, human eyesight needs to be verified by other people, or technology, as misinterpretations may lead to dangerous outcomes. Hence, to alleviate this issue, in this proof-of-concept we developed a portable and compact computer vision system using Raspberry Pi 3B+ and a digital camera as a powerful tool to objectively analyze physical indicators such as pH test strips. The algorithm is based on a series of thresholding, localization, color extraction and calculations that averages the multiple BGR parameters into a single value for comparison with a custom pH library. Thus, it compares BGR values to determine the most appropriate pH level based on the smallest difference in BGR between a test strip and a library. Case examples are described where inaccuracies of typical pH strip readings are compensated and corrected by the system.
2. Jin Wan Kim
Implementation of A* Shortest Path Finding Algorithm in a Transport Robot with Robust Turning Mechanism
J. W. Kim, N. Timothy, A. A. Salim, E. Taniara and E. Steven, "Implementation of A* Shortest Path Finding Algorithm in a Transport Robot with Robust Turning Mechanism," Conference Paper, 1st International Conference on Technology Innovation and Its Applications (ICTIIA), IEEE Xplore, pp. 1-6, DOI: 10.1109/ICTIIA54654.2022.9935869. (2022).
IEEE Xplore Link:
Abstract
Autonomous transportation is a rapidly emerging field of technology with potential applications ranging from safe delivery of goods in disaster sites or hospitals to intelligent warehouse logistics management. However, an overwhelming majority of this emerging process focuses on high-end quality products with prices which deny entrance into the field for most. Herein, we demonstrate the development of a proof-of-concept autonomous line-following robot that could serve as a low-cost budget solution for this domain. Rather than using performance-demanding AI approaches, the utilization of the A* path finding algorithm in combination with a line-following movement approach enables the use of the low-cost single-board computer, raspberry pi. Infrared sensors are used in complement with a grid of black lines to enable the robot's movement. Additionally, this work dabbles in the computational performance of the A * algorithm alongside the development of a highly robust turning mechanism.
3. Reuben Ailin Santoso
Voice Assisted Guide for the Blind using QR Codes and Wearable Smartphones
R. A. Santoso, D. H. Hareva, D. Sentausa and E. Steven, "Voice Assisted Guide for the Blind using QR Codes and Wearable Smartphones," Conference Paper, 1st International Conference on Technology Innovation and Its Applications (ICTIIA), IEEE Xplore, pp. 1-6, DOI: 10.1109/ICTIIA54654.2022.9935994. (2022).
IEEE Xplore Link:
Abstract
Visual disabilities are a recurring issue that negatively impacts many lives. Many existing public spaces are inaccessible to blind people. Although, many studies aim at assistive technology solutions, scalability of such solutions remains a challenge. Thus, the aim of this study is to develop a scalable solution to improving the accessibility of public spaces for blind people via a voice assisted guide mobile application system. The system is based on a pair of QR code reading and planning apps. The QR reader uses the built-in camera to read QR codes and output auditory cues for the blind users with one-touch operation. The QR planner allows other users to customize a given space with contextual QR code messages for guiding the blind user as they navigate the space. The flexibility of the apps' potential applications is demonstrated through case simulations along with analysis of the operating parameters that are hoped to enable wide scale deployment in various public spaces.
4. Olivia Lim
Companion: Mental Health Mobile Applications for Students
O. O. Lim, D. H. Hareva, E. Steven, D. Sentausa and F. A. Abineno, "Companion: Mental Health Mobile Applications for Students," Conference Paper, 1st International Conference on Technology Innovation and Its Applications (ICTIIA), IEEE Xplore, pp. 1-6, doi: 10.1109/ICTIIA54654.2022.9935882. (2022).
IEEE Xplore Link:
Abstract
In this time and age, mental health and technology plays a big role in society. The app Companion is a mental health app which targets students during their struggle in life, where they may feel there is no one to talk to and no one to reach for help. The app aims to be a safe place for students to pour their thoughts and feelings out as they consult with qualified doctors. In this work, we provide a brief overview of the often overlooked issue in our society regarding mental health, especially during adolescence phase of student life. Companion app is built with students as its main community of interest while focusing on simplicity and long-term usability. The User Interface is built using Figma, turned into a real application without coding through the Bravo Studio platform, and integrated to a firebase realtime database with Airtable. We hope our app could make a safe space for students to lighten their pressure in life and help them heal their mental health.