Mobile Applications

HifoCap app This is a plethora of small, related research projects. The first one was a collaborative pilot project with Drs. Rodrigo Romero and Sergio Cabrera of ECE, entitled HifoCap: A Wearable System for Detecting High Frequency Oscillations in EEGS of the Human Brain. The goal of the project was to investigate a wearable system for automatic detection of scalp high frequency oscillations (HFO). A wearable HifoCap device (cap) senses cortical signals and processes them with an embedded system. The main processing steps include amplification, filtering, and HFO detection. EEG waves containing HFOs are wirelessly transmitted to a smart phone (or tablet) using a personal area network protocol such as Bluetooth. An app running on the smart phone receives EEG waves for recording, time stamping, plotting, logging, and wireless transmission using a local area network protocol such as IEEE 802.11 to a cloud storage server. A future generation of the system may include full raw data acquisition and storage in a cloud server to support multiple types of off-line analysis.

HifoCap app Our research team, consisting of Oliver Singayigaya (MSSwE), Javier Garcia (MSEng), John Ramirez (BS), Brian Adriana Escobar (BS), and Brain Espinosa (BS), was responsible for developing an Android app for EEG data visualization and cloud server transfer. We identified many interesting challenges in developing a data intensive, soft real time system on non-dedicated Android devices such as smartphones and tablets. We classified these challenges based on their causes, e.g., interrupts such as incoming calls and notifications, lack of control on app's lifecycle (especially, suspension and destruction), garbage collection, high communication bandwidth (1.96 Mbps), long and continuous running, and network coverage outage. We proposed possible solutions to some of these challenges -- e.g., disabling/removing unpredictable services, minimizing the garbage collection time, selective decoding and visualization of EEG samples, optimization of network I/O, and light UI -- and showed the effectiveness of the proposed solutions by developing a prototype [Cheon, Romero and Garcia 2017]. We also observed and learned that the best practices for writing Java programs are not always best practices for Android applications [Cheon 2016]. They can be sources of memory performance issues [Escobar and Cheon 2017].

We recently become more interested in applying established software engineering principles, techniques, and methods to mobile application developments [Cheon 2012]. Examples include applications of model-driven development [Cheon and Barua 2018] multiplatform application development [Speicher and Cheon 2018] [Cheon 2019], and code reuse [Cheon, Chavez and Castro 2019].

  1. Yoonsik Cheon, Carlos Chavez and Ubaldo Castro. Code Reuse between Java and Android Applications, Proceedings of the 14th International Conference on Software Technologies (ICSOFT), Prague, Czech Republic, July 26-28, 2019, pages 246-253. [DOI: 10.5220/0007843702460253] [PDF]
  2. Yoonsik Cheon. Multiplatform Application Development for Android and Java. 17th IEEE/ACIS International Conference on Software Engineering, Management and Applications, Honolulu, Hawaii, May 29-31, 2019. [PDF]
  3. Terry J. Speicher and Yoonsik Cheon, Composing a Cross-platform Development Environment Using Maven, Workshop on Regional Consortium for Foundations, Research and Spread of Emerging Technologies in Computing Sciences, Juarez, Mexico, Nov. 8-9, 2018, pp. 68-80. [PDF]
  4. Yoonsik Cheon and Aditi Barua. Model Driven Development for Android Apps. Proceedings of the 2018 International Conference on Software Engineering Research & Practice, Las Vegas, Nevada, July 30 - August 2, 2018, pages 17-22. [PDF]
  5. Adriana Escobar De La Torre and Yoonsik Cheon, Impacts of Java Language Features on the Memory Performances of Android Apps, Technical Report 17-84, Department of Computer Science, University of Texas at El Paso, El Paso, TX, September 2017. [PDF]
  6. Yoonsik Cheon, Rodrigo Romero, and Javier Garcia, HifoCap: An Android App for Wearable Health Devices, Advances in Digital Technologies, Proceedings of the 8-th International Conference on Applications of Digital Information and Web Technologies, Volume 295 of Frontiers in Artificial Intelligence and Applications, pages 178--192, IOS Press, 2017. [DOI: 10.3233/978-1-61499-773-3-178]
  7. Yoonsik Cheon, Are Java Programming Best Practices Also Best Practices for Android? Technical Report 16-76, Department of Computer Science, University of Texas at El Paso, El Paso, TX, December 2016. [PDF]
  8. Yoonsik Cheon, Extending Java for Android Programming, International Conference on Software Engineering Research and Practice, Las Vegas, Nevada, July 16-19, 2012. [PDF]

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