A Model Driven Approach for State Management in Mobile Applications
With the paradigm shift from desktop to mobile applications and the growing demand for mobile devices has awakened the interest of the IT industry on how to tackle the development of mobile applications. Managing application state is hard in building modern mobile applications. As application complexity increases, it becomes increasingly difficult to keep the track of changing state and mapping those changes back to user interface. State management is challenging due to its low-level implementation complexity. To overcome above mentioned issue, there is a strong need to introduce model driven approach for state management in mobile applications. This paper proposes a Unified Modeling Language profile for Mobile Application State Management (UMASM) to simplify mobile application state management requirements. Particularly, UMASM is capable of representing mobile state management requirements at higher abstraction level. This provides the strong basis to automatically generate low level implementations in target platform like Redux from high level UMASM models. The applicability of UMASM is validated through e-banking application case study.
Modeling and Code Generation of Android Applications Using Acceleo
The development of mobile applications becomes increasingly popular in our daily life due to the intensive use of applications in mobile devices like smartphones and tablets. The diversity and variety of mobile operating systems (Android, iOS, Black Berry, Windows Phone, etc.) make software engineers in front of a big challenge to develop the same application for these different platforms. This paper presents a methodology based on the Model Driven Architecture (MDA) to develop mobile applications according to the principle “develops once, use everywhere”. Our approach exploits UML modeling and Acceleo to generate specific code in order to accelerate and facilitate the development of mobile applications.
Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG Signals
Parkinson’s disease (PD) is a common neurodegenerative disease that causes involuntary muscle movements and tremor among other symptoms. One approach to diagnosing this disease is by analyzing the electroencephalography (EEG) signals of the patients. However, due to the complexity of this type of signal, more advanced feature extraction and classification methods are required. The goal of this study is to combine six well-known features in EEG analysis with eight higher order statistical features and use them for classification of early stage PD (1st and 2nd stage) from a healthy control group. After extracting the required features, eight classifiers are employed to classify the signals.