Mobile development companies aim to maximize the return on investments by making their mobile applications (Apps) available on different mobile platforms. Consequently, the same App is developed several times; each time the developer uses the programming languages and development tools of a specific platform. Therefore, there is a need to have cross-platform mobile applications development solutions that enable the developers to develop the App once and run it everywhere. The Integrated Cross-Platform Mobile Applications Development (ICPMD) solution is one of the attempts that enables the developers to use the most popular programming languages like Java for Android and C# for Windows Phone 8 (WP8). ICPMD is used to transform both the source code and user interface to another language to generate full Apps on the target platform. This paper extends ICPMD by proposing a new code conversion approach based on XSLT and Regular Expressions to ease the conversion process. In addition, it provides the assessment method to compare the ICPMD efficiency with competing approaches. Several Apps are converted from WP8 to Android and vice versa. The ICPMD evaluation results show reasonable improvement over commercial cross-platform mobile development tools (Titanium and Xamarin).

Restoring vision is no longer impossible as a result of recent advances in neural interfaces. Successful demonstrations of retinal implants motivate the development of more effective visual prostheses. The thalamic Lateral Geniculate Nucleus (LGN) is one potential deep-brain interfacing site for visual prostheses. A main challenge in developing thalamic as well as other visual prostheses is optimizing the parameters of electrical stimulation. This paper introduces a Kalman-based optimal encoder whose function is to determine the optimal electrical stimulation parameters required to induce a certain visual sensation. The performance of the proposed approach is demonstrated using a probabilistic model of LGN neurons. Results demonstrate a significant similarity between neuronal responses obtained using electrical stimulation and the responses obtained using the corresponding visual stimuli with a mean correlation of 0.62 (P <; 0.01, n = 54). These results indicate the efficacy of the proposed optimal encoder in driving LGN neurons to induce visual sensations.