Earticle

With the growing popularity of the iPhone, there is a constantly increasing demand of users for mobile applications of iPhone, in which the alarm clock application is contained. But existing alarm clock application is not favored by many users because of functional singleness. This paper presents an iOS-based mobile application on alarm clock with online radio support, and designs and implements its framework by virtue of Objective-C and SQLite in the Xcode 4.5.2 development environment, based on the popular three-tier MVC software design structure. After the application is installed and deployed on iPhone, it is available to users by providing some functions such as auto-playing radio, setting radio alarm clock, turning off the alarm by shaking iPhone, sleeping timer by radio, reserving radio program, binding and sharing microblog. This proposed iOS-based mobile application provides users with more choices and allows the user to enjoy a more colorful leisure time. The practical test from some people shows that the proposed application is very popular with users, which also illustrates its practicability and effectiveness.

Development of computing environments and mobile devices lead to rapid growth of smart device, mobile OS and application market. Especially, the mobile platform sector with major runners of Apple and Google, compose the core technology for mobile applications. iOS and Android represent mobile platforms each supporting individual execution environment, development tools and development methodology. Applications and contents developed in each platform inevitably have exclusive properties in the other platforms. Therefore, generally it is not possible to execute a mobile application on a different platform. In order to provide service to multiple platforms by portability, additional costs and development period is required. Particularly, the lifespan and development duration of mobile applications are becoming shorter at this point in time. Therefore, fast development and deployment is required and has been standing out as a weakness. Smart Cross Platform is a virtual machine based solution developed to resolve this weakness. In this paper, Smart Cross Platform's content execution component, Smart Virtual Machine based on an independent neutral language was designed and implemented to be run in iOS. In the Smart Virtual Machine, the programmer can make a program not being restricted to the development language of a particular platform environment, making it is easier to port previous contents or provide service of a single program to multiple platforms.

The iOS platform developed by Apple is the world’s most advanced mobile operating system, continually redefining what people can do with a mobile device. Bada platform developed by Samsung is a smart phone platform, which is created for wide range of device. "Bada" is a Korean word that means "ocean" and "seashore". "bada" in itself embodies the open possibilities of the ocean. Due to the use of different smart phone platforms, game developers must create content designed specifically for each platform or use a conversion process to provide game content to consumers. In this paper, to resolve this problem, the iOS-to-Bada automatic smart game converter was designed to automatically translate game contents from the iOS platform to the Bada platform for smart phones. Through the iOS-to-Bada converter, resources such as images and sounds can be converted, APIs can be converted using a platform mapping engine. These and all other content conversion functions were examined. Test results indicate that the graphics, image output, sound output, and other functions of converted Bada games were equivalent to those of the iOS games before conversion.

The iOS platform developed by Apple is the world’s most advanced mobile operating system, continually redefining what people can do with a mobile device. The Android platform developed by Google is an optimized platform for mobile devices with a perfect combination of an operating system, middleware, and application programs. Given that mobile communication companies choose different smart phone platforms, game developers must create content designed specifically for each platform or use a conversion process to provide game content to consumers. In this paper, to resolve this problem, the iOS-to-Android automatic smart game converter was designed to automatically translate game contents from the iOS platform to the Android platform for smart phones. Through the iOS-to-Android converter, resources such as images and sounds can be converted, APIs can be converted using a platform mapping engine. These and all other content conversion functions were examined. Test results indicate that the graphics, image output, sound output, and other functions of converted Android games were equivalent to those of the iOS games before conversion.

본 논문에서는 iOS 플랫폼을 위한 스마트 가상기계(Smart Virtual Machine)를 설계하고 구현하였다. iOS 플랫폼을 위한 스마트 가상기계는 가상기계 기반의 컴파일러가 C/C++나 Java 언어로 작성된 프로그램과 의미적으로 동등한 중간 코드를 생성하면 이를 입력으로 받아 실행시켜주는 프로그램이다. 스마트 가상기계는 실행 파일인 SEF(Smart Executable Format) 파일을 입력으로 받아 실행하며, SEF 로더와 인터프리터, 내장 라이브러리, 실행 환경으로 구성되어 있다. 스마트 가상기계는 스마트 기기 등에 탑재되어 하드웨어 독립적으로 실행되며 어플리케이션 형태로 실행이 가능하므로 플랫폼에 독립적인 장점을 가진다. 또한, 프로그래머는 개발 언어의 제약 없이 프로그램을 작성할 수 있고, 프로그램을 플랫폼 독립적으로 실행할 수 있다.

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.