Yu-Seok Bae

Architecture for interoperability of services between an ACAP receiver and home networked devices

As digital convergence of home networking and broadcasting technologies is being accelerated, it becomes increasingly important to achieve interoperability of not only heterogeneous devices but also various types of services. Therefore, in this paper, we describe the design and implementation of architecture for interoperability of data broadcasting and home networking services based on the ACAP middleware and the UMB stack. The proposed architecture provides a TV-centric user interface capable of managing home networking services using TV sets, by applying Java TV Xlet interface and HAVi user interface APIs for home networking sendees such as data broadcasting services. In addition, it provides data broadcasting services through metadata and media streaming for home networked devices without ACAP middleware.

Effective encoding of TV-Anytime metadata using EXI

The TVA metadata allows consumers to find, navigate, and manage contents through a variety of terminal devices including PDR, DTV, and IPTV. In general, a TVA metadata description is delivered from a content provider to a terminal device over a transport link. Because such metadata description can become very large, it is essential to encode each fragment of the metadata description in a compressed format before the delivery and then decode the encoded fragments at the terminal device. This paper proposes a new encoding procedure for TVA metadata based on the EXI and presents performance comparison results between the proposed encoding procedure and the existing TVA encoding procedure.

Design and implementation of an adaptive middleware based on the universal middleware bridge for heterogeneous home networks

As home networks become more complex and dynamic, it is crucial to support seamless interoperability through automatic reconfiguration and robustness through the efficient handling of faults. This paper presents an adaptive middleware that provides an adaptive autonomic configuration for the heterogeneous home networks and an autonomous fault management that includes fault diagnosis and recovery from unexpected faults such as device plug-outs, network link failures, service failures, and other such incidents. The proposed adaptive middleware is based on the universal middleware bridge (UMB) to guarantee interoperability and robustness in the middleware layer appropriate to heterogeneous home networks.

An error messages clustering-based fault management framework for adaptive home network middleware

This paper proposes an autonomous fault management framework consists of efficient fault detection, analysis and fault recovery based on clustering error messages. Most home network middlewares provide simple rule-based fault processing mechanisms optimized only to handle error messages individually. The proposed architecture focuses on detecting and analyzing error messages generated by identical faults as collectively as possible. For this, three relation graphs are used to decide the fault range and to find error messages. Message filters are also used to omit analyzing the previously found faults. Predefined fault recovery rules are used to handling the analyzed results and reconfigure home networks automatically. The proposed framework is implemented on an adaptive home network middleware based on the Universal Middleware Bridge (UMB) that provides adaptive autonomic configuration for the heterogeneous home networks.

A seamless remote user interface system supporting multi-screen services in smart devices

Many smart devices getting introduced to the market provide multimedia services and various interactive applications. Efficient collaboration among these devices helps improve user convenience, user mobility, and multi-screen services. In this paper, we propose an architecture for a seamless remote user interface system supporting multi-screen services in smart devices that guarantees smooth user interface transition through efficient collaboration.

Adaptive Transformation for a Scalable User Interface Framework Supporting Multi-screen Services

Nowadays many smart devices getting introduced to the market have different performance and capabilities in terms of CPU, memory, screen size, screen resolution, and etc. In this paper, we propose an adaptive transformation for a scalable user interface (SUI) framework supporting multi-screen services which is capable of providing a uniform user experience irrespective of devices of varying size and capabilities. The proposed adaptive transformation dynamically adapts user interfaces to make it suitable for smart devices using transformation policy including layout, content, and appearance of user interfaces as well as device profiles regarding device capabilities.

Architecture for fast object detection supporting CPU-GPU hybrid and distributed computing

This paper describes architecture for fast object detection that integrates uniform local binary patterns (ULBP) with convolutional neural networks (CNN). The proposed architecture also supports CPU-GPU hybrid and distributed computing based on the Hadoop distributed computing platform considering large-scale image big data.

A GPU-enabled Face Detection System in the Hadoop Platform Considering Big Data for Images

With the advent of the era of digital big data, the Hadoop platform has become widely used in various fields. However, the Hadoop MapReduce framework suffers from problems related to the increase of the name nodes main memory and map tasks for the processing of large number of small files. In addition, a method for running C++-based tasks in the MapReduce framework is required in order to conjugate GPUs supporting hardware-based data parallelism in the MapReduce framework. Therefore, in this paper, we present a face detection system that generates a sequence file for images to process big data for images in the Hadoop platform. The system also deals with tasks for GPU-based face detection in the MapReduce framework using Hadoop Pipes. We demonstrate a performance increase of around 6.8-fold as compared to a single CPU process.

Adaptive Transformation Engine Supporting Multi-Screen Services Using Smart Devices

Various types of smart devices including smartphones, smart pads, and smart TVs are being introduced to the market but have different size and capabilities. Thus, it is crucial to consider device capabilities to provide seamless multi-screen services in conjunction with these devices. In this paper, we present an adaptive transformation engine to efficiently support smooth multi-screen services using smart devices. The proposed engine dynamically adapts user interfaces to make it suitable for smart devices. In addition, it supports real-time media transcoding and streaming so as to provide these devices with suitable media contents.

A scalable user interface framework for multi-screening

Many smart devices have different performance and capabilities in terms of CPU, memory, screen size, screen resolution, dots per inch, and etc. Nevertheless, it is very important to provide a uniform user experience regarding the user interface across devices of varying size and capabilities. In this paper, we propose a scalable user interface framework using adaptive transformation for multi-screening considering size, performance, and capabilities of smart devices.