• The KIPS Transactions:PartD
• /
• v.11D no.2
• /
• pp.443-452
• /
• 2004

The paper presents an intelligent space management system for ubiquitous computing. The system is basically a home/office automation system that could control light, electronic key, and home appliances such as TV and audio. On top of these basic capabilities, there are four elegant features in the system. First, we can access the system using either a cellular Phone or using a browser on the PC connected to the Internet, so that we control the system at any time and any place. Second, to provide more human-oriented interface, we integrate voice recognition functionalities into the system. Third, the system supports not only reactive services but also proactive services, based on the regularities of user behavior. Finally, by exploiting embedded technologies, the system could be run on the hardware that has less-processing power and storage. We have implemented the system on the embedded board consisting of StrongARM CPU with 205MHz, 32MB SDRAM, 16MB NOR-type flash memory, and Relay box. Under these hardware platforms, software components such as embedded Linux, HTK voice recognition tools, GoAhead Web Server, and GPIO driver are cooperated to support user-friendly intelligent space.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.2
• /
• pp.503-523
• /
• 2022

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.

• Journal of Internet Computing and Services
• /
• v.6 no.4
• /
• pp.47-58
• /
• 2005

There are two major type of flash memory products, namely, NAND-type and NOR-type flash memory. NOR-type flash memory is generally deployed as ROM BIOS code storage because if offers Byte I/O and fast read operation. However, NOR-type flash memory is more expensive than NAND-type flash memory in terms of the cost per byte ratio, and hence NAND type flash memory is more widely used as large data storage such as embedded Linux file systems. In this paper, we designed an efficient flash memory file system based an Embedded system and presented to make up for reduced to Swapping a weak System Performance to flash file system using NAND-type flash memory, then proposed Swapping algorithm insured to an Execution time. Based on Implementation and simulation studies, Then, We improved performance bases on NAND-type flash memory to the requirement of the embedded system.

• Journal of Computing Science and Engineering
• /
• v.11 no.4
• /
• pp.121-129
• /
• 2017

Feature selection has been widely established as an efficient technique for microarray data analysis. Feature selection aims to search for the most important feature/gene subset of a given dataset according to its relevance to the current target. Unsupervised feature selection is considered to be challenging due to the lack of label information. In this paper, we propose a novel method for unsupervised feature selection, which incorporates embedded learning and $l_{2,1}-norm$ sparse regression into a framework to select genes in microarray data analysis. Local tangent space alignment is applied during embedded learning to preserve the local data structure. The $l_{2,1}-norm$ sparse regression acts as a constraint to aid in learning the gene weights correlatively, by which the proposed method optimizes for selecting the informative genes which better capture the interesting natural classes of samples. We provide an effective algorithm to solve the optimization problem in our method. Finally, to validate the efficacy of the proposed method, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method obtains quite promising performance.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.4
• /
• pp.608-615
• /
• 2006

Embedded System is one of important factor of ubiquitous computing, and it has various application areas. In this paper, we develop an educational contents for the education of young children using portable embedded machine; it shows embedded system can be applied to education area as well as to industry area. The system is similar to portable game machine, and it is easy to use everywhere. It also can download new contents from host computer or internet. The developed contents forms game and multimedia to derive children's interest.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.2
• /
• pp.96-102
• /
• 2014

The embedded processor market has grown rapidly and consistently with the appearance of mobile devices. In an embedded system, the power consumption and execution time are important factors affecting the performance. The system performance is determined by both hardware and software. Although the hardware architecture is high-end, the software runs slowly due to the low quality of codes. This study compared the performance of two major compilers, LLVM and GCC on a32-bit EISC embedded processor. The dynamic instructions and static code sizes were evaluated from these compilers with the EEMBC benchmarks.LLVM generally performed better in the ALU intensive benchmarks, whereas GCC produced a better register allocation and jump optimization. The dynamic instruction count and static code of GCCwere on average 8% and 7% lower than those of LLVM, respectively.

• Journal of KIISE:Computer Systems and Theory
• /
• v.33 no.9
• /
• pp.642-649
• /
• 2006

In order to recognize a situation from a environment which provides an intelligent service, we propose state-transition based situation modeling which is suitable for a low computing power and restricted resources like embedded systems, and we designed its application to a situation-action converter(SAC)which is consist of two parts; situation detector recognized wanted situations and action generator generated various control actions. Then, we implemented a situation manager for smart scheduler service by using a SAC which is installed to a ARM processor based embedded Linux evaluation board.

• Journal of Internet Computing and Services
• /
• v.7 no.4
• /
• pp.11-22
• /
• 2006

Software product-lines methodology is the software development paradigm to build the target system by customizing the variable part of software assets according to requirements. To attain this, the commonalities and variabilities of the system family should be modeled explicitly at early stage. Although the researches on general software product-lines are active, the researches on component-based real-time embedded software product-lines are rather inactive. In this paper a code generation system to support the functional variabilities via feature model and generate the code for synchronization via state model is proposed to increase the productivity of the development of the real-time embedded software product-lines.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.15 no.2
• /
• pp.51-60
• /
• 2020

Optical character recognition (OCR) has been studied for decades because it is very useful in a variety of places. Nowadays, OCR's performance has improved significantly due to outstanding deep learning technology. Thus, there is an increasing demand for commercial-grade but affordable OCR systems. We have developed a low-cost, high-performance OCR system for the industry with the cheapest embedded developer kit that supports GPU acceleration. To achieve high accuracy for industrial use on limited computing resources, we chose a state-of-the-art text recognition algorithm that uses an end-to-end deep learning network as a baseline model. The model was then improved by replacing the feature extraction network with the best one suited to our conditions. Among the various candidate networks, EfficientNet-B3 has shown the best performance: excellent recognition accuracy with relatively low memory consumption. Besides, we have optimized the model written in TensorFlow's Python API using TensorFlow-TensorRT integration and TensorFlow's C++ API, respectively.

• Electronics and Telecommunications Trends
• /
• v.34 no.6
• /
• pp.133-144
• /
• 2019

Although deep learning-based visual image recognition technology has evolved rapidly, most of the commonly used methods focus solely on recognition accuracy. However, the demand for low latency and low power consuming image recognition with an acceptable accuracy is rising for practical applications in edge devices. For example, most Internet of Things (IoT) devices have a low computing power requiring more pragmatic use of these technologies; in addition, drones or smartphones have limited battery capacity again requiring practical applications that take this into consideration. Furthermore, some people do not prefer that central servers process their private images, as is required by high performance serverbased recognition technologies. To address these demands, the object and scene recognition technologies for mobile/embedded devices that enable optimized neural networks to operate in mobile and embedded environments are gaining attention. In this report, we briefly summarize the recent trends and issues of object and scene recognition technologies for mobile and embedded devices.