• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1329-1336
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• 2016

This paper develops a 360 degree video system using multiple off-the-shelf webcams and a set of embedded boards. Existing 360 degree cameras have shortcomings that they do not support real-time video generation since recorded videos should be copied to computers or smartphones which then provide stitching. Another shortcoming is that wide FoV(Field of View) cameras are not able to provide sufficiently high resolution. Moreover, resulting images are visually distorted bending straight lines. By employing an array of 65 degree FoV webcams, we were able to generate videos on the spot and achieve over 6K resolution with much less distortion. We describe the configuration and algorithms of the proposed system. The performance evaluation results of our early stage prototype system are presented.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.2
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• pp.65-71
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• 2015

It has been known that GMM (Gaussian Mixture Model) based speaker identification systems using ML (Maximum Likelihood) and WMR (Weighting Model Rank) demonstrate very high performances. However, such systems are not so effective under practical environments, in terms of real time processing, because of their high calculation costs. In this paper, we propose a new speaker-pruning algorithm that effectively reduces the calculation cost. In this algorithm, we select 20% of speaker models having higher likelihood with a part of input speech and apply MWMR (Modified Weighted Model Rank) to these selected speaker models to find out identified speaker. To verify the effectiveness of the proposed algorithm, we performed speaker identification experiments using TIMIT database. The proposed method shows more than 60% improvement of reduced processing time than the conventional GMM based system with no pruning, while maintaining the recognition accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.1
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• pp.1-8
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• 2016

We proposed a load unbalancing scheduling method for energy-efficient multi-core embedded systems considering DVFS (Dynamic Voltage/Frequency Scaling) power consumption and task characteristics. It is a new kind of scheduler which combines load balancing and load unbalancing technique. The purpose of the method is to effectively utilize energy without much effect in performance. In this paper, we conduct experiments on energy consumption and performance using the previous load balancing and unbalancing techniques and our proposed technique. The proposed technique reduced energy consumption more than 13.7% when compared to other algorithms. As a result, the proposed technique shows low energy consumption without much decline in the performance and is adequate for energy-efficient multi-core embedded systems.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1616-1625
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• 2007

Recently, embedded systems role as control systems instead of mechanical control systems in many parts of vehicles. In cases that embedded systems are used controling the electric signal, it is important to secure the reliability of a software within embedded systems. In this paper, the test platform for securing the reliability and real-time characteristic of the embedded system that controls electric signal of All Wheel Steering Control System in a Bi-modal tram is proposed. The platform is built on a HIL (Hardware In the Loop) architecture. Through the HIL platform, various vehicle conditions, driver activities and environment conditions can be successfully tested without actual driving, hence improving the reliability of the embedded system for the All Wheel Steering Control System.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.928-930
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• 2019

방송에서는 당사자의 동의 없이 얼굴을 노출 시키거나, 유해물질로 판단되는 물체의 노출을 금지하고 있다. 기존의 처리방식으로 편집자가 촬영된 영상을 직접 편집하거나, 촬영 시 가리개를 이용하는 방법을 사용한다. 이러한 방법은 번거롭고, 실수로 인해 얼굴이나 유해물질이 방송에 그대로 노출될 수 있다. 본 논문에서는 딥러닝 기반의 객체탐지 모델과 동일인 판단 모델을 사용하여 편집 과정을 자동으로 처리하고 후처리뿐만 아니라 실시간 방송에서의 적용을 위해 추가적으로 객체추적 알고리즘을 도입하여 처리속도를 높이는 방안을 제시한다.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1462-1471
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• 2019

Real-time embedded systems have become pervasive in general industry. They also began to be applied in such domains as avionics, automotive, aerospace, healthcare, and industrial Internet. However, the system failure of such domains could result in catastrophic consequences. Runtime software testing is required in such domains that demands very high accuracy. Traditional runtime software testing based on handwork is very inefficient and time consuming. Hence, test automation methodologies in runtime is demanding. In this paper, we introduce a software testing system that translates a real-time software into a monitorable real-time software. The monitorable real-time software means the software provides the monitoring information in runtime. The monitoring target are time constraints of the input real-time software. We anticipate that our system lessens the burden of runtime software testing.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.6
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• pp.295-311
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• 2019

The smart product market is growing year by year and is being used in many areas. There are various ways of interacting with smart products and users by inputting voice recognition, touch and finger movements. It is most important to detect an accurate hand region as a whole step to recognize hand movement. In this paper, we propose a method to detect accurate hand region in real time in various environments. A conventional method of detecting a hand region includes a method using depth information of a multi-sensor camera, a method of detecting a hand through machine learning, and a method of detecting a hand region using a color model. Among these methods, a method using a multi-sensor camera or a method using a machine learning requires a large amount of calculation and a high-performance PC is essential. Many computations are not suitable for embedded systems, and high-end PCs increase or decrease the price of smart products. The algorithm proposed in this paper detects the hand region using the color model, corrects the problems of the existing hand detection algorithm, and detects the accurate hand region based on various experimental environments.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.428-442
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• 2022

A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficient scheduling and management of transport routes. In this study, we introduce a lightweight head detection network using deep learning applicable to an embedded system. Currently, object detection algorithms using deep learning have been found to be successful. However, these algorithms essentially need a graphics processing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network using certain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using the proposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK 710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiency of the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system, we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances of exiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite the fact that only the CPU was used.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.3
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• pp.137-143
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• 2013

This paper describes an implementation of face detection technology for a real-time application on the Android platform. Java class of Face-Detection for detection of human face is provided by the Android API. However, this function is not suitable to apply for the real-time applications due to inadequate detection speed and accuracy. In this paper, the AdaBoost based classification method which utilizes Local Binary Pattern (LBP) histogram is employed for face detection. The face detection module has been developed by C/C++ language for high-speed image processing, and this module is included to the Android platform using the Java Native Interface (JNI). The experiments were carried out in the Java-based environment and JNI-based environment. The experimental results have shown that the performance of JNI-based is faster than Java-based method and our system is well enough to apply for real-time applications.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.173-182
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• 2015

Motion control systems are widely deployed in various industrial automation processes. The motion controller, which is a key element of motion control systems, has stringent real-time constraints. The controller must provide a short and deterministic control message transmission cycle, and minimize the actuation deviation among motor drives. To meet these requirements, hardware-based proprietary controllers have been prevalent. However, since it is becoming difficult for such an approach to meet increasing needs of system interoperability and scalability, nowadays, software-based universal motion controllers are regarded as their substitutes. Recently, embedded motion controller solutions are gaining attention due to low cost and relatively high performance. In this paper, we designed and implemented an embedded motion controller on an ARM-based evaluation board by using Xenomai real-time kernel and other open source software components. We also measured and analyzed the performance of our embedded controller under a realistic test-bed environment. The experimental results show that our embedded motion controller can provide relatively deterministic performance with synchronized control of three motor axis at 2 ms control cycle.