• Title/Summary/Keyword: Single Board Computing

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Computational Analysis and Measurement for SDR-based Spectrum Sensing System Design on Single Board Computer (소프트웨어 정의 라디오 기반 스펙트럼 센싱 시스템 설계를 위한 단일 보드 컴퓨터 내 연산 분석 및 측정 연구)

  • Kim, Joon Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.12
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    • pp.1650-1658
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    • 2019
  • In recent years, IoT device and platform become widely popular and the computing performance and capabilities of IoT devices are also getting improved. However, the size and computing resources of IoT devices, especially small single board computer, are limited in a way that the design and implementation of the system should be carefully considered to operate on the devices. Recently, SDR technologies are adapting in IoT devices and can perform various radio systems. Thorough analysis and investigation of computer performances on small single board computer are necessary for its usage. In this paper, we present the results of computing resources measurement and analysis on small single-board computers. At first, we consider to design SDR based spectrum sensing for single board computer, investigate various key factors and propose a design procedure that can affect performance of the system with experiments.

Performance Measurement of Single-board System for Mobile BCI System (이동식 BCI 시스템을 위한 싱글보드 시스템의 성능측정)

  • Lee, Hyo Jong;Kim, Hyun Kyu;Gao, Yongbin
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.3
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    • pp.136-144
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    • 2015
  • The EEG system can be classified as a wired or wireless device. Each device used for the medical or entertainment purposes. The collected EEG signals from sensor are analyzed using feature extractions. A wireless EEG system provides good portability and convenience, however, it requires a mobile system that has heavy computing power. In this paper a single board system is proposed to handle EEG signal processing for BCI applications. Unfortunately, the computing power of a single board system is limited unlike general desktop systems. Thus, parallel approach using multiple single board systems is investigated. The parallel EEG signal processing system that we built demonstrates superlinear speedup for an EEG signal processing algorithm.

An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance

  • Srinivasan, Kathiravan;Chang, Chuan-Yu;Huang, Chao-Hsi;Chang, Min-Hao;Sharma, Anant;Ankur, Avinash
    • Journal of Information Processing Systems
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    • v.14 no.4
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    • pp.989-1009
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    • 2018
  • Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

A Development of Shoes Cleaner Control System using Raspberry Pi

  • Deukchang Hyun
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.7
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    • pp.21-32
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    • 2024
  • Since leather shoes cannot be washed with water, there is a need for a cleaning method that can remove extraneous substance from the inside and outside of shoes and senitize the inside of shoes without using water. For this purpose, this paper develops a shoes cleaning machine control system that automatically controls the entire process of shoes cleaning in a shoes cleaning machine that quickly cleans the inside and outside of shoes using compressed air, sterilization solution. The developed system uses Rasberry Pi, a general purpose single board computer(SBC), to control various actuators of the shoes cleaning machine. The shoes cleaning machine operated by the developed system shows a sterilization efficiency of more than 99% and an odor removal efficiency of more than 86% in a cleaning time of less than 1 minute.

An Embedded Solution for Fast Navigation and Precise Positioning of Indoor Mobile Robots by Floor Features (바닥 특징점을 사용하는 실내용 정밀 고속 자율 주행 로봇을 위한 싱글보드 컴퓨터 솔루션)

  • Kim, Yong Nyeon;Suh, Il Hong
    • The Journal of Korea Robotics Society
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    • v.14 no.4
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    • pp.293-300
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    • 2019
  • In this paper, an Embedded solution for fast navigation and precise positioning of mobile robots by floor features is introduced. Most of navigation systems tend to require high-performance computing unit and high quality sensor data. They can produce high accuracy navigation systems but have limited application due to their high cost. The introduced navigation system is designed to be a low cost solution for a wide range of applications such as toys, mobile service robots and education. The key design idea of the system is a simple localization approach using line features of the floor and delayed localization strategy using topological map. It differs from typical navigation approaches which usually use Simultaneous Localization and Mapping (SLAM) technique with high latency localization. This navigation system is implemented on single board Raspberry Pi B+ computer which has 1.4 GHz processor and Redone mobile robot which has maximum speed of 1.1 m/s.

Structural Improvement for Crack of Integrated Circuit in Single Board Computer by Structure Analysis (단일보드컴퓨터 구조해석을 통한 집적회로 균열현상의 구조적 개선)

  • Ryu, Jeong-min;Lee, Yong-jun;Sohn, Kwonil
    • Journal of Advanced Navigation Technology
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    • v.23 no.5
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    • pp.460-465
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    • 2019
  • In this study, we aim to derive a solution from the structural analysis for electrical failure of single board computers for computing navigation information. By analyzing the characteristic factor, we identify that crack occur on the central processing unit board due to a certain structural problem, and that the physical effect by the crack make communication function be impossible to perform, which it causes booting error. In order to find the location of excessive stress causing the crack, structural analysis for the single board computer is done. From the structural analysis, the areas where stress concentration occurs are identified, and improvement methods changing the structures are developed. As a result, we shows that stresses are reduced entirely on the stress distribution for the improved structure. In addition, heat analysis shows that changing the structure to reduce stresses is not affect to the heat radiation, and the thermal resistance of the actual equipment is verified by measuring the temperature of the heat sink applied with the improved structure.

Radiation tolerance of a small COTS single board computer for mobile robots

  • West, Andrew;Knapp, Jordan;Lennox, Barry;Walters, Steve;Watts, Stephen
    • Nuclear Engineering and Technology
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    • v.54 no.6
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    • pp.2198-2203
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    • 2022
  • As robotics become more sophisticated, there are a growing number of generic systems being used for routine tasks in nuclear environments to reduce risk to radiation workers. The nuclear sector has called for more commercial-off-the-shelf (COTS) devices and components to be used in preference to nuclear specific hardware, enabling robotic operations to become more affordable, reliable, and abundant. To ensure reliable operation in nuclear environments, particularly in high-gamma facilities, it is important to quantify the tolerance of electronic systems to ionizing radiation. To deliver their full potential to end-users, mobile robots require sophisticated autonomous behaviors and sensing, which requires significant computational power. A popular choice of computing system, used in low-cost mobile robots for nuclear environments, is the UP Core single board computer. This work presents estimates of the total ionizing dose that the UP Core running the Robot Operating System (ROS) can withstand, through gamma irradiation testing using a Co-60 source. The units were found to fail on average after 111.1 ± 5.5 Gy, due to faults in the on-board power management circuitry. Its small size and reasonable radiation tolerance make it a suitable candidate for robots in nuclear environments, with scope to use shielding to enhance operational lifetime.

A Study on the Effect of the Name Node and Data Node on the Big Data Processing Performance in a Hadoop Cluster (Hadoop 클러스터에서 네임 노드와 데이터 노드가 빅 데이터처리 성능에 미치는 영향에 관한 연구)

  • Lee, Younghun;Kim, Yongil
    • Smart Media Journal
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    • v.6 no.3
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    • pp.68-74
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    • 2017
  • Big data processing processes various types of data such as files, images, and video to solve problems and provide insightful useful information. Currently, various platforms are used for big data processing, but many organizations and enterprises are using Hadoop for big data processing due to the simplicity, productivity, scalability, and fault tolerance of Hadoop. In addition, Hadoop can build clusters on various hardware platforms and handle big data by dividing into a name node (master) and a data node (slave). In this paper, we use a fully distributed mode used by actual institutions and companies as an operation mode. We have constructed a Hadoop cluster using a low-power and low-cost single board for smooth experiment. The performance analysis of Name node is compared through the same data processing using single board and laptop as name nodes. Analysis of influence by number of data nodes increases the number of data nodes by two times from the number of existing clusters. The effect of the above experiment was analyzed.

The Design and implementation of a Low Noise Amplifier for DSRC using GaAs MESFET (GaAs MESFET을 이용한 DSRC용 LNA MMIC 설계 및 구현)

  • Moon, Tae-Jung;Hwang, Sung-Bum;Kim, Byoung-Kook;Ha, Young-Chul;Hur, Hyuk;Song, Chung-Kun;Hong, Chang-Hee
    • Proceedings of the IEEK Conference
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    • 2002.06b
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    • pp.61-64
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    • 2002
  • We have optimally designed and implemented by a monolithic microwave integrated circuit(MMIC) the low noise amplifier(LNA) of 5.8GHz band composed of receiver front-end(RFE) in a on-board equipment system for dedicated short range communication using a depletion-mode GaAs MESFET. The LNA is provided with two active devices, matching circuits, and two drain bias circuits. Operating at a single supply of 3V and a consumption current of 18㎃, The gain at center frequency 5.8GHz is 13.4dB, Noise figure(NF) is 1.94dB, Input 3rd order intercept point(lIPS) is 3dBm, and Input return loss(5$_{11}$) and Output return loss(S$_{22}$) is -l8dB and -13.3dB, respectively. The circuit size is 1.2$\times$O.7$\textrm{mm}^2$.EX>.>.

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An improvement of the learning speed through Improved Reinforcement Learning on Jul-Gonu Game (개선된 강화학습을 이용한 줄고누게임의 학습속도개선)

  • Shin, Yong-Woo;Chung, Tae-Choong
    • Journal of Internet Computing and Services
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    • v.10 no.3
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    • pp.9-15
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    • 2009
  • It takes quite amount of time to study a board game because there are many game characters and different stages are exist for board games. Also, the opponent is not just a single character that means it is not one on one game, but group vs. group. That is why strategy is needed, and therefore applying optimum learning is a must. This paper used reinforcement learning algorithm for board characters to learn, and so they can move intelligently. If there were equal result that both are considered to be best ones during the course of learning stage, Heuristic which utilizes learning of problem area of Jul-Gonu was used to improve the speed of learning. To compare a normal character to an improved one, a board game was created, and then they fought against each other. As a result, improved character's ability was far more improved on learning speed.

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