• Title/Summary/Keyword: Propagation Software

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Wire Optimization and Delay Reduction for High-Performance on-Chip Interconnection in GALS Systems

  • Oh, Myeong-Hoon;Kim, Young Woo;Kim, Hag Young;Kim, Young-Kyun;Kim, Jin-Sung
    • ETRI Journal
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    • v.39 no.4
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    • pp.582-591
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    • 2017
  • To address the wire complexity problem in large-scale globally asynchronous, locally synchronous systems, a current-mode ternary encoding scheme was devised for a two-phase asynchronous protocol. However, for data transmission through a very long wire, few studies have been conducted on reducing the long propagation delay in current-mode circuits. Hence, this paper proposes a current steering logic (CSL) that is able to minimize the long delay for the devised current-mode ternary encoding scheme. The CSL creates pulse signals that charge or discharge the output signal in advance for a short period of time, and as a result, helps prevent a slack in the current signals. The encoder and decoder circuits employing the CSL are implemented using $0.25-{\mu}m$ CMOS technology. The results of an HSPICE simulation show that the normal and optimal mode operations of the CSL achieve a delay reduction of 11.8% and 28.1%, respectively, when compared to the original scheme for a 10-mm wire. They also reduce the power-delay product by 9.6% and 22.5%, respectively, at a data rate of 100 Mb/s for the same wire length.

Learning Module Design for Neural Network Processor(ERNIE) (신경회로망칩(ERNIE)을 위한 학습모듈 설계)

  • Jung, Je-Kyo;Kim, Yung-Joo;Dong, Sung-Soo;Lee, Chong-Ho
    • Proceedings of the KIEE Conference
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    • 2003.11b
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    • pp.171-174
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    • 2003
  • In this paper, a Learning module for a reconfigurable neural network processor(ERNIE) was proposed for an On-chip learning. The existing reconfigurable neural network processor(ERNIE) has a much better performance than the software program but it doesn't support On-chip learning function. A learning module which is based on Back Propagation algorithm was designed for a help of this weak point. A pipeline structure let the learning module be able to update the weights rapidly and continuously. It was tested with five types of alphabet font to evaluate learning module. It compared with C programed neural network model on PC in calculation speed and correctness of recognition. As a result of this experiment, it can be found that the neural network processor(ERNIE) with learning module decrease the neural network training time efficiently at the same recognition rate compared with software computing based neural network model. This On-chip learning module showed that the reconfigurable neural network processor(ERNIE) could be a evolvable neural network processor which can fine the optimal configuration of network by itself.

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A Software Quality Prediction Model Without Training Data Set (훈련데이터 집합을 사용하지 않는 소프트웨어 품질예측 모델)

  • Hong, Euy-Seok
    • The KIPS Transactions:PartD
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    • v.10D no.4
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    • pp.689-696
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    • 2003
  • Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone are used for identifying trouble spots of software system in analysis or design phases. Many criticality prediction models for identifying fault-prone modules using complexity metrics have been suggested. But most of them need training data set. Unfortunately very few organizations have their own training data. To solve this problem, this paper builds a new prediction model, KSM, based on Kohonen SOM neural networks. KSM is implemented and compared with a well-known prediction model, BackPropagation neural network Model (BPM), considering internal characteristics, utilization cost and accuracy of prediction. As a result, this paper shows that KSM has comparative performance with BPM.

DEVELOPING THE CLOUD DETECTION ALGORITHM FOR COMS METEOROLOGICAL DATA PROCESSING SYSTEM

  • Chung, Chu-Yong;Lee, Hee-Kyo;Ahn, Hyun-Jung;Ahn, Hyoung-Hwan;Oh, Sung-Nam
    • Proceedings of the KSRS Conference
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    • v.1
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    • pp.200-203
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    • 2006
  • Cloud detection algorithm is being developed as major one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-1R and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithm and preliminary test result of both algorithms.

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Design of Neurofuzzy Networks by Means of Linear Fuzzy Inference and Its Application to Software Engineering (선형 퍼지추론을 이용한 뉴로퍼지 네트워크의 설계와 소프트웨어 공학으로의 응용)

  • Park, Byoung-Jun;Park, Ho-Sung;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • 2002.07d
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    • pp.2818-2820
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    • 2002
  • In this paper, we design neurofuzzy networks architecture by means of linear fuzzy inference. The proposed neurofuzzy networks are equivalent to linear fuzzy rules, and the structure of these networks is composed of two main substructures, namely premise part and consequence part. The premise part of neurofuzzy networks use fuzzy space partitioning in terms of all variables for considering correlation between input variables. The consequence part is networks constituted as first-order linear form. The consequence part of neurofuzzy networks in general structure(for instance ANFIS networks) consists of nodes with a function that is a linear combination of input variables. But that of the proposed neurofuzzy networks consists of not nodes but networks that are constructed by connection weight and itself correspond to a linear combination of input variables functionally. The connection weights in consequence part are learned by back-propagation algorithm. For the evaluation of proposed neurofuzzy networks. The experimental results include a well-known NASA dataset concerning software cost estimation.

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A Design of an AES-based Security Chip for IoT Applications using Verilog HDL (IoT 애플리케이션을 위한 AES 기반 보안 칩 설계)

  • Park, Hyeon-Keun;Lee, Kwangjae
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.67 no.1
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    • pp.9-14
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    • 2018
  • In this paper, we introduce an AES-based security chip for the embedded system of Internet of Things(IoT). We used Verilog HDL to implement the AES algorithm in FPGA. The designed AES module creates 128-bit cipher by encrypting 128-bit plain text and vice versa. RTL simulations are performed to verify the AES function and the theory is compared to the results. An FPGA emulation was also performed with 40 types of test sequences using two Altera DE0-Nano-SoC boards. To evaluate the performance of security algorithms, we compared them with AES implemented by software. The processing cycle per data unit of hardware implementation is 3.9 to 7.7 times faster than software implementation. However, there is a possibility that the processing speed grow slower due to the feature of the hardware design. This can be solved by using a pipelined scheme that divides the propagation delay time or by using an ASIC design method. In addition to the AES algorithm designed in this paper, various algorithms such as IPSec can be implemented in hardware. If hardware IP design is set in advance, future IoT applications will be able to improve security strength without time difficulties.

Noisy Power Quality Recognition System using Wavelet based Denoising and Neural Networks (웨이블릿 기반 잡음제거와 신경회로망을 이용한 잡음 전력 품질 인식 시스템)

  • Chong, Won-Yong
    • Journal of the Institute of Convergence Signal Processing
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    • v.13 no.2
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    • pp.91-98
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    • 2012
  • Power Quality (PQ) signal such as sag, swell, harmonics, and impulsive transients are the major issues in the operations of the power electronics based devices and microprocessor based equipments. The effectiveness of wavelet based denoising techniques and recognizing different power quality events with noise has been presented in this paper. The algorithms involved in the noisy PQ recognition system are the wavelet based denoising and the back propagation neural networks. Also, in order to verify the real-time performances of the noisy PQ recognition systems under the noisy environments, SIL(Software In the Loop) and PIL(Processor In the Loop) were carried out, resulting in the excellent recognition performances.

Developing the Cloud Detection Algorithm for COMS Meteorolgical Data Processing System

  • Chung, Chu-Yong;Lee, Hee-Kyo;Ahn, Hyun-Jung;Ahn, Myoung-Hwan;Oh, Sung-Nam
    • Korean Journal of Remote Sensing
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    • v.22 no.5
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    • pp.367-372
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    • 2006
  • Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

A Study on Network Operation Structure and DataLink Protocol for Interworking of Ground Network ALL-IP at Next-Military Satellite Communication (차기군위성통신에서 지상망 ALL-IP 연동을 위한 네트워크 운용구조 및 데이터링크 프로토콜 연구)

  • Lee, Changyoung;Kang, Kyungran;Shim, Yong-hui
    • Journal of the Korea Institute of Military Science and Technology
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    • v.21 no.6
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    • pp.826-841
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    • 2018
  • The military satellite communication of ROK military, ANASIS is designed for analog data such as voice and streaming data. ANASIS cannot fully support ALL-IP communications due to its long propagation delay. The next generation satellite communication system is being designed to overcome the limitation. Next generation satellite communications system considers both high-speed and low-speed networks to support various operating environment. The low-speed satellite supports both broadband and narrow-band communication. This network works as the infrastructure for of wide-area internetworking over multiple AS's in the terrestrial network. It requires minimum satellite frequency and minimum power and works without PEP and router. In this paper, we propose a network operation structure to enable the inter-operation between high and low-speed satellite networks. In addition, we propose a data link protocol for low speed satellite networks.

Real-Time Hand Pose Tracking and Finger Action Recognition Based on 3D Hand Modeling (3차원 손 모델링 기반의 실시간 손 포즈 추적 및 손가락 동작 인식)

  • Suk, Heung-Il;Lee, Ji-Hong;Lee, Seong-Whan
    • Journal of KIISE:Software and Applications
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    • v.35 no.12
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    • pp.780-788
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    • 2008
  • Modeling hand poses and tracking its movement are one of the challenging problems in computer vision. There are two typical approaches for the reconstruction of hand poses in 3D, depending on the number of cameras from which images are captured. One is to capture images from multiple cameras or a stereo camera. The other is to capture images from a single camera. The former approach is relatively limited, because of the environmental constraints for setting up multiple cameras. In this paper we propose a method of reconstructing 3D hand poses from a 2D input image sequence captured from a single camera by means of Belief Propagation in a graphical model and recognizing a finger clicking motion using a hidden Markov model. We define a graphical model with hidden nodes representing joints of a hand, and observable nodes with the features extracted from a 2D input image sequence. To track hand poses in 3D, we use a Belief Propagation algorithm, which provides a robust and unified framework for inference in a graphical model. From the estimated 3D hand pose we extract the information for each finger's motion, which is then fed into a hidden Markov model. To recognize natural finger actions, we consider the movements of all the fingers to recognize a single finger's action. We applied the proposed method to a virtual keypad system and the result showed a high recognition rate of 94.66% with 300 test data.