• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.529-535
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• 2011

The HF communication method is capable of communicating short and long distances without a separate relaying method and is used as the primary/secondary communication method in other nations. However, the Korean military strongly discouraged the use of the method due to issues regarding the skip zone and the fact that the usable frequency changes according to irregularities in the ionosphere. The NVIS communication is less susceptible to noise than typical communications using ionosphere reflection, and is also able to communicate short distances containing skip zones. In this paper, we inspect the NVIS communication methods of foreign nations in order to facilitate the use of HF communications, as well as provide solutions to the issues mentioned above. This paper explains the concept of NVIS communication, and investigates how the Korean military is implementing HF communications based on actual communications data of military corps. Based on this result, we have verified the possibility of overcoming skip zones through NVIS communications, and have considered the applicability of a prediction program in order to enhance the efficiency of HF communications.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.59-64
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• 2011

In this paper, the lumped element model parameter estimation method and its implemented estimation software for fabricated floating mass transducers of IMEHDs have been presented so that the estimated parameter values could be compared with the designed ones and applied to predict the output performance when the transducers were implanted into human ears. The presented method is based on the sequential quadratic programming (SQP) for estimating parameters in the transducer's lumped model and has been implemented by the use of LabVIEW graphical language. Using the implemented estimation software, the accuracy of parameter estimation has been verified and our implemented estimation method has been evaluated by the comparison of the estimated transducer parameter values with the designed ones for a practically fabricated floating mass transducer for IMEHDs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.147-153
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• 2018

Beacon enables one to measure distance indoors based on low-power Bluetooth low energy (BLE) technology, while GPS (Global Positioning System) only can be used outdoors. In measuring indoor distance using Beacon, RSSI (Received Signal Strength Indication) is considered as the one of the key factors, however, it is influenced by various environmental factors so that it causes the huge gap between the estimated distance and the real. In order to handle this issue, we propose the adaptive ANN (Artificial Neural Network) based approach to measuring the exact distance using Beacon. First, we has carried out the preprocessing of the RSSI signals by applying the extended Kalman filter and the signal stabilization filter into decreasing the noise. Then, we suggest the multi-layered ANNs, each of which layer is learned by specific training data sets. The results showed an average error of 0.67m, a precision of 0.78.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.34-41
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• 2001

In an IMBE (Improved Multi-band Excitation) speech coder, initial pitch estimation occupies most of the total computing time for the coder due to complex cost function and exhaustive search over candidate pitches. Future frames in initial pitch estimation cause inevitable time delay. Therefore, it is difficult to implement a real-time coder. Furthermore, unvoiced frames use the unnecessary pitch estimation as in the voiced frames. In this paper, each frame is determined voiced or unvoiced by Dyadic Wavelet Transform (DyWT) and, then, initial pitch estimation is performed only for voiced frame. Therefore different pitch estimation algorithms are employed between voiced and unvoiced frames incurring reduced time delay at transmitter and receiver. Simulation result show that the relative complexity of initial pitch estimation is reduced by 23％, and the processing time decreases down to 1/10 ∼ 1/1l of the IMBE coder while speech quality is almost maintained.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.13-20
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• 1990

This paper describes an interframe image coding using motion compensated and classified vector quantizer (MC-CVQ). It is essential to carefully encode blocks with significant pels in motion compensated vector quantizers (MCVQ). In this respect, we propose a new CVQ algorithm which is appropriate to the coding of interframe prediction error after motion compensation. In order to encode an image efficiently at a low bit rate, we partition each block, which is the processing element in MC, into equally sized 4 vectors, and classify vectors into 15 classes according to the position of significant pels. Vectors in each class are then encoded by the vector quantizer with the codebook independently designed for the class. The computer simulation shows that the signal-to-noise ratio and the average bit rate of MC-CVQ are 35-37dB and 0.2-0.25bit/pel, respectively, for the videophone or video conference type image.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.4 s.310
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• pp.35-44
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• 2006

The network induced transmission delay deteriorates the performance and stability of the real-time distributed control system on LonWorks over IP (LonWorks/IP) virtual device network (VDN). LonWorks/IP virtual device network is an integrated form of LonWorks device network and IP data network. The time delay in servo control on the LonWorks/IP-based VDN has highly stochastic nature. In the real-time distributed servo applications for predictive maintenance on the factory floor, timely response is essential.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.144-152
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• 1996

In this paper, a pulse-mode digital multilayer neural network with a massively parallel yet compact and flexible network architecture is presented. Algebraicneural operations are replaced by stochastic processes using pseudo-random pulse sequences and simple logic gates are used as basic computing elements. The distributions of the results from the stochastic processes are approximated using the hypergeometric distribution. A statistical model of the noise(error) is developed to estimate the relative accuracy associated with stochastic computing in terms of mean and variance. Numerical character recognition problems are applied to the network to evaluate the network performance and to justify the validity of analytic results based on the developed statistical model. The network architectures are modeled in VHDL using the mixed descriptions of gate-level and register transfer level (RTL). Experiments show that the statistical model successfully predicts the accuracy of the operations performed in the network and that the character classification rate of the network is competitive to that of ordinary Back-Propagation networks.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.3
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• pp.311-318
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• 2004

It is required to design the RF link with sufficiently stable signal margin to minimize bit errors and improve the quality of received data in the telemetry system modulated digitally like PCM/FM. In case of the vehicle flying at a high speed, the variation of the gain pattern between transmitting and receiving antenna and the fee space loss due to flight distance cause the fluctuation of link. In this paper, KSR(Korea Sounding Rocket)- III, the first domestic liquid rocket which was successfully launched in Nov. 2002 is introduced. The SNR(signal-to-noise ratio) variation of the telemetry signal which was measured at S-band ground station, the one which was simulated considering the flight trajectory, and the attitude variation such as roll, pitch and yaw are compared, analyzed, and agree very well. In addition, two virtual flying situations are simulated and evaluated-only one antenna is equipped in one case, and rocket is roll-free in the other.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.391-399
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• 2012

This paper proposes a particle filter approach for SLAM(Simultaneous Localization and Mapping) of a mobile robot. The SLAM denotes estimation of both the robot location and map while the robot navigates in an unknown environment without map. The proposed method estimates robot location simultaneously with the locations of the ultrasonic beacons which constitute landmarks for navigation. The particle filter method represents the locations of the robot and landmarks in probabilistic manner by the distribution of particles. The method takes care of the uncertainty of the landmarks' location as well as that of the robot motion. Therefore, the locations of the landmarks are updated including uncertainty at every sampling time. Performance of the proposed method is verified through simulation and experiments. The method yields practically useful mapping information even if the range data from the landmarks include random noise. Also, it provides more accurate and robust estimation of the robot location than the usual least squares methods or dead-reckoning method.

• The Journal of the Acoustical Society of Korea
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• v.24 no.1
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• pp.58-68
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• 2005

In this paper, we propose a scheme to improve discriminative property in the feature compensation method for robust speech recognition under noisy environments. The estimation of noisy speech model used in existing feature compensation methods do not guarantee the computation of posterior probabilities which discriminate reliably among the Gaussian components. Estimation of Posterior probabilities is a crucial step in determining the discriminative factor of the Gaussian models, which in turn determines the intelligibility of the restored speech signals. The proposed scheme employs minimum classification error (MCE) training for estimating the parameters of the noisy speech model. For applying the MCE training, we propose to identify and determine the 'competing components' that are expected to affect the discriminative ability. The proposed method is applied to feature compensation based on parallel combined mixture model (PCMM). The performance is examined over Aurora 2.0 database and over the speech recorded inside a car during real driving conditions. The experimental results show improved recognition performance in both simulated environments and real-life conditions. The result verifies the effectiveness of the proposed scheme for increasing the performance of robust speech recognition systems.