• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.18-27
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• 2009

In this paper, we propose a frame rate conversion algorithm using adaptive search-based motion estimation (ME). The proposed ME method uses recursive search, 3-step search, and single predicted search as candidates for search strategy. The best method among the three candidates is adaptively selected on a block basis according to the predicted motion type. The adaptation of the search method improves the accuracy of the estimated motion vectors while curbing the increase of computational load. To support the proposed ME method, an entire image is divided into three regions with different motion types. Experimental results show that the proposed FRC method achieves better image quality than existing algorithms in both subjective and objective measures.

• Journal of Convergence for Information Technology
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• v.10 no.5
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• pp.1-7
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• 2020

This paper proposes a link adaptation method for Underwater Internet of Things (IoT), which reduces power consumption of sensor nodes and improves the throughput of network in underwater IoT network. Adaptive Modulation and Coding (AMC) technique is one of link adaptation methods. AMC uses the strong correlation between Signal Noise Rate (SNR) and Bit Error Rate (BER), but it is difficult to apply in underwater IoT as it is. Therefore, we propose the machine learning based AMC technique for underwater environments. The proposed Modulation Coding and Scheme (MCS) prediction model predicts transmission method to achieve target BER value in underwater channel environment. It is realistically difficult to apply the predicted transmission method in real underwater communication in reality. Thus, this paper uses the high accuracy BER prediction model to measure the performance of MCS prediction model. Consequently, the proposed AMC technique confirmed the applicability of machine learning by increase the probability of communication success.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2070-2086
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• 2014

As a kind of personal lifelog data, activity data have been considered as one of the most compelling information to understand the user's habits and to calibrate diagnoses. In this paper, we proposed a robust algorithm to sampling rates for human activity recognition, which identifies a user's activity using accelerations from a triaxial accelerometer in a smartphone. Although a high sampling rate is required for high accuracy, it is not desirable for actual smartphone usage, battery consumption, or storage occupancy. Activity recognitions with well-known algorithms, including MLP, C4.5, or SVM, suffer from a loss of accuracy when a sampling rate of accelerometers decreases. Thus, we start from particle swarm optimization (PSO), which has relatively better tolerance to declines in sampling rates, and we propose PSO with an adaptive boundary correction (ABC) approach. PSO with ABC is tolerant of various sampling rate in that it identifies all data by adjusting the classification boundaries of each activity. The experimental results show that PSO with ABC has better tolerance to changes of sampling rates of an accelerometer than PSO without ABC and other methods. In particular, PSO with ABC is 6%, 25%, and 35% better than PSO without ABC for sitting, standing, and walking, respectively, at a sampling period of 32 seconds. PSO with ABC is the only algorithm that guarantees at least 80% accuracy for every activity at a sampling period of smaller than or equal to 8 seconds.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.3
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• pp.404-416
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• 2015

Small-area, low-power coarse and fine frequency detectors (FDs) are proposed for an adaptive bandwidth referenceless CDR with a wide range of input data rate. The coarse FD implemented with two flip-flops eliminates harmonic locking as long as the initial frequency of the CDR is lower than the target frequency. The fine FD samples the incoming input data by using half-rate four phase clocks, while the conventional rotational FD samples the full-rate clock signal by the incoming input data. The fine FD uses only a half number of flip-flops compared to the rotational FD by sharing the sampling and retiming circuitry with PLL. The proposed CDR chip in a 65-nm CMOS process satisfies the jitter tolerance specifications of both USB 3.0 and USB 3.1. The proposed CDR works in the range of input data rate; 2 Gb/s ~ 8 Gb/s at 1.2 V, 4 Gb/s ~ 11 Gb/s at 1.5 V. It consumes 26 mW at 5 Gb/s and 1.2 V, and 41 mW at 10 Gb/s and 1.5 V. The measured phase noise was -97.76 dBc/Hz at the 1 MHz frequency offset from the center frequency of 2.5 GHz. The measured rms jitter was 5.0 ps at 5 Gb/s and 4.5 ps at 10 Gb/s.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.422-428
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• 2023

Ultrafast ultrasound imaging has been applied to various imaging approaches, including shear wave elastography, ultrafast Doppler, and super-resolution imaging. However, these methods are still challenging in real-time implementation for three Dimension (3D) or portable applications because of their massive data rate required. In this paper, we proposed an adaptive quantization method that effectively reduces the data rate of large Radio Frequency (RF) data. In soft tissue, ultrasound backscatter signals require a high dynamic range, and thus typical quantization used in the current systems uses the quantization level of 10 bits to 14 bits. To alleviate the quantization level to expand the application of ultrafast ultrasound imaging, this study proposed a depth-sectional quantization approach that reduces the quantization errors. For quantitative evaluation, Field II simulations, phantom experiments, and in vivo imaging were conducted and CNR, spatial resolution, and SSIM values were compared with the proposed method and fixed quantization method. We demonstrated that our proposed method is capable of effectively reducing the quantization level down to 3-bit while minimizing the image quality degradation.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.815-821
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• 2009

Information processing in variable and noisy environments is usually accomplished by means of adaptive filters. Among various adaptive algorithms, Least Mean Square (LMS) has become the most popular for its robustness, good tracking capabilities and simplicity, both in terms of computational load and easiness of implementation. In practical application of the LMS algorithm, the most important key parameter is the Step Size. As is well known, if the Step Size is large, the convergence rate of the algorithm will be rapid, but the steady state mean square error (MSE) will increase. On the other hand, if the Step Size is small, the steady state MSE will be small, but the convergence rate will be slow. Many researches have been proposed to alleviate this drawback by using a variable Step Size. In this paper, a new variable Step Size LMS(VSSLMS) called Categorized VSSLMS (CVSSLMS) is proposed. CVSSLMS updates the Step Size by categorizing the current status of the gradient, hence significantly improves the convergence rate. The performance of the proposed algorithm was verified from the view point of convergence rate, Excessive Mean Square Error(EMSE), and complexity through experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1198-1205
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• 2000

In this paper, we introduce a modified interference cancellation scheme to overcome MAI in DS-CDMA. Among ICs(Interference Cancellers), PIC(Parallel IC) requires the more complexity, and SIC(Successive IC) faces the problems of the long delay time. Most of all, the adaptive detector achieves the good BER performance using the adaptive Inter conducted iteration algorithm. So it requires many iterations. To resolve the problems of them, we propose an improved adaptive detector that the received signal removed MAI through the sorting scheme and the cancellation method are fed into the adaptive filter. Because the improved input signal is fed into the adaptive filter, it has the same BER performance only using smaller iterations than the conventional adaptive detector, and the proposed detector having adaptive filter requires less complexity than the other detectors.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.640-645
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• 2004

This paper proposes a direct adaptive control method using self-recurrent wavelet neural network (SRWNN) for stable path tracking of mobile robots. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). Unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. For this ability of the SRWNN, the SRWNN is used as a controller with simpler structure than the WNN in our on-line control process. The gradient-descent method with adaptive learning rates (ALR) is applied to train the parameters of the SRWNN. The ALR are derived from discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7C
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• pp.907-914
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• 2004

In this paper, we propose an application of the receiver antenna switching to an conventional adaptive modulation system and derived the optimal antenna switching threshold of the system to maximize the average transmission bit rate and analyzed its performance. Also, we compare the performances of the presented scheme with those of an adaptive modulation using the antenna selection diversity and the one with a single antenna in terms of the average number of bits per symbol and the probability of no transmission. Performance comparison results show that the proposed system has an SNR gain of 1.4 dB over the adaptive modulation using a single antenna when the average number of bits per a symbol is two and yields an SNR gain of 6 dB for maintaining the probability of no transmission at the level of 0.1.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.3-7
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• 2001

In active sonar system using CW signal, when the noise included reverberation has not the white characteristics, the CFAR detector estimates high threshold. Because of this reason it cannot detect targets and not resolve the closely spaced multiple targets. In order to solve these problems, we propose an adaptive reverberation rejection filter The proposed filter is composed of an adaptive filter and a fixed filter with its coefficients. To study the performance of the proposed adaptive reverberation rejection filter, various experiments have been performed under In moving active sonar environments. As a results, the proposed method has the improved performance than the previous methods.