• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4600-4616
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• 2021

In cognitive radio networks where unlicensed secondary users opportunistically access to licensed spectrum unused by licensed primary users, spectrum sensing is one of the key issues in order to effectively use the frequency resource. For enhancing the sensing performance in energy detection-based spectrum sensing, spatial diversity based on multiple antennas is utilized. However, the sensing performance can be degraded when antennas are spatially correlated, resulting in inducing the harmful interference to primary users. To overcome this problem, in this paper, an advanced energy detector is proposed. In the proposed sensing method, a weight matrix based on the eigenvalues of the spatial channels without any prior information on the primary signals is defined and utilized. In numerical simulations, it is shown that the proposed detector outperforms the conventional detector with regard to false-alarm and detection probabilities when antenna are spatially correlated.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.4
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• pp.165-173
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• 2019

In this paper, we apply a discrete wavelet packet transform (DWPT) to reduce the influence of interference in global positioning system (GPS) signals and compare the interference mitigation performance of various wavelets. By applying DWPT to the received signal, we can gradually divide the received signal band into low-pass and high-pass bands. After calculating the average power for the separate bands, we can determine whether there is interference by comparing the value with the given threshold. For a band that includes interference, we can reconstruct the whole band signal using inverse DWPT (IDWPT) after applying a nulling method that sets all of the wavelet coefficients to 0. The reconstructed signals are correlated with the pseudorandom noise (PRN) codes to acquire GPS signals. The performance evaluation is based on the number of satellite signals whose peak ratio (defined as the ratio of the first and second correlation peak values in the acquisition stage) exceeds the threshold. In this paper, we compare and evaluate the performance of 6 wavelets including Haar, Daubechies, Symlets, Coiflets, Biorthogonal Splines, and Discrete Meyer.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.465-474
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• 1994

This study is concerned with a method which helps human to generate EMG signals accurately and consistently to make reliable design samples of function discriminator for prosthetic arm control. We intend to ensure a signal accuracy and consistency by training human as a signal generation source. For the purposes, we construct a human training system using a digital computer, which generates visual graphes to compare real target motion trajectory with the desired one, to observe EMG signals and their features. To evaluate the effect which affects a feature variance and a feature separability between motion classes by the human training system, we select 4 features such as integral absolute value, zero crossing counts, AR coefficients and LPC cepstrum coefficients. We perform a experiment four times during 2 months. The experimental results show that the hu- man training system is effective for accurate and consistent EMG signal generation and reduction of a feature variance, but is not correlated for a feature separability, The cepstrum coefficient is the most preferable among the used features for reduction of variance, class separability and robustness to a time varing property of EMG signals.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.882-885
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• 2003

In this paper, nonlinear echo cancellation using a correlation LMS (CLMS) algorithm is proposed to cancel the undesired nonlinear echo signals generated in the hybrid system of the telephone network. In the telephone network, the echo signals may result the degradation of the network performance. Furthermore, digital to analog converter (DAC) and analog to digital converter (ADC) may be the source of the nonlinear distortion in the hybrid system. The adaptive filtering technique based on the nonlinear Volterra filter has been the general technique to cancel such a nonlinear echo signals in the telephone network. But in the presence of the double-talk situation, the error signal for tap adaptations will be greatly larger, and the near-end signal can cause any fluctuation of tap coefficients, and they may diverge greatly. To solve a such problem, the correlation LMS (CLMS) algorithm can be applied as the nonlinear adaptive echo cancellation algorithm. The CLMS algorithm utilizes the fact that the far-end signal is not correlated with a near-end signal. Accordingly, the residual error for the tap adaptation is relatively small, when compared to that of the conventional normalized LMS algorithm. To demonstrate the performance of the proposed algorithm, the DAC of hybrid system of the telephone network is considered. The simulation results show that the proposed algorithm can cancel the nonlinear echo signals effectively and show robustness under the double-talk situations.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.471-478
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• 2024

Effective physical fatigue monitoring is crucial for ensuring the health, safety, and productivity of construction workers, given the physically demanding nature of their work and the challenging environment in which they operate. In recent years, wearable sensors have shown growing potential for physical fatigue monitoring among construction workers. However, such fatigue assessment methods exhibit a significant gap as they often overlook the impact of inter-individual variability, such as differences in height, weight, and body mass index, on physiological signals that indicate physical fatigue. Therefore, this study aimed to investigate the role of personal factors in altering physiological responses, thereby improving the reliability and accuracy of fatigue monitoring using wearable physiological sensors. To explore the impact of these inter-individual factors, we experimentally analyzed the relationship between personal characteristics, physiological signals, and physical fatigue. Our findings reveal that although the inter-individual factors may not be directly correlated with fatigue levels, they significantly affect fatigue through their influence on physiological signals. Incorporation of these factors into a random forest predictive model significantly enhanced its predictive performance. Furthermore, integrating personal features with other variables to create new features in the physical fatigue prediction model notably improves its accuracy, highlighting the potential for developing personalized fatigue detection systems.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.16 no.2
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• pp.140-145
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• 2005

Background and Objectives : Voice perturbation measures, such as jitter and shimmer has been importantly used for diagnosis and treatment efficacy of laryngeal dysfunction. This study was conducted to investigate validity of newly developed multi-channel voice analyzer program by comparing with MDVP, PRAAT, TF32. In addition, we compared the voice perturbation measures with different voice analyzer program by type of signals. Materials and Methods : Nineteen mild-severe dysphonic patients participated in our study. Fundamental frequency, jitter and shimmer values were obtained from different voice analyzer program using the same sustained/ah/phonation. Results : Fundamental frequency and shimmer were highly correlated whereas jitter was weakly correlated between newly developed multi-channel voice analyzer program and the others though different pitch computation algorithm except MDVP, In addition, Type 2 and 3 signals were weakly correlated than Type 1. Conclusion : In the clinical setting, clinician may have sufficient information of voice analyzer and control conditions properly for severity of pathologic voice before voice perturbation measure to obtain reliable results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.4
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• pp.386-392
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• 2009

This paper reports on the research which investigates acoustic signals acquired in progressive compressing, hole blanking, shearing and burr compacting process. The work piece is the head pin of the electric connector, whose raw material is the preformed steel bar. An acoustic sensor was set on the bed of hydraulic press. Because the acquired signals include the dynamic characteristics generated for all the processes, it is required to investigate signal characteristics corresponding to unit process. The corresponding dynamic characteristics to the respective process were first studied by analyzing the signals respectively acquired from compressing, blanking and compacting process. The combined signals were then periodically analyzed from the grinding to the grinding in the sound frequency domain and in the ultrasonic wave. The frequency of around 9 kHz in the sound frequency domain was much correlated to the tool wear. The characteristic frequency in the acoustic emission domain between 100 kHz and 500 kHz was not only clearly observed right after tool grinding but its amplitude was also related to the wear. The frequency amplitudes of 160 kHz and 320 kHz were big enough to be classified by the noise. The noise amplitudes are getting bigger, and their energy was much bigger as coming to the next regrinding. The signal analysis was based on the real time data and its frequency spectrum by Fourier Transform. As a result, the acousto-ultrasonic signals were much related to the tool wear progression.

• Journal of Biosystems Engineering
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• v.23 no.1
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• pp.57-66
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• 1998

This study was conducted to investigate the relationships between parameters affecting internal quality of watermelon and its acoustic characteristics. The measuring and analyzing system was established to study the acoustic characteristics of watermelon. Algorithms for analyzing sound signals were developed. Sound signals which was detected with the microphone were filtered, and their spectrum was computed by means of the Fast Fourier Transform. As watermelon changed from the unripe stage to ripe, acoustic waves in time domain became complicated, and several components appeared in frequency domain. The correlationship was investigated between some parameters affecting internal quality of the watermelon and several peak frequencies. Results indicated that weight, density and sugar content had high correlations with several frequencies(the first peak frequency, the second peak frequency, and the third peak frequency). And the sugar content and the volume of the watermelon were highly correlated with the third peak frequency.

• Journal of Power System Engineering
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• v.7 no.1
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• pp.82-85
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• 2003

This study is to look at the effect for deformation of Polyethylene, on the wave forms produced by tensile test. Signals collected were then classified visually into three types according to their shapes in the time and frequency domain. Each type should contain signals which could be correlated to a certain micro failure mechanism that occurs during the tensile process. The result showed that the acoustic emission method could be effectively used for analysis of fracture mechanism in Polyethylene structures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.374-378
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• 2003

The asymptotic performance of the two-sample locally optimum rank detector for random signals buried in signal-dependent noise and additive noise is consigered in this paper. It is shown that the locally optimum rank detector, a nonparametric detector, has reasonable asymptotic performance for a class of correlated random signals, compared with the locally optimum detector. It is noteworthy that the the two-sample locally optimum rank detector perform almost the same with the one-sample locally optimum rank detector.