• ETRI Journal
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• v.32 no.2
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• pp.214-221
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• 2010

Radio frequency (RF) subsampling can be used by radio receivers to directly down-convert and digitize RF signals. A goal of a cognitive radio/software defined ratio (CR/SDR) receiver design is to place the analog-to-digital converter (ADC) as near the antenna as possible. Based on this, a band-pass sampling (BPS) frontend for CR/SDR is proposed and verified. We present a receiver architecture based second-order BPS and signal processing techniques for a digital RF frontend. This paper is focused on the benefits of the second-order BPS architecture in spectrum sensing over a wide frequency band range and in multiband receiving without modification of the RF hardware. Methods to manipulate the spectra are described, and reconstruction filter designs are provided. On the basis of this concept, second-order BPS frontends for CR/SDR systems are designed and verified using a hardware platform.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.93-98
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• 2020

Research on underwater sensor networks is increasing due to such reasons as marine resource management, maritime disaster prediction and military protection. Many underwater sensor networks performs wireless communication using an acoustic sound wave band signal having a relatively low frequency. So the digital part of their modem can take charge of carrier band signal processing. To enable this, the sampling rate of the baseband band signal should be increased to a sampling rate at which carrier band signal processing is possible. In this paper, we designed a sampling rate increasing circuit based on a CIC interpolator for underwater sensor nodes. The CIC interpolator has a simple circuit structure. However, since the CIC interpolator has a large attenuation of the pass band and a wide transition band, an inverse sinc LPF is added to compensate for frequency response of the CIC interpolator. The proposed interpolator was verified in time domain and frequency domain using ModelSim and Matlab.

• Speech Sciences
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• v.10 no.2
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• pp.249-258
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• 2003

This study attempted to examine a speaker identification method using difference sum and correlation coefficient determined from a pair of intensity level matrices of band-pass-filtered numeric sounds produced by ten female speakers of similar age and height. Subjects recorded three digit numbers at a quiet room at a sampling rate of 22 kHz on a personal computer. Collected data were band-pass-filtered at five different band ranges. Then, matrices of five intensity levels at 100 proportional time points were obtained. Pearson correlation coefficients and the sum of absolute intensity differences between a pair of given matrices were determined within and across the speakers. Results showed that very high correlation coefficient and small difference sum generally occurred within each speaker but some individual variation was also observed. Thus, the matrix pair with a higher coefficient and a smaller difference sum was averaged to form each individual's model. Comparison among the speakers yielded generally low coefficients and large differences, which suggests successful speaker identification, but among them there were a few cases with very high coefficients and small differences. Future studies will focus on finer band ranges and additional spectral parameters at some peak points of the intensity contour at a low frequency band.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.44-50
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• 2002

The fast Fourier transform (FFT) is a good method to estimate spectral density, but the frequency resolution is limited to the sampling window, and thus the precise characteristics of the spectral density for short signals are not clear. To solve the limitation, a multiple band-pass filter was introduced to estimate the precise time course of the spectral density for flash visual evoked potentials (VEPs). Signals were recorded during -200 and 600 ms using balanced noncephalic electrodes, and sampled at 1 K Hz in 12 bits. With 1 Hz and 10 ms resolutions, spectral density was estimated between 10 and 100 Hz. Background powers at the alpha-and beta-bands were high over the posterior scalp, and powers around 200ms were evoked at the same bands over the same region, corresponding to P110 and N165 of VEPs. normalized's spectral density showed evoked powers around 200 ms and suppressed powers following the evoked powers over the posterior scalp. The evoked powers above the 20Hz band were not statistically significant. However, the gamma band was significantly evoked intra-individually; details in the gamma bands were varied among the subjects. Details of spectral density were complicated even for a simple task such as watching flashes; both synchronization and desynchronization occurred with different distributions and different time courses.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.385-390
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• 2006

This paper examines the sampling and jitter specifications and considerations for Global Navigation Satellite Systems (GNSS) software receivers. Software radio (SWR) technologies are being used in the implementation of communication receivers in general and GNSS receivers in particular. With the advent of new GPS signals, and a range of new Galileo and GLONASS signals soon becoming available, GNSS is an application where SWR and software-defined radio (SDR) are likely to have an impact. The sampling process is critical for SWR receivers, where it occurs as close to the antenna as possible. One way to achieve this is by BandPass Sampling (BPS), which is an undersampling technique that exploits aliasing to perform downconversion. BPS enables removal of the IF stage in the radio receiver. The sampling frequency is a very important factor since it influences both receiver performance and implementation efficiency. However, the design of BPS can result in degradation of Signal-to-Noise Ratio (SNR) due to the out-of-band noise being aliased. Important to the specification of both the ADC and its clocking Phase- Locked Loop (PLL) is jitter. Contributing to the system jitter are the aperture jitter of the sample-and-hold switch at the input of ADC and the sampling-clock jitter. Aperture jitter effects have usually been modeled as additive noise, based on a sinusoidal input signal, and limits the achievable Signal-to-Noise Ratio (SNR). Jitter in the sampled signal has several sources: phase noise in the Voltage-Controlled Oscillator (VCO) within the sampling PLL, jitter introduced by variations in the period of the frequency divider used in the sampling PLL and cross-talk from the lock line running parallel to signal lines. Jitter in the sampling process directly acts to degrade the noise floor and selectivity of receiver. Choosing an appropriate VCO for a SWR system is not as simple as finding one with right oscillator frequency. Similarly, it is important to specify the right jitter performance for the ADC. In this paper, the allowable sampling frequencies are calculated and analyzed for the multiple frequency BPS software radio GNSS receivers. The SNR degradation due to jitter in a BPSK system is calculated and required jitter standard deviation allowable for each GNSS band of interest is evaluated. Furthermore, in this paper we have investigated the sources of jitter and a basic jitter budget is calculated that could assist in the design of multiple frequency SWR GNSS receivers. We examine different ADCs and PLLs available in the market and compare known performance with the calculated budget. The results obtained are therefore directly applicable to SWR GNSS receiver design.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.313-317
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• 2001

The approach to predict time series without neglecting the fluctuation in a short period is tried by using a digital FIR filter and a neural network. The differential waveform of the Nikkei average closing price is filtered by the FIR band-pass filter of 101 length. It is filtered into the five frequency bands of 0-1Hz, 1-2Hz, 2-3Hz, 3-4Hz and 4-5Hz by setting the sampling frequency 10Hz. The each filtered waveform is learned and forecasted by the neural network. The neural network of the back propagation method is adopted in the learning the waveform. By inputting the data of 20 days in the past, the prediction of 10 days ahead is carried out. After learning the time series of each frequency band by the neural network, the predicted data far each frequency band are obtained. The predicted waveforms of each frequency band are synthesized to obtain a final forecast. The waveform can be forecasted well as a whole.

• The Journal of the Acoustical Society of Korea
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• v.14 no.5
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• pp.89-93
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• 1995

To reduce the redundancy within samples that resulted from uniform sampling method, nonuniform sampling or nonredundant-sample coding methods can be considered. However, it is well known that when conventional nonuniform sampling methods are applied directly to speech signal, the required amount of data is comparable to or mure than that by uniform sampling method like PCM. To overcome this problem, a new nonuniform sampling method is proposed, in which nonuniform sampling is applied to the low-pass filtered speech signal and higher band is compensated by 8 colored Gaussian random noise with various noise levels. By this method, speech signal waveform can be encoded by 1.8 times larger compression ratio than the conventional nonuniform sampling method.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.3
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• pp.225-231
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• 2013

This paper presents an improved signal processing technique in the square-wave-type voltage injection method for IPMSM sensorless drives. Since the sensorless method based on the square-wave voltage injection does not use low-pass filters to get an error signal for estimating rotor position and allows the frequency of the injected voltage signal to be high, the sensorless drive system may achieve an enhanced control bandwidth and reduced acoustic noise. However, this sensorless method still requires low-pass and band-pass filters to extract the fundamental component current and the injected frequency component current from the motor current, respectively. In this paper, these filters are replaced by simple arithmetic operations so that the time delay for estimating the rotor position can be effectively reduced to only one current sampling. Hence, the proposed technique can simplify its whole signal process for the IPMSM sensorless control using the square-wave-type voltage injection. The proposed technique is verified by the experiment on the 800W IPMSM drive system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.9-15
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• 2015

A CMOS band-pass delta-sigma modulator(BPDSM) and cascode class-E power amplifier have been developed CMOS for Class-S power amplifier applications. The BPDSM is operating at 1-GHz sampling frequency, which converts a 250-MHz sinusoidal signal to a pulse-width modulated digital signal without the quantization noise. The BPDSM shows a 25-dB SQNR(Signal to Quantization Noise Ratio) and consumes a power of 24 mW at an 1.2-V supply voltage. The class-E power amplifier exhibits an 18.1 dBm of the maximum output power with a 25% drain efficiency at a 3.3-V supply voltage. The BPDSM and class-E PA were fabricated in the Dongbu's 110-nm CMOS process.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.682-688
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• 1994

LSM(Least-Squares Method) has inherent limitation that precise system identification over wide frequency band is difficult especially at low frequency hand. In this paper we propose to use decimation, a spectrum analysis method widely used in signal processing. The merits of decimation are the flexibility of selection of the frequency hand concerned and the function of LPF(Low Pass Filter). In this paper, frequency-domain is divided into separate frequency bands which will be combined into full frequency-domain by using MDM(Multiple Decimation Method). In this way, free selection of sampling frequency for each hand is possible and the low frequency oscillation modes of LSM are avoided.