scholarly journals Digital demodulators for analog signals: comparative analysis and simulation

2021 ◽  
Vol 2094 (2) ◽  
pp. 022048
Author(s):  
T V Kudinova ◽  
G A Osipov ◽  
F A Nanay
Keyword(s):  
Comparative Analysis ◽  
Phase Modulation ◽  
Limited Memory ◽  
Radio Monitoring ◽  
Analog Signals ◽  
Fm Signals ◽  
Narrowband Signal ◽  
Frequency Domains ◽  
Modulation Signal ◽  
Programmable Devices

Abstract The paper examines digital demodulators for two commonly used techniques of modulating analog signals: amplitude modulation (AM) and frequency modulation (FM). The described demodulators can be used to perform the radio monitoring of narrowband signal ranges including FM broadcasting stations as well as license-free CB, LPD, PMR bands. The demodulators considered in this work are intended for programmable devices with limited memory and computing resources, for example, for STM32F407 microcontrollers and similar ones. The paper presents the analysis and simulation of demodulators for AM signals, FM signals with low modulation indices and for FM signals without restriction on the modulation indices. In addition, the authors demonstrate how to demodulate the phase-modulation signal using a quadrature demodulator. The number of operations that are available for demodulation is limited by IF multiplication and filtering. The simulation of the analyzed demodulation algorithms was carried out in the Scilab environment which is a free analogue of the Matlab environment. To explain the principle of operation of demodulators, block diagrams and graphs of signals in time and frequency domains are shown.

Time and frequency domains characteristics comparative analysis for light output waveform of fault-free and faulty LED lamps

Optik ◽  
2020 ◽  
Vol 223 ◽  
pp. 165540
Author(s):  
Fukang Sun ◽  
Qiansheng Fang ◽  
Jianxia Xie ◽  
Bailing Chen ◽  
Yuhang Shang ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Light Output ◽  
Output Waveform ◽  
Frequency Domains ◽  
Time And Frequency Domains

Phase-Modulation Fluorometer Using a Dynode-Voltage Burst-Modulated Photomultiplier Tube

2005 ◽  
Vol 59 (8) ◽  
pp. 1049-1053 ◽  
Author(s):  
Tetsuo Iwata ◽  
Tsutomu Araki
Keyword(s):  
Phase Modulation ◽  
Load Resistance ◽  
Photomultiplier Tube ◽  
Duty Ratio ◽  
Fluorescence Signal ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Modulation Signal ◽  
Combined Technique

We propose a new scheme for a phase-modulation fluorometer (PMF) in which a photomultiplier tube (PMT) is used as a photo-detector whose gain is modulated sinusoidally with a burst signal of period T and duty ratio 0.5. The carrier wave of the burst modulation signal is synchronized with an incident fluorescence signal. In order to modulate the gain of the PMT, one of the dynodes in the PMT was deeply biased and the burst signal was superimposed. Because the fluorescence signal is converted to a direct current (dc) signal by the PMT internal modulation, we can make the value of the load resistance of the PMT relatively large under the condition τ ≤ T/2, where τ is a time constant of a low-pass filter attached to the output of the PMT. The proposed scheme brings about advantages in sensitivity and noise immunity in detecting weak fluorescence in comparison with those of the conventional PMF. The combined technique of the burst modulation of the gain of the PMT and the alternating current (ac) signal detection alleviates the influence of the background light.

scholarly journals Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 251
Author(s):  
Polash Dey ◽  
Emam Hossain ◽  
Md. Ishtiaque Hossain ◽  
Mohammed Armanuzzaman Chowdhury ◽  
Md. Shariful Alam ◽  
...  
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Recurrent Neural Networks ◽  
Stock Price ◽  
Short Term Memory ◽  
Time Intervals ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Frequency Domains ◽  
Time Series Analyses

Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks’ prices follow the same trend, a single model cannot be used to predict the movement of all types of stock’s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs—simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)—and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies’ datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability.

scholarly journals Characterization of Frequency Domains of Bandlimited Frame Multiresolution Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1050
Author(s):  
Zhihua Zhang
Keyword(s):  
Frequency Domain ◽  
Multiresolution Analysis ◽  
Background Noise ◽  
Sparse Reconstruction ◽  
Time Frequency ◽  
Processing Data ◽  
Narrowband Signal ◽  
Frequency Domains ◽  
Frame Multiresolution Analysis

Framelets have been widely used in narrowband signal processing, data analysis, and sampling theory, due to their resilience to background noise, stability of sparse reconstruction, and ability to capture local time-frequency information. The well-known approach to construct framelets with useful properties is through frame multiresolution analysis (FMRA). In this article, we characterize the frequency domain of bandlimited FMRAs: there exists a bandlimited FMRA with the support of frequency domain G if and only if G satisfies G⊂2G, ⋃m2mG≅Rd, and G\G2⋂G2+2πν≅∅(ν∈Zd).

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