Articles
IET Communications (17518628)19(1)
This paper investigates the momentary extraction of a part of the frequency components of the signal resulting from the frequency modulation process. Based on this analysis, a new method for extracting frequency information is proposed. The changes of the instantaneous periods of the FM signal are investigated, and a simple method to determine the instantaneous frequency of the signal in the time domain is proposed. The proposed method was evaluated through simulations and compared against conventional FM demodulation techniques. In similar SNR, the relative error power of the proposed method is approximately 2 dB less than other conventional types of FM demodulators. © 2025 The Author(s). IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
IET Communications (17518628)18(17)pp. 993-1001
This paper investigates the changes in the waveform of a sinusoidal carrier resulting from amplitude modulation (AM) process. Based on this analysis, a novel method for extracting amplitude information is proposed. The proposed method uses the behaviour of the amplitude limitation which does not significantly affect the slope of the sinusoidal signal near zero crossing points. A simple comparator is used to convert the changes in sinusoidal slope near zero crossing points into pulse width changes. A simple circuit is proposed which keeps the output pulse width of the comparator constant by a simple control loop. The accuracy of the method is evaluated through simulation and is experimentally tested. If the modulation index is high and the amplitude of the input signal to the detector is limited, the proposed method can yield up at least 9 dB improvement in relative error power. However, if the modulation index is small, the improvement in relative error power can be at least 35 dB compared to other conventional types of AM demodulators. © 2024 The Author(s). IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
IET Science, Measurement and Technology (17518822)17(9)pp. 351-360
Low-frequency noise, generated inherently by the number or mobility fluctuation of carriers, is a crucial concern for the design of analog and digital circuits. Unified modelling based on experimental validation of near-DC noise in amplifiers is a long-standing open problem. This article develops a model for low-frequency noise by deriving new bounds for carrier capturing and releasing. According to the proposed model, a measurement system is suggested that operates in a wide frequency range and even at very low frequencies. The system is noise-tolerant, since the amplifier is selected based on acceptable noise levels. Among the advantages are the independence from specialized structural noise models for each component and the low cost of the measurement system. The evaluation results show that the proposed method leads to a promising improvement in the low-frequency noise measuring and is superior to conventional models in the normalized root mean square error indicator. Findings reveal that the proposed measurement method can estimate the flicker noise around the DC frequency, and the proposed model agrees reasonably with the proposed measurement circuit. © 2023 The Authors. IET Science, Measurement & Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Biomedical Signal Processing and Control (17468108)80
Biomedical signals are frequently contaminated by colored noise; consequently, noise recognition and reduction are critical to biomedical systems. Conventional techniques have not been sufficiently focused on noise classification and using dominant noise, which facilitates noise recognition and reduction. In addition, the dependence of previous methods on threshold and calibration parameters decreases the performance of noise reduction methods. Hence, this paper enriches empirical mode decomposition (EMD) by dominant noise and presents an adaptive denoising method based on deep learning. The proposed method first identifies the dominant noise using mode decomposition and a two-step long short-term memory (LSTM) deep classifier. Then, detected intrinsic features are used for noise-aided mode decomposition. Finally, denoising is adaptively performed using the most relevant components to the dominant noise. This method is capable of accurately classifying and suppressing white and colored noise. The proposed method is validated by ECG and audio datasets. The evaluation results show that the suggested method leads to a promising improvement in noise classification, moreover, noise reduction is superior to the conventional methods in terms of SNR and RMSE criteria. As a result, the suggested method can impact noise reduction performance in real-world applications by utilizing dominant noise detection. © 2022 Elsevier Ltd