Jingzhi Zhang

Bio: Jingzhi Zhang is an academic researcher. The author has contributed to research in topics: Independent component analysis & Compressed sensing. The author has an hindex of 1, co-authored 3 publications receiving 2 citations.

Target Signal Extraction Method Based on Enhanced ICA with Reference

Abstract: Target signal extraction has a great potential for applications. To solve the problem of error extraction of target signals in the current constrained independent component analysis (cICA) method, an enhanced independent component analysis with reference (EICA-R) method is proposed. The new algorithm establishes a unified cost function, which combines the negative entropy contrast function and the distance metric function. The EICA-R method transforms the constrained optimization problem into unconstrained optimization problem to overcome the problem of threshold setting of distance metric function in constrained optimization problem. The theoretical analysis and simulation experiment show that the proposed EICA-R algorithm overcomes the problem of the error extraction of the existing algorithm and improves the reliability of the target signal extraction.

Enhanced ICA-R Algorithm Based Steady Mixed Signals Separation

Abstract: To solve the problem of steady mixed signal separation, we propose an enhanced reference independent component analysis (ICA-R) method by combining the negative entropy contrast function and the distance measurement function of the target signal. The new algorithm transforms the constrained optimization problem into an unconstrained optimization problem. The convergence of the enhanced ICA-R method is proved theoretically. Simulation results show that the algorithm proposed in this paper improves the robustness of target signal extraction.

The Fingerprinting Analysis of Power Amplifier Predistortion Error from Wideband Signals

Abstract: Radio frequency fingerprinting is caused by the hardware non-ideality, especially the analog components’ non-ideality. With the rapidly increasing demands for higher data transmission rate, the requirements for efficient modulation schemes and wider channel bandwidth are continuously increasing, which needs digital predistortion (DPD) to eliminate the nonlinear and memory effect of the power amplifier. Aiming at the power amplifier predistortion error, this paper analyzes and identifies typical DPD architectures. Firstly, a power amplifier behavior model which coefficients extracted from a real PA is used. Then, three typical DPD architectures are employed to compensate the PA’s nonlinear and memory effect respectively. The DPD and PA architecture is stimulated by a 5 MHz long term evolution (LTE) signal. Finally, the three DPD and PA architectures are identified above a neural network classifier using overall AM-AM and AM-PM performance. The analysis and simulation results demonstrate the effectiveness of the proposed method.

Hankel Structured Low Rank and Sparse Representation Via L0-Norm Optimization for Compressed Ultrasound Plane Wave Signal Reconstruction

Abstract: Ultrasound plane wave imaging is widely used in many applications thanks to its capability in reaching high frame rates. However, the amount of data acquisition and storage in a period of time can become a bottleneck in ultrasound system design for thousands frames per second. In our previous study, we proposed a low-rank and joint-sparse model to reduce the amount of sampled channel data of focused beam imaging by considering all the received data as a 2D matrix. However, for a single plane wave transmission, the number of channels is limited and the low-rank property of the received data matrix is no longer achieved. In this study, a L 0 -norm based Hankel structured low-rank and sparse model is proposed to reduce the channel data. An optimization algorithm, based on the alternating direction method of multipliers (ADMM), is proposed to efficiently solve the resulting optimization problem. The performance of the proposed approach was evaluated using the data published in Plane Wave Imaging Challenge in Medical Ultrasound (PICMUS) in 2016. Results on channel and plane wave data show that the proposed method is better adapted to the ultrasound channel signal and can recover the image with fewer samples than the conventional CS method.

A Simple Scheme for 2FSK Signal Extraction Based on Independent Component Analysis with Cosine Pulse Reference Signal

Abstract: In this paper, we propose a simple scheme for 2FSK signal extraction based on independent component analysis with reference (ICA-R) in noisy and disturbed channel condition, and only one of the two carrier frequencies of the source 2FSK signal is required as the a priori information to generate a cosine pulse signal as the reference signal at receiver. Simulations under various channel conditions, such as different jamming signal type, Signal-to-Jamming Ratio (SJR) and Signal-to-Noise Ratio (SNR), are carried out to investigate the effectiveness and extraction performance of the proposed method. The simulation results show that the proposed method takes a good extraction effect when the carrier frequency of the reference signal equals to either one of the two carrier frequencies of the source 2FSK signal, and the anti-noise performance under strong interference condition is almost the same with the theoretical result in A WGN channel. Moreover, the anti-jamming performance keeps well even when the SJR is as low as -50 dB. Besides, we find that the wrong convergence phenomenon results from the inherent threshold parameter problem in ICA-R is solved in our proposed scheme.

Similarity-and-Independence-Aware Beamformer with Iterative Casting and Boost Start for Target Source Extraction Using Reference.

Abstract: Target source extraction is significant for improving human speech intelligibility and the speech recognition performance of computers. This study describes a method for target source extraction, called the similarity-and-independence-aware beamformer (SIBF). The SIBF extracts the target source using a rough magnitude spectrogram as the reference signal. The advantage of the SIBF is that it can obtain a more accurate signal than the spectrogram generated by target-enhancing methods such as speech enhancement based on deep neural networks. For the extraction, we extend the framework of deflationary independent component analysis (ICA) by considering the similarities between the reference and extracted target sources, in addition to the mutual independence of all the potential sources. To solve the extraction problem by maximum-likelihood estimation, we introduce three source models that can reflect the similarities. The major contributions of this study are as follows. First, the extraction performance is improved using two methods, namely boost start for faster convergence and iterative casting for generating a more accurate reference. The effectiveness of these methods is verified through experiments using the CHiME3 dataset. Second, a concept of a fixed point pertaining to accuracy is developed. This concept facilitates understanding the relationship between the reference and SIBF output in terms of accuracy. Third, a unified formulation of the SIBF and mask-based beamformer is realized to apply the expertise of conventional BFs to the SIBF. The findings of this study can also improve the performance of the SIBF and promote research on ICA and conventional beamformers. Index Terms: beamformer, independent component analysis, source separation, speech enhancement, target source extraction

Similarity-and-Independence-Aware Beamformer With Iterative Casting and Boost Start for Target Source Extraction Using Reference

Abstract: Target source extraction is significant for improving human speech intelligibility and the speech recognition performance of computers. This study describes a method for target source extraction, called the similarity-and-independence-aware beamformer (SIBF). The SIBF extracts the target source using a rough magnitude spectrogram as the reference signal. The advantage of the SIBF is that it can obtain a more accurate signal than the spectrogram generated by target-enhancing methods such as speech enhancement based on deep neural networks. For the extraction, we extend the framework of deflationary independent component analysis (ICA) by considering the similarities between the reference and extracted target sources, in addition to the mutual independence of all the potential sources. To solve the extraction problem by maximum-likelihood estimation, we introduce three source models that can reflect the similarities. The major contributions of this study are as follows. First, the extraction performance is improved using two methods, namely boost start for faster convergence and iterative casting for generating a more accurate reference. The effectiveness of these methods is verified through experiments using the CHiME3 dataset. Second, a concept of a fixed point pertaining to accuracy is developed. This concept facilitates understanding the relationship between the reference and SIBF output in terms of accuracy. Third, a unified formulation of the SIBF and mask-based beamformer is realized to apply the expertise of conventional BFs to the SIBF. The findings of this study can also improve the performance of the SIBF and promote research on ICA and conventional beamformers. Index Terms: beamformer, independent component analysis, source separation, speech enhancement, target source extraction

A Simple Scheme for 2FSK Signal Extraction Based on Independent Component Analysis with Cosine Pulse Reference Signal

Abstract: In this paper, we propose a simple scheme for 2FSK signal extraction based on independent component analysis with reference (ICA-R) in noisy and disturbed channel condition, and only one of the two carrier frequencies of the source 2FSK signal is required as the a priori information to generate a cosine pulse signal as the reference signal at receiver. Simulations under various channel conditions, such as different jamming signal type, Signal-to-Jamming Ratio (SJR) and Signal-to-Noise Ratio (SNR), are carried out to investigate the effectiveness and extraction performance of the proposed method. The simulation results show that the proposed method takes a good extraction effect when the carrier frequency of the reference signal equals to either one of the two carrier frequencies of the source 2FSK signal, and the anti-noise performance under strong interference condition is almost the same with the theoretical result in A WGN channel. Moreover, the anti-jamming performance keeps well even when the SJR is as low as -50 dB. Besides, we find that the wrong convergence phenomenon results from the inherent threshold parameter problem in ICA-R is solved in our proposed scheme.

Similarity-and-Independence-Aware Beamformer: Method for Target Source Extraction using Magnitude Spectrogram as Reference.

Abstract: This study presents a novel method for source extraction, referred to as the similarity-and-independence-aware beamformer (SIBF). The SIBF extracts the target signal using a rough magnitude spectrogram as the reference signal. The advantage of the SIBF is that it can obtain an accurate target signal, compared to the spectrogram generated by target-enhancing methods such as the speech enhancement based on deep neural networks (DNNs). For the extraction, we extend the framework of the deflationary independent component analysis, by considering the similarity between the reference and extracted target, as well as the mutual independence of all potential sources. To solve the extraction problem by maximum-likelihood estimation, we introduce two source model types that can reflect the similarity. The experimental results from the CHiME3 dataset show that the target signal extracted by the SIBF is more accurate than the reference signal generated by the DNN. Index Terms: semiblind source separation, similarity-and-independence-aware beamformer, deflationary independent component analysis, source model