Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data

A class of algorithms based on subspace projection is widely used in the denoising of magnetoencephalography (MEG) signals. Setting the dimension of the interference (external) subspace matrix of these algorithms is the key to balancing the denoising effect and the degree of signal distortion. However, most current methods for estimating the dimension threshold rely on experience, such as observing the signal waveforms and spectrum, which may render the results too subjective and lacking in quantitative accuracy. Therefore, this study proposes a method to automatically estimate a suitable threshold. Time-frequency transformations are performed on the evoked state data to obtain the neural signal of interest and the noise signal in a specific time-frequency band, which are then used to construct the objective function describing the degree of noise suppression and signal distortion. The optimal value of the threshold in the selected range is obtained using the weighted-sum method. Our method was tested on two classical subspace projection algorithms using simulation and two sensory stimulation experiments. The thresholds estimated by the proposed method enabled the algorithms to achieve the best waveform recovery and source location error. Therefore, the threshold selected in this method enables subspace projection algorithms to achieve the best balance between noise removal and neural signal preservation in subsequent MEG analyses.

APNet: Adaptive projection network for medical image denoising

BACKGROUND

In clinical medicine, low-dose radiographic image noise reduces the quality of the detected image features and may have a negative impact on disease diagnosis.

OBJECTIVE

In this study, Adaptive Projection Network (APNet) is proposed to reduce noise from low-dose medical images.

METHODS

APNet is developed based on an architecture of the U-shaped network to capture multi-scale data and achieve end-to-end image denoising. To adaptively calibrate important features during information transmission, a residual block of the dual attention method throughout the encoding and decoding phases is integrated. A non-local attention module to separate the noise and texture of the image details by using image adaptive projection during the feature fusion.

RESULTS

To verify the effectiveness of APNet, experiments on lung CT images with synthetic noise are performed, and the results demonstrate that the proposed approach outperforms recent methods in both quantitative index and visual quality. In addition, the denoising experiment on the dental CT image is also carried out and it verifies that the network has a certain generalization.

CONCLUSIONS

The proposed APNet is an effective method that can reduce image noise and preserve the required image details in low-dose radiographic images.

Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction

High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method—minimum eigenvector collaborative representation discriminant projection—to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation. On the other hand, we maintain the collaborative representation relationship of samples in the projection subspace to enhance the discriminability of the extracted features. Also, the between-class scatter of the reconstructed samples is used to improve the robustness of the projection space. The experimental results on the COIL-20 image object database, ORL, and FERET face databases, as well as Isolet database demonstrate the effectiveness of the proposed method, especially in low dimensions and small training sample size.

A Two-Stage Interference Suppression Scheme Based on Antenna Array for GNSS Jamming and Spoofing

Jamming and spoofing are the two main types of intentional interference for global navigation satellite system (GNSS) receivers. Due to the entirely different signal characteristics they have, a few techniques can deal with them simultaneously. This paper proposes a two-stage interference suppression scheme based on antenna arrays, which can detect and mitigate jamming and spoofing before the despreading of GNSS receivers. First, a subspace projection was adopted to eliminate the high-power jamming signals. The output signal is still a multi-dimensional vector so that the spatial processing technique can be used in the next stage. Then, the cyclostationarity of GNSS signals were fully excavated to reduce or even remove the noise component in the spatial correlation matrix. Thus, the signal subspace, including information of the power and the directions-of-arrival (DOAs) of the GNSS signals, can be obtained. Next, a novel cyclic correlation eigenvalue test (CCET) algorithm was proposed to detect the presence of a spoofing attack, and the cyclic music signal classification (Cyclic MUSIC) algorithm was employed to estimate the DOAs of all the navigation signals. Finally, this study employed a subspace projection again to eliminate the spoofing signals and provide a higher gain for authentic satellite signals through beamforming. All the operations were performed on the raw digital baseband signal so that they did not introduce additional computational complexity to the GNSS receiver. The simulation results show that the proposed scheme not only suppresses jamming and spoofing effectively but also maximizes the power of the authentic signals. Nonetheless, the estimated DOA of spoofing signals may be helpful for the interference source positioning in some applications.