Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model.

EEG decoding based on motor imagery is an important part of brain-computer interface technology and is an important indicator that determines the overall performance of the brain-computer interface. Due to the complexity of motor imagery EEG feature analysis, traditional classification models rely heavily on the signal preprocessing and feature design stages. End-to-end neural networks in deep learning have been applied to the classification task processing of motor imagery EEG and have shown good results. This study uses a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network to obtain spatial information and temporal correlation from EEG signals. The use of cross-layer connectivity reduces the network gradient dispersion problem and enhances the overall network model stability. The effectiveness of this network model is demonstrated on the BCI Competition IV dataset 2a by integrating CNN, BiLSTM and ResNet (called CLRNet in this study) to decode motor imagery EEG. The network model combining CNN and BiLSTM achieved 87.0% accuracy in classifying motor imagery patterns in four classes. The network stability is enhanced by adding ResNet for cross-layer connectivity, which further improved the accuracy by 2.0% to achieve 89.0% classification accuracy. The experimental results show that CLRNet has good performance in decoding the motor imagery EEG dataset. This study provides a better solution for motor imagery EEG decoding in brain-computer interface technology research.

Remove Artifacts from a Single-Channel EEG Based on VMD and SOBI.

With the development of portable EEG acquisition systems, the collected EEG has gradually changed from being multi-channel to few-channel or single-channel, thus the removal of single-channel EEG signal artifacts is extremely significant. For the artifact removal of single-channel EEG signals, the current mainstream method is generally a combination of the decomposition method and the blind source separation (BSS) method. Between them, a combination of empirical mode decomposition (EMD) and its derivative methods and ICA has been used in single-channel EEG artifact removal. However, EMD is prone to modal mixing and it has no relevant theoretical basis, thus it is not as good as variational modal decomposition (VMD) in terms of the decomposition effect. In the ICA algorithm, the implementation method based on high-order statistics is widely used, but it is not as effective as the implementation method based on second order statistics in processing EMG artifacts. Therefore, aiming at the main artifacts in single-channel EEG signals, including EOG and EMG artifacts, this paper proposed a method of artifact removal combining variational mode decomposition (VMD) and second order blind identification (SOBI). Semi-simulation experiments show that, compared with the existing EEMD-SOBI method, this method has a better removal effect on EOG and EMG artifacts, and can preserve useful information to the greatest extent.

Excellent repeatability, all-sapphire Fabry Perot optical Pressure sensor based on wet etching and direct bonding for Harsh Environment Applications.

In this paper, we proposed an all-sapphire-based extrinsic Fabry-Perot interferometer (EFPI) sensor based on wet etching and the direct bonding process. Temperature measured by the EFPI is used to calibrate pressure measurement. The problem of repeatable measurement of dynamic pressure in a harsh environment is solved. The EFPI sensor can be applied in the temperature range of 25°C to 800°C and the pressure range environment of 0MPa to 5MPa. The pressure sensitivity of 355.8nm/MPa and the temperature sensitivity of 1.64nm/°C are obtained by a cross-correlation function (CCF) algorithm to interrogate the optical sensing system. Therefore, the proposed sensor has a great potential for pressure monitoring, such as jet engines, industrial gas turbine, and so on due to its 8×8mm size and compact structure.

All-sapphire-based fiber-optic pressure sensor for high-temperature applications based on wet etching.

In this paper, we proposed an all-sapphire-based extrinsic Fabry-Perot interferometer (EFPI) pressure sensor based on an optimized wet etching process, aiming to improve the quality of the interference signal. The sapphire pressure sensitive diaphragm (SPSD) was fabricated by wet etching solutions with different mixture ratios of H3PO4 and H2SO4 at 280°C. The differences of mixture ratios affect the surface roughness of SPSD. SPSDs with surface roughness of 3.91nm and 0.39nm are obtained when the mixture ratios of H3PO4 and H2SO4 is 1:1 and 1:3, respectively. We constructed pressure sensing test system adopting these two kinds of SPSD and performed comparative test. The experiment results show that the demodulation jump can be solved and cavity length fluctuation is decreased to ±5nm when the surface roughness of SPSD is 0.39nm.

Underdetermined Blind Source Separation of Synchronous Orthogonal Frequency Hopping Signals Based on Single Source Points Detection.

This paper considers the complex-valued mixing matrix estimation and direction-of-arrival (DOA) estimation of synchronous orthogonal frequency hopping (FH) signals in the underdetermined blind source separation (UBSS). A novel mixing matrix estimation algorithm is proposed by detecting single source points (SSPs) where only one source contributes its power. Firstly, the proposed algorithm distinguishes the SSPs by the comparison of the normalized coefficients of time frequency (TF) points, which is more effective than existing detection algorithms. Then, mixing matrix of FH signals can be estimated by the hierarchical clustering method. To sort synchronous orthogonal FH signals, a modified subspace projection method is presented to obtain the DOAs of FH. One superiority of this paper is that the estimation accuracy of the mixing matrix can be significantly improved by the proposed SSPs detection criteria. Another superiority of this paper is that synchronous orthogonal FH signals can be sorted in underdetermined condition. The experimental results demonstrate the efficiency of the two proposed algorithms.

Design and implementation of hybrid CORDIC algorithm based on phase rotation estimation for NCO.

The numerical controlled oscillator has wide application in radar, digital receiver, and software radio system. Firstly, this paper introduces the traditional CORDIC algorithm. Then in order to improve computing speed and save resources, this paper proposes a kind of hybrid CORDIC algorithm based on phase rotation estimation applied in numerical controlled oscillator (NCO). Through estimating the direction of part phase rotation, the algorithm reduces part phase rotation and add-subtract unit, so that it decreases delay. Furthermore, the paper simulates and implements the numerical controlled oscillator by Quartus II software and Modelsim software. Finally, simulation results indicate that the improvement over traditional CORDIC algorithm is achieved in terms of ease of computation, resource utilization, and computing speed/delay while maintaining the precision. It is suitable for high speed and precision digital modulation and demodulation.

Sparse array angle estimation using reduced-dimension ESPRIT-MUSIC in MIMO radar.

Sparse linear arrays provide better performance than the filled linear arrays in terms of angle estimation and resolution with reduced size and low cost. However, they are subject to manifold ambiguity. In this paper, both the transmit array and receive array are sparse linear arrays in the bistatic MIMO radar. Firstly, we present an ESPRIT-MUSIC method in which ESPRIT algorithm is used to obtain ambiguous angle estimates. The disambiguation algorithm uses MUSIC-based procedure to identify the true direction cosine estimate from a set of ambiguous candidate estimates. The paired transmit angle and receive angle can be estimated and the manifold ambiguity can be solved. However, the proposed algorithm has high computational complexity due to the requirement of two-dimension search. Further, the Reduced-Dimension ESPRIT-MUSIC (RD-ESPRIT-MUSIC) is proposed to reduce the complexity of the algorithm. And the RD-ESPRIT-MUSIC only demands one-dimension search. Simulation results demonstrate the effectiveness of the method.

Rao and Wald tests for adaptive detection in partially homogeneous environment with a diversely polarized antenna.

This study considers Rao test and Wald test for adaptive detection based on a diversely polarized antenna (DPA) in partially homogeneous environment. The theoretical expressions for the probability of false alarm and detection are derived, and constant false alarm rate (CFAR) behaviour is remarked on. Furthermore, the monotonicities of detection probability of the two detectors are proved, and a polarization optimization detection algorithm to enhance the detection performance is proposed. The numerical simulations are conducted to attest to the validity of the above theoretical analysis and illustrate the improvement in the detection performance of the proposed optimization algorithm.

Correlation between Serum Oxidative Stress Level and Serum Uric Acid and Prognosis in Patients with Hepatitis B-Related Liver Cancer before Operation.

Aiming to explore the correlation between preoperative serum oxidative stress level and serum uric acid and prognosis of hepatitis B-related liver cancer, the clinical data of 712 patients with hepatitis B-related liver cancer from January 2019 to December 2020 were retrospectively analyzed. By using the receiver operating curve, the optimal critical values of preoperative superoxide dismutase (SOD), malondialdehyde (MDA), and serum uric acid (SUA) are determined. The single-factor and multifactor Cox models are applied to screen out the suspicious factors affecting the prognosis of patients with hepatitis B-related liver cancer. According to the survival status of patients, the optimal thresholds of SOD, MDA, and SUA before operation were 58.055/mL, 10.825 nmol/L, and 312.77 nmol/L, respectively. The results of univariate analysis show that the prognosis of patients is significantly correlated with preoperative SOD, MDA, and SUA levels and TNM staging (P < 0.05). Additionally, multivariate analysis demonstrates that preoperative SOD < 58.055 U/mL and SUA ≥ 312.770 mmol/L and TNM stage III-IV are independent risk factors for postoperative prognosis (P < 0.05). Our study suggests that SOD, SUA, and TNM staging have certain value in judging the early prognosis of patients with hepatitis B-related liver cancer. Patients with high preoperative SOD level and low preoperative SUA level can obtain better prognosis.

Unidirectional rotating coordinate rotation digital computer algorithm based on rotational phase estimation.

The improved coordinate rotation digital computer (CORDIC) algorithm gives high precision and resolution phase rotation, but it has some shortages such as high iterations and big system delay. This paper puts forward unidirectional rotating CORDIC algorithm to solve these problems. First, using under-damping theory, a part of unidirectional phase rotations is carried out. Then, the threshold value of angle is determined based on phase rotation estimation method. Finally, rotation phase estimation completes the rest angle iterations. Furthermore, the paper simulates and implements the numerical control oscillator by Quartus II software and Modelsim software. According to the experimental results, the algorithm reduces iterations and judgment of sign bit, so that it decreases system delay and resource utilization and improves the throughput. We always analyze the error brought by this algorithm. It turned out that the algorithm has a good application prospect in global navigation satellite system and channelized receiver.