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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. [ABSTRACT FROM AUTHOR]

Recently, microstructured optical fibers have become the subject of extensive research as they can be employed in many civilian and military applications. One of the recent areas of research is to enhance the normalized responsivity (NR) to acoustic pressure of the optical fiber hydrophones by replacing the conventional single mode fibers (SMFs) with hollow-core photonic bandgap fibers (HC-PBFs). However, this needs further investigation. In order to fully understand the feasibility of using HC-PBFs as acoustic pressure sensors and in underwater communication systems, it is important to study their modal properties in this environment. In this paper, the finite element solver (FES) COMSOL Multiphysics is used to study the effect of underwater acoustic pressure on the effective refractive index neff of the fundamental mode and discuss its contribution to NR. Besides, we investigate, for the first time to our knowledge, the effect of underwater acoustic pressure on the effective area Aeff and the numerical aperture (NA) of the HC-PBF.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The compressed sensing (CS) radar receiver based on the theory of CS has a great advantage in receiving wide-band signal and can receive high-frequency radar signal at a sampling rate far lower than the traditional receiver. However, in order to obtain the information of the target signal, the parameters of the reconstructed signal still need to be measured by conventional methods. In order to make full use of the compressed sampling and the characteristics of radar signals, this study proposed a cascading dictionary matching pursuit algorithm based on block-sparsity (BS-CDMP) to achieve fast identification and reconstruction of received signals. First, a signal-level redundant dictionary is constructed for quickly identifying the signal in which the authors are interested in. Then, they study the feasibility of cascading the signal-level redundant dictionary and the DFT basis as a whole sparsity dictionary. Finally, the block sparsity is introduced for the selection of different parts of the dictionary during greedy iteration. Experimental results show that their proposed algorithm has considerable and universal reconfiguration performance and can directly perform fast reconstruction and recognition of the target signal. [ABSTRACT FROM AUTHOR]

In the development of analog signal compressive sensing (CS), the degradation of reconstruction performance under noise is the main bottleneck because the CS framework is very sensitive to noise. This paper proposes an adaptive channelization-based orthogonal matching pursuit algorithm (C-OMP) combining the channelization and the adaptive iteration methods. The proposed C-OMP has two steps: channel screening and global iteration. Based on the proposed method, the original signal can be recovered adaptively in high probability of success with fewer observations under the noise background. Simultaneously, the noise can be reduced as much as possible to enhance the output signal-to-noise ratio (SNR) by excluding the noise channel during the channel screening and separating noise atoms during the global iteration. The relationship between the probability of successful reconstruction and the number of observations is mathematically analyzed. Furthermore, the parameter settings, computational complexity, and output SNR are analytically evaluated. The simulation results confirm the analytical results and further demonstrate the effectiveness and advantages of the C-OMP in the noise environment. Overall, the proposed algorithm considerably improves the performance of the analog signal CS in the practical noisy environment. [ABSTRACT FROM AUTHOR]

This correspondence paper studies joint beamforming optimization and time allocation for a wireless powered two-way hybrid-relay network. As a general relaying architecture accommodating the existing relaying architectures in the literature as two special cases, we consider the two-way communication system where both energy-harvesting (EH) relays and non-EH relays exist. We aim to maximize the minimum rate by jointly optimizing the distributed beamforming vector at the relays, the energy beamforming matrix at the non-EH relays, and the EH time allocation with the energy constraints at the relays. The formulated problem is intractable to solve because the objective function and the energy constraints are highly nonconvex. We solve the optimization problem using the successive convex approximation. Simulations demonstrate that the proposed iterative algorithm considerably outperforms any existing approaches. [ABSTRACT FROM AUTHOR]

This paper studies the joint beamforming design and time allocation for a two-way energy harvesting (EH) relay network, where a single-antenna base station and a single-antenna user communicate with each other through multiple-single antenna EH relay nodes, and an external multiantenna power beacon wirelessly power the relays. Unlike the existing studies that assumed the symmetric two-way relaying scenario, we assume a more practical scenario of asymmetric two-way relaying considering different traffic demands on the downlink and uplink. In this paper, we design the optimal beamforming vectors at the relays and at the power beacon jointly with the time allocation between information and EH transmissions, in the sense of maximizing the sum rate of the asymmetric information transmission under the EH constraints at the relays, and the power constraint at the power beacon. The formulated optimization problem is nonconvex, and thus, it is difficult to solve. To solve this challenging problem, we propose an iterative algorithm based on the semidefinite relaxation and the successive convex approximation techniques. Also, to reduce the computational complexity, we propose an efficient noniterative scheme based on the maximum-ratio transmission. The performance of the proposed schemes is demonstrated through simulations. [ABSTRACT FROM AUTHOR]

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.

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 H 3 PO 4 and H 2 SO 4 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 H 3 PO 4 and H 2 SO 4 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.

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., Competing Interests: The authors declare no conflict of interest.

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.