An Improved Toeplitz Approximation Method for Coherent DOA Estimation in Impulsive Noise Environments.

Direction of arrival (DOA) estimation is an important research topic in array signal processing and widely applied in practical engineering. However, when signal sources are highly correlated or coherent, conventional subspace-based DOA estimation algorithms will perform poorly due to the rank deficiency in the received data covariance matrix. Moreover, conventional DOA estimation algorithms are usually developed under Gaussian-distributed background noise, which will deteriorate significantly in impulsive noise environments. In this paper, a novel method is presented to estimate the DOA of coherent signals in impulsive noise environments. A novel correntropy-based generalized covariance (CEGC) operator is defined and proof of boundedness is given to ensure the effectiveness of the proposed method in impulsive noise environments. Furthermore, an improved Toeplitz approximation method combined CEGC operator is proposed to estimate the DOA of coherent sources. Compared to other existing algorithms, the proposed method can avoid array aperture loss and perform more effectively, even in cases of intense impulsive noise and low snapshot numbers. Finally, comprehensive Monte-Carlo simulations are performed to verify the superiority of the proposed method under various impulsive noise conditions.

Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise.

Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.

DOA Estimation for Massive MIMO Systems with Unknown Mutual Coupling Based on Block Sparse Bayesian Learning.

Obtaining accurate angle parameters using direction-of-arrival (DOA) estimation algorithms is crucial for acquiring channel state information (CSI) in massive multiple-input multiple-output (MIMO) systems. However, the performance of the existing algorithms deteriorates severely due to mutual coupling between antenna elements in practical engineering. Therefore, for solving the array mutual coupling, the array output signal vector is modeled by mutual coupling coefficients and the DOA estimation problem is transformed into block sparse signal reconstruction and parameter optimization in this paper. Then, a novel sparse Bayesian learning (SBL)-based algorithm is proposed, in which the expectation-maximum (EM) algorithm is used to estimate the unknown parameters iteratively, and the convergence speed of the algorithm is enhanced by utilizing the approximate approximation. Moreover, considering the off-grid error caused by discretization processes, the grid refinement is carried out using the polynomial roots to realize the dynamic update of the grid points, so as to improve the DOA estimation accuracy. Simulation results show that compared with the existing algorithms, the proposed algorithm is more robust to mutual coupling and off-grid error and can obtain better estimation performance.

Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems.

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system's achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

Design optimization of coronary stent based on finite element models.

This paper presents an effective optimization method using the Kriging surrogate model combing with modified rectangular grid sampling to reduce the stent dogboning effect in the expansion process. An infilling sampling criterion named expected improvement (EI) is used to balance local and global searches in the optimization iteration. Four commonly used finite element models of stent dilation were used to investigate stent dogboning rate. Thrombosis models of three typical shapes are built to test the effectiveness of optimization results. Numerical results show that two finite element models dilated by pressure applied inside the balloon are available, one of which with the artery and plaque can give an optimal stent with better expansion behavior, while the artery and plaque unincluded model is more efficient and takes a smaller amount of computation.

[An FES system based on dual axis inclinometer in foot drop treatment].

The foot drop functional electrical stimulation (FES) system consisting of various sensors has been widely applied to the disease of the foot drop. However, the current system is limited to the research on walking on the ground and ignores other important actions of foot in one's daily life, such as walking up and down the stairs, squatting and lying down, etc. In this work, we applied the dual axis angle sensor to the system of the foot drop FES for the first time. Such a system can not only stimulate the foot drop during normal walking, but also identify squatting, sitting, and lying down etc. and furthermore, the system can switch off automatically. In the meanwhile, it can also detect falls and other dangerous actions. The accuracy of our system can achieve 100%, 81.9%, 95.8%, 99.0% and 66.9% for normal walking, sitting-standing, walking up the stairs, walking down the stairs and squatting-standing respectively.

[Study on the method and system for falling detection based on the inclinometer].

A falling is a risky incident of safety and health of human. It may cause serious injuries, such as bone fracture, and even death. A falling detection method based on inclinometer is described. At first, we collect angle data recorded by a wearable inclinometer placed at subject's waist. The angular data are transmitted to PC through a wireless data transmission device. Then, the falling duration is divided into three phases: the state of fall, the impact phase, and the posture phase. We make threshold-based fall-detection decisions in every phase after feature extraction and analysis of the short-time angle data. Finally, a robust falling detection result is given by comprehensive considerations of the three phases decisions. The experiment results proved that the accuracy of our falling detection method was up to 97.23% without undetected falls.

[The analysis method of the Hilbert spectrum entropy of dividing frequency range for signals of heart rate variability].

The signal analysis of heart rate variability (HRV) has been very significant for heart disease of aided diagnosis, monitoring and evaluation. We proposed a new method of HRV signal analysis based on the Hilbert spectrum entropy dividing frequency range. According to Hilbert spectrum characteristics of the multi-resolution and the characteristic of HRV signal frequency spectrum, the Hilbert time-frequency spectrum entropy of HRV signal in different frequency range and the full frequency Hilbert time-frequency spectrum entropy with weighting factor were calculated. This approach was analyzed after the appropriate separation for various physiological factors based on the frequency range and it is more conducive to reflect the physiological and the pathological characteristics. Applying the new approach to the actual HRV signal of the MIT-BIH standard database, we obtained the results which showed that this method could effectively differentiate from the sample group for the young, the elder and the patients with atrial fibrillation, and for the sample group for the healthy persons and CHF patients, the performance in statistical analysis was superior to those of the general time-frequency entropy method. The approach could provide an effective analysis method for clinical HRV signal.

Automated brain structures segmentation from PET/CT images based on landmark-constrained dual-modality atlas registration.

Automated brain structures segmentation in positron emission tomography (PET) images has been widely investigated to help brain disease diagnosis and follow-up. To relieve the burden of a manual definition of volume of interest (VOI), automated atlas-based VOI definition algorithms were developed, but these algorithms mostly adopted a global optimization strategy which may not be particularly accurate for local small structures (especially the deep brain structures). This paper presents a PET/CT-based brain VOI segmentation algorithm combining anatomical atlas, local landmarks, and dual-modality information. The method incorporates local deep brain landmarks detected by the Deep Q-Network (DQN) to constrain the atlas registration process. Dual-modality PET/CT image information is also combined to improve the registration accuracy of the extracerebral contour. We compare our algorithm with the representative brain atlas registration methods based on 86 clinical PET/CT images. The proposed algorithm obtained accurate delineation of brain VOIs with an average Dice similarity score of 0.79, an average surface distance of 0.97 mm (sub-pixel level), and a volume recovery coefficient close to 1. The main advantage of our method is that it optimizes both global-scale brain matching and local-scale small structure alignment around the key landmarks, it is fully automated and produces high-quality parcellation of the brain structures from brain PET/CT images.

[Estimation of evoked potentials based on MD criterion and Givens matrix in non-Gaussian noise environments].

Traditional EP analysis is developed under the condition that the background noises in EP are Gaussian distributed. Alpha stable distribution, a generalization of Gaussian, is better for modeling impulsive noises than Gaussian distribution in biomedical signal processing. Conventional blind separation and estimation method of evoked potentials is based on second order statistics (SOS). In this paper, we propose a new algorithm based on minimum dispersion criterion and Givens matrix. The simulation experiments show that the proposed new algorithm is more robust than the conventional algorithm.

Detection and characterization of hepatocellular carcinoma in rats with liver cirrhosis: diagnostic value of combined use of MR positive and negative contrast agents.

BACKGROUND: Gadolinium-enhanced multi-phase dynamic imaging has improved the accuracy of the diagnosis of hypervascular hepatocellular carcinoma (HCC), but using gadolinium-enhanced dynamic imaging alone is problematic in evaluating hypovascular HCC. This work aimed at evaluating the combined use of superparamagnetic iron oxide (SPIO)-enhanced and gadolinium set in distinguishing HCCs from regenerative nodules (RNs) in a rat model induced by diethylnitrosamine (DEN). METHODS: DEN-induced HCC model rats (n=40) and control rats (n=10) were studied. From weeks 16 to 19 after DEN administration, 4 animals were scanned every week. The hepatic changes were tested with a 1.5 Tesla magnet, and MR images of SPIO-enhanced and gadolinium set were obtained. According to the pathologic changes, the tumorigenesis was divided into HCC and RN (diameter of nodules > or =3 mm). Diagnostic accuracy of the combined SPIO-enhanced and gadolinium set and the gadolinium set alone was evaluated using receiver-operating characteristic curves. Sensitivity and specificity of the combined SPIO-enhanced and gadolinium set and the gadolinium set alone were calculated. RESULTS: The listed tests were completed in 29 rats (21 treated and 8 controls). One hundred and six nodules (82 HCCs, 24 RNs) were analyzed. The Az value and sensitivity with the combined SPIO-enhanced and gadolinium set (Az 0.94, sensitivity 0.96) were higher than those with the gadolinium set alone (Az 0.92, sensitivity 0.89). Using the combined SPIO-enhanced and gadolinium set led to detection of 6 nodules which were negative in the gadolinium set alone and 3 nodules were correctly characterized. CONCLUSION: Using the combined SPIO-enhanced and gadolinium set improved the detectability of HCCs and the SPIO-enhanced imaging compensated for the gadolinium set in differentiating HCCs from RNs in a rat model.

[A novel method to quantitatively analyze the fatty liver ultrasonic images based on multiresolution fractal Brownian motion model and genetic algorithm].

Medical ultrasonic imaging is frequently used for diagnosing the fatty liver disease. In order to help doctors diagnose fatty liver disease more precisely, we need to construct a quantitative assessment system, and in this paper, we propose a method to construct such system with the use of multiresolution fractal Brownian motion model and the genetic algorithm. In such a way, a set of standards can help doctors diagnose the degree of the fatty liver disease more precisely.

[An EMD based epileptic spike detection method].

The automatic spike detection in EEG is significant in both diagnosing illness and alleviating the heavy labour force of the doctor. This paper proposes a new EMD based method to complete spike detection. It decomposes a signal into a few intrinsic mode functions (IMF), and then applies the nonlinear energy operator (NEO) to the first IMF to complete the automatic detection. Sufficient results are obtained by applying this method to the spike detection of the simulation signal and the real epileptic EEG signal.

Quasi-periodic fluctuation in Donchin's speller signals and its potential use for asynchronous control.

When we examine the event-related potential (ERP) responses of Donchin's brain-computer interface (BCI) speller, a type of quasi-periodic fluctuation (FLUC) overlapping with the ERP components can be observed; this fluctuation is traditionally treated as interference. However, if the FLUC is detectable in a working BCI, it can be used for asynchronous control, i.e. to indicate whether the BCI is under the control state (CS) or under the non-control idle state (NC). Asynchronous control is an important issue to address to enable BCI's practical use. In this paper, we examine the characteristics of the FLUC and explore the possibility of using the FLUC for asynchronous control of the BCI. For detecting the FLUC, we propose a method based on the power spectrum and evaluate the detection rates in a simulation. As a result, high true positive rates (TPRs) and low false positive rates (FPRs) are obtained. Our work reveals that the FLUC is of great value for implementing an asynchronous BCI.

Deblurring traffic sign images based on exemplars.

Motion blur appearing in traffic sign images may lead to poor recognition results, and therefore it is of great significance to study how to deblur the images. In this paper, a novel method for deblurring traffic sign is proposed based on exemplars and several related approaches are also made. First, an exemplar dataset construction method is proposed based on multiple-size partition strategy to lower calculation cost of exemplar matching. Second, a matching criterion based on gradient information and entropy correlation coefficient is also proposed to enhance the matching accuracy. Third, L0.5-norm is introduced as the regularization item to maintain the sparsity of blur kernel. Experiments verify the superiority of the proposed approaches and extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.

Robust propagation velocity estimation of gastric electrical activity by least mean p-norm blind channel identification.

The propagation of the gastric slow wave is one of the most important spatial characteristics of gastric electrical activity (GEA). The time delay estimation (TDE) is an effective approach to quantitatively assessing the propagation velocity of GEA. Traditional TDE analyses are developed under the condition reported that the background noise in GEA analysis is Gaussian distributed. Due to the effects of spikes and/or motion artifacts, the GEA obtained from gastric serosal electrodes often contains sharp transitions. This paper proposes robust time delay estimation based on least mean p-norm blind channel identification (BCILMP) under alpha-stable noise condition. Compared with the least mean square time delay estimation (LMSTDE), the BCILMP provides better performance in the impulsive noise environments. The robustness of the proposed method is demonstrated through computer simulations in both Gaussian and alpha-stable noise environments. The results of the propagation velocity of real data obtained from gastric serosal electrodes in gastroparetic patients show that the propagation velocity in gastroparetic patients is slower than in the normal subjects reported in the literature, and the slow-wave propagation is directed proximally to distally from the corpus toward the pylorus but not all the variability of the propagation velocity increases monotonously.

[Spike extraction of epileptic waves in EEG based on EMD].

The automatic spike detection in EEG is significant in both diagnosing epilepsy and alleviating the heavy labor force of the doctors. This paper proposes an empirical model decomposition (EMD) based epileptic spike detection method. It extracts the high frequency components related to spikes in EEG signal by EMD, and it detects the spikes by calculating the instantaneous amplitude of the high component with Hilbert transform. The results of experiments show that the method works well.

[The sample entropy and its application in EEG based epilepsy detection].

It is of great importance for the detection of epilepsy in clinical applications. Based on the limitations of the common used approximate entropy (ApEn) in the epilepsy detection, this paper analyzes epileptic EEG signals with the sample entropy (SampEn) approach, a new method for signal analysis with much higher precision than that of the ApEn. Data analysis results show that the values from both ApEn and SampEn decrease significantly when the epilepsy is burst. Furthermore, the SampEn is more sensitive to EEG changes caused by the epilepsy, about 15%-20% higher than the results of the ApEn.

[Research on chaotic behavior of epilepsy electroencephalogram of children based on independent component analysis algorithm].

In this paper, Independent component analysis (ICA) was first adopted to isolate the epileptiform signals from the background Electroencephalogram (EEG) signals. Then, by using the phase space reconstruct techniques from a time series and the quantitative criterions and rules of system chaos, different phases of the epileptiform signals were analyzed and calculated. Through the comparative research with the analyses of the phase plots, the power spectra, the computation of the correlation dimensions and the Lyapunov exponents of the physiologyical and the epileptiform signals, the following conclusions were drawn: (1) The phase plots, the power spectra, the correlation dimensions and the Lyapunov exponents of the EEG independent components reflect the general dynamical characteristics of brains, which can be taken as a quantitative index to weigh the healthy states of brains. (2) Under normal physiological conditions, the EEG signals are chaotic, while under epilepsy conditions the signals approach regularity.

[An EMD based time-frequency distribution and its application in EEG analysis].

Hilbert-Huang transform (HHT) is a new time-frequency analytic method to analyze the nonlinear and the non-stationary signals. The key step of this method is the empirical mode decomposition (EMD), with which any complicated signal can be decomposed into a finite and small number of intrinsic mode functions (IMF). In this paper, a new EMD based method for suppressing the cross-term of Wigner-Ville distribution (WVD) is developed and is applied to analyze the epileptic EEG signals. The simulation data and analysis results show that the new method suppresses the cross-term of the WVD effectively with an excellent resolution.