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.

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.

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.

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.

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.

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.

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.

Performance improvement of ERP-based brain-computer interface via varied geometric patterns.

Recently, many studies have been focusing on optimizing the stimulus of an event-related potential (ERP)-based brain-computer interface (BCI). However, little is known about the effectiveness when increasing the stimulus unpredictability. We investigated a new stimulus type of varied geometric pattern where both complexity and unpredictability of the stimulus are increased. The proposed and classical paradigms were compared in within-subject experiments with 16 healthy participants. Results showed that the BCI performance was significantly improved for the proposed paradigm, with an average online written symbol rate increasing by 138% comparing with that of the classical paradigm. Amplitudes of primary ERP components, such as N1, P2a, P2b, N2, were also found to be significantly enhanced with the proposed paradigm. In this paper, a novel ERP BCI paradigm with a new stimulus type of varied geometric pattern is proposed. By jointly increasing the complexity and unpredictability of the stimulus, the performance of an ERP BCI could be considerably improved.

Phonocardiogram signal compression using sound repetition and vector quantization.

BACKGROUND: A phonocardiogram (PCG) signal can be recorded for long-term heart monitoring. A huge amount of data is produced if the time of a recording is as long as days or weeks. It is necessary to compress the PCG signal to reduce storage space in a record and play system. In another situation, the PCG signal is transmitted to a remote health care center for automatic analysis in telemedicine. Compression of the PCG signal in that situation is necessary as a means for reducing the amount of data to be transmitted. Since heart beats are of a cyclical nature, compression can make use of the similarities in adjacent cycles by eliminating repetitive elements as redundant. This study proposes a new compression method that takes advantage of these repetitions. METHODS: Data compression proceeds in two stages, a training stage followed by the compression as such. In the training stage, a section of the PCG signal is selected and its sounds and murmurs (if any) decomposed into time-frequency components. Basic components are extracted from these by clustering and collected to form a dictionary that allows the generative reconstruction and retrieval of any heart sound or murmur. In the compression stage, the heart sounds and murmurs are reconstructed from the basic components stored in the dictionary. Compression is made possible because only the times of occurrence and the dictionary indices of the basic components need to be stored, which greatly reduces the number of bits required to represent heart sounds and murmurs. The residual that cannot be reconstructed in this manner appears as a random sequence and is further compressed by vector quantization. What we propose are quick search parameters for this vector quantization. RESULTS: For normal PCG signals the compression ratio ranges from 20 to 149, for signals with median murmurs it ranges from 14 to 35, and for those with heavy murmurs, from 8 to 20, subject to a degree of distortion of ~5% (in percent root-mean-square difference) and a sampling frequency of 4kHz. DISCUSSION: We discuss the selection of the training signal and the contribution of vector quantization. Performance comparisons between the method proposed in this study and existing methods are conducted by computer simulations. CONCLUSIONS: When recording and compressing cyclical sounds, any repetitive components can be removed as redundant. The redundancies in the residual can be reduced by vector quantization. The method proposed in this study achieves a better performance than existing methods.

A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI.

PURPOSE: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three-dimensional lesions from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) of the breast. METHODS: The authors propose a new background distribution-based active contour model using level set (BDACMLS) to segment lesions in breast DCE-MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure function which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors' method. Finally, the performance of the proposed method is evaluated by several region-based metrics such as the overlap ratio. RESULTS: Forty-two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinoma in situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). CONCLUSIONS: A new active contour model method has been developed and shown to successfully segment breast DCE-MRI three-dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak-edge-passed problem, and improve the robustness in segmenting different lesions.

Cirrhosis classification based on texture classification of random features.

Accurate staging of hepatic cirrhosis is important in investigating the cause and slowing down the effects of cirrhosis. Computer-aided diagnosis (CAD) can provide doctors with an alternative second opinion and assist them to make a specific treatment with accurate cirrhosis stage. MRI has many advantages, including high resolution for soft tissue, no radiation, and multiparameters imaging modalities. So in this paper, multisequences MRIs, including T1-weighted, T2-weighted, arterial, portal venous, and equilibrium phase, are applied. However, CAD does not meet the clinical needs of cirrhosis and few researchers are concerned with it at present. Cirrhosis is characterized by the presence of widespread fibrosis and regenerative nodules in the hepatic, leading to different texture patterns of different stages. So, extracting texture feature is the primary task. Compared with typical gray level cooccurrence matrix (GLCM) features, texture classification from random features provides an effective way, and we adopt it and propose CCTCRF for triple classification (normal, early, and middle and advanced stage). CCTCRF does not need strong assumptions except the sparse character of image, contains sufficient texture information, includes concise and effective process, and makes case decision with high accuracy. Experimental results also illustrate the satisfying performance and they are also compared with typical NN with GLCM.

Reinvestigation of the relationship between the amplitude of the first heart sound to cardiac dynamics.

The relationships between the amplitude of the first heart sound (S1) and the rising rate of left ventricular pressure (LVP) concluded in previous studies were not consistent. Some researchers believed the relationship was positively linear; others stated the relationship was only positively correlated. To further investigate this relationship, this study simultaneously sampled the external phonocardiogram, electrocardiogram, and intracardiac pressure in the left ventricle in three anesthetized dogs, while invoking wide hemodynamic changes using various doses of epinephrine. The relationship between the maximum amplitude of S1 and the maximum rising rate of LVP and the relationship between the amplitude of dominant peaks/valleys and the corresponding rising rate of LVP were examined by linear, quadratic, cubic, and exponential models. The results showed that the relationships are best fit by nonlinear exponential models.

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.

Modeling of heart sound morphology and analysis of the morphological variations induced by respiration.

In this study, each peak/valley of a heart sound was modeled by a Gaussian curve and characterized by amplitude, timing, and supporting width. This model was applied to analyze the morphological variations induced by respiration in 12 subjects. It was observed that the morphology exhibited regular behaviors with respiration. The amplitude of the prominent peaks and valleys of S2 (the second heart sound) were commonly attenuated during expiration and were accentuated during inspiration whereas no consistent observations were obtained for S1 (the first heart sound). The supporting width of S1 commonly decreased with expiration and increased with inspiration whereas the supporting width of S2 displayed no significant changes during respiration. For all subjects, the delay of S1 increased during inspiration and decreased during expiration. However, the delay of the aortic component increased during expiration and decreased during inspiration. The pulmonary component of S2 was observed in 7 of 12 subjects, and the delay was opposite to that of the aortic component. The opposing delays yielded a splitting between the two components of S2 that increased during inspiration and decreased during expiration. The delay pattern was the most consistent observation in all subjects. These results suggest that a quantitative analysis of morphological variations, particularly the delay pattern, could be used as a non-invasive continuous monitoring method of hemodynamic change during respiratory cycles.

[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.

Heart sound cancellation from lung sound record using cyclostationarity.

From the mechanism of heart sound generation, it is known that heart sounds are cyclic following the frequency of the heartbeat. This paper proposes a short-time cyclic frequency spectrum to calculate the instantaneous cycle frequency (ICF) of heart sounds as an estimation of the frequency of the heartbeat. Heart sounds in a lung sound record are detected with the assistance of ICF. Lung sounds (LSs) are recovered by removing heart sounds from the LS record. An LS record is the only input signal source; no other reference signal is necessary. Evaluation by visual inspection, auditory listening and spectral analysis all show that heart sounds are successfully cancelled without hampering the main components of lung sounds.

Post stimulus effects of high frequency biphasic electrical current on a fibre's conductibility in isolated frog nerves.

OBJECTIVE: High frequency biphasic (HFB) electrical currents are widely used in nerve blocking studies. Their safety margins largely remain unknown and need to be investigated. APPROACH: This study, exploring the post stimulus effects of HFB electrical currents on a nerve's conductibility, was performed on bullfrog sciatic nerves. Both compound action potentials (CAPs) and differential CAPs (DCAPs, i.e. control CAPs subtracted by CAPs following HFB currents) were obtained, and N1 and N2 components, which were the first and second upward components of DCAPs, were used for analyses of the effects introduced by HFB electrical stimulation. MAIN RESULTS: First, HFB currents of 10 kHz at a completely blocking threshold were applied for 5 s. The maximum amplitudes and conducting velocities of the CAPs were significantly (P < 0.02) decreased within the observed period (60 s) following HFB currents. The DCAPs displayed clear N1 and N2 components, demonstrating respectively the losses of the fibres' normal conductibility and the appearances of new delayed conductions. Decreases of N1 amplitudes along time, regarded as the recovery of the nerve's conductibility, exhibited two distinct phases: a fast one lasting several seconds and a slow one lasting longer than 5 min. Further tests showed a linear relationship between the HFB stimulation durations and recovering periods of N1 amplitudes. Supra-threshold blocking did not cause higher N1 amplitudes. SIGNIFICANCE: This study indicates that HFB electrical currents lead to long lasting post stimulus reduction of a nerve's conductibility, which might relate to potential nerve injuries. A possible mechanism, focusing on changes in intracellular and periaxonal ionic concentrations, was proposed to underlie the reduction of the nerve's conductibility and potential nerve injuries. Greater caution and stimulation protocols with greater safety margins should be explored when utilizing HFB electrical current to block nerve conductions.

Optimum heart sound signal selection based on the cyclostationary property.

Noise often appears in parts of heart sound recordings, which may be much longer than those necessary for subsequent automated analysis. Thus, human intervention is needed to select the heart sound signal with the best quality or the least noise. This paper presents an automatic scheme for optimum sequence selection to avoid such human intervention. A quality index, which is based on finding that sequences with less random noise contamination have a greater degree of periodicity, is defined on the basis of the cyclostationary property of heart beat events. The quality score indicates the overall quality of a sequence. No manual intervention is needed in the process of subsequence selection, thereby making this scheme useful in automatic analysis of heart sound signals.

[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.

Nonrigid medical image registration based on mesh deformation constraints.

Regularizing the deformation field is an important aspect in nonrigid medical image registration. By covering the template image with a triangular mesh, this paper proposes a new regularization constraint in terms of connections between mesh vertices. The connection relationship is preserved by the spring analogy method. The method is evaluated by registering cerebral magnetic resonance imaging (MRI) image data obtained from different individuals. Experimental results show that the proposed method has good deformation ability and topology-preserving ability, providing a new way to the nonrigid medical image registration.