Search

Objective: This paper presents an asyn-chronous electrooculography (EOG)-based human–machine interface (HMI) for smart home environmental control with the purpose of providing daily assistance for severe spinal cord injury (SCI) patients. Methods: The proposed HMI allows users to interact with a smart home environment through eye blinking. Specifically, several buttons, each corresponding to a control command, randomly flash on a graphical user interface. Each flash of the buttons functions as a visual cue for the user to blink. To issue a control command, the user can blink synchronously with the flashes of the corresponding button. Through detecting blinks based on the recorded EOG signal, the target button and its corresponding control command are determined. Seven SCI patients participated in an online experiment, during which the patients were required to control a smart home environment including household electrical appliances, an intelligent wheelchair, as well as a nursing bed via the proposed HMI. Results: The average false operation ratio in the control state was 4.1%, whereas during the idle state, no false operations occurred. Conclusion: All SCI patients were able to control the smart home environment using the proposed EOG-based HMI with satisfactory performance in terms of the false operation ratio in both the control and the idle states. Significance: The proposed HMI offers a simple and effective approach for patients with severe SCIs to control a smart home environment. Therefore, it is promising to assist severe SCI patients in their daily lives.

Weighted ℓ1 minimization (WL1M) is a general and powerful framework for reconstructing sparse signals from underdetermined measurements. The performance improvement of WL1M owes to the incorporation of additional structural priors of signals by means of its weights. However, the selection of weights relies on hand-crafted designs in existing works, so that high-order structural priors of signals are hard to be captured. This paper proposes a data-driven method, namely RBM-WL1M, to alleviate this situation. In the RBM-WL1M, restricted Boltzmann machines (RBMs) are employed to learn the prior distribution of the signals from training data; furthermore, utilizing the RBM, high frequency support set and non-zero probabilities for each of the entries in signals can be estimated effectively, which are used to appropriately select the weights. In our experiments, the proposed framework demonstrates superior performance over several state-of-the-art CS methods on the Physikalisch-Technische Bundesanstalt(PTB) Diagnostic ECG Data set.

Exploring the coupling relationship between urban land and carbon emissions (CE) is one of the important premises for coordinating the urban development and the ecological environment. Due to the influence of the scale effect, a systematic evaluation of the CE at different scales will help to develop more reasonable strategies for low-carbon urban planning. However, corresponding studies are still lacking. Hence, two administrative scales (e.g., region and county) in Chongqing were selected as experimental objects to compare and analyze the CE at different scales using the spatiotemporal coupling and coupling coordination models. The results show that urban land and carbon emissions presented a significant growth trend in Chongqing at different scales from 2000 to 2015. The strength of the spatiotemporal coupling relationship between urban land and total carbon emissions gradually increased with increasing scale. At the regional scale, the high coupling coordination between urban land and total carbon emissions was mainly concentrated in the urban functional development region. Additionally, the high coupling coordination between urban land and carbon emission intensity (OI) was still located in the counties within the metropolitan region of Chongqing, but the low OI was mainly distributed in the counties in the northeastern and southeastern regions of Chongqing at the county level. This study illustrates the multiscale trend of CE and suggests differentiated urban land and carbon emission reduction policies for controlling urban land sprawl and reducing carbon emissions.

Land urbanization (LU) and population urbanization (PU) maintain the nature of spatiotemporal heterogeneity in China. As a municipality directly administered by the central government in the mode of “large cities and large rural areas”, Chongqing’s urbanization process is the epitome of China’s urbanization process. This paper examines the spatiotemporal variability of LU and PU in Chongqing on the basis of nighttime light data, the elasticity coefficient of the coupling relationship, and GWR. The results show that (1) the urban land and urban population in Chongqing grew notably from 2008 to 2018, with average annual growth rates of 9.4% and 2.3%, respectively. (2) The coupling coordination coefficient of LU and PU in Chongqing was 0.24, and the total number of districts and counties with uncoordinated development increased, but the overall uncoordinated situation gradually improved over the period. (3) The influence of PU on LU in each district and county increased year by year, and it showed a decreasing trend from southwest to northeast in Chongqing, which indicates that LU was increasingly adapted to the construction needs of PU. The gap between LU and PU widened due to the household registration system, land fiscal policies and other policies. After the reform of the household registration system and the adjustment of new pilot policies targeting the construction of new-type urbanization, the coupling relationship between LU and PU was gradually improving to the coordinated mode. The findings indicate that Chinese urban areas should adhere to the principle of new-type urbanization construction and carry out scientific land planning strategies, strictly controlling land expansion to promote the reasonable development of population growth.

To solve online continuous time-varying convex quadratic-programming problems constrained by a time-varying linear-equality, a novel varying-parameter convergent-differential neural network (termed as VP-CDNN) is proposed and analyzed. Different from fixed-parameter convergent-differential neural network (FP-CDNN), such as the gradient-based recurrent neural network, the classic Zhang neural network (ZNN), and the finite-time ZNN (FT-ZNN), VP-CDNN is based on monotonically increasing time-varying design-parameters. Theoretical analysis proves that VP-CDNN has super exponential convergence and the residual errors of VP-CDNN converge to zero even under perturbation situations, which are both better than traditional FP-CDNN and FT-ZNN. Computer simulations based on different activation functions are illustrated to verify the super exponential convergence performance and strong robustness characteristics of the proposed VP-CDNN. A robot tracking example is finally presented to verify the effectiveness and availability of the proposed VP-CDNN.

Electromagnetic (E/MEG) brain source imaging involves challenging problems that make it particularly difficult to estimate both the locations and extents of extended sources. In this study, we propose a new method called Variation Sparse Source Imaging based on Conditional Mean of the posterior (VSSI-CM), which is built upon a Bayesian framework, to reconstruct extended E/MEG generators. Based on the proposed framework, VSSI-CM can employ various spatial priors (e.g., the Laplace prior) to explore sparseness of current sources in transform domains (e.g., the variation transform in this study). Considering the complexity of posterior density in the estimated sources, we propose using the posterior mean instead of the typical maximum a posterior (MAP) estimate as a more accurate inverse solution. The posterior mean is obtained by fitting an approximated Gaussian distribution to the intractable true posterior distribution. An efficient double-loop algorithm is also proposed using convex analysis skills. Validation using synthetic and human experimental data sets indicates that VSSI-CM outperforms the well-studied L2-norm methods (i.e., sLORETA and dSPM) and the sparse constrained methods that explore sparseness in the original source domain. The estimates from VSSI-CM are also more accurate than that from MAP.

Background Currently, it is challenging to detect the awareness of patients who suffer disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which do not depend on the behavioral response of patients, may serve for detecting the awareness in patients with DOC. However, we must develop effective BCIs for these patients because their ability to use BCIs does not as good as healthy users. Methods Because patients with DOC generally do not exhibit eye movements, a gaze-independent audiovisual BCI is put forward in the study where semantically congruent and incongruent audiovisual number stimuli were sequentially presented to evoke event-related potentials (ERPs). Subjects were required to pay attention to congruent audiovisual stimuli (target) and ignore the incongruent audiovisual stimuli (non-target). The BCI system was evaluated by analyzing online and offline data from 10 healthy subjects followed by being applied to online awareness detection in 8 patients with DOC. Results According to the results on healthy subjects, the audiovisual BCI system outperformed the corresponding auditory-only and visual-only systems. Multiple ERP components, including the P300, N400 and late positive complex (LPC), were observed using the audiovisual system, strengthening different brain responses to target stimuli and non-target stimuli. The results revealed the abilities of three of eight patients to follow commands and recognize numbers. Conclusions This gaze-independent audiovisual BCI system represents a useful auxiliary bedside tool to detect the awareness of patients with DOC.

The emerging compressive sensing (CS) paradigm holds considerable promise for improving the energy efficiency of wireless body sensor networks, which enables nodes to employ a sample rate significantly below Nyquist while still able to accurately reconstruct signals. In this paper, we propose a weighted $\ell _{1,2}$ minimization method for multichannel electrocardiogram (ECG) reconstruction by exploiting both the interchannel correlation and multisource prior in wavelet domain. A sufficient and necessary condition for exact recovery via the proposed method is derived. Based upon the condition, the performance gain of the proposed method is analyzed theoretically. Furthermore, a reconstruction error bound of the proposed method is obtained, which indicates that the proposed method is stable and robust in recovering sparse and compressible signals from noisy measurements. Extensive experiments utilizing Physikalisch-Technische Bundesanstalt diagnostic ECG database and open-source electrophysiological toolbox fetal ECG database show that significant performance improvements, in terms of compression rate and reconstruction quality, can be obtained by the proposed method compared with the state-of-the-art CS-based methods.

Goal : P300 spellers are among the most popular types of brain–computer interfaces (BCIs) and are extremely useful assistive devices that enable severely disabled patients to communicate. However, P300 speller performances should be further improved to translate laboratory designs into practical applications. We aimed to design a new speller paradigm that could evoke higher event-related potentials (ERPs) than traditional P300 spellers, thus improving the performance of BCI systems. Methods : We proposed a new P300 speller paradigm based on three-dimensional (3-D) stereo visual stimuli. In this paradigm, flashing buttons are presented in 3-D stereo form. We designed two experiments, one that tested a traditional two-dimensional (2-D) speller and another that tested the proposed 3-D speller. Twelve healthy volunteers participated in our experiments. We compared the ERPs elicited by the 2-D speller and the 3-D speller, and we also compared the classification accuracy, information transfer rate (ITR), and user workload between the two paradigms. Results : The 3-D P300 speller elicited higher amplitudes of P300 waveforms than the traditional 2-D P300 speller. The online experimental results showed that the classification accuracy and the ITR were significantly improved with the 3-D P300 speller. We also found that the user workload of the 3-D P300 speller was significantly lower than that of the 2-D P300 speller. Conclusion : The proposed 3-D P300 speller based on stereo visual stimuli outperformed a traditional 2-D P300 speller. This finding indicates that our 3-D paradigm offers a new method that will improve the performance of P300 BCI systems.

Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised (CRS-R). Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness (DOCs). Brain-computer interface (BCI) can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expression. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R.

To remedy the discontinuity phenomenon existing in the infinity-norm velocity minimization (INVM) scheme, prevent the occurrence of high joint velocity, and decrease the joint-angle drift of redundant robot manipulators, a new type of tricriteria optimization-coordination-motion (TCOCM) scheme is proposed and investigated for dual-redundant-robot manipulators to track complex end-effector paths. Besides, the proposed scheme considers joint physical constraints (i.e., joint-angle limits and joint-velocity limits) and guarantees the joint velocity to approach zero at the end of tasks. Such a new-type TCOCM scheme combines the minimum velocity norm (MVN), repetitive motion planning (RMP), and INVM solutions via two weighting factor, and thus is termed MVN-RMP-INVM-TCOCM scheme. The proposed scheme consists of two subschemes, i.e., subscheme for the left robot manipulator and subscheme for the right robot manipulator. To control the dual arms simultaneously and collaboratively, two subschemes are reformulated as two general quadratic programming (QP) problems and further unified into one QP formulation. The unified QP problem is then solved by a simplified linear-variational-inequalities-based primal–dual neural network solver. Computer simulation results based on dual PUMA560 robot manipulators are illustrated to substantiate the advantage, efficacy, and applicability of the proposed MVN-RMP-INVM-TCOCM scheme to resolve the redundancy of dual-robot manipulators.

Visual pursuit assessment is extensively applied in the behavioral scale-based clinical examination of patients with disorders of consciousness (DOC). However, this assessment is challenging because it relies on behavioral markers, and these patients severely lack behavioral responses. Brain-computer interfaces (BCIs) may provide a potential solution to detect brain responses to external stimuli without requiring behavioral expressions. A BCI system was designed to simulate visual pursuit detection in the coma recovery scale-revised (CRS-R). The graphical user interface included four buttons, one that moved on the screen and three that did not. These buttons flashed in a random order. The patients were prompted to follow the moving button. Based on the collected electroencephalography data, the algorithm determined whether the patient focused on the moving target. Among the 14 DOC patients who participated in the assessments based on the BCI system and the CRS-R, four patients exhibited visual pursuit, and three were nonresponsive in both assessments. More importantly, seven patients who did not exhibit visual pursuit in CRS-R were detected to be responsive to the moving target stimuli in the BCI assessment. Furthermore, five out of seven recovered consciousness to some degree and showed visual pursuit in the second CRS-R assessment. The proposed BCI system is better able to detect visual pursuit than the behavioral scale-based assessment and thus can assist in clinically evaluating the challenging population of DOC patients.

In order to solve the joint-angular-drift problems of redundant robot manipulators, a novel varying-parameter convergent-differential neural network (VP-CDNN) is proposed and exploited. To do so, a quadratic program (QP)-based feedback-considered joint-angular-drift-free (FC-JADF) scheme is first designed and presented. The FC-JADF scheme adopted in this paper is composed of an optimization criterion simultaneously optimizing quadratic and linear terms, and a velocity layer kinematic equation with adding feedback. Second, the FC-JADF scheme is formulated as a standard QP. Third, the VP-CDNN is proposed to solve the resultant standard QP problem. The Lyapunov theory proves that the proposed VP-CDNN solver can globally converge to an optimal solution to the standard QP problem corresponding to redundant robot manipulators, and the joint-angular-drift problems are solved. Two computer simulations and physical experiments based on a six-degree-of-freedom Kinova Jaco $^2$ robot, i.e., a starfish path and a cardioid path, verify the effectiveness, accuracy, safety, and practicability of the QP-based FC-JADF scheme and the VP-CDNN solver for solving the joint-angular-drift problems of redundant robot manipulators.

In this paper, a real-time temperature measurement system was implemented using a field programmable gate array (FPGA) via tunable diode laser absorption spectroscopy. The system can be used to measure the average temperature along the laser path. The wavelength modulation spectroscopy (WMS) method was applied to realize temperature measurement. First, a recursive peak detection algorithm was proposed to improve the accuracy of main peak detection of WMS signal and reduce the computational complexity. Second, an FPGA was facilitated to realize: 1) a real-time quadrature demodulator to extract the signals at frequencies of $2f $ and $1f$ and calculate the WMS- $2f/1f $ signals on chip and 2) in-situ accurate main peak detection of the WMS- $2f/1f $ signals and hence target gas temperature measurement. Third, experiments were conducted to verify the real-time performance of the system. Experimental results show that the proposed algorithm and hardware design can implement continuous temperature measurement with a temporal resolution of up to 0.25 ms and the measuring rate can reach 4 kHz.

In off-line training of motor imagery-based brain-computer interfaces (BCIs), to enhance the generalization performance of the learned classifier, the local information contained in test data could be used to improve the performance of motor imagery as well. Further considering that the covariance matrices of electroencephalogram (EEG) signal lie on Riemannian manifold, in this paper, we construct a Riemannian graph to incorporate the information of training and test data into processing. The adjacency and weight in Riemannian graph are determined by the geodesic distance of Riemannian manifold. Then, a new graph embedding algorithm, called bilinear regularized locality preserving (BRLP), is derived upon the Riemannian graph for addressing the problems of high dimensionality frequently arising in BCIs. With a proposed regularization term encoding prior information of EEG channels, the BRLP could obtain more robust performance. Finally, an efficient classification algorithm based on extreme learning machine is proposed to perform on the tangent space of learned embedding. Experimental evaluations on the BCI competition and in-house data sets reveal that the proposed algorithms could obtain significantly higher performance than many competition algorithms after using same filter process.

Most of the existing clustering methods have difficulty in processing complex nonlinear data sets. To remedy this deficiency, in this paper, a novel data model termed Hybrid K-Nearest-Neighbor (HKNN) graph, which combines the advantages of mutual k-nearest-neighbor graph and k-nearest-neighbor graph, is proposed to represent the nonlinear data sets. Moreover, a Clustering method based on the HKNN graph (CHKNN) is proposed. The CHKNN first generates several tight and small subclusters, then merges these subclusters by exploiting the connectivity among them. In order to select the optimal parameters for CHKNN, we further propose an internal validity index termed K-Nearest-Neighbor Index (KNNI), which can also be used to evaluate the validity of nonlinear clustering results by varying a control parameter. Experimental results on synthetic and real-world data sets, as well as that on the video clustering, have demonstrated the significant improvement on performance over existing nonlinear clustering methods and internal validity indices.

Biological signals, including electroencephalography (EEG) and electrooculography (EOG), are often used to develop switches, which represent a class of typical asynchronous human–computer interfaces (HCIs) in which control and idle states need to be distinguished based on a criterion. Determining a satisfactory criterion for rapid and accurate discrimination between control and idle states remains a challenging issue, as EEG signals are highly noisy and nonstationary, and EOG signals are highly affected by unintended/spontaneous eye movements. Therefore, most existing EEG- or EOG-based switches are characterized by disadvantages of long response times (RTs) or high false positive rates (FPRs). The primary contribution of this work is the development of a novel EOG-based switch design, in which a visual trigger mechanism is introduced to guide the users’ blinks and to assist in detecting blinks. Specifically, the graphical user interface (GUI) includes a switch button that flashes once per 1.2 s. The user is instructed to blink synchronously with the flashes of the switch button to issue an on/off command while a single-channel EOG signal is collected. A waveform detection algorithm is applied to the ongoing EOG signal, which discriminates the intended and unintended blinks mainly based on the synchrony between the blink and the switch buttons flash. Once an intended blink, i.e., a blink corresponding to a button’s flash, is detected, the system issues an on/off command. As one application, the proposed EOG-based switch is used to produce start/stop commands for a wheelchair. Several online experiments were conducted with ten healthy subjects. An average accuracy of 99.5%, an RT of 1.3 s for issuing a switch command in the control state, and an average FPR of 0.10/min in the idle state were achieved. The experimental results therefore demonstrate the effectiveness of the single-channel EOG-based switch.

Epileptic seizures are caused by excessive, synchronized activity of large groups of neurons. In human electroencephalograph (EEG), they are reflected by multiple epileptic characteristic waves. Based on the idea of template matching, this paper presents a patient-specific approach for the automatic detection of epileptic seizures. In our method, a set of wavelets are constructed based on the epileptic characteristic waves extracted from training EEG signals, and then continuous wavelet transform (CWT) is performed on the recorded EEG. The coefficients of CWT reflect the similarity of the recorded EEG and the epileptic characteristic waveforms and thus can be used to detect if the epileptic characteristic waveforms exist in the EEG. After applying data fusion to the CWT coefficient matrices corresponding to the multiple constructed wavelets, the boundaries of seizures can be determined. In the experiment, our constructed wavelets performed better in the detection of epileptic characteristic waves compared to the Daubechies wavelet. We analyzed the EEG of 10 patients and our method detected 32 out of 34 seizures and declared five false detections. Therefore, our method is promising for the automatic detection of epileptic seizures and the real-time monitoring of patients’ EEG signal.

Many reward-based crowdfunding platforms encourage entrepreneurs to introduce their projects and make a personal appeal with a video clip. In this study, we investigate the impact of such a pitch video on financing outcomes. Grounded in social perception literature, we propose that effective use of nonverbal cues in a pitch video increases funding success. We coded and analyzed videos of crowdfunding campaigns and found that an entrepreneur could improve the funding outcomes by gazing less, appearing early, and reducing speech hesitations in a pitch video. We also found that smiling has no impact on funding success.

Semi-supervised learning has been applied in brain–computer interfaces (BCIs) to reduce calibration time for user. For example, a sequential updated self-training least squares support vector machine (SUST-LSSVM) was devised for online semi-supervised P300 speller. Despite its good performance, the computational complexity becomes too high after several updates, which hinders its practical online application. In this paper, we present a self-training regularized weighted online sequential extreme learning machine (ST-RWOS-ELM) for P300 speller. It achieves much lower complexity compared to SUST-LSSVM without affecting the spelling accuracy performance. The experimental results validate its effectiveness in the P300 system.

Multivariate pattern analysis (MVPA) approach applied to neuroimaging data, such as functional magnetic resonance imaging (fMRI) data, has received a great deal of attention because of its sensitivity to distinguishing patterns of neural activities associated with different stimuli or cognitive states. Generally, when using MVPA approach to decode the mental states or stimuli, a set of discriminative variables (e.g., voxels) is first selected. However, in most of existing MVPA methods, the selected variables do not contain all informative variables, since these selected variables are sufficient for decoding. In this paper, we propose a multivariate pattern analysis method based on sparse representation for decoding the brain states and localizing category-specific brain activation areas corresponding to two experimental conditions/tasks at the same time. Unlike traditional MVPA approaches, this method is designed to find informative variables as many as possible. We applied the proposed method to two judgement experiments: a gender discrimination and an emotion discrimination task, data analysis results demonstrate its effectiveness and potential applications.

Electrooculography (EOG) signals, which can be used to infer the intentions of a user based on eye movements, are widely used in human-computer interface (HCI) systems. Most existing EOG-based HCI systems incorporate a limited number of commands because they generally associate different commands with a few different types of eye movements, such as looking up, down, left, or right. This paper presents a novel single-channel EOG-based HCI that allows users to spell asynchronously by only blinking. Forty buttons corresponding to 40 characters displayed to the user via a graphical user interface are intensified in a random order. To select a button, the user must blink his/her eyes in synchrony as the target button is flashed. Two data processing procedures, specifically support vector machine (SVM) classification and waveform detection, are combined to detect eye blinks. During detection, we simultaneously feed the feature vectors extracted from the ongoing EOG signal into the SVM classification and waveform detection modules. Decisions are made based on the results of the SVM classification and waveform detection. Three online experiments were conducted with eight healthy subjects. We achieved an average accuracy of 94.4% and a response time of 4.14 s for selecting a character in synchronous mode, as well as an average accuracy of 93.43% and a false positive rate of 0.03/min in the idle state in asynchronous mode. The experimental results, therefore, demonstrated the effectiveness of this single-channel EOG-based speller.

Objective: This study proposes an event-related potential (ERP) brain-computer interface (BCI)-based environmental control system that integrates household electrical appliances, a nursing bed, and an intelligent wheelchair to provide daily assistance to paralyzed patients with severe spinal cord injuries (SCIs). Methods: An asynchronous mode is used to switch the environmental control system on or off or to select a device (e.g., a TV) for achieving self-paced control. In the asynchronous mode, we introduce several pseudo-keys and a verification mechanism to effectively reduce the false operation rate. By contrast, when the user selects a function of the device (e.g., a TV channel), a synchronous mode is used to improve the accuracy and speed of BCI detection. Two experiments involving six SCI patients were conducted separately in a nursing bed and a wheelchair, and the patients were instructed to control the nursing bed, the wheelchair, and household electrical appliances (an electric light, an air conditioner, and a TV). Results: The average false rate of BCI commands in the control state was 10.4%, whereas the average false operation ratio was 4.9% (a false BCI command might not necessarily results in a false operation according to our system design). During the idle state, there was an average of 0.97 false positives/min, which did not result in any false operations. Conclusion: All SCI patients could use the proposed ERP BCI-based environmental control system satisfactorily. Significance: The proposed ERP-based environmental control system could be used to assist patients with severe SCIs in their daily lives.

Over the last 20 years, studies have provided greater insight into disorders of consciousness (DOC), also known as altered state of consciousness. Increased brain residual functions have been identified in patients with DOC due to the successful application of novel next-generation imaging technologies. Many unconscious patients have now been confirmed to retain considerable cognitive functions. It is hoped that greater insight regarding the psychological state of patients may be achieved through the use of functional magnetic resonance imaging and brain-computer interfaces. However, issues surrounding the research and treatment of DOC remain problematic. These include differing opinions on the definition of consciousness, difficulties in diagnosis, assessment, prognosis and/or treatment, and newly emerging ethical, legal and social issues. To overcome these, appropriate care must be offered to patients with DOC by clinicians and families, as DOC patients may now be considered to live in more than just a vegetative state. The present article reviews the controversy surrounding the definition of consciousness and the reliability of novel technologies, prognostic prediction, communication with DOC patients and treatment methods. The ethical and social issues surrounding the treatment of DOC and future perspectives are also considered.

A flip-flop circuit hardened against soft errors is presented in this paper. This design is an improved version of Quatro for further enhanced soft-error resilience by integrating the guard-gate technique. The proposed design, as well as reference Quatro and regular flip-flops, was implemented and manufactured in a 65-nm CMOS bulk technology. Experimental characterization results of their alpha and heavy ions soft-error rates verified the superior hardening performance of the proposed design over the other two circuits.

Three neural networks and three numerical methods are investigated, developed, and compared to solve a repetitive motion planning (RMP) scheme for remedying joint-drift problems of redundant robot manipulators. Three recurrent neural networks, i.e., a dual neural network, a linear variational inequality (LVI)-based primal-dual neural network, and a simplified LVI-based primal-dual neural network, are recurrent and real time, and they do not need to be trained in advance. Three numerical methods, i.e., the 94LVI method, the E47 method, and the M4 method, are time discrete and ready to conduct in digital computers. All these solutions have global linear convergence. Computer simulations and physical robot experiments verify that they are all effective to solve the RMP scheme. The comparisons show that neural networks are more accurate and faster than numerical methods on the same simulated condition under the majority normal circumstances. Furthermore, numerical methods are easy to be applied in digital computers since they are time discrete.

In motor imagery brain–computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals carry important discriminative information. In this paper, we intend to classify motor imagery EEG signals by exploiting the fact that the space of SPD matrices endowed with Riemannian distance is a high-dimensional Riemannian manifold. To alleviate the overfitting and heavy computation problems associated with conventional classification methods on high-dimensional manifold, we propose a framework for intrinsic sub-manifold learning from a high-dimensional Riemannian manifold. Considering a special case of SPD space, a simple yet efficient bilinear sub-manifold learning (BSML) algorithm is derived to learn the intrinsic sub-manifold by identifying a bilinear mapping that maximizes the preservation of the local geometry and global structure of the original manifold. Two BSML-based classification algorithms are further proposed to classify the data on a learned intrinsic sub-manifold. Experimental evaluation of the classification of EEG revealed that the BSML method extracts the intrinsic sub-manifold approximately $5\times $ faster and with higher classification accuracy compared with competing algorithms. The BSML also exhibited strong robustness against a small training dataset, which often occurs in BCI studies.

Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training. This paper presents a Fully Guided Network (FGN) for few-shot instance segmentation. FGN perceives FSIS as a guided model where a so-called support set is encoded and utilized to guide the predictions of a base instance segmentation network (i.e., Mask R-CNN), critical to which is the guidance mechanism. In this view, FGN introduces different guidance mechanisms into the various key components in Mask R-CNN, including Attention-Guided RPN, Relation-Guided Detector, and Attention-Guided FCN, in order to make full use of the guidance effect from the support set and adapt better to the inter-class generalization. Experiments on public datasets demonstrate that our proposed FGN can outperform the state-of-the-art methods., Accepted by CVPR 2020, 10 pages, 6 figures

Pulmonary hypertension is common in chronic kidney disease (CKD) patients. However, the prognostic value of pulmonary hypertension in Chinese predialytic CKD patients is rarely reported. We evaluated the relevant factors and prognostic value of pulmonary hypertension in CKD patients. This retrospective cohort study enrolled 1092 predialytic patients from The Third Affiliated Hospital of Sun Yat-Sen University from May 1st, 2011, to December 31st, 2016. Data of interest were retrieved from electronic medical records. Pulmonary hypertension was defined as pulmonary arterial systolic pressure (PASP) ≥ 35 mmHg by echocardiology. All participants were followed from the date of the first echocardiography examination. The primary endpoints were all-cause mortality and cardiovascular mortality. The secondary endpoint was end-stage renal disease (ESRD) defined as starting renal replacement therapy. The prevalence of pulmonary hypertension was 15.9% in the study population. For CKD stage 1, 2, 3a, 3b, 4 and 5, the prevalence was 6.0%, 9.6%, 17.2%, 13.3%, 20.7% and 26.6%, respectively. Older age, lower left ventricular ejection fraction, anemia and higher pulse pressure were independently associated with pulmonary hypertension in CKD patients. In multivariate Cox regression analysis, pulmonary hypertension was the independent risk factor for cardiovascular mortality, but not of all-cause mortality and ESRD. Pulmonary hypertension is not rare in early CKD patients. Patients with older age, anemia, higher pulse pressure and compromised heart function were more likely to comorbid pulmonary hypertension. Pulmonary hypertension maybe a sign of worse cardiovascular outcome in CKD patients.

Energy-resolved computed tomography (ErCT) with a photon counting detector concurrently produces multiple CT images corresponding to different photon energy ranges. It has the potential to generate energy-dependent images with improved contrast-to-noise ratio and sufficient material-specific information. Since the number of detected photons in one energy bin in ErCT is smaller than that in conventional energy-integrating CT (EiCT), ErCT images are inherently more noisy than EiCT images, which leads to increased noise and bias in the subsequent material estimation. In this work, we first deeply analyze the intrinsic tensor properties of two-dimensional (2D) ErCT images acquired in different energy bins and then present a ${F}$ ull- ${S}$ pectrum-knowledge-aware Tensor analysis and processing (FSTensor) method for ErCT reconstruction to suppress noise-induced artifacts to obtain high-quality ErCT images and high-accuracy material images. The presented method is based on three considerations: (1) 2D ErCT images obtained in different energy bins can be treated as a 3-order tensor with three modes, i.e., width, height and energy bin, and a rich global correlation exists among the three modes, which can be characterized by tensor decomposition. (2) There is a locally piecewise smooth property in the 3-order ErCT images, and it can be captured by a tensor total variation regularization. (3) The images from the full spectrum are much better than the ErCT images with respect to noise variance and structural details and serve as external information to improve the reconstruction performance. We then develop an alternating direction method of multipliers algorithm to numerically solve the presented FSTensor method. We further utilize a genetic algorithm to tackle the parameter selection in ErCT reconstruction, instead of manually determining parameters. Simulation, preclinical and synthesized clinical ErCT results demonstrate that the presented FSTensor method leads to significant improvements over the filtered back-projection, robust principal component analysis, tensor-based dictionary learning and low-rank tensor decomposition with spatial-temporal total variation methods.

Objective: A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials (SSVEPs) in the EEG signal and blink-related electrooculography (EOG) signals. Methods: Twelve buttons corresponding to 12 characters are included in the graphical user interface (GUI). These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simultaneously highlighted by changing their sizes. The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded. A multifrequency band-based canonical correlation analysis (CCA) method is applied to the EEG data to detect the evoked SSVEPs, whereas the EOG data are analyzed to identify the user's blinks. Finally, the target character is identified based on the SSVEP and blink detection results. Results: Ten healthy subjects participated in our experiments and achieved an average information transfer rate (ITR) of 105.52 bits/min, an average accuracy of 95.42%, an average response time of 1.34 s and an average false-positive rate (FPR) of 0.8%. Conclusion: The proposed BCI generates multiple commands with a high ITR and low FPR. Significance: The hybrid asynchronous BCI has great potential for practical applications in communication and control.

Currently, whether the urban development in China satisfies Zipf&rsquo, s law across different scales is still unclear. Thus, this study attempted to explore whether China&rsquo, s urban development satisfies Zipf&rsquo, s law across different scales from the National Polar-Orbiting Partnership&rsquo, s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data. First, the NPP-VIIRS data were corrected. Then, based on the Zipf law model, the corrected NPP-VIIRS data were used to evaluate China&rsquo, s urban development at multiple scales. The results showed that the corrected NPP-VIIRS data could effectively reflect the state of urban development in China. Additionally, the Zipf index (q) values, which could express the degree of urban development, decreased from 2012 to 2018 overall in all provinces, prefectures, and counties. Since the value of q was relatively close to 1 with an R2 value &gt, 0.70, the development of the provinces and prefectures was close to the ideal Zipf&rsquo, s law state. In all counties, q &gt, 1 with an R2 value &gt, 0.70, which showed that the primate county had a relatively stronger monopoly capacity. When the value of q &lt, 1 with a continuous declination in the top 2000 counties, the top 250 prefectures, and the top 20 provinces in equilibrium, there was little difference in the scale of development at the multiscale level with an R2 &gt, 0.90. The results enriched our understanding of urban development in terms of Zipf&rsquo, s law and had valuable implications for relevant decision-makers and stakeholders.

Stereo-electroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes, which are currently used for epileptic diagnosis. In this study, we implemented a P300-based Brain Computer Interface (BCI) using SEEG signals. 40 buttons corresponding to 40 numbers displayed in a graphical user interface (GUI) were intensified in a random order. To select a number, the user could focus on the corresponding button when it was flashing. Five epileptic patients implanted with SEEG electrodes attended the experiment and achieved an average online accuracy of 97.33%. Moreover, through single contact decoding and simulated online analysis, we found that these subjects achieved an average accuracy of 82.00% using a single channel of signal. These results indicated that our SEEG-based BCI had a high performance, which was mainly because of the high quality of SEEG signals.

This chapter offers a comprehensive review and explanation of crowdfunding in China. It first extensively describes the status of crowdfunding in China by presenting the market facts, insights, and current regulations of the four main crowdfunding models (reward-based, equity-based, loan-based, and donation-based) respectively. Next, through a detailed up-to-date literature review of the key literature of Chinese crowdfunding, three main research streams are summarized and discussed covering success drivers, comparative studies of Chinese versus other contexts of crowdfunding practice, and applications of crowdfunding for creative and sustainability projects. Finally, both practical and research implications are presented together with the challenges and future research directions on crowdfunding in China.

Additional file 1. Questionnaire on Social Network and Job Burnout of Primary Health Care Workers in Rural

Survival prediction is a typical task in computer-aided diagnosis with many clinical applications. Existing approaches to survival prediction are mostly based on the classic Cox model, which mainly focus on learning a hazard or survival function rather than the survival time, largely limiting their practical uses. In this paper, we present a Censoring-Aware Deep Ordinal Regression (CDOR) to directly predict survival time from pathological images. Instead of relying on the Cox model, CDOR formulates survival prediction as an ordinal regression problem, and particularly introduces a censoring-aware loss function to train the deep network in the presence of censored data. Experiment results on publicly available dataset demonstrate that, the proposed CDOR can achieve significant higher accuracy in predicting survival time.

A triple modular redundancy (TMR) based design technique for double cell upsets (DCUs) mitigation is investigated in this paper. This technique adds three extra self-voter circuits into a traditional TMR structure to enable the enhanced error correction capability. Fault-injection simulations show that the soft error rate (SER) of the proposed technique is lower than 3% of that of TMR. The implementation of this proposed technique is compatible with the automatic digital design flow, and its applicability and performance are evaluated on an FIFO circuit.

Most existing brain-computer Interfaces (BCIs) are designed to control a single assistive device, such as a wheelchair, a robotic arm or a prosthetic limb. However, many daily tasks require combined functions which can only be realized by integrating multiple robotic devices. Such integration raises the requirement of the control accuracy and is more challenging to achieve a reliable control compared with the single device case. In this study, we propose a novel hybrid BCI with high accuracy based on electroencephalogram (EEG) and electrooculogram (EOG) to control an integrated wheelchair robotic arm system. The user turns the wheelchair left/right by performing left/right hand motor imagery (MI), and generates other commands for the wheelchair and the robotic arm by performing eye blinks and eyebrow raising movements. Twenty-two subjects participated in a MI training session and five of them completed a mobile self-drinking experiment, which was designed purposely with high accuracy requirements. The results demonstrated that the proposed hBCI could provide satisfied control accuracy for a system that consists of multiple robotic devices, and showed the potential of BCI-controlled systems to be applied in complex daily tasks.

Introduction This study aimed to assess the prevalence of hypertension and to explore the disparities of its risk factors among urban and rural elderly. Method Data of hypertensive patients were collected from the China Health and Retirement Longitudinal Study (CHARLS) 2015. Stratified sample households were selected from 450 villages or communities of 150 counties from 28 provinces. Multivariable logistic regression was performed to analyze the factors correlated with hypertension. Results Prevalence of HBP was 47.6% (95% CI [45.2%–50.1%]) in total and it was close between urban and rural population (48.6% vs 47.2%). Factors associated with HBP were different between urban and rural areas. In urban areas, hypertension was significantly associated with literacy and diabetes in both genders, high BMI level and smoke quitters in males, and physical activity and dyslipidemia in females. In rural areas, hypertension was significantly associated with older age, higher BMI level in both males and females, and dyslipidemia in males. Conclusions The prevalence are about the same among urban and rural residents, but their risk factors vary from each other. Disparity in the risk factors between urban and rural population should be taken into consideration for further intervention.

Background This study aimed to reveal the effects of the connectedness of primary health care (PHC) workers in social networks on their job burnout. Methods Cross-sectional survey data of rural PHC workers in China were analyzed. A total of 663 respondents were enrolled. Chi-square and cumulative logistic regression were used to determine the effects of the connectedness of PHC workers in social networks on their job burnout. Results PHC workers in rural China had high levels of emotional exhaustion (24.1%), depersonalization (15.7%), and lack of personal accomplishment (34.7%). More than half of the participants were in the middle connectedness level in terms of their advisory (70.4%) and friendship (70.3%) networks. The degree of emotional exhaustion seemed to increase when participants had a low connectedness in their friendship networks (β = 0.769, 95% CI = 0.080–1.458, P = 0.029). Respondents with the middle level of connectedness in advisory networks had higher levels of depersonalization (β = 0.739, 95% CI = 0.130–1.348, P = 0.017) and lack of personal accomplishment (β = 0.583, 95% CI = 0.111–1.055, P = 0.015) than those with the high degree of connectedness in advisory networks. Conclusions The connectedness of PHC workers in social networks influenced their job burnout. Thus, organizations should establish an informal communication platform and information feedback mechanism, promote and manage friendship networks, and help PHC workers overcome emotional exhaustion. Managers should also encourage individuals with a high level of connectedness in advisory networks play the role of “opinion leader” so that they can help others mitigate burnout.

Deep convolutional neural networks have been used to classify environmental sound recently. The classification system with high performance often requires a large well-labled dataset. The cost of tagging audio segments correctly and completely is quite high thus the deep learning models need to have high generalization ability if a weakly-tagged dataset is used. An algorithm named Weighting Filters algorithm(WF) which can be considered as an improved algorithm based on Dropout is proposed in this paper to enhance the generalization ability of models. To implement the Weighting Filters algorithm, an extra layer trained by backpropagation algorithm is introduced to produce a series of weighted filters. The simulation results show that the Weighting Filters algorithm is an effective way to improve the generalization ability of the model. Further more, a deep convolutional neural network using weighting filters algorithm is proposed for the applications of environmental sound classification. The main contributions of this paper are as follows: First, we proposed an effective algorithm WF based on Dropout, and secondly, we proposed a CNN-based framework using WF(CNN-WF) for environmental sound classification. The results obtained on ESC-50 demonstrate that the CNN-based framework we proposed has considerable performance for environmental sound classification.

Cosmic radiation phenomena such as Solar Particle Events cause high radiation flux lasting from hours to days, thus increasing the probability of Single-Event Upsets (SEUs) for several orders of magnitude. In space applications it is necessary, therefore, to monitor the SEU rate in order to ensure timely detection of high radiation levels and efficient protection of radiation-sensitive circuits. This work proposes an approach combining the SEU monitoring and data storage functions in the same on-chip Static Random Access Memory (SRAM) module, with negligible cost and overheads compared to traditional stand-alone SEU monitors. Furthermore, it also enables the detection of permanent faults in SRAM. The proposed monitor is intended to be further integrated into a highly dependable and self-adaptive multiprocessing platform in which it will drive the selection of the multiprocessor operating modes. Thus, a dynamic trade-off between reliability, performance and power consumption in real-time can be achieved.

Effectively evaluating the effects of urban forms on CO

Enhanced beta oscillations (13-30 Hz) in the subthalamic nucleus (STN) have been associated with clinical impairment in Parkinson’s disease (PD), such as rigidity and slowing of movement, with the suppression of STN beta activity through medication or deep brain stimulation correlating with improvement in these symptoms. Recent studies have also emphasized the importance of the time dynamics of the STN beta oscillations in the pathology of PD. An increased probability of prolonged beta bursts, defined as periods when beta band power exceeds a certain threshold, was more closely related to motor symptoms than average power; and the occurrence of beta bursts just before a go cue slows cued movements. Here we adopted a sequential neurofeedback-behaviour task paradigm to investigate whether patients with PD can learn to suppress pathological beta oscillations recorded from STN with neurofeedback training and whether the training improves the motor performance. Results from twelve patients showed that, compared with the control condition, the neurofeedback training led to reduced incidence and duration of beta bursts in the STN local field potential (LFP) and also reduced the synchrony between the STN LFP and cortical activities measured through EEG in the beta frequency band. The changes were accompanied by a reduced reaction time in cued movements. These results suggest that volitional suppression of beta bursts facilitated by neurofeedback training could help improve movement initialisation in Parkinson’s disease.Significance StatementOur study suggests that a neurofeedback paradigm which focuses on the time dynamics of the target neural signal can facilitate volitional suppression of pathological beta oscillations in the STN in Parkinson’s disease. Neurofeedback training was accompanied by reduced reaction time in cued movements, but associated with increased tremor in tremulous patients. The results strengthen the link between subthalamic beta oscillations and motor impairment, and also suggest that different symptom-specific neural signals could be targeted to improve neuromodulation strategies, either through brain stimulation or neurofeedback training, for patients with tremor and bradykinesia-rigidity.

Single event transients (SETs) stand out as one of the major causes of soft errors in nanoscale CMOS integrated circuits. To reduce the need for exhaustive circuit simulations in the design of radiation-hard integrated circuits, the cost-effective approaches for characterization and modeling of SET generation effects in standard logic cells are required. In this work, a SPICE-based methodology for characterization of SET generation effects, employing two different SET current models, is presented. Based on the acquired simulation results, the empirical models for the two main SET generation metrics (SET critical charge and SET pulse width) are derived. The SET generation models and the respective model parameters are intended to be used as inputs for the higher-level analysis of SET effects in digital circuits designed with the characterized standard logic cells. By storing the model parameters for each gate in the look-up table, instead of storing the raw data obtained from SPICE simulations, the amount of characterization data can be significantly reduced, allowing to speed up the subsequent SET analysis of a complex circuit.

In recent years, the rent-free farmland transfer has become increasingly prominent in rural China. By employing multiple data sources, this study estimates the extent of rent-free land transfer from 1986 to 2015 and develops a multilevel logit model to identify its drivers. The results show that the extent of rent-free land transfer has been increasing, especially since 2002, and 30% to 50% of the land that has been transferred does not receive any rent, both in terms of the number of land parcels and the land area. There are obvious regional differences for the rent-free phenomenon in the process of land transfer, especially in mountainous areas, where the proportion is more than 70%. The cost-benefit analysis at the land parcel level shows that the average net income of rent-free land per mu that has been transferred (1 mu = 1/15 ha) is at a loss of 66 yuan, at 35 yuan. The rent-free land transfer is affected by factors at the levels of the land parcel, farmer and village. The land parcel size, quality level and irrigation condition are the key factors at the land parcel level, the ratio of the non-agricultural income in the family and land fragmentation are the key factors at the farmer level, and the inter-village gap is the key factor at the village level. The land parcel features account for approximately 40% of the variation in the rent-free land transfer. In sum, the increase in the labour cost is the direct reason for the rent-free land transfer, and land parcel features and the marginalization of mountainous areas are the preconditions for the rent-free land transfer. A rent-free land transfer is reasonable to some extent, but it is not an informal land transfer, and land rent does not play an effective role in allocating land resources. In this process, the idle or abandoned land resources in mountainous areas become relatively rich. The government should reasonably introduce urban capital and fully tap relatively rich land resources to realize the land capital appreciation and develop the local economy, and it should further regulate the land rental market and lay an emphasis on the integration of land resources, while moderating scale operations in mountainous areas.