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Image segmentation is an important step in image processing, but contemporary segmentation algorithms have problems such as poor anti-noise performance, over-segmentation, and imprecise results. To solve these problems, the authors proposed an adaptive image segmentation algorithm under the constraint of edge posterior probability. This algorithm first resolves the problem of over-segmentation by improving the watershed algorithm. Then, the algorithm automatically decides whether to adopt the edge threshold segmentation resulting from the watershed algorithm based on the proposed edge posterior probability model. Experiments showed that the proposed algorithm has excellent anti-noise performance, highly precise segmentation result, and are useful in effectively segmenting low-contrast images.

Accurate dynamic parameters are required in the dynamic model–based robot motion controller designing. Experiment-based robot parameter identification is the only way to obtain them effectively, in...

In this article, a yaw channel controller with linear active disturbance rejection control is proposed for high-performance attitude tracking of an unmanned helicopter. In this control scheme, the linear extended state observer serves as a compensator, which can effectively reject parametric uncertainties and external disturbances. In this case, the attitude tracking controller is designed according to the input–output relation of yaw model. As a controlled plant, the yaw dynamical model is first obtained through the system identification method. Subsequently, in order to optimize the linear active disturbance rejection control applied to yaw control, an artificial bee colony algorithm is introduced to solve the problem of parameter tuning. Finally, comparative studies are carried out. The results from simulation and flight experiment show that the proposed controller provides better performances than the traditional controller.

A new discrete time delay control design is presented and investigated for the joint-space control of hydraulic manipulators in this article. Thanks to the time delay estimation technique, our new designed controller no longer needs the detailed system model, which is very fascinating for practical applications. Also, the proposed discrete time delay control method requires no acceleration information to be obtained through quadratic differential, which is usually essential for the continuous-time version time delay control method of hydraulic manipulators. This may result in fewer requirements of computational effort or unavailability of sensors for obtaining the acceleration signal. Furthermore, the newly proposed discrete time delay control requires no future predicted values of the desired trajectory or system states compared with the traditional discrete time delay control method, which is more suitable for practical applications. The corresponding stability is analyzed using the bounded input bounded output stability theory. The easiness for actual applications and robustness against disturbance are verified through some 2-degree-of-freedom experiments.

Objectives The aim of our study was to evaluate the clinical application of color Doppler flow imaging (CDFI) and acoustic radiation force impulse (ARFI) for the diagnosis of acute rejection after liver transplantation. Methods B-Mode CDFI and ARFI assessments were performed in 76 patients who underwent biopsy after liver transplantation at our institution, between October 2011 and October 2014. The study group included 56 patients with acute rejection confirmed by biopsy, with 20 patients whose liver function recovered within 1 month of transplantation forming the control group. Anteroposterior diameter of the liver, hemodynamic index (consisting of the portal vein diameter, portal vein flow velocity, and hepatic vein flow waveform), and ARFI shear wave velocity (SWV) were measured. We used logistic regression modeling and receiver operating curve to evaluate between-group differences. Results Compared with the control group, patients with acute rejection exhibited increased anteroposterior diameter (P = .035) and change in hemodynamic index (P = .021), including increased portal vein diameter, decreased portal vein flow, and loss of triphasic waveform of hepatic vein flow. Acoustic radiation force impulse SWV was markedly increased in the acute rejection group (P

A new nonlinear robust control scheme is proposed and investigated for the trajectory tracking control problem of an underwater vehicle-manipulator system (UVMS) using the discrete time delay estimation (DTDE) technique. The proposed control scheme mainly has two parts: the DTDE part and the desired dynamics part. The former one is applied to properly estimate and compensate the complex unknown lumped dynamics of the system, using the intentionally time-delayed system's information. The latter one is used to obtain the desired dynamic characteristic of the closed-loop control system. Thanks to the DTDE technique, the proposed control scheme no longer requires the detailed system dynamic information or the acceleration signals, bringing in good feasibility for actual applications and satisfactory control performance. The stability of the closed-loop control system is analyzed and proved using the bounded input bounded out stability theory. Finally, nine degree of freedoms (DOFs) simulation and seven DOFs pool experiment studies were conducted to demonstrate the effectiveness of the proposed control scheme with an UVMS developed in our laboratory. Corresponding results show that our proposed control scheme can ensure satisfactory control performance with relative small control gains and obtain a precision of 0.064 m for the end effector in the task space.

The forward kinematics is the basis of the design and control of the parallel robots. This paper aims to provide an efficient solution to the forward kinematics of a class of six-degrees-of-freedom parallel robots for real-time applications. With a unit dual quaternion used as the generalized coordinates of the robot system, the forward kinematic equations are derived to be a set of quadratic ones. An efficient algorithm is proposed to get the actual solution to them. The convergence and singularity problems of the new algorithm have been discussed. We have provided a convergence strategy and revealed the internal relation of the singularity with that of the parallel robot, proving the feasibility of the algorithm and giving the working condition in the practical applications. The new algorithm have been compared to the Newton's method for an 8-U P S parallel robot, resulting in the time consumptions of 0.2187 milliseconds and 14.25 milliseconds respectively. And then we perform a simulation of the state-feedback control for an 8- P US parallel robot. The two examples present the applications of the new algorithm and demonstrate its validity and efficiency.

This paper presents a novel control strategy to compensate hysteretic nonlinearity and achieve precise positioning control of a cell puncture mechanism driven by a piezoelectric actuator (PEA). A dynamic model of the cell puncture mechanism is developed based on the Bouc-Wen model. Parameters of the nonlinear model are identified by particle swarm optimization. The strategy of feedforward (FF) control and sliding mode feedback (FB) control based on the Bouc-Wen inverse model is further developed to position the cell puncture mechanism. Zebrafish embryo is used as the validation object, wherein a cell micropuncture experiment is successfully performed. Proportional-integral sliding mode FB control plus FF control has a simple structure and exhibits excellent performance. Thus, this method can be easily extended to other micro-or nanopositioning mechanisms based on PEAs and adopted in practical applications.

Multi-mosquito object detection and 2D pose estimation are essential steps towards fully automated extracting PfSPZ-infected mosquito salivary glands for manufacture of PfSPZ Vaccine, which has been shown to protect against malaria in multiple clinical trials in the US, Europe, and Africa. This paper presents a deep learning approach to perform cluster condition classification and bounding box detection of multiple mosquitoes in an image. It also estimates the 2D pose of each non-clustered mosquito by body part detection. This approach is based on two popular convolutional neural network (CNN) architectures, Mask R-CNN and DeeperCut. In addition, we propose a cascaded image processing approach to achieve the multi-mosquito detection, cluster condition classification, and body parts detection in a multi-step manner. We compare the two approaches in terms of their functionality, robustness, accuracy, and speed. We hope our effective approaches would push forward the automation of PfSPZ Vaccine production to facilitate the prevention and elimination of this disease worldwide.

Low visibility weather has seriously affected daily traffic management and safety of life and property. Aiming at the problem that highway in low visibility environment is prone to traffic accidents, a highway visibility detection method based on video surveillance is proposed, and the surveillance video data of each section are analyzed. Based on the traditional dark channel prior, the highway visibility detection method converted the surveillance video frame into gray image, and extracted the air transmittance and atmospheric light value contained in the surveillance image by combining the adaptive guided filtering algorithm and quadtree image segmentation algorithm. Then based on the visibility calculation model, the visibility distance of the road was estimated according to the actual distance and offset angle between the monitoring equipment and the detection object. The experimental results show that the optimized image processing method can solve the problems of low image clarity, color distortion, and poor scene adaptability. The detection of visibility error rate meets the accuracy requirements of highway visibility detection in China. Finally, a road real-time visibility detection and warning system is applied based on the corresponding early warning strategies of each visibility level.

A time delay control (TDC) scheme is developed in this paper for the trajectory tracking control purpose of cable-driven manipulators with nonsingular terminal sliding mode (NTSM). The proposed control scheme utilizes TDC as the basic control frame to enjoy the attractive model-free, while uses the NTSM error dynamics to guarantee good control performance under lumped disturbances. The core component of TDC is the time delay estimation (TDE), which uses the time delayed signals of the system to estimate and compensate the lumped system dynamics. The proposed method is easy to use in complicated practical situations thanks to TDC and can provide with high tracing accuracy and fast dynamical response benefiting from NTSM. The overall stability of the closed-loop control system is analyzed using Lyapunov stability theory. Finally, comparative simulations were performed to verify the effectiveness and superiorities of our newly developed control method over the traditional TDC method.

Vehicle tracking is a hot research direction in the Intelligent Transportation System (ITS). To deal with the traditional Camshift algorithm can not achieve multi-vehicle tracking, and tracking-lost of the vehicle under large proportion occlusion, a novel multi-vehicle tracking method based on linked list of associated vehicles is proposed in this paper. In order to initialize the search window, vehicle detection is used to obtain the target vehicle's location and size. Each tracked vehicle establishes a track list to realize multi-vehicle tracking. The algorithm uses Bhattacharyya distance to occlusion identification, and uses Kalman filter to predict the location of vehicles, accurate vehicle tracking can be realized under occlusion. Experimental results show that the algorithm can achieve a robust and accurate vehicle tracking under different traffic.

Image segmentation is a crucial topic in image analysis and understanding, and the foundation of target detection and recognition. Image segmentation, essentially, can be considered as classifying the image according to the consistency of the region and the inconsistency between regions, it is widely used in medical and criminal investigation, cultural relic identification, monitoring and so forth. There are two outstanding common problems in the existing segmentation algorithm, one is the lack of accuracy, and the other is that it is not widely applicable. The main contribution of this paper is to present a novel segmentation method based on the information entropy theory and multi-scale transform contour constraint. Firstly, the target contour is initially obtained by means of a multi-scale sample top-hat and bottom-hat transform and an improved watershed method. Subsequently, in terms of this initial contour, the interesting areas can be finely segmented out with an innovative 3D flow entropy method. Finally, the sufficient synthetic and real experiments proved that the proposed algorithm can greatly improve the segmentation effect. In addition, it is widely applicable.

A miniature resonant tactile sensor for tissue stiffness detection in robot-assisted minimally invasive surgery is proposed in this article. The proposed tactile sensor can detect tissue stiffness based on the principle of the resonant frequency shift when it contacts with tissue of different stiffness. A PZT (lead zirconate titanate) bimorph works simultaneously as the actuator and the sensing element, which is helpful for simplifying the structure. The resonant frequency shift can be deduced by measuring the electrical impedance of the PZT bimorph, since there will be an abrupt change of the impedance when resonance occurs. A unique structure of an Archimedean spiral metal sheet is introduced to restrict the outer size of the sensor within 10 mm and to keep the resonant frequency low. A theoretical model is established. Finite element method analyses are carried out to validate the working principle and it meets the theoretical model quite well. Several silicone samples are tested with the sensor and the results show that the proposed sensor is capable of measuring tissue stiffness within the range of 0–2 MPa, detecting and locating lumps inside tissue.

The development of a method to simulate desired approach turbulence characteristics and improve the typhoon resistance ability for complex building and transmission tower-line system in a multiple controlled fan wind tunnel is described. A case study is investigated using a closed-circuit single controlled fan wind tunnel model of the Full-Scale Test Facility at the State Grid Fujian Electric Power Research Institute for State Grid Wind Tunnel Laboratory. A small-scale model of the closed-circuit single controlled fan wind tunnel is designed and established to simulate the turbulence characteristics and develop the target turbulent flow generating methods efficiently. The results reveal that the value of the maximum average wind speed can reach 18.2 m/s and the maximum circular frequency is 7 Hz. Here, the mean wind speed of the sinusoidal wave flow is 15.1 m/s at the peak of amplitude of 3.8 m/s. The generated sinusoidal wave flow has a high correlation coefficient at 94.3% with the target.

Current Reinforcement Learning (RL) algorithms struggle with long-horizon tasks where time can be wasted exploring dead ends and task progress may be easily reversed. We develop the SPOT framework, which explores within action safety zones, learns about unsafe regions without exploring them, and prioritizes experiences that reverse earlier progress to learn with remarkable efficiency. The SPOT framework successfully completes simulated trials of a variety of tasks, improving a baseline trial success rate from 13% to 100% when stacking 4 cubes, from 13% to 99% when creating rows of 4 cubes, and from 84% to 95% when clearing toys arranged in adversarial patterns. Efficiency with respect to actions per trial typically improves by 30% or more, while training takes just 1-20k actions, depending on the task. Furthermore, we demonstrate direct sim to real transfer. We are able to create real stacks in 100% of trials with 61% efficiency and real rows in 100% of trials with 59% efficiency by directly loading the simulation-trained model on the real robot with no additional real-world fine-tuning. To our knowledge, this is the first instance of reinforcement learning with successful sim to real transfer applied to long term multi-step tasks such as block-stacking and row-making with consideration of progress reversal. Code is available at https://github.com/jhu-lcsr/good_robot ., Comment: Accepted to the journal IEEE Robotics and Automation Letters (RA-L) and to be presented at IROS 2020. This is a minor update to v3. 8 pages, 6 figures, 3 tables, 1 algorithm. Code is available at https://github.com/jhu-lcsr/good_robot and a video overview is at https://youtu.be/MbCuEZadkIw

Background: Piezoelectric actuators are widely used in many micro/nano-manipulation applications, but their positioning accuracy are badly affected by their inherent nonlinear hysteresis and creep. To solve this problem, this paper presents a new robust motion-control method for piezoelectric actuators with fast nonsingular terminal sliding mode based on time delay estimation. Method: The proposed controller needs no detailed information about the hysteresis and other nonlinearities of the system, leading to a simple and model-free characteristic due to the time delay estimation and ensures fast convergence and high tracking accuracy thanks to the nonsingular terminal sliding-mode surface and fast terminal sliding mode -type reaching law. A robust exact differentiator is adopted to estimate the velocity and accelerationinformation online, and it overcomes the limitation of only available position measurements. The finite-time convergence and stability of the closed-loop system are proved by using a Lyapunov function. Results: Experimental results show that the proposed control strategy has faster convergence and higher tracking precision compared with a traditional time delay control. Conclusion: The proposed control strategy can be widely used as an effective control method for high-precision motion control of piezoelectric actuators.

Worldwide countries are recognising the need for and significance of universal health coverage (UHC), however, health inequality continues to persist. This study evaluates the status and equity of residents&rsquo, demand for and utilisation of health services and expenditure by considering the three components of universal health coverage, urban-rural differences, and different income groups. Sample data from China&rsquo, s Fifth Health Service Survey were analysed and the &lsquo, five levels of income classification&rsquo, were used to classify people into income groups. This study used descriptive analysis and concentration index and concentration curve for equity evaluation. Statistically significant differences were found in the demand and utilisation of health services between urban and rural residents. Rural residents&rsquo, demand and utilisation of health services decreased with an increase in income and their health expenditure was higher than that of urban residents. Compared with middle- and high-income rural residents, middle- and lower-income rural residents faced higher hospitalisation expenses, and, compared with urban residents, equity in rural residents&rsquo, demand and utilisation of health services, and annual health and hospitalisation expenditures, were poorer. Thus, equity of health service utilisation and expenditure for urban and rural residents with different incomes remain problematic, requiring improved access and health policies.

For the trajectory tracking purpose of cable-driven robots, a novel robust control method is proposed and investigated in this paper. The proposed method consists of three elements, i.e. a time-delay estimation (TDE) element and a nonsingular fast terminal sliding mode (NFTSM) control element and a feedforward compensation. The TDE technique is utilized to estimate the lumped system dynamics in a simple but effective way, and then ensures an attractive model-free control scheme. The NFTSM control element is adopted to guarantee precise and robust control performance with continuous and singularity-free control efforts. Moreover, the feedforward compensation is also utilized to further enhance the control performance by taking advantages of the system dynamics. Due to this feedforward compensation element, higher control performance has been ensured compared with the traditional TDE-based control. Thanks to above three elements, the proposed method is practical to use and highly accurate and strongly robust. Comparative simulations and experiments were conducted to verify the effectiveness and superiorities of our proposed control method over the recently proposed one.

Fluorescent conjugated polymers are widely used for trace detection of explosives due to their super-quenching effect. In this work, a hyper-branched conjugated polymer P2 and a linear conjugated polymer P1 were synthesized. Compared with P1, P2 showed the superior sensitivity toward 2,4,6-trinitrophenol (PA). The Stern-Volmer constants (Ksv) of P2 (1.56 × 104 M−1) was 2.7 times higher than that of P1 (0.57 × 104 M−1), and the LODs to PA of P2 (8.8 μM) was 3.2 times lower than that of P1 (24 μM). These data indicated the enhanced super-quenching effect in P2. Due to the electrons transfer among the conjugated backbone of polymer, the super-quenching effect of P1 with one-dimensional (1D) configuration may be defined as “length quenching”, while that of P2 with two-dimensional (2D) configuration can be defined as “Area quenching”. The 2D conjugation nature of P2 provided multi migration channel for excitons, which resulted in the remarkably enhanced super-quenching.

Two tiphenylamine-based materials SM-1 and SM-2 were synthesized via simple one-step synthetic progress, which exhibits aggregation induced emission (AIE) in DMSO/H2O (1/9, v/v) solution due to intermolecular interactions and the restriction of intramolecular rotation in the aggregated state. The relationships between their structures and photophysical properties were studied via spectroscopic methods and density functional theory (DFT) calculations. The single crystal data of SM-1 and SM-2 revealed that multiple intermolecular interactions between molecules. Due to the excellent AIE nature of the two compounds. SM-1 and SM-2 were highly sensitive toward PA in the aggregated state. The quenching constant (KSV) of SM-2 was 5.33 × 104 M−1, and SM-1 was 1.1 × 105 M−1, with limit of detection (LOD) were 1.9 μM and 93 ppb for SM-1 and SM-2, respectively. For this reason, it shows that AIE materials are promising candidate in detection of explosives.

Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients.

Robot-assisted therapy affords effective advantages to the rehabilitation training of patients with motion impairment problems. To meet the challenge of integrating the active participation of a patient in robotic training, this study presents an admittance-based patient-active control scheme for real-time intention-driven control of a powered upper limb exoskeleton. A comprehensive overview is proposed to introduce the major mechanical structure and the real-time control system of the developed therapeutic robot, which provides seven actuated degrees of freedom and achieves the natural ranges of human arm movement. Moreover, the dynamic characteristics of the human-exoskeleton system are studied via a Lagrangian method. The patient-active control strategy consisting of an admittance module and a virtual environment module is developed to regulate the robot configurations and interaction forces during rehabilitation training. An audiovisual game-like interface is integrated into the therapeutic system to encourage the voluntary efforts of the patient and recover the neural plasticity of the brain. Further experimental investigation, involving a position tracking experiment, a free arm training experiment, and a virtual airplane-game operation experiment, is conducted with three healthy subjects and eight hemiplegic patients with different motor abilities. Experimental results validate the feasibility of the proposed scheme in providing patient-active rehabilitation training.

The prognostic significance of hypoxia inducible factor-1α (HIF-1α) expression in tumors and the levels of preoperative hemoglobin in clear cell renal cell carcinoma (ccRCC), with or without concomitant chronic obstructive pulmonary disease (COPD), was investigated. A total of 128 patients with ccRCC who underwent surgery were analyzed using retrospective methods. Overall survival (OS) and progression free survival (PFS) were analyzed with clinicopathological variables, including preoperative hemoglobin levels, COPD and the levels of HIF‑1α expression, by Kaplan‑Meier survival analysis. Levels of HIF‑1α expression were detected by immunohistochemistry and the multivariate analysis was performed by proportional hazards regression. High levels of HIF‑1α expression were associated with a higher pathological stage and histological grade in patients with ccRCC (P

In order to meet the requirement of continuous pouring in many varieties and small batches in casting production, a mobile heavy load pouring robot is developed based on a new 4-UPU parallel mechanism due to its strong carrying capacity. Firstly, the instantaneous motion characteristics of the novel 4-UPU parallel mechanism with four degrees of freedom (DOF) are analyzed using screw theory. By using the geometric method, both the forward and inverse kinematic solutions of the proposed robot system are given out. Secondly, based on a common pouring ladle, the volume change of pouring liquid in pouring process and the relationship between tilting angular velocity and flow rate are analyzed, and the results show that the shape of the ladle and the design of the pouring mouth have great influence on the tilting model. It is an important basis for the division of the sectional model. Finally, a numerical example is given to verify the effectiveness of the developed tilting model. The mapping relation between the tilting model and the parallel mechanism shows that the pouring flow can be adjusted by controlling the movement of parallel manipulator. The research of this paper provides an important theoretical basis for the position control of mobile heavy load pouring robot and the research of pouring speed control.

To deal with the problem of inverse dynamics modelling for dual arm robot, a recursive inverse dynamics modelling method based on decoupled natural orthogonal complement is presented. In this model, the concepts and methods of Decoupled Natural Orthogonal Complement matrices are used to eliminate the constraint forces in the Newton-Euler kinematic equations, and the screws is used to express the kinematic and dynamics variables. On this basis, the paper has developed a special simulation program with symbol software of Mathematica and conducted a simulation research on the a dual-arm robot. Simulation results show that the proposed method based on decoupled natural orthogonal complement can save an enormous amount of CPU time that was spent in computing compared with the recursive Newton-Euler kinematic equations and the results is correct and reasonable, which can verify the reliability and efficiency of the method.

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.

This paper presents a new method to derive the closed-form solution of the forward kinematics of the symmetrical Gough-Stewart platform, in which both the fixed base and the moving platform are symmetrical hexagons circumscribed in a circle, respectively. Firstly, analyzing the coupling relationships between the position and orientation variables of the moving platform, and their geometry meanings, eleven compatible algebra equations are obtained. Then, based those compatible equations obtained earlier, the orthogonal complement method is used to eliminate the redundant variables. In the end, the problem of the forward kinematics analysis of the symmetrical Gough-Stewart platform parallel mechanism is expressed as a 14th-degree univariate polynomial equation; and an example is presented to verify the correctness of the new method.

An image registration method based on feature similarity analysis combined with improved adaptive cloud particle model is proposed. Firstly, the SURF algorithm is used to extract the feature points of the image. Innovatively using the spatial gray distance to match the feature vectors. Then we disturb characteristic coordinates to increase micro displacement, finally we introduce the improved particle swarm optimization algorithm with quantum behavior. The whole spatial transformation parameters are classified and the iterative evolution of different weights is carried out, the velocity evolution formula of cloud particle swarm is optimized by means of local and global transformation parameters. It shows that the proposed method can effectively avoid the local optimization of the transform parameters by the multi resolution remote sensing image registration experiments. Compared with the original method, the new method has a higher matching accuracy. In addition, the improved adaptive particle swarm optimization algorithm can effectively eliminate the false registration.

The signal recorded by a low resolution hyperspectral remote sensor from a given pixel, letting alone the effects of the complex terrain, is a mixture of substances. To improve the accuracy of classification and sub-pixel object detection, hyperspectral unmixing(HU) is a frontier-line in remote sensing area. Unmixing algorithm based on geometric has become popular since the hyperspectral image possesses abundant spectral information and the mixed model is easy to understand. However, most of the algorithms are based on pure pixel assumption, and since the non-linear mixed model is complex, it is hard to obtain the optimal endmembers especially under a highly mixed spectral data. To provide a simple but accurate method, we propose a minimum volume simplex analysis constrained (MVSAC) unmixing algorithm. The proposed approach combines the algebraic constraints that are inherent to the convex minimum volume with abundance soft constraint. While considering abundance fraction, we can obtain the pure endmember set and abundance fraction correspondingly, and the final unmixing result is closer to reality and has better accuracy. We illustrate the performance of the proposed algorithm in unmixing simulated data and real hyperspectral data, and the result indicates that the proposed method can obtain the distinct signatures correctly without redundant endmember and yields much better performance than the pure pixel based algorithm.

To solve the trajectory tracking control problem of underwater vehicle-manipulator system (UVMS) under strong nonlinear couplings and complex parametric uncertainties and unknown external disturbance, a nonlinear robust control method based on time delay estimation (TDE) was proposed and investigated in this paper. The core idea of this method is that builds the basic architecture of the controller with TDE, and realizes the online estimation and real time compensation of the complex unknown lumped dynamics for the system. Afterwards, a designed desired dynamic equation is used to regulate the system control performance. Benefitting from the compensation of the unknown lumped dynamics and the model-free nature of TDE, the proposed method has good robustness and practical easy usage. Finally, the effective of the proposed method was demonstrated by comparative numerical simulations with a 9 degree of freedoms (DOFs) UVMS.

Singularity analysis is an important problem in the field of parallel mechanism, how to obtain a concise and analytical expression of the singularity locus has been the focus of the study for a long time. This paper presents a new method for the singularity analysis of the Stewart platform parallel mechanism. Firstly, the rotation matrix is described by quaternion; utilizing length constraint equations of the extensible limbs and properties of the quaternion, seven equivalent equations are obtained, and the variables of position and orientation are decoupled preliminarily. Secondly, by taking the derivative of seven the equivalent equations with respect to time, a new kind of Jacobian matrix is obtained, which reflects the mapping relationship between the change rate of the lengths of extensible limbs and the change rate of variables of position and orientation of the moving platform. Finally, the analytical expression of the singularity locus is derived from calculating the determinant of the new Jacobian matrix; when the quaternion are transformed into Rodriguez parameters, there are only 258 items in the fully expanded analytical expression of the singularity locus. Not only can this method be used to study the singularity problem of Stewart platform parallel mechanism, but it can also be used to study the singularity free workspace of the mechanism.

A 1000V, high-voltage planar micro-supercapacitor directly fabricated on a flexible polymer substrate has been demonstrated to successfully power an electrostatic cantilever microresonator for more than 1 minute. Direct-write laser engraving of Kapton tape (polyimide) was used to fabricate each 200V micro-supercapacitor rapidly in a single step at ambient condition for the first time. To actuate the microresonator, five 200V micro-supercapacitors were connected in series to attain a 1000V output in a stable and safe manner. We demonstrated that laser engraving is a highly feasible and cost-effective technique for fabricating very high-voltage energy storage devices that are portable, flexible, and lightweight.

With the increasing use of fingerprint authentication systems on portable devices, fake fingerprint detection has become growing important because fingerprints can be easily spoofed from a variety of available fabrication materials. Recently, many smartphones are hacked successfully by 2D fake fingerprint, which is a serious threat to authentication security. In order to enhance the robustness against fake fingerprint type, this paper proposes a novel 2D fake fingerprint detection method for portable devices based on improved light Convolutional Neural Networks (CNN). To evaluate the performance of the proposed method, a new 2D fake fingerprint dataset including three fabrication materials is created from capacitive fingerprint scanner. In addition, batch normalization and global average pooling are integrated to optimize the network. Experimental results show that the proposed method has high accuracy, strong robustness and good real-time performance, and can meet the requirements on portable devices.

Adrenocortical carcinoma (ACC) is a rare, but highly aggressive type of tumor with an incidence of one to two per million annually. Adrenocortical carcinosarcoma is an exceptional variant of ACC, which is characterized by the presence of histological regions of carcinoma and sarcoma. To date, to the best of our knowledge, there have only been 12 reported cases of adrenocortical carcinosarcoma. In the present study, a case of primary, non-functional adrenocortical carcinosarcoma is described, as well as a review of the literature to raise awareness of this particularly rare type of malignant neoplasm that is associated with a worse diagnosis and prognosis than adrenocortical carcinoma. In the present study, the patient underwent a laparoscopic left adrenalectomy and the tumor was dissected without complication from the left kidney. Microscopic observations showed the tumor comprised of epithelial and spindle cell components. The patient did not exhibit signs of tumor recurrence at the one-month follow-up. The potential diagnosis of adrenocortical carcinosarcoma must be considered when diagnosing adrenal malignancies in adults. In addition, comphrensive imunohistochemical staining may be required to identify possible sarcomatous patterns. To the best of our knowledge, the present case is the first to report an incidence of adrenocortical carcinosarcoma in China. Details of the patient are presented and the pathology of adrenocortical carcinosarcoma is discussed.

Object Neurocan is a major form of growth-inhibitory molecule (growth-IM) that suppresses axonal regeneration after neural injury. Bone marrow stromal cells (MSCs) have been shown to inhibit neurocan expression in vitro and in animal models of cerebral ischemia. Therefore, the present study was designed to investigate the effects of treatment of MSCs impregnated with collagen scaffolds on neurocan expression after traumatic brain injury (TBI). Methods Adult male Wistar rats were injured with controlled cortical impact and treated with saline, human MSCs (hMSCs) (3 × 106) alone, or hMSCs (3 × 106) impregnated into collagen scaffolds (scaffold + hMSCs) transplanted into the lesion cavity 7 days after TBI (20 rats per group). Rats were sacrificed 14 days after TBI, and brain tissues were harvested for immunohistochemical studies, Western blot analyses, laser capture microdissections, and quantitative real-time reverse transcriptase polymerase chain reaction (qRT-PCR) to evaluate neurocan protein and gene expressions after various treatments. Results Animals treated with scaffold + hMSCs after TBI showed increased axonal and synaptic densities compared with the other groups. Scaffold + hMSC treatment was associated with reduced TBI-induced neurocan protein expression and upregulated growth-associated protein 43 (GAP-43) and synaptophysin expression in the lesion boundary zone. In addition, animals in the scaffold + hMSC group had decreased neurocan transcription in reactive astrocytes after TBI. Reduction of neurocan expression was significantly greater in the scaffold + hMSC group than in the group treated with hMSCs alone. Conclusions The results of this study show that transplanting hMSCs with scaffolds enhances the effect of hMSCs on axonal plasticity in TBI rats. This enhanced axonal plasticity may partially be attributed to the downregulation of neurocan expression by hMSC treatment after injury.

Chitosan/Ag–montmorillonite (CS/Ag–MMT) nanocomposite was obtained via solution intercalation. The X-ray diffraction and transmission electron microscopy results indicated the successful formation of intercalated-and-exfoliated nanostructures at low Ag–MMT contents and intercalated-and-flocculated nanostructures at high Ag–MMT contents. The CS/Ag–MMT nanocomposite with 7 wt% Ag–MMT presented a very high inhibition ratio against Staphylococcus aureus (>70%), and its bacterial inhibition zone diameter was 3.2 mm larger than that of CS, which showed remarkable antibacterial activity. When the Ag–MMT content was 5 wt%, the E′ of CS/Ag–MMT was increased to 3,261 MPa, which was 195% higher than CS and exhibited excellent mechanic property. The DMA test results showed the Tg of CS/Ag–MMT shifted to a higher temperature, suggesting the Ag–MMT interlayer not only interacted quite strongly with the reactive groups of CS but also restricted the motion of CS macromolecules, which was also verified by Fourier transform infrared spectrum measurements. POLYM. COMPOS., 35:1980–1988, 2014. © 2014 Society of Plastics Engineers

Nogo-A is a major form of growth inhibitory molecule (growth-IM) which inhibits axonal regeneration and neurite regrowth after neural injury. Bone marrow stromal cells (MSCs) have been shown to inhibit Nogo-A expression in vitro and in cerebral ischemic animal models. The present study was designed to investigate the effects of treatment with human MSCs (hMSCs) impregnated into collagen scaffolds on the expression of Nogo-A and axonal plasticity after traumatic brain injury (TBI). Adult male Wistar rats were injured with controlled cortical impact and treated either with saline, hMSCs-alone or hMSCs impregnated into collagen scaffolds (scaffold+hMSC) transplanted into the lesion cavity 7 days after TBI. Rats were sacrificed 14 days after TBI and brain tissues were harvested for immunohistochemical studies, Western blot analysis, laser capture microdissections and qRT-PCR to evaluate axonal density and Nogo-A protein and gene expressions. Our data showed that treatment of TBI with scaffold+hMSC significantly decreased TBI-induced Nogo-A protein expression and increased axonal density compared to saline and hMSC-alone treatments. In addition, scaffold+hMSC transplantation decreased Nogo-A transcription in oligodendrocytes after TBI. Scaffold+hMSC treatment was superior to hMSC-alone treatment in suppressing Nogo-A expression and enhancing axonal regeneration after TBI. Our data suggest that transplanting hMSCs with scaffolds down-regulates Nogo-A transcription and protein expression which may partially contribute to the enhanced axonal regeneration after TBI.

Artificial tendon is a kind of bionic actuation system capable of imitating the transmission characteristics of skeleton tendon. This paper presents a new compliant single-tendon-sheath artificial tendon actuator inspired by Hill-type muscle model. The proposed artificial tendon can perform variable compliance properties with two linear springs applying onto the tendon-sheath element. Detailed development and modeling of the actuation system is given, and transmission characteristics are analyzed and validated via simulations and experiments. Then, a bending angle measurement method is developed to compensate the variation of tendon-sheath configuration during transmission process. A model-based inverse control strategy with bending angle compensation is developed for displacement and force control without distal feedback. Trajectory tracking experiments are conducted to verify the effectiveness of the proposed control strategy. Experimental results indicate that the control errors of displacement and force control without distal feedback could be limited within 7.07% and 13.62%, respectively.

Smart material electrohydrostatic actuator based on giant magnetostrictive transducer can meet the requirements of smaller vehicles like the unmanned combat air vehicle as well as in rotating environments like helicopter rotors. In order to analyze and improve the performance of smart material electrohydrostatic actuator, the dynamic hysteresis nonlinear model of giant magnetostrictive transducer is developed based on the relationship between complex permeability and magnetic energy power loss in giant magnetostrictive material, and the inverse model is also derived to design the inverse compensator for improving the linearity of giant magnetostrictive transducer. The experiments show that with the help of inverse compensator, phase lag between the input control signal and the output displacement of giant magnetostrictive transducer is decreased. In addition, the simulation results show that because of the effect of inverse compensator, the flow rate is increased and the growth rate increases with the increase of excitation frequency.

Silver loaded montmorillonite (Ag-MMT) was prepared by ion exchange reaction under microwave irradiation. The effects of microwave irradiation time, AgNO3 concentration, and microwave power on the silver content of Ag-MMT were investigated using the Volhard method. The time of ion exchange reaction was found to be greatly shortened under microwave irradiation. The most suitable reaction condition was in 0.2 m AgNO3 under 100% microwave power irradiation for 4 min, and the silver content was determined and was found to be 1.23 wt%. The results of the Fourier transform infrared spectrum (FT-IR) and Ag+ release tests confirmed the successful loading of silver and its good slow-release property. The X-ray diffraction (XRD) patterns and ultraviolet-visible (UV-vis) spectra proved the existence of metallic Ag nanoparticles. The Ag-MMT showing a large inhibition zone, high inhibition ratio, and low minimum inhibitory concentration (MIC) value against Staphylococcus aureus ATCC6535 presented a very good antibacterial property.

Object This study was designed to investigate how transplantation into injured brain of human bone marrow stromal cells (hMSCs) impregnated in collagen scaffolds affects axonal sprouting in the spinal cord after traumatic brain injury (TBI) in rats. Also investigated was the relationship of axonal sprouting to sensorimotor functional recovery after treatment. Methods Adult male Wistar rats (n = 24) underwent a controlled cortical impact injury and were divided into three equal groups (8 rats/group). The two treatment groups received either hMSCs (3 × 106) alone or hMSC (3 × 106)–impregnated collagen scaffolds transplanted into the lesion cavity. In the control group, saline was injected into the lesion cavity. All treatments were performed 7 days after TBI. On Day 21 after TBI, a 10% solution of biotinylated dextran amine (10,000 MW) was stereotactically injected into the contralateral motor cortex to label the corticospinal tract (CST) originating from this area. Sensorimotor function was tested using the modified neurological severity score (mNSS) and foot-fault tests performed on Days 1, 7, 14, 21, 28, and 35 after TBI. Spatial learning was tested with Morris water maze test on Days 31–35 after TBI. All rats were sacrificed on Day 35 after TBI, and brain and spinal cord (cervical and lumbar) sections were stained immunohistochemically for histological analysis. Results Few biotinylated dextran amine–labeled CST fibers crossing over the midline were found in the contralateral spinal cord transverse sections at both cervical and lumbar levels in saline-treated (control) rats. However, hMSC-alone treatment significantly increased axonal sprouting from the intact CST into the denervated side of the gray matter of both cervical and lumbar levels of the spinal cord (p < 0.05). Also, this axonal sprouting was significantly more in the scaffold+hMSC group compared with the hMSC-alone group (p < 0.05). Sensorimotor functional analysis showed significant improvement of mNSS (p < 0.05) and foot-fault tests (p < 0.05) in hMSC-alone and scaffold+hMSC-treated rats compared with controls (p < 0.05). Functional improvement, however, was significantly greater in the scaffold+hMSC group compared with the hMSC-alone group (p < 0.05). Morris water maze testing also showed significant improvement in spatial learning in scaffold+hMSC and hMSC-alone groups compared with the control group (p < 0.05), with rats in the scaffold+hMSC group performing significantly better than those in the hMSC-alone group (p < 0.05). Pearson correlation data showed significant correlation between the number of crossing CST fibers detected and sensorimotor recovery (p < 0.05). Conclusions Axonal plasticity plays an important role in neurorestoration after TBI. Transplanting hMSCs with scaffolds enhances the effect of hMSCs on axonal sprouting of CST fibers from the contralateral intact cortex into the denervated side of spinal cord after TBI. This enhanced axonal regeneration may at least partially contribute to the therapeutic benefits of treating TBI with hMSCs.

Recently, camera anomaly detection has attracted increasing interest in order to generate real-time alerts of camera malfunction for video surveillance systems. The existing camera anomaly detection methods still haven't enough ability to detect comprehensive types of anomaly, and lack the self-improvement ability in the case of miscarriage of justice by self-learning. So, this paper proposes a morphological analysis and deep learning based camera anomaly detection method to detect comprehensive types of anomaly. Morphological analysis is used to detect simple camera anomalies to accelerate the processing speed, and deep learning is utilized to detect complicated camera anomalies to improve the accuracy. The experimental results show that the detection accuracy of the proposed method achieves more than 95%.

This paper proposes an untethered self-folding locomotive paper robot. Its self-folding locomotion is similar to the opening and folding of the Chinese hand-held fan. We achieve this fan-like self-folding motion by the special design of paper cases and utilization of a novel 3D sticky pneumatic actuator. The actuator is a bladder with adhesive backs. Its inflation and contraction are controlled by two miniature pumps respectively. The opening and folding motions of the robot are synchronized with the inflation and contraction of the actuator. The electronics are clock-based controlled by an Arduino board. Instead of relying on the friction force, the robot utilizes the moment produced by the gravitational force to achieve locomotion. Thus, the robot is able to operate on surfaces where the friction coefficients are small. The average speed of its locomotion is 0.825cm/s. The robot features low cost, accessibility and instant-fabrication. The materials and equipment used for fabrications are paper cardboards, plastic membrane, tapes, tubes, scissors, miniature pumps, batteries, relays, solenoid valves, and Arduino boards which are all widely used in and easily obtained from daily life.

Aimed at the coordinated workspace and motion trajectory of the redundant dual-arm robot, the corresponding methods of workspace analysis and trajectory planning are proposed in this paper. Firstly, forward kinematics model of redundant dual-arm robot was established by D-H parameter method, and its inverse kinematics analytic solutions were given according to the decoupling configuration of robot. Thereafter, Monte Carlo method was proposed in analyzing the coordinated workspace, and non-uniform rational B-spline (NURBS) curves were used for the trajectory fitting. At last, the rationality of the redundant dual-arm robot structure design, and the feasibility of NURBS curve method for the trajectory fitting were verified by a visual simulation in the environment of MATLAB Robotics Toolbox.

With the deepening of the urbanization process in China, the construction of public engineering also develop rapidly, public engineering has non-competitive and non-exclusive characteristics, the core of its decision-making is value thinking. Based on the philosophy of value and stakeholder theory, this paper firstly defines the core stakeholders of public works, using VBS method to analyze the value demands of key stakeholders, building core value system of public engineering, and using structure entropy method to determine the index weight, finally the paper study the core value system evaluation by Planguage method. The objective of the paper is to provide reference for the investment decisions theoretical and empirical studies of public engineering.