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With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network’s advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated.

This paper presents a low-profile reconfigurable antenna array capable of five radiation-pattern modes for vehicular communication applications. The antenna array consists of four antenna elements, each containing four square patches. Exciting one of the square patches generates a broadside radiation. A square parasitic patch is added at the rear of the excited patch, and two square parasitic patches are placed at the front. By optimizing the design of these parasitic patches, including the treatment of center slotting and addition of shorting pins, the antenna element achieves an end-fire beam with a certain tilt angle. On this basis, a reconfigurable feeding network is designed with 1:1 and 1:4 output modes. By connecting the reconfigurable feeding network to the four antenna elements and altering the on/off states of the PIN diodes in the feeding network, a reconfigurable antenna with four end-fire beams and one omnidirectional beam in its radiation pattern is realized. Measurement results demonstrate an excellent impedance bandwidth, radiation pattern, and gain performance in all modes. The four end-fire and one omnidirectional radiation characteristics make it highly suitable for vehicular communication applications.

Extrinsic parameter calibration is the foundation and prerequisite for LiDAR and camera data fusion of the autonomous system. This technology is widely used in fields such as autonomous driving, mobile robots, intelligent surveillance, and visual measurement. The learning-based method is one of the targetless calibrating methods in LiDAR and camera calibration. Due to its advantages of fast speed, high accuracy, and robustness under complex conditions, it has gradually been applied in practice from a simple theoretical model in just a few years, becoming an indispensable and important method. This paper systematically summarizes the research and development of this type of method in recent years. According to the principle of calibration parameter estimation, learning-based calibration algorithms are divided into two categories: accurate calibrating estimation and relative calibrating prediction. The evolution routes and algorithm frameworks of these two types of algorithms are elaborated, and the methods used in the algorithms’ steps are summarized. The algorithm mechanism, advantages, limitations, and applicable scenarios are discussed. Finally, we make a summary, pointing out existing research issues and trends for future development.

Most of the existing metasurfaces are effective for the incident wave with the specific circularly polarized (CP) or linearly polarized (LP) state, that is the polarization-sensitive metasurface. This drawback dramatically hinders the practical use of the metasurface. Herein, this paper presents a strategy of polarization-insensitive transmissive microwave metasurfaces to manipulate the incident wave with arbitrary CP and LP states. The metasurface consists of polarization-insensitive unit cells. For the left circularly polarized (LCP) and right circularly polarized (RCP) incident waves, the same abrupt phase covering 0° to 360° can be realized by combining the Pancharatnam–Berry (PB) and resonant phases. As the arbitrary LP wave can decompose into the LCP and RCP waves, metasurfaces consisting of designed unit cells are valid for any polarization states. The polarization-insensitive transmissive microwave metalens and orbital angular momentum multiplexing metasurface working at 23 GHz are devised for verification. Simulation and measurement results verify the availability of the approach. The proposed method is suitable for designing microwave-transmissive metasurfaces capable of polarization insensitivity.

A general limitation in assessing the accuracy of land cover mapping is the availability of ground truth data. At sites where ground truth is not available, potentially inaccurate proxy datasets are used for sub-field-scale resolution investigations at large spatial scales, i.e., in the Contiguous United States. The USDA/NASS Cropland Data Layer (CDL) is a popular agricultural land cover dataset due to its high accuracy (>80%), resolution (30 m), and inclusions of many land cover and crop types. However, because the CDL is derived from satellite imagery and has resulting uncertainties, comparisons to available in situ data are necessary for verifying classification performance. This study compares the cropland mapping accuracies (crop/non-crop) of an optical approach (CDL) and the radar-based crop area (CA) approach used for the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) L- and S-band mission but using Sentinel-1 C-band data. CDL and CA performance are compared to ground truth data that includes 54 agricultural production and research fields located at USDA’s Beltsville Agricultural Research Center (BARC) in Maryland, USA. We also evaluate non-crop mapping accuracy using twenty-six built-up and thirteen forest sites at BARC. The results show that the CDL and CA have a good pixel-wise agreement with one another (87%). However, the CA is notably more accurate compared to ground truth data than the CDL. The 2017–2021 mean accuracies for the CDL and CA, respectively, are 77% and 96% for crop, 100% and 94% for built-up, and 100% and 100% for forest, yielding an overall accuracy of 86% for the CDL and 96% for CA. This difference mainly stems from the CDL under-detecting crop cover at BARC, especially in 2017 and 2018. We also note that annual accuracy levels varied less for the CA (91–98%) than for the CDL (79–93%). This study demonstrates that a computationally inexpensive radar-based cropland mapping approach can also give accurate results over complex landscapes with accuracies similar to or better than optical approaches.

Path planning and tracking control is an essential part of autonomous vehicle research. In terms of path planning, the artificial potential field (APF) algorithm has attracted much attention due to its completeness. However, it has many limitations, such as local minima, unreachable targets, and inadequate safety. This study proposes an improved APF algorithm that addresses these issues. Firstly, a repulsion field action area is designed to consider the velocity of the nearest obstacle. Secondly, a road repulsion field is introduced to ensure the safety of the vehicle while driving. Thirdly, the distance factor between the target point and the virtual sub-target point is established to facilitate smooth driving and parking. Fourthly, a velocity repulsion field is created to avoid collisions. Finally, these repulsive fields are merged to derive a new formula, which facilitates the planning of a route that aligns with the structured road. After path planning, a cubic B-spline path optimization method is proposed to optimize the path obtained using the improved APF algorithm. In terms of path tracking, an improved sliding mode controller is designed. This controller integrates lateral and heading errors, improves the sliding mode function, and enhances the accuracy of path tracking. The MATLAB platform is used to verify the effectiveness of the improved APF algorithm. The results demonstrate that it effectively plans a path that considers car kinematics, resulting in smaller and more continuous heading angles and curvatures compared with general APF planning. In a tracking control experiment conducted on the Carsim–Simulink platform, the lateral error of the vehicle is controlled within 0.06 m at both high and low speeds, and the yaw angle error is controlled within 0.3 rad. These results validate the traceability of the improved APF method proposed in this study and the high tracking accuracy of the controller.

With the advancement of vehicle electrification and intelligence, distributed drive electric trucks have emerged as the preferred choice for heavy-duty electric trucks. However, the control of yaw stability remains a significant issue. To tackle this concern, this study introduces a layered control strategy for yaw moment. Specifically, the upper layer utilizes a yaw moment controller based on linear quadratic regulator (LQR) to compute the additional yaw moment required. Additionally, in order to enhance the performance of the yaw moment controller, the weight matrix in LQR is optimized using a hybrid Genetic Algorithm and Particle Swarm Optimization algorithm (GA-PSO). The lower layer consists of a torque distribution layer, which establishes an objective function for minimizing tire utilization rate. Quadratic Programming algorithm is then employed to compute the optimal torque distribution value, thereby improving the vehicle’s stability. Subsequently, the stability control effects of the vehicle are simulated and compared on the Matlab/Simulink Trucksim joint simulation platform using four control strategies: the proposed control strategy, SMC, LQR, and without yaw moment control. These simulations are conducted under two working conditions: serpentine and double lane change. The results demonstrate that the proposed approach reduces the average yaw rate by 14.4%, 19.6%, and 42.15% while optimizing the average sideslip angle by 25.9%, 24.8%, and 52.3% in comparison to the other three control strategies. Consequently, the proposed control strategy significantly enhances the driving stability of the vehicle. Furthermore, the optimized allocation method reduces the average tire utilization rate by 42.6% in contrast to the average allocation method, thereby improving the stability control margin of the vehicle. These findings successfully validate the efficiency of the yaw stability control strategy presented in this article.

Super-resolution (SR) images based on deep networks have achieved great accomplishments in recent years, but the large number of parameters that come with them are not conducive to use in equipment with limited capabilities in real life. Therefore, we propose a lightweight feature distillation and enhancement network (FDENet). Specifically, we propose a feature distillation and enhancement block (FDEB), which contains two parts: a feature-distillation part and a feature-enhancement part. Firstly, the feature-distillation part uses the stepwise distillation operation to extract the layered feature, and here we use the proposed stepwise fusion mechanism (SFM) to fuse the retained features after stepwise distillation to promote information flow and use the shallow pixel attention block (SRAB) to extract information. Secondly, we use the feature-enhancement part to enhance the extracted features. The feature-enhancement part is composed of well-designed bilateral bands. The upper sideband is used to enhance the features, and the lower sideband is used to extract the complex background information of remote sensing images. Finally, we fuse the features of the upper and lower sidebands to enhance the expression ability of the features. A large number of experiments show that the proposed FDENet both produces less parameters and performs better than most existing advanced models.

Owing to the remarkable development of deep learning algorithms, defect detection techniques based on deep neural networks have been extensively applied in industrial production. Most existing surface defect detection models assign equal costs to the classification errors among different defect categories but do not strictly distinguish them. However, various errors can generate a great discrepancy in decision risk or classification costs and then produce a cost-sensitive issue that is crucial to the manufacturing process. To address this engineering challenge, we propose a novel supervised classification cost-sensitive learning method (SCCS) and apply it to improve YOLOv5 as CS-YOLOv5, where the classification loss function of object detection was reconstructed according to a new cost-sensitive learning criterion explained by a label–cost vector selection method. In this way, the classification risk information from a cost matrix is directly introduced into the detection model and fully exploited in training. As a result, the developed approach can make low-risk classification decisions for defect detection. It is applicable for direct cost-sensitive learning based on a cost matrix to implement detection tasks. Using two datasets of a painting surface and a hot-rolled steel strip surface, our CS-YOLOv5 model outperforms the original version with respect to cost under different positive classes, coefficients, and weight ratios, but also maintains effective detection performance measured by mAP and F1 scores.

P(VDF-TrFE) is a promising material for flexible acoustic devices owing to its good piezoelectric performance and excellent stretchability. However, the high density of internal pores and large surface roughness of the conventional P(VDF-TrFE) results in a high propagation attenuation for acoustic waves, which limits its use in flexible acoustic devices. In this paper, a novel method based on two-step annealing is proposed to effectively remove the pores inside the P(VDF-TrFE) film and reduce its surface roughness. The obtained P(VDF-TrFE) film possesses excellent characteristics, including a high breakdown strength of >300 kV/mm, a high-purity β-phase content of more than 80%, and high piezoelectric coefficients (d33) of 42 pm/V. Based on the low-porosity β-phase P(VDF-TrFE) film, we fabricated flexible film bulk acoustic resonators (FBARs) which exhibit high sharp resonance peaks. The pressure sensor was made by sandwiching the FBARs with two PDMS microneedle patches. Heartbeat and respiration rate monitoring were achieved using the pressure sensor. This work demonstrates the feasibility of high-performance flexible piezoelectric acoustic resonators based on low-porosity P(VDF-TrFE) films, which could see wider applications in the wearable sensors for both physical and chemical sensing.

To achieve low-cost and robustness, an indoor location system using simple visual tags is designed by comprehensively considering accuracy and computation complexity. Only the color and shape features are used for tag detection, by which both algorithm complexity and data storage requirement are reduced. To manage the nonunique problem caused by the simple tag features, a fast query and matching method is further presented by using the view field of the camera and the tag azimuth. Then, based on the relationship analysis between the spatial distribution of tags and location error, a pose and position estimation method using the weighted least square algorithm is designed and works together with the interactive algorithm by the designed switching strategy. By using the techniques presented, a favorable balance is achieved between the algorithm complexity and the location accuracy. The simulation and experiment results show that the proposed method can manage the singular problem of the overdetermined equations effectively and attenuate the negative effect of unfavorable label groups. Compared with the ultrawide band technology, the location error is reduced by more than 62%.

Amid increasing demands for planetary exploration, wide-range autonomous exploration is still a great challenge for existing planetary rovers, which calls for new planetary rovers with novel locomotive mechanisms and corresponding control strategies. This paper proposes a novel wheeled–legged mechanism for the design of planetary rovers. The leg suspension utilizes a rigid–flexible coupling mechanism with a hybrid serial–parallel topology. First, the kinematic model is derived. Then, a control strategy for the wheeled–legged rover that includes a trajectory tracking module based on the model predictive control, the steering strategy, and the wheel speed allocation algorithm is proposed. After that, three groups of cosimulations with different trajectories and speeds, and experiments are carried out. Results of both the simulations and experiments validate the proposed control method.

With the increase in global energy demand, the exploration and development of natural gas hydrate in sea has become a research hotspot in recent years. However, the environmental problems that may be brought about by large-scale harvesting are still concerns. The terrain monitoring of the trial harvesting area can effectively prevent the geological disasters that may be caused by the development of hydrates. Therefore, we have developed a new terrain monitoring device, which can work in the deep sea for a long time. Firstly, the structure of the sensor arrays and bus-type control system of the device are introduced. Secondly, an arc model with an interpolation method is used for reconstruction of the monitored terrain. Thirdly, after the accuracy of the sensing arrays are verified in laboratory, the device was placed in the Shenhu area of the South China Sea for more than 6 months of in-situ monitoring. Finally, we analyzed the data and concluded that the terrain of the monitored area was relatively flat, where the maximum subsidence was 12.3 cm and the maximum uplift was 2.75 cm.

In order to accurately evaluate the flow stability of the flow standard facility, the flow fluctuation in the standard facility needs to be accurately measured. However, the flow fluctuation signal is always superimposed with the fluctuation signal of the measuring flowmeter or measurement system (mainly noise), which leads to inaccurate measurement of the flow fluctuation and even an unreliable evaluation result of the flow stability. In addition, when there are multiple fluctuation sources, flow fluctuations with different frequencies are superimposed together, which is extremely unfavorable for evaluating the impact of flow fluctuation with different single frequencies. In this paper, a new measuring method was proposed to obtain the fluctuation signal and the flow fluctuation based on singular value decomposition (SVD). Simulation experiments on the fluctuation signal (single frequency and multiple frequencies) under different levels of noise were conducted, and simulation results showed that the proposed method could accurately obtain the fluctuation signal and the flow fluctuation, even under high noise. Finally, an experimental platform was set-up based on a water flow standard facility and a flow fluctuation generator, and experiments on the output signal of a venturi flowmeter were carried out. The experiment results showed that the proposed method could effectively obtain the fluctuation signal and accurately measure the flow fluctuation.

The cascading launch and cooperative work of lander and rover are the pivotal methods to achieve lunar zero-distance exploration. The separated design results in a heavy system mass that requires more launching costs and a limited exploration area that is restricted to the vicinity of the immovable lander. To solve this problem, we have designed a six-legged movable repetitive lander, called “HexaMRL”, which congenitally integrates the function of both the lander and rover. However, achieving a buffered landing after a failure of the integrated drive units (IDUs) in the harsh lunar environment is a great challenge. In this paper, we systematically analyze the fault-tolerant capacity of all possible landing configurations in which the number of remaining normal legs is more than two and design the landing algorithm to finish a fault-tolerant soft-landing for the stable configuration. A quasi-incentre stability optimization method is further proposed to increase the stability margin during supporting operations after landing. To verify the fault-tolerant landing performance on the moon, a series of experiments, including five-legged, four-legged and three-legged soft-landings with a vertical landing velocity of −1.9 m/s and a payload of 140 kg, are successfully carried out on a 5-DoF lunar gravity ground-testing platform. The HexaMRL with fault-tolerant landing capacity will greatly promote the development of a next-generation lunar prober.

Lane detection is a significant technology for autonomous driving. In recent years, a number of lane detection methods have been proposed. However, the performance of fast and slim methods is not satisfactory in sophisticated scenarios and some robust methods are not fast enough. Consequently, we proposed a fast and robust lane detection method by combining a semantic segmentation network and an optical flow estimation network. Specifically, the whole research was divided into three parts: lane segmentation, lane discrimination, and mapping. In terms of lane segmentation, a robust semantic segmentation network was proposed to segment key frames and a fast and slim optical flow estimation network was used to track non-key frames. In the second part, density-based spatial clustering of applications with noise (DBSCAN) was adopted to discriminate lanes. Ultimately, we proposed a mapping method to map lane pixels from pixel coordinate system to camera coordinate system and fit lane curves in the camera coordinate system that are able to provide feedback for autonomous driving. Experimental results verified that the proposed method can speed up robust semantic segmentation network by three times at most and the accuracy fell 2% at most. In the best of circumstances, the result of the lane curve verified that the feedback error was 3%.

Manipulators with multi degree-of-freedom (DOF) are widely used for the peg-in-hole task. Compared with manipulators, six-legged robots have better mobility performance apart from completing operational tasks. However, there are nearly no previous studies of six-legged robots performing the peg-in-hole task. In this article, a peg-in-hole approach for six-legged robots is studied and experimented with a six-parallel-legged robot. Firstly, we propose a method whereby a vision sensor and a force/torque (F/T) sensor can be used to explore the relative location between the hole and peg. According to the visual information, the robot can approach the hole. Next, based on the force feedback, the robot plans the trajectory in real time to mate the peg and hole. Then, during the insertion, admittance control is implemented to guarantee the smooth insertion. In addition, during the whole assembly process, the peg is held by the gripper and attached to the robot body. Connected to the body, the peg has sufficient workspace and six DOF to perform the assembly task. Finally, experiments were conducted to prove the suitability of the approach.

Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.

Coordinate identification between vision systems and robots is quite a challenging issue in the field of intelligent robotic applications, involving steps such as perceiving the immediate environment, building the terrain map and planning the locomotion automatically. It is now well established that current identification methods have non-negligible limitations such as a difficult feature matching, the requirement of external tools and the intervention of multiple people. In this paper, we propose a novel methodology to identify the geometric parameters of 3D vision systems mounted on robots without involving other people or additional equipment. In particular, our method focuses on legged robots which have complex body structures and excellent locomotion ability compared to their wheeled/tracked counterparts. The parameters can be identified only by moving robots on a relatively flat ground. Concretely, an estimation approach is provided to calculate the ground plane. In addition, the relationship between the robot and the ground is modeled. The parameters are obtained by formulating the identification problem as an optimization problem. The methodology is integrated on a legged robot called “Octopus”, which can traverse through rough terrains with high stability after obtaining the identification parameters of its mounted vision system using the proposed method. Diverse experiments in different environments demonstrate our novel method is accurate and robust.

Objective: To establish an electrochemical immunosensor for the determination of serum trypsin levels using a multiwall carbon nanotubes (MWCNTs)-composite-modified electrode. Method: A MWCNT composite coated on the surface of bare gold electrodes was used for fixation of an anti-trypsin antibody. The assembly process and the performance indicators, including sensitivity, linear range of detection, anti-jamming performance, and stability, of the electrochemical immunosensor were examined by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Results: With optimized experimental conditions, the difference of the current value measured by differential pulse voltammetry (DPV) showed a linear relationship with the concentration of serum trypsin within 0.10–100 ng/mL. The detection limit for trypsin using this sensor was 0.002 ng/mL. Conclusions: The electrochemical immunosensor built using the MWCNT-composite-modified electrode is simple to operate and has a fast response time, along with a wide linear range, high sensitivity, and accuracy, making it suitable for serum trypsin detection.

A Highway Intelligent Space System (HISS) is proposed to study vehicle environment perception in this paper. The nature of HISS is that a space sensors system using laser, ultrasonic or radar sensors are installed in a highway environment and communication technology is used to realize the information exchange between the HISS server and vehicles, which provides vehicles with the surrounding road information. Considering the high-speed feature of vehicles on highways, when vehicles will be passing a road ahead that is prone to accidents, the vehicle driving state should be predicted to ensure drivers have road environment perception information in advance, thereby ensuring vehicle driving safety and stability. In order to verify the accuracy and feasibility of the HISS, a traditional vehicle-mounted sensor system for environment perception is used to obtain the relative driving state. Furthermore, an inter-vehicle dynamics model is built and model predictive control approach is used to predict the driving state in the following period. Finally, the simulation results shows that using the HISS for environment perception can arrive at the same results detected by a traditional vehicle-mounted sensors system. Meanwhile, we can further draw the conclusion that using HISS to realize vehicle environment perception can ensure system stability, thereby demonstrating the method’s feasibility.

Piezoelectric fiber composites (PFC) have shown excellent performance in the areas of vibration control and deformation control. The viscosity and rigidity of the epoxy resin before and after curing, respectively, were very important factors that affected the performance of PFC. In this paper, Aradite 2020, DP 460, and DP 490 epoxy resins, with the viscosities of 0.15, 25.0, and 250.0 Pa·s, respectively, were employed to encapsulate the piezoelectric fiber composite. The PFC that was packaged with Araldite 2020 had the best free strain of 1420 ppm and tip displacement of 17.8 mm. DP 490 caused the lowest performance of PFC, due to the highest viscosity. When the environmental temperature increased from −40 to +80 °C, the free strain of PFC with Aradite 2020 increased at first and then decreased, reaching a maximum value of 1440 ppm at 30 °C, which was mainly related to the mismatch of the resin/ceramic thermal expansion coefficient.

The authors wish to make the following corrections to their paper [...]

This work demonstrates a highly sensitive pressure sensor that was fabricated using carbon microcoils (CMCs) and polydimethylsiloxane (PDMS). CMCs were grown by chemical vapor deposition using various ratios of Fe-Sn catalytic solution. The pressure sensor has a sandwiched structure, in which the as-grown CMCs were inserted between two PDMS layers. The pressure sensor exhibits piezo-resistivity changes in response to mechanical loading using a load cell system. The yields of the growth of CMCs at a catalyst proportion of Fe:Sn = 95:5 reach 95%. Experimental results show that the sensor achieves a high sensitivity of 0.93%/kPa from the CMC yield of 95%. The sensitivity of the pressure sensor increases with increasing yield of CMCs. The demonstrated pressure sensor shows the advantage of high sensitivity and is suitable for mass production.

Although Worldview-2 (WV) images (non-pansharpened) have 2-m resolution, the re-visit times for the same areas may be seven days or more. In contrast, Planet images are collected using small satellites that can cover the whole Earth almost daily. However, the resolution of Planet images is 3.125 m. It would be ideal to fuse these two satellites images to generate high spatial resolution (2 m) and high temporal resolution (1 or 2 days) images for applications such as damage assessment, border monitoring, etc. that require quick decisions. In this paper, we evaluate three approaches to fusing Worldview (WV) and Planet images. These approaches are known as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal Data Fusion (FSDAF), and Hybrid Color Mapping (HCM), which have been applied to the fusion of MODIS and Landsat images in recent years. Experimental results using actual Planet and Worldview images demonstrated that the three aforementioned approaches have comparable performance and can all generate high quality prediction images.

Carboxyl-functionalized semiconducting polymer dots (Pdots) were synthesized as an energy donor by the nanoprecipitation method. A black hole quenching dye (BHQ-labelled thrombin aptamers) was used as the energy acceptor, and fluorescence resonance energy transfer between the aptamers and Pdots was used for fluorescence quenching of the Pdots. The addition of thrombin restored the fluorescence intensity. Under the optimized experimental conditions, the fluorescence of the system was restored to the maximum when the concentration of thrombin reached 130 nM, with a linear range of 0–50 nM (R2 = 0.990) and a detection limit of 0.33 nM. This sensor was less disturbed by impurities, showing good specificity and signal response to thrombin, with good application in actual samples. The detection of human serum showed good linearity in the range of 0–30 nM (R2 = 0.997), with a detection limit of 0.56 nM and a recovery rate of 96.2–104.1%, indicating that this fluorescence sensor can be used for the detection of thrombin content in human serum.

The recently developed direct phase measuring deflectometry (DPMD) method can directly measure the three-dimensional (3D) shape of specular objects with discontinuous surfaces, but requires a calibrated distance between a reference plane and liquid crystal display screen. Because the plane and screen are different distances from the imaging device, they cannot be clearly captured given the limited depth of field (DOF) of the lens. Therefore, existing machine vision-based methods cannot be used to effectively calibrate a DPMD system. In this paper, a new distance calibration method that uses a mirror with a hollow ring matrix pattern and a mobile stage is presented. The direction of the mobile stage in the camera coordinate system is determined by the mirror’s pattern at several positions in the camera’s DOF so that the reference position outside of the DOF can be calculated. The screen’s position can also be calibrated by displaying patterns at a known scale. Therefore, the required distance is accurately obtained in the camera coordinate system. Evaluation results show that the maximum value of the absolute error is less than 0.031 mm. The experimental results on an artificial stepped mirror and a reflected diamond distribution surface demonstrate the accuracy and practicality of the proposed method.

The fast development in the fields of integrated circuits, photovoltaics, the automobile industry, advanced manufacturing, and astronomy have led to the importance and necessity of quickly and accurately obtaining three-dimensional (3D) shape data of specular surfaces for quality control and function evaluation. Owing to the advantages of a large dynamic range, non-contact operation, full-field and fast acquisition, high accuracy, and automatic data processing, phase-measuring deflectometry (PMD, also called fringe reflection profilometry) has been widely studied and applied in many fields. Phase information coded in the reflected fringe patterns relates to the local slope and height of the measured specular objects. The 3D shape is obtained by integrating the local gradient data or directly calculating the depth data from the phase information. We present a review of the relevant techniques regarding classical PMD. The improved PMD technique is then used to measure specular objects having discontinuous and/or isolated surfaces. Some influential factors on the measured results are presented. The challenges and future research directions are discussed to further advance PMD techniques. Finally, the application fields of PMD are briefly introduced.

A novel technique was developed, that was high throughput simultaneousscreening of multiple resistance protein expression based on a protein array system. Themethod combined the advantage of the specificity of enzyme-linked immunosorbentassays with the sensitivity and high throughput of microspot. In this system, the multipleresistance protein arrays were created by spotting the captured antibodies onto the glassslide. The arrays were then incubated with cell samples of leukemia patients. The boundproteins were recognized by biotin-conjugated antibodies and detected by CCD.Experiments demonstrated that three multiple resistance proteins, including Pgp, MRPand BCRP which are members of the ATP-binding-cassette (ABC) superfamily ofmembrane transporters could be simultaneously detected using this new approach.Research work shows the result is coincident with flow cytometry (FCM) (P>0.01). Itprovided a methodology to develop many high-density protein array systems to detect avariety of proteins. The protein arrays will provide a powerful tool to identify theleukemia cell protein expression and rapidly validate their MDR determination.