To achieve high-efficiency delivery of bio-ink， a novel cell delivery device based on air-assisted atomization was developed，and its atomization characteristics and their effect on cell viability were studied. According to the Rosin-Rammler droplet size distribution law， the uniformity index was quantitatively evaluated. Multi-frame image superposition and local threshold binarization were used to extract the boundary of atomization from a video， so that the atomization angle could be analyzed. By combining the BM3D denoising and binarization algorithm to accurately locate the droplet positions， we could calculate the velocity of the droplets. The atomization height and the liquid flow rate are positively correlated to the diameter of the droplets. The pressure of the assistant air has a noticeable effect on the droplet uniformity when this pressure is higher than 60 kPa. By controlling the liquid flow rate and spray height， the spray area can be adjusted to a wide range of 50-1 800 mm2. The velocity of droplets was greatly affected by the spray height. At a spray height of 50 mm， the velocity reached maximum value， with an average value as high as 14 m/s. Both the spray height and the auxiliary air flow rate significantly affect the viability of HaCaT cells. The rate of cell activation was 64.01±0.86% and 90.24±0.73% at a spray height of 50 mm and 100 mm， respectively （the cell activation rate was 92.98±3.21% in the negative control group）. Within 72 hours after spraying， the viability of HaCaT cells was consistent with that of unsprayed cells. The novel atomization device can be used for high-efficiency delivery of bio-ink， and the relevant research results also provide guidance for the optimal design of atomization parameters to achieve spraying of areas of different size and high cell-activation rate delivery.

The influence of SAR speckle noise makes it difficult for the existing state-of-the art algorithms to guarantee the repeatability rate of feature points when extracting them from optical and SAR images owing to the nonlinear radiation differences between optical and SAR remote sensing images， which consequently reduce the matching performance. To address the above problems， a Harris feature point extraction algorithm based on phase congruency moment feature is proposed. Firstly， blocking strategy was used to divide the input image into several image blocks； secondly， phase congruency intermediate moments were defined； then， phase congruency multi-moment maps were calculated for each image block； and finally， a voting strategy was designed on the phase congruency multi-moment maps. The feature points that appeared more than half of the time on the multi-moment image were selected as the final feature points. In this study， the simulated optical and SAR images were used as experimental data， and three different feature point detection algorithms were selected for comparison with the proposed algorithm. Experimental results showed that the proposed algorithm can overcome the influence of nonlinear radiation differences between optical and SAR remote sensing images and the SAR speckle noise， improving the repeatability rate of feature points effectively. The registration results on the real optical and SAR images showed that， compared with the other three algorithms， the matching points increased by 23， 26， and 35 pairs and the root mean square error decreased by 12.6%， 37.2%， and 40.8%， respectively. The performance of registration algorithm was improved effectively.

To improve the shielding performance of a multilayer magnetically shielded structure and to further reduce the construction cost of a magnetically shielded room， this study proposes to treat the magnetically shielded structure as a multi-objective function optimization problem， and to optimize the parameters of the shielded lamination structure using the non-dominated sorting genetic algorithm-II （NSGAII） under the constraints of feasible construction cost and quality construction composition. In this study， the NSGAII algorithm is improved using a segmental crossover strategy with an adaptive variation operator called CSA-NSGAII to solve the problems of the traditional NSGAII algorithm of uneven population convergence distribution， poor global search ability， and easily falling into a local optimum. Compared with the original NSGAII algorithm， NSGAII-SDR， g-NSGAII， and MOEA/D algorithms， the CSA-NSGAII is beneficial in GD， IGD， and spacing， indicating that the proposed CSA-NSGAII algorithm achieves improved convergence performance and a more uniform population distribution. By applying the algorithm proposed in this paper to the multi-objective optimization design problem of the magnetic shielding structure， the experimental results show that the optimized stacked structure can， on average， save approximately 14% of the construction costs while achieving the same shielding performance， and can achieve approximately 70 dB of shielding performance in a Helmholtz coil with an interference amplitude of 32 000 nT and frequency of 1 Hz.

Currently， pedestrian detection in multiple scenes is a research hotspot in the field of computer vision. Deep learning has attracted considerable attention and can provide high detection accuracy； however， the subsequent high-complexity operations seriously limit its application on mobile platforms. To address this problem， this paper proposes a lightweight pedestrian detection algorithm for multiple scenes. Firstly， a deep and shallow feature fusion network is constructed to learn the texture features of multi-scale pedestrians. Secondly， a cross-dimensional feature-guided attention module is designed to retain the interactive information between channels and spaces in the process of feature extraction. Finally， the redundant channels in the model are trimmed using the pruning strategy， to reduce the algorithm complexity. In addition， an adaptive Gamma correction algorithm is designed to reduce the influence of external disturbances， such as illumination and shadows， on the detection results of multiple scenes. The experimental results show that the proposed method can compress the model volume to 10 MB， and the processing speed can reach 93 Frame/s while achieving similar detection accuracy， which outperforms the current mainstream methods.

To improve the point accuracy of along-track stereoscopic imaging， a larger coverage area must be obtained； that is， the effective area for stereoscopic imaging must be improved. Herein， first， a method for attitude planning of along-track dual/triple perspective stereoscopic imaging is designed， which adopts the earth ellipsoid model and considers the earth rotation and satellite rolling direction maneuver. Next， to reduce the maneuver time and to increase the area that can be imaged by the satellite in a single shot， a method called path planning fast maneuver control is designed considering the actuator moment and angular momentum constraints， including two parts. According to a situation wherein the angular velocity of the satellite is limited owing to the torque and angular momentum constraints of the actuator， a three-axis maneuver attitude planner with the shortest path and continuous angular acceleration is designed based on the invariant rotation axis constraint. An attitude maneuver algorithm combining angular acceleration feed-forward and internal–external loop control is further designed to improve the dynamic performance of attitude tracking. Finally， a mathematical simulation is conducted with the parameters of the Jilin-1 satellite， whereby the control accuracy is better than 0.02°， the stability is better than 0.001 （°）/s， and the imaging duration is no less than 10 s. During the in-orbit test， the stereo image pairs and digital surface models of Urumqi city are obtained， with an effective coverage area of 1600 km2 and an overlap rate of dual-view imaging exceeding 97%， which verifies the feasibility and effectiveness of the along-track dual-view stereo imaging planning and control method.

A novel ultrasonic-vibration-composite electrical-discharge-machining （EDM） small-hole high-speed machining system is designed for small-hole machining of difficult-to-machine metals， such as nickel-based superalloys. The system can simultaneously realize the functions of electrode ultrasonic vibration， electrode rotation， and water flush out at the center of the electrode. It can be easily employed for other EDM machines. A series of small-hole machining experiments are carried out on the superalloy GH4099. By altering the machining current and time， indexes such as the material removal rate （MRR）， tool electrode wear rate （TWR）， and hole morphology are compared. The experimental results reveal that ultrasonic-vibration-composite machining can effectively improve the MRR and reduce the relative TWR during low-current machining. Under the same conditions， when the peak current is 1 A， the hole depth processed by this system is 89.62% higher than that of ordinary EDM. When the peak current is 6 A， the MRR increases by 46.42% compared with that for ordinary EDM. The electrode wear rate is reduced by 25.85%. Thus， we can conclude that the proposed system features a notably stable machining process that provides a convenient and efficient solution for EDM finishing of difficult-to-machine metals.

RB-SiC is as an ideal material for manufacturing large space mirrors owing to its excellent properties such as low density， high thermal conductivity， and good thermal stability. However， it is easy to introduce processing damage during the precision machining of its formed surface. Therefore， flexible scribing experiments were conducted to investigate the effect of flexible grinding process with abrasive belts on the material. A comparative experiment using RB-SiC with rigid/flexible contact was conducted， and the average sub-surface injury ratio of flexibility and the rigid scribing was found to be 0.677 and 0.823， respectively. Based on this， the orthogonal scribing experiments with different normal pressure， abrasive angle， and scribing speed were performed. The results showed that the influence of normal pressure and abrasive angle on material damage was more significant. Meanwhile， with the increase in normal pressure， decrease in abrasive angle， and increase in scribing speed， the degree of material damage became larger. Finally， by examining the scribing with multiple diamond abrasive grains， it was concluded that compared with the scribing of single diamond abrasive grains， the surface of the material is not severely broken or damaged， and the depth of the subsurface damage layer was less than 10 μm. This study will provide basic guidance for the flexible grinding of abrasive belts for RB-SiC.

A piezoelectric positioning stage is driven and amplified by piezoelectric ceramic and flexible hinges， which can provide high positioning accuracies and response speeds. Thus， it is widely used in various precision/ultra-precision positioning fields. However， the primary challenge presented by the piezoelectric positioning stage is the inherent hysteresis nonlinear characteristics of piezoelectric ceramics， which significantly affects the positioning and tracking accuracy of the piezoelectric positioning stage. Hence， a hysteresis modeling method based on the Hammerstein structure and an input-output feedback linearization control strategy is proposed herein. First， hysteresis modeling based on the Hammerstein structure is proposed， and the parameters are estimated. Subsequently， based on the Hammerstein model， a tracking controller is designed via an input–output feedback linearization control strategy. Finally， the proposed Hammerstein model and the designed tracking controller are experimentally verified on a piezoelectric positioning stage. The experimental results of model identification reveal that the proposed Hammerstein model can effectively fit the hysteresis nonlinearity between the input and output of the piezoelectric positioning stage and that its root mean square error is less than 0.5 μm. Meanwhile， the experimental results of trajectory tracking indicate that the designed tracking controller can track the desired signal （amplitude 60 μm； frequency 100 Hz） with a root mean square error of 0.926 6 μm. Compared with the feedforward compensation tracking control based on the modified rate-dependent Prandtl-Ishlinskii （MRPI） model and the compound tracking control of feedforward compensation based on the MRPI model and proportional-integral-derivative feedback， the proposed model offers an accuracy improvement of 81.22% and 46.25%， respectively.

The rapid and efficient detection and identification of pathogenic bacteria have long been the focus of many research fields， such as life sciences， medical diagnosis， food safety， and environmental monitoring. Microfluidic chip analysis technology provides efficient methods and platforms for the research and detection of bacteria and other microorganisms. The combination of microfluidic chips and surface-enhanced Raman scattering （SERS） spectroscopy detection technology provides many advantages and allows rapid identification and detection of pathogenic bacteria in biological samples. In this paper， we review the SERS analysis technology and its applications in microfluidic chip analysis. First， we introduce improved substrate materials for SERS and compare their effectiveness. The methods and techniques used to integrate SERS substrates on microfluidic chips are then systematically reviewed. Next， we discuss methods for the external injection of a nano metal sol into the microfluidic channel， embedding a solid nano structure in the microfluidic chip detection area， and the in-situ fabrication of nano structure substrates in the microfluidic channel. Finally， we provide an overview of the progress and future prospects of the application of the microfluidic chip analysis method integrated with SERS detection technology for the identification and quantitative detection of pathogenic bacteria.

To solve the nonlinear unmixing problem of hyperspectral remote sensing images， a multi-graph regularized multi-kernel nonnegative matrix factorization （MGMKNMF） method is proposed， and the multi-graph regularization term in multi-kernel space is constructed. Moreover， the MGMKNMF objective function， which includes multi-graph in multi-kernel regularization， multi-kernel weights regularization， and multi-graph weights regularization terms， is constructed. MGMKNMF can update the multi-kernel and multi-graph weights during the process of learning endmembers and abundance， and precisely construct the graph of the input data in the appropriate multi-kernel space， thereby solving the problem of selecting the graph and kernel weights. Compared with the single kernel function used in kernel nonnegative matrix factorization （KNMF）， multiple kernel functions can determine a more appropriate kernel space. Further， compared with the single graph in graph regularized nonnegative matrix factorization （GNMF）， multiple graphs describe the relationship between samples in different ways， which is more accurate and reliable than the single graph. The experimental results with two real measured datasets and two simulated datasets show that the presented MGMKNMF algorithm is effective. Compared with GNMF， non-pure pixels kernel nonnegative matrix factorization kernel sparse non-negative matrix factorization， kernel-based nonlinear spectral unmixing with dictionary pruning methods， and multi-graph regularized kernel nonnegative matrix factorization method， the average spectral angle distance （SAD） values of the proposed MGMKNMF are the best， that is， 0.092 1 and 0.097 0 on the real Cuprite dataset and Jasper ridge dataset， respectively. The average SAD values of MGMKNMF on the Hapke and generalized bilinear model simulated datasets are 0.137 5 and 0.145 6， respectively. Finally， the root mean square error values are 0.050 6 and 0.057 0， respectively.

To solve the problems of weak texture and reflection in the local magnification observation of jewelry， mineral， and metal samples， a multiview stereo three-dimensional （3D） reconstruction algorithm based on feature extraction is proposed. The lens of the microscope is fixed at a fixed angle， and the image sequence is obtained via a multi-angle imaging of the 3D specimen by moving the carrier platform. By combining the advantages of the Harris and SIFT algorithms， the SIFT algorithm in the original SFM reconstruction is improved to the Harris-SIFT algorithm for feature extraction and matching， which improves the performance of feature information extraction in weak texture regions of microscopic images. By using the full convolution neural network combined with the depth residual network to estimate and predict the depth of the input image， the predicted depth information is combined with the MVS depth map through the threshold method， the MVS depth map is modified， the dense point cloud of the object is reconstructed， and more point clouds are reconstructed with structural integrity. Experiments performed using a VHX-6000 digital microscope system show that the number of point clouds reconstructed using this algorithm is 31.25% higher than that reconstructed by the original MVS reconstruction algorithm， and the overall reconstruction time is reduced by 21.16%.