Continuous blood glucose detection is of great significance in the diagnosis and treatment of diabetes. In this study, an integrated, automated minimally invasive blood glucose continuous detection instrument was designed. Interstitial fluid (ISF) was transdermally extracted by a microfluidic chip, and the volume of the ISF was accurately measured by the single-chip computer. The glucose contained in the fluid was detected by enzyme colorimetry. Then, continuous blood glucose detection was achieved using the correlation of glucose concentration between the interstitial fluid and blood. The glucose concentration in the ISF was obtained by controlling the sample injection, reagent quantification, and absorbance detection in the enzyme colorimetric optical detection module. Owing to the small volume of the ISF extracted through the skin and dispersed on the surface of the skin, in order to facilitate collection, the extracted ISF was diluted with normal saline. To measure the low concentration of the diluted ISF, 10 concentrations in the range of 1—50 mg/dL were selected for absorbance measurement. The absorbance model was established based on the spectral data and glucose concentration. The results show that the glucose detection method has good linearity in the range of 1—50 mg/dL, and the relative standard deviation is less than 0.65%. The instrument is an automation control system and can provide basis for the diagnosis of diabetes. The feasibility of the instrument is verified by test experiments. The instrument has good application prospects for continuous blood glucose monitoring.

A laser interferometry system was built to accurately realize the frequency identification of low-frequency underwater targets. An improved algorithm was proposed based on the Phase-Generated Carrier (PGC) demodulation technology using the arctangent demodulation algorithm. First, a set of interference experiment systems was built using discrete optical elements under an optical darkroom. Water surface waves were introduced into the reference optical path as a high-frequency carrier, and the interference signal was obtained by interference with the main reflector of measuring and reference light. Next, sine mixing of the carrier signal is introduced based on the arctangent algorithm of the PGC demodulation technique. Using the power comparison algorithm, the group with stronger power was selected between two groups of orthogonal signals for demodulation, so as to avoid demodulation distortion caused by the initial carrier phase. Finally, the anti-interference ability was demonstrated by experiments under substantial mechanical disturbance. Experimental results show that the lower detection limit of the underwater sound source is approximately 30 Hz, and the frequency detection accuracy obtained by multiple detections is better than 1 Hz at frequencies above 100 Hz. The improved arctangent demodulation algorithm can effectively avoid demodulation distortion and has a strong anti-interference ability.

The spatial-filtering velocimetry method has the advantages of having a simple structure, good stability, and strong applicability. However, the traditional linear-array Charged Coupled Device (CCD) spatial-filtering velocity measurement method requires that the direction of the CCD array be the same as the direction of motion of the object whose velocity is to be measured . Therefore, this method is not suitable for measuring complex flow fields. To solve this problem, this study proposes a spatial-filtering velocity- measurement method based on an area-array CCD camera. A series of area-array CCD output images were collected from the measurement area. Interlaced sampling was performed in the horizontal and vertical directions of the image to simulate the multi-slit spatial-filtering characteristics, and an optical non-contact measurement of flow particle velocity around an obstacle was realized. Moreover, regarding the characteristics of the power spectrum density of complex flow fields, using an energy center-of-gravity correction spectrum improves system measurement accuracy. The system was calibrated by adjusting the speed of the conveyor to achieve measurements at different speeds with an average error of less than 4%. In addition, the debris-flow velocity-field distribution simulated with glass sand was also measured using this system. Finally, the influence of spatial period and duration on the measurement results was discussed, which demonstrate that the measured velocity reaches a plateau for sampling over 0.5 s and the spatial resolution is improved to be 1.28 mm.

A 635 nm and 10.5 μm dual-band coherent optical infrared transmission interferometer was designed. This integrated system employs a visible semiconductor laser at 635 nm and an infrared semiconductor laser at 10.5 μm as the interference light source. Through tests with this integrated system, a common optical path between the interference of the visible light band and the infrared light band could be determined. The common mode of the mechanical phase shift system and the visible light co-alignment were used in the dual-band optical path, and the phase shifter long-stroke error was calibrated at 10.5 μm based on the segment stagnation point of the 635 nm testing light. The accuracy of the developed dual-wavelength infrared interferometer system was measured, where the values of PV, RMS, and RMS repeatability were better than 0.05λ, 0.01λ, and 0.001λ, respectively. The interferometer was used to test an aspherical mirror with off-axis amount of 800 nm and dimensions of 400 mm×400 mm, where the maximum deviation of the measured edge value is 21.9 μm. Furthermore, the interferometer enables high-accurate surface measurements during the entire grinding process of large-aperture off-axis aspheric components.

To realize a time-efficient and wider range of measurements of piston errors of segmented mirror, a method based on the coherence of the Fraunhofer diffraction pattern of two half circles in visible light was proposed to detect the piston errors of the segmented mirror. In this method, the noncoherent diffraction pattern of the two half circulars was used as a template, and the cross-correlation algorithm was used to calculate the cross-correlation coefficient between the template pattern and the actual diffraction pattern. To achieve coarse co-phasing detection of piston errors of the segmented mirror, the threshold value of the cross correlation coefficient was set as 0.85. The proposed method has advantages such as time-efficiency, better energy usage ratio, infinite detection range, and a better measurement accuracy. The method was validated by theoretical, analytical, and numerical simulations. An active optics and co-phasing experimental system of the segmented mirror was built to measure and adjust the piston errors of the segmented mirror. The segmented mirror consists of four hexagonal segments whose edge to edge length is 100 mm and the curvature radius is 2 000 mm. Initially, to fine co-focus the segmented mirror, a shack-Hartmann sensor and active optics technology were used. Then, the coherence of the Fraunhofer diffraction pattern of the two half circulars in visible light was used to detect the piston errors of the segmented mirror, which can be adjusted using active optics technology. The experimental results show that the detection range is infinite and the measurement accuracy is better than ±250 nm. Theoretical, analytical, and experimental results demonstrated that the proposed method is suitable for segmented mirror coarse co-phasing measurement and adjustment.

A system of three mirrors was proposed for beam shaping. This assembly of mirrors can convert a Gaussian distribution spot into a uniform linear distributed toroidal surface with conic and high order terms. Standard surfaces were utilized to control the beam propagation direction and energy distribution. The default merit function and optical modeling were combined to incorporate operands to optimize the systems efficiency. Rectangular and line fields with uniform energy distribution in one direction were successfully demonstrated. This has direct application to illumination or scanning in optical coherence tomography. In the case of the latter, the line is linear within a specific scanning range of 10 mm×11 mm of the first mirror. It can be concluded that this system can facilitate a simpler CT setup and is effective over a wide range of wavelengths.

To meet the increasing demands of Inertial Confinement Fusion (ICF) shock velocity passive measurement, an optical collection system was designed. The 300—800 nm apochromatic optical system was designed using fluoride optical glasses, which have high transmission in the ultraviolet band. The imaging system has two lenses. The light beams between the two lenses were parallel, and the distance between the lenses could be varied to meet the size demands for different assembly positions. The machine visions on both sides of the first lens permit the system to achieve automatic homing. Five parallel laser beams were produced with one of them being on axis and lighting the target to obtain a clear image on the slit of the streak camera, and the other four surrounding the axis, lighting the second lens and focused on the imaging plane. The position of the separated optical elements present after the second lens can be easily fixed with the focusing lasers. The five laser beams can be terminated or turned on automatically. The first lens has an aperture of F/#4.0, and the collection system has a 10 μm object space resolution in 300—800 nm, and a 5 μm object space resolution in 532 nm. This optical system can passively measure the shock velocity by collecting the spectral radiance of the target. It can also act as the common path to transmit the probe laser and receive the Doppler-shift singles of VISAR simultaneously. This optical system could meet the demand of measuring the ICF shock velocity.

The maximum likelihood detection algorithm produces the best bit error performance in optical spatial modulation systems, but the complexity of the decoding process limits its application in practical settings. To address this deficiency, a transmission signal with sparse characteristics was generated by mapping an optical spatial modulation laser vector and a pulse position modulation vector together. A compressed sensing-based optical spatial modulation signal detection method was proposed based on the resulting transmission signal with sparse characteristics and the utilization of an orthogonal matching pursuit algorithm. Simulation results show that this method can greatly reduce computational complexity at the expense of a small increase in the performance error. In comparison with the maximum likelihood detection algorithm, the complexity of the proposed method is reduced by 99.54% when 64 lasers are used. Moreover, this approach is more suitable for wireless optical communication systems with large-scale lasers, due to the introduction of sparsity.

Aiming at fulfilling the requirement of 25 Hz high scanning frequency of the airborne whiskbroom scanning spectrometer, a swing mirror with three reflectors is designed. Firstly, by revealing the relationship between the reflection area and the deviation of the rotation axis and comparing performance of several swing mirrors with the different numbers of reflectors. The arrangement of three reflectors around the rotation axis of swing mirror was adopted. Thus rotating at a speed of 600 r/min the mirror was able to achieve 1 800 scans within a single minute. At the same time, the swing mirror used uniform rotation to reduce the difficulty in controlling and improve the stability of the sweep-scanning system. Through topology optimization and integrated optimization, the weight of the swing mirror was reduced to 5.28 kg and the lightweight ratio was 58%. Finally, calculated by the finite element simulation, the RMS of the reflector was better than λ/20 in 1 g gravity and centrifugal force at 600 r/min rotate speed environment and the fundamental frequency is 1 199 Hz. This verifies that the swing mirror is of high mechanical stability. It can satisfy the higher scanning frequency requirement of airborne whiskbroom scanning spectrometer.

In order to reduce the influence of the geometrical error of rotary axis on the accuracy of turntable-tilting head type five-axis machine tools, a Position-Independent Geometric Error(PIGE) measurement and identification method based on the measurement of the Double Ball Bar(DBB) is proposed. Firstly, based on multi-body system theory and homogeneous coordinate transformation method, the position-independent geometric error model of turntable-tilting head type five-axis machine tool position was established. Four measurement model based on circular path measurement were established according to the influence factors of geometric errors under different motion states of rotary axes, and 10 position-independent geometric errors were identified. Secondly, the numerical simulation was carried out by using the established geometric error model to quantify the influence of 10 geometric errors on the measurement trajectory of the rotary axes. Finally, the geometric error compensation was conducted to validate the validity of the proposed measurement and identification methods, and the measurement trajectories before and after the position-independent geometric error compensation were compared. The average compensation rate of the position-independent geometric errors of the 10 positions is 70.4%, and the maximum compensation rate is 88.4%. The experimental results show that the proposed modeling and identification method can be used to detect the accuracy of rotary axis, at the same time which can be used for the machine tool accuracy evaluation and provide the guidance for improving the accuracy.

Structural stability of vacuum chamber assembly significantly affects alignment precision of laser beam and target in Inertial Confinement Fusion(ICF) facility. Structural stability optimization for vacuum chamber assembly is processed in this paper. Dynamic performance of vacuum chamber assembly was first studied. Natural frequencies and vibration modes were analyzed. Then influences of supporting frusta structural parameters on natural frequencies were investigated. And the model of influence was established. Last, the optimization of the supporting frusta structural parameters was carried out and the first natural frequency was improved to 14.44 Hz that satisfied the stability requirement of the ICF facility.

This study proposes an improved Monte Carlo method, considering that the traditional method lacks precision while calculating the workspace of a robot. The improved Monte Carlo method comprises two stages. In the first stage, a seed workspace is generated using the traditional Monte Carlo method. In the second stage, the seed workspace is expanded based on the normal distribution, and each region in the obtained workspace can be accurately described by setting an accuracy threshold in the process of expansion. Taking into account the characteristics of the normal distribution, to improve the efficiency of the expansion, dynamically adjustable standard deviations are used. Based on the obtained workspace, a voxel algorithm is proposed to determine the volume of the workspace. The algorithm for searching the boundary has been designed to locate the boundary as well as the non-boundary of the workspace. Refining the boundary alone reduces the calculation time and the resulting error. In order to verify the validity and practicability of the algorithm, the improved Monte Carlo method and the proposed volumetric algorithm were simulated and analyzed using a 9-degrees-of-freedom super-redundant serial robot. The results show that when the number of sampling points is the same, the boundary of the workspace generated by the improved Monte Carlo method is smoother and the noise is smaller. When the accurate workspace is obtained, the number of sampling points needed by the improved method is only 4.67% that of the traditional method. The designed volumetric algorithm is also more efficient, with a relative error less than 1%. The volume of workspace thus obtained can be used to evaluate the performance of a serial robot, which lays a theoretical foundation for the subsequent optimization of serial robot configuration.

The clearance size of the air bearing of the dynamic pressure gyro motor is an important index to determine the motor performance and running stability. To improve the precision, automation degree, and rapid batch measurement capacity of the clearance measurement for the dynamic pressure gyro motor, a measurement equipment was developed. In this work, the internal clearance was transformed into an external micro-displacement by applying external force to the rotor. The equipment, designed based on a modular concept, was composed of a clamping module, an automatic force application module, and a displacement measurement module. The clamping module provided resilient double-clamp mounting of the stator, which was conducive to protecting the motor and ensuring smooth force application. The automatic force application module was a 3-D precision motion platform integrating one triaxial force transducer. During measurement, the rotor and force transducer were connected by a pneumatic gripper, thereby the rotor would follow the motion of the platform and generate a relative displacement because of the internal clearance. The force transducer was used for precise control of the force application and the self-aligning centering of the stator. The displacement measurement module was a 2-D precision motion platform integrating double inductance probes, which measured micro-displacement based on a relative measurement principle. Experimental results show that the measurement accuracy is superior to 0.3 μm. The equipment is capable of controllable and continuous force application, making it suitable for batch measurement.

Solution-assisted laser processing was proposed to solve the problem of surface quality in engineering ceramics during laser processing. First, the energy transfer of the laser through a water layer, heat transfer of the laser acting on an underwater solid matter, and action mechanism of the auxiliary laser processing of the hydrostatic water and water jet were analyzed after referring to relevant research at home and abroad. A complex beam-processing system of the parabola jet and ultrasonic-vibration-assisted laser was constructed, and silicon nitride ceramics were tested under different processing conditions. Scanning electron microscopy was used to detect the morphology of the grooves, and the contour of the cut section was observed using a laser-scanning confocal microscope. Our research shows that the absorption and convection of water reduce the effective energy of the laser ablation materials and the etching rate during the water-jet-assisted laser processing of silicon nitride ceramic materials. Simultaneously, owing to the existence of a water layer, the energy distribution in the processing area changes, which results in widening of the notch. The groove depth decreases by 30% and the width increases by 21% when the laser current is 200 A, frequency is 50 Hz, and pulse width is 0.6 ms. Because the water and silicon nitride are hydrolyzed by the laser action, water vapor, material steam, molten particles, bubbles, and others are washed away in the water-flow direction, which is beneficial for the improvement in the processing efficiency and surface quality.

To satisfy the demand of rich-resin defect detection in the so-called low-porosity carbon fiber reinforced composite (CFRP) with porosity close to zero, an ultrasonic testing methodology was proposed in this article. The denoising methods, attenuation suppression method, and 3D imaging technology for rich-resin identification are investigated, and low-porosity CFRP rich-resin detection software was developed. The rich resin was detected in four steps. First, the resonant frequency was estimated, and the high-frequency stochastic noise was suppressed. Second, variational mode decomposition (VMD) was used to separate the resonant structure noise and extract the low-frequency component. The low-frequency component consisted of the front-wall echo, back-wall echo, rich-resin reflection signal, and remaining coherent noise made up of the interlayer reflection signals and material scattering noise. Third, the instant amplitude ratio was introduced to correct the envelop attenuation of the low-frequency component and describe the local reflectivity of the low-porosity CFRP. Finally, the multi-threshold Otsu method was used to search the threshold of the rich-resin detection, resulting in the elimination of interference and finishing the detection of rich resin. Further, multi-view imaging was performed on the test results, and the rich resin was identified in the 3D, C-scan, and B-scan imaging processes. The experimental results show that when the VMD mode was set to two and the classes in the multi-threshold Otsu method are set to three, a rich-resin reflection signal can be detected. When the threshold in the multi-view imaging is set to 0.15, the rich resin can be effectively characterized.

To improve the dynamic performance of a piezoelectric fast steering mirror in the space-telescope image-stabilization system, the dynamic hysteresis compensation and control of a piezoelectric actuator are investigated. According to the inversion complexity of the PI model based on the generalized Play operator and the asymmetry of hysteresis curves, a PI inverse model based on the generalized Stop operator is constructed to compensate the hysteresis nonlinearity. The Hammerstein model is applied to model the dynamic hysteresis of the piezoelectric actuator and to describe the static nonlinearity and rate-dependent properties of the Hammerstein hysteresis model using the generalized PI and auto-regressive exogenous models, respectively. A compound counter strategy that combines the feedforward compensation and linear quadratic Gauss (LQG) optimal control algorithm is proposed to solve the hysteresis rate dependent model uncertainty. The adaptive differential evolution algorithm is used to identify the model parameters and tune the controller parameters. The test results show that the dynamic hysteresis model can effectively describe the hysteresis curve of the piezoelectric actuator in the frequency range of 1—100 Hz, fitting tracking root mean square errors from 0.077 1 μm (at 1 Hz) to 0.512 3 μm (at 100 Hz), and relative errors from 0.003 1 (at 1 Hz) to 0.020 9 (at 100 Hz). The tracking accuracy of the LQG control algorithm increases by 48.6% and 27.02%, respectively, compared with the direct feedforward and PID controls, in the real-time tracking of the variable-frequency target displacement with an amplitude of 24.5 μm.

The light and small pan-tilt system of a multi-rotor unmanned aerial vehicle is a complicated electromechanical servo system that requires a number of desirable properties such as light weight and rapid response to be realized. We focus on the inherent limitations of the traditional methods in the electromechanical servo-system design to present a multi-objective optimization method for a mechatronic system based on the bandwidth. For the structure system, seven-dimensional parameters were selected using sensitivity analysis in which the mass and first-order natural frequency are considered as the optimization goals. For the control system, the controller parameters in the rate and position loops are selected as design variables in which the rise time, regulating time, and integral absolute error (IAE) are considered as the optimization goals. In the optimization process, an optimization method that combines the approximation model and multi-objective genetic algorithm is proposed to reduce the complexity, improve the efficiency, and improve the overall optimization ability during the optimization process. Simulations are then conducted to verify the proposed method. The results show that, compared with the original model of the control and structure systems, the mass, IAE, regulating time, and rise time are reduced by 8.8%, 54.8%, 81.9%, and 53.4%, respectively. Finally, a modal pan-tilt experiment is carried out by hammering, and the error in the experimental and optimization results is found to be 13.4%. Therefore, the optimization method is effective.

Following the increase in the aperture of space optical telescopes, the inconsistency in space and ground mechanical environment results in serious degradation of the system image quality in orbit. Gravity needs to be compensated during the alignment and test procedure of a space telescope. However, quantitative analysis and optimization methods for unloading the point coordinate have not been completely developed. First, the deformation mechanism of a large-aperture telescope under 1g gravity was studied. According to the separate closed-loop location and closed-loop mass, a mathematical algorithm was developed to optimize the position of the unloading points. By performing co-simulation, we were able to optimize the coordinates. Subsequently, simulation experiments were performed to verify the actual effect of the telescope model under different unloading parameters. The closed-loop location analysis method improves the displacement of the highly sensitive optical components from 370 μm, 36″ to 72.9 μm, 0.3″. The maximum relative deviation of the closed-loop mass method from the set value is approximately 74%. The closed-loop location gravity-compensation method can achieve a value closer to 0 g. However, its error sensitivity is high, and realizing the engineering results is difficult. The total unloading rate is approximately 75% based on the closed-loop mass gravity-compensation model, and the sensitivity is low. This model can satisfy the demand for mechanical environment of semi-physical simulation experiments using gravity unloading.

Traditional template matching methods suffer from heavy occlusion, intense background change and non-rigid deformation. A multi-scale saliency template matching method is proposed in this article in order to deal with such conditions. The method extracted saliency and multi-scale features in parallel. On the one hand, the template and the target images were first divided into grids of different scales using spatial pyramid model. Deformable Diversity Similarity (DDIS) was calculated under such different grids. On the other hand, saliency map of the template image was calculated using saliency segmentation method. Such saliency map s are then used to weight the scores calculated by DDIS under different grids. Finally, the final score map is calculated by fusing the score maps under different grids. The method proposed achieves 2.9% AUC(Area Under Curve) improvement compared with original DDIS method. Experiments show that salient object segmentation helps the method to focus more on object than background, therefore improve the robustness to background changes and occlusion. Besides, spatial pyramid model makes the method to consider information from different scale, for example, local contours and structural features of an object. Combining these two factors raises the matching accuracy significantly.

Majority of the techniques currently employed for detecting illegal videos were primarily based on video content analysis, which in turn was computationally intensive and cannot be applied for real-time detection of large video traffic flow. To address the said issue, the novel approach of real-time video source identification was proposed in this paper for preventing the transmission of illegal videos. In contrast to conventional approached based on content analysis, the proposed approach blocked the transmission of potentially illegal videos by detecting the camera sources that had previously produced illegal videos. If a video call was performed from a cellphone that had previously produced illegal videos, a security warning was issued for blocking the video transmission. The proposed approach was based on the assumption that video streams were more likely to be illegal if they have been produced from a camera that was used earlier for distributing illegal videos. Hence, the primary objective of the proposed approach was to develop a real-time and reliable method for camera source identification. To achieve this two objective, the following three aspects were taken into consideration. Firstly, a database comprising hundred videos was established for evaluation. The videos in the database were obtained from twenty-five cellphones of various brands and models, wherein each video indicated the corresponding camera source. Secondly, a simplified and feasible method was developed for real-time video source identification. Finally, a novel feature decision-making model with multiple integrated fingerprint features was incorporated for enhancing the reliability of video source identification. Obtained experimental results indicate that the proposed approach for camera source identification can meet real-time requirements and is significantly superior to conventional approaches. Furthermore, in the case of Android cellphones, the accuracy of camera source identification is found to be 98.161%, which corroborates the feasibility of the proposed approach.

A novel image coloration fusion method in oRGB color space is presented for the enhancement of thermal infrared and visible night-vision images. We chose to construct a pseudo-color image and to execute color transfer in the same oRGB color space because this new space is reasonably simple, has intuitive axes, and is well-suited for computational applications. In order to preserve detail information from the source images in the final coloration image, the luminance component of the pseudo-color image is assigned to the fused image by a multi-scale top-hat transform. The two color components of the pseudo-color image are constructed according to the differences between the dual-band images and the color statistics of the reference image. To avoid gray-scale compression or over-saturation that can be easily caused by the linear color transfer method, we applied histogram matching rather than linear transformation to the two color components in the color transfer process. Experiments show that our method produces a coloration image with a more natural color distribution. Furthermore, visual features such as salient features and overall brightness and contrast are superior to those produced by other methods.

In order to realize the over fitting problem existing in Back Propagation (BP) neural networks, a neural prediction model based on Pearson correlation was designed. It replaces the error function in a BP neural network based on error back propagation with the Pearson correlation function. By means of gradient ascent, the adjustment of connection weights and biases in training process is derived. Meanwhile, momentum is added to this adjustment to improve the convergence speed of the network. The Pearson correlation BP prediction model is built with weight threshold limiting and an increasing learning rate to prevent overfitting. Time series prediction experiments on a standard dataset were performed. The results demonstrate that compared with improved the radial basis function and BP neural networks, the Pearson correlation BP neural network reduces root-mean-square error, and time to convergence in multi-factor time series prediction. Therefore, the Pearson correlation BP neural network realizes the integration of correlation analysis with neural networks, is able to ensure efficiency, and can solve fitting problems in the same time as other methods with higher accuracy.

In the domain of single image super-resolution, algorithms based on Gaussian process regression neither exploit the association relationships among similar patches, nor do they discriminate between these patches with similar properties to augment the volume of the training set, which leads to obvious noise and artifacts in reconstructed high-resolution images. To overcome this problem, a new super-resolution algorithm based on multi-task Gaussian process regression is proposed. This algorithm introduces the idea of multi-task learning to partition the input low-resolution image into overlapped patches and considers the super-resolution process of each patch as a task. In the process of modeling similar tasks, the parameter set obtained by optimal solving for representing the commonness and difference gives generalization ability, improves prediction accuracy improved, makes the reconstructed high-resolution image clear and sharp, and suppresses noise and artifacts significantly. A large number of experiments to process common testing images and a public image test set subjectively and objectively demonstrate that this algorithm is superior to similar state of the art algorithms, and the peak signal to noise ratio is approximately 0.5 dB higher than that of other common super-resolution algorithms.

In order to solve the problem that energy image species (EIS) are susceptible to human movement time and position shift, i.e., it is difficult to describe the details of human behaviors, in this paper a method of human behavior recognition was present based on pyramid gradient histogram (PHOG) fusion features and a multi-class Adaboost classifier. This method first calculated the average motion energy image (AMEI) and the enhanced motion energy image (EMEI) of an objects silhouette images after human body registration, and then it extracted the PHOG features of AMEI and EMEI and series them together to form a kind of multi-level feature descriptor of human behavior. Finally, a look-up table-based real Adaboost (LUT-Real Adaboost) algorithm was utilized to realize human behavior recognition by designing a multi-class classifier. Experimental results show that the correct recognition rate in typical depth-included human action datasets is 97.6% by using this method, which is higher than that of other classifiers using single feature description and support vector machine. This reveals that, by combining global and local features, the proposed method can effectively describe the detailed active features of human behavior at different scales, enhance the description ability of human behavior characteristics, and improve recognition performance.

Considering the problem that traditional image enhancement methods based on a normalized incomplete beta function (NIBF) have difficultly obtaining optimal parameters automatically and that enhancement effects are limited by the dynamic range of the image, a method of NIBF remote sensing image automatic enhancement based on an adaptive quantum genetic algorithm was proposed. First, from the image color depth, the maximum and minimum spectral measurement levels were introduced into the image to be enhanced to expand its dynamic range. Secondly, the parameters of NIBF were encoded into quantum chromosomes using quantum bits, and several quantum chromosomes were set as the initial parameter population. The parameter population was measured and decoded, the decoded value was input as a parameter of NIBF, and the image was transformed by spectral measure to obtain the corresponding enhanced image population. Then, edge images of each individual in the enhanced image population were extracted using the eight-direction edge detection template. The fitness function of individual quality in the parameter population was defined by edge intensity, edge number, and entropy measure, and each parameter in the parameter population was evaluated and retained, the best parameters of individuals were recorded. In the proposed evolutionary strategy, the quantum rotation gate was used to evolve the quantum chromosomes toward to the direction of maximum fitness level, and the size of the quantum rotation angle was adaptively adjusted according to the difference of each generations fitness and evolutionary algebra. The best parameters of NIBF were the individuals with the most fitness in the finally evolved parameter population, and the corresponding spectral measure transformation curve was generated to determine the mapping relationship between the input and output spectral measure, so optimal automatic enhancement of the image was achieved. The blind/referenceless image spatial quality assessment indicators increase by 122.2%, the natural image quality assessment indicators increased by 71.8%, and the running time is 10.758 s. The proposed algorithm satisfies the requirements of automation, robustness, and high efficiency in remote sensing image enhancement.

Natural color fusion of long-wave infrared and visible images is helpful to observers for quick perception of the environment and discovery of targets. The coaxial optical structure enjoys the unique advantages in terms of improving the quality of the fused image and simplifying the registration algorithm. According to the requirements in battlefield detection, surveillance, and so on, a real-time coaxial color fusion system based on TMS320DM642 DSP and ATmega32 MCU was designed in the present work. In the fusion system, the long-wave infrared imaging system is combined in parallel with the visible imaging system. The optical path was separated by a beam splitter and a mirror. The dual-band images were absolutely registered using field-of-view matching and optical-axis calibration. The image processing program was developed based on a DSP/BIOS embedded operating system. A natural color fusion algorithm based on a CbCr look-up table was adopted according to the features of the video image. The fusion algorithm was optimized according to the hardware features. The fusion time of a single-frame image with resolution of 720 pixel×576 pixel is 9.18 ms, which satisfies the real-time fusion requirements of 25 frames per second. The fusion image exhibits a natural color, and the hot targets in the scene can be readily identified. The research on related problems presented in this paper can provide references to the design and implementation of a coaxial image fusion system.

To solve the problems of irregular hole shape and low accuracy of surface registration in an incomplete 3D model-repair process, a model-repair method was proposed to effectively maintain the natural hole boundary and restore the model surface details. First, we traced all points with unequal numbers of one-neighborhood edges and one-neighborhood triangles to detect the hole boundaries of the model. A method based on 2D mesh numbers was proposed to determine the matching candidate set for the incomplete model, and the optimal matching model was predicted based on the vertex-position error, edge-transition error, and orthogonal constraint, which were represented by double sparstiy. Then, the curvature was combined, cosine value of the boundary contour angle, and length of the line segments of the adjacent boundary points centered on the same boundary vertex to construct the feature descriptors that can effectively express the alignment relationship between the incomplete and fragmented models. Finally, the second-order umbrella operator was used to make the repair boundary of the model smooth. The experimental results demonstrate that the repair time is reduced by 19%—26% and the repair error is reduced by 35%, on average. This method avoids the limitations of large cracks and poorly realized surface details of the current model-restoration methods and can quickly and effectively repair the damaged model.