The Shack-Hartmann wavefront sensor (SHWS) is an essential tool for wavefront sensing in adaptive optical microscopes. However, the distorted spots induced by the complex wavefront challenge its detection performance. Here, we propose a deep learning based wavefront detection method which combines point spread function image based Zernike coefficient estimation and wavefront stitching. Rather than using the centroid displacements of each micro-lens, this method first estimates the Zernike coefficients of local wavefront distribution over each micro-lens and then stitches the local wavefronts for reconstruction. The proposed method can offer low root mean square wavefront errors and high accuracy for complex wavefront detection, and has potential to be applied in adaptive optical microscopes.
To maximize signal collection in nonlinear optical microscopy, non-descanned epi-detection is generally adopted for in vivo imaging. However, because of severe scattering in biological samples, most of the emitted fluorescence photons go beyond the collection angles of objectives and thus cannot be detected. Here, we propose an extended detection scheme to enhance the collection of scattered photons in nonlinear fluorescence microscopy using a silicon photomultiplier array ahead of the front apertures of objectives. We perform numerical simulations to demonstrate the enhanced fluorescence collection via extended epi-detection in the multi-photon fluorescence imaging of human skin and mouse brain through craniotomy windows and intact skulls. For example, with red fluorescence emission at a depth of 600 μm in human skin, the increased collection can be as much as about 150% with a 10×, 0.6-NA objective. We show that extended epi-detection is a generally applicable, feasible technique for use in nonlinear fluorescence microscopy to enhance signal detection.
Most existing light field (LF) super-resolution (SR) methods either fail to fully use angular information or have an unbalanced performance distribution because they use parts of views. To address these issues, we propose a novel integration network based on macro-pixel representation for the LF SR task, named MPIN. Restoring the entire LF image simultaneously, we couple the spatial and angular information by rearranging the four-dimensional LF image into a two-dimensional macro-pixel image. Then, two special convolutions are deployed to extract spatial and angular information, separately. To fully exploit spatial-angular correlations, the integration resblock is designed to merge the two kinds of information for mutual guidance, allowing our method to be angular-coherent. Under the macro-pixel representation, an angular shuffle layer is tailored to improve the spatial resolution of the macro-pixel image, which can effectively avoid aliasing. Extensive experiments on both synthetic and real-world LF datasets demonstrate that our method can achieve better performance than the state-of-the-art methods qualitatively and quantitatively. Moreover, the proposed method has an advantage in preserving the inherent epipolar structures of LF images with a balanced distribution of performance.
A single strategy used in the firefly algorithm (FA) cannot effectively solve the complex optimal scheduling problem. Thus, we propose the FA with division of roles (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy Cauchy mutation; the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.
Target tracking is one of the hottest topics in the field of drone research. In this paper, we study the multiple unmanned aerial vehicles (multi-UAV) collaborative target tracking problem. We propose a novel tracking method based on intention estimation and effective cooperation for UAVs with inferior tracking capabilities to track the targets that may have agile, uncertain, and intelligent motion. For three classic target motion modes, we first design a novel trajectory feature extraction method with the least dimension and maximum coverage constraints, and propose an intention estimation mechanism based on the environment and target trajectory features. We propose a novel Voronoi diagram, called MDA-Voronoi, which divides the area with obstacles according to the minimum reachable distance and the minimum steering angle of each UAV. In each MDA-Voronoi region, the maximum reachable region of each UAV is defined, the upper and lower bounds of the trajectory coverage probability are analyzed, and the tracking strategies of the UAVs are designed to effectively reduce the tracking gaps to improve the target sensing time. Then, we use the Nash Q-learning method to design the UAVs’ collaborative tracking strategy, considering factors such as collision avoidance, maneuvering constraints, tracking cost, sensing performance, and path overlap. By designing the reward mechanism, the optimal action strategies are obtained as the control input of the UAVs. Finally, simulation analyses are provided to validate our method, and the results demonstrate that the algorithm can improve the collaborative target tracking performance for multiple UAVs with inferior tracking capabilities.
This study focuses on the multiphase flow properties of crowd motions. Stability is a crucial forewarning factor for the crowd. To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd, a novel motion structure analysis model is established based on purposiveness, and is used to describe the continuity of pedestrians’ pursuing their own goals. We represent the crowd with self-driven particles using a destination-driven analysis method. These self-driven particles are trackable feature points detected from human bodies. Then we use trajectories to calculate these self-driven particles’ purposiveness and select trajectories with high purposiveness to estimate the common destinations and the inherent structure of the crowd. Finally, we use these common destinations and the crowd structure to evaluate the behavior of newly arriving pedestrians and crowd stability. Our studies show that the purposiveness parameter is a suitable descriptor for middle-density human crowds, and that the proposed destination-driven analysis method is capable of representing complex crowd motion behaviors. Experiments using synthetic and real data and videos of both human and animal crowds have been conducted to validate the proposed method.
Multicomponent oxide (GaxIn1-x)2O3 films are prepared on (0001) sapphire substrates to realize a tunable band-gap by magnetron sputtering technology followed by thermal annealing. The optical properties and band structure evolution over the whole range of compositions in ternary compounds (GaxIn1-x)2O3 are investigated in detail. The X-ray diffraction spectra clearly indicate that (GaxIn1-x)2O3 films with Ga content varying from 0.11 to 0.55 have both cubic and monoclinic structures, and that for films with Ga content higher than 0.74, only the monoclinic structure appears. The transmittance of all films is greater than 86% in the visible range with sharp absorption edges and clear fringes. In addition, a blue shift of ultraviolet absorption edges from 380 to 250 nm is noted with increasing Ga content, indicating increasing band-gap energy from 3.61 to 4.64 eV. The experimental results lay a foundation for the application of transparent conductive compound (GaxIn1-x)2O3 thin films in photoelectric and photovoltaic industry, especially in display, light-emitting diode, and solar cell applications.
A set of semiconductor laser pulse seed sources based on an embedded chip is proposed. The greatest feature is that the optical pulse frequency and width can be independently adjusted in real time. The pulse seed sources can be switched independently and online from the gain-switched mode to the quasi-continuous wave mode to obtain optimal optical parameters for specific applications. To explore the physical mechanism of the semiconductor laser source, the rate equation that describes the carrier-photon transient change in a semiconductor laser cavity is numerically derived and solved. Subsequently, problems that need to be considered while designing the drive circuit are identified. The system evaluation indicates that the optical pulse frequency adjustment range is 250 Hz to 42 MHz, and the narrowest optical pulse output width is 80 ps. The pulse seed source can drive semiconductor lasers with different central wavelengths (1064, 1550, and 1970 nm), and can also simultaneously drive two semiconductor lasers and output dual-band optical pulses. It can be used as a seed source for general high-power optical systems, and exhibits good application value and extensive market prospects.
In the semiconductor manufacturing industry, the dynamic model of a controlled object is usually obtained from a frequency sweeping method before motion control. However, the existing isolators cannot properly isolate the disturbance of the inertial force on the platform base during frequency sweeping (the frequency is between 0 Hz and the natural frequency). In this paper, an adjustable anti-resonance frequency controller for a dual-stage actuation semi-active vibration isolation system (DSA-SAVIS) is proposed. This system has a significant anti-resonance characteristic; that is, the vibration amplitude can drop to nearly zero at a particular frequency, which is called the anti-resonance frequency. The proposed controller is designed to add an adjustable anti-resonance frequency to fully use this unique anti-resonance characteristic. Experimental results show that the closed-loop transmissibility is less than −15 dB from 0 Hz to the initial anti-resonance frequency. Furthermore, it is less than −30 dB around an added anti-resonance frequency which can be adjusted from 0 Hz to the initial anti-resonance frequency by changing the parameters of the proposed controller. With the proposed controller, the disturbance amplitude of the payload decays from 4 to 0.5 mm/s with a reduction of 87.5% for the impulse disturbance applied to the platform base. Simultaneously, the system can adjust the anti-resonance frequency point in real time by tracking the frequency sweeping disturbances, and a good vibration isolation performance is achieved. This indicates that the DSA-SAVIS and the proposed controller can be applied in the guarantee of an ultra-low vibration environment, especially at frequency sweeping in the semiconductor manufacturing industry.
This study analyzes the problem of robust stability of fractional-order delay systems of neutral type under actuator saturation. A Lyapunov–Krasovskii (LK) function is constructed and conditions of the asymptotic robust stability of such systems are given, which are formulated by linear matrix inequalities (LMIs), using the Lyapunov direct method. An algorithm is introduced to compute the gain of the state feedback controller for extending the domain of attraction. The theoretical results are validated using some numerical examples.