GedankenNet: Self-supervised learning of hologram reconstruction using physics consistency

The past decade has witnessed transformative applications of deep learning in various computational imaging, sensing and microscopy tasks. Due to the supervised learning schemes employed, most of these methods depend on large-scale, diverse, and labeled training data. The acquisition and preparation of such training image datasets are often laborious and costly, also leading to biased estimation and limited generalization to new types of samples. Here, we report a self-supervised learning model, termed GedankenNet, that eliminates the need for labeled or experimental training data, and demonstrate its effectiveness and superior generalization on hologram reconstruction tasks. Without prior knowledge about the sample types to be imaged, the self-supervised learning model was trained using a physics-consistency loss and artificial random images that are synthetically generated without any experiments or resemblance to real-world samples. After its self-supervised training, GedankenNet successfully generalized to experimental holograms of various unseen biological samples, reconstructing the phase and amplitude images of different types of objects using experimentally acquired test holograms. Without access to experimental data or the knowledge of real samples of interest or their spatial features, GedankenNet's self-supervised learning achieved complex-valued image reconstructions that are consistent with the Maxwell's equations, meaning that its output inference and object solutions accurately represent the wave propagation in free-space. This self-supervised learning of image reconstruction tasks opens up new opportunities for various inverse problems in holography, microscopy and computational imaging fields.Comment: 30 pages, 6 Figure

All-optical image denoising using a diffractive visual processor

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.Comment: 21 Pages, 7 Figure

A Survey of Bioinspired Jumping Robot: Takeoff, Air Posture Adjustment, and Landing Buffer

A bioinspired jumping robot has a strong ability to overcome obstacles. It can be applied to the occasion with complex and changeable environment, such as detection of planet surface, postdisaster relief, and military reconnaissance. So the bioinspired jumping robot has broad application prospect. The jumping process of the robot can be divided into three stages: takeoff, air posture adjustment, and landing buffer. The motivation of this review is to investigate the research results of the most published bioinspired jumping robots for these three stages. Then, the movement performance of the bioinspired jumping robots is analyzed and compared quantitatively. Then, the limitation of the research on bioinspired jumping robots is discussed, such as the research on the mechanism of biological motion is not thorough enough, the research method about structural design, material applications, and control are still traditional, and energy utilization is low, which make the robots far from practical applications. Finally, the development trend is summarized. This review provides a reference for further research of bioinspired jumping robots

Configuration Synthesis and Performance Analysis of Finger Soft Actuator

Compared with the traditional rigid finger actuator, the soft actuator has the advantages of light weight and good compliance. This type of finger actuator can be used for data acquisition or finger rehabilitation training, and it has broad application prospects. The motion differences between the soft actuator and finger may cause extrusion deformation at the binding point, and the binding forces along nonfunctional direction may reduce drive efficiency. In order to reduce the negative deformation of soft structure and improve comfort, the configuration synthesis and performance analysis of the finger soft actuator were conducted for the present work. The configuration synthesis method for soft actuator was proposed based on the analysis of the physiological structure of the finger, and the soft actuator can make the human-machine closed-loop structure including n joints (n=1, 2, 3) meet the requirement of DOF (degrees of freedom). Then the typical feasible configurations were enumerated. The different typical configurations were analyzed and compared based on the establishment of mathematical models and simulation analysis. Results show that the configuration design method is feasible. This study offers a theoretical basis for designing the configuration of finger soft actuator

Annales de la Société Royale des Sciences Médicales et Naturelles de Bruxelles

We consider the expansion of co-compact convex hypersurfaces in Minkowski space by functions of their curvature, and prove under quite general conditions that solutions are asymptotic to the self-similar expanding hyperboloid. In particular this implies a convergence result for a class of special solution of the cross-curvature ow of negatively curved Riemannian metrics on three-manifolds

Stress-dilatancy behaviors of coarse granular soils in three-dimensional stress space

The influence of the intermediate principal stress ratio (i.e., the b-value) on the stress-dilatancy relation of coarse granular soils was investigated by a series of true triaxial compression tests conducted under the constant-. p\u27 and constant-. b loading conditions. It was observed that the peak and critical state friction angles increased with decreasing confining pressure or increasing b-value. An increase in the confining pressure or b-value resulted in a decrease in the maximum dilatancy angle. In addition, the relationship between the excess friction angle (i.e., the difference between the peak and critical state angles) and maximum dilatancy angle was greatly influenced by the b-value. A linear equation incorporating the effect of the b-value was proposed for this stress-dilatancy relationship. The perditions of the proposed equation could be in good agreement with the test data of coarse granular soils. The proposed b-dependent stress-dilatancy equation could be useful in the foundation of a constitutive model for granular soils

Creep behavior of EPS composite soil

EPS composite soil is one type of premixed lightweight fills studied by numerous researchers. However, one aspect that has not been fully understood is the creep behaviors which may have significant effect on the design and application of EPS composite soil. In this paper, the results of a series of oedometer creep tests and triaxial undrained creep tests on EPS composite soil were presented. Four main influencing factors were identified and their effects on the creep behaviors of EPS composite soil were studied. Three well established creep models, namely, Findley model, Singh & Mitchell model, and Mesri model, were used to simulate the creep behavior of EPS composite soil. This study shows that the Findley creep model fits the test results the best. A semi-empirical creep model was also proposed to model the creep behavior under axisymmetric conditions. In this model, the creep strain was divided into instant and viscous elastic strain as well as instant and viscous plastic strain which were simulated by element models and empirical equations, respectively. It was shown that the proposed creep model was able to precisely predict the creep strain of EPS composite soil