In this paper, we propose a new complex-valued dense atrous neural network (CDANN) for phase-only hologram (POH) generation. The network architecture integrates a complex-valued partial convolut

In this paper, we propose a new complex-valued dense atrous neural network (CDANN) for phase-only hologram (POH) generation. The network architecture integrates a complex-valued partial convolution (C-PConv) module into the down-sampling stages of dual U-Net structures, enhancing computational efficiency through selective channel-wise processing. To improve feature extraction, we introduce a novel complex-value dense atrous convolution (DAC) module, which employs four cascaded branches with multi-scale atrous convolutions to capture intricate features while maintaining spatial resolution. Additionally, we integrate a spatial pyramid pooling (SPP) module into the U-Net architecture to encode multi-scale contextual features derived from the DAC module. This hierarchical integration expands the U-Net’s receptive field while facilitating cross-layer feature fusion. The proposed method achieves an average signal-to-noise ratio (PSNR) of 32.19 dB and an average structural similarity index (SSIM) of 0.892 within a running time of 24 ms, outperforming conventional approaches. Experiments confirm significant improvements in both reconstruction quality and computational efficiency, making CDANN suitable for real-time holographic displays.show less

In past two decades, optogenetic technology has developed to be the most accurate method for investigating or treatment of neural correlated diseases. Currently, the applications of optogenetic

In past two decades, optogenetic technology has developed to be the most accurate method for investigating or treatment of neural correlated diseases. Currently, the applications of optogenetic technology have been expanded from the initial central nervous system to peripheral nervous system, circulatory system, locomotor system, alimentary system, urinary system and so on. This review summarized the recent progresses of optogenetic technology in biomedical applications through two categories: to activate neural impulse or to inhibit neural impulse. The involved diseases include Alzheimer's disease, Ischemic stroke, Parkinson's disease, Epilepsy, Spinal cord injury, Cardiac arrhythmias, chronic kidney disease etc. Furthermore, the basic and clinical researches of optogenetic technology in visual restoration are highlighted. Eventually, the challenges of optogenetic technology for clinical applications are discussed.show less

Ce3+-doped gadolinium-based borosilicate (GBSCx) glass scintillators with ultra-high concentration of 16 mol% were synthesized in ambient atmosphere for future Calorimeter application. The valen

Ce3+-doped gadolinium-based borosilicate (GBSCx) glass scintillators with ultra-high concentration of 16 mol% were synthesized in ambient atmosphere for future Calorimeter application. The valence state of Ce was preciously controlled in the glass by X-ray absorption near edge structure (XANES) spectrum. With the increased Ce3+ concentration, the NBO/BO ratio decreases notably from 5.15 to 0.56. The GBSCx glass scintillators exhibit the broad photoluminescence (PL) band within 350-550 nm regions, with a maximum PL quantum yield (PL QY) of 60.6%. In X-ray excited luminescence (XEL), the integral intensity of the GBSC2 glass is 18.4% compared to the BGO crystal. Meanwhile, it has the highest light yield of 1043 ph/MeV with an energy resolution of 28.4% at 662 keV under γ-ray excitation. When the doped concentration of Ce3+ exceeds 4 mol%, the proportion of light yield within 1 μs integral gate exceeds 95%, which conforms to the requirement of fast time response. Interestingly, the concentration quenching effect of high concentration Ce3+ (x≤14) does not occur in the glass scintillators under γ-ray excitation. With the increase of Ce3+ concentration, both the fast (100-18 ns) and slow (1000-59 ns) components of scintillation decay time decrease dramatically. Therefore, the developed GBSCx glass scintillators, featured with the reasonable light yield and fast time response, have a promising application in future high energy physics (HEP) experiments.show less

Robust three-dimensional (3D) recognition across different viewing angles is crucial for dynamic applications such as autonomous navigation and augmented reality; however, the application of the

Robust three-dimensional (3D) recognition across different viewing angles is crucial for dynamic applications such as autonomous navigation and augmented reality; however, the application of the technology remains challenging owing to factors such as orientation, deformation, and noise. Wave-based analogous computing, particularly diffraction neural networks, constitutes a scan-free, energy-efficient means of mitigating these issues with strong resilience to environmental disturbances. Herein, we present a real-time all-directional 3D object recognition and distortion correction system using a deep knowledge prior diffraction neural network (DNN). Our approach effectively addressed complex two-dimensional (2D) and 3D distortions by optimizing the metasurface parameters with minimal training data and refining them using diffraction neural networks. Experimental results demonstrate that the system can effectively rectify distortions and recognizes objects in real time, even under varying perspectives and multiple complex distortions. In 3D recognition, the prior DNN reliably identifies both dynamic and static objects, maintaining stable performance despite arbitrary orientation changes, highlighting its adaptability to complex and dynamic environments. Our system can function either as a preprocessing tool for imaging platforms or as a stand-alone solution, facilitating 3D recognition tasks such as motion sensing and facial recognition. It offers a scalable solution for high-speed recognition tasks in dynamic and resource-constrained applications.show less