A5101664962- Neural Networks and Reservoir Computing
- Photonic and Optical Devices
- Advanced Fluorescence Microscopy Techniques
- Optical Network Technologies
- Liver Disease Diagnosis and Treatment
- Adipose Tissue and Metabolism
- Cell Image Analysis Techniques
- Optical Coherence Tomography Applications
- Photoacoustic and Ultrasonic Imaging
- Advanced Memory and Neural Computing
- Advanced Optical Sensing Technologies
- Spinal Cord Injury Research
- Circadian rhythm and melatonin
- CCD and CMOS Imaging Sensors
- Electronic Packaging and Soldering Technologies
- Neural dynamics and brain function
- Birth, Development, and Health
- Acute Ischemic Stroke Management
- Advanced Neural Network Applications
- Parallel Computing and Optimization Techniques
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Selenium in Biological Systems
- Pancreatic function and diabetes
- Dietary Effects on Health
- Lipid metabolism and disorders
Nanjing Medical University
2013-2025
Jiangsu Province Hospital
2018-2025
Tsinghua University
2018-2025
Anhui Medical University
2025
National Clinical Research Center for Digestive Diseases
2025
Powerchina Huadong Engineering Corporation (China)
2025
PowerChina (China)
2025
Xiamen University
1995-2024
Sir Run Run Shaw Hospital
2024
National Engineering Research Center for Information Technology in Agriculture
2022-2024
In this Letter we propose the Fourier-space diffractive deep neural network (F-D^{2}NN) for all-optical image processing that performs advanced computer vision tasks at speed of light. The F-D^{2}NN is achieved by placing extremely compact modulation layers Fourier plane or both and imaging planes an optical system, where nonlinearity introduced from ferroelectric thin films. We demonstrated can be trained with learning algorithms saliency detection high-accuracy object classification.
Training an artificial neural network with backpropagation algorithms to perform advanced machine learning tasks requires extensive computational process. This paper proposes implement the algorithm optically for in situ training of both linear and nonlinear diffractive optical networks, which enables acceleration speed improvement energy efficiency on core computing modules. We demonstrate that gradient a loss function respect weights layers can be accurately calculated by measuring forward...
Abstract Photonic computing enables faster and more energy-efficient processing of vision data 1–5 . However, experimental superiority deployable systems remains a challenge because complicated optical nonlinearities, considerable power consumption analog-to-digital converters (ADCs) for downstream digital vulnerability to noises system errors 1,6–8 Here we propose an all-analog chip combining electronic light (ACCEL). It has systemic energy efficiency 74.8 peta-operations per second watt...
Abstract A fundamental challenge in fluorescence microscopy is the photon shot noise arising from inevitable stochasticity of detection. Noise increases measurement uncertainty and limits imaging resolution, speed sensitivity. To achieve high-sensitivity beyond shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time suppression. Based on our previous framework DeepCAD, reduced number network parameters by 94%, memory consumption 27-fold processing time...
Abstract Planar digital image sensors facilitate broad applications in a wide range of areas 1–5 , and the number pixels has scaled up rapidly recent years 2,6 . However, practical performance imaging systems is fundamentally limited by spatially nonuniform optical aberrations originating from imperfect lenses or environmental disturbances 7,8 Here we propose an integrated scanning light-field sensor, termed meta-imaging to achieve high-speed aberration-corrected three-dimensional...
The pursuit of artificial general intelligence (AGI) continuously demands higher computing performance. Despite the superior processing speed and efficiency integrated photonic circuits, their capacity scalability are restricted by unavoidable errors, such that only simple tasks shallow models realized. To support modern AGIs, we designed Taichi-large-scale chiplets based on an diffractive-interference hybrid design a distributed architecture has millions-of-neurons capability with...
The cultivated [Glycine max (L) Merr.] and wild soja Siebold & Zucc.] soybean species comprise wide variation in seed composition traits. Compared to soybean, contains low protein, high oil, sucrose. In this study, an interspecific population was derived from a cross between G. (Williams 82) (PI 483460B). This recombinant inbred line (RIL) of 188 lines sequenced at 0.3× depth. Based on 91 342 single nucleotide polymorphisms (SNPs), recombination events RILs were defined, high-resolution bin...
Reviewing the history of development artificial intelligence (AI) clearly reveals that brain science has resulted in breakthroughs AI, such as deep learning. At present, although developmental trend AI and its applications surpassed expectations, an insurmountable gap remains between human intelligence. It is urgent to establish a bridge research, including link from connection knowing simulating brain. The first steps toward this goal are explore secrets by studying new brain-imaging...
The rapid development of artificial intelligence (AI) facilitates various applications from all areas but also poses great challenges in its hardware implementation terms speed and energy because the explosive growth data. Optical computing provides a distinctive perspective to address this bottleneck by harnessing unique properties photons including broad bandwidth, low latency, high efficiency. In review, we introduce latest developments optical for different AI models, feedforward neural...
Endowed with the superior computing speed and energy efficiency, optical neural networks (ONNs) have attracted ever-growing attention in recent years. Existing architectures are mainly single-channel due to lack of advanced connection interaction operators, solving simple tasks such as hand-written digit classification, saliency detection, etc. The limited capacity scalability ONNs restrict implementation machine vision. Herein, we develop Monet: a multichannel network architecture for...
The metaverse is attracting considerable attention recently. It aims to build a virtual environment that people can interact with the world and cooperate each other. In this survey paper, we re-introduce in new framework based on broad range of technologies, including perception which enables us precisely capture characteristics real world, computation supports large requirement over large-scale data, reconstruction builds from one, cooperation facilitates long-distance communication...
Following the explosive growth of global data, there is an ever-increasing demand for high-throughput processing in image transmission systems. However, existing methods mainly rely on electronic circuits, which severely limits throughput. Here, we propose end-to-end all-optical variational autoencoder, named photonic encoder-decoder (PED), maps physical system into optical generative neural network. By modeling noises as variation latent space, PED establishes a large-scale unsupervised...
Abstract Widefield microscopy can provide optical access to multi-millimeter fields of view and thousands neurons in mammalian brains at video rate. However, tissue scattering background contamination results signal deterioration, making the extraction neuronal activity challenging, laborious time consuming. Here we present our deep-learning-based widefield neuron finder (DeepWonder), which is trained by simulated functional recordings effectively works on experimental data achieve...
Ultrafast dynamic machine vision in the optical domain can provide unprecedented perspectives for high-performance computing. However, owing to limited degrees of freedom, existing photonic computing approaches rely on memory's slow read/write operations implement processing. Here, we propose a spatiotemporal architecture match highly parallel spatial with high-speed temporal and achieve three-dimensional plane. A unified training framework is devised optimize physical system network model....
Optical computing promises to improve the speed and energy efficiency of machine learning applications
Abstract Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced issues of energy scaling walls, hindering them from sustainable performance improvement iterative multi-task learning. Referring to another modality light, photonic has been progressively applied in high-efficient neuromorphic systems. Here, we...
The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report integrated that achieves performance beyond a commercial with 5×, NA 0.1 objective only at 0.15 cm3 0.5 g, whose five orders magnitude smaller than conventional microscope. To achieve this, progressive optimization pipeline proposed which systematically optimizes both aspherical lenses diffractive elements over 30 times memory reduction compared to the end-to-end...
Abstract Despite a wealth of research linking the triglyceride glucose index (TyG index) to metabolic diseases. However, little evidence links TyG all-cause or CVD mortality in middle-aged and elderly individuals with type 2 diabetes (T2D). This study analyzed data from 2998 patients T2D who participated National Health Nutrition Examination Survey (NHANES) between 1999 2018. The were investigated using Cox regression models. nonlinear association can be understood help restricted cubic...