Computing dimension for a reconfigurable photonic tensor processing core based on silicon photonics.

In the rapidly evolving field of artificial intelligence, integrated photonic computing has emerged as a promising solution to address the growing demand for high-performance computing with ultrafast speed and reduced power consumption. This study presents what we believe is a novel photonic tensor processing core (PTPC) on a chip utilizing wavelength division multiplexing technology to perform parallel multiple vector-matrix multiplications concurrently, allowing for reconfigurable computing dimensions without changing the hardware scale. Specifically, this architecture significantly enhances the number of operations in convolutional neural networks, making it superior to other photonic computing systems.

A Data Enhancement Algorithm for DDoS Attacks Using IoT.

With the rapid development of the Internet of Things (IoT), the frequency of attackers using botnets to control IoT devices in order to perform distributed denial-of-service attacks (DDoS) and other cyber attacks on the internet has significantly increased. In the actual attack process, the small percentage of attack packets in IoT leads to low accuracy of intrusion detection. Based on this problem, the paper proposes an oversampling algorithm, KG-SMOTE, based on Gaussian distribution and K-means clustering, which inserts synthetic samples through Gaussian probability distribution, extends the clustering nodes in minority class samples in the same proportion, increases the density of minority class samples, and improves the amount of minority class sample data in order to provide data support for IoT-based DDoS attack detection.

Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model.

The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described.

Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins.

With the rapid development of information technology, great changes have taken place in the way of managing, analyzing, and using data in all walks of life. Using deep learning algorithm for data analysis in the field of medicine can improve the accuracy of disease recognition. The purpose is to realize the intelligent medical service mode of sharing medical resources among many people under the dilemma of limited medical resources.

Realization of low-cross-talk half-wavelength pitch waveguide array on a silicon-on-insulator platform.

Realizing a densely packed waveguide antenna array is of great importance in light detection and ranging (LIDAR), owing to its suppressed grating lobes. In this work, a low-cross-talk half-wavelength pitch silicon waveguide array is proposed and experimentally demonstrated. It has a periodic arrangement of silicon strip nanophotonic waveguides, between which deep-subwavelength silicon strips are placed.

Deep Transfer Learning-Based Multi-Modal Digital Twins for Enhancement and Diagnostic Analysis of Brain MRI Image.

Objective: it aims to adopt deep transfer learning combined with Digital Twins (DTs) in Magnetic Resonance Imaging (MRI) medical image enhancement.

Methods: MRI image enhancement method based on metamaterial composite technology is proposed by analyzing the application status of DTs in medical direction and the principle of MRI imaging. On the basis of deep transfer learning, MRI super-resolution deep neural network structure is established.

Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics.

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced.

Fuzzy System Based Medical Image Processing for Brain Disease Prediction.

The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm.

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion.

The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins.

Metabolic and Transcriptional Analysis of Recombinant Saccharomyces Cerevisiae for Xylose Fermentation: A Feasible and Efficient Approach.

Lignocellulose is an abundant xylose-containing biomass found in agricultural wastes, and has arisen as a suitable alternative to fossil fuels for the production of bioethanol. Although Saccharomyces cerevisiae has been thoroughly used for the production of bioethanol, its potential to utilize lignocellulose remains poorly understood. In this work, xylose-metabolic genes of Pichia stipitis and Candida tropicalis, under the control of different promoters, were introduced into S.

Characterizing microring resonators using optical frequency domain reflectometry.

A novel, to the best of our knowledge, method to extract optical microring resonators' loss characteristics is proposed and demonstrated using optical frequency domain reflectometry (OFDR). Compared with the traditional optical transmission measurement method, the spatial-resolved backscattering optical signals obtained from the OFDR can clearly show the resonance mode's increased optical path length due to its circulation inside the resonator. By further processing the backscattered optical signals, loaded $Q$-factors of several resonators can be accurately determined.

Design of a low-crosstalk half-wavelength pitch nano-structured silicon waveguide array.

An ultra-compact half-wavelength pitch silicon waveguide array with very low crosstalk is proposed and analyzed in this work. We first show the design of a pair of low-crosstalk silicon waveguides with only half-wavelength spacing, where the placement of two thin silicon strips asymmetrically in between the waveguides is key to having very low crosstalk. We next extend this nano-structured two-waveguide design to form a low-crosstalk half-wavelength pitch silicon waveguide array.

High performance ultra-compact SOI waveguide crossing.

Waveguide crossing is an important integrated photonic component that will be routinely used for high-density and large-scale photonic integrated circuits, such as optical switches and routers. Several techniques have been reported in achieving high performance waveguide crossings on a silicon-on-insulator photonic platform, i.e.

Analyzing modal power in multi-mode waveguide via machine learning.

A machine learning assisted modal power analyzing scheme designed for optical modes in integrated multi-mode waveguides is proposed and studied in this work. Convolutional neural networks (CNNs) are successfully trained to correlate the far-field diffraction intensity patterns of a superposition of multiple waveguide modes with its modal power distribution. In particular, a specialized CNN is trained to analyze thin optical waveguides, which are single-moded along one axis and multi-moded along the other axis.

General analysis of SRS-limited high-power fiber lasers and design strategy.

In this paper, a spectral model by incorporating SRS effect is proposed and established, which is feasible for analyzing the SRS effect both in high-power fiber oscillator and master oscillator power amplifier (MOPA) system. The theoretical results show that the SRS effect is tightly related to the bandwidths of the fiber Bragg gratings (FBGs) and it can be efficiently suppressed by optimizing the bandwidth of the FBGs. Besides, the established theoretical model is also feasible for analyzing the influence of seed power on the SRS effect.

Investigation of stimulated Raman scattering effect in high-power fiber amplifiers seeded by narrow-band filtered superfluorescent source.

In this paper the stimulated Raman scattering (SRS) effect in high-power fiber amplifiers seeded by the narrow-band filtered superfluorescent source (SFS) is firstly analyzed both theoretically and experimentally. Spectral models for the formation of the SFS and the spectral evolution in high-power fiber amplifiers seeded by filtered SFS are proposed. It is found that the SRS effect in high-power fiber amplifiers depends on the spectral width of the filtered SFS seed.

293 W, GHz narrow-linewidth, polarization maintaining nanosecond fiber amplifier with SBS suppression employing simultaneous phase and intensity modulation.

We present a new method of SBS suppression in fiber amplifier system by employing simultaneously phase and intensity modulation. In this way, a GHz narrow-linewidth polarization-maintaining (PM) all-fiber pulsed laser is obtained based on a master oscillator power amplifier (MOPA) configuration. The pulsed seed is generated from a single-frequency continuous wave (CW) laser at 1064 nm by simultaneous modulation using an electro-optic intensity modulator (EOIM) and an electro-optic phase modulator (EOPM).

3.15 kW direct diode-pumped near diffraction-limited all-fiber-integrated fiber laser.

We demonstrate a direct diode-pumped all-fiber-integrated fiber laser based on master oscillator power amplifier configuration at 1080 nm, producing maximum output power of 3.15 kW with corresponding optical to optical efficiency of 75.1%.