arXiv subjects

Jianping Yao

Publications and source records attributed to Jianping Yao.

At least 19 recordsLinked to original sources

arXiv2026

Integrated Microwave Photonics: From Material Platforms to Systems-on-Chip

In this paper, recent advances in integrated microwave photonics (IMWP) are reviewed, including material platforms, integration technologies, and system functionalities. Emerging opportunities and future perspectives are discussed. Silicon (Si) and silicon nitride (SiN) provide dense routing, programmable filtering, and low-loss delay; indium phosphide (InP) supplies optical gain, light generation, and high-speed photodetection; thin-film lithium niobate (TFLN) offers linear and broadband electro-optic conversion; and photonic integration is emerging as a practical path to synergize complementary capabilities. Recent work shows a marked shift from isolated modulators, filters, and delay lines toward chip- and module-level systems, ranging from signal processing engines, real-time spectrum sensing, to full-spectrum wireless links, fiber-wireless conversion, silicon beamforming, integrated radar, programmable processors, and photonic convolution engines with on-chip sources and detectors. As these demonstrations become more mature and complete, the key questions move beyond bandwidth and efficiency toward system integration, microwave packaging, calibration stability, analog link performance, and application-level validation.

physics.optics↗

arXiv2026

The Information Rate of Fiber-Wireless Communication Systems Based on Photonic Generation of RF Signals

High-capacity fiber-wireless communication systems operating at high frequencies increasingly rely on photonic generation of radio-frequency (RF) signals. In these systems, optical signals are transmitted over optical fibers and detected by photodetectors, where RF signals are generated at frequencies equal to the difference between the optical carrier frequencies. A major performance-limiting impairment is the phase noise of the generated RF signals, which originates from the phase noise of the optical sources. In this paper, we develop comprehensive probabilistic models for the two principal configurations of fiber-wireless communication systems employing photonic RF generation. Based on these models, we propose an efficient numerical framework for calculating the information rate (IR). Numerical simulations are performed to validate the efficiency of the proposed algorithm and the results provide design guidelines for high-performance fiber-wireless systems.

physics.optics↗

arXiv2026

LSAI: A Large Small AI Model Codesign Framework for Agentic Robot Scenarios

The development of Artificial Intelligence (AI) has enabled agentic robots an appealing paradigm for various applications, such as research and rescue in complex environment. In this context, the next wireless communication technology facilitates robot cooperation for efficient environment sensing and exploration. However, traditional AI solutions cannot always provide reasonable resource utilization decisions, which makes it challenging to achieve both accurate and low-latency research and rescue. To address this issue, we propose a, LSAI, a large small AI model codesign framework to achieve highly accurate and real-time robot cooperation with deep interaction between large AI model and small AI model. We first propose an attention-based model aggregation for LAI construction. It can assist agentic robots in accurately sensing physical environments. Next, we design an adaptive model splitting and update algorithm to enable the robots to perform accurate path planning for high-efficiency environment sensing with low energy consumption. Finally, we demonstrate the effectiveness of our proposed LSAI framework. The simulation results indicate that our solution achieves sensing accuracy of up to 20.4% while reducing sensing cooperation latency by an average of 17.9% compared to traditional AI solutions.

eess.SY↗

arXiv2025

Synergistic Antenna-Modulator Integration for Monolithic Photonic RF Receiver

Integrated radio-frequency (RF) photonics plays a pivotal role in wireless communications, sensing, and radar due to its large intrinsic bandwidth, remote distribution capability, and compact footprint. However, despite significant advances in photonic integrated circuits (PICs), the practical deployment of these systems remains constrained by the bulky nature of essential RF components (e.g., bulky antennas, amplifiers, and cables), especially in covert, conformal, and space-constrained applications. To overcome these limitations, monolithic electronic-photonic integrated circuits (EPICs), enabling miniaturized and synergistic integration of both RF and photonic components, are gaining notable attention. As a groundbreaking advancement, we demonstrate a novel photonic RF receiver that monolithically integrates a bow-tie antenna and a microring modulator on a thin-film lithium niobate platform. The chip innovatively leverages dual-resonance enhancement mechanism, RF resonance from the antenna and optical resonance from the microring, to significantly boost the RF-to-optical conversion efficiency. A record-high figure of merit (FOM) of 3.88 W-1/2 is achieved within a compact footprint of 2*1.7 mm2. As the first demonstrations, the integrated receiver is deployed in an integrated sensing and communication (ISAC) system, achieving centimeter-level radar ranging accuracy and 3.2 Gbps wireless communication capacity, as well as real-time video transmission function in moving scenarios. This seminal work paves a new way for covert, conformal, and miniaturized frontends in wireless communication and sensing applications, including body area networks, unmanned aerial vehicles, high-speed vacuum maglevs, and electronic warfare systems.

physics.optics↗

arXiv2025

Microwave Photonics for Space-Ground Connectivity

Future space-ground communication networks require a seamless fusion of technologies that combine the all-weather reliability of microwave links with the ultra-high data capacity of near-infrared optical systems. Achieving this vision demands compact, robust, and multifunctional hardware, yet monolithic integration of these fundamentally distinct domains has remained elusive. Here, we present the first monolithically integrated silicon photonic chip that bridges microwave and optical domains for dual-band free-space communications and dynamic beamforming. The chip integrates a microwave true time delay (TTD) beamforming network, an optical phased array (OPA) beamforming network, and an optical coherent transceiver, all on a silicon-on-insulator (SOI) platform. By uniting the strengths of microwave resilience, optical bandwidth, and coherent detection sensitivity, this photonic integrated circuit represents a critical step toward reconfigurable, interference-resistant, high-throughput links for satellites, UAVs, and ground stations. Experimental demonstrations confirm two-dimensional dynamic beam steering in both bands 24.9 deg x 18.5 deg at microwave frequencies and 10 deg x 4.7 deg in the optical domain. In a 5-meter free-space link, the chip achieves error-free transmission at 10 Gbps for microwave and 80 Gbps per wavelength in the near infrared band. These results establish integrated microwave photonics as a promising platform for bridging Earth and orbit through compact, dual-band, beamforming-enabled transceivers.

physics.optics↗

arXiv2025

Wine Characterisation with Spectral Information and Predictive Artificial Intelligence

The purpose of this paper is to use absorbance data obtained by human tasting and an ultraviolet-visible (UV-Vis) scanning spectrophotometer to predict the attributes of grape juice (GJ) and to classify the wine's origin, respectively. The approach combined machine learning (ML) techniques with spectroscopy to find a relatively simple way to apply them in two stages of winemaking and help improve the traditional wine analysis methods regarding sensory data and wine's origins. This new technique has overcome the disadvantages of the complex sensors by taking advantage of spectral fingerprinting technology and forming a comprehensive study of the employment of AI in the wine analysis domain. In the results, Support Vector Machine (SVM) was the most efficient and robust in both attributes and origin prediction tasks. Both the accuracy and F1 score of the origin prediction exceed 91%. The feature ranking approach found that the more influential wavelengths usually appear at the lower end of the scan range, 250 nm (nanometers) to 420 nm, which is believed to be of great help for selecting appropriate validation methods and sensors to extract wine data in future research. The knowledge of this research provides new ideas and early solutions for the wine industry or other beverage industries to integrate big data and IoT in the future, which significantly promotes the development of 'Smart Wineries'.

cs.LG↗

arXiv2025

Multi-output Deep-Supervised Classifier Chains for Plant Pathology

Plant leaf disease classification is an important task in smart agriculture which plays a critical role in sustainable production. Modern machine learning approaches have shown unprecedented potential in this classification task which offers an array of benefits including time saving and cost reduction. However, most recent approaches directly employ convolutional neural networks where the effect of the relationship between plant species and disease types on prediction performance is not properly studied. In this study, we proposed a new model named Multi-output Deep Supervised Classifier Chains (Mo-DsCC) which weaves the prediction of plant species and disease by chaining the output layers for the two labels. Mo-DsCC consists of three components: A modified VGG-16 network as the backbone, deep supervision training, and a stack of classification chains. To evaluate the advantages of our model, we perform intensive experiments on two benchmark datasets Plant Village and PlantDoc. Comparison to recent approaches, including multi-model, multi-label (Power-set), multi-output and multi-task, demonstrates that Mo-DsCC achieves better accuracy and F1-score. The empirical study in this paper shows that the application of Mo-DsCC could be a useful puzzle for smart agriculture to benefit farms and bring new ideas to industry and academia.

cs.CV↗

arXiv2025

UAV-Enabled Secure ISAC Against Dual Eavesdropping Threats: Joint Beamforming and Trajectory Design

In this work, we study an unmanned aerial vehicle (UAV)-enabled secure integrated sensing and communication (ISAC) system, where a UAV serves as an aerial base station (BS) to simultaneously perform communication with a user and detect a target on the ground, while a dual-functional eavesdropper attempts to intercept the signals for both sensing and communication. Facing the dual eavesdropping threats, we aim to enhance the average achievable secrecy rate for the communication user by jointly designing the UAV trajectory together with the transmit information and sensing beamforming, while satisfying the requirements on sensing performance and sensing security, as well as the UAV power and flight constraints. To address the non-convex nature of the optimization problem, we employ the alternating optimization (AO) strategy, jointly with the successive convex approximation (SCA) and semidefinite relaxation (SDR) methods. Numerical results validate the proposed approach, demonstrating its ability to achieve a high secrecy rate while meeting the required sensing and security constraints.

cs.IT↗

arXiv2025

Sensing-Assisted Channel Prediction in Complex Wireless Environments: An LLM-Based Approach

This letter studies the sensing-assisted channel prediction for a multi-antenna orthogonal frequency division multiplexing (OFDM) system operating in realistic and complex wireless environments. In this system,an integrated sensing and communication (ISAC) transmitter leverages the mono-static sensing capability to facilitate the prediction of its bi-static communication channel, by exploiting the fact that the sensing and communication channels share the same physical environment involving shared scatterers. Specifically, we propose a novel large language model (LLM)-based channel prediction approach,which adapts pre-trained text-based LLM to handle the complex-matrix-form channel state information (CSI) data. This approach utilizes the LLM's strong ability to capture the intricate spatiotemporal relationships between the multi-path sensing and communication channels, and thus efficiently predicts upcoming communication CSI based on historical communication and sensing CSI data. Experimental results show that the proposed LLM-based approach significantly outperforms conventional deep learning-based methods and the benchmark scheme without sensing assistance.

eess.SP↗

arXiv2025

Silicon Micro-Disk Resonator Crossbar Array for High-Speed and High-Density Photonic Convolution Processing

Advanced artificial intelligence (AI) algorithms, particularly those based on artificial neural networks, have garnered significant attention for their potential applications in areas such as image recognition and natural language processing. Notably, neural networks make heavy use of matrix-vector multiplication (MVM) operations, causing substantial computing burden on existing electronic computing systems. Optical computing has attracted considerable attention that can perform optical-domain MVM at an ultra-high speed. In this paper, we introduce a novel silicon photonic micro-disk resonator (MDR) crossbar signal processor designed to support matrix-vector multiplication (MVM) with both high processing speed and enhanced computational density. The key innovation of the proposed MDR crossbar processor is the placement of two MDRs at each crosspoint, enabling simultaneous routing and weighting functions. This design effectively doubles the computational density, improving overall performance. We fabricate a silicon photonic MDR crossbar processor, which is employed to perform convolutional tasks in a convolutional neural network (CNN). The experimental results demonstrate that the photonic processor achieves a classification accuracy of 96% on the MNIST dataset. Additionally, it is capable of scaling to a computational speed of up to 160 tera-operations per second (TOPS) and a computational density as high as 25.6 TOPS/mm2. Our approach holds significant promise for enabling highly efficient, scalable on-chip optical computing, with broad potential applications in AI and beyond.

physics.optics↗

arXiv2025

A photonic integrated processor for multiple parallel computational tasks

Optical networks with parallel processing capabilities are significant in advancing high-speed data computing and large-scale data processing by providing ultra-width computational bandwidth. In this paper, we present a photonic integrated processor that can be segmented into multiple functional blocks, to enable compact and reconfigurable matrix operations for multiple parallel computational tasks. Fabricated on a silicon-on-insulator (SOI) platform, the photonic integrated processor supports fully reconfigurable optical matrix operations. By segmenting the chip into multiple functional blocks, it enables optical matrix operations of various sizes, offering great flexibility and scalability for parallel computational tasks. Specifically, we utilize this processor to perform optical convolution operations with various kernel sizes, including reconfigurable three-channel 1x1 convolution kernels and 2x2 real-valued convolution kernels, implemented within distinct segmented blocks of the chip. The multichannel optical 1x1 convolution operation is experimentally validated by using the deep residual U-Net, demonstrating precise segmentation of pneumonia lesion region in lung CT images. In addition, the capability of the 2x2 optical convolution operation is also experimentally validated by constructing an optical convolution layer and integrating an electrical fully connected layer, achieving ten-class classification of handwritten digit images. The photonic integrated processor features high scalability and robust parallel computational capability, positioning it a promising candidate for applications in optical neural networks.

physics.optics↗

arXiv2024

Microwave Photonic Multi-Mode Injection-Locked Frequency Divider With a Wide Operational Range Based on an Optoelectronic Oscillator

We propose and implement a microwave photonic multi-mode injection-locked frequency divider (ILFD) with a wide frequency operational range based on an optoelectronic oscillator (OEO). In the OEO, a Mach-Zehnder modulator (MZM) and a photodetector (PD) are employed to construct a frequency multiplier to achieve an N-1 times frequency multiplication, which is then mixed with an external injection signal at an electrical mixer in the OEO loop. By adjusting the round-trip gain and time delay of the OEO loop, a radio frequency (RF) signal with a frequency that is 1/N that of the injection signal is generated, thus N times frequency division is achieved. Theoretical analysis and experimental verification are conducted to evaluate the effectiveness of the proposed ILFD. The results demonstrate that the system can divide a RF signal from 2.6 to 20.8 GHz to 1.3 to 1.95 GHz with different frequency division factors ranging from 2 to 13. A significant improvement in phase noise of 35.11 dB is also obtained at a frequency offset of 100 kHz when the frequency division factor is 13.

physics.optics↗

arXiv2024

Seamless Integration and Implementation of Distributed Contact and Contactless Vital Sign Monitoring

Real-time vital sign monitoring is gaining immense significance not only in the medical field but also in personal health management. Facing the needs of different application scenarios of the smart and healthy city in the future, the low-cost, large-scale, scalable, and distributed vital sign monitoring system is of great significance. In this work, a seamlessly integrated contact and contactless vital sign monitoring system, which can simultaneously implement respiration and heartbeat monitoring, is proposed. In contact vital sign monitoring, the chest wall movement due to respiration and heartbeat is translated into changes in the optical output intensity of a fiber Bragg grating (FBG). The FBG is also an important part of radar signal generation for contactless vital sign monitoring, in which the chest wall movement is translated into phase changes of the radar de-chirped signal. By analyzing the intensity of the FBG output and phase of the radar de-chirped signal, real-time respiration and heartbeat monitoring are realized. In addition, due to the distributed structure of the system and its good integration with the wavelength-division multiplexing optical network, it can be massively scaled by employing more wavelengths. A proof-of-concept experiment is carried out. Contact and contactless respiration and heartbeat monitoring of three people are simultaneously realized. During a monitoring time of 60 s, the maximum absolute measurement errors of respiration and heartbeat rates are 1.6 respirations per minute and 2.3 beats per minute, respectively. The measurement error does not have an obvious change even when the monitoring time is decreased to 5 s.

eess.SY↗

arXiv2024

Seamlessly merging radar ranging/imaging, wireless communications, and spectrum sensing, for 6G empowered by microwave photonics

Integration of radar, wireless communications, and spectrum sensing is being investigated for 6G with an increased spectral efficiency. Microwave photonics (MWP), a technique that combines microwave engineering and photonic technology to take advantage of the wide bandwidth offered by photonics for microwave signal generation and processing is considered an effective solution for the implementation of the integration. In this paper, an MWP-assisted joint radar, wireless communications, and spectrum sensing (JRCSS) system that enables precise perception of the surrounding physical and electromagnetic environments while maintaining high-speed data communication is proposed and demonstrated. Communication signals and frequency-sweep signals are merged in the optical domain to achieve high-speed radar ranging and imaging, high-data-rate wireless communications, and wideband spectrum sensing. In an experimental demonstration, a JRCSS system supporting radar ranging with a measurement error within $\pm$ 4 cm, two-dimensional imaging with a resolution of 25 $\times$ 24.7 mm, wireless communications with a data rate of 2 Gbaud, and spectrum sensing with a frequency measurement error within $\pm$ 10 MHz in a 6-GHz bandwidth, is demonstrated.

eess.SP↗

arXiv2023

Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches

Deep learning plays an important role in modern agriculture, especially in plant pathology using leaf images where convolutional neural networks (CNN) are attracting a lot of attention. While numerous reviews have explored the applications of deep learning within this research domain, there remains a notable absence of an empirical study to offer insightful comparisons due to the employment of varied datasets in the evaluation. Furthermore, a majority of these approaches tend to address the problem as a singular prediction task, overlooking the multifaceted nature of predicting various aspects of plant species and disease types. Lastly, there is an evident need for a more profound consideration of the semantic relationships that underlie plant species and disease types. In this paper, we start our study by surveying current deep learning approaches for plant identification and disease classification. We categorise the approaches into multi-model, multi-label, multi-output, and multi-task, in which different backbone CNNs can be employed. Furthermore, based on the survey of existing approaches in plant pathology and the study of available approaches in machine learning, we propose a new model named Generalised Stacking Multi-output CNN (GSMo-CNN). To investigate the effectiveness of different backbone CNNs and learning approaches, we conduct an intensive experiment on three benchmark datasets Plant Village, Plant Leaves, and PlantDoc. The experimental results demonstrate that InceptionV3 can be a good choice for a backbone CNN as its performance is better than AlexNet, VGG16, ResNet101, EfficientNet, MobileNet, and a custom CNN developed by us. Interestingly, empirical results support the hypothesis that using a single model can be comparable or better than using two models. Finally, we show that the proposed GSMo-CNN achieves state-of-the-art performance on three benchmark datasets.

cs.CV↗

arXiv2023

Machine Learning for Leaf Disease Classification: Data, Techniques and Applications

The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular.

cs.CV↗

arXiv2022

Tera-sample-per-second arbitrary waveform generation in the synthetic dimension

The synthetic dimension opens new horizons in quantum physics and topological photonics by enabling new dimensions for field and particle manipulations. The most appealing property of the photonic synthetic dimension is its ability to emulate high-dimensional optical behavior in a unitary physical system. Here we show that the photonic synthetic dimension can transform technical problems in photonic systems between dimensionalities, providing unexpected solutions to technical problems that are otherwise challenging. Specifically, we propose and experimentally demonstrate a photonic Galton board (PGB) in the temporal synthetic dimension, in which the temporal high-speed challenge is converted into a spatial fiber-optic length matching problem, leading to the experimental generation of tera-sample-per-second arbitrary waveforms. Limited by the speed of the measurement equipment, waveforms with sampling rates of up to 341.53 GSa/s are recorded. Our proposed PGB operating in the temporal synthetic dimension breaks the speed limit in a physical system, bringing arbitrary waveform generation into the terahertz regime. The concept of dimension conversion offers possible solutions to various physical dimension-related problems, such as super-resolution imaging, high-resolution spectroscopy, time measurement, etc.

physics.optics↗

arXiv2022

Cellular-Connected UAV with Adaptive Air-to-Ground Interference Cancellation and Trajectory Optimization

This letter studies a cellular-connected unmanned aerial vehicle (UAV) scenario, in which a UAV user communicates with ground base stations (GBSs) in cellular uplink by sharing the spectrum with ground users (GUs). To deal with the severe air-to-ground (A2G) co-channel interference, we consider an adaptive interference cancellation (IC) approach, in which each GBS can decode the GU's messages by adaptively switching between the modes of IC (i.e., precanceling the UAV's resultant interference) and treating interference as noise (TIN). By designing the GBSs' decoding modes, jointly with the wireless resource allocation and the UAV's trajectory control, we maximize the UAV's data-rate throughput over a finite mission period, while ensuring the minimum data-rate requirements at individual GUs. We propose an efficient algorithm to solve the throughput maximization problem by using the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that our proposed design significantly improves the UAV's throughput as compared to the benchmark schemes without the adaptive IC and/or trajectory optimization.

cs.IT↗