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Ling He

Publications and source records attributed to Ling He.

At least 19 recordsLinked to original sources
arXiv2026

STAR-RIS-Assisted Integrated Sensing, Secure Communication, and Power Transfer: A Transmit Power Minimization Framework

Evolving wireless networks call for architectures that unify sensing, communication, and wireless power transfer. Although integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT) have validated dual-function transmission, the combination of integrated sensing, secure communication, and power transfer (ISSCPT) remains largely unexplored, in part due to the tight coupling among design variables. To address this coupling and expand spatial degrees of freedom, we turn to intelligent metasurfaces: while a conventional reconfigurable intelligent surface (cRIS) reflects only to one side and thus limits coverage and flexibility, a simultaneously transmitting and reflecting RIS (STAR-RIS) enables full-space wave control, making it a natural vehicle for power-efficient ISSCPT. We study a STAR-RIS-assisted ISSCPT system and pose a central question: How much transmit power is required to operate such a system? We formulate a transmit-power minimization problem that jointly optimizes transmit and receive beamforming and the STAR-RIS configuration, and solve it via alternating optimization with successive convex approximation, second-order cone programming, and eigenvalue decomposition. Simulations show that the proposed STAR-RIS-assisted design outperforms cRIS and no-RIS baselines, and quantify the additional transmit power required by ISSCPT relative to ISAC and secure SWIPT, clarifying security-sensing-power tradeoffs in metasurface-assisted systems.

eess.SP↗
arXiv2026

Design of Uplink ISAC Systems with Cooperative Sensing: Power Control and Receive Beamforming

Integrated sensing and communication (ISAC) has emerged as a key paradigm for next-generation wireless systems, which allows wireless resources to be used for data transmission and target sensing simultaneously. In this paper, multi-user collaborative target detection in the uplink ISAC system is investigated. To incorporate the target sensing functionality, the system relies on the reuse of uplink signals from the communication users. Specifically, we analyze an uplink multi-user single-input multiple-output (MU-SIMO) communication system with bistatic sensing. Using the channel statistics, we formulate the problem of joint optimal pilot and data power allocation to maximize the uplink ergodic sum rate while meeting communication and sensing quality-of-service (QoS) requirements. To address this non-convex problem, we propose an alternating optimization (AO)-based iterative framework, where the joint power allocation problem is decomposed into two sub-problems. Specifically, the pilot power allocation is optimized using a penalty dual decomposition (PDD)-based gradient ascent algorithm, while the data power allocation is solved via successive convex approximation (SCA). Once the long-term power allocation is determined, the base station (BS) estimates the instantaneous channels using a minimum mean-squared error (MMSE) estimator. Subsequently, based on the estimated instantaneous channel state information (CSI), the receive beamforming for communication users is optimized via another SCA-based method to maximize the sum rate. Meanwhile, the optimal receive beamforming for the target is obtained in closed-form through eigenvalue decomposition (EVD).

eess.SP↗
arXiv2026

Achievable Rate Optimization for Large Flexible Intelligent Metasurface Assisted Downlink MISO under Statistical CSI

The integration of electromagnetic metasurfaces into wireless communications enables intelligent control of the propagation environment. Recently, flexible intelligent metasurfaces (FIMs) have evolved beyond conventional reconfigurable intelligent surfaces (RISs), enabling three-dimensional surface deformation for adaptive wave manipulation. However, most existing FIM-aided system designs assume perfect instantaneous channel state information (CSI), which is impractical in large-scale networks due to the high training overhead and complicated channel estimation. To overcome this limitation, we propose a robust statistical-CSI-based optimization framework for downlink multiple-input single-output (MISO) systems with FIM-assisted transmitters. A block coordinate ascent (BCA)-based iterative algorithm is developed to jointly optimize power allocation and FIM morphing, maximizing the average achievable sum rate. Simulation results show that the proposed statistical-CSI-driven FIM design significantly outperforms conventional rigid antenna arrays (RAAs), validating its effectiveness and practicality.

eess.SP↗
arXiv2024

Accelerating FRB Search: Dataset and Methods

Fast Radio Burst (FRB) is an extremely energetic cosmic phenomenon of short duration. Discovered only recently and with its origin still unknown, FRBs have already started to play a significant role in studying the distribution and evolution of matter in the universe. FRBs can only be observed through radio telescopes, which produce petabytes of data, rendering the search for FRB a challenging task. Traditional techniques are computationally expensive, time-consuming, and generally biased against weak signals. Various machine learning algorithms have been developed and employed, all of which require substantial datasets. We here introduce the FAST dataset for Fast Radio bursts EXploration (FAST-FREX), built upon the observations obtained by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our dataset comprises 600 positive samples of observed FRB signals from three sources and 1000 negative samples of noise and Radio Frequency Interference (RFI). Furthermore, we provide a machine learning algorithm, Radio Single-Pulse Detection Algorithm Based on Visual Morphological Features (RaSPDAM), with significant improvements in efficiency and accuracy for FRB search. We also employed the benchmark comparison between conventional single-pulse search softwares, namely PRESTO and Heimdall, and RaSPDAM. RaSPDAMv2 achieves an average precision of 97% and an average recall of 83%, with notable enhancements in computational performance. Future machine learning algorithms can use this as a reference point to measure their performance and help the potential improvements. By enabling more accurate and efficient detection of transient radio events, our work facilitates the FRB and pulsars search pipeline, enhances the potential for discovering new astrophysical phenomena.

astro-ph.IM↗
arXiv2024

The space of totally real flat minimal surfaces in the Quaternionic projective space HP^3

We prove that the moduli space of all noncongruent linearly full totally real flat minimal immersions from the complex plane C into HP^3 that do not lie in CP^3 has three components, each of which is a manifold of real dimension 6. As an application, we give a description of the moduli space of all noncongruent linearly full totally real flat minimal tori in HP^3 that do not lie in CP^3.

math.DG↗
arXiv2024

Research on the Application of Large Language Models in Automatic Question Generation: A Case Study of ChatGLM in the Context of High School Information Technology Curriculum

This study investigates the application effectiveness of the Large Language Model (LLMs) ChatGLM in the automated generation of high school information technology exam questions. Through meticulously designed prompt engineering strategies, the model is guided to generate diverse questions, which are then comprehensively evaluated by domain experts. The evaluation dimensions include the Hitting(the degree of alignment with teaching content), Fitting (the degree of embodiment of core competencies), Clarity (the explicitness of question descriptions), and Willing to use (the teacher's willingness to use the question in teaching). The results indicate that ChatGLM outperforms human-generated questions in terms of clarity and teachers' willingness to use, although there is no significant difference in hit rate and fit. This finding suggests that ChatGLM has the potential to enhance the efficiency of question generation and alleviate the burden on teachers, providing a new perspective for the future development of educational assessment systems. Future research could explore further optimizations to the ChatGLM model to maintain high fit and hit rates while improving the clarity of questions and teachers' willingness to use them.

cs.CY↗
arXiv2024

Application of Large Language Models in Automated Question Generation: A Case Study on ChatGLM's Structured Questions for National Teacher Certification Exams

This study delves into the application potential of the large language models (LLMs) ChatGLM in the automatic generation of structured questions for National Teacher Certification Exams (NTCE). Through meticulously designed prompt engineering, we guided ChatGLM to generate a series of simulated questions and conducted a comprehensive comparison with questions recollected from past examinees. To ensure the objectivity and professionalism of the evaluation, we invited experts in the field of education to assess these questions and their scoring criteria. The research results indicate that the questions generated by ChatGLM exhibit a high level of rationality, scientificity, and practicality similar to those of the real exam questions across most evaluation criteria, demonstrating the model's accuracy and reliability in question generation. Nevertheless, the study also reveals limitations in the model's consideration of various rating criteria when generating questions, suggesting the need for further optimization and adjustment. This research not only validates the application potential of ChatGLM in the field of educational assessment but also provides crucial empirical support for the development of more efficient and intelligent educational automated generation systems in the future.

cs.CY↗
arXiv2024

Wearable Device-Based Real-Time Monitoring of Physiological Signals: Evaluating Cognitive Load Across Different Tasks

This study employs cutting-edge wearable monitoring technology to conduct high-precision, high-temporal-resolution (1-second interval) cognitive load assessment on electroencephalogram (EEG) data from the FP1 channel and heart rate variability (HRV) data of secondary vocational students. By jointly analyzing these two critical physiological indicators, the research delves into their application value in assessing cognitive load among secondary vocational students and their utility across various tasks. The study designed two experiments to validate the efficacy of the proposed approach: Initially, a random forest classification model, developed using the N-BACK task, enabled the precise decoding of physiological signal characteristics in secondary vocational students under different levels of cognitive load, achieving a classification accuracy of 97%. Subsequently, this classification model was applied in a cross-task experiment involving the National Computer Rank Examination (Level-1), demonstrating the method's significant applicability and cross-task transferability in diverse learning contexts. Conducted with high portability, this research holds substantial theoretical and practical significance for optimizing teaching resource allocation in secondary vocational education, as well as for cognitive load assessment methods and monitoring. Currently, the research findings are undergoing trial implementation in the school.

cs.HC↗
arXiv2024

EPIDetect: Video-based convulsive seizure detection in chronic epilepsy mouse model for anti-epilepsy drug screening

In the preclinical translational studies, drug candidates with remarkable anti-epileptic efficacy demonstrate long-term suppression of spontaneous recurrent seizures (SRSs), particularly convulsive seizures (CSs), in mouse models of chronic epilepsy. However, the current methods for monitoring CSs have limitations in terms of invasiveness, specific laboratory settings, high cost, and complex operation, which hinder drug screening efforts. In this study, a camera-based system for automated detection of CSs in chronically epileptic mice is first established to screen potential anti-epilepsy drugs.

cs.CV↗
arXiv2021

Delay Aware Secure Offloading for NOMA-Assisted Mobile Edge Computing in Internet of Vehicles

In this paper, a multi-vehicle multi-task nonorthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system with passive eavesdropping vehicles is investigated. To heighten the performance of edge vehicles, we propose a vehicle grouping pairing method, which utilizes vehicles near the MEC as full-duplex relays to assist edge vehicles. For promoting transmission security, we employ artificial noise to interrupt eavesdropping vehicles. Furthermore, we derive the approximate expression of secrecy outage probability of the system. The combined optimization of vehicle task division, power allocation, and transmit beamforming is formulated to minimize the total delay of task completion of edge vehicles. Then, we design a power allocation and task scheduling algorithm based on genetic algorithm to solve the mixed-integer nonlinear programming problem. Numerical results demonstrate the superiority of our proposed scheme in terms of system security and transmission delay.

cs.IT↗
arXiv2020

Totally Real Flat Minimal Surface in Hyperquadric

In this paper, we study geometry of totally real minimal surfaces in the complex hyperquadric $Q_{N-2}$, and obtain some characterizations of the harmonic sequence generated by these minimal immersions. For totally real flat surfaces that are minimal in both $Q_{N-2}$ and $\mathbb{C}P^{N-1}$, we determine them for $N=4, 5, 6$, and give a classification theorem when they are Clifford solutions.

math.DG↗
arXiv2020

SK-Unet: an Improved U-net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR

In the clinical environment, myocardial infarction (MI) as one com-mon cardiovascular disease is mainly evaluated based on the late gadolinium enhancement (LGE) cardiac magnetic resonance images (CMRIs). The auto-matic segmentations of left ventricle (LV), right ventricle (RV), and left ven-tricular myocardium (LVM) in the LGE CMRIs are desired for the aided diag-nosis in clinic. To accomplish this segmentation task, this paper proposes a modified U-net architecture by combining multi-sequence CMRIs, including the cine, LGE, and T2-weighted CMRIs. The cine and T2-weighted CMRIs are used to assist the segmentation in the LGE CMRIs. In this segmentation net-work, the squeeze-and-excitation residual (SE-Res) and selective kernel (SK) modules are inserted in the down-sampling and up-sampling stages, respective-ly. The SK module makes the obtained feature maps more informative in both spatial and channel-wise space, and attains more precise segmentation result. The utilized dataset is from the MICCAI challenge (MS-CMRSeg 2019), which is acquired from 45 patients including three CMR sequences. The cine and T2-weighted CMRIs acquired from 35 patients and the LGE CMRIs acquired from 5 patients are labeled. Our method achieves the mean dice score of 0.922 (LV), 0.827 (LVM), and 0.874 (RV) in the LGE CMRIs.

eess.IV↗
arXiv2019

On holomorphic two-spheres with constant curvature in the complex Grassmann manifold G(2,n)

In this paper, the theory of functions of one complex variable is explored to study linearly full unramified holomorphic two-spheres with constant curvature in $G(2,n)$ satisfying that the generated harmonic sequence degenerates at position $2$. Firstly, we determine the value distribution of the curvature and give the explicit characterization of such holomorphic two-spheres in terms of a polynomial equation. Then, applying this characterization, many examples of non-homogeneous constantly curved holomorphic two-spheres are constructed.

math.DG↗
arXiv2019

Totally Real Flat Minimal Surfaces in Quaternionic Projective Spaces

In this paper, we study totally real minimal surfaces in the quaternionic projective space $\mathbb{H}P^n$. We prove that the linearly full totally real flat minimal surfaces of isotropy order $n$ in $\mathbb{H}P^n$ are two surfaces in $\mathbb{C}P^n$, one of which is the Clifford solution, up to symplectic congruence.

math.DG↗
arXiv2016

CUNet: A Compact Unsupervised Network for Image Classification

In this paper, we propose a compact network called CUNet (compact unsupervised network) to counter the image classification challenge. Different from the traditional convolutional neural networks learning filters by the time-consuming stochastic gradient descent, CUNet learns the filter bank from diverse image patches with the simple K-means, which significantly avoids the requirement of scarce labeled training images, reduces the training consumption, and maintains the high discriminative ability. Besides, we propose a new pooling method named weighted pooling considering the different weight values of adjacent neurons, which helps to improve the robustness to small image distortions. In the output layer, CUNet integrates the feature maps gained in the last hidden layer, and straightforwardly computes histograms in non-overlapped blocks. To reduce feature redundancy, we implement the max-pooling operation on adjacent blocks to select the most competitive features. Comprehensive experiments are conducted to demonstrate the state-of-the-art classification performances with CUNet on CIFAR-10, STL-10, MNIST and Caltech101 benchmark datasets.

cs.CV↗
arXiv2016

A Hierarchical Distributed Processing Framework for Big Image Data

This paper introduces an effective processing framework nominated ICP (Image Cloud Processing) to powerfully cope with the data explosion in image processing field. While most previous researches focus on optimizing the image processing algorithms to gain higher efficiency, our work dedicates to providing a general framework for those image processing algorithms, which can be implemented in parallel so as to achieve a boost in time efficiency without compromising the results performance along with the increasing image scale. The proposed ICP framework consists of two mechanisms, i.e. SICP (Static ICP) and DICP (Dynamic ICP). Specifically, SICP is aimed at processing the big image data pre-stored in the distributed system, while DICP is proposed for dynamic input. To accomplish SICP, two novel data representations named P-Image and Big-Image are designed to cooperate with MapReduce to achieve more optimized configuration and higher efficiency. DICP is implemented through a parallel processing procedure working with the traditional processing mechanism of the distributed system. Representative results of comprehensive experiments on the challenging ImageNet dataset are selected to validate the capacity of our proposed ICP framework over the traditional state-of-the-art methods, both in time efficiency and quality of results.

cs.CV↗