arXiv subjects

Xinrui Li

Publications and source records attributed to Xinrui Li.

35 records · Page 2Linked to original sources

arXiv2024

Sparse MIMO for ISAC: New Opportunities and Challenges

Multiple-input multiple-output (MIMO) has been a key technology of wireless communications for decades. A typical MIMO system employs antenna arrays with the inter-antenna spacing being half of the signal wavelength, which we term as compact MIMO. Looking forward towards the future sixth-generation (6G) mobile communication networks, MIMO system will achieve even finer spatial resolution to not only enhance the spectral efficiency of wireless communications, but also enable more accurate wireless sensing. To this end, by removing the restriction of half-wavelength antenna spacing, sparse MIMO has been proposed as a new architecture that is able to significantly enlarge the array aperture as compared to conventional compact MIMO with the same number of array elements. In addition, sparse MIMO leads to a new form of virtual MIMO systems for sensing with their virtual apertures considerably larger than physical apertures. As sparse MIMO is expected to be a viable technology for 6G, we provide in this article a comprehensive overview of it, especially focusing on its appealing advantages for integrated sensing and communication (ISAC) towards 6G. Specifically, assorted sparse MIMO architectures are first introduced, followed by their new benefits as well as challenges. We then discuss the main design issues of sparse MIMO, including beam pattern synthesis, signal processing, grating lobe suppression, beam codebook design, and array geometry optimization. Last, we provide numerical results to evaluate the performance of sparse MIMO for ISAC and point out promising directions for future research.

cs.IT↗

arXiv2024

Observation of temporal topological boundary states of light in a momentum bandgap

Topological phases have prevailed across diverse disciplines, spanning electronics, photonics, and acoustics. Hitherto, the understanding of these phases has centred on energy (frequency) bandstructures, showcasing topological boundary states at spatial interfaces. Recent strides have uncovered a unique category of bandstructures characterized by gaps in momentum, referred to as momentum bandgaps or k gaps, notably driven by breakthroughs in photonic time crystals. This discovery hints at abundant topological phases defined within momentum bands, alongside a wealth of topological boundary states in the time domain. Here, we report the first experimental observation of k-gap topology in a large-scale optical temporal synthetic lattice, manifesting as temporal topological boundary states. These boundary states are uniquely situated at temporal interfaces between two subsystems with distinct k-gap topology. Counterintuitively, despite the exponential amplification of k-gap modes within both subsystems, these topological boundary states exhibit decay in both temporal directions. Our findings mark a significant pathway for delving into k gaps, temporal topological states, and time-varying physics.

physics.optics↗

arXiv2024

DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans

Gastrointestinal (GI) tract cancers pose a global health challenge, demanding precise radiotherapy planning for optimal treatment outcomes. This paper introduces a cutting-edge approach to automate the segmentation of GI tract regions in magnetic resonance imaging (MRI) scans. Leveraging advanced deep learning architectures, the proposed model integrates Inception-V4 for initial classification, UNet++ with a VGG19 encoder for 2.5D data, and Edge UNet for grayscale data segmentation. Meticulous data preprocessing, including innovative 2.5D processing, is employed to enhance adaptability, robustness, and accuracy. This work addresses the manual and time-consuming segmentation process in current radiotherapy planning, presenting a unified model that captures intricate anatomical details. The integration of diverse architectures, each specializing in unique aspects of the segmentation task, signifies a novel and comprehensive solution. This model emerges as an efficient and accurate tool for clinicians, marking a significant advancement in the field of GI tract image segmentation for radiotherapy planning.

eess.IV↗

arXiv2024

Linear Relative Pose Estimation Founded on Pose-only Imaging Geometry

How to efficiently and accurately handle image matching outliers is a critical issue in two-view relative estimation. The prevailing RANSAC method necessitates that the minimal point pairs be inliers. This paper introduces a linear relative pose estimation algorithm for n $( n \geq 6$) point pairs, which is founded on the recent pose-only imaging geometry to filter out outliers by proper reweighting. The proposed algorithm is able to handle planar degenerate scenes, and enhance robustness and accuracy in the presence of a substantial ratio of outliers. Specifically, we embed the linear global translation (LiGT) constraint into the strategies of iteratively reweighted least-squares (IRLS) and RANSAC so as to realize robust outlier removal. Simulations and real tests of the Strecha dataset show that the proposed algorithm achieves relative rotation accuracy improvement of 2 $\sim$ 10 times in face of as large as 80% outliers.

cs.CV↗

arXiv2023

Multi-User Modular XL-MIMO Communications: Near-Field Beam Focusing Pattern and User Grouping

In this paper, we investigate multi-user modular extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems, where modular extremely large-scale uniform linear array (XL-ULA) is deployed at the base station (BS) to serve multiple single-antenna users. By exploiting the unique modular array architecture and considering the potential near-field propagation, we develop sub-array based uniform spherical wave (USW) models for distinct versus common angles of arrival/departure (AoAs/AoDs) with respect to different sub-arrays/modules, respectively. Under such USW models, we analyze the beam focusing patterns at the near-field observation location by using near-field beamforming. The analysis reveals that compared to the conventional XL-MIMO with collocated antenna elements, modular XL-MIMO can provide better spatial resolution by benefiting from its larger array aperture. However, it also incurs undesired grating lobes due to the large inter-module separation. Moreover, it is found that for multi-user modular XL-MIMO communications, the achievable signal-to-interference-plus-noise ratio (SINR) for users may be degraded by the grating lobes of the beam focusing pattern. To address this issue, an efficient user grouping method is proposed for multi-user transmission scheduling, so that users located within the grating lobes of each other are not allocated to the same time-frequency resource block (RB) for their communications. Numerical results are presented to verify the effectiveness of the proposed user grouping method, as well as the superior performance of modular XL-MIMO over its collocated counterpart with densely distributed users.

cs.IT↗

arXiv2023

Real higher-order Weyl photonic crystal

Higher-order Weyl semimetals are a family of recently predicted topological phases simultaneously showcasing unconventional properties derived from Weyl points, such as chiral anomaly, and multidimensional topological phenomena originating from higher-order topology. The higher-order Weyl semimetal phases, with their higher-order topology arising from quantized dipole or quadrupole bulk polarizations, have been demonstrated in phononics and circuits. Here, we experimentally discover a class of higher-order Weyl semimetal phase in a three-dimensional photonic crystal (PhC), exhibiting the concurrence of the surface and hinge Fermi arcs from the nonzero Chern number and the nontrivial generalized real Chern number, respectively, coined a real higher-order Weyl PhC. Notably, the projected two-dimensional subsystem with kz = 0 is a real Chern insulator, belonging to the Stiefel-Whitney class with real Bloch wavefunctions, which is distinguished fundamentally from the Chern class with complex Bloch wavefunctions. Our work offers an ideal photonic platform for exploring potential applications and material properties associated with the higher-order Weyl points and the Stiefel-Whitney class of topological phases.

cond-mat.mes-hall↗

arXiv2023

Near-Field Beam Focusing Pattern and Grating Lobe Characterization for Modular XL-Array

In this paper, we investigate the near-field modelling and analyze the beam focusing pattern for modular extremely large-scale array (XL-array) communications. As modular XL-array is physically and electrically large in general, the accurate characterization of amplitude and phase variations across its array elements requires the non-uniform spherical wave (NUSW) model, which, however, is difficult for performance analysis and optimization. To address this issue, we first present two ways to simplify the NUSW model by exploiting the unique regular structure of modular XL-array, termed sub-array based uniform spherical wave (USW) models with different or common angles, respectively. Based on the developed models, the near-field beam focusing patterns of XL-array communications are derived. It is revealed that compared to the existing collocated XL-array with the same number of array elements, modular XL-array can significantly enhance the spatial resolution, but at the cost of generating undesired grating lobes. Fortunately, different from the conventional far-field uniform plane wave (UPW) model, the near-field USW model for modular XL-array exhibits a higher grating lobe suppression capability, thanks to the non-linear phase variations across the array elements. Finally, simulation results are provided to verify the near-field beam focusing pattern and grating lobe characteristics of modular XL-array.

cs.IT↗

arXiv2023

Meta-lenses for differential imaging based on weak measurement

All-optical information communication, processing and computation have received substantial interest of both fundamental and applied research due to its unrivaled speed and broad bandwidth. Compared to its electronic counterpart, photons seldom interact with each other which makes them obtain a long coherence time on one hand and relieved from heavy energy dissipation on the other. However, one of the hindrances to achieve all-optical circuits is the large volume of all-optical devices to achieve specific functionalities. In this work, we propose and demonstrate experimentally three meta-lenses for differential imaging employing the framework of weak measurement: (1) partial differential lens, (2) total differential lens and (3) second order differential lens compatible with the requirement of miniaturization to achieve all-optical technology. Based on Fresnel-lens-like structures, our meta-lenses incorporated the previous weak-measurement compartment into wavelength scale, which induces a miniature differential operation system as a result. In addition to its potential importance in heavily integrated all-optical neural networks, the differential lens can be easily incorporated in the existing imaging systems like a conventional lens without increasing the complexity of the system of interest.

physics.optics↗

arXiv2023

A note on $R$-linear convergence of nonmonotone gradient methods

Nonmonotone gradient methods generally perform better than their monotone counterparts especially on unconstrained quadratic optimization. However, the known convergence rate of the monotone method is often much better than its nonmonotone variant. With the aim of shrinking the gap between theory and practice of nonmonotone gradient methods, we introduce a property for convergence analysis of a large collection of gradient methods. We prove that any gradient method using stepsizes satisfying the property will converge $R$-linearly at a rate of $1-λ_1/M_1$, where $λ_1$ is the smallest eigenvalue of Hessian matrix and $M_1$ is the upper bound of the inverse stepsize. Our results indicate that the existing convergence rates of many nonmonotone methods can be improved to $1-1/κ$ with $κ$ being the associated condition number.

math.OC↗

arXiv2022

Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis

Through a study of multi-gas mixture datasets, we show that in multi-component spectral analysis, the number of functional or non-functional principal components required to retain the essential information is the same as the number of independent constituents in the mixture set. Due to the mutual in-dependency among different gas molecules, near one-to-one projection from the principal component to the mixture constituent can be established, leading to a significant simplification of spectral quantification. Further, with the knowledge of the molar extinction coefficients of each constituent, a complete principal component set can be extracted from the coefficients directly, and few to none training samples are required for the learning model. Compared to other approaches, the proposed methods provide fast and accurate spectral quantification solutions with a small memory size needed.

cs.LG↗

arXiv2022

Applying Back Propagation Algorithm and Analytic Hierarchy Process to Environment Assessment

This paper designs a new and scientific environmental quality assessment method, and takes Saihan dam as an example to explore the environmental improvement degree to the local and Beijing areas. AHP method is used to assign values to each weight 7 primary indicators and 21 secondary indicators were used to establish an environmental quality assessment model. The conclusion shows that after the establishment of Saihan dam, the local environmental quality has been improved by 7 times, and the environmental quality in Beijing has been improved by 13%. Then the future environmental index is predicted. Finally the Spearson correlation coefficient is analyzed, and it is proved that correlation is 99% when the back-propagation algorithm is used to test and prove that the error is little.

cs.CY↗

arXiv2022

A gradient method exploiting the two dimensional quadratic termination property

The quadratic termination property is important to the efficiency of gradient methods. We consider equipping a family of gradient methods, where the stepsize is given by the ratio of two norms, with two dimensional quadratic termination. Such a desired property is achieved by cooperating with a new stepsize which is derived by maximizing the stepsize of the considered family in the next iteration. It is proved that each method in the family will asymptotically alternate in a two dimensional subspace spanned by the eigenvectors corresponding to the largest and smallest eigenvalues. Based on this asymptotic behavior, we show that the new stepsize converges to the reciprocal of the largest eigenvalue of the Hessian. Furthermore, by adaptively taking the long Barzilai--Borwein stepsize and reusing the new stepsize with retard, we propose an efficient gradient method for unconstrained quadratic optimization. We prove that the new method is $R$-linearly convergent with a rate of $1-1/κ$, where $κ$ is the condition number of Hessian. Numerical experiments show the efficiency of our proposed method.

math.OC↗

arXiv2022

GES Model :Combining Pearson Correlation Coefficient Analysis with Multilayer Perceptron

With the development of technological progress, mining on asteroids is becoming a reality. This paper focuses on how to distribute asteroid mineral resources in a reasonable way to ensure global equity. To distribute asteroid resources fairly, 7 primary indicators and 20 secondary indicators are introduced to build a mathematical model to evaluate global equity and the weights are given by Analytic Hierarchy Process (AHP). Then Global Equity Score(GES) Model based on 12 primary indicators and 40 secondary indicators is built and TOPSIS method is applied to rank all countries. A t-distribution probability density function is applied to simulate the rate of asteroid mining. The Backward Algorithm is applied to quantitatively measure the impact of changing indicators on global equity. Then Pearson correlation coefficient analysis is conducted for each indicator, and t-test is performed lastly. The results demonstrate that asteroid mining promotes global equity that poor countries can be allocated slightly more mineral resources, and a schedule of the implementation of each measure is given. To gain more insight, sensitivity analysis is conducted and the results demonstrate that scores vary less than 7%. It can be concluded that our GES model have great potential as its robustness, accuracy and strengths.

cs.CE↗

arXiv2022

Modular Extremely Large-Scale Array Communication: Near-Field Modelling and Performance Analysis

This paper investigates wireless communications based on a new antenna array architecture, termed modular extremely large-scale array (XL-array), where an extremely large number of antenna elements are regularly arranged on a common platform in a modular manner. Each module consists of a flexible/moderate number of antenna elements, and different modules are separated with an inter-module spacing that is typically much larger than the inter-element spacing/signal wavelength for ease of deployment. By properly modelling the variations of signal phase, amplitude and projected aperture across different array modules/elements, we develop the new channel model and analyze the signal-to-noise ratio (SNR) performance of the modular XL-array based communications. Under the practical non-uniform spherical wave (NUSW) model, the closed-form expression of the maximum achievable SNR is derived in terms of key geometric parameters, including the total planar array size, module separation distances along each dimension, as well as the user's location in the three-dimensional (3D) space. Besides, the asymptotic SNR scaling laws are revealed as the number of modules along different dimensions goes to infinity. Moreover, we show that our developed near-field modelling and performance analysis include the existing ones for the collocated XL-array, the far-field uniform plane wave (UPW) model, as well as the one-dimensional (1D) modular extremely large-scale uniform linear array (XL-ULA) as special cases. Extensive simulation results are provided to validate our obtained results.

cs.IT↗

arXiv2022

Near-Field Modelling and Performance Analysis of Modular Extremely Large-Scale Array Communications

This letter studies a new array architecture, termed as modular extremely large-scale array (XL-array), for which a large number of array elements are arranged in a modular manner. Each module consists of a moderate number of array elements and the modules are regularly arranged with the inter-module space typically much larger than signal wavelength to cater to the actual mounting structure. We study the mathematical modelling and conduct the performance analysis for modular XL-array communications, by considering the non-uniform spherical wave (NUSW) characteristic that is more suitable than the conventional uniform plane wave (UPW) assumption for physically large arrays. A closed-form expression is derived for the maximum signal-to-noise ratio (SNR) in terms of the geometries of the modular XL-array, including the total array size and module separation, as well as the user's location. The asymptotic SNR scaling law is revealed as the size of modular array goes to infinity. Furthermore, we show that the developed modelling and performance analysis include the existing results for collocated XL-array or far-field UPW assumption as special cases. Numerical results demonstrate the importance of near-field modelling for modular XL-array communications since it leads to significantly different results from the conventional far-field UPW modelling.

cs.IT↗

arXiv2019

Image Retargetability

Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions, while preserving its visually and semantically important content. However, not all images can be equally well processed that way. In this work, we introduce the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated retargetability rating problem. To train and analyze this model, we have collected a database which contains retargetability scores and meaningful image attributes assigned by six expert raters. Experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show applications of image retargetability in retargeting method selection, retargeting method assessment and photo collage generation.

cs.CV↗

arXiv2017

Unsupervised state representation learning with robotic priors: a robustness benchmark

Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot. As in Jonschkowski et al. 2015, we learn in an unsupervised way using prior knowledge about the world as loss functions called robotic priors and extend this approach to high dimension richer images to learn a 3D representation of the hand position of a robot from RGB images. We propose a quantitative evaluation of the learned representation using nearest neighbors in the state space that allows to assess its quality and show both the potential and limitations of robotic priors in realistic environments. We augment image size, add distractors and domain randomization, all crucial components to achieve transfer learning to real robots. Finally, we also contribute a new prior to improve the robustness of the representation. The applications of such low dimensional state representation range from easing reinforcement learning (RL) and knowledge transfer across tasks, to facilitating learning from raw data with more efficient and compact high level representations. The results show that the robotic prior approach is able to extract high level representation as the 3D position of an arm and organize it into a compact and coherent space of states in a challenging dataset.

cs.AI↗