arXiv subjects

Xiaoxiao Wu

Publications and source records attributed to Xiaoxiao Wu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Multi-channel frequency router based on valley-Hall metacrystals

Topological photonics has revolutionized manipulations of electromagnetic waves by leveraging various topological phases proposed originally in condensed matters, leading to robust and error-immune signal processing. Despite considerable efforts, a critical challenge remains in devising frequency routers operating at a broadband frequency range with limited crosstalk. Previous designs usually relied on fine tuning of parameters and are difficult to be integrated efficiently and compactly. Here, targeting the demand for frequency-selective applications in on-chip photonics, we explore a topological approach to photonic frequency router via valley-Hall metacrystals. Diverging from the majority of studies which focuses on zigzag interfaces, our research shifts the attention to armchair interfaces within an ABA sandwich-like structure, where a single column of type-B metacrystal acts as a perturbation in the background type-A metacrystal. Essentially, through tuning a single geometric parameter of the type-B metacrystal, this configuration gives rise to interface states within a customized frequency band, enabling signal routing with limited crosstalk to meet specified demands. Moreover, this concept is practically demonstrated through a photonic frequency router with three distinct channels, experimentally exhibiting robust wave transmissions with excellent agreement with the design. This investigation manifests possible applications of the armchair interfaces in valley-Hall photonic systems and advances development of photonic devices that are both compact and efficient. Notably, the approach is naturally compatible with on-chip photonics and integration, which could benefit telecommunications and optical computing applications.

physics.app-ph↗

arXiv2024

Few-Shot Medical Image Segmentation with Large Kernel Attention

Medical image segmentation has witnessed significant advancements with the emergence of deep learning. However, the reliance of most neural network models on a substantial amount of annotated data remains a challenge for medical image segmentation. To address this issue, few-shot segmentation methods based on meta-learning have been employed. Presently, the methods primarily focus on aligning the support set and query set to enhance performance, but this approach hinders further improvement of the model's effectiveness. In this paper, our objective is to propose a few-shot medical segmentation model that acquire comprehensive feature representation capabilities, which will boost segmentation accuracy by capturing both local and long-range features. To achieve this, we introduce a plug-and-play attention module that dynamically enhances both query and support features, thereby improving the representativeness of the extracted features. Our model comprises four key modules: a dual-path feature extractor, an attention module, an adaptive prototype prediction module, and a multi-scale prediction fusion module. Specifically, the dual-path feature extractor acquires multi-scale features by obtaining features of 32{\times}32 size and 64{\times}64 size. The attention module follows the feature extractor and captures local and long-range information. The adaptive prototype prediction module automatically adjusts the anomaly score threshold to predict prototypes, while the multi-scale fusion prediction module integrates prediction masks of various scales to produce the final segmentation result. We conducted experiments on publicly available MRI datasets, namely CHAOS and CMR, and compared our method with other advanced techniques. The results demonstrate that our method achieves state-of-the-art performance.

cs.CV↗

arXiv2024

Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation

In recent years, deep learning based on Convolutional Neural Networks (CNNs) has achieved remarkable success in many applications. However, their heavy reliance on extensive labeled data and limited generalization ability to unseen classes pose challenges to their suitability for medical image processing tasks. Few-shot learning, which utilizes a small amount of labeled data to generalize to unseen classes, has emerged as a critical research area, attracting substantial attention. Currently, most studies employ a prototype-based approach, in which prototypical networks are used to construct prototypes from the support set, guiding the processing of the query set to obtain the final results. While effective, this approach heavily relies on the support set while neglecting the query set, resulting in notable disparities within the model classes. To mitigate this drawback, we propose a novel Support-Query Prototype Fusion Network (SQPFNet). SQPFNet initially generates several support prototypes for the foreground areas of the support images, thus producing a coarse segmentation mask. Subsequently, a query prototype is constructed based on the coarse segmentation mask, additionally exploiting pattern information in the query set. Thus, SQPFNet constructs high-quality support-query fused prototypes, upon which the query image is segmented to obtain the final refined query mask. Evaluation results on two public datasets, SABS and CMR, show that SQPFNet achieves state-of-the-art performance.

cs.CV↗

arXiv2024

Multi-Task Learning for Fatigue Detection and Face Recognition of Drivers via Tree-Style Space-Channel Attention Fusion Network

In driving scenarios, automobile active safety systems are increasingly incorporating deep learning technology. These systems typically need to handle multiple tasks simultaneously, such as detecting fatigue driving and recognizing the driver's identity. However, the traditional parallel-style approach of combining multiple single-task models tends to waste resources when dealing with similar tasks. Therefore, we propose a novel tree-style multi-task modeling approach for multi-task learning, which rooted at a shared backbone, more dedicated separate module branches are appended as the model pipeline goes deeper. Following the tree-style approach, we propose a multi-task learning model for simultaneously performing driver fatigue detection and face recognition for identifying a driver. This model shares a common feature extraction backbone module, with further separated feature extraction and classification module branches. The dedicated branches exploit and combine spatial and channel attention mechanisms to generate space-channel fused-attention enhanced features, leading to improved detection performance. As only single-task datasets are available, we introduce techniques including alternating updation and gradient accumulation for training our multi-task model using only the single-task datasets. The effectiveness of our tree-style multi-task learning model is verified through extensive validations.

cs.CV↗

arXiv2024

Experimental demonstration of a thermal-EM concentrator for enhancing EM signals and converging heat fluxes simultaneously

Simultaneously concentrating EM waves and heat fluxes to the same target region within an on-chip system carries substantial academic research importance and practical application value. Nevertheless, existing researches are primarily aimed at the design and experimentation of concentrators for individual EM waves or temperature fields. In this work, a thermal-EM concentrator, capable of simultaneously concentrating EM waves and heat fluxes, is designed using transformation optics/thermodynamics and fabricated with engineered EM-thermal metamaterials. The concentrating effects of the proposed thermal-EM concentrator on the thermal fluxes and EM waves are verified through numerical simulations and experimental measurements, respectively, which are in good agreement with each other. Both numerically simulated and experimentally measured results demonstrate the concentrating capability of the proposed thermal-EM concentrator, which can concentrate broadband TM-polarized EM waves ranging from 8-12 GHz and heat/cold flows to the same target region within an on-chip operating environment. The thermal-EM concentrator exhibits a thermal focusing efficiency close to 100% and more than three times enhancement of the magnetic field at the designed center frequency of 10 GHz. The proposed thermal-EM concentrator can be utilized for efficient cooling for the specified component and simultaneously enhancing the EM antenna's radiation/reception efficiency within an on-chip system.

physics.gen-ph↗

arXiv2023

Multi-Attention Fusion Drowsy Driving Detection Model

Drowsy driving represents a major contributor to traffic accidents, and the implementation of driver drowsy driving detection systems has been proven to significantly reduce the occurrence of such accidents. Despite the development of numerous drowsy driving detection algorithms, many of them impose specific prerequisites such as the availability of complete facial images, optimal lighting conditions, and the use of RGB images. In our study, we introduce a novel approach called the Multi-Attention Fusion Drowsy Driving Detection Model (MAF). MAF is aimed at significantly enhancing classification performance, especially in scenarios involving partial facial occlusion and low lighting conditions. It accomplishes this by capitalizing on the local feature extraction capabilities provided by multi-attention fusion, thereby enhancing the algorithm's overall robustness. To enhance our dataset, we collected real-world data that includes both occluded and unoccluded faces captured under nighttime and daytime lighting conditions. We conducted a comprehensive series of experiments using both publicly available datasets and our self-built data. The results of these experiments demonstrate that our proposed model achieves an impressive driver drowsiness detection accuracy of 96.8%.

cs.CV↗

arXiv2023

Demonstration of acoustic high-order Stiefel-Whitney semimetal in bilayer graphene sonic crystals

Recently, higher-order topological phases have endowed materials many exotic topological phases. For three-dimensional (3D) higher-order topologies, it hosts topologically protected 1D hinge states or 0D corner states, which extend the bulk-boundary correspondence of 3D topological phases. Meanwhile, the enrichment of group symmetries with exploration of projective symmetry algebras redefined the fundamentals of nontrivial topological matter with artificial gauge fields, leading to the discovery of new topological phases in classical wave systems. In this Letter, we construct an acoustic topological semimetal characterized by both the first and the second Stiefel-Whitney (SW) topological charges by utilizing the projective symmetry. Different from conventional high-order topologies with multiple bulk-boundary correspondences protected by different class topological invariants, acoustic high-order Stiefel-Whitney semimetal (HOSWS) has two different bulk-edge correspondences protected by only one class (SW class) topological invariant. Two types of topological hinge and surface states are embedded in bulk bands at the same frequency, featuring similar characteristics of bound states in the continuum (BICs). In experiments, we demonstrate the existence of high-quality surface state and hinge state at the interested frequency window with polarized intensity field distributions.

cond-mat.mes-hall↗

arXiv2022

Latency Minimization for Multiuser Computation Offloading in Fog-Radio Access Networks

This paper considers computation offloading in fog-radio access networks (F-RAN), where multiple user equipments (UEs) offload their computation tasks to the F-RAN through a number of fog nodes. Each UE can choose one of the fog nodes to offload its task, and each fog node may serve multiple UEs. Depending on the computation burden at the fog nodes, the tasks may be computed by the fog nodes or further offloaded to the cloud via capacity-limited fronthaul links. To compute all UEs' tasks as fast as possible, joint optimization of UE-Fog association, radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs. This min-max problem is formulated as a mixed integer nonlinear program (MINP). We first show that the MINP can be reformulated as a continuous optimization problem, and then employ the majorization minimization (MM) approach to find a solution. The MM approach that we develop is unconventional in that -- each MM subproblem can be solved inexactly with the same provable convergence guarantee as the conventional exact MM, thereby reducing the complexity of each MM iteration. In addition, we also consider a cooperative offloading model, where the fog nodes compress-and-forward their received signals to the cloud. Under this model, a similar min-max latency optimization problem is formulated and tackled again by the inexact MM approach. Simulation results show that the proposed algorithms outperform some heuristic offloading strategies, and that the cooperative offloading can better exploit the transmission diversity to attain better latency performance than the non-cooperative one.

eess.SP↗

arXiv2021

Secure Blockchain Platform for Industrial IoT with Trusted Computing Hardware

As a disruptive technology that originates from cryptocurrency, blockchain provides a trusted platform to facilitate industrial IoT (IIoT) applications. However, implementing a blockchain platform in IIoT scenarios confronts various security challenges due to the rigorous deployment condition. To this end, we present a novel design of secure blockchain based on trusted computing hardware for IIoT applications. Specifically, we employ the trusted execution environment (TEE) module and a customized security chip to safeguard the blockchain against different attacking vectors. Furthermore, we implement the proposed secure IIoT blockchain on the ARM-based embedded device and build a small-scale IIoT network to evaluate its performance. Our experimental results show that the secure blockchain platform achieves a high throughput (150TPS) with low transaction confirmation delay (below 66ms), demonstrating its feasibility in practical IIoT scenarios. Finally, we outline the open challenges and future research directions.

cs.CR↗

arXiv2021

Blockchain for Transactive Energy Management of Distributed Energy Resources in Smart Grid

This work presents the design and implementation of a blockchain system that enables the trustable transactive energy management for distributed energy resources (DERs). We model the interactions among DERs, including energy trading and flexible appliance scheduling, as a cost minimization problem. Considering the dispersed nature and diverse ownership of DERs, we develop a distributed algorithm to solve the optimization problem using the alternating direction method of multipliers (ADMM) method. Furthermore, we develop a blockchain system, on which we implement the proposed algorithm with the smart contract, to guarantee the transparency and correctness of the energy management. We prototype the blockchain in a small-scale test network and evaluate it through experiments using real-world data. The experimental results validate the feasibility and effectiveness of our design.

cs.DC↗

arXiv2021

Blockchain-Based Decentralized Energy Management Platform for Residential Distributed Energy Resources in A Virtual Power Plant

The advent of distributed energy resources (DERs), such as distributed renewables, energy storage, electric vehicles, and controllable loads, \rv{brings} a significantly disruptive and transformational impact on the centralized power system. It is widely accepted that a paradigm shift to a decentralized power system with bidirectional power flow is necessary to the integration of DERs. The virtual power plant (VPP) emerges as a promising paradigm for managing DERs to participate in the power system. In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP. Specifically, users can interact with each other to trade energy for mutual benefits and provide network services, such as feed-in energy, reserve, and demand response, through the VPP. To respect the users' independence and preserve their privacy, we design a decentralized optimization algorithm to optimize the users' energy scheduling, energy trading, and network services. Then we develop a prototype blockchain network for VPP energy management and implement the proposed algorithm on the blockchain network. By experiments using real-world data-trace, we validated the feasibility and effectiveness of our algorithm and the blockchain system. The simulation results demonstrate that our blockchain-based VPP energy management platform reduces the users' cost by up to 38.6% and reduces the overall system cost by 11.2%.

eess.SY↗

arXiv2021

Distributed-Memory Tensor Completion for Generalized Loss Functions in Python using New Sparse Tensor Kernels

Tensor computations are increasingly prevalent numerical techniques in data science, but pose unique challenges for high-performance implementation. We provide novel algorithms and systems infrastructure which enable efficient parallel implementation of algorithms for tensor completion with generalized loss functions. Specifically, we consider alternating minimization, coordinate minimization, and a quasi-Newton (generalized Gauss-Newton) method. By extending the Cyclops library, we implement all of these methods in high-level Python syntax. To make possible tensor completion for very sparse tensors, we introduce new multi-tensor primitives, for which we provide specialized parallel implementations. We compare these routines to pairwise contraction of sparse tensors by reduction to hypersparse matrix formats, and find that the multi-tensor routines are more efficient in theoretical cost and execution time in experiments. We provide microbenchmarking results on the Stampede2 supercomputer to demonstrate the efficiency of the new primitives and Cyclops functionality. We then study the performance of the tensor completion methods for a synthetic tensor with 10 billion nonzeros and the Netflix dataset, considering both least squares and Poisson loss functions.

cs.DC↗

arXiv2021

Manually Tunable Ventilated Metamaterial Absorbers

For most acoustic metamaterials, once they have been fabricated, their operating frequencies and functions cannot be adjusted, which is an intrinsic barrier for development of realistic applications. The study to overcome this limit has become a significant issue in acoustic metamaterial engineering. Although with the advance of metamaterials in the past two decades, a series of methods such as electric or magnetic control have been proposed, most of them can only work in the condition of no fluid passage. Some metamaterials with large transmission losses have been proposed, but the sounds are essentially reflected rather than absorbed. Here, to overcome this intrinsic difficulty, we propose a ventilated sound absorber that can be manually tuned in a large range after being manufactured. During the tuning which is achieved through an intricately designed slider, high-performance absorption and ventilation are both ensured. The tunable ventilated sound absorber is demonstrated experimentally and the effective model of coupled lossy oscillators can be employed to understand its mechanism. The manually tunable ventilated metamaterial has the potential application values in various complicated pipe systems that require frequency adjustment and it also establishes the foundation for future development of active tunable ventilated acoustic metamaterials.

physics.app-ph↗

arXiv2020

Ultra-open High-efficiency Ventilated Metamaterial Absorbers with Customized Broadband Performance

High-efficiency absorption of low-frequency sounds (< 1000 Hz) while maintaining a free flow of fluids remains a significant challenge in acoustical engineering due to the rigid trade-off between absorption and ventilation performances. Although ongoing advances in acoustic metamaterials have unlocked unprecedented possibilities and various metamaterial absorbers have been proposed, most of them only work adequately in the condition of no sound transmissions. Unfortunately, such condition requires a complete block of fluid channels due to longitudinal nature of sounds, which allows them to penetrate any small holes. Otherwise, their absorption performance could be drastically degraded and often cannot exceed 50%. This basic trade-off between absorption and ventilation performances definitely constrains their applications in daily scenarios where free air flows are necessary. Though some ventilated sound barriers with large transmission loss have been demonstrated, they essentially only reflect sounds, which are still there and even may be reflected back. Here, to overcome this general difficulty, we propose and demonstrate an ultra-open ventilated metamaterial absorber. The absorber, aiming at low-frequency sounds, simultaneously ensures high-performance absorption and ventilation, confirmed in experiments. Their mechanism is understood from an effective model of coupled lossy oscillators. Furthermore, the absorbers can be simply stacked to work in a customized broadband, while maintaining a good ventilation. The demonstrated absorber provides a clear scheme for achieving high-performance absorption and ventilation at low frequencies, necessary for applications in environment with free air flows.

physics.app-ph↗

arXiv2020

Deterministic scheme for two-dimensional type-II Dirac points and experimental realization in acoustics

Low-energy electrons near Dirac/Weyl nodal points mimic massless relativistic fermions. However, as they are not constrained by Lorentz invariance, they can exhibit tipped-over type-II Dirac/Weyl cones which provide highly anisotropic physical properties and responses, creating unique possibilities. Recently, they have been observed in several quantum and classical systems. Yet, there is still no simple and deterministic strategy to realize them since their nodal points are accidental degeneracies, unlike symmetry-guaranteed type-I counterparts. Here, we propose a band-folding scheme for constructing type-II Dirac points, and we use a tight-binding analysis to unveil its generality and deterministic nature. Through realizations in acoustics, type-II Dirac points are experimentally visualized and investigated using near-field mappings. As a direct effect of tipped-over Dirac cones, strongly tilted kink states originating from their valley-Hall properties are also observed. This deterministic scheme could serve as platform for further investigations of intriguing physics associated with various strongly Lorentz-violating nodal points.

cond-mat.mes-hall↗

arXiv2019

Interlayer Topological Transport and Devices based on Layer Pseudospins in Photonic Valley-Hall Phases

Valley-Hall phases, first proposed in two-dimensional (2D) materials, originate from nontrivial topologies around valleys which denote local extrema in momentum space. Since they are extended into classical systems, their designs draw inspirations from existing quantum counterparts, and their transports show similar topological protections. In contrast, it is recently established in acoustics that layer pseudospins in valley-Hall phases can give rise to special valley-Hall edge states with fundamentally different transport behaviors at the interfaces compared with various 2D materials. Their realization in other classical systems, such as photonics, would allow us to design topological insulators beyond quantum inspirations. In this work, we show that layer pseudospins exist in photonic valley-Hall phases, using vertically coupled designer surface plasmon crystals, a non-radiative system in open environment supporting tightly-confined propagating modes. The negligible thermal and radiative losses in our structure pave the way for our direct observations of the layer pseudospins and associated topological phenomena stemmed from them in both real and reciprocal spaces. Photonic devices that manipulate signals based on layer pseudospins of the topological phases, such as layer convertors and layer-selected delay lines, are experimentally demonstrated, confirming the potential applications of layer pseudospins as a new degree of freedom carrying information.

physics.optics↗

arXiv2018

Su-Schrieffer-Heeger Model Inspired Acoustic Interface States and Edge States

If a full band gap closes and then reopens when we continuously deform a periodic system while keeping its symmetry, a topological phase transition usually occurs. A common model demonstrating such a topological phase transition in condensed matter physics is the Su-Schrieffer-Heeger (SSH) model. As well known, two distinct topological phases emerge when the intracell hopping is tuned from smaller to larger with respect to the intercell hopping in the model. The former case is topologically trivial, while the latter case is topologically non-trivial. Here, we design a 1D periodic acoustic system in exact analogy to the SSH model. The unit cell of the acoustic system is composed of two resonators and two junction tubes connecting them. We show that the topological phase transition happens in our acoustic analog when we tune the radii of the junction tubes which control the intercell and intracell hoppings. The topological phase transition is characterized by the abrupt change of the geometric Zak phase. The topological interface states between non-trivial and trivial phases of our acoustic analog are experimentally measured, and the results agree very well with the numerical values. Further, we show that topologically non-trivial phases of our acoustic analog of the SSH model can support edge states, on which the discussion is absent in previous works about topological acoustics. The edge states are robust against localized defects and perturbations.

physics.class-ph↗

arXiv2018

Designing topological interface states in phononic crystals based on the full phase diagrams

The topological invariants of a periodic system can be used to define the topological phase of each band and determine the existence of topological interface states within a certain bandgap. Here, we propose a scheme based on the full phase diagrams, and design the topological interface states within any specified bandgaps. As an example, here we propose a kind of one-dimensional phononic crystals. By connecting two semi-infinite structures with different topological phases, the interface states within any specific bandgap or their combinations can be achieved in a rational manner. The existence of interface states in a single bandgap, in all odd bandgaps, in all even bandgaps, or in all bandgaps, are verified in simulations and experiments. The scheme of full phase diagrams we introduce here can be extended to other kinds of periodic systems, such as photonic crystals and designer plasmonic crystals.

physics.app-ph↗