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Researcher profile

He Huang

He Huang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionArtificial Intelligencegr-qcInformation Theorymath.ITCryptography and SecurityDistributed, Parallel, and Cluster Computingeess.IVeess.SPHuman-Computer InteractionMachine LearningNetworking and Internet Architecturephysics.plasm-ph

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Published work

13 published item(s)

preprint2026arXiv

AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers

The widespread use of earphones has enabled various sensing applications, including activity recognition, health monitoring, and context-aware computing. Among these, earphone-based user authentication has become a key technique by leveraging unique biometric features. However, existing earphone-based authentication systems face key limitations: they either require explicit user interaction or active speaker output, or suffer from poor accessibility and vulnerability to environmental noise, which hinders large-scale deployment. In this paper, we propose a passive authentication system, called AccLock, which leverages distinctive features extracted from in-ear BCG signals to enable secure and unobtrusive user verification. Our system offers several advantages over previous systems, including zero-involvement for both the device and the user, ubiquitous, and resilient to environmental noise. To realize this, we first design a two-stage denoising scheme to suppress both inherent and sporadic interference. To extract user-specific features, we then propose a disentanglement-based deep learning model, HIDNet, which explicitly separates user-specific features from shared nuisance components. Lastly, we develop a scalable authentication framework based on a Siamese network that eliminates the need for per-user classifier training. We conduct extensive experiments with 33 participants, achieving an average FAR of 3.13% and FRR of 2.99%, which demonstrates the practical feasibility of AccLock.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dynamical analysis and statefinder of Barrow holographic dark energy

Based on the holographic principle and the Barrow entropy, Barrow holographic dark energy had been proposed. In order to analyze the stability and the evolution of Barrow holographic dark energy, we, in this paper, apply the dynamical analysis and statefinder methods to Barrow holographic dark energy with different IR cutoff and interacting terms. In the case of using Hubble horizon as IR cutoff with the interacting term $Q=\fracλ{H}ρ_{m}ρ_{D}$, we find this model is stable and can be used to describe the whole evolution of the universe when the energy transfers from the pressureless matter to the Barrow holographic dark energy. When the dynamical analysis method is applied to this stable model, an attractor corresponding to an accelerated expansion epoch exists and this attractor can behave as the cosmological constant. Furthermore, the coincidence problem can be solved in this case. Then, after using the statefinder analysis method to this model, we find this model can be discriminated from the standard $Λ$CDM model. Finally, we have discussed the turning point of Hubble diagram in Barrow holographic dark energy and find the turning point does not exist in this model.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Emergent scenario in mimetic gravity

The emergent scenario provides a possible way to avoid the big bang singularity by assuming that the universe originates from an Einstein static state. Therefore, an Einstein static universe stable under perturbations is crucial to a successful implementation of the emergent mechanism. In this paper, we analyze the stability of the Einstein static universe against the scalar perturbations in the mimetic theory and find that stable Einstein static solutions exist under certain conditions in this theory. In the original mimetic gravity, the Einstein static universe is unstable. Then, we find that the universe can naturally exit from the initial static state, evolve into an inflationary era and then exit from the inflationary era. Thus, the emergent scenario can be used to resolve the big bang singularity in the mimetic theory.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation

Glioma is the most common and aggressive brain tumor. Magnetic resonance imaging (MRI) plays a vital role to evaluate tumors for the arrangement of tumor surgery and the treatment of subsequent procedures. However, the manual segmentation of the MRI image is strenuous, which limits its clinical application. With the development of deep learning, a large number of automatic segmentation methods have been developed, but most of them stay in 2D images, which leads to subpar performance. Moreover, the serious voxel imbalance between the brain tumor and the background as well as the different sizes and locations of the brain tumor makes the segmentation of 3D images a challenging problem. Aiming at segmenting 3D MRI, we propose a model for brain tumor segmentation with multiple encoders. The structure contains four encoders and one decoder. The four encoders correspond to the four modalities of the MRI image, perform one-to-one feature extraction, and then merge the feature maps of the four modalities into the decoder. This method reduces the difficulty of feature extraction and greatly improves model performance. We also introduced a new loss function named "Categorical Dice", and set different weights for different segmented regions at the same time, which solved the problem of voxel imbalance. We evaluated our approach using the online BraTS 2020 Challenge verification. Our proposed method can achieve promising results in the validation set compared to the state-of-the-art approaches with Dice scores of 0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhanced tumor, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulation

Avatar refers to a representative of a physical user in the virtual world that can engage in different activities and interact with other objects in metaverse. Simulating the avatar requires accurate human pose estimation. Though camera-based solutions yield remarkable performance, they encounter the privacy issue and degraded performance caused by varying illumination, especially in smart home. In this paper, we propose a WiFi-based IoT-enabled human pose estimation scheme for metaverse avatar simulation, namely MetaFi. Specifically, a deep neural network is designed with customized convolutional layers and residual blocks to map the channel state information to human pose landmarks. It is enforced to learn the annotations from the accurate computer vision model, thus achieving cross-modal supervision. WiFi is ubiquitous and robust to illumination, making it a feasible solution for avatar applications in smart home. The experiments are conducted in the real world, and the results show that the MetaFi achieves very high performance with a PCK@50 of 95.23%.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Stability analysis of the Tsallis holographic dark energy model

Using the generalized Tsallis entropy, the Tsallis holographic dark energy(THDE) was proposed recently. In this paper we analyze the cosmological consequences of the THDE model with an interaction between dark energy and dark matter $Q=H(αρ_{m}+βρ_{D})$. We find that the THDE model can explain the current accelerated cosmic expansion, and it is stable under certain conditions. Furthermore, through investigating the dynamical analysis, we find that there exists an attractor which represents an accelerated expansion phase of the universe. When $β=0$, this attractor corresponds to a dark energy dominated de Sitter solution and the universe can evolve into an era which is depicted by the $Λ$CDM model. The age of universe in this model is also explored.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cooperative Communications for Internet of Everything in B5G/6G Hybrid and Ubiquitous Networks: Foundation, Further Optimization and Solutions

Cooperative Communications (CC) has been one of most critical communication technologies which plays a founding role on Internet of Everything in B5G/6G networks. As 5G communications standard is gradually established recently, core communications technologies with CC are further studied to significantly improve communication quality and develop new communications scenarios for B5G/6G ubiquitous networks. Considering that CC has been regarded as foundation theory which widely exists in future multiple B5G/6G hybrid scenarios, such as, Cognitive Internet of Things (CIOT) networks, UAVs communications, air-space-ground of integrated networks, underwater acoustic communication and so on, besides it is closely combined with other key technologies, for examples, Massive MIMO, NOMA, Full-duplex transmission, Polar code and so on. Hence, in this paper we review foundation of CC for Internet of Everything in B5G/6G multiple heterogeneous CC networks, and compare fundamental CC algorithms to reveal key of performance improvement. Furthermore we propose that collective communications ideology is theory of foundation to realize communications for arbitrary two points as source/destination devices, sensors, relays, IOT nodes and so on in future.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

FDMA with Layers-based Optimized Mobile Relays Subsets Algorithm in B5G/6G Cognitive IoT Networks

In view of noteworthy communications performance improvements for future B5G/6G (such as cognitive Internet of Things, space-ground integration network and so on), cooperative communications (CC) diversity with relays selection algorithms have been extensively studied to significantly improve communications quality, but so far there is still a lot of potential optimization work with CC schemes. In this paper, in the light of NP-hard problem of subsets relays selection, further studies for theorems of relays subsets with K-layers power allocation standard have been put forward to explore better performance in B5G/6G cognitive IoT (Internet of Things) networks, we propose unified layers-based optimized mobile relays subsets algorithms for full-duplex (FD) non-orthogonal multiple access (NOMA) to greatly improve transmission rate. After revealing and taking into account fundamental properties of relays, such as mobile relays nodes state, relays locations, fading characteristics and so on, optimized FD-NOMA algorithm based on these relays features has been presented to improve transmission validity, and a related series of relays subsets theorems have been derived and proved, then minimum upper bound of maximum transmission rates has been estimated to reveal two-way balanced optimal transmission conclusion for FD-NOMA. In general, proposed general and optimized algorithm can be used in multiple future cooperative communications scenarios in B5G/6G networks such as cognitive IoT. Simulations results show that proposed scheme has several times transmission rates than other classical relays selection algorithms

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Generalized Optimal Two-way Relays Subsets Pairings in Cloud-based Region Cognitive Networks

Communication reliability improving is one of most important research requirements in cognitive networks, as 5G communications technology rapidly develop nowadays. In this paper, we propose generalized optimal cloud-based region relays subsets paring model in underlay dual-hop cognitive networks, this unified model reveals three relays nodes characteristics of cloud-based cooperative networks in the nearby area- A subset, only needs receiving for first hop, B subset, just receive and forward, C subset, only forwarding. In addition, this generalized model can be converted into various classical relay selection algorithms when A, B and C subsets are taken as special selection values [Table II]. Furthermore, we put forward optimal relays subsets pairings and replacement algorithm flowchart to improve minimum outage probability (OP) for communication reliability, and prove that optimal relays subsets pairings (A, B, C) will better guarantee reliability of communication, comparing other popular relays selection schemes. Simulation results show that optimal relays paired subsets are exist and this generalized algorithm enormously reduces OP, comparing other selection algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Instance Scale Normalization for image understanding

Scale variation remains a challenging problem for object detection. Common paradigms usually adopt multiscale training & testing (image pyramid) or FPN (feature pyramid network) to process objects in a wide scale range. However, multi-scale methods aggravate more variations of scale that even deep convolution neural networks with FPN cannot handle well. In this work, we propose an innovative paradigm called Instance Scale Normalization (ISN) to resolve the above problem. ISN compresses the scale space of objects into a consistent range (ISN range), in both training and testing phases. This reassures the problem of scale variation fundamentally and reduces the difficulty of network optimization. Experiments show that ISN surpasses multi-scale counterpart significantly for object detection, instance segmentation, and multi-task human pose estimation, on several architectures. On COCO test-dev, our single model based on ISN achieves 46.5 mAP with a ResNet-101 backbone, which is among the state-of-the-art (SOTA) candidates for object detection.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge

Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. It is more challenging than its single-label counterpart. On one hand, the unconstrained number of labels assigned to each image makes the model more easily overfit to those seen classes. On the other hand, there is a large semantic gap between seen and unseen classes in the existing multi-label classification datasets. To address these difficult issues, this paper introduces a novel multi-label zero-shot classification framework by learning to transfer from external knowledge. We observe that ImageNet is commonly used to pretrain the feature extractor and has a large and fine-grained label space. This motivates us to exploit it as external knowledge to bridge the seen and unseen classes and promote generalization. Specifically, we construct a knowledge graph including not only classes from the target dataset but also those from ImageNet. Since ImageNet labels are not available in the target dataset, we propose a novel PosVAE module to infer their initial states in the extended knowledge graph. Then we design a relational graph convolutional network (RGCN) to propagate information among classes and achieve knowledge transfer. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed approach.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Penetration of a supersonic particle at the interface in a binary complex plasma

The penetration of a supersonic particle at the interface was studied in a binary complex plasma. Inspired by the experiments performed in the PK-3 Plus Laboratory on board the International Space Station, Langevin dynamics simulations were carried out. The evolution of Mach cone at the interface was observed, where a kink of the lateral wake front was observed at the interface. By comparing the evolution of axial and radial velocity, we show that the interface solitary wave is non-linear. The dependence of the background particle dynamics in the vicinity of the interface on the penetration direction reveals that the disparity of the mobility may be the cause of various interface effects.

0 citations0 reviewsNo code linkedOpen access