arXiv subjects

Feng Zhao

Publications and source records attributed to Feng Zhao.

At least 181 records · Page 10Linked to original sources

arXiv2022

8D Parameters Estimation for Bistatic EMVS-MIMO Radar via the nested PARAFAC

In this letter, a novel nested PARAFAC algorithm was proposed to improve the 8D parameters estimation performance for the bistatic EMVS-MIMO radar. Firstly, the outer part PARAFAC algorithm was carried out to estimate the receive spatial response matrix and its first way factor matrix. For the estimated first way factor matrix, a theory is given to rearrange its data into an new matrix, which is the mode-1 unfolding matrix of a three-way tensor. Then, the inner part PARAFAC algorithm was used to estimate the transmit steering vector matrix, the transmit spatial response matrix and the receive steering vector matrix. Thus, the transmit 4D parameters and receive 4D parameters can be accurately located via the abovementioned process. Compared with the original PARAFAC algorithm, the proposed nested PARAFAC algorithm can avoid additional reconstruction process when estimating the transmit/receive spatial response matrix. Moreover, the proposed algorithm can offer a highly-accurate 8D parameters estimaiton than that of the original PARAFAC algorithm. Simulated results verify the effectiveness of the proposed algorithm.

eess.SP↗

arXiv2022

AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition

2D+3D facial expression recognition (FER) can effectively cope with illumination changes and pose variations by simultaneously merging 2D texture and more robust 3D depth information. Most deep learning-based approaches employ the simple fusion strategy that concatenates the multimodal features directly after fully-connected layers, without considering the different degrees of significance for each modality. Meanwhile, how to focus on both 2D and 3D local features in salient regions is still a great challenge. In this letter, we propose the adaptive fusion network with masks (AFNet-M) for 2D+3D FER. To enhance 2D and 3D local features, we take the masks annotating salient regions of the face as prior knowledge and design the mask attention module (MA) which can automatically learn two modulation vectors to adjust the feature maps. Moreover, we introduce a novel fusion strategy that can perform adaptive fusion at convolutional layers through the designed importance weights computing module (IWC). Experimental results demonstrate that our AFNet-M achieves the state-of-the-art performance on BU-3DFE and Bosphorus datasets and requires fewer parameters in comparison with other models.

cs.CV↗

arXiv2022

AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection

Object detection through either RGB images or the LiDAR point clouds has been extensively explored in autonomous driving. However, it remains challenging to make these two data sources complementary and beneficial to each other. In this paper, we propose \textit{AutoAlign}, an automatic feature fusion strategy for 3D object detection. Instead of establishing deterministic correspondence with camera projection matrix, we model the mapping relationship between the image and point clouds with a learnable alignment map. This map enables our model to automate the alignment of non-homogenous features in a dynamic and data-driven manner. Specifically, a cross-attention feature alignment module is devised to adaptively aggregate \textit{pixel-level} image features for each voxel. To enhance the semantic consistency during feature alignment, we also design a self-supervised cross-modal feature interaction module, through which the model can learn feature aggregation with \textit{instance-level} feature guidance. Extensive experimental results show that our approach can lead to 2.3 mAP and 7.0 mAP improvements on the KITTI and nuScenes datasets, respectively. Notably, our best model reaches 70.9 NDS on the nuScenes testing leaderboard, achieving competitive performance among various state-of-the-arts.

cs.CV↗

arXiv2022

BLOWN: A Blockchain Protocol for Single-Hop Wireless Networks under Adversarial SINR

Known as a distributed ledger technology (DLT), blockchain has attracted much attention due to its properties such as decentralization, security, immutability and transparency, and its potential of servicing as an infrastructure for various applications. Blockchain can empower wireless networks with identity management, data integrity, access control, and high-level security. However, previous studies on blockchain-enabled wireless networks mostly focus on proposing architectures or building systems with popular blockchain protocols. Nevertheless, such existing protocols have obvious shortcomings when adopted in wireless networks where nodes may have limited physical resources, may fall short of well-established reliable channels, or may suffer from variable bandwidths impacted by environments or jamming attacks. In this paper, we propose a novel consensus protocol named Proof-of-Channel (PoC) leveraging the natural properties of wireless communications, and develop a permissioned BLOWN protocol (BLOckchain protocol for Wireless Networks) for single-hop wireless networks under an adversarial SINR model. We formalize BLOWN with the universal composition framework and prove its security properties, namely persistence and liveness, as well as its strengths in countering against adversarial jamming, double-spending, and Sybil attacks, which are also demonstrated by extensive simulation studies.

cs.CR↗

arXiv2021

Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification

Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID is more challenging and exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised representation learning with contrastive objectives, in this paper, we design an Unsupervised Pre-training framework for ReID based on the contrastive learning (CL) pipeline, dubbed UP-ReID. During the pre-training, we attempt to address two critical issues for learning fine-grained ReID features: (1) the augmentations in CL pipeline may distort the discriminative clues in person images. (2) the fine-grained local features of person images are not fully-explored. Therefore, we introduce an intra-identity (I$^2$-)regularization in the UP-ReID, which is instantiated as two constraints coming from global image aspect and local patch aspect: a global consistency is enforced between augmented and original person images to increase robustness to augmentation, while an intrinsic contrastive constraint among local patches of each image is employed to fully explore the local discriminative clues. Extensive experiments on multiple popular Re-ID datasets, including PersonX, Market1501, CUHK03, and MSMT17, demonstrate that our UP-ReID pre-trained model can significantly benefit the downstream ReID fine-tuning and achieve state-of-the-art performance. Codes and models will be released to https://github.com/Frost-Yang-99/UP-ReID.

cs.CV↗

arXiv2021

MFEViT: A Robust Lightweight Transformer-based Network for Multimodal 2D+3D Facial Expression Recognition

Vision transformer (ViT) has been widely applied in many areas due to its self-attention mechanism that help obtain the global receptive field since the first layer. It even achieves surprising performance exceeding CNN in some vision tasks. However, there exists an issue when leveraging vision transformer into 2D+3D facial expression recognition (FER), i.e., ViT training needs mass data. Nonetheless, the number of samples in public 2D+3D FER datasets is far from sufficient for evaluation. How to utilize the ViT pre-trained on RGB images to handle 2D+3D data becomes a challenge. To solve this problem, we propose a robust lightweight pure transformer-based network for multimodal 2D+3D FER, namely MFEViT. For narrowing the gap between RGB and multimodal data, we devise an alternative fusion strategy, which replaces each of the three channels of an RGB image with the depth-map channel and fuses them before feeding them into the transformer encoder. Moreover, the designed sample filtering module adds several subclasses for each expression and move the noisy samples to their corresponding subclasses, thus eliminating their disturbance on the network during the training stage. Extensive experiments demonstrate that our MFEViT outperforms state-of-the-art approaches with an accuracy of 90.83% on BU-3DFE and 90.28% on Bosphorus. Specifically, the proposed MFEViT is a lightweight model, requiring much fewer parameters than multi-branch CNNs. To the best of our knowledge, this is the first work to introduce vision transformer into multimodal 2D+3D FER. The source code of our MFEViT will be publicly available online.

cs.CV↗

arXiv2021

Disentangle Your Dense Object Detector

Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding. However, the current training pipeline for dense detectors is compromised to lots of conjunctions that may not hold. In this paper, we investigate three such important conjunctions: 1) only samples assigned as positive in classification head are used to train the regression head; 2) classification and regression share the same input feature and computational fields defined by the parallel head architecture; and 3) samples distributed in different feature pyramid layers are treated equally when computing the loss. We first carry out a series of pilot experiments to show disentangling such conjunctions can lead to persistent performance improvement. Then, based on these findings, we propose Disentangled Dense Object Detector (DDOD), in which simple and effective disentanglement mechanisms are designed and integrated into the current state-of-the-art dense object detectors. Extensive experiments on MS COCO benchmark show that our approach can lead to 2.0 mAP, 2.4 mAP and 2.2 mAP absolute improvements on RetinaNet, FCOS, and ATSS baselines with negligible extra overhead. Notably, our best model reaches 55.0 mAP on the COCO test-dev set and 93.5 AP on the hard subset of WIDER FACE, achieving new state-of-the-art performance on these two competitive benchmarks. Code is available at https://github.com/zehuichen123/DDOD.

cs.CV↗

arXiv2021

MVT: Mask Vision Transformer for Facial Expression Recognition in the wild

Facial Expression Recognition (FER) in the wild is an extremely challenging task in computer vision due to variant backgrounds, low-quality facial images, and the subjectiveness of annotators. These uncertainties make it difficult for neural networks to learn robust features on limited-scale datasets. Moreover, the networks can be easily distributed by the above factors and perform incorrect decisions. Recently, vision transformer (ViT) and data-efficient image transformers (DeiT) present their significant performance in traditional classification tasks. The self-attention mechanism makes transformers obtain a global receptive field in the first layer which dramatically enhances the feature extraction capability. In this work, we first propose a novel pure transformer-based mask vision transformer (MVT) for FER in the wild, which consists of two modules: a transformer-based mask generation network (MGN) to generate a mask that can filter out complex backgrounds and occlusion of face images, and a dynamic relabeling module to rectify incorrect labels in FER datasets in the wild. Extensive experimental results demonstrate that our MVT outperforms state-of-the-art methods on RAF-DB with 88.62%, FERPlus with 89.22%, and AffectNet-7 with 64.57%, respectively, and achieves a comparable result on AffectNet-8 with 61.40%.

cs.CV↗

arXiv2021

The Nancy Grace Roman Space Telescope Coronagraph Instrument (CGI) Technology Demonstration

The Coronagraph Instrument (CGI) on the Nancy Grace Roman Space Telescope will demonstrate the high-contrast technology necessary for visible-light exoplanet imaging and spectroscopy from space via direct imaging of Jupiter-size planets and debris disks. This in-space experience is a critical step toward future, larger missions targeted at direct imaging of Earth-like planets in the habitable zones of nearby stars. This paper presents an overview of the current instrument design and requirements, highlighting the critical hardware, algorithms, and operations being demonstrated. We also describe several exoplanet and circumstellar disk science cases enabled by these capabilities. A competitively selected Community Participation Program team will be an integral part of the technology demonstration and could perform additional CGI observations beyond the initial tech demo if the instrument performance warrants it.

astro-ph.IM↗

arXiv2021

Refined Eulerian numbers and ballot permutations

A ballot permutation is a permutation π such that in any prefix of π the descent number is not more than the ascent number. In this article, we obtained a formula in close form for the multivariate generating function of {A(n,d,j)}, which denote the number of permutations of length n with d descents and j as the first letter. Besides, by a series of calculations with generatingfunctionology, we confirm a recent conjecture of Wang and Zhang for ballot permutations.

math.CO↗

arXiv2021

wChain: A Fast Fault-Tolerant Blockchain Protocol for Multihop Wireless Networks

This paper presents $\mathit{wChain}$, a blockchain protocol specifically designed for multihop wireless networks that deeply integrates wireless communication properties and blockchain technologies under the realistic SINR model. We adopt a hierarchical spanner as the communication backbone to address medium contention and achieve fast data aggregation within $O(\log N\logΓ)$ slots where $N$ is the network size and $Γ$ refers to the ratio of the maximum distance to the minimum distance between any two nodes. Besides, $\mathit{wChain}$ employs data aggregation and reaggregation, and node recovery mechanisms to ensure efficiency, fault tolerance, persistence, and liveness. The worst-case runtime of $\mathit{wChain}$ is upper bounded by $O(f\log N\logΓ)$, where $f=\lfloor \frac{N}{2} \rfloor$ is the upper bound of the number of faulty nodes. To validate our design, we conduct both theoretical analysis and simulation studies, and the results only demonstrate the nice properties of $\mathit{wChain}$, but also point to a vast new space for the exploration of blockchain protocols in wireless networks.

cs.DC↗

arXiv2020

Reproducing Scientific Experiment with Cloud DevOps

The reproducibility of scientific experiment is vital for the advancement of disciplines based on previous work. To achieve this goal, many researchers focus on complex methodology and self-invented tools which have difficulty in practical usage. In this article, we introduce the Cloud DevOps infrastructure from software engineering community and shows how it can be used effectively for heterogeneous agents to reproduce experiments for computer science related disciplines. DevOps can be enabled using freely available cloud computing machines for medium-sized experiment and self-hosted computing engines for large-scale computing, thus powering researchers to share their experiment result with others in a more reliable way.

cs.DC↗

arXiv2020

Moments of the multivariate Beta distribution

In this paper, we extend Beta distribution to 2 by 2 matrix and give the analytical formula for its moments. Our analytical formula can be used to analyze the asymptotic behavior of Beta distribution for 2 by 2 matrix.

stat.AP↗

arXiv2020

P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds

Towards 3D object tracking in point clouds, a novel point-to-box network termed P2B is proposed in an end-to-end learning manner. Our main idea is to first localize potential target centers in 3D search area embedded with target information. Then point-driven 3D target proposal and verification are executed jointly. In this way, the time-consuming 3D exhaustive search can be avoided. Specifically, we first sample seeds from the point clouds in template and search area respectively. Then, we execute permutation-invariant feature augmentation to embed target clues from template into search area seeds and represent them with target-specific features. Consequently, the augmented search area seeds regress the potential target centers via Hough voting. The centers are further strengthened with seed-wise targetness scores. Finally, each center clusters its neighbors to leverage the ensemble power for joint 3D target proposal and verification. We apply PointNet++ as our backbone and experiments on KITTI tracking dataset demonstrate P2B's superiority (~10%'s improvement over state-of-the-art). Note that P2B can run with 40FPS on a single NVIDIA 1080Ti GPU. Our code and model are available at https://github.com/HaozheQi/P2B.

cs.CV↗

arXiv2019

Threshold Voltage Improvement and Leakage Reduction of AlGaN/GaN HEMTs Using Dual-Layer SiNx Stressors

In this work, AlGaN/GaN HEMTs with dual-layer SiNx stressors (composed of a low-stress layer and a high-stress layer) were investigated. The low-stress padding layer solved the surface damage problem caused during the deposition of the high-stress SiNx, and provided a good passivated interface. The HEMTs with dual-layer stressors showed a 1 V increase in the threshold voltage (Vth) with comparable on-current and RF current gain to those without stressors. Moreover, the off-current (I_off) was shown to be reduced by one to three orders of magnitude in the strained devices as a result of the lower electric field in AlGaN, which suppressed the gate injection current. The dual-layer stressor scheme supports strain engineering as an effective approach in the pursuit of the normally-off operation of AlGaN/GaN HEMTs.

physics.app-ph↗

arXiv2019

Silicon Nitride Stress Liner Impacts on the Electrical Characteristics of AlGaN/GaN HEMTs

Due to the piezoelectric nature of GaN, the 2DEG in AlGaN/GaN HEMT could be engineered by strain. In this work, SiNx deposited using dual-frequency PECVD was used as a stressor. The output performance of the devices was dominated by the surface passivation instead of the stress effect. However, the threshold voltage was increased by the induced stress, supporting strain engineering as an effective approach to pursue the normally-off operation of AlGaN/GaN HEMTs.

physics.app-ph↗

arXiv2019

Key Technologies for the Wide Field Infrared Survey Telescope Coronagraph Instrument

The Wide Field Infrared Survey Telescope (WFIRST) Coronagraph Instrument (CGI) is a high-contrast imager and integral field spectrograph that will enable the study of exoplanets and circumstellar disks at visible wavelengths. Ground-based high-contrast instrumentation has fundamentally limited performance at small working angles, even under optimistic assumptions for 30m-class telescopes. There is a strong scientific driver for better performance, particularly at visible wavelengths. Future flagship mission concepts aim to image Earth analogues with visible light flux ratios of more than 10^10. CGI is a critical intermediate step toward that goal, with a predicted 10^8-9 flux ratio capability in the visible. CGI achieves this through improvements over current ground and space systems in several areas: (i) Hardware: space-qualified (TRL9) deformable mirrors, detectors, and coronagraphs, (ii) Algorithms: wavefront sensing and control; post-processing of integral field spectrograph, polarimetric, and extended object data, and (iii) Validation of telescope and instrument models at high accuracy and precision. This white paper, submitted to the 2018 NAS Exoplanet Science Strategy call, describes the status of key CGI technologies and presents ways in which performance is likely to evolve as the CGI design matures.

astro-ph.IM↗

arXiv2018

Enabling Low-power Duplex Visible Light Communication

The new generation of LED-based illuminating infrastructures has enabled a "dual-paradigm" where LEDs are used for both illumination and communication purposes. The ubiquity of lighting makes visible light communication (VLC) well suited for communication with mobile devices and sensor nodes in indoor environment. Existing research on VLC has primarily been focused on advancing the performance of one-way communication. In this paper, we present Retro-VLC, a low-power duplex VLC system that enables a mobile device to perform bi-directional communication with the illuminating LEDs over the same light carrier. The design features a retro-reflector fabric that backscatters light, an LCD shutter that modulates information bits on the backscattered light carrier, and several low-power optimization techniques. We have prototyped the Reader system and made a few battery-free tag devices. Experimental results show that the tag can achieve a 10kbps downlink speed and 0.5kbps uplink speed over a distance of 2.4m. We also outline several potential applications of the proposed Retro-VLC system.

cs.NI↗