Ultra-wideband THz/IR Metamaterial Absorber based on Doped Silicon

Metamaterial-based absorbers have been extensively investigated in the terahertz (THz) range with ever increasing performances. In this paper, we propose an all-dielectric THz absorber based on doped silicon. The unit cell consists of a silicon cross resonator with an internal cross-shaped air cavity. Numerical results suggest that the proposed absorber can operate from THz to mid-infrared, having an average power absorption of >95% between 0.6 and 10 THz. Experimental results using THz time-domain spectroscopy show a good agreement with simulations. The underlying mechanisms for broadband absorptions are attributed to the combined effects of multiple cavities modes formed by silicon resonators and bulk absorption in the substrate, as confirmed by simulated field patterns. This ultra-wideband absorption is polarization insensitive and can operate across a wide range of the incident angle. The proposed absorber can be readily integrated into silicon-based platforms and is expected to be used in sensing, imaging, energy harvesting and wireless communications systems.Comment: 6 pages, 5 figure

Efficient frequency-domain channel equalisation methods for OFDM visible light communications

The authors present efficient frequency-domain channel estimation methods based on the intra-symbol frequency-domain averaging (ISFA), minimum mean squared error (MMSE) and weighted inter-frame averaging (WIFA) schemes for the orthogonal frequency division multiplexing (OFDM) visible light communications (VLC) system. OFDM-VLC with quadrature phase shift keying, 16- and 64-quadrature amplitude modulation mapping is experimentally demonstrated. Compared with the conventional least square channel estimation method, ISFA, MMSE and WIFA offer improved performance with MMSE offering the best performance in terms of the error vector magnitude but at the cost of high complexity. The authors show that the WIFA can improve the estimation accuracy of time-varying VLC optical channel

Experimental Demonstration of OFDM/OQAM Transmission for Visible Light Communications

We propose a modified orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) scheme for visible light communications (VLC). The OFDM/OQAM VLC system can efficiently boost the data rate, and combat multipath induced the inter symbol interference (ISI) and inter carrier interference (ICI). To combat the effect of intrinsic imaginary interference, intrasymbol frequency-domain averaging and minimum mean squared error (MMSE), combined with interference approximation method, are proposed. The experiment results show that the proposed system offers similar bit error rate performance to that of OFDM, while the bit rate is increased by 9% for the elimination of cyclic-prefix and guard band

MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration

Capturing voxel-wise spatial correspondence across distinct modalities is crucial for medical image analysis. However, current registration approaches are not practical enough in terms of registration accuracy and clinical applicability. In this paper, we introduce MambaMorph, a novel multi-modality deformable registration framework. Specifically, MambaMorph utilizes a Mamba-based registration module and a fine-grained, yet simple, feature extractor for efficient long-range correspondence modeling and high-dimensional feature learning, respectively. Additionally, we develop a well-annotated brain MR-CT registration dataset, SR-Reg, to address the scarcity of data in multi-modality registration. To validate MambaMorph's multi-modality registration capabilities, we conduct quantitative experiments on both our SR-Reg dataset and a public T1-T2 dataset. The experimental results on both datasets demonstrate that MambaMorph significantly outperforms the current state-of-the-art learning-based registration methods in terms of registration accuracy. Further study underscores the efficiency of the Mamba-based registration module and the lightweight feature extractor, which achieve notable registration quality while maintaining reasonable computational costs and speeds. We believe that MambaMorph holds significant potential for practical applications in medical image registration. The code for MambaMorph is available at: https://github.com/Guo-Stone/MambaMorph

Graduate Employment Prediction with Bias

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students' employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework

1st Place Solution of Egocentric 3D Hand Pose Estimation Challenge 2023 Technical Report:A Concise Pipeline for Egocentric Hand Pose Reconstruction

This report introduce our work on Egocentric 3D Hand Pose Estimation workshop. Using AssemblyHands, this challenge focuses on egocentric 3D hand pose estimation from a single-view image. In the competition, we adopt ViT based backbones and a simple regressor for 3D keypoints prediction, which provides strong model baselines. We noticed that Hand-objects occlusions and self-occlusions lead to performance degradation, thus proposed a non-model method to merge multi-view results in the post-process stage. Moreover, We utilized test time augmentation and model ensemble to make further improvement. We also found that public dataset and rational preprocess are beneficial. Our method achieved 12.21mm MPJPE on test dataset, achieve the first place in Egocentric 3D Hand Pose Estimation challenge

DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs

Existing nighttime unmanned aerial vehicle (UAV) trackers follow an "Enhance-then-Track" architecture - first using a light enhancer to brighten the nighttime video, then employing a daytime tracker to locate the object. This separate enhancement and tracking fails to build an end-to-end trainable vision system. To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts. Without a separate enhancer, DCPT directly encodes anti-dark capabilities into prompts using a darkness clue prompter (DCP). Specifically, DCP iteratively learns emphasizing and undermining projections for darkness clues. It then injects these learned visual prompts into a daytime tracker with fixed parameters across transformer layers. Moreover, a gated feature aggregation mechanism enables adaptive fusion between prompts and between prompts and the base model. Extensive experiments show state-of-the-art performance for DCPT on multiple dark scenario benchmarks. The unified end-to-end learning of enhancement and tracking in DCPT enables a more trainable system. The darkness clue prompting efficiently injects anti-dark knowledge without extra modules. Code and models will be released.Comment: Under revie