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Researcher profile

Yu Xue

Yu Xue contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionMachine LearningArtificial Intelligenceeess.SPEmerging TechnologiesNeural and Evolutionary Computing

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Published work

7 published item(s)

preprint2026arXiv

A 2.5 $μ$W 30 nV/$\surd$Hz Instrumentation Amplifier for Bioimpedance Sensors with Source Degenerated Current Mirror and DTMOS Transistor

This paper proposes a low-power and low-noise instrumentation amplifier (IA) tailored for bioimpedance sensing applications. The design originates from a gain-boosted flipped voltage follower (FVF) transconductance (TC) stage and integrates two complementary circuit techniques to improve the noise performance. To achieve an optimal balance between input-referred noise and available voltage headroom, a source-degenerated current mirror (SDCM) is adopted, resulting in reducing the input-referred noise by 7.95% compared with a conventional current mirror structure. In addition, a dynamic threshold MOSFET (DTMOS) scheme is employed to enhance the effective transconductance, leading to a further 11.66% reduction in input-referred noise. Simulated in a 28 nm CMOS process demonstrate that the proposed IA achieves an input-referred noise floor of 30 nV/$\surd$Hz and a bandwidth of 1.44 MHz, while consuming only 2.5 $μ$W from a 0.8 V supply. Compared to the baseline design, the proposed approach achieves a 32.4% reduction in power consumption without degrading noise performance. The complete design parameters are open-sourced in this paper, to ensure reproducibility and facilitate future developments.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism

Neural architecture search (NAS) has emerged as a powerful paradigm that enables researchers to automatically explore vast search spaces and discover efficient neural networks. However, NAS suffers from a critical bottleneck, i.e. the evaluation of numerous architectures during the search process demands substantial computing resources and time. In order to improve the efficiency of NAS, a series of methods have been proposed to reduce the evaluation time of neural architectures. However, they are not efficient enough and still only focus on the accuracy of architectures. Beyond classification accuracy, real-world applications increasingly demand more efficient and compact network architectures that balance multiple performance criteria. To address these challenges, we propose the SMEMNAS, a pairwise comparison relation-assisted multi-objective evolutionary algorithm based on a multi-population mechanism. In the SMEMNAS, a surrogate model is constructed based on pairwise comparison relations to predict the accuracy ranking of architectures, rather than the absolute accuracy. Moreover, two populations cooperate with each other in the search process, i.e. a main population that guides the evolutionary process, while a vice population that enhances search diversity. Our method aims to discover high-performance models that simultaneously optimize multiple objectives. We conduct comprehensive experiments on CIFAR-10, CIFAR-100 and ImageNet datasets to validate the effectiveness of our approach. With only a single GPU searching for 0.17 days, competitive architectures can be found by SMEMNAS which achieves 78.91% accuracy with the MAdds of 570M on the ImageNet. This work makes a significant advancement in the field of NAS.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation

Cardiac magnetic resonance (CMR) segmentation underpins quantitative assessment of ventricular structure and function, yet reliable delineation remains difficult due to low tissue contrast, fuzzy boundaries, and inter scan variability. We present CardiacNAS, an evolutionary neural architecture search (NAS) framework that couples a UNet like supernet with a cardiac aware search space spanning depth width, kernel size, filter size, attention, fusion, activation, dropout, and residual scaling. The search is explicitly resource aware, jointly optimizing dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) versus model size and floating point operations (FLOPs) under fixed compute budgets. Candidate architectures are instantiated from the supernet, trained with proxy budgets, and evolved through crossover, mutation, and elitist selection. We evaluate on the ACDC dataset and compare against six state of the art methods, using qualitative comparisons, learning curve analyses, and design factor correlation studies. The resulting model attains 93.22% average DSC and 4.73 mm HD95 with 3.58M parameters and 14.56 GFLOPs, demonstrating a favorable accuracy efficiency trade off. Analyses indicate that searched attention and fusion choices, together with residual scaling, contribute to improved boundary fidelity and stability. CardiacNAS offers a principled, resource aware approach to deployable CMR segmentation with transparent reporting of architectural complexity and compute budgets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick

With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods explore deep learning on ASSC. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming especially when dealing with multi-classification tasks. In this paper, we propose an efficient five-sleep-stage classification method using convolutional neural networks (CNNs) with a novel data processing trick and we design neural architecture search (NAS) technique based on genetic algorithm (GA), NAS-G, to search for the best CNN architecture. Firstly, we attach each kernel with an adaptive coefficient to enhance the signal processing of the inputs. This can enhance the propagation of informative features and suppress the propagation of useless features in the early stage of the network. Then, we make full use of GA's heuristic search and the advantage of no need for the gradient to search for the best architecture of CNN. This can achieve a CNN with better performance than a handcrafted one in a large search space at the minimum cost. We verify the convergence of our data processing trick and compare the performance of traditional CNNs before and after using our trick. Meanwhile, we compare the performance between the CNN generated through NAS-G and the traditional CNNs with our trick. The experiments demonstrate that the convergence of CNNs with data processing trick is faster than without data processing trick and the CNN with data processing trick generated by NAS-G outperforms the handcrafted counterparts that use the data processing trick too.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ConAM: Confidence Attention Module for Convolutional Neural Networks

The so-called "attention" is an efficient mechanism to improve the performance of convolutional neural networks. It uses contextual information to recalibrate the input to strengthen the propagation of informative features. However, the majority of the attention mechanisms only consider either local or global contextual information, which is singular to extract features. Moreover, many existing mechanisms directly use the contextual information to recalibrate the input, which unilaterally enhances the propagation of the informative features, but does not suppress the useless ones. This paper proposes a new attention mechanism module based on the correlation between local and global contextual information and we name this correlation as confidence. The novel attention mechanism extracts the local and global contextual information simultaneously, and calculates the confidence between them, then uses this confidence to recalibrate the input pixels. The extraction of local and global contextual information increases the diversity of features. The recalibration with confidence suppresses useless information while enhancing the informative one with fewer parameters. We use CIFAR-10 and CIFAR-100 in our experiments and explore the performance of our method's components by sufficient ablation studies. Finally, we compare our method with a various state-of-the-art convolutional neural networks and the results show that our method completely surpasses these models. We implement ConAM with the Python library, Pytorch, and the code and models will be publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

HDAM: Heuristic Difference Attention Module for Convolutional Neural Networks

The attention mechanism is one of the most important priori knowledge to enhance convolutional neural networks. Most attention mechanisms are bound to the convolutional layer and use local or global contextual information to recalibrate the input. This is a popular attention strategy design method. Global contextual information helps the network to consider the overall distribution, while local contextual information is more general. The contextual information makes the network pay attention to the mean or maximum value of a particular receptive field. Different from the most attention mechanism, this article proposes a novel attention mechanism with the heuristic difference attention module, HDAM. HDAM's input recalibration is based on the difference between the local and global contextual information instead of the mean and maximum values. At the same time, to make different layers have a more suitable local receptive field size and increase the exibility of the local receptive field design, we use genetic algorithm to heuristically produce local receptive fields. First, HDAM extracts the mean value of the global and local receptive fields as the corresponding contextual information. Then the difference between the global and local contextual information is calculated. Finally HDAM uses this difference to recalibrate the input. In addition, we use the heuristic ability of genetic algorithm to search for the local receptive field size of each layer. Our experiments on CIFAR-10 and CIFAR-100 show that HDAM can use fewer parameters than other attention mechanisms to achieve higher accuracy. We implement HDAM with the Python library, Pytorch, and the code and models will be publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Partial Connection Based on Channel Attention for Differentiable Neural Architecture Search

Differentiable neural architecture search (DARTS), as a gradient-guided search method, greatly reduces the cost of computation and speeds up the search. In DARTS, the architecture parameters are introduced to the candidate operations, but the parameters of some weight-equipped operations may not be trained well in the initial stage, which causes unfair competition between candidate operations. The weight-free operations appear in large numbers which results in the phenomenon of performance crash. Besides, a lot of memory will be occupied during training supernet which causes the memory utilization to be low. In this paper, a partial channel connection based on channel attention for differentiable neural architecture search (ADARTS) is proposed. Some channels with higher weights are selected through the attention mechanism and sent into the operation space while the other channels are directly contacted with the processed channels. Selecting a few channels with higher attention weights can better transmit important feature information into the search space and greatly improve search efficiency and memory utilization. The instability of network structure caused by random selection can also be avoided. The experimental results show that ADARTS achieved 2.46% and 17.06% classification error rates on CIFAR-10 and CIFAR-100, respectively. ADARTS can effectively solve the problem that too many skip connections appear in the search process and obtain network structures with better performance.

0 citations0 reviewsNo code linkedOpen access