Mengda Yang

Wuhan University

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i-10 index

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Hongxin Hu

University at Buffalo, State University of New York

Xiaoyang Xu

Changzhou University

Juan Wang

Wuhan University

Wenzhe Yi

Wuhan University

Dequan Zheng

Harbin University of Commerce

Wenzhe Yi

Wuhan University

Ziang Li

Wuhan University

Chengzhi Zhou

First Affiliated Hospital of Guangzhou Medical University

Juan Wang

Wuhan University

Chunling Yang

Harbin Institute of Technology

Cooperative Institutions

Wuhan University

Harbin Institute of Technology

Chinese Academy of Sciences

Qingdao Agricultural University

University of Chinese Academy of Sciences

Shanghai Jiao Tong University

Kunming University of Science and Technology

Institute of Electronics

Heilongjiang Institute of Technology

Fudan University

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Research on infrared decoy movement characteristics and simulation

Yang Chun-ling Chen Yu Mengda Yang

Infrared decoy is a common method of infrared countermeasures(IRCM). In order to research the corresponding anti-interference strategy, it is necessary to establish an IR decoy movement model. This paper researches on the movement characteristics of IR decoy, probes into the calculation method for IR decoy aerodynamic parameters, then establishes a relatively accurate movement model, at last, simulates the model based on computer simulation which provides the basis for further research on the IR decoy.

Decoy

10.1109/iciea.2012.6361037

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Citations (3)

The frequency estimation of weak signal based on chaotic system fusion technology

2009 ICCAS-SICE (2009)

Yanchun Xu Chunling Yang Xiaodong Qu Mengda Yang Quan Yang

In this paper we use fusion technology based on Rossler chaotic oscillator and proportional differential control technology to detect the frequency of the weak signal. The dynamic character of Rossler equation is analyzed and it is immune against the white noise and the interference signal having larger frequency difference with the intrinsic frequency of chaotic oscillator. And proportional differential control is used to regulate the system to periodic state and the method of spectrum analysis to detect the unknown frequency. The simulation result verified that the presented method is effective and it is very feasible.

SIGNAL (programming language)

Source

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A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning

arXiv (Cornell University) (2024)

Xiaoyang Xu Mengda Yang Wenzhe Yi Ziang Li Juan Wang

Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However, these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces a new semi-honest Data Reconstruction Attack on SL, named Feature-Oriented Reconstruction Attack (FORA). In contrast to prior works, FORA relies on limited prior knowledge, specifically that the server utilizes auxiliary samples from the public without knowing any client's private information. This allows FORA to conduct the attack stealthily and achieve robust performance. The key vulnerability exploited by FORA is the revelation of the model representation preference in the smashed data output by victim client. FORA constructs a substitute client through feature-level transfer learning, aiming to closely mimic the victim client's representation preference. Leveraging this substitute client, the server trains the attack model to effectively reconstruct private data. Extensive experiments showcase FORA's superior performance compared to state-of-the-art methods. Furthermore, the paper systematically evaluates the proposed method's applicability across diverse settings and advanced defense strategies.

Feature (linguistics)

10.48550/arxiv.2405.04115

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Design of on-line UPS based on boost converter

2009 ICCAS-SICE (2009)

Chunyang Gu Zhibing Yang Chunling Yang Mengda Yang

Design and build up a new on-line digital UPS based on boost topological structure with a new kind of single-chip computer, STC12C5412AD, realizing sine wave and square wave output, and shifting among 50Hz, 60 Hz, and 400Hz of the supply for adopting into different industrial standards and applications. Such UPS system contains the module of intelligent charging from storage battery and thorough security functions, characterized by the output of un-interruption and high stability, low distortion, free of interference, low cost and multi-function.

Sine wave

Line (geometry)

Distortion (music)

Square wave

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Citations (9)

Penetralium: Privacy-Preserving and Memory-Efficient Neural Network Inference at the Edge

Mengda Yang Wenzhe Yi Juan Wang Hongxin Hu Xiaoyang Xu

The proliferation of artificial intelligence and edge computing has led to an increase in the deployment of proprietary deep learning models on third-party edge servers or devices to power mission-critical applications. However, this trend raises concerns about model privacy, particularly on untrusted edge platforms. Protecting model privacy in such scenarios requires addressing challenges such as untrustworthy model deployment environments, resource-constrained Trusted Execution Environments (TEE), and vulnerability to privacy inference attacks. To address these challenges, this paper proposes Penetralium, a system-algorithm jointly optimized model inference system on edge computing platforms. Penetralium runs models in the TEE by building an underlying computational engine. We propose an adaptive decomposition algorithm that builds a computing pipeline for models, which adapts to the underlying trusted components. Additionally, Penetralium uses a lightweight confidence score perturbation policy to protect against advanced privacy inference attacks on deep learning models. Experimental results demonstrate that Penetralium provides strong security guarantees with reasonable performance. The system not only reduces inference latency and memory consumption overhead but also improves the overall robustness of the system against advanced attacks.

10.2139/ssrn.4654317

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Low Dosage of Sugammadex Might Not Induce Laryngospasm

PubMed (2019)

Tsai-Shan Wu Zhi‐Fu Wu Mengda Yang Hou‐Chuan Lai

Sugammadex

Laryngospasm

10.6859/aja.201906_57(2).0005

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High-performance VLSI architecture of adaptive decision feedback equalizer based on predictive parallel branch slicer (PPBS) scheme

IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2004)

Mengda Yang An-Yeu Wu Jyh-Ting Lai

Among existing works of high-speed pipelined adaptive decision feedback equalizer (ADFE), the pipelined ADFE using relaxed look-ahead technique results in a substantial hardware saving than the parallel processing or Look-ahead approaches. However, it suffers from both the signal-to-noise ratio (SNR) degradation and slow convergence rate. In this paper, we employ the predictive parallel branch slicer (PPBS) to eliminate the dependencies of the present and past decisions so as to reduce the iteration bound of decision feedback loop of the ADFE. By adding negligible hardware complexity overheads, the proposed architecture can help to improve the output mean-square error (MSE) of the ADFE compared with the Relaxed Look-ahead ADFE architecture. Moreover, we show the superior performance of the proposed pipelined ADFE by using theoretical derivations and computer simulation results. A VLSI design example using Avant! 0.35-/spl mu/m CMOS standard cell library is also illustrated. From the post-layout simulation results, we can see that the PPBS scheme requires only 38.4% gate count overhead, but it can help to reduce the critical path from 7.06 to 4.69 ns so as to meet very high-speed data transmission systems.

Critical path method

Standard cell

Performance Improvement

10.1109/tvlsi.2003.820521

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Citations (5)

High-performance adaptive decision feedback equalizer based on predictive parallel branch slicer scheme

Mengda Yang An-Yeu Wu

Among the existing works of high-speed pipelined Adaptive Decision Feedback Equalizer (ADFE), the pipelined ADFE using the relaxed look-ahead technique results in a substantial hardware saving than the parallel processing or Look-ahead approaches. However, it suffers from both the SNR degradation and slow convergence rate. In this paper, we employ the Predictive Parallel Branch Slicer (PPBS) to eliminate the dependencies of the present and past decisions so as to reduce the iteration bound of Decision Feedback Loop of the ADFE. By adding negligible hardware complexity overheads, the proposed architecture can help to improve the output Mean-square-error (MSE) of the ADFE compared with the Relaxed Look-ahead ADFE architecture. Moreover, we show the superior performance of the proposed pipelined ADFE by theoretical derivations and computer simulation results.

Look-ahead

Performance Improvement

Degradation

Feedback loop

Branch predictor

Adaptive equalizer

10.1109/sips.2002.1049696

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Enhance the trust between IoT devices, mobile apps, and the cloud based on blockchain

Journal of Network and Computer Applications (2023)

Juan Wang Wenzhe Yi Mengda Yang Jiaci Ma Shengzhi Zhang