paper_authors: Van Hieu Nguyen, Gia Thien Luu, Thien Van Luong, Mai Xuan Trang, Philippe Ravier, Olivier Buttelli
for: 本研究旨在evaluating muscle after-fatigue condition based on surface electromyography (sEMG) signals, which has been overlooked in previous studies.
methods: 该方法使用了amplitude-based, spectral-based, 和muscle fiber conduction velocity (CV) parameters to analyze muscle fatigue indicators at various maximal voluntary contraction (MVC) levels, 以及每个MVC水平的contractions time.
results: 研究结果显示在after-fatigue condition下, muscle activation undergoes significant changes, including higher CV, power spectral density shifting to the right, and longer contraction time until exhaustion compared to before-fatigue and fatigue conditions.Abstract
This study introduces a novel muscle activation analysis based on surface electromyography (sEMG) signals to assess the muscle's after-fatigue condition. Previous studies have mainly focused on the before-fatigue and fatigue conditions. However, a comprehensive analysis of the after-fatigue condition has been overlooked. The proposed method analyzes muscle fatigue indicators at various maximal voluntary contraction (MVC) levels to compare the before-fatigue, fatigue, and after-fatigue conditions using amplitude-based, spectral-based, and muscle fiber conduction velocity (CV) parameters. In addition, the contraction time of each MVC level is also analyzed with the same indicators. The results show that in the after-fatigue condition, the muscle activation changes significantly in the ways such as higher CV, power spectral density shifting to the right, and longer contraction time until exhaustion compared to the before-fatigue and fatigue conditions. The results can provide a comprehensive and objective evaluation of muscle fatigue and recovery, which can be helpful in clinical diagnosis, rehabilitation, and sports performance.
摘要
Translation notes:* "surface electromyography" (sEMG) is translated as "表面电 MYography" (bǎo miàn diàn MYography)* "maximal voluntary contraction" (MVC) is translated as "最大自愿收缩" (zuì dà zì yù shōu)* "muscle fiber conduction velocity" (CV) is translated as "肌细胞传导速度" (jiāo xì bāng chuán dào)* "power spectral density" is translated as "能量 спектраль密度" (néngyè spèktrum míngdé)* "contractions time" is translated as "收缩时间" (shōu shū shíjiān)
Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM
results: 实验结果显示，TransD3D-IM可以对DM-IM-3D-OFDM系统中的信号探测进行优化，并且比现有的IM基于模型的探测器更高的传输可靠性。此外，TransD3D-IM也可以大幅提高传输速率，并且具有更好的响应性。Abstract
In this paper, we propose a deep learning-based signal detector called TransD3D-IM, which employs the Transformer framework for signal detection in the Dual-mode index modulation-aided three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the data bits are conveyed using dual-mode 3D constellation symbols and active subcarrier indices. As a result, this method exhibits significantly higher transmission reliability than current IM-based models with traditional maximum likelihood (ML) detection. Nevertheless, the ML detector suffers from high computational complexity, particularly when the parameters of the system are large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is also not impressive enough. To overcome this limitation, our proposal applies a deep neural network at the receiver, utilizing the Transformer framework for signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation results demonstrate that our detector attains to approach performance compared to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness than the existing deep learning-based detector while considerably reducing runtime complexity in comparison with the benchmarks.
摘要
在这篇论文中，我们提出了一种深度学习基于的信号探测器，即TransD3D-IM，该探测器使用Transformer框架进行信号探测在三个维度（3D）相互频分复用（DM-IM-3D-OFDM）系统中。在这个系统中，数据位通过 dual-mode 3D 象限符号和活动子频点来传输。因此，这种方法在现有的IM-based模型中显示出了明显更高的传输可靠性。然而，ML 探测器在系统参数较大时会受到高计算复杂性的限制。而Log-Likelihood Ratio 算法，作为 DM-IM-3D-OFDM 系统中的低复杂度探测器，也并不够印象。为了解决这个问题，我们的提案使用了一个深度神经网络，通过Transformer框架进行 DM-IM-3D-OFDM 系统中的信号探测。实验结果表明，我们的探测器可以准确地探测 DM-IM-3D-OFDM 系统中的信号，并且比模型基于接收器更具有 approached 性。此外，TransD3D-IM 还表现了更高的鲁棒性，而且可以在对比 benchmarks 时大幅减少运行时复杂性。
A Public Information Precoding for MIMO Visible Light Communication System Based on Manifold Optimization
For: 这 paper 的目的是设计一种 omnidirectional precoding，以便在 MIMO-VLC 网络中传输公共信息的信号。* Methods: 该 paper 使用了一种最大化可 achievable 率的方法，以提高发送信号的能量效率和bit error rate。它还考虑了所有 LED 的平均发送功率是相同的约束。* Results: simulation 结果表明，提案的 omnidirectional precoding 可以实现更高的Received Mean Power 和 bit error rate，相比于传统的无precoding方法。Abstract
Visible light communication (VLC) is an attractive subset of optical communication that provides a high data rate in the access layer of the network. The combination of multiple inputmultiple output (MIMO) with a VLC system leads to a higher speed of data transmission named as MIMO-VLC system. In multi-user (MU) MIMO-VLC, a LED array transmits signals for users. These signals are categorized as signals of private information for each user and signals of public information for all users. The main idea of this paper is to design an omnidirectional precoding to transmit the signals of public information in the MUMIMO-VLC network. To this end, we propose to maximize the achievable rate which leads to maximizing the received mean power at the possible location of the users. Besides maximizing the achievable rate, we consider equal mean transmission power constraint in all LEDs to achieve higher power efficiency of the power amplifiers used in the LED array. Based on this we formulate an optimization problem in which the constraint is in the form of a manifold and utilize a gradient method projected on the manifold to solve the problem. Simulation results indicate that the proposed omnidirectional precoding can achieve superior received mean power and bit error rate with respect to the classical form without precoding utilization.
摘要
可见光通信（VLC）是一个吸引人的光学通信子集，它在网络访问层提供高速的数据传输率。将多输入多输出（MIMO）技术与VLC系统结合，称为MIMO-VLC系统，可以提高数据传输速率。在多用户（MU）MIMO-VLC网络中，LED数组发送用户信号。这些信号分为每个用户的专用信息信号和所有用户的公共信息信号。本文的主要想法是设计一种全方位预编码，以在MUMIMO-VLC网络中传输公共信息信号。为此，我们提出了最大化可 achievable 率的目标，以最大化用户可能位置上接收到的平均功率。此外，我们还考虑了所有LED的平均发射功率均衡限制，以提高发射器使用的电力增效。基于这些假设，我们形式化了一个优化问题，并使用梯度法在杯 manifold上解决问题。实验结果表明，我们提出的全方位预编码可以在比较高的接收平均功率和比特错误率下获得优于经典预编码无使用情况。
Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort
paper_authors: Kangkyu Kwon, Yoon Jae Lee, Yeongju Jung, Ira Soltis, Chanyeong Choi, Yewon Na, Lissette Romero, Myung Chul Kim, Nathan Rodeheaver, Hodam Kim, Michael S. Lloyd, Ziqing Zhuang, William King, Susan Xu, Seung-Hwan Ko, Jinwoo Lee, Woon-Hong Yeo
For: This paper aims to address the limitations of current facial respirators by developing a novel smart N-95 filtering facepiece respirator with self-fit adjusting functionality and air quality monitoring.* Methods: The proposed respirator incorporates a humidity sensor made of laser-induced graphene and a pressure sensor array based on dielectric elastomeric sponge to monitor the respirator’s contact with the user’s face and provide closed-loop feedback for self-fit adjustment.* Results: The self-fit adjusting mode and elastomeric lining of the proposed respirator improve the fit factor by an average of 3.20 and 5 times at maximum, compared to current commercial respirators.Abstract
The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the face of the user, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the limitations.
摘要
covid-19 疫情的普及使我们的生活方式发生了深刻的改变，而 facial respirator 也成为了我们日常生活中的必备品。然而，现有的 respirator 具有许多限制，例如覆盖人类面部多样化大小和形状的能力不足，可能导致戴着 respirator 的效果受损。此外，现有的 facial respirator 也无法在长期使用时提供空气质量的信息。我们在这里展示了一种新型的智能 N-95 滤袋面罩，它具有滤袋面罩内部湿度感应器和压力感应器透过自适应调节功能，以提高面罩的适合度和效果。使用 LIG 将滤袋面罩内部湿度感应器，而基于导电塑胶泡的压力感应器则用于监控用户面罩的接触情况，提供关键的感应信息。因此，这个自适应调节模式加上塑料膜装饰，使面罩的适合度提高了 3.20 和 5 倍的平均和最大值。我们预期这个实验证明将提供现有商业 respirator 改进的可能性。