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1. A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

   https://doi.org/10.3390/sci2030053  

   Zhang, Jian; Soangra, Rahul; E. Lockhart, Thurmon (September 2020, Sci)

   One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions. 

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2. Adaptive RF Fingerprint Decomposition in Micro UAV Detection based on Machine Learning

   https://doi.org/10.1109/ICASSP39728.2021.9414985  

   Xu, Chengtao; He, Fengyu; Chen, Bowen; Jiang, Yushan; Song, Houbing (June 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   null (Ed.)

   Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources. 

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3. Automatic Utility Pole Inclination Angle Measurement Using Unmanned Aerial Vehicle and Deep Learning

   Zhu, Zanbo; Zhang, Jing; Alam, Md Morshedul; Tokgoz, Berna Eren; Hwang, Seokyon (May 2019, Proceedings of the Institute of Industrial and Systems Engineers Annual conference 2019)

   Measuring the inclination angles of utility poles of the electric power distribution lines is critical to maintain power distribution systems and minimize power outages, because the poles are very vulnerable to natural disasters. However, traditional human-based pole inspection methods are very costly and require heavy workloads. In this paper, we propose a novel pole monitoring system to measure the inclination angle of utility poles from images captured by unmanned aerial vehicle (UAV) automatically. A state-of-the-art deep learning neural network is used to detect and segment utility poles from UAV street view images, and computer vision techniques are used to calculate the inclination angles based on the segmented poles. The proposed method was evaluated using 64 images with 84 utility poles taken in different weather conditions. The pole segmentation accuracy is 93.74% and the average inclination angle error is 0.59 degrees, which demonstrate the efficiency of the proposed utility pole monitoring system. 

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4. Nanoscale imaging of Gilbert damping using signal amplitude mapping

   https://doi.org/10.1063/5.0023455  

   Wu, Guanzhong; Cheng, Yang; Guo, Side; Yang, Fengyuan; Pelekhov, Denis V; Hammel, P Chris (January 2021, Applied Physics Letters)

   Ferromagnetic resonance force microscopy (FMRFM) is a powerful scanned probe technique that uses sub-micrometer-scale, spatially localized standing spin wave modes (LMs) to perform local ferromagnetic resonance (FMR) measurements. Here, we show the spatially resolved imaging of Gilbert damping in a ferromagnetic material (FM) using FMRFM. Typically damping is measured from the FMR linewidth. We demonstrate an approach to image the spatial variation of Gilbert damping utilizing the LM resonance peak height to measure the LM resonance cone angle. This approach enables determination of damping through field-swept FMRFM at a single excitation frequency. The extreme force sensitivity of ∼2 fN at room temperature can resolve changes of Gilbert damping as small as ∼2×10−4 at 2 GHz, corresponding to ∼0.16 Oe in FMR linewidth resolution. This high sensitivity, high spatial resolution, and single frequency imaging of Gilbert damping creates the opportunity to study spin interactions at the interface between an insulating FM and a small volume of nonmagnetic material such as atomically thin two-dimensional materials. 

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5. A new method for solving the linearized 1D Vlasov–Poisson system yielding a new class of solutions

   https://doi.org/10.1063/5.0201816  

   Lee, Frank_M; Shadwick, B_A (May 2024, Physics of Plasmas)

   We describe a new method for solving the linearized 1D Vlasov–Poisson system by using properties of Cauchy-type integrals. Our method remedies critical flaws of the two standard methods, reveals a previously unrecognized Gaussian-in-time-like decay, and can also account for an externally applied electric field. The Landau approximation involves deforming the Bromwich contour around the poles closest to the real axis due to the analytically continued dielectric function, finding the long-time behavior for a stable system: Landau damping. Jackson's generalization encircles all poles while sending the contour to infinity, assuming its contribution vanishes, which is not true in general. This gives incorrect solutions for physically reasonable configurations and can exhibit pathological behavior, of which we show examples. The van Kampen method expresses the solution for a stable equilibrium as a continuous superposition of waves, resulting in an opaque integral. Case's generalization includes unstable systems and predicts a decaying discrete mode for each growing discrete mode, an apparent contradiction to both the Jackson solution and ours. We show, without imposing additional constraints, that the decaying modes are never present in the time evolution due to an exact cancellation with part of the continuum. Our solution is free of integral expressions, is obtained using algebra and Laurent series expansions, does not rely on analytic continuations, and results in a correct asymptotically convergent form in the case of infinite sums. The analysis used can be readily applied in higher-dimensional, electromagnetic systems and also provides a new technique for evaluating certain inverse Laplace transforms. 

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