Surface electromyography (sEMG) is crucial in sports science, offering insights into muscle activation patterns. However, standard devices like the Delsys Trigno Wireless System are relatively costly. This study prese...
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ISBN:
(数字)9798350350821
ISBN:
(纸本)9798350350838
Surface electromyography (sEMG) is crucial in sports science, offering insights into muscle activation patterns. However, standard devices like the Delsys Trigno Wireless System are relatively costly. This study presents a prototype that simultaneously obtains sEMG and IMU signals to measure muscle activity. The presence of noise necessitates signal processing techniques to be applied to the raw signal, including feed-forward comb, adaptive, and wavelet filters. Evaluation against a reference signal revealed that the wavelet filter performed best, exhibiting the lowest MSE and highest SNR scores. Specifically, it achieved MSE scores of 2.62E-10 in the time domain, 6.25E-22 in PSD, and SNR scores 13.96 for squats. These findings underscore the effectiveness of the wavelet filter in reducing sEMG signal noise.
A new computationally efficient filtering algorithm for reconstruction of the first harmonic of a periodic signal is presented. The algorithm allows to recover the combustion quality information from the engine speed ...
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A new computationally efficient filtering algorithm for reconstruction of the first harmonic of a periodic signal is presented. The algorithm allows to recover the combustion quality information from the engine speed measurements which are noise contaminated. The algorithm is applied to the torque estimation problem for a V8 spark ignition engine.
Two new fast gradient algorithms are presented employing convergence factors which are optimized in the least-squares sense, and which perform two-dimensional block adaptive filtering. These two algorithms are termed ...
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Two new fast gradient algorithms are presented employing convergence factors which are optimized in the least-squares sense, and which perform two-dimensional block adaptive filtering. These two algorithms are termed the two-dimensional optimum block algorithm with individual adaptation of parameters (TDOBAI) and the two-dimensional optimum block adaptive algorithm (TDOBA). Using computer simulation, the convergence properties of the TDOBAI and TDOBA algorithm are investigated and compared with the two-dimensional block least-mean-square (TDBLMS) algorithm which uses a convergence factor that is constant for each 2D coefficient at each block iteration. It is also shown that for the TDOBAI and TDOBA algorithms, the convergence, speed and accuracy of adaptation are greatly improved at the expense of a modest increase in computational complexity, as compared to the TDBLMS algorithm. The effectiveness of the algorithms is demonstrated in 2D system modeling, restoration (2D additive noise cancellation) and enhancement of artificially degraded images.< >
Traditional acoustic source localization techniques attempt to determine the current location of an acoustic source from data obtained at an array of sensors during the current time only. Recently, state-space methods...
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Traditional acoustic source localization techniques attempt to determine the current location of an acoustic source from data obtained at an array of sensors during the current time only. Recently, state-space methods have been proposed that use particle filters to perform recursive estimation of the current source location using all previous data. We present an overview of these particle filter algorithms, and formulate performance measures for determining their ability to track a moving source. We present results of experiments using reverberant data recorded in a real room, and show that steered beamforming methods have improved performance over GCC-based approaches.
This paper compares four image filtering algorithms on common data sets for various signal to noise ratios and white Gaussian noise. The algorithms are the median filter, the Wallis filter, the reduced update Kalman f...
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This paper compares four image filtering algorithms on common data sets for various signal to noise ratios and white Gaussian noise. The algorithms are the median filter, the Wallis filter, the reduced update Kalman filter, and a multiple model, decision-directed filter. This comparison produces some surprising results from both a subjective (visual error) and a mean square error (MSE) viewpoint.
This work treats the performance evaluation of lscr p -based algorithms, such as lscr 1 , lscr 2 , and lscr 4 . Moreover, a variable weight mixed-norm lscr 2 -lscr 4 algorithm is also included in this study and shown...
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This work treats the performance evaluation of lscr p -based algorithms, such as lscr 1 , lscr 2 , and lscr 4 . Moreover, a variable weight mixed-norm lscr 2 -lscr 4 algorithm is also included in this study and shown to result in a better performance than each of the lscr 2 -and lscr 4 -based algorithms. The performance evaluation is carried out for noise with uniform distribution and Gaussian distribution. Detailed simulations are performed to assess the behavior of these algorithms under these noise environments.
An adaptive noise canceller (ANC) with two adaptive filters (MF and SF) is built. Based on this architecture, we propose several algorithms called A/spl mu/LMS or modified A/spl mu/LMS. By using sinusoidal signals, th...
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ISBN:
(纸本)7563504028
An adaptive noise canceller (ANC) with two adaptive filters (MF and SF) is built. Based on this architecture, we propose several algorithms called A/spl mu/LMS or modified A/spl mu/LMS. By using sinusoidal signals, the simulation results demonstrate that these new algorithms have good performance in noise cancellation. The main advantages of these proposed algorithms are that they have a high stability, a fast convergence rate and a computational efficiency.
Over the past years, research has highlighted the importance of enhancing the performance of e-mail spam filters to eliminate the risk of false negative e-mails. On the other hand, the problem of false positive e-mail...
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Over the past years, research has highlighted the importance of enhancing the performance of e-mail spam filters to eliminate the risk of false negative e-mails. On the other hand, the problem of false positive e-mails got less attention despite the fact that it is critical and may cause a failure in the delivery of important e-mails. The aim of this research is to provide a solution to reduce the rate of false positive e-mails. It addresses the problem by exploring the behavior of the existing e-mail spam filters and highlighting the different reasons behind the failure of e-mail delivery. Based on this investigation, we developed an algorithm that helps e-mail users in ensuring the deliverability of their emails. The proposed algorithm is based on reversing the mechanism of spam filters on the client-side.
Standard particle filters cannot handle dynamic systems with unknown fixed parameters. In this paper, we extend the recently proposed cost-reference particle filtering methodology (CRPF) to jointly estimate the time-v...
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Standard particle filters cannot handle dynamic systems with unknown fixed parameters. In this paper, we extend the recently proposed cost-reference particle filtering methodology (CRPF) to jointly estimate the time-varying state and the static parameters of a dynamic system. In particular, we introduce three strategies that allow assigning costs to the random samples in the state-space independently of the fixed parameters. Asymptotic results that illuminate the relationships among the methods are derived, and computer simulation results are presented to illustrate their practical implementation in a vehicle navigation problem.
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