2,130 research outputs found
A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device
This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beats·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beats·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beats·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate
A Novel Method for Acoustic Noise Cancellation
Over the last several years Acoustic Noise Cancellation (ANC) has been an active area of research and various adaptive techniques have been implemented to achieve a
better online acoustic noise cancellation scheme. Here we introduce the various adaptive techniques applied to ANC viz. the LMS algorithm, the Filtered-X LMS algorithm, the Filtered-S LMS algorithm and the Volterra Filtered-X LMS algorithm and try to understand their performance through various simulations. We then take up the problem of cancellation of external acoustic feedback in hearing aid. We provide three different models to achieve the feedback cancellation. These are - the adaptive FIR Filtered-X LMS, the adaptive IIR LMS and the adaptive IIR PSO models for
external feedback cancellation. Finally we come up with a comparative study of the performance of these models based on the normalized mean square error minimization provided by each of these feedback cancellation schemes
Adaptive frequency domain identification for ANC systems using non-stationary signals
The problem of identification of secondary path in active noise control applications is dealt with fundamentally using time-domain adaptive filters. The use of adaptive frequency domain subband identification as an alternative has some significant advantages which are overlooked in such applications. In this paper two different delayless subband adaptive algorithms for identification of an unknown secondary path in an ANC framework are utilized and compared. Despite of reduced computational complexity and increase convergence rate this approach allows us to use non-stationary audio signals as the excitation input to avoid injection of annoying white noise. For this purpose two non-stationary music and speech signals are used for identification. The performances of the algorithms are measured in terms of minimum mean square error and convergence speed. The results are also compared to a fullband algorithm for the same scenario. The proposed delayless algorithms have a closed loop structure with DFT filterbanks as the analysis filter. To eliminate the delay in the signal path two different weights transformation schemes are compared
On use of averaging in FxLMS algorithm for single-channel feedforward ANC systems
科研費報告書収録論文(課題番号:15560314/研究代表者:川又政征/多次元ディジタルフィルタの最適設計とその画像・映像処理への応用
LMS Adaptive Filters for Noise Cancellation: A Review
This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted
Estimation-based synthesis of H∞-optimal adaptive FIR filtersfor filtered-LMS problems
This paper presents a systematic synthesis procedure for H∞-optimal adaptive FIR filters in the context of an active noise cancellation (ANC) problem. An estimation interpretation of the adaptive control problem is introduced first. Based on this interpretation, an H∞ estimation problem is formulated, and its finite horizon prediction (filtering) solution is discussed. The solution minimizes the maximum energy gain from the disturbances to the predicted (filtered) estimation error and serves as the adaptation criterion for the weight vector in the adaptive FIR filter. We refer to this adaptation scheme as estimation-based adaptive filtering (EBAF). We show that the steady-state gain vector in the EBAF algorithm approaches that of the classical (normalized) filtered-X LMS algorithm. The error terms, however, are shown to be different. Thus, these classical algorithms can be considered to be approximations of our algorithm. We examine the performance of the proposed EBAF algorithm (both experimentally and in simulation) in an active noise cancellation problem of a one-dimensional (1-D) acoustic duct for both narrowband and broadband cases. Comparisons to the results from a conventional filtered-LMS (FxLMS) algorithm show faster convergence without compromising steady-state performance and/or robustness of the algorithm to feedback contamination of the reference signal
IMPLEMENTATION OF NOISE CANCELLATION WITH HARDWARE DESCRIPTION LANGUAGE
The objective of this project is to implement noise cancellation technique on an FPGA
using Hardware Description Language. The performance of several adaptive algorithms is
compared to determine the desirable algorithm used for adaptive noise cancellation
system. The project will focus on the implementation of adaptive filter with least-meansquares
(LMS) algorithm or normalized least-mean-squares (NLMS) algorithm to cancel
acoustic noises. This noise consists of extraneous or unwanted waveforms that can
interfere with communication. Due to the simplicity and effectiveness of adaptive noise
cancellation technique, it is used to remove the noise component from the desired signal.
The project is divided into four main parts: research, Matlab simulation, ModelSim
simulation and hardware implementation. The project starts with research on several noise
cancellation techniques, and then with Matlab code, Simulink and FDA tool, the adaptive
noise cancellation system is designed with the implementation of the LMS algorithm,
NLMS algorithm and recursive-least-square algorithm to remove the interference noise.
By using the Matlab code and Simulink, the noise that interfered with a sinusoidal signal
and a record of music can be removed. The original signal in turns can be retrieved from
the noise corrupted signal by changing the coefficient of the filter. Since filter is the
important component in adaptive filtering process, the filter is designed first before adding
adaptive algorithm. A Finite Impulse Response (FIR) filter is designed and the desired
result of functional simulation and timing simulation is obtained through ModelSim and
Integrated Software Environment (ISE) software and FPGA implementation. Finally the
adaptive algorithm is added to the filter, and implemented in the FPGA. The noise is
greatly reduced in Matlab simulation, functional simulation and timing simulation. Hence
the results of this project show that noise cancellation with adaptive filter is feasible
A constrained, total-variation minimization algorithm for low-intensity X-ray CT
Purpose: We develop an iterative image-reconstruction algorithm for
application to low-intensity computed tomography (CT) projection data, which is
based on constrained, total-variation (TV) minimization. The algorithm design
focuses on recovering structure on length scales comparable to a detector-bin
width.
Method: Recovering the resolution on the scale of a detector bin, requires
that pixel size be much smaller than the bin width. The resulting image array
contains many more pixels than data, and this undersampling is overcome with a
combination of Fourier upsampling of each projection and the use of
constrained, TV-minimization, as suggested by compressive sensing. The
presented pseudo-code for solving constrained, TV-minimization is designed to
yield an accurate solution to this optimization problem within 100 iterations.
Results: The proposed image-reconstruction algorithm is applied to a
low-intensity scan of a rabbit with a thin wire, to test resolution. The
proposed algorithm is compared with filtered back-projection (FBP).
Conclusion: The algorithm may have some advantage over FBP in that the
resulting noise-level is lowered at equivalent contrast levels of the wire.Comment: This article has been submitted to "Medical Physics" on 9/13/201
Adaptive Feedforward Compensation Algorithms for Active Vibration Control with Mechanical Coupling and Local Feedback - a unified approach
Adaptive feedforward broadband vibration (or noise) compensation is currently used when a correlated measurement with the disturbance (an image of the disturbance) is available. Most of the active vibration control systems feature an internal "positive" mechanical feedback between the compensation system and the reference source (a correlated measurement with the disturbance). Such systems have often also a feedback control loop for reducing the effect of disturbances. Therefore the adaptive feedforward compensation algorithms should take into account this structure. For stability reasons the adaptation algorithms requires the implementation of a filter on observed data or a filtering of the residual acceleration in order to satisfy some passivity conditions. The paper proposes new algorithms for the adaptive feedforward compensation in this context with both filtering of data and of the residual acceleration and using an "Integral + Proportional" (IP) adaptation as a means for accelerating the transients as well as for relaxing the positive real conditions required by the stability analysis. The paper also shows that the main interest in filtering the residual acceleration is to shape in the frequency domain the power spectral density (PSD) of the residual acceleration. The algorithms have been applied to an active vibration control (AVC) system and real time results illustrating the advantages of the proposed algorithms are presented
Analysis and implementation of active noise control strategies using Piezo and EAP actuators
Currently noise cancellation, which affects the lives of people and in the workplace is
achieved through the active noise reduction. This measure is not expensive as passive or
semi active measures also permits adequate air conduction in duct ventilation systems.
The system control is achieved through a suitable location of the phase in the cancelling
noise signal relative to the signal primary noise.
Algorithms have been developed and strategies for active noise reduction and its implementation
and experimental testing on duct ventilation. The actives elements used
are Piezo Actuators and EAP as speakers; Individual and collective operation of the
aforementioned actuators is examined. The work was evaluated as follows:
Analysis of previous research on existing algorithms for active noise reduction.
Study the strategies of simulation and implementation for active noise control algorithms
designed.Tesi
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