Optimization on Tracking Performance of RLS Algorithm Using Transfer Function of Butterworth Low Pass Filter

2013 ◽  
Vol 850-851 ◽  
pp. 856-859
Author(s):  
Si Wei Tan ◽  
Zhi Liang Ren
Keyword(s):  
Signal Processing ◽  
Transfer Function ◽  
Interference Cancellation ◽  
Tracking Performance ◽  
Forgetting Factor ◽  
Rls Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Variable Forgetting Factor

In order to get further optimizations on tracking performance of the RLS algorithm in nonstationarity signal processing, and to simplify the variable forgetting factor updating model, a novel variable forgetting factor updating model based on Butterworth low pass filter transfer function was proposed. The relationship between the variable forgetting factor and the tracking performance of RLS algorithm was analyzed. The updating model proposed in this paper was built based on the modification of the transfer function of Butterworth low pass filter. The model function fit the theoretical variation curve of the variable forgetting factor well. It also could be adjusted by parameters of the function order and the critical point according to the different applications. In addition, the computation of the updating model was simple and convenience. The RLS algorithm with the updating model was tested in an adaptive interference cancellation system. Some conclusions were drawn from the simulation results. The optimized RLS algorithm had a better tracking performance in nonstationarity signal processing as well as small stationary errors after convergence.

ANALYSIS AND IMPROVEMENT TECHNIQUES FOR THE TRANSFER FUNCTION OF A PLANAR LOW - PASS FILTER

2016 ◽  
Vol 15 (12) ◽  
pp. 2579-2586
Author(s):  
Adina Racasan ◽  
Calin Munteanu ◽  
Vasile Topa ◽  
Claudia Pacurar ◽  
Claudia Hebedean
Keyword(s):  
Transfer Function ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

scholarly journals Study of modular transfer function-based optieal low-pass filter evaluation model and experiment

2008 ◽  
Vol 57 (5) ◽  
pp. 2854
Author(s):  
Qi Xun-Jun ◽  
Lin Bin ◽  
Cao Xiang-Qun ◽  
Chen Yu-Qing
Keyword(s):  
Transfer Function ◽  
Evaluation Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Norepinephrine reuptake, baroreflex dynamics, and arterial pressure variability in rats

2000 ◽  
Vol 279 (4) ◽  
pp. R1257-R1267 ◽  
Author(s):  
Delphine Bertram ◽  
Christian Barrès ◽  
Yong Cheng ◽  
Claude Julien
Keyword(s):  
Time Delay ◽  
Transfer Function ◽  
Fixed Time ◽  
Baroreceptor Reflex ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Mayer Waves ◽  
Frequency Responses ◽  
Low Pass ◽  
Norepinephrine Reuptake

This study examined the effect of norepinephrine reuptake blockade with desipramine (DMI) on the spontaneous variability of the simultaneously recorded arterial pressure (AP) and renal sympathetic nerve activity (SNA) in conscious rats. Acute DMI administration (2 mg/kg iv) depressed AP Mayer waves (∼0.4 Hz) and increased low-frequency (<0.2 Hz) components of AP variability. DMI decreased renal SNA variability, especially due to the abolition of oscillations related to Mayer waves. To examine whether DMI-induced changes in AP and renal SNA variabilities could be explained by alterations in the dynamic characteristics of the baroreceptor reflex loop, the frequency responses of mean AP to aortic depressor nerve stimulation were studied in urethan-anesthetized rats. DMI accentuated the low-pass filter properties of the transfer function without significantly altering the fixed time delay. The frequency responses of iliac vascular conductance to stimulation of the lumbar sympathetic chain were studied in an additional group of anesthetized rats. DMI did not markedly alter the low-pass filter properties of the transfer function and slightly increased the fixed time delay. These results suggest that the DMI-induced decrease in the dynamic gain of the baroreceptor reflex is responsible for the decreased spontaneous renal SNA variability and the accompanying increased AP variability. The “slowing down” of baroreflex responses cannot be attributed to an effect of DMI at the vascular neuroeffector junction.

Parietal Representation of Hand Velocity in a Copy Task

2005 ◽  
Vol 93 (1) ◽  
pp. 508-518 ◽  
Author(s):  
Bruno B. Averbeck ◽  
Matthew V. Chafee ◽  
David A. Crowe ◽  
Apostolos P. Georgopoulos
Keyword(s):  
Transfer Function ◽  
Linear Model ◽  
Parietal Cortex ◽  
Neural Activity ◽  
Nonlinear Models ◽  
Linear Representation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Filter Width ◽  
Low Pass

We recorded neural activity from ensembles of neurons in areas 5 and 2 of parietal cortex, while two monkeys copied triangles, squares, trapezoids, and inverted triangles and used both linear and nonlinear models to predict the hand velocity from the neural activity of the ensembles. The linear model generally outperformed the nonlinear model, suggesting a reasonably linear relation between the neural activity and the hand velocity. We also found that the average transfer function of the linear model fit to individual cells was a low-pass filter because the neural response had considerable high-frequency power, whereas the hand velocity only had power at frequencies below ∼5 Hz. Increasing the width of the transfer function, up to a width of 700–800 ms, improved the fit of the model. Furthermore, the Rsqr of the linear model improved monotonically with the number of cells in the ensemble, saturating at 60–80% for a filter width of 700 ms. Finally, it was found that including an interaction term, which allowed the transfer function to shift with the eye position, did not improve the fit of the model. Thus ensemble neural responses in superior parietal cortex provide a high-fidelity, linear representation of hand kinematics within our task.

Digital All-Pass Filter Modeling Based on Desired Group Delay Using Advanced Material: A Review on the Signal Processing Part

2015 ◽  
Vol 815 ◽  
pp. 338-342
Author(s):  
Faizah Abu Bakar ◽  
Sohiful Anuar Zainol Murad ◽  
Rizalafande Che Ismail ◽  
Muzamir Isa
Keyword(s):  
Transfer Function ◽  
Group Delay ◽  
Sampling Frequency ◽  
Bilinear Transformation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Phase Type ◽  
Low Pass ◽  
Group Delays ◽  
Delay Variation

This paper presents a review on three types of techniques in designing digital all-pass filters based on group delay. All the three methods use the same basic concept rooting back to the requirement of a stable transfer function of the filter which should be a minimum-phase type, and the denominator group delay. The most optimized of the three is chosen to be implemented in MATLAB in order to decrease the group delay variation of a 5th order Chebyshev low-pass filter with cut-off frequency of 160 MHz. The digital transfer function of the low-pass filter is obtained from the analog transfer function by means of bilinear transformation. The sampling frequency of the digital LPF is 100 times the cut-off ffrequency to retain the response of the analog LPF. Both of the filters are then cascaded together and the overall group delays variations are analyzed. The variations of group delay shows a reduction but the price paid is the increase of the overall group delay of the system.

A 2nd-Order Continuous Time Delta-Sigma Modulator for Photoplethysmography Analog Front-End

2020 ◽  
Author(s):  
Eka Fitrah Pribadi ◽  
Rajeev Kumar Pandey ◽  
Paul C.-P. Chao
Keyword(s):  
Transfer Function ◽  
Continuous Time ◽  
Supply Voltage ◽  
Signal To Noise Ratio ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Delta Sigma Modulator ◽  
Delta Sigma ◽  
Low Pass ◽  
Order Continuous

Abstract A brief presents a 2nd order continuous-time delta-sigma modulator (CT-DSM) using a low pass filter to reduce the slew rate requirement of the output swing of the first integrator. By adding the low pass filter, the desired transfer function of the CT-DSM is altered. Thus a feed-forward based compensation circuit is introduced to transform the altered transfer function to the original condition. The CT-DSM is designed with a bandwidth of 100 Hz to satisfy the requirement of photoplethysmogram (PPG) detection. The CT-DSM is simulated using CMOS 180 nm technology with the layout area 460 μm × 460 μm. The circuit uses a 1.8 V supply voltage and consumes 35.61 μW. The signal-to-noise ratio of the CT-DSM is 101.2 dB, while the SFDR is 99.1 dB.

Myoelectric-Controlled Exoskeletal Elbow Robot to Suppress Essential Tremor: Extraction of Elbow Flexion Movement Using STFTs and TDNN

2012 ◽  
Vol 24 (1) ◽  
pp. 141-149 ◽  
Author(s):  
Takeshi Ando ◽  
◽  
Masaki Watanabe ◽  
Keigo Nishimoto ◽  
Yuya Matsumoto ◽  
...  
Keyword(s):  
Signal Processing ◽  
Essential Tremor ◽  
Voluntary Movement ◽  
Elbow Flexion ◽  
Myoelectric Signal ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Short Time ◽  
Short Time Fourier Transformation

Essential tremor is the most common of all involuntary movements. Many patients with an upper-limb tremor have serious difficulties in performing daily activities. We developed a myoelectric-controlled exoskeletal robot to suppress tremor. In this article, we focus on developing a signal processing method to extract voluntary movement from a myoelectric in which the voluntary movement and tremor were mixed. First, a Low-Pass Filter (LPF) and Neural Network (NN) were used to recognize the tremor patient’s movement. Using these techniques, it was difficult to recognize the movement accurately because the myoelectric signal of the tremor patient periodically oscillated. Then, Short-Time Fourier Transformation (STFT) and NN were used to recognize the movement. This method was more suitable than LPF and NN. However, the recognition timing at the start of the movement was late. Finally, a hybrid algorithm for using both short and long windows’ STFTs, which is a kind of “mixture of experts,” was proposed and developed. With this type of signal processing, elbow flexion was accurately recognized without the time delay in starting the movement.

Neuronal uptake affects dynamic characteristics of heart rate response to sympathetic stimulation

1999 ◽  
Vol 277 (1) ◽  
pp. R140-R146 ◽  
Author(s):  
Tsutomu Nakahara ◽  
Toru Kawada ◽  
Masaru Sugimachi ◽  
Hiroshi Miyano ◽  
Takayuki Sato ◽  
...  
Keyword(s):  
Heart Rate ◽  
Transfer Function ◽  
Lag Time ◽  
Stimulation Frequency ◽  
Neuronal Uptake ◽  
Sympathetic Stimulation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Dynamic Gain

Recently, studies in our laboratory involving the use of a Gaussian white noise technique demonstrated that the transfer function from sympathetic stimulation frequency to heart rate (HR) response showed dynamic characteristics of a second-order low-pass filter. However, determinants for the characteristics remain to be established. We examined the effect of an increase in mean sympathetic stimulation frequency and that of a blockade of the neuronal uptake mechanism on the transfer function in anesthetized rabbits. We found that increasing mean sympathetic stimulation frequency from 1 to 4 Hz significantly ( P < 0.01) decreased the dynamic gain of the transfer function without affecting other parameters, such as the natural frequency, lag time, or damping coefficient. In contrast, the administration of desipramine (0.3 mg/kg iv), a neuronal uptake blocking agent, significantly ( P < 0.01) decreased both the dynamic gain and the natural frequency and prolonged the lag time. These results suggest that the removal rate of norepinephrine at the neuroeffector junction, rather than the amount of available norepinephrine, plays an important role in determining the low-pass filter characteristics of the HR response to sympathetic stimulation.

An Improved Harmonic Current Detection Method Based on Variable Forgetting Factor RLS Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2459-2463
Author(s):  
Minh Guang Vu ◽  
Wei Han ◽  
Da Zhi Wang ◽  
Yun Lu Li
Keyword(s):  
Dynamic Process ◽  
Detection Algorithm ◽  
Recursive Least Squares ◽  
Forgetting Factor ◽  
Harmonic Current ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Variable Forgetting Factor ◽  
The Impact ◽  
Current Detection

The effects of compensating and restraining the power system harmonics using active power filter are determined by the detection precision and its dynamic response characters. To improve both of them, a harmonic current detection algorithm based on variable forgetting factor Recursive Least-Squares algorithm is presented. The occurrence of the dynamic process is identified firstly by the judgment condition which is given by the algorithm, and then the forgetting factor is assigned dynamically, so that the convergent speed is significantly improved. The algorithm overcomes the impact of low-pass filter of traditional p-q or ip-iq algorithm, and releases the contradiction cased by the conflicting requirements of forgetting factor value between steady process and dynamic process. So it has better dynamic performance. Simulation and experiments prove the validity and feasibility of the approaches.

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