scholarly journals Sampling frequency affects the processing of Actigraph raw acceleration data to activity counts

2016 ◽  
Vol 120 (3) ◽  
pp. 362-369 ◽  
Author(s):  
Jan Christian Brønd ◽  
Daniel Arvidsson
Keyword(s):  
Physical Activity ◽  
Frequency Band ◽  
Sampling Frequency ◽  
Signal Frequency ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Acceleration Data ◽  
Varied Degree ◽  
Band Pass ◽  
Acceleration Signals

ActiGraph acceleration data are processed through several steps (including band-pass filtering to attenuate unwanted signal frequencies) to generate the activity counts commonly used in physical activity research. We performed three experiments to investigate the effect of sampling frequency on the generation of activity counts. Ideal acceleration signals were produced in the MATLAB software. Thereafter, ActiGraph GT3X+ monitors were spun in a mechanical setup. Finally, 20 subjects performed walking and running wearing GT3X+ monitors. Acceleration data from all experiments were collected with different sampling frequencies, and activity counts were generated with the ActiLife software. With the default 30-Hz (or 60-Hz, 90-Hz) sampling frequency, the generation of activity counts was performed as intended with 50% attenuation of acceleration signals with a frequency of 2.5 Hz by the signal frequency band-pass filter. Frequencies above 5 Hz were eliminated totally. However, with other sampling frequencies, acceleration signals above 5 Hz escaped the band-pass filter to a varied degree and contributed to additional activity counts. Similar results were found for the spinning of the GT3X+ monitors, although the amount of activity counts generated was less, indicating that raw data stored in the GT3X+ monitor is processed. Between 600 and 1,600 more counts per minute were generated with the sampling frequencies 40 and 100 Hz compared with 30 Hz during running. Sampling frequency affects the processing of ActiGraph acceleration data to activity counts. Researchers need to be aware of this error when selecting sampling frequencies other than the default 30 Hz.

Envelope Analysis Based on the Combination of Morlet Wavelet and Kurtogram

2012 ◽  
Vol 490-495 ◽  
pp. 305-308
Author(s):  
Yu Liang ◽  
Yu Guo ◽  
Chuan Hui Wu ◽  
Yan Gao
Keyword(s):  
Frequency Band ◽  
Filter Banks ◽  
Morlet Wavelet ◽  
Center Frequency ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Filter Bandwidth ◽  
Envelope Analysis ◽  
Band Pass ◽  
Complex Morlet Wavelet

Envelope analysis based on the combination of complex Morlet wavelet and Kurtogram have advantages of automatic calculation of the center frequency and bandwidth of required band-pass filter. However, there are some drawbacks in the traditional algorithm, which include that the filter bandwidth is not -3dB bandwidth and the analysis frequency band covered by the filter-banks are inconsistent at different levels. A new algorithm is introduced in this paper. Through it, both optimal center frequency and bandwidth of band-pass filter in the envelop analysis can be obtained adaptively. Meanwhile, it ensures that the filters in the filter-banks are overlapped at the point of -3dB bandwidth and the consistency of frequency band that the filter-banks covered.

SELF-TUNING BIQUAD BAND-PASS FILTER

2013 ◽  
Vol 22 (03) ◽  
pp. 1350008 ◽  
Author(s):  
GORAN JOVANOVIĆ ◽  
DARKO MITIĆ ◽  
MILE STOJČEV ◽  
DRAGAN ANTIĆ
Keyword(s):  
Dynamic Range ◽  
Signal Frequency ◽  
Band Pass Filter ◽  
Central Frequency ◽  
Pass Filter ◽  
Input Signal Frequency ◽  
Bicmos Technology ◽  
Self Tuning ◽  
Band Pass ◽  
Constant Bandwidth

One approach to design self-tuning gm-C biquad band-pass filter is considered in this paper. The phase control loop is introduced to force filter central frequency to be equal to input signal frequency what is achieved by adjusting the amplifier transconductance gm. Thanks to that, the filter is robust to parameter perturbations and it can be used as a selective amplifier. In the full tuning range, it has a constant maximum gain at central frequency as well as a constant bandwidth. The 0.25 μm SiGe BiCMOS technology was used during design and verification of the band-pass filter. The filter has 26 dB gain, quality factor Q = 20 and central frequency up to 150 MHz. Simulation results indicate that the total in-band noise is 59 μV rms , the output third intercept point OIP3 = 4.36 dB and the dynamic range is 35 dB. Maximal power consumption at 3 V power supply is 1.115 mW.

Fast Locking Time Biquadratic Band-Pass Filter Utilizing Nonlinear Sliding-Mode Controller

2020 ◽  
pp. 2150154
Author(s):  
Darko Mitić ◽  
Goran Jovanović ◽  
Mile Stojčev ◽  
Dragan Antić
Keyword(s):  
Input Signal ◽  
Sliding Mode ◽  
Design Procedure ◽  
Signal Frequency ◽  
Sliding Mode Controller ◽  
Band Pass Filter ◽  
Central Frequency ◽  
Pass Filter ◽  
Bicmos Technology ◽  
Band Pass

This paper considers design procedure of fast locking time self-tuning [Formula: see text] biquadratic band-pass filter with nonlinear sliding mode control. A sliding mode controller is building block of the phase control loop (PCL) involved to push central frequency to reach input signal frequency very fast, approximately 100–200[Formula: see text]ns. The sliding mode controller is realized by using a tunable delay line, enabling optimal filter locking time for different input signal frequencies. The filter possesses low sensitivity to component discrepancy and is applied as a selective amplifier. The 0.13[Formula: see text][Formula: see text]m SiGe BiCMOS technology has been utilized for design and verification of the presented filter. This filter has central frequency up to 220[Formula: see text]MHz, quality factor [Formula: see text] and 25[Formula: see text]dB gain.

Feature extraction of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy

2020 ◽  
pp. 107754632092566 ◽  
Author(s):  
HongChao Wang ◽  
WenLiao Du
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Sparse Decomposition ◽  
Vibration Data ◽  
Fault Features ◽  
Band Pass

As the key rotating parts in machinery, it is crucial to extract the latent fault features of rolling bearing in machinery condition monitoring to avoid the occurrence of sudden accidents. Unfortunately, the latent fault features are hard to extract by using the traditional signal processing method such as envelope demodulation because the effect of envelope demodulation is influenced strongly by the degree of background noise. Sparse decomposition, as a new promising method being able of capturing the latent fault feature components buried in the vibration signal, has attracted a lot of attentions, especially the predefined dictionary-based sparse decomposition methods. However, the feature extraction effect of the predefined dictionary-based sparse decomposition depends on whether the prior knowledge of the analyzed signal is sufficient or not. To overcome the above problems, a feature extraction method of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy is proposed in the article. First, a self-learned sparse atomics method is applied on the early weak vibration signal of rolling bearing and several self-learned atomics are obtained. Then, the self-learned atomics owing bigger kurtosis values are selected and used to reconstruct the vibration signal to remove the other interference signals. Subsequently, the frequency band entropy method is used to analyze the reconstructed vibration signal, and the optimal parameter of band-pass filter could be calculated. At last, the reconstructed vibration signal is filtered using the optimal band-pass filter, envelope demodulation on the filtered signal is applied, and better fault feature is extracted. The feasibility and effectiveness of the proposed method are verified through the vibration data of the accelerated fatigue life test of rolling bearing. Besides, the analysis results of the same vibration data using Autogram and spectral kurtosis methods are also presented to highlight the superiority of the proposed method.

scholarly journals Bandwidth Enhancement and Frequency Scanning Array Antenna Using Novel UWB Filter Integration Technique for OFDM UWB Radar Applications in Wireless Vital Signs Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3155 ◽  
Author(s):  
MuhibUr Rahman ◽  
Mahdi NaghshvarianJahromi ◽  
Seyed Mirjavadi ◽  
Abdel Hamouda
Keyword(s):  
Frequency Band ◽  
Vital Signs ◽  
Array Antenna ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Bandwidth Enhancement ◽  
Beam Array ◽  
Vital Signs Monitoring ◽  
Frequency Scanning ◽  
Band Pass

This paper presents the bandwidth enhancement and frequency scanning for fan beam array antenna utilizing novel technique of band-pass filter integration for wireless vital signs monitoring and vehicle navigation sensors. First, a fan beam array antenna comprising of a grounded coplanar waveguide (GCPW) radiating element, CPW fed line, and the grounded reflector is introduced which operate at a frequency band of 3.30 GHz and 3.50 GHz for WiMAX (World-wide Interoperability for Microwave Access) applications. An advantageous beam pattern is generated by the combination of a CPW feed network, non-parasitic grounded reflector, and non-planar GCPW array monopole antenna. Secondly, a miniaturized wide-band bandpass filter is developed using SCSRR (Semi-Complementary Split Ring Resonator) and DGS (Defective Ground Structures) operating at 3–8 GHz frequency band. Finally, the designed filter is integrated within the frequency scanning beam array antenna in a novel way to increase the impedance bandwidth as well as frequency scanning. The new frequency beam array antenna with integrated band-pass filter operate at 2.8 GHz to 6 GHz with a wide frequency scanning from the 50 to 125-degree range.

SOFM for Image Compression Based on Spatial Frequency Band-Pass Filter and Vector Quantization

2020 ◽  
pp. 388-403
Author(s):  
Shadi M. S. Hilles ◽  
Volodymyr P. Maidaniuk
Keyword(s):  
Image Compression ◽  
Spatial Frequency ◽  
Vector Quantization ◽  
Frequency Band ◽  
Compression Ratio ◽  
Band Pass Filter ◽  
Small Signal ◽  
Pass Filter ◽  
Band Pass ◽  
Three Stages

This chapter presents image compression based on SOFM and vector quantization (VQ). The purpose of this chapter is to show the significance of SOFM with bandpass filter in process of image compression to increase compression ratio and to enhance image compression effectiveness. Image compression by SOFM model is presented and consists of three stages: The first is band-pass filter. The result experiments used Lena.bmp, girl256.bmp, and show compression in block image 16x16 given best compression ratio with a small signal-noise ratio (SNR).

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