An automatic seismic signal detection algorithm based on the Walsh transform

1981 ◽  
Vol 71 (4) ◽  
pp. 1351-1360
Author(s):  
Tom Goforth ◽  
Eugene Herrin
Keyword(s):  
Signal Detection ◽  
Real Time ◽  
Seismic Signal ◽  
Detection Algorithm ◽  
Machine Language ◽  
Walsh Transform ◽  
Time Signal ◽  
Data Systems ◽  
Detection Capability ◽  
Short Period

abstract An automatic seismic signal detection algorithm based on the Walsh transform has been developed for short-period data sampled at 20 samples/sec. Since the amplitude of Walsh function is either +1 or −1, the Walsh transform can be accomplished in a computer with a series of shifts and fixed-point additions. The savings in computation time makes it possible to compute the Walsh transform and to perform prewhitening and band-pass filtering in the Walsh domain with a microcomputer for use in real-time signal detection. The algorithm was initially programmed in FORTRAN on a Raytheon Data Systems 500 minicomputer. Tests utilizing seismic data recorded in Dallas, Albuquerque, and Norway indicate that the algorithm has a detection capability comparable to a human analyst. Programming of the detection algorithm in machine language on a Z80 microprocessor-based computer has been accomplished; run time on the microcomputer is approximately 110 real time. The detection capability of the Z80 version of the algorithm is not degraded relative to the FORTRAN version.

A WEARABLE BIOFEEDBACK SYSTEM SUPPORTING REAL-TIME PACED BREATHING TRAINING AND PHYSIOLOGICAL MONITORING

2013 ◽  
Vol 25 (02) ◽  
pp. 1350018 ◽  
Author(s):  
Zheng-Bo Zhang ◽  
Hao Wu ◽  
Jie-Wen Zheng ◽  
Wei-Dong Wang ◽  
Bu-Qing Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Breathing Pattern ◽  
Detection Algorithm ◽  
Biofeedback Training ◽  
Physiological Data ◽  
Time Signal ◽  
Physiological Monitoring ◽  
Breathing Rate ◽  
Beneficial Effects ◽  
Biofeedback System

Slow and regular breathing can generate beneficial effects on cardiovascular system and reduce stress. Breathing pacer is usually helpful for a user to learn to control breathing and restore an optimal breathing pattern. In this paper, a wearable physiological monitoring system supporting real-time breathing biofeedback is presented. An elastic T-shirt with two inductive bands integrated in the positions of rib cage (RC) and abdomen (AB) is used as a motherboard both for physiological monitoring and respiratory biofeedback. Physiological signals such as RC and AB respiration, electrocardiography (ECG), photoplethysmograph (PPG) and artery pulse wave (APW) can be sampled, stored and transmitted wirelessly. When this system is used in biofeedback applications, respiratory signals are processed in real-time by a peak-detection algorithm to recognize the concurrent breathing pattern. By comparing the actual breathing rate with the guiding breathing rate, an audio biofeedback is generated by playing music audios stored in the Micro-SD card through an MP3 decoder chip VS1053. With this design, multiple functions of physiological monitoring, real-time signal processing and audio biofeedback were integrated in one wearable system. Experiment showed that through audio biofeedback this system can guide the user to practice a slow and regular breathing effectively. Physiological data recorded from a Yoga practitioner during meditation demonstrated the capability of the system to acquire cardiopulmonary physiological data during slow breathing. This system is a useful tool both for breathing biofeedback training and its related scientific researches.

FPGA-based lifting wavelet processor for real-time signal detection

2004 ◽  
Vol 84 (10) ◽  
pp. 1931-1940 ◽  
Author(s):  
Koichi Kuzume ◽  
Koichi Niijima ◽  
Shigeru Takano
Keyword(s):  
Signal Detection ◽  
Real Time ◽  
Time Signal ◽  
Lifting Wavelet ◽  
Wavelet Processor

A real-time digital seismic event detection and recording system for network applications

1982 ◽  
Vol 72 (6A) ◽  
pp. 2339-2348
Author(s):  
Andrew J. Michael ◽  
Stephen P. Gildea ◽  
Jay J. Pulli
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Seismic Event ◽  
Weather Conditions ◽  
Recording System ◽  
Walsh Transform ◽  
Long Period ◽  
Short Period ◽  
Seismic Event Detection ◽  
Period Data

abstract A real-time digital seismic event detection and recording system has been developed for the MIT Seismic Network. The system has been designed specifically for an environment of low natural seismic activity and for surface stations which are often influenced by weather conditions and cultural noise. The system runs on an HP-1000 computer and can handle up to 16 channels of short- and long-period data. The structure of the system centers around the event detectors, one for short-period data and one for long-period data. These detectors base their decisions on a metric computed from the Walsh transform of the data. This allows them to detect changes in the amplitude of the waveform as well as frequency shifts. Detections at several stations are correlated to prevent glitches from triggering the detector. Present operation successfully saves those events that are large enough for analysis and leaves 23 of the computer available for general timesharing use.

scholarly journals A Novel Adaptive Noise Resistance Method Used for AIS Real-Time Signal Detection

2020 ◽  
Vol 29 (2) ◽  
pp. 327-336
Author(s):  
Shuaiheng Huai ◽  
Shufang Zhang ◽  
Xiaoye Wang ◽  
Jingbo Zhang
Keyword(s):  
Signal Detection ◽  
Real Time ◽  
Time Signal ◽  
Noise Resistance ◽  
Resistance Method ◽  
Adaptive Noise
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