Detection and identification algorithm of the S1 and S2 heart sounds

2016 ◽  
Author(s):  
Fatima Chakir ◽  
Abdelilah Jilbab ◽  
Chafik Nacir ◽  
Ahmed Hammouch ◽  
Amir Hajjam El Hassani
Keyword(s):  
Heart Sounds ◽  
Identification Algorithm ◽  
Detection And Identification

scholarly journals Large-Scale Survey of CampylobacterSpecies in Human Gastroenteritis by PCR and PCR–Enzyme-Linked Immunosorbent Assay

1999 ◽  
Vol 37 (12) ◽  
pp. 3860-3864 ◽  
Author(s):  
A. J. Lawson ◽  
J. M. J. Logan ◽  
G. L. O'neill ◽  
M. Desai ◽  
J. Stanley
Keyword(s):  
Immunosorbent Assay ◽  
Large Scale ◽  
Enzyme Linked Immunosorbent Assay ◽  
Mixed Infections ◽  
Identification Algorithm ◽  
Detection And Identification ◽  
Significant Difference ◽  
Sporadic Cases ◽  
Low Incidence ◽  
Campylobacter Infection

A PCR-based study of the incidence of enteropathogenic campylobacter infection in humans was done on the basis of a detection and identification algorithm consisting of screening PCRs and species identification by PCR-enzyme-linked immunosorbent assay. This was applied to DNA extracted from 3,738 fecal samples from patients with sporadic cases of acute gastroenteritis, submitted by seven regional Public Health Laboratories in England and Wales over a 2-year period. The sending laboratories had cultured “Campylobacterspp.” from 464 samples. The PCR methodologies detected 492Campylobacter-positive samples, and the combination of culture and PCR yielded 543 Campylobacter-positive samples. There was identity (overlap) for 413 samples, but 79 PCR-positive samples were culture negative, and 51 culture-positive samples were PCR negative. While there was no statistically significant difference between PCR and culture in detection of C. jejuni-C. coli(PCR, 478 samples; culture, 461 samples), PCR provided unique data about mixed infections and non-C. jejuni and non- C. coli campylobacters. Mixed infections withC. jejuni and C. coli were found in 19 samples, and mixed infection with C. jejuni and C. upsaliensis was found in one sample; this was not apparent from culture. Eleven cases of gastroenteritis were attributed to C. upsaliensis by PCR, three cases were attributed to C. hyointestinalis, and one case was attributed to C. lari. This represents the highest incidence of C. hyointestinalis yet reported from human gastroenteritis, while the low incidence of C. larisuggests that it is less important in this context.

scholarly journals Detection and Identification of Loss of Efficiency Faults of Flight Actuators

2015 ◽  
Vol 25 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Daniel Ossmann ◽  
Andreas Varga
Keyword(s):  
Fault Detection ◽  
Surface Damage ◽  
Fault Detection And Diagnosis ◽  
Identification Algorithm ◽  
Detection Techniques ◽  
Fast Detection ◽  
Detection And Identification ◽  
Robust Fault Detection ◽  
Input Signals ◽  
Detection And Diagnosis

Abstract We propose linear parameter-varying (LPV) model-based approaches to the synthesis of robust fault detection and diagnosis (FDD) systems for loss of efficiency (LOE) faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative) LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults) is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system’s detection and identification characteristics under realistic conditions.

scholarly journals The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals

Algorithms ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 129
Author(s):  
Zhiyong Sheng ◽  
Dandan Qu ◽  
Yuan Zhang ◽  
Dan Yang
Keyword(s):  
Optical Fiber ◽  
Embedded System ◽  
Real Time ◽  
Digital Signal Processor ◽  
Warning System ◽  
Vibration Signal ◽  
Identification Algorithm ◽  
The Real ◽  
Detection And Identification ◽  
Time Requirements

With the continuous development of optical fiber sensing technology, the Optical Fiber Pre-Warning System (OFPS) has been widely used in various fields. The OFPS identifies the type of intrusion based on the detected vibration signal to monitor the surrounding environment. Aiming at the real-time requirements of OFPS, this paper presents a fast algorithm to accelerate the detection and recognition processing of optical fiber intrusion signals. The algorithm is implemented in an embedded system that is composed of a digital signal processor (DSP). The processing flow is divided into two parts. First, the dislocation processing method is adopted for the sum processing of original signals, which effectively improves the real-time performance. The filtered signals are divided into two parts and are parallel processed by two DSP boards to save time. Then, the data is input into the identification module for feature extraction and classification. Experiments show that the algorithm can effectively detect and identify the optical fiber intrusion signals. At the same time, it accelerates the processing speed and meets the real-time requirements of OFPS for detection and identification.

scholarly journals A Multihypothesis Sequential Probability Test for Fault Detection and Identification of Vehicles' Ultrasonic Parking Sensors

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
Mamoun F. Abdel-Hafez
Keyword(s):  
Fault Detection ◽  
Measurement Noise ◽  
Experimental Results ◽  
Identification Algorithm ◽  
Ultrasonic Sensors ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Correct Hypothesis ◽  
Sequential Probability

This paper presents a sequential fault detection and identification algorithm for detecting a fault in a vehicle's ultrasonic parking sensors. The algorithm identifies a bias fault in any of the ultrasonic sensors by computing the probability of having that bias fault given a carefully constructed measurement residual that is only a function of the measurement noise and the possible measurement fault. A set of bias hypotheses is assumed and initially given equal alarm probability. It is assumed that only one sensor will acquire a bias at any given time. Once the probability of a hypothesis approaches 1, that hypothesis is declared as the correct hypothesis and the bias associated with the hypothesis is removed from the sensors' reading. The accuracy and convergence characteristics of the proposed algorithm are verified using experimental results. This study is essential to ensure accurate operation of vehicle's ultrasonic parking sensors.

Detection and identification of S1 and S2 heart sounds using wavelet decomposition method

2015 ◽  
Vol 08 (06) ◽  
pp. 1550078 ◽  
Author(s):  
Ali Tavakoli Golpaygani ◽  
Nahid Abolpour ◽  
Kamran Hassani ◽  
Kourosh Bajelani ◽  
D. John Doyle
Keyword(s):  
Wavelet Decomposition ◽  
Heart Sound ◽  
Detection System ◽  
Automatic Segmentation ◽  
Heart Sounds ◽  
Segmentation Algorithm ◽  
Discrete Wavelet ◽  
Detection And Identification ◽  
Heart Disorders ◽  
Heart Sound Segmentation

Phonocardiogram (PCG), the digital recording of heart sounds is becoming increasingly popular as a primary detection system for diagnosing heart disorders and it is relatively inexpensive. Electrocardiogram (ECG) is used during the PCG in order to identify the systolic and diastolic parts manually. In this study a heart sound segmentation algorithm has been developed which separates the heart sound signal into these parts automatically. This study was carried out on 100 patients with normal and abnormal heart sounds. The algorithm uses discrete wavelet decomposition and reconstruction to produce PCG intensity envelopes and separates that into four parts: the first heart sound, the systolic period, the second heart sound and the diastolic period. The performance of the algorithm has been evaluated using 14,000 cardiac periods from 100 digital PCG recordings, including normal and abnormal heart sounds. In tests, the algorithm was over 93% correct in detecting the first and second heart sounds. The presented automatic segmentation algorithm using wavelet decomposition and reconstruction to select suitable frequency band for envelope calculations has been found to be effective to segment PCG signals into four parts without using an ECG.

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