Localized Acoustic-Event Measurement Probe: Connector Confirmation Utilizing Acoustic Signatures

2020 ◽  
Author(s):  
Brian Skoglind ◽  
Travis Roberts ◽  
Sourabh Karmakar ◽  
Cameron Turner ◽  
Laine Mears
Keyword(s):  
Background Noise ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Consumer Products ◽  
Noise Mitigation ◽  
Acoustic Event ◽  
Electrical Connection ◽  
Acoustic Signatures ◽  
Electrical Connector ◽  
Electrical Connections

Abstract Electrical connections in consumer products are typically made manually rather than through automated assembly systems due to the high variety of connector types and connector positions, and the soft flexible nature of their structures. Manual connections are prone to failure through missed or improper connections in the assembly process and can lead to unexpected downtime and expensive rework. Past approaches for registering connection success such as vision verification or Augmented Reality have shown limited ability to verify correct connection state. However, the feasibility of an acoustic-based verification system for electrical connector confirmation has not been extensively researched. One of the major problems preventing acoustic based verification in a manufacturing or assembly environment is the typically low signal to noise ratio (SNR) between the sound of an electrical connection and the diverse soundscape of the plant. In this study, a physical means of background noise mitigation and signature amplification are investigated in order to increase the SNR between the electrical connection and the plant soundscape in order to improve detection. The concept is that an increase in the SNR will lead to an improvement in the accuracy and robustness of an acoustic event detection and classification system. Digital filtering has been used in the past to deal with low SNRs, however, it runs the risk of filtering out potential important features for classification. A sensor platform is designed to filter out and reduce background noise from the plant without effecting the raw acoustic signal of the electrical connection, and an automated detection algorithm is presented. The solution is over 75% effective at detecting and classifying connections.

Selecting the Optimal FM System for Children With Cochlear Implants

2008 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Erin C. Schafer
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Empirical Research ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Evidence Based ◽  
Signal To Noise ◽  
Speech Processor ◽  
System Input ◽  
Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

scholarly journals CMUT-Based Sensor for Acoustic Emission Application: Experimental and Theoretical Contributions to Sensitivity Optimization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2042
Author(s):  
Redha Boubenia ◽  
Patrice Le Moal ◽  
Gilles Bourbon ◽  
Emmanuel Ramasso ◽  
Eric Joseph
Keyword(s):  
Signal Processing ◽  
Signal To Noise Ratio ◽  
Ultrasonic Transducer ◽  
Operating Conditions ◽  
Geometrical Parameters ◽  
Signal To Noise ◽  
Coupling Conditions ◽  
Sensor Structure ◽  
Noise Ratio ◽  
Electrical Connections

The paper deals with a capacitive micromachined ultrasonic transducer (CMUT)-based sensor dedicated to the detection of acoustic emissions from damaged structures. This work aims to explore different ways to improve the signal-to-noise ratio and the sensitivity of such sensors focusing on the design and packaging of the sensor, electrical connections, signal processing, coupling conditions, design of the elementary cells and operating conditions. In the first part, the CMUT-R100 sensor prototype is presented and electromechanically characterized. It is mainly composed of a CMUT-chip manufactured using the MUMPS process, including 40 circular 100 µm radius cells and covering a frequency band from 310 kHz to 420 kHz, and work on the packaging, electrical connections and signal processing allowed the signal-to-noise ratio to be increased from 17 dB to 37 dB. In the second part, the sensitivity of the sensor is studied by considering two contributions: the acoustic-mechanical one is dependent on the coupling conditions of the layered sensor structure and the mechanical-electrical one is dependent on the conversion of the mechanical vibration to electrical charges. The acoustic-mechanical sensitivity is experimentally and numerically addressed highlighting the care to be taken in implementation of the silicon chip in the brass housing. Insertion losses of about 50% are experimentally observed on an acoustic test between unpackaged and packaged silicon chip configurations. The mechanical-electrical sensitivity is analytically described leading to a closed-form amplitude of the detected signal under dynamic excitation. Thus, the influence of geometrical parameters, material properties and operating conditions on sensitivity enhancement is clearly established: such as smaller electrostatic air gap, and larger thickness, Young’s modulus and DC bias voltage.

Head Instability Detection for Testing Process in Perpendicular Magnetic Recording System

2013 ◽  
Vol 770 ◽  
pp. 319-322 ◽  
Author(s):  
Piya Kovintavewat ◽  
Santi Koonkarnkhai ◽  
Aimamorn Suvichakorn
Keyword(s):  
Magnetic Recording ◽  
Signal To Noise Ratio ◽  
Disk Drive ◽  
Detection Algorithm ◽  
Recording System ◽  
High Signal ◽  
Signal To Noise ◽  
Perpendicular Magnetic Recording ◽  
Read Head ◽  
Pass Through

During hard disk drive (HDD) testing process, the magneto-resistive read (MR) head is analyzed and checked if the head is defective or not. Baseline popping (BLP) is one of the crucial problems caused by head instability, whose effect can distort the readback signal to the extent of causing possible sector read failure. Without BLP detection algorithm, the defective read head might pass through HDD assembling process, thus producing an unreliable HDD. This situation must be prevented so as to retain customer satisfaction. This paper proposes a simple (but efficient) BLP detection algorithm for perpendicular magnetic recording systems. Results show that the proposed algorithm outperforms the conventional one in terms of both the percentage of detection and the percentage of false alarm, when operating at high signal-to-noise ratio.

Fiducial visibility on planar images during motion-synchronized tomotherapy treatments

2022 ◽  
Author(s):  
William Ferris ◽  
Larry Albert DeWerd ◽  
Wesley S Culberson
Keyword(s):  
Management System ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Position Error ◽  
Signal To Noise ◽  
Motion Management ◽  
Future Studies ◽  
Planar Images ◽  
Noise Ratio

Abstract Objective: Synchrony® is a motion management system on the Radixact® that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. Approach: A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. Main Results: The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. Significance: This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.

scholarly journals An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data

2021 ◽  
Vol 28 (2) ◽  
pp. 247-256
Author(s):  
Siming He ◽  
Jian Guan ◽  
Xiu Ji ◽  
Hang Xu ◽  
Yi Wang
Keyword(s):  
Data Processing ◽  
Spread Spectrum ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Induced Polarization ◽  
High Resistivity ◽  
Observation System ◽  
Pseudorandom Sequence ◽  
Identification Algorithm ◽  
The Time Domain

Abstract. In spread spectrum induced polarization (SSIP) data processing, attenuation of background noise from the observed data is the essential step that improves the signal-to-noise ratio (SNR) of SSIP data. The time-domain spectral induced polarization based on pseudorandom sequence (TSIP) algorithm has been proposed to improve the SNR of these data. However, signal processing in background noise is still a challenging problem. We propose an enhanced correlation identification (ECI) algorithm to attenuate the background noise. In this algorithm, the cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Then the frequency-domain IP (FDIP) method is used for extracting the frequency response of the observation system. Experiments on both synthetic and real SSIP data show that the ECI algorithm will not only suppress the background noise but also better preserve the valid information of the raw SSIP data to display the actual location and shape of adjacent high-resistivity anomalies, which can improve subsequent steps in SSIP data processing and imaging.

scholarly journals Detection of Incoherent Signal In Molecular Communication Based on Channel Impulse Response

2021 ◽  
Vol 2136 (1) ◽  
pp. 012039
Author(s):  
Shanchao Wen
Keyword(s):  
Impulse Response ◽  
Background Noise ◽  
Molecular Diffusion ◽  
Detection Algorithm ◽  
Channel Impulse Response ◽  
Molecular Communication ◽  
Computing Power ◽  
Traditional Scheme ◽  
Molecular Signal ◽  
The Moment

Abstract In order to solve the problem of intercode interference (ISI) and background noise caused by molecular diffusion in molecular communication, Honda analyzed and studied four methods to resist ISI signal, and analyzed the characteristics of the received signal at the moment. A reliable incoherent molecular signal detection algorithm independent of channel impulse response (CIR) is proposed, and an adaptive threshold calculation method is designed, and the theoretical value of bit error rate (BER) is given. The simulation results show that the proposed scheme BER is lower than the traditional scheme BER under the same computational complexity, so it has a wide application prospect in the nanoscale molecular communication system with limited computing power.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

Theoretical Methods of Images Processing in Optoelectronic Systems

Biometrics ◽  
2017 ◽  
pp. 361-381
Author(s):  
Tatyana Strelkova ◽  
Vladimir Kartashov ◽  
Alexander P. Lytyuga ◽  
Alexander I. Strelkov
Keyword(s):  
Mathematical Model ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Optical Signal ◽  
Optoelectronic System ◽  
Output Plane ◽  
Mean Values ◽  
Optical Signals ◽  
Images Processing ◽  
Detection Characteristics

The chapter covers development of mathematical model of signals in output plane of optoelectronic system with registration of optical signals from objects. Analytical forms for mean values and dispersion of signal and interference components of photo receiver response are given. The mathematical model can be used as a base with detection algorithm development for optical signal from objects. An algorithm of signals' detection in output plane of optoelectronic system for the control is offered. The algorithm is synthesized taking into account corpuscular and statistical properties of optical signals. Analytical expressions for mean values and signal and noise components dispersion are cited. These expressions can be used for estimating efficiency of the offered algorithm by the criterion of detection probabilistic characteristics and criterion of signal/noise relation value. The possibility of signal detection characteristics improvement with low signal-to-noise ratio is shown.

An Efficient Block Mode Detection Algorithm for Scalable Video Coding using Probability Model

2015 ◽  
Vol 4 (2) ◽  
pp. 42-55 ◽  
Author(s):  
L. Balaji ◽  
K.K. Thyagharajan ◽  
A. Dhanalakshmi
Keyword(s):  
Signal To Noise Ratio ◽  
Probability Model ◽  
Scalable Video Coding ◽  
Rate Distortion ◽  
Detection Algorithm ◽  
Base Layer ◽  
Scalable Video ◽  
Coding Efficiency ◽  
Macro Block ◽  
Video Model

H.264 / AVC expansion is H.264 / SVC which is applicable in environments that demand video streaming. This paper delivers an algorithm to shorten computational complexity and extend coding efficiency by determining the mode speedily. In this writing, the authors talk a fast mode resolution algorithm with less complexity unlikely the traditional joint scalable video model (JSVM). Their algorithm end mode hunt by a probability model defined. This model is address for both intra-mode and inter-mode predictions of base layer and enhancement layers in a macro block (MB). The estimated rate distortion cost (RDC) for modes among layers is custom to determine the best mode of each MB. The experimental results show that the authors' algorithm realizes 26.9% of encoding time when compared with the JSVM reference software with smallest reduction in peak signal to noise ratio (PSNR).

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