Signal Quality Assessment Research Articles

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The influence of the ionisation energy on the quality of spectral fingerprints recorded by static head space-mass spectrometry (SHS-MS) was studied on cheese, wine and flavoured soda. The parameters used to assess signal quality were noise level of spectra, total signal abundance, range of mass fragments above noise, signal-to-noise ratio of each mass fragment, and discriminating power of the spectral fingerprints. These parameters were measured at eight levels of ionisation energy ranging from 35 to 95 eV. Overall, up to 70–80 eV the quality of the mass spectra increased. Fine analysis of the mass spectra shows that the levels of ionisation energy that yielded the best signal-to-noise ratios were specific to the material being studied. This work also shows that a thorough study of the spectral noise helps optimise the spectral acquisition parameters and their exploitation in predictive models.

Influence of ionisation energy on the quality of static head space-mass spectrometry spectral fingerprints

The influence of the ionisation energy on the quality of spectral fingerprints recorded by static head space-mass spectrometry (SHS-MS) was studied on cheese, wine and flavoured soda. The parameters used to assess signal quality were noise level of spectra, total signal abundance, range of mass fragments above noise, signal-to-noise ratio of each mass fragment, and discriminating power of the spectral fingerprints. These parameters were measured at eight levels of ionisation energy ranging from 35 to 95 eV. Overall, up to 70–80 eV the quality of the mass spectra increased. Fine analysis of the mass spectra shows that the levels of ionisation energy that yielded the best signal-to-noise ratios were specific to the material being studied. This work also shows that a thorough study of the spectral noise helps optimise the spectral acquisition parameters and their exploitation in predictive models.

Tracking poles with an autoregressive model: a confidence index for the analysis of the intrapartum cardiotocogram

Clinicians often rely upon the cardiotocogram, a display of the fetal heart rate and maternal uterine activity (UA) over time, as a means of monitoring fetal health during labour. Fetal health can be monitored adequately only when the signal quality of the cardiotocogram is good. We propose an automated assessment of UA signal quality in order to create a confidence index for subsequent analysis of the intrapartum cardiotocogram. We use an autoregressive (AR) model of the UA to estimate the power at the contraction frequency, with high power indicative of “good” UA signal quality. 5th, 10th, and 15th-order AR models are used to assess the signal quality of 12 intrapartum UA traces as “good/medium” or “poor”. We compare our results to two experts' visual assessments of signal quality. The 10th-order model exhibits the highest percent agreement rate of 62%. It also exhibits the most balanced false positive and false negative rates, where “good” or “medium” signal quality is considered a positive and “poor” signal quality a negative. The 10th-order model can therefore be used as a confidence index to reduce the errors made in the identification of uterine contractions in the UA trace and in the subsequent analysis of the cardiotocogram as a whole.

Equipment and Strategies for Signal Quality Monitoring for Digital Television Transmission Networks

This paper identifies the problems of objective assessment of signal quality on digital TV networks. It aims also to present equipment for automatic audio and video quality assessment designed in the Quovadis project. Monitoring of signals on digital TV networks sets specific technical constraints that have to be taken into account.

Multichannel audio signal compression and quality assessment for AV communications

This paper describes an algorithm for multichannel audio signal compression using the psychoacoustic model, an implementation and the result on quality assessment of real-time operating multichannel audio codec, and a multiplexing technique of synchronization for AV communication. The algorithm which is based on the MPEG-2 audio standard, utilizes psychoacoustic modeling, sub-band analysis and synthesis, and bit allocation for information as key technology. The audio codec was implemented on real-time operation by using a flexible DSP system in which an encoder and a decoder are independently designed for processing a formatted digital audio bit stream, and can compress the multichannel audio data of about 3848 kbits/s into 384 kbits/s. A subjective test for quality assessment of multichannel audio codec was performed using double blind and triple stimuli with hidden reference method. In the test condition with 17 subjects and ten test materials, the test result with 95% confidence intervalshows that all test materials except one (pitch pipe sound) are awarded mean difference grades better than −0.6 and this audio codec is acceptable for AV communication or broadcasting. Finally, a multiplexing method to control time stamp for synchronization of AV communication is, in this paper, introduced from the implementation of HDTV system using this codec.

A technique for the detection, decomposition and analysis of the EMG signal

In the present paper we have described a system for acquiring, processing and decomposing EMG signals for the purpose of extracting as many motor unit action potential trains as possible with the greatest level of accuracy. This system consists of 4 main sections. The first section consists of methodologies for signal acquisition and quality verification. Three channels of EMG signals are acquired using a quadripolar needle electrode designed to enhance discrimination among different MUAPs. An automated experiment control system is devised to free the experimenter from the burden of experiment detailed surveillance and bookkeeping; and to allow on-line assessment of the EMG signal quality in terms of decomposition suitability. The second section consists of methodologies for signal sampling and conditioning. The EMG signal is bandpass filtered (between 1 kHz and 10 kHz), sampled and compressed by eliminating parts of the signal under a preset threshold level. The third section consists of a signal decomposition technique where motor unit action potential trains are extracted from the EMG signal using a highly computer assisted interactive algorithm. The algorithm uses a continuously updated template matching routine and firing statistics to identify MUAPs in the EMG signal. The templates of the MUAPs are continuously updated to enable the algorithm to function even when the shape of a specific MUAP undergoes slow variations. The fourth section deals with ways in which to analyze and display the results. The more frequently used representation formats are: (1) display of MUAP wave shapes; (2) impulse trains representing motor unit firings; (3) IPI plots where time interval between successive firings of the same motor unit is plotted vs. time of the muscle contraction; (4) firing rate plots where the estimated time-varying mean firing rate of the detected motor units is plotted vs. time of the muscle contraction. The performance of the system has been tested in terms of: (1) consistency among results obtained by different operators; (2) accuracy evaluated on synthetic EMG signal; (3) indirect measure of accuracy on real EMG signal by comparing results pertaining the same motor unit action potential trains derived by two EMG signals, independently and simultaneously recorded from two different electrodes.