Envelope Features Research Articles

Energy, environmental and economic analysis of electric vapour compression and gas driven absorption heat pumps for single-family houses

The aim of this paper is to compare energy consumptions, CO2 emissions, and operative costs of condensing boilers, electric vapour compression heat pumps and gas driven absorption heat pumps to provide space heating and domestic hot water. The analysis is performed for 140 m2 single-family houses in five different Italian cities whose envelope features depend on the location. For each location, two different envelope conditions are considered. The first one is a non-insulated building, while the second one is the same building, but an external thermal insulation is added on vertical walls and roof. To avoid internal renovation, radiators are maintained as emission system. Combined dynamic simulations are performed to appreciate building and system interactions. A 6 second time step is set to evaluate properly interactions and the DHW profile demand. In addition, the GHP dynamic model is a grey box model experimentally validated. The results show that electric vapour compression heat pumps reach the highest non-renewable primary energy savings (>32%) compared to condensing boilers, but their operative costs are higher due to the higher specific cost of electricity in Italy. Gas driven absorption heat pumps achieve a lower consumption reduction than electric heat pumps (>22%), but they have also the minimum operative cost among the three technologies.

Genome-wide identification of novel genes involved in Corynebacteriales cell envelope biogenesis using Corynebacterium glutamicum as a model.

Corynebacteriales are Actinobacteria that possess an atypical didermic cell envelope. One of the principal features of this cell envelope is the presence of a large complex made up of peptidoglycan, arabinogalactan and mycolic acids. This covalent complex constitutes the backbone of the cell wall and supports an outer membrane, called mycomembrane in reference to the mycolic acids that are its major component. The biosynthesis of the cell envelope of Corynebacteriales has been extensively studied, in particular because it is crucial for the survival of important pathogens such as Mycobacterium tuberculosis and is therefore a key target for anti-tuberculosis drugs. In this study, we explore the biogenesis of the cell envelope of Corynebacterium glutamicum, a non-pathogenic Corynebacteriales, which can tolerate dramatic modifications of its cell envelope as important as the loss of its mycomembrane. For this purpose, we used a genetic approach based on genome-wide transposon mutagenesis. We developed a highly effective immunological test based on the use of anti-cell wall antibodies that allowed us to rapidly identify bacteria exhibiting an altered cell envelope. A very large number (10,073) of insertional mutants were screened by means of this test, and 80 were finally selected, representing 55 different loci. Bioinformatics analyses of these loci showed that approximately 60% corresponded to genes already characterized, 63% of which are known to be directly involved in cell wall processes, and more specifically in the biosynthesis of the mycoloyl-arabinogalactan-peptidoglycan complex. We identified 22 new loci potentially involved in cell envelope biogenesis, 76% of which encode putative cell envelope proteins. A mutant of particular interest was further characterized and revealed a new player in mycolic acid metabolism. Because a large proportion of the genes identified by our study is conserved in Corynebacteriales, the library described here provides a new resource of genes whose characterization could lead to a better understanding of the biosynthesis of the envelope components of these bacteria.

Use of the Total Focusing Method with the Envelope Feature

This paper presents how to compute the envelope of a total focusing method (TFM) image and the benefits of using this TFM envelope as part of a code-compliant solution. The TFM envelope is obtained by computing the norm of two different TFM images; namely, a first TFM image computed using the standard acquired full matrix capture (FMC), and a second TFM image computed using the Hilbert-transformed FMC. The resulting TFM envelope image provides a better basis for the amplitude-based sizing technique, as it is more robust to amplitude variation compared with a standard oscillatory TFM image at an identical grid resolution. Therefore, with respect to the standard oscillatory TFM, a coarser grid resolution can be set for the TFM envelope, consequently reducing the total amount of computation effort and ultimately increasing the resulting acquisition rate.

Research on Subhealth Diagnosis Method for Resistance of Urban Rail Transit Door System

The rail vehicle door system is one of the key components of rail vehicles. Its failure rate accounts for more than 30% of vehicle failures. By analyzing early warnings provided by subhealth data from the door system, the efficiency and reliability of their health maintenance can be effectively improved and stable operation of the door system can also be guaranteed. In this paper, early-stage resistance changes in the subhealth state of rail vehicle door systems are considered as the research object. Firstly, the distribution rules for the motor parameters are studied, and the time-domain and normal operating envelope features of the operating motor are extracted. Secondly, subhealth conditions with different resistances are simulated using a test rig, and the experimental data are applied to summarize the rules. According to the subhealth types and the distribution of features, diagnostic rules for subhealth are formulated. To check the possibility of fault diagnosis, a verification using running rail vehicle door system data is carried out in MATLAB. The results reveal that the misdiagnosis rate of resistance subhealth is 0% while the rate of missed diagnoses is 2%. Meanwhile, the diagnostic process based on the established rules is relatively efficient. This method is suitable for application for resistance subhealth diagnosis of urban rail vehicle door systems.

Air pollution particulate matter as a potential carrier of SARS-CoV-2 to the nervous system and/or neurological symptom enhancer: arguments in favor.

Entry receptor for SARS-CoV-2 is expressed in nasal epithelial cells, and nasal delivery pathway can be a key feature of transmission. Here, a possibility of interaction of SARS-CoV-2 with air pollution particulate matter (PM) was considered. It was shown in our recent studies that water-suspended plastic and wood smoke aerosol PM and carbon-containing nanoparticles from burning organics can interact with the plasma membrane of brain nerve terminals presumably due to their lipid components. COVID-19 patients have neurological symptoms, viral particles were found in the brain, SARS-CoV-2 enters the cells via fusion of lipid viral envelope with the plasma membranes of infected cells, and so viral envelop can contain lipid components of the host neuronal membranes. Therefore, interaction of SARS-CoV-2 envelope with PM is possible in water surrounding. After drying, PM can serve as a carrier for transmission of SARS-CoV-2 immobilized at their surface. Moreover, PM and SARS-CoV-2 per se can enter human organism during nasal inhalation, and they both use the same nose-to-brain delivery pathways moving along axons directly to the brain, influencing the nervous system and exocytosis/endocytosis in nerve cells. Thus, PM can aggravate neurological symptoms of SARS-CoV-2 and vice versa, due to their identical nose-to-brain delivery mechanism and possible interference of neuronal effects. In addition, different types of PM because of their ability to interact with the plasma membranes of nerve cells can facilitate unspecific SARS-CoV-2 entrance to the cells, and can influence envelope features of SARS-CoV-2. Detailed studies are required to analyze interaction of SARS-CoV-2 with PM.

Automatic Contraction Detection Using Uterine Electromyography

Electrohysterography (EHG) is a promising technique for pregnancy monitoring and preterm risk evaluation. It allows for uterine contraction monitoring as early as the 20th gestational week, and it is a non-invasive technique based on recording the electric signal of the uterine muscle activity from electrodes located in the abdominal surface. In this work, EHG-based contraction detection methodologies are applied using signal envelope features. Automatic contraction detection is an important step for the development of unsupervised pregnancy monitoring systems based on EHG. The exploratory methodologies include wavelet energy, Teager energy, root mean square (RMS), squared RMS, and Hilbert envelope. In this work, two main features were evaluated: contraction detection and its related delineation accuracy. The squared RMS produced the best contraction (97.15 ± 4.66%) and delineation (89.43 ± 8.10%) accuracy and the lowest false positive rate (0.63%). Despite the wavelet energy method having a contraction accuracy (92.28%) below the first-rated method, its standard deviation was the second best (6.66%). The average false positive rate ranged between 0.63% and 4.74%—a remarkably low value.

MmbR, a master transcription regulator that controls fatty acid β-oxidation genes in Mycolicibacterium smegmatis.

An unusually high lipid content and a complex lipid profile are the most distinctive features of the mycobacterial cell envelope. However, our understanding of the regulatory mechanism underlying mycobacterial lipid metabolism is limited, and the major regulators responsible for lipid homeostasis remain to be characterized. Here, we identified MmbR as a novel master regulator that is essential for maintaining lipid homeostasis in Mycolicibacterium smegmatis. We found that MmbR controls fatty acid β-oxidation and modulates biofilm formation in Mycolicibacterium smegmatis. Although MmbR possesses the properties of nucleoid-associated proteins, it acts as a TetR-like transcription factor, directly regulating and intensively repressing the expression of a group of core genes involved in fatty acid β-oxidation. Furthermore, both long-chain acyl-Coenzyme A and fatty acids appear to regulate the signal molecules modulated by MmbR. The deletion of mmbR led to a significant reduction in intracellular fatty acid content and a decrease in the relative lipid composition of the biofilm. The lack of mmbR led to morphological changes in the mycobacterial colony, defects in biofilm formation and enhanced sensitivity to anti-tuberculosis drugs. Our study is the first to establish a link between the transcriptional regulation of fatty acid β-oxidation genes and lipid homeostasis in mycobacteria.

High gamma cortical processing of continuous speech in younger and older listeners

Neural processing along the ascending auditory pathway is often associated with a progressive reduction in characteristic processing rates. For instance, the well-known frequency-following response (FFR) of the auditory midbrain, as measured with electroencephalography (EEG), is dominated by frequencies from ∼100 Hz to several hundred Hz, phase-locking to the acoustic stimulus at those frequencies. In contrast, cortical responses, whether measured by EEG or magnetoencephalography (MEG), are typically characterized by frequencies of a few Hz to a few tens of Hz, time-locking to acoustic envelope features. In this study we investigated a crossover case, cortically generated responses time-locked to continuous speech features at FFR-like rates. Using MEG, we analyzed responses in the high gamma range of 70–200 Hz to continuous speech using neural source-localized reverse correlation and the corresponding temporal response functions (TRFs). Continuous speech stimuli were presented to 40 subjects (17 younger, 23 older adults) with clinically normal hearing and their MEG responses were analyzed in the 70–200 Hz band. Consistent with the relative insensitivity of MEG to many subcortical structures, the spatiotemporal profile of these response components indicated a cortical origin with ∼40 ms peak latency and a right hemisphere bias. TRF analysis was performed using two separate aspects of the speech stimuli: a) the 70–200 Hz carrier of the speech, and b) the 70–200 Hz temporal modulations in the spectral envelope of the speech stimulus. The response was dominantly driven by the envelope modulation, with a much weaker contribution from the carrier. Age-related differences were also analyzed to investigate a reversal previously seen along the ascending auditory pathway, whereby older listeners show weaker midbrain FFR responses than younger listeners, but, paradoxically, have stronger cortical low frequency responses. In contrast to both these earlier results, this study did not find clear age-related differences in high gamma cortical responses to continuous speech. Cortical responses at FFR-like frequencies shared some properties with midbrain responses at the same frequencies and with cortical responses at much lower frequencies.

Unsupervised hidden semi-Markov model for automatic beat onset detection in 1D Doppler ultrasound

Objective: One dimensional (1D) Doppler ultrasound (DUS) is commonly used for fetal health assessment, during both regular prenatal visits and labor. It is used in preference to ECG and other modalities because of its simplicity and cost. To date, all analysis of such data has been confined to a smoothed, windowed heart rate estimation derived from the 1D DUS signal, reducing the potential of short-term variability information. A first step in improving the assessment of short-term variability of the fetal heart rate (FHR) is through implementing an accurate beat detector for 1D DUS signals. Approach: This work presents an unsupervised probabilistic segmentation method enabled by a hidden semi-Markov model (HSMM). The proposed method employs envelope and spectral features for an online segmentation of fetal 1D DUS signal. The beat onsets and fetal cardiac beat-to-beat intervals are then estimated from the segmentations. For this work, two data sets were used, including 1D DUS recordings from five fetuses recorded in Germany, comprising 6521 beats and 45.06 minutes of data (dataset 1). Simultaneous fetal ECG (fECG) was used as the reference for beat timing. Dataset 2, comprising 4044 beats captured from 17 subjects in the UK was hand scored for beat location and was used as an independent held-out test set. Leave-one-out subject cross-validation was used for parameter tuning on dataset 1. No retraining was performed for dataset 2. To assess the performance of the beat onset detection, the root mean square error (RMSE), F1 score, sensitivity, positive predictivity (PPV) and the error in several standard common heart rate variability metrics were used. These metrics were evaluated on three fiducial points: (1) beat onset, (2) beat offset, and (3) middle of beat interval. Main results: In dataset 1, the proposed method provided an RMSE of 20 ms, F1 score of 97.5 %, a Se of 97.6%, and a PPV of 97.3%. In dataset 2, the proposed method achieved an RMSE of 26 ms, an F1 score of 98.5 %, a Se of 98.0 % and a PPV of 98.9 %. It was also determined that the best beat-to-beat interval was derived from the onset of each beat. For the dataset 2, significant correlations were found in all short term heart rate variability metrics tested, both in the time and frequency domain. Only the proportion of successive normal-to-normal interval differences greater than 20 ms (pNN20) exhibited a significant absolute difference. Significance: This work presents the first-ever description of an algorithm to identify cardiac beats with 1D DUS, closely matching the fetal ECG-derived beats, to enable short-term heart rate variability analysis. The novel algorithm proposed requires no human labeling of data, and could have applicability beyond 1D DUS to other similar highly variable time series.

Heart sound segmentation via Duration Long–Short Term Memory neural network

Heart sound segmentation, which aims at detecting the first and second heart sound in phonocardiogram, is an essential step to automatically analyze heart valve diseases. Recently, the neural network-based methods have demonstrated their promising performance in segmenting the heart sound data. However, the methods also suffer from serious limitations due to the used envelope features. The reason is largely due to that the envelope features cannot effectively model the intrinsic sequential characteristic, resulting in the poor utilization of the duration information of heart cycles. In this paper, we propose a Duration Long–Short Term Memory network (Duration LSTM) to effectively address this problem by incorporating the duration features. The proposed method is investigated in the real-world phonocardiogram dataset (Massachusetts Institute of Technology heart sounds database) and compared with the two representatives of the existing state-of-the-art methods, the experimental results demonstrate that the proposed method has the promising performance on different tolerance windows. In addition, the proposed model also has some advantages in the impact of recording length and the phenomenon of the end effect.

Dynamic modelling and thermoeconomic analysis of micro wind turbines and building integrated photovoltaic panels

During the past few years a significant effort has been performed in order to promote the use of renewable energy sources. However, one of the main barriers for a mature commercialization is due to the unpredictability of the renewable power production, mainly in case of wind and solar energy. Unfortunately, electric storage devices are often poorly profitable. Therefore, some more stable renewable energy systems must be designed. In this framework, this paper presents a novel hybrid renewable system consisting of Building Integrated PhotoVoltaic panels and small-scale Wind Turbines and double-stage heat pumps. This combination is very promising since it reduces the typical fluctuations of solar or wind systems, achieving a more stable profile of the overall power production. A detailed dynamic simulation model is developed in TRNSYS environment, including validated models for all the components and a suitable thermoeconomic analysis. A case study is implemented for a hotel building, where the space heating and cooling energy is supplied by an electrically driven reversible air-to-water Heat Pump, supplied by the electricity produced by Building Integrated PhotoVoltaic panels and Wind Turbines. the thermal energy recovered from the HP desuperheater is coupled with the thermal energy produced by a two-stage cascade cycle Heat Pump to produce domestic hot water. Results are presented in terms of hourly, monthly and yearly system performance data as well as by discussing the results of a detailed sensitivity analysis performed to detect the optimum configuration and weather zone of this hybrid renewable system. An analysis of the building envelope features is also performed, according to the nearly zero energy buildings target. Results showed that the combination of photovoltaic and wind technologies allows one to significantly enhance the stability of the renewable power production. Results also show that the use of heat pumps leads to a reduction of the primary energy demand for building space heating/cooling and domestic hot water by 30%. A payback period of about 5.2 years is obtained and the optimum configuration suggests adopting one 20 kW Wind Turbine for the selected case study. • Building integrated photovoltaic panels coupled with small size wind turbines. • Dynamic simulation model developed with TRNSYS software. • Sensitivity analysis on the building envelope features and weather zones. • Simple payback period of about 4.4 years for two 20 kW wind turbines.

Nonnegative Bounded Convolutional Sparse Learning Method for Envelope Feature Deconvolution

This article considers the problem of extracting periodic envelope features from observation signals modulated by subcritically damped mechanical systems. Due to insufficient envelope structure descriptions, popular two-stage blind deconvolution techniques incur a significant performance degradation in envelope feature deconvolution tasks. Leveraging sparse learning framework, a nonnegative bounded convolutional sparse learning model (NBconvSLM), is then proposed to address it in this article, and meanwhile, a nonconvex multiblock alternating direction method of multiplier (ADMM) solver is developed to rapidly achieve satisfying deconvolutional solutions. The main highlight of NBconvSLM is that nonnegative sparse regularizer is exploited to effectively describe envelope feature structure, and bounded regularizer is further introduced to sufficiently mitigate deconvolutional envelope amplitude enlargement problem. Regularizer’ roles are verified through a set of numerical experiments. Meanwhile, algorithmic performance and superiority are profoundly evaluated with respect to state-of-the-art deconvolutional techniques. Engineering application to generator bearing fault detection further corroborates algorithmic effectiveness in latent periodic feature recognition.

Including Measures of High Gamma Power Can Improve the Decoding of Natural Speech From EEG.

The human auditory system is highly skilled at extracting and processing information from speech in both single-speaker and multi-speaker situations. A commonly studied speech feature is the amplitude envelope which can also be used to determine which speaker a listener is attending to in those multi-speaker situations. Non-invasive brain imaging (electro-/magnetoencephalography [EEG/MEG]) has shown that the phase of neural activity below 16 Hz tracks the dynamics of speech, whereas invasive brain imaging (electrocorticography [ECoG]) has shown that such processing is strongly reflected in the power of high frequency neural activity (around 70-150 Hz; known as high gamma). The first aim of this study was to determine if high gamma power scalp recorded EEG carries useful stimulus-related information, despite its reputation for having a poor signal to noise ratio. Specifically, linear regression was used to investigate speech envelope and attention decoding in low frequency EEG, high gamma power EEG, and in both EEG signals combined. The second aim was to assess whether the information reflected in high gamma power EEG may be complementary to that reflected in well-established low frequency EEG indices of speech processing. Exploratory analyses were also completed to examine how low frequency and high gamma power EEG may be sensitive to different features of the speech envelope. While low frequency speech tracking was evident for almost all subjects as expected, high gamma power also showed robust speech tracking in some subjects. This same pattern was true for attention decoding using a separate group of subjects who participated in a cocktail party attention experiment. For the subjects who showed speech tracking in high gamma power EEG, the spatiotemporal characteristics of that high gamma tracking differed from that of low-frequency EEG. Furthermore, combining the two neural measures led to improved measures of speech tracking for several subjects. Our results indicated that high gamma power EEG can carry useful information regarding speech processing and attentional selection in some subjects. Combining high gamma power and low frequency EEG can improve the mapping between natural speech and the resulting neural responses.

Effects of envelope features on wind flow and pollutant exposure in street canyons

Traffic pollution has posed a serious threat to the health of near-road city residents and pedestrians, especially in high-density cities. However, the influences of envelope features, including balconies, overhangs and wing walls, on pollutant exposure to near road residents and pedestrians have not been fully understood. This paper investigates the effects of three commonly-used envelope features on wind flow and pollutant exposure to residents in street canyon with three different aspect ratios. The evaluation metrics of personal intake fraction and daily pollutant exposure are used to quantitatively assess the influences caused by different envelope features on healthy risk of near-road residents and pedestrians, alongside with wind flow pattern and pollutant distribution. The results show that these envelope features have increased the risk of pollutant exposure for the leeward side residents, while the risk of pollutant exposure for the windward side residents is reduced for most cases, in particular for the first floor. This observation is especially prominent when the canyon has the highest aspect ratio among the tested ratios, with the increased ratio of personal intake fraction reaching up to 540%. Moreover, the pollutant concentration is overall higher on leeward side of upstream building than that of windward side of downstream building. These findings can help urban planners and architects to build healthy and sustainable urban environment.

Heart Sound Segmentation Using Bidirectional LSTMs With Attention.

This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection of the heart state. We propose the use of recurrent neural networks and exploit recent advancements in attention based learning to segment the PCG signal. This allows the network to identify the most salient aspects of the signal and disregard uninformative information. The proposed method attains state-of-the-art performance on multiple benchmarks including both human and animal heart recordings. Furthermore, we empirically analyse different feature combinations including envelop features, wavelet and Mel Frequency Cepstral Coefficients (MFCC), and provide quantitative measurements that explore the importance of different features in the proposed approach. We demonstrate that a recurrent neural network coupled with attention mechanisms can effectively learn from irregular and noisy PCG recordings. Our analysis of different feature combinations shows that MFCC features and their derivatives offer the best performance compared to classical wavelet and envelop features. Heart sound segmentation is a crucial pre-processing step for many diagnostic applications. The proposed method provides a cost effective alternative to labour extensive manual segmentation, and provides a more accurate segmentation than existing methods. As such, it can improve the performance of further analysis including the detection of murmurs and ejection clicks. The proposed method is also applicable for detection and segmentation of other one dimensional biomedical signals.

Artificial neural network classification of surface reflectors and volume scatterers using sequential echoes acquired with a biomimetic audible sonar.

This paper investigates classifying two target groups, surface reflectors (SR) and volume scatterers (VS), using echo envelope features. SR targets have convex surface patches that exhibit echo persistence over aspect angle, while VS targets are composed of random range-distributed and oriented reflectors producing echoes that become uncorrelated with small changes in aspect angle. The SR target group contains single-post (P1) and multiple-post (PM) types and the VS group contains Ficus benjamina (F) and Schefflera arboricola (S) foliage types with leaf areas that differ by a factor of 4. A biomimetic sonar emitting audible clicks acquired sequences of up to three binaural echoes from target views separated by 18°. Two artificial neural networks performing linear and nonlinear classification first differentiated SR/VS target groups and then P1/PM and F/S types. Classification performance improved with echo number, from a single monaural echo to three pairs of binaural echoes, demonstrating the benefit of sequential echoes. Linear and nonlinear classification of SR/VS targets achieved a minimum generalization error probability PEG = 0.003. Nonlinear P1/PM classification achieved PEG = 0.009 that was four times smaller than linear classification. Nonlinear F/S classification achieved PEG = 0.220, indicating that envelope features by themselves are inadequate to accurately differentiate foliage targets.

Analysis of Monaural and Binaural Statistical Properties for the Estimation of Distance of a Target Speaker

The paper presents an auditory distance perception model that is based on the extraction of statistical properties from monaural and binaural features in a reverberant room environment. The developed framework has considered both mono and stereo speech signals originated from different distances at various reverberation time periods. Hence, two models, namely single-channel monaural statistics and binaural-channel monaural statistics, have been discussed in this study. The distance-dependent statistical features from fused monaural coefficients, namely cepstral and envelope features, are chosen as an input to the different classification algorithms such as Gaussian mixture model-expectation maximization, support vector machine and random forest for the estimation of distance of a desired target user. The monaural coefficients are extracted in addition with the binaural cues, such as interaural time and level differences and interaural coherence (ITD, ILD and IC) for the binaural speech signals and eventually applied for the estimation of distance. The proposed monaural and binaural models observe an average of more than 5% better results compared to existing baseline techniques even at lower signal-to-noise ratio, 0 dB.

Automatic Recognition of Fundamental Heart Sound Segments From PCG Corrupted With Lung Sounds and Speech

Automated recognition of fundamental heart sound segments (FHSS) from Phonocardiogram (PCG) is the preliminary step before clinical parameters extraction to detect the presence of abnormality if any. PCG acquisition systems are usually based on microphones. These microphones apart from cardiac sounds will also pick up non-cardiac sounds like lung sounds and speech. The recognition of FHSS is challenging in the presence of non-cardiac events. Deep learning techniques like convolutional neural network (CNN) and recurrent neural network (RNN) are suitable for automated FHSS. However, it will be shown that their performance is degraded in the presence of interference like lung sounds, and speech. Hence in this work, a combination of conventional signal processing technique with deep neural network (DNN) is proposed to enhance the accuracy of automated FHSS. The conventional signal processing technique is based on EWT which can adaptively design the filter banks based on the type of interference. For DNN, U-Net is considered. The method involves the segmentation of PCG using EWT and recognition of FHSS using U-Net based DNN. Envelope features are extracted from the EWT based reconstructed signal and used for training the U-Net based DNN to recognize FHSS. To further improve the recognition accuracy of FHSS, delineation parameters obtained from EWT are incorporated for temporal modeling with the outcomes of U-Net based DNN. The performance of the proposed method is analyzed using both real-time signals and signals taken from standard databases like the Physionet database, and Littmann’s lung sound library. Realtime PCG is acquired using an in-house developed PCG acquisition system. The proposed U-Net based DNN with the EWT method achieves FHSS recognition accuracy of 91.17% for PCG with lung sound interference and 90.78% for PCG with speech interference. The proposed method significantly improves the accuracy of FHSS recognition compared to long short term memory (LSTM), and gated recurrent unit (GRU).

Leak detection in a gas pipeline using spectral portrait of acoustic emission signals

This paper proposes a reliable method to detect leaks and recognize their various sizes in a gas pipeline based on the spectral portrait of acoustic emission (AE) signals. Through analyzing the relation of leak syndromes and AE signals in the frequency domain, this paper demonstrates that leak identification only by signatures obtained from measured individual signals might be unreliable due to strong attenuation in wave propagation along pipelines. The proposed method constructs a vector quantity in the frequency domain from two AE signals simultaneously acquired by two sensor channels, combined with a wave propagation model. This quantity characterizes the spectral portrait of AE signals emitted from a leak, its signatures therefore reflect leakage syndromes the same as leak signals, whereas those of individual sensor-measurements involve considerable distortions. Hence, the accuracy and reliability of leak detection and recognition are improved by the approach using the spectral portrait-related quantity. This paper also describes bank filters designed to encode the spectral envelope of AE signals instead of observing their total continuous frequency span. Finally, a trained multi-class support vector machine (SVM) classifier is responsible for the leak categorization, exploiting a set of the most discriminative encoded spectral envelope (ESE) features. The experimental results show that the proposed method outperforms conventional algorithms in pattern separability quantified by Kullback-Leibler distance and classification accuracy.

Enhancement of the heart sound envelope using the logistic function amplitude moderation method

This paper presents a new method to extract the envelope of the fundamental heart sound (S1 and S2) using the logistic function. The sigmoid characteristic of the logistic function is incorporated to segregate S1, and S2 signal intensities from silent or noise interfered systolic and diastolic intervals in a heart sound cycle. This signal intensity transformation brings uniformity to the envelope peak of S1 and S2 sound by inclining the transform intensity distribution towards the upper asymptote of the sigmoid curve. The proposed logistic function based amplitude moderation (LFAM) envelogram method involves finding the critical upper amplitude (xuc) above which the signals will be categorized as loud sound and the critical lower amplitude (xlc) below which the signal will be considered as noise. These critical values are regressively obtained from the signal itself by histogram analysis of intensity distribution. The performance is evaluated on noisy PCG dataset taken from PhysioNet/Computing in Cardiology Challenge 2016. The LFAM envelope yields better hill-valley discrimination of heart sounds from its silent/noisy signal intervals. The enhance heart sound envelope peaks are better than conventional methods. The proposed envelope feature is evaluated for heart sound segmentation using HSMM. There is a significant improvement in segmentation accuracy, especially at a low signal-to-noise ratio. The best average F1 score is 97.73%.