Basic Signal Research Articles

ECG Signal Classification Review

The heart is an important part of the human body, functioning to pump blood through the circulatory system. Heartbeats generate a signal called an ECG signal. ECG signals or electrocardiogram signals are basic raw signals to identify and classify heart function based on heart rate. Its main task is to analyze each signal in the heart, whether normal or abnormal. This paper discusses some of the classification methods which most frequently used to classify ECG signals. These methods include pre-processing, feature extraction, and classification methods such as MLP, K-NN, SVM, CNN, and RNN. There were two stages of ECG classification, the feature extraction stage and the classification stage. Before ECG features were extracted, raw ECG signal data first processed in the pre-processing stage because ECG signals were not necessarily free of noise. Noise will cause a decrease in accuracy during the classification process. After features were extracted, ECG signals were then classified with the classification method. Neural Network methods such as CNN and RNN are best to use since they can give better accuracy. For further research, the machine learning method needs to be improved to get high accuracy and high precision in the ECG signals classification.

Когнитивный подход к интерпретации художественного текста

The research is concerned with the cognitive approach to literary text interpretation which presupposes certain models of understanding. It discusses the problems of multidimensional structure of the literary text, the basic linguistic signals put in the position of foregrounding and the role of cultural concepts in decoding authors world picture. Special attention is paid to the principles of foregrounding, linguistic iconicity, redundancy and economy, which are considered to be cognitive principles of information distribution in a literary text and perform conceptually significant functions, highlighting the most essential information. It is substantiated that in the process of literary text interpretation, attention should be paid to the representation of cultural concepts, the processes of conceptualization and distribution of information in a literary text, the distribution of conceptual features according to the field principle.

Toward Workable and Cost-Efficient Monitoring of Unstable Rock Compartments with Ambient Noise

Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring advanced signal processing techniques. In this article, we paved the way toward an innovative edge-computing approach tested on ambient vibration records made during the bolting of a ~760 m3 limestone rock column (Vercors, France). First, we established some guidelines for prone-to-fall rock column AVB–SHM by comparing several basic, computing-efficient, seismic parameters (i.e., Fast Fourier Transform, Horizontal to Vertical and Horizontal to Horizontal Spectral Ratios). All three parameters performed well in revealing the unstable compartment’s fundamental resonance frequency. HHSR appeared as the most consistent spectral estimator, succeeding in revealing both the fundamental and higher modes. Only the fundamental mode should be trustfully monitored with HVSR since higher peaks may be artifacts. Then, the first application of a novelty detection algorithm on an unstable rock column AVB–SHM case study showed the following: the feasibility of automatic removing the adverse thermomechanical fluctuations in column’s dynamic parameters based on machine learning, as well as the systematic detection of clear, permanent change in column’s dynamic behavior after grout injection and hardening around the bolts (i1 and i2). This implementation represents a significant workload reduction, compared to physical-based algorithms or numerical twin modeling, and shows better robustness with regard to instrumentation gaps. We believe that edge-computing monitoring systems combining basic seismic signal processing techniques and automatic detection algorithms could help facilitate AVB–SHM of remote natural structures such as prone-to-fall rock compartments.

Task specific Brain Synchronization in EEG based Speech and Speech Imagery Procedures

Speech being a complicated cognitive skill of the brain, retrieving verbal information from spoken and imagined speech using electrophysiological signal is widely under research to assist the impaired. Electroencephalography has been a successful medium in analyzing the temporal and behavioral changes happening in the brain during any task. In this work an endeavor has been made to distinguish such changes while speaking and envisioning speaking the phoneme of vowel ‘a’. The brain activity was recorded from volunteers while speaking and envisioning speaking the vowel ‘a’ and the basic signal processing routines were applied. The EEG sub-band frequencies were extracted using Fast Fourier Transform (fft). The synchronization indices were assessed for the segmented signals of various sub-band frequencies and the behavioral changes were enlisted. It was seen that the left hemisphere of the brain functioned synchronously during speech task and an inverse phenomenon was observed during the imagery task. Hence the possibility of reconstructing the broken or incomplete speech from the directly recorded brain activity has reached a step ahead towards the milestone. This work exhibits the analysis of brain connectivity parameters while speaking and imagining a vowel ‘a’. Speech imagery can be used to decode the thoughts for patients who are unable to express their thoughts, using non-invasive techniques.

Theoretical Derivation of Student Basic Signal Monitoring System Based on Software Engineering

At present, in China’s higher education institutions, with the rapid progress of software engineering theory, this paper adopts the signal monitoring system method and derives the mathematical analog signal strength monitoring system. It is optimized in combination with the software engineering wearable system. By using the heart rate acquisition module to improve the performance, using the temperature acquisition module empirical formula, and finally through the software engineering statistical comparison analysis, it is proved that the student basic signal monitoring system has a significant effect and can play a role in preventing students from fainting or sudden death. positive effect.

Postabortion care availability, facility readiness and accessibility in Nigeria and Côte d'Ivoire.

Postabortion care (PAC) is an essential component of emergency obstetric care (EmOC) and is necessary to prevent unsafe abortion-related maternal mortality, but we know little regarding the preparedness of facilities to provide PAC services, the distribution of these services and disparities in their accessibility in low-resource settings. To address this knowledge gap, this study aims to describe PAC service availability, evaluate PAC readiness and measure inequities in access to PAC services in seven states of Nigeria and nationally in Côte d’Ivoire. We used survey data from reproductive-age women and the health facilities that serve the areas where they live. We linked facility readiness information, including PAC-specific signal functions, to female data using geospatial information. Findings revealed less than half of facilities provide basic PAC services in Nigeria (48.4%) but greater PAC availability in Côte d’Ivoire (70.5%). Only 33.5% and 36.9% of facilities with the capacity to provide basic PAC and only 23.9% and 37.5% of facilities with the capacity to provide comprehensive PAC had all the corresponding signal functions in Nigeria and Côte d’Ivoire, respectively. With regard to access, while ∼8 out of 10 women of reproductive age in Nigeria (81.3%) and Côte d’Ivoire (79.9%) lived within 10 km of a facility providing any PAC services, significantly lower levels of the population lived <10 km from a facility with all basic or comprehensive PAC signal functions, and we observed significant inequities in access for poor, rural and less educated women. Addressing facilities’ service readiness will improve the quality of PAC provided and ensure postabortion complications can be treated in a timely and effective manner, while expanding the availability of services to additional primary-level facilities would increase access—both of which could help to reduce avoidable abortion-related maternal morbidity and mortality and associated inequities.

A Computational Study of the S2 State in the Oxygen-Evolving Complex of Photosystem II by Electron Paramagnetic Resonance Spectroscopy.

The S2 state produces two basic electron paramagnetic resonance signal types due to the manganese cluster in oxygen-evolving complex, which are influenced by the solvents, and cryoprotectant added to the photosystem II samples. It is presumed that a single manganese center oxidation occurs on S1 → S2 state transition. The S2 state has readily visible multiline and electron paramagnetic resonance signals and hence it has been the most studied of all the Kok cycle intermediates due to the ease of experimental preparation and stability. The S2 state was studied using electron paramagnetic resonance spectroscopy at X-band frequencies. The aim of this study was to determine the spin states of the signal. The multiline signal was observed to arise from a ground state spin ½ centre while the 4.1 signal generated at ≈140 K NIR illumination was proposed to arise from a spin center with rhombic distortion. The ‘ground’ state 4.1 signal was generated solely or by conversion from the multiline. The data analysis methods used involved numerical simulations of the experimental spectra on relevant models of the oxygen-evolving complex cluster. A strong focus in this paper was on the ‘ground’ state 4.1 signal, whether it is a rhombic spin state signal or an axial spin state signal. The data supported an X-band CW-EPR-generated 4.1 signal as originating from a near rhombic spin 5/2 of the S2 state of the PSII manganese cluster.

Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas

Fatigue-induced human error is a leading cause of accidents. The purpose of this exploratory study in China was to perform field tests to measure fatigue psychophysiological parameters, such as electrocardiography (ECG), electromyography (EMG), pulse, blood pressure, reaction time and vital capacity (VC), in miners in high-altitude and cold areas and to perform multi-feature information fusion and fatigue identification. Forty-five miners were randomly selected as subjects for a field test, and feature signals were extracted from 90 psychophysiological features as basic signals for fatigue analysis. Fatigue sensitivity indices were obtained by Pearson correlation analysis, t-test and receiver operating characteristic (ROC) curve performance evaluation. The ECG time-domain, ECG frequency-domain, EMG, VC, systolic blood pressure (SBP), and pulse were significantly different after miner fatigue. The support vector machine (SVM) and random forest (RF) techniques were used to classify and identify fatigue by information fusion and factor combination. The optimal fatigue classification factors were ECG-FD (CV Accuracy = 85.0%) and EMG (CV Accuracy = 90.0%). The optimal combination of factors was ECG-TD + ECG-FD + EMG (CV accuracy = 80.0%). Furthermore, SVM machine learning had a good recognition effect. This study shows that SVM and RF can effectively identify miner fatigue based on fatigue-related factor combinations. ECG-FD and EMG are the best indicators of fatigue, and the best performance and robustness are obtained with three-factor combination classification. This study on miner fatigue identification provides a reference for research on clinical medicine and the identification of human fatigue under high-altitude, cold and low-oxygen conditions.

A Performance Analysis of Wavelet based LTE-OFDM with Multi-equalizers

A rapid change in usage of mobile data and mobile based applications such as online gaming, mobile TV etc. motivates the mobile wireless communication system to work on new emerging trends Long Term Evolution (LTE). LTE-4G architecture provides an optimized packet based radio access technology. The basic signal format of LTE is Orthogonal Frequency Division Multiplexing (OFDM) Modulation. This modulation scheme satisfies the spectrum flexibility requirement in LTE. LTE-MIMO-OFDM with IFFT and FFT transform introduced to provide the cost-effective solution for very wide carriers with high PAPR. Inter-symbol Interference (ISI) and inter carrier interference (ICI) are predominant factors which exist in the wireless communication channel. Existing methods of ISI and ICI reduction to improve signal quality have been achieved using conventional OFDM techniques. However, a better modulation scheme is required to achieve high data rate, low latency and flexible bandwidth deployment. This paper has proposed a Wavelet based LTE-MIMO-OFDM modulation scheme with multi equalizer that improves the performance of system by reducing PAPR and BER in MCM. Experimental results show the performance of this scheme and the results is compared with existing MMSE and Multi equalizer MMSE and achieves a flexible spectral efficiency with reduction of PAPR and BER.

The Insect Type 1 Tyramine Receptors: From Structure to Behavior.

Tyramine is a neuroactive compound that acts as neurotransmitter, neuromodulator, and neurohormone in insects. Three G protein-coupled receptors, TAR1-3, are responsible for mediating the intracellular pathway in the complex tyraminergic network. TAR1, the prominent player in this system, was initially classified as an octopamine receptor which can also be activated by tyramine, while it later appeared to be a true tyramine receptor. Even though TAR1 is currently considered as a well-defined tyramine receptor and several insect TAR1s have been characterized, a defined nomenclature is still inconsistent. In the last years, our knowledge on the structural, biochemical, and functional properties of TAR1 has substantially increased. This review summarizes the available information on TAR1 from different insect species in terms of basic structure, its regulation and signal transduction mechanisms, and its distribution and functions in the brain and the periphery. A special focus is given to the TAR1-mediated intracellular signaling pathways as well as to their physiological role in regulating behavioral traits. Therefore, this work aims to correlate, for the first time, the physiological relevance of TAR1 functions with the tyraminergic system in insects. In addition, pharmacological studies have shed light on compounds with insecticidal properties having TAR1 as a target and on the emerging trend in the development of novel strategies for pest control.

Detection of water seepage in lake body using ground penetrating radar method

Ground Penetrating Radar (GPR) is a geophysical method used to identification subsurface condition at a certain depth, using a source of electromagnetic waves in the form of radar. Geoscanners Akula A9000C and Antenna Gecko 60 with 60 MHz antenna frequency were used for data acquisition for this study. Survey data was conducted as many four lines, located on the south and east sides of the lake, with the distance range from 0 to 145 m. This paper reports basic signal processing GPR data using matGPR for the analysis and modelling. The results show that there is anomaly which is suspected as water seepage appear on depth of 2 m. On the lengthwise line, anomalies show on a distance of 20 m and 35 m (line A) and 125 m and 135 m (line B), while on the crosswise line anomalies show on a distance of 35 m and 50 m (line C), 25 m and 95 m (line D). Water seepage can be found in this area as it is formed by alluvial depositional environment, remembering that the characteristics and rocks lithology in this area can stimulate water infiltration process. Water seepage will cause erosion of the lake and can collapse the lake’s structure.

SNR Enhancement of Back Scattering Signals for Bistatic Radar Based on BeiDou GEO Satellites

Using scattering signals of the global navigation satellite system (GNSS) for target detection has become one of the research hotspots. This technology faces the difficulty of low signal-to-noise ratio (SNR) target echoes. Since BeiDou geostationary orbit (GEO) satellites provide the opportunity to form a bistatic radar with some advantages, such as fixed coverage area and quasi monostatic configuration to avoid the interference from the direct signals, the target echoes may have regular phases that are beneficial to SNR enhancement. This study uses BeiDou GEO satellites and ground fixed receivers to form a bistatic radar and analyzes the phase changes in the reflected signal caused by the target, then gives two methods for SNR enhancement corresponding to two applications: deformation monitoring and ship detection. This paper first introduces the basic signal processing including the intermediate frequency (IF) signal collector and the range compression in the software receiver, then describes the basic SNR enhancement method, i.e., increasing coherence integration time (CIT), and shows its limitation by two target cases: static metal reflector on the land and ships in the water. After that, this study provides an improved SNR enhancement method based on Doppler and range compensation in the moving ship detection case. The experiment shows that by the SNR enhancement, the SNRs of target echo signals in range/Doppler domain increase more than 4 dB on average. This study also demonstrates the bistatic radar’s potential for monitoring surface deformation.

On generation of a stochastic GW background by the scattering on relic wormholes

We study collective features of the scattering of gravitational waves on relic wormholes and normal matter objects. We derive and solve the GW energy transport equation and show that the scattered signal lies in the same frequency spectrum bands as the basic signal. The scattering forms long living tails which always accompany the basic signal and have a universal form. The scattering on normal matter objects forms tails which have always the retarded character, while wormholes lead to advanced tails as well. In addition, wormholes may produce considerably stronger effect when the total energy in tails detected on the Earth exceeds that in the incident direct wave. In both cases the retarding tails have a long living character when the mean amplitude behaves with time as hsim 1/sqrt{t+R/c}. For a single GW event the echo tails give only a tiny contribution to the mean amplitude. However such tails accumulate with events and form a stochastic GW background which may be observed by the contribution to the noise.

Predicting protein condensate formation using machine learning

Membraneless organelles are liquid condensates, which form through liquid-liquid phase separation. Recent advances show that phase separation is essential for cellular homeostasis by regulating basic cellular processes, including transcription and signal transduction. The reported number of proteins with the capacity to mediate protein phase separation (PPS) is continuously growing. While computational tools for predicting PPS have been developed, obtaining a proteome-wide overview of PPS probabilities has remained challenging. Here, we present a phase separation analysis and prediction (PSAP) machine-learning classifier that, based solely on the amino acid content of a training set of known PPS proteins, can determine the phase separation likelihood for each protein in a given proteome. Through comparison with PPS databases, existing predictors, and experimental evidence, we demonstrate the validity and advantages of the PSAP classifier. We anticipate that the PSAP predictor provides a useful tool for future research aimed at identifying phase separating proteins in health and disease.

Half Gaussian-based wavelet transform for pooling layer for convolution neural network

Pooling methods are used to select most significant features to be aggregated to small region. In this paper, anew pooling method is proposed based on probability function. Depending on the fact that, most information is concentrated from mean of the signal to its maximum values, upper half of Gaussian function is used to determine weights of the basic signal statistics, which is used to determine the transform of the original signal into more concise formula, which can represent signal features, this method named half gaussian transform (HGT). Based on strategy of transform computation, Three methods are proposed, the first method (HGT1) is used basic statistics after normalized it as weights to be multiplied by original signal, second method (HGT2) is used determined statistics as features of the original signal and multiply it with constant weights based on half Gaussian, while the third method (HGT3) is worked in similar to (HGT1) except, it depend on entire signal. The proposed methods are applied on three databases, which are (MNIST, CIFAR10 and MIT-BIH ECG) database. The experimental results show that, our methods are achieved good improvement, which is outperformed standard pooling methods such as max pooling and average pooling.

Alleviation of extensive visual pathway dysfunction by a remyelinating drug in a chronic mouse model of multiple sclerosis.

Visual deficits are among the most prevalent symptoms in patients with multiple sclerosis (MS). To understand deficits in the visual pathway during MS and potential treatment effects, we used experimental autoimmune encephalomyelitis (EAE), the most commonly used animal model of MS. The afferent visual pathway was assessed in vivo using optical coherence tomography (OCT), electroretinography (ERG), and visually evoked cortical potentials (VEPs). Inflammation, demyelination, and neurodegeneration were examined by immunohistochemistry ex vivo. In addition, an immunomodulatory, remyelinating agent, the estrogen receptor β ligand chloroindazole (IndCl), was tested for its therapeutic potential in the visual pathway. EAE produced functional deficits in visual system electrophysiology, including suppression of ERG and VEP waveform amplitudes and increased signal latencies. Therapeutic IndCl rescued overall visual system latency by VEP but had little impact on amplitude or ERG findings relative to vehicle. Faster VEP conduction in IndCl‐treated mice was associated with enhanced myelin basic protein signal in all visual system structures examined. IndCl preserved retinal ganglion cells (RGCs) and oligodendrocyte density in the prechiasmatic white matter, but similar retinal nerve fiber layer thinning by OCT was noted in vehicle and IndCl‐treated mice. Although IndCl differentially attenuated leukocyte and astrocyte staining signal throughout the structures analyzed, axolemmal varicosities were observed in all visual fiber tracts of mice with EAE irrespective of treatment, suggesting impaired axonal energy homeostasis. These data support incomplete functional recovery of VEP amplitude with IndCl, as fiber tracts displayed persistent axon pathology despite remyelination‐induced decreases in latencies, evidenced by reduced optic nerve g‐ratio in IndCl‐treated mice. Although additional studies are required, these findings demonstrate the dynamics of visual pathway dysfunction and disability during EAE, along with the importance of early treatment to mitigate EAE‐induced axon damage.

Cargo transport through the nuclear pore complex at a glance.

Bidirectional transport of macromolecules across the nuclear envelope is a hallmark of eukaryotic cells, in which the genetic material is compartmentalized inside the nucleus. The nuclear pore complex (NPC) is the major gateway to the nucleus and it regulates nucleocytoplasmic transport, which is key to processes including transcriptional regulation and cell cycle control. Accordingly, components of the nuclear transport machinery are often found to be dysregulated or hijacked in diseases. In this Cell Science at a Glance article and accompanying poster, we provide an overview of our current understanding of cargo transport through the NPC, from the basic transport signals and machinery to more emerging aspects, all from a 'cargo perspective'. Among these, we discuss the transport of large cargoes (>15 nm), as well as the roles of different cargo properties to nuclear transport, from size and number of bound nuclear transport receptors (NTRs), to surface and mechanical properties.

Motor Fault Detection using Current and Sound: A comparative Study

Early detection of motor faults can save the motor from subsequent deterioration into more severe conditions, and thus can save lot of maintenance costs. In this work, we have developed automatic motor fault detection system based on artificial neural-network (ANN) using two basic signals: current and sound. Motor condition is captured through current and sound signals which are then preprocessed, and a compressed feature vector is created in their frequency domain. Feature vectors from different motor conditions are used to train a neural network (NN). Once the training is done, the NN-system is ready to monitor motor health condition. Our experimental results show that our NN-based system can successfully detect different motor faults unto 99% accuracy. Both current and sound signals are thoroughly compared under different operating conditions of motors, where both single and three phase motors are considered. The hardware of the system comprised of low-cost CT (current transformer) and microphone that leads to a very cost effective solution. Detailed comparative results are presented that show the suitability of current, sound and their hybrid signal in different scenario. The robustness of the system is evaluated under different conditions such as environmental noise and system’s parameters.

Availability, utilization and quality of emergency obstetric care services in Sousse, Tunisia.

Introductionemergency obstetric care (EmOC) is a high-impact priority intervention strongly recommended for improving maternal health outcomes. The objectives of this study were to assess the availability, utilization, and quality of emergency obstetric care services in the Governorate of Sousse (Tunisia).Methodsa cross-sectional study was conducted among public health facilities which performed deliveries in Sousse in 2017. Data were collected by consulting clinical records and registers and interviewing staff using WHO EmOC tools. Emergency obstetric care (EmOC) indicators were calculated.Resultsonly the University maternity Unit functioned as full comprehensive EmOC facility. No other public facility provided all the 7 Basic EmOC signal functions 3 months prior to the survey. The unperformed signal functions were: administration of parenteral antibiotics, manual removal of placenta and assisted vaginal delivery. The number of EmOC facilities was 0.72 per 500,000 inhabitants. The met need for EmOC was 89.5%. The proportion of caesarean section was 24.2%. The direct obstetric case fatality rate was 0.159% and intrapartum and very early neonatal death rate was 0.65%.Conclusionraising maternity facilities to a minimum level of basic EmOC status would be a major contributing step towards maternal mortality reduction.

Novel Insight into the Role of Endoplasmic Reticulum Stress in the Pathogenesis of Myocardial Ischemia-Reperfusion Injury.

Impaired function of the endoplasmic reticulum (ER) is followed by evolutionarily conserved cell stress responses, which are employed by cells, including cardiomyocytes, to maintain and/or restore ER homeostasis. ER stress activates the unfolded protein response (UPR) to degrade and remove abnormal proteins from the ER lumen. Although the UPR is an intracellular defense mechanism to sustain cardiomyocyte viability and heart function, excessive activation initiates ER-dependent cardiomyocyte apoptosis. Myocardial ischemia/reperfusion (I/R) injury is a pathological process occurring during or after revascularization of ischemic myocardium. Several molecular mechanisms contribute to the pathogenesis of cardiac I/R injury. Due to the dual protective/degradative effects of ER stress on cardiomyocyte viability and function, it is of interest to understand the basic concepts, regulatory signals, and molecular processes involved in ER stress following myocardial I/R injury. In this review, therefore, we present recent findings related to the novel components of ER stress activation. The complex effects of ER stress and whether they mitigate or exacerbate myocardial I/R injury are summarized to serve as the basis for research into potential therapies for cardioprotection through control of ER homeostasis.