Identification Of Signals Research Articles

Decreased Noise and Identification of Very Low Voltage Signals Using a Novel Electrophysiology Recording System.

The interpretation of intracardiac electrograms recorded from conventional electrophysiology recording systems is frequently impacted by powerline (50/60 Hz) noise and distortion due to notch filtering. This study compares unipolar electrograms recorded simultaneously from a conventional electrophysiology recording system and one of two 3D mapping systems (control system) with those from a novel system (ECGenius, CathVision ApS) designed to reduce noise without the need for conventional filtering. Unipolar electrograms were recorded simultaneously from nine consecutive patients undergoing catheter ablation for AF (five patients), atrioventricular nodal re-entrant tachycardia (three patients), or ventricular tachycardia (one patient) over the course of 1 week in 2020. The noise spectral power of the novel system (49-51 Hz) was 6.1 ± 6.2 times lower than that of the control system. Saturation artefact following pacing (duration 97 ± 85 ms) occurred in eight control recordings and no novel system recordings (p<0.001). High frequency, low amplitude signals and fractionated electrograms apparent on unfiltered novel system unipolar recordings were not present on control recordings. Control system notch filtering obscured His bundle electrograms observable without such filtering using the novel system and induced electrogram distortion that was not present on novel system recordings. Signal saturation occurred in five of seven control system recordings but none of the novel system recordings. In this study, novel system recordings exhibited less noise and fewer signal artefacts than the conventional control system and did not require notch filtering that distorted electrograms on control recordings. The novel recording system provided superior electrogram data not apparent with conventional systems.

Interactions of Different Streptomyces Species and Myxococcus xanthus Affect Myxococcus Development and Induce the Production of DK-Xanthenes.

The co-culturing of microorganisms is a well-known strategy to study microbial interactions in the laboratory. This approach facilitates the identification of new signals and molecules produced by one species that affects other species' behavior. In this work, we have studied the effects of the interaction of nine Streptomyces species (S. albidoflavus, S. ambofaciens, S. argillaceus, S. griseus, S. lividans, S. olivaceus, S. parvulus, S. peucetius, and S. rochei) with the predator bacteria Myxococcus xanthus, five of which (S. albidoflavus, S. griseus, S. lividans, S. olivaceus, and S. argillaceus) induce mound formation of M. xanthus on complex media (Casitone Yeast extract (CYE) and Casitone tris (CTT); media on which M. xanthus does not form these aggregates under normal culture conditions. An in-depth study on S. griseus-M. xanthus interactions (the Streptomyces strain producing the strongest effect) has allowed the identification of two siderophores produced by S. griseus, demethylenenocardamine and nocardamine, responsible for this grouping effect over M. xanthus. Experiments using pure commercial nocardamine and different concentrations of FeSO4 show that iron depletion is responsible for the behavior of M. xanthus. Additionally, it was found that molecules, smaller than 3 kDa, produced by S. peucetius can induce the production of DK-xanthenes by M. xanthus.

MsCNN-LSTM perimeter intrusion vibration signal identification method based on ultra-weak FBG arrays

To improve the recognition rate of ultra-weak fiber Bragg grating (UWFBG) arrays in perimeter security monitoring, this paper proposes a msCNN-LSTM combinatorial model recognition method, which combines an improved multi-scale convolutional neural network (msCNN) and a long short-term memory neural network (LSTM) to identify intrusion behaviors more accurately by synchronously extracting the multi-scale structural features and time-dependent relationships of vibration signals. Using msCNN to cross-learn multi-layer features, the hidden information between features at different scales can be obtained. The attention mechanism was applied to recalibrate the combined features extracted from the shallower layers, which enhanced the expression ability of features. Finally, the time dependence is analyzed by LSTM. Experimental results show that the scheme can effectively distinguish five typical intrusion behaviors, and the average recognition rate can reach 97.84%.

Autism spectrum disorder detection using variable frequency complex demodulation of the electroretinogram

The early diagnosis of neurodevelopmental conditions such as autism spectrum disorder (ASD), is an unmet need. One difficulty is the identification of a biological signal that relates to the ASD phenotype. The electroretinogram (ERG) waveform has been identified as a possible signal that could categorize neurological conditions such as ASD. The ERG waveform is derived from the electrical activity of photoreceptors and retinal neurons in response to a brief flash of light and provides an indirect 'window' into the central nervous system. Traditionally, the waveform is analyzed in the time-domain, but more recently time-frequency spectrum (TFS) analysis of ERG has been successfully carried out using discrete wavelet transformation (DWT) to characterize the morphological features of the signal. In this study, we propose the use of a high resolution TFS technique, namely variable frequency complex demodulation (VFCDM), to decompose the ERG waveform based on two signal flash strengths to build machine learning (ML) models to categorize ASD. ERG waveforms from N = 217 subjects (71 ASD, 146 control), at two different flash strengths, 446 and 113 Troland seconds (Td.s), from both right and left eyes were included. We analyzed the raw ERG waveforms using DWT and VFCDM. We computed features from the TFSs and trained ML models such as Random Forest, Gradient Boosting, Support Vector Machine to classify ASD from controls. ML models were validated using a subject independent validation strategy, and we found that the ML models with VFCDM features outperformed models using the DWT, achieving an area under the receiver operating characteristics curve of 0.90 (accuracy = 0.81, sensitivity = 0.85, specificity = 0.78). We found that the higher frequency range (80–300 Hz) included more relevant information for classifying ASD compared to the lower frequencies. We also found that the stronger flash strength of 446 Td.s in the right eye provided the best classification result which supports VFCDM analysis of the ERG waveform as a potential tool to aid in the identification of the ASD phenotype.

Detection of Internal Wire Broken in Mining Wire Ropes Based on WOA–VMD and PSO–LSSVM Algorithms

To quantitatively identify internal wire breakage damage in mining wire ropes, a wire rope internal wire breakage signal identification method is proposed. First, the whale optimization algorithm is used to find the optimal value of the variational mode decomposition parameter [K,α] to obtain the optimal combination of the parameters, which reduces the signal noise with a signal-to-noise ratio of 29.29 dB. Second, the minimum envelope entropy of the noise reduction signal is extracted and combined with the time-domain features (maximum and minimum) and frequency-domain features (frequency–amplitude average, average frequency, average power) to form a fusion feature set. Finally, we use a particle swarm optimization–least squares support vector machine model to identify the internal wire breakage of wire ropes. The experimental results show that the method can effectively identify the internal wire rope breakage damage, and the average recognition rate is as high as 99.32%, so the algorithm can greatly reduce the system noise and effectively identify the internal damage signal of the wire rope, which is superior to a certain extent.

СЕМАНТИЧНИЙ АСПЕКТ ФОРМУВАННЯ МОТИВАЦІЙНОЇ СПРОМОЖНОСТІ СПОРТИВНОГО КОМПЛЕКСУ ЗАКЛАДУ ВИЩОЇ ОСВІТИ

At the moment, the issue of forming and maintaining the physical and mental health of the population of Ukraine is more relevant than ever. The system of physical education in educational institutions is considered as one of the ways to promote physical culture and sports, as well as to form a healthy lifestyle among the population. However, it is possible to identify two main shortcomings of domestic SCs of higher education institutions, namely: the inconsistency of the existing range of premises and sports spaces with modern needs and the low motivational capacity of the architectural and spatial component of the sports environment of higher education institutions. Thus, we face the issue of forming a motivationally capable environment of the SC of HEIs, which would allow us to increase the interest of young people in physical culture and sports. It should be borne in mind that the work of perception is apperceptive (adding to the perceived) and is mainly determined by the subject's own and borrowed experience. The perception of the architectural (artificial) environment is not reduced to visual assessment, but is formed in relation to the functional content and human needs in the environment of a certain purpose. The functional aspect involves ensuring that the nomenclature of structural elements of the AC of a higher education institution meets the requirements of the training process and a certain level of comfort. The aesthetic aspect involves the analysis of morphological features of the environment in terms of compositional and artistic requirements. The semantic aspect of the formation of the environment of the SC of the HEI involves the analysis of functional and morphological features of the spatial organisation in terms of their semantic significance. The semantics of architecture is defined as sign mental formations embodied in specific forms, due to which the four main functions of the system of meanings are realised: orientation - search and finding of signals; interpretation - separation of signals (content, features) and identification of relations between them; identification of signals - establishing the identity of semantic values; recognition - comparison of the received signals with the system of standards (features) with further decoding of input data. Semantics of form - involves the expression of the embedded ideas and information by means of architectural and compositional expressiveness and the form of the object. Visual accessibility of the training process -provides free access to the visual perception of the functional process of a part of the sports spacesof the complex by unexpected spectators and visitors. Sports symbols - filling the environment withsports symbols is another effective means of informing visitors and casual spectators about thefunctional component of the complex and its focus on sports. The harmonious integration of sportssymbols into the architectural and artistic solution will increase both the information content of theenvironment and its attractiveness. The use of architectural semantics in the formation of thearchitectural environment of the SC of a higher education institution will increase its motivationalcapacity, which will positively affect the process of physical education and the promotion of sportsand a healthy lifestyle.

OODT: LOS Signal Identification for Acoustic Indoor Localization From Stream Perspective

Acoustic-based technologies have attracted more attention in indoor ranging-based localization and tracking applications. To achieve high-accuracy ranging measurements, the precise identification of line-of-sight(LOS) acoustic signals is essential. This paper proposes a novel Off-Online Dynamic Training method(OODT) to identify LOS acoustic signals from stream perspective based on a few training data in dynamic indoor environment. The dynamic online training method is proposed to identify the unlabeled acoustic feature stream by the Parent-Child model, utilizing the dynamic prior probability from time series information and a selection strategy based on the prediction risk. Then, the trustworthy pseudo-labeled streaming samples are accumulated into the Child-Models by online learning in real-time. To reduce the impact of discarding the untrustworthy LOS signals, the Off-Online retraining method is proposed to incorporate the spatial information into the Parent-Models, which uses the category distribution of all pseudo-labeled streaming samples as the prior probability for iterative retraining. Subsequently, a new round of the dynamic online training is conducted to update the pseudo-labels of feature stream. Experiments demonstrate that the dynamic online training method has a higher identification precision of LOS acoustic signals from stream perspective, reaching 98% and more than 93.06% in above-ground and underground experimental scenarios. Moreover, the adaption for the concept drift is also verified with identification precision of 97.98% and 98.23%, respectively. The Off-Online retraining method further optimize the performance of the Parent-Child models in a single scenario. In conclusion, the proposed OODT method can autonomously identify dynamic acoustic signals with strong robustness and scenario drift adaptability.

Signal detection and material identification method for loose particles inside sealed relays based on fusion classification model

In this study, the authors combined the research on loose particle signal and component signal identification with the research on loose particle material identification for the first time, providing comprehensive and reliable loose particle detection results. Based on this, a signal detection and material identification method for loose particles inside sealed relays based on fusion classification model is proposed. Due to the limitations of technical means and confidentiality management, the authors made a real sealed relay sample, and took it as the research object. Through the steps of data acquisition, signal processing, feature engineering, and model training, the dedicated component identification feature library and material identification feature library was constructed, respectively, the component identification model and material identification model based on parameter-optimized SVM with linear kernel and XGBoost was trained, respectively. For the seal relay to be tested, through the steps of data acquisition, signal processing and feature engineering, the data set to be tested was created. The component identification model was used to identify component signals with loose particle signals, and the material identification model was used to identify the materials of loose particles. The majority voting process was used to convert the classification results into identification results, resulting in loose particle detection and material identification results. In addition, the general procedure steps of the proposed method for physical testing were given, and the identification accuracy for device-level loose particle detection was newly proposed. The loose particle testing event containing thirty-seven identification tasks shows that, the achieved identification accuracy was 97.30%, and 92.16% of the average classification accuracy was achieved by the component identification model, 80.41% of the average classification accuracy was achieved by the material identification model. This effectively demonstrates the feasibility and practicality of the proposed method in this paper. It is an important supplement to the loose particle detection research, and provides references for signal detection in similar fields.

(B)On(e)-cohistones and the epigenetic alterations at the root of bone cancer.

Identification of mutations in histones in a number of human neoplasms and developmental syndromes represents the most compelling evidence to date for a causal role of epigenetic perturbations in human disease. In most cases, these mutations have gain of function properties that cause deviation from normal developmental processes leading to embryo defects and/or neoplastic transformation. These exciting discoveries represent a step-change in our understanding of the role of chromatin (dys)regulation in development and disease. However, the mechanisms of action of oncogenic histone mutations (oncohistones) remain only partially understood. Here, we critically assess existing literature on oncohistones focussing mainly on bone neoplasms. We show how it is possible to draw parallels with some of the cell-autonomous mechanisms of action described in paediatric brain cancer, although the functions of oncohistones in bone tumours remain under-investigated. In this respect, it is becoming clear that histone mutations targeting the same residues display, at least in part, tissue-specific oncogenic mechanisms. Furthermore, it is emerging that cancer cells carrying oncohistones can modify the surrounding microenvironment to support growth and/or alter differentiation trajectories. A better understanding of oncohistone function in different neoplasms provide potential for identification of signalling that could be targeted therapeutically. Finally, we discuss some of the main concepts and future directions in this research area, while also drawing possible connections and parallels with other cancer epigenetic mechanisms.

Amine-recognizing domain in diverse receptors from bacteria and archaea evolved from the universal amino acid sensor

Bacteria possess various receptors that sense different signals and transmit information to enable an optimal adaptation to the environment. A major limitation in microbiology is the lack of information on the signal molecules that activate receptors. Signals recognized by sensor domains are poorly reflected in overall sequence identity, and therefore, the identification of signals from the amino acid sequence of the sensor alone presents a challenge. Biogenic amines are of great physiological importance for microorganisms and humans. They serve as substrates for aerobic and anaerobic growth and play a role of neurotransmitters and osmoprotectants. Here, we report the identification of a sequence motif that is specific for amine-sensing sensor domains that belong to the Cache superfamily of the most abundant extracellular sensors in prokaryotes. We identified approximately 13,000 sensor histidine kinases, chemoreceptors, receptors involved in second messenger homeostasis and Ser/Thr phosphatases from 8,000 bacterial and archaeal species that contain the amine-recognizing motif. The screening of compound libraries and microcalorimetric titrations of selected sensor domains confirmed their ability to specifically bind biogenic amines. Mutants in the amine-binding motif or domains that contain a single mismatch in the binding motif had either no or a largely reduced affinity for amines. We demonstrate that the amine-recognizing domain originated from the universal amino acid-sensing Cache domain, thus providing insight into receptor evolution. Our approach enables precise "wet"-lab experiments to define the function of regulatory systems and therefore holds a strong promise to enable the identification of signals stimulating numerous receptors.

Enhancing Wearable Gait Monitoring Systems: Identifying Optimal Kinematic Inputs in Typical Adolescents.

Machine learning-based gait systems facilitate the real-time control of gait assistive technologies in neurological conditions. Improving such systems needs the identification of kinematic signals from inertial measurement unit wearables (IMUs) that are robust across different walking conditions without extensive data processing. We quantify changes in two kinematic signals, acceleration and angular velocity, from IMUs worn on the frontal plane of bilateral shanks and thighs in 30 adolescents (8-18 years) on a treadmills and outdoor overground walking at three different speeds (self-selected, slow, and fast). Primary curve-based analyses included similarity analyses such as cosine, Euclidean distance, Poincare analysis, and a newly defined bilateral symmetry dissimilarity test (BSDT). Analysis indicated that superior-inferior shank acceleration (SI shank Acc) and medial-lateral shank angular velocity (ML shank AV) demonstrated no differences to the control signal in BSDT, indicating the least variability across the different walking conditions. Both SI shank Acc and ML shank AV were also robust in Poincare analysis. Secondary parameter-based similarity analyses with conventional spatiotemporal gait parameters were also performed. This normative dataset of walking reports raw signal kinematics that demonstrate the least to most variability in switching between treadmill and outdoor walking to help guide future machine learning models to assist gait in pediatric neurological conditions.

Radio Frequency Fingerprint Identification of WiFi Signals Based on Federated Learning for Different Data Distribution Scenarios

Radio Frequency Fingerprint Identification of WiFi Signals Based on Federated Learning for Different Data Distribution Scenarios

Functional clustering of neuronal signals with FMM mixture models

The identification of unlabeled neuronal electric signals is one of the most challenging open problems in neuroscience, widely known as Spike Sorting. Motivated to solve this problem, we propose a model-based approach within the mixture modeling framework for clustering oscillatory functional data called MixFMM. The core of the approach is the FMM (Frequency Modulated Möbius) waves, which are non-linear parametric time functions, flexible enough to describe different oscillatory patterns and simple enough to be estimated efficiently. In particular, specific model parameters describe the phase, amplitude and shape of the waveforms. A mixture model is defined using FMM waves as basic functions and gaussian errors, and an EM algorithm is proposed for estimating the parameters. Spike Sorting (SS) has received considerable attention in the literature, and different functional clustering approaches have been considered. We have conducted a fair comparative analysis of the MixFMM with three competitors. Two of them are traditional methods in functional clustering and widely used in Spike Sorting. The third is an approach that has proven superior to many others solving Spike Sorting problems. The datasets used for validation include benchmarking simulated and real cases. The internal and external validation indexes confirm a better performance of the MixFMM on real data sets against the three competitors and an outstanding performance in simulated data against traditional approaches.

Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach

Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach

Penetrance of missense mutation of OR2AT4 and OR9G1/9 gene polymorphism increases risk factor in azoospermic patients

Objectives: In human, olfactory receptor (ORs) gene polymorphism regulate testicular development and physiological functions during spermatogenesis. However, the etiopathology of OR gene in male infertility is still restricted that required further studies. Basic mechanism involves the identification of peripheral signals based on specific chemoreceptors site that are expressed on the surface of mature spermatozoa inside the female reproductive tract. These receptors belong to G-protein couple receptors (GPCR) of superfamily and the roles of ORs gene linked to sperm chemotaxis through chemokinesis. In the present study, we have studied the role of Olfactory receptor family 2 subfamilies AT member 4 (OR2AT4) and Olfactory receptor family 9 subfamily G member 1/9 (OR9G1/9) genes polymorphism in clinically diagnosed azoospermic patients. Materials and Methods: Systematically, GTG banded karyotypes were developed using short term lymphocyte cultures, fluorescence in situ hybridization using SRY probe and PCR based microdeletion of Y-chromosome was characterized using STS marker (SY255). Whole exome sequencing (WES) based analysis was performed in azoospermic patients to identify the involvement of de-novo mutation in OR2AT4 and OR9G1/OR9G9 genes mutation followed by bioinformatics tools used for the analysis to identify changes in respective amino acids. Results: OR2AT4 showing missense mutation after substitution of nucleotide (G→A) followed by lack of change in amino acid (Gln→Gln), but in OR9G1/G9 gene showing changes of substitution of nucleotide thymidine into adenine (T→A), whereas valine is replaced by glutamine (Val→ Glu), similarly thymidine is substituted by cysteine (T→C), after decode phenylalanine is replaced by leucine (Phe→Leu). Further, 3D protein helical structure was developed with the help of bioinformatics on the basis of amino acids coded protein and compare with mutated amino acid or truncated protein residues with the help of iTASSER server. Conclusion: Present study demonstrate the role of OR2AT4 and OR9G1/9 gene polymorphism elucidates the functional role of protamine in ectopic receptors followed by increasing “risk” of developing male infertility that leads the clinical condition defined as non-obstructive azoospermia.

Radio Frequency Fingerprinting Identification of Few-Shot Wireless Signals Based on Deep Metric Learning

As a cross-protocol endogenous security mechanism, the physical layer-based radio frequency (RF) fingerprinting can effectively enhance the existing password-based application layer authentication utilizing the hardware differences of wireless devices, which is unique and cannot be counterfeited by a third party. However, the recognition performance of the deep learning physical layer fingerprint recognition algorithm drops sharply in the case of a small number of signal samples. This paper analyzes the feasibility and proposes the few-shot wireless signal classification network based on deep metric learning (FSig-Net). FSig-Net reduces the model’s dependence on big data by adaptively learning the feature distance metric. We use 8 mobile phones and 18 Internet of Things (IoT) modules as targets for identification. When the number of single-type samples is only 10, the recognition accuracy of mobile phones can reach 98.28%, and the recognition accuracy of IoT devices can reach 98.20%.

Microseismic Monitoring of the Fracture Nucleation Mechanism and Early Warning for Cavern Rock Masses

The rock mass is susceptible to instability and damage during cavern construction. The blast-induced cracking process of the rock mass contains a wealth of information about the precursors of instability, and the identification of fracture nucleation signals is a prerequisite for effective hazard warning. A laboratory mechanical test and microseismic (MS) monitoring were carried out in the Baihetan Cavern to investigate the fracture nucleation process in the rock mass. MS monitoring shows that pre-existing microcracks were closed or new cracks were generated under the action of high stress, which caused the migration of microcracks. As the crack density increases, the fracture interaction gradually increases. The study of the rock fracture nucleation mechanism helps to reveal the MS sequences during the rock fracture process, and the fore-main shock was found in the MS sequence during access tunnel excavation. This study can effectively provide guidance for the early warning of rock mass failure and the stability analysis of underground caverns under blasting excavation disturbance.

An improved CNN‐LSTM network for modulation identification relying on periodic features of signal

AbstractAiming at the problem that modulated signals' classification and recognition accuracy is not high in the complex and changeable electromagnetic environment, this paper improves the CNN‐LSTM network and proposes a modulation recognition method of the CNN‐LSTM network (Intra‐InterNet) based on the periodic characteristics of the signal. The method uses the feature extraction ability of the convolutional neural network to extract the correlation features within the cycle and between adjacent cycles of different kinds of modulated signals. At the same time, the relevant features of the signals of non‐adjacent cycles are extracted by using the characteristics of the LSTM. This feature extraction method effectively enhances the feature extraction capability of the network by utilizing the periodicity of the modulated signal. Through the identification and verification of various modulation signals, the experimental results show that: under the signal‐to‐noise ratio of −6 dB ∼ 6 dB, the average identification accuracy rate reaches 87.29%, which effectively improves the accuracy of signal modulation mode identification.

Machine Learning-Enhanced Biomass Pressure Sensor with Embedded Wrinkle Structures Created by Surface Buckling.

Flexible piezoresistive sensors are core components of many wearable devices to detect deformation and motion. However, it is still a challenge to conveniently prepare high-precision sensors using natural materials and identify similar short vibration signals. In this study, inspired by microstructures of human skins, biomass flexible piezoresistive sensors were prepared by assembling two wrinkled surfaces of konjac glucomannan and k-carrageenan composite hydrogel. The wrinkle structures were conveniently created by hardness gradient-induced surface buckling and coated with MXene sheets to capture weak pressure signals. The sensor was applied to detect various slight body movements, and a machine learning method was used to enhance the identification of similar and short throat vibration signals. The results showed that the sensor exhibited a high sensitivity of 5.1 kPa-1 under low pressure (50 Pa), a fast response time (104 ms), and high stability over 100 cycles. The XGBoost machine learning model accurately distinguished short voice vibrations similar to those of individual English letters. Moreover, experiments and numerical simulations were carried out to reveal the mechanism of the wrinkle structure preparation and the excellent sensing performance. This biomass sensor preparation and the machine learning method will promote the optimization and application of wearable devices.

Adverse event signal extraction from cancer patients’ narratives focusing on impact on their daily-life activities

Adverse event (AE) management is important to improve anti-cancer treatment outcomes, but it is known that some AE signals can be missed during clinical visits. In particular, AEs that affect patients’ activities of daily living (ADL) need careful monitoring as they may require immediate medical intervention. This study aimed to build deep-learning (DL) models for extracting signals of AEs limiting ADL from patients’ narratives. The data source was blog posts written in Japanese by breast cancer patients. After pre-processing and annotation for AE signals, three DL models (BERT, ELECTRA, and T5) were trained and tested in three different approaches for AE signal identification. The performances of the trained models were evaluated in terms of precision, recall, and F1 scores. From 2,272 blog posts, 191 and 702 articles were identified as describing AEs limiting ADL or not limiting ADL, respectively. Among tested DL modes and approaches, T5 showed the best F1 scores to identify articles with AE limiting ADL or all AE: 0.557 and 0.811, respectively. The most frequent AE signals were “pain or numbness”, “fatigue” and “nausea”. Our results suggest that this AE monitoring scheme focusing on patients’ ADL has potential to reinforce current AE management provided by medical staff.