Set Of Signals Research Articles

An improved Bi-LSTM method based on heterogeneous features fusion and attention mechanism for ECG recognition

Electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. However, extracting powerful deep features from ECG signal for recognition is still a challenging problem today due to the variable abnormal rhythms and noise distribution. This work proposes a Bi-LSTM algorithm based on heterogeneous features fusion and attention mechanism (HFFAM + Bi-LSTM). Combining the empirical features and the features learned by the deep learning network, HFFAM + Bi-LSTM can comprehensively extract the temporal frequency information and spatial structure information of the ECG signal. Meanwhile, a novel attention mechanism based on improved DTW (AM-DTW) is designed to analyze and control the fusion process of features. The role of AM-DTW in HFFAM + Bi-LSTM is twofold, one is to measure the feature similarity between ECG signal sets with different labels using the improved DTW, and the other is to distinguish the features into isomorphic and heterogeneous features as well as adaptive weighting of the features. It is worth mentioning that overly similar isomorphic features are filtered out to further optimize the algorithm. Thus, HFFAM + Bi-LSTM has the advantage of strengthening the heterogeneous information in the feature subspace while accounting for the isomorphic features. The accuracy of HFFAM + Bi-LSTM reaches up to 98.1 % and 97.1 % on the simulated and real datasets, respectively. Compared to the all benchmark models, the classification accuracy of HFFAM + Bi-LSTM is 1.3 % higher than the best. The experiments also demonstrate that HFFAM + Bi-LSTM has better performance compared with existing methods, which provides a new scheme for automatic detection of ECG signal.

Parameters estimation for the Hammerstein‐Wiener models with colored noise based on hybrid signals

SummaryA three‐stage estimation approach of the Hammerstein‐Wiener model with colored noise using hybrid signals is considered in this article. The Hammerstein‐Wiener model where a linear dynamic block is embedded between two static nonlinear elements, in which two nonlinear elements represented by two independent neural fuzzy models and a linear element represented by autoregressive exogenous model. The designed hybrid signals that consist of separable signals and random signals are devoted to estimating independently. First, the characteristics of separable signals in the action of the static nonlinear element are analyzed, then the output nonlinear element parameters are estimated utilizing two groups of separable signals with a multiple. Moreover, least squares based on correlation analysis method is applied to estimate linear element using one set of separable signals, which handles the interference of colored process noise. Finally, the recursive extended least squares algorithm is derived to estimate the input nonlinear element and the autoregressive moving average noise model, which improves parameters estimation accuracy owing to estimating colored noise model parameters in recursive estimate process. The feasibility of the presented estimation technique is demonstrated by an illustrative simulation example and a practical nonlinear process.

A deep neural network based reverse radio spectrogram search algorithm

Abstract Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more sophisticated RFI rejection algorithms. The ‘needle in a haystack’ nature of searches for transients and technosignatures requires us to develop methods that can determine whether a signal of interest has unique properties, or is a part of some larger set of pernicious RFI. In the past, this vetting has required onerous manual inspection of very large numbers of signals. In this paper, we present a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained a β-variational autoencoder on signals returned by an energy detection algorithm. We then adapted a positional embedding layer from classical transformer architecture to a embed additional metadata, which we demonstrate using a frequency-based embedding. Next we used the encoder component of the β-variational autoencoder to extract features from small (∼715 Hz, with a resolution of 2.79 Hz per frequency bin) windows in the radio spectrogram. We used our algorithm to conduct a search for a given query (encoded signal of interest) on a set of signals (encoded features of searched items) to produce the top candidates with similar features. We successfully demonstrate that the algorithm retrieves signals with similar appearance, given only the original radio spectrogram data. This algorithm can be used to improve the efficiency of vetting signals of interest in technosignature searches, but could also be applied to a wider variety of searches for ‘lookalike’ signals in large astronomical data sets.

Convolutional Neural Network-Based Classification of Steady-State Visually Evoked Potentials with Limited Training Data

One approach employed in brain–computer interfaces (BCIs) involves the use of steady-state visual evoked potentials (SSVEPs). This article examines the capability of artificial intelligence, specifically convolutional neural networks (CNNs), to improve SSVEP detection in BCIs. Implementing CNNs for this task does not require specialized knowledge. The subsequent layers of the CNN extract valuable features and perform classification. Nevertheless, a significant number of training examples are typically required, which can pose challenges in the practical application of BCI. This article examines the possibility of using a CNN in combination with data augmentation to address the issue of a limited training dataset. The data augmentation method that we applied is based on the spectral analysis of the electroencephalographic signals (EEG). Initially, we constructed the spectral representation of the EEG signals. Subsequently, we generated new signals by applying random amplitude and phase variations, along with the addition of noise characterized by specific parameters. The method was tested on a set of real EEG signals containing SSVEPs, which were recorded during stimulation by light-emitting diodes (LEDs) at frequencies of 5, 6, 7, and 8 Hz. We compared the classification accuracy and information transfer rate (ITR) across various machine learning approaches using both real training data and data generated with our augmentation method. Our proposed augmentation method combined with a convolutional neural network achieved a high classification accuracy of 0.72. In contrast, the linear discriminant analysis (LDA) method resulted in an accuracy of 0.59, while the canonical correlation analysis (CCA) method yielded 0.57. Additionally, the proposed approach facilitates the training of CNNs to perform more effectively in the presence of various EEG artifacts.

Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals

When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is crucial for acquiring electrocardiographic signals. Constant displacement positions are essential for ensuring the consistency of the ECG signal when used for individual identification. However, achieving constant placement of ECG electrode patches in every trial for data acquisition is challenging. This is because different individuals may attach patches, and even when the same person attaches them, it may be difficult to specify the exact position. Therefore, gathering ECG data from various locations is necessary. However, this process requires a substantial amount of labor and time, owing to the requirement for multiple attempts. Nonetheless, persisting with these efforts enables the endurance of some ECG differences. To reduce labor and time, we propose a semi-supervised domain adaptation for individual identification using ECG signals. The method operates with a full set of original ECG signals and a small set of ECG signals from different placements to account for the differences between the signals in the generative adversarial network (CycleGAN). Specifically, to train the CycleGAN, the ECG signals were transformed into time–frequency representations, and the trained generator was used to generate ECG signals to expand the small set of ECG signals from different placements. Subsequently, both the original and generated signals were used to train the classifier for individual identification. This scenario can also be applied to the classification of ECG signals from different sensors. The PTB-ECG dataset was used for this experiment. We found that the proposed method showed higher accuracy than when only the original ECG signals were used for the training classifier.

Vibration-based rotation speed estimation for Industrial IoT

The Industrial Internet of Things (IIoT) uses wireless vibration sensors to predict machine health. In many cases, communication bandwidth and sensor processing resources are limited, and machine kinematics is not available at the sensor level. Consequently, only basic condition indicators, such as overall signal energy or peak-to-peak values, are computed at the sensor and transmitted to the cloud for further analysis. The behavior of these indicators is strongly influenced by machine speed, which is not available in many cases. This paper presents a novel and straightforward method for estimating machine relative speed based on vibrations, enhancing the interpretation of changes in basic condition indicators. The method does not require any prior knowledge about the machine's kinematics or expected speed changes. It was evaluated on simulated signals and wind turbine vibrations, providing a general solution for estimating varying rotating speed when no supplementary information is available. The method estimates rotating speed up to a constant factor in a set of signals (or signal segments) by optimally utilizing peak frequency statistics in the spectrum. The method demonstrates high accuracy under different operating conditions and noise and holds the potential to be used in a variety of IIoT applications.

Economic justification of the choice of development of an explored oil and gas field

The paper presents a study that combines two methods: clustering of fields using machine learning algorithms and building a regression model for forecasting the cost and economic efficiency of an oil and gas field. In the course of this study, a neural network of direct propagation was developed, which is used to forecast the cost of developing wells in oil and gas fields, taking into account all technical parameters. The resulting neural network, formed on the basis of algorithms of input data, forms output signals when any set of input signals of the training set is applied to the input of the network. The resulting neural network expresses patterns that are present in the input data. This network turns out to be the functional equivalent of some model of dependencies between variables. Indicators of 15 wells were used to create the ANN model. The main task of the model is to determine the cluster of a new deposit. Conventional designations (x - for exogenous (explored factors and y - for actual calculated cost data). Input data (x) for neural network training were: The smallest thickness of oil-bearing formations; The largest thickness of oil-bearing formations; Gas factor; Reservoir temperature; Porosity; Permeability ; Oil and gas content in the formation; Occurrence depth; Water flow rate; Oil flow rate; Gas flow rate; Volume of extracted oil; Volume of extracted gas; Design depth of the well. After dividing the educational sample into classes, a model of the dependence of the following factors on the input was created for each cluster: Approximate cost of well construction without VAT (thousands of dollars); Estimated cost of well construction including VAT (thousands of dollars); Profit (thousands of dollars); Cost of well construction (thousands of dollars); The cost of 1 m of penetration without VAT (dollars); The cost of 1 m of penetration including VAT (dollars). Data, on the training sample for which these indicators were known, the accuracy of the forecast was checked. The error did not exceed 5%. Then, calculations were made for explored wells, but those where the economic indicators are unknown. Based on the calculated well development cost values, the efficiency factor was calculated as a fraction of the predicted development cost divided by the explored reserves. And it is recommended for development that explored field, which has the lowest indicator.

Multiple corporate and functional performance feedback and problemistic search

AbstractIn this study, we examine how decision makers assess and respond to more than two performance feedback regarding goals across different hierarchical levels. Building on Cyert and March's seminal work and later research on pattern perception, we argue that pattern simplicity and pattern predictability of multiple (functional) feedback help decision makers to better guide problemistic search and generate more plausible solutions to fix a serious corporate performance shortfall. In a longitudinal sample of 97 global incumbent pharmaceutical firms, we find that a simpler and more predictable set of functional performance signals strengthens the relationship between poor performance on a corporate goal (i.e., ROA) and the number of functional solutions (i.e., number of new product development projects) that a firm initiates. We discuss the implications of our findings to the performance feedback and managerial search literature.

Sensitivity analysis by differential importance measure for unsupervised fault diagnostics

Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique.

Belief Updating Beyond the Two-State Setting

Heuristics and biases in probabilistic belief updating have typically been examined in simple two-state experimental settings. We argue that the two-state setting has probabilistic properties that do not extend to settings with more states. With three states, we find that individuals apply similar heuristics, such as representativeness and anchoring, when providing posterior probability distributions. However, because of the different normative benchmark, the use of these heuristics results in different biases for point estimates. In particular, we demonstrate that the well-known finding of stronger underinference for larger signal sets does not translate from the two-state to the three-state setting. Our findings caution against an indiscriminate transfer of updating biases observed in two-state settings to a broad set of real-world applications. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: This work was supported by the Fritz Thyssen Stiftung [Grant 20.21.0.023WW]. Supplemental Material: The data and online appendix are available at https://doi.org/10.1287/mnsc.2022.00513 .

Self-supervised learning minimax entropy domain adaptation for the underwater target recognition

With wide research of intelligent methods, studies on underwater target recognition have been making rapid progress. However, various marine conditions may cause data distribution mismatch between the collected signal sets, reducing model recognition performance. To mitigate the negative impact of data divergence, this paper uses the domain adaptation methods in target recognition and proposes an improved domain adaptation frame, self-supervised learning minimax entropy. Firstly, based on the minimax entropy method (MME), the prediction consistency is utilized to determine pseudo-labels, and the loss weight is introduced to deal with the misaligned target domain data. Then, a self-supervised learning mechanism is designed to ensure consistency of prediction results during training. Three different features, including the constant-Q transform (CQT), Mel spectrum, and Mel-frequency cepstral coefficient (MFCC), are used to verify the performance of domain adaptation methods. The experimental results show that applying domain adaptations can effectively improve the recognition performance of the models under most experimental conditions, and the improved frame has higher average recognition accuracy than other domain adaptation methods in the experiments.

Bidirectional EMD-RLS: Performance analysis for denoising in speech signal

Empirical Mode Decomposition (EMD) is an alternative decomposition method that allows the analysis of non-stationary and non-linear signals, decomposing them into components called Intrinsic Mode Functions (IMFs). In this paper, we introduce the bidirectional combination between EMD and the Recursive Least Squares (RLS) adaptive filter and compare it to Least Mean Square (LMS), EMD-LMS and RLS methods. The hybrid algorithm relies on the determination of vertical (IMFs indexes) and horizontal (temporal indexes) weighting coefficients. We evaluated the effectiveness of the methods by filtering sets of speech signals artificially corrupted by white Gaussian noise. To find the method that provides the greatest noise reduction and the best speech quality, we took into account the Signal-to-Noise Ratio and the Perceptual Evaluation of Speech Quality (PESQ) score. The analyzed cases showed that EMD-RLS can provide results as good as the other methods, but with a shorter learning curve than LMS and EMD-LMS, despite the higher computational cost. For a single training iteration, the methods presented PESQ scores on average equivalent to each other. However, they showed a significant difference in performance regarding noise reduction, with the best result obtained by RLS, followed by EMD-RLS, EMD-LMS and LMS.

Finite Control Set Model Predictive Control (FCS-MPC) for Enhancing the Performance of a Single-Phase Inverter in a Renewable Energy System (RES)

A single-phase five-level T-type topology has been investigated in this article. This topology has emerged as a viable option for renewable energy systems (RES) due to its inherent benefits. The finite control set model predictive control (FCS-MPC) strategy has been implemented to this topology in order to improve the performance and overall reliability of the system. This control strategy empowers the inverter to predict future behavior based on a discrete set of control signals, enabling precise modulation and high-speed response to system dynamics. In the realm of RES, integration of FCS-MPC with multilevel inverters (MLIs) holds great potential to enhance energy conversion efficiency, grid integration, and overall system reliability. The article is structured to present an overview of the evolving landscape of power electronic systems, and the advantages of FCS-MPC. This paper provides a comprehensive analysis of the FCS-MPC control strategy applied to the single-phase five-level T-type topology. The study covers various aspects including the theoretical framework, hardware development, and experimental evaluation. It is obvious from the analysis that this inverter topology is reliable. Several redundant states make it fault-tolerant which helps in maintaining the output voltage at the same level even in the fault conditions. Additionally, the results show that the output load voltage is maintained at the same level irrespective of load change. Also, output load voltage has maintained the high-quality sinusoidal characteristics as the total harmonic distortion (THD) is very low. With all these features, this system is suitable within the framework of RES.

A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation

Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS–SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods.

An observer's perspective of the Unruh and Hawking effects—Using coherent signals to extract information from a black hole

The Unruh effect is one of the first calculations that explain what one would see when transiting between an inertial reference frame in its quantum field vacuum state and a non-inertial (specifically, uniformly accelerating) reference frame. The inertial reference frame's vacuum state would not correspond to the vacuum state of the non-inertial frame. The observer in the non-inertial frame would see radiation, with a corresponding Bose distribution and a temperature proportional to the acceleration. In this paper, I compute the response of this non-inertial observer to a single frequency mode in the inertial frame and deduce that, indeed, the cumulative distribution (over the observer's proper time) of frequencies observed by the accelerating observer would be the Bose distribution with a temperature proportional to the acceleration. The conclusion is that the Unruh effect (and the related Hawking effect) is generic, in that it would appear with any incoming incoherent state and the Bose distribution is obtained as a consequence of the non-inertial frame's motion, rather than some special property of the quantum vacuum. As a consequence of the analysis of a coherent set of signals, I show how to extract information from the spectrum that an accelerated observer would see (as well as from the radiation from a black hole).

Characterization and Quantitation of N-Nitroso Duloxetine Impurity in Duloxetine Hydrochloride Drug Substance

The nitrosamine family of chemicals is a large group of carcinogens found in human diets and other environmental media. In animal experiments, it has been shown that N-nitroso compounds can cause cancer, hence the presence of nitrosamine impurities are important to evaluate and need to be quantified. This work shows the content of N-nitroso duloxetine in duloxetine (DXT) hydrochloride, before proceeding to the quantification of impurity, the impurity standard must be well characterized by analytical techniques and it places a vigorous role, i.e. N-nitroso DXT molecular weight conformed by mass spectral data and the atoms connectivity was confirmed by NMR spectral data (1H, 13C, COSY, HSQC and HMBC) and thermal analyses (DSC and TGA) were also performed. In N-nitroso DXT, two sets of signals were typically observed, indicating the presence of asymmetric N-nitroso DXT, concluding with drift functional theory (DFT) for E-, Z-isomers related to the nitroso group in N-nitroso DXT. The medical agency recommends a specified limit of 0.83 ppm for the acceptable intake (AI) and the maximum daily dosage. The determination of N- itroso DXT was performed by liquid chromatography mass spectrometry (LC-MS) followed by electron spray ionization with triple quadrupole.

Synthesis of Organosilicon‐Based Thioether Ligand Class Showing Defined S‐Stereogenic Coordination in Crystalline Palladium(II) Complexes

AbstractA synthesis route to aromatic organosilicon‐based thioether ligands could be established and utilised for three congeners 2. These ligands were suitable for complexation with palladium(II) chloride forming three new complexes 3. The single crystal X‐ray diffraction and temperature‐dependent 1H and 13C‐NMR analysis of 3 are presented. The elucidated solid‐state structures suggested that one pair of diastereomers is favoured in crystalline phase and is controllable by the aromatic substituent. However, at room temperature in solution, the conformation at the sulphur atoms can fluctuate with a coalescence temperature for 1H‐NMR around 24 °C. At −31 °C, two signal sets could be observed since the inversion is slower than the 1H‐NMR timescale at lower temperatures.

Multi-Signal Multifractal Detrended Fluctuation Analysis for Uncertain Systems —Application to the Energy Consumption of Software Programs in Microcontrollers

Uncertain systems are those wherein some variability is observed, meaning that different observations of the system will produce different measurements. Studying such systems demands the use of statistical methods over multiple measurements, which allows overcoming the uncertainty, based on the premise that a single measurement is not representative of the system’s behavior. In such cases, the current multifractal detrended fluctuation analysis (MFDFA) method cannot offer confident conclusions. This work presents multi-signal MFDFA (MS-MFDFA), a novel methodology for accurately characterizing uncertain systems using the MFDFA algorithm, which enables overcoming the uncertainty of the system by simultaneously considering a large set of signals. As a case study, we consider the problem of characterizing software (Sw) consumption. The difficulty of the problem mainly comes from the complexity of the interactions between Sw and hardware (Hw), as well as from the high uncertainty level of the consumption measurements, which are affected by concurrent Sw services, the Hw, and external factors such as ambient temperature. We apply MS-MFDFA to generate a signature of the Sw consumption profile, regardless of the execution time, the consumption levels, and uncertainty. Multiple consumption signals (or time series) are built from different Sw runs, obtaining a high frequency sampling of the instant input current for each of them while running the Sw. A benchmark of eight Sw programs for analysis is also proposed. Moreover, a fully functional application to automatically perform MS-MFDFA analysis has been made freely available. The results showed that the proposed methodology is a suitable approximation for the multifractal analysis of a large number of time series obtained from uncertain systems. Moreover, analysis of the multifractal properties showed that this approach was able to differentiate between the eight Sw programs studied, showing differences in the temporal scaling range where multifractal behavior is found.

Signal setting design to reduce noise emissions in a connected environment

To meet the zero-pollution target on noise by 2030, it is necessary to develop mobility management policies that directly act on noise exposure. This paper proposes traffic signal optimisation in an environment with fully connected vehicles (CVs), combining noise pollution minimisation with total travel time minimisation to improve traffic flow. Traffic signal optimisation was tested on a network with signalised interacting junctions, comparing different approaches and scenarios based on short-range communication between the infrastructure and CVs approaching the junctions. The results show that the proposed traffic control method may be adopted to effectively reduce the impact of traffic noise and improve traffic performance.

Low level of tonic interferon signalling is associated with enhanced susceptibility to SARS-CoV-2 variants of concern in human lung organoids

ABSTRACT There is tremendous heterogeneity in the severity of COVID-19 disease in the human population, and the mechanisms governing the development of severe disease remain incompletely understood. The emergence of SARS-CoV-2 variants of concern (VOC) Delta (B.1.617.2) and Omicron (B.1.1.529) further compounded this heterogeneity. Virus replication and host cell damage in the distal lung is often associated with severe clinical disease, making this an important site to consider when evaluating pathogenicity of SARS-CoV-2 VOCs. Using distal human lung organoids (hLOs) derived from multiple human donors, we compared the fitness and pathogenicity of SARS-CoV-2 VOC Delta and Omicron, along with an ancestral clade B variant D614G, and evaluated donor-dependent differences in susceptibility to infection. We observed substantial attenuation of Omicron in hLOs and demonstrated enhanced susceptibility to Omicron and D614G replication in hLOs from one donor. Transcriptomic analysis revealed that increased susceptibility to SARS-CoV-2 infection in these hLOs was associated with reduced tonic interferon signaling activity at baseline. We show that hLOs can be used to model heterogeneity of SARS-CoV-2 pathogenesis in humans, and propose that variability in tonic interferon signaling set point may impact susceptibility to SARS-CoV-2 VOCs and subsequent COVID-19 disease progression.