Signal Samples Research Articles

Detection of Traffic Anomalies Based on Their Frame Wavelet Transformations Processing

Relevance. The active transition to a massive digital infrastructure based on Internet of Things (IoT) technology has brought telecommunications networks to the level of dominant information resources. The one-time increase in the number of existing Internet services is inextricably linked to the growing variety of network anomalies on telecommunications equipment. In turn, existing methods of detecting network threats do not allow timely assessment of network traffic, which is characterized by a large number of parameters, and the detected anomalies from external interference do not have pronounced patterns. The purpose of the study is to increase the efficiency of detecting traffic anomalies based on the results of processing its frame wavelet transform. The scientific task is to develop scientific and methodological approaches that allow effective analysis and timely detection of anomalies in network traffic. A comparative review of search methods for detecting network traffic anomalies, algorithms for detecting uncontrolled anomalies, traffic analysis methods based on local emission factor, binary trees, optical emission spectroscopy. Decision. The results of the study of the possibility of detecting anomalies in the bitstream traffic based on the results of its multiple-variable transformation in the Haar wavelet basis are considered. The choice for further processing of the coefficients of the traffic decomposition matrix along the time shift variable is justified. It is proved that multiple-scale transformations not only increase the structural differences in traffic, but also open up the possibility of localization of anomalies that caused these differences. The scientific novelty of the work is determined by the author's approach to detecting network traffic anomalies during the transition from the direct representation of a signal in the form of its discrete samples to coefficients formed from the matrices of its wavelet transformations, and, as a result, increasing its contrast with other signals with a similar structure. Theoretical significance. The necessity and sufficiency of using wavelet coefficients instead of time samples of signals in the basis of the parent wavelet from the matrix of the generated frame is proved. The relationship between the Hurst indicators and the coefficients of the cross-correlation functions has been established. Practical significance. The results obtained in the work, in the future, can be used in the construction of models for evaluating network traffic in conditions of deliberate, as well as methods for searching and synthesizing effective methods of protection against them.

Research on impedance measurement based on Gallium Nitride Transistor

Abstract Impedance measurement has broad applications in fields such as electrochemistry, biology, medicine, and food safety. Typically, frequency response analyzers are used for small signal disturbance measurements, but they come with drawbacks such as high cost, large size, and limited excitation capabilities. To address the need for wide-band, large-signal, and portable impedance measurement in electrochemical applications, a novel scheme based on a Gallium Nitride device excitation circuit and amplitude-phase discrimination module is proposed. A multiphase cascade frequency-doubling buck circuit is used to inject disturbance signals into the impedance under test, and impedance parameters are obtained using discrete fourier transformafter signal sampling. By varying the frequency of the disturbance signal, impedance information across different frequencies can be captured. In this approach, the amplitude-phase discrimination module replaces the frequency response analyzer, reducing the overall size of the measurement system, while the power device-based excitation circuit enables large signal disturbances.

Leveraging Multi-level Correlations for Imputing Monitoring Data in Water Supply Systems Using Graph Signal Sampling Theory

Leveraging Multi-level Correlations for Imputing Monitoring Data in Water Supply Systems Using Graph Signal Sampling Theory

SOAMC: A Semi-Supervised Open-Set Recognition Algorithm for Automatic Modulation Classification

Traditional automatic modulation classification methods operate under the closed-set assumption, which proves to be impractical in real-world scenarios due to the diverse nature of wireless technologies and the dynamic characteristics of wireless propagation environments. Open-set environments introduce substantial technical challenges, particularly in terms of detection effectiveness and computational complexity. To address the limitations of modulation classification and recognition in open-set scenarios, this paper proposes a semi-supervised open-set recognition approach, termed SOAMC (Semi-Supervised Open-Set Automatic Modulation Classification). The primary objective of SOAMC is to accurately classify unknown modulation types, even when only a limited subset of samples is manually labeled. The proposed method consists of three key stages: (1) A signal recognition pre-training model is constructed using data augmentation and adaptive techniques to enhance robustness. (2) Feature extraction and embedding are performed via a specialized extraction network. (3) Label propagation is executed using a graph convolutional neural network (GCN) to efficiently annotate the unlabeled signal samples. Experimental results demonstrate that SOAMC significantly improves classification accuracy, particularly in challenging scenarios with limited amounts of labeled data and high signal similarity. These findings are critical for the practical identification of complex and diverse modulation signals in real-world wireless communication systems.

An Ultrawide Bandwidth Digital Backend System Based on PFB Algorithm for QTT

For the planning of the QiTai radio Telescope ultrawide bandwidth low-frequency pulsar receiving system, we designed and implemented a Field-Programmable Gate Array (FPGA)+CPU/GPU hybrid architecture digital backend system based on the Polyphase FilterBank (PFB) channeling algorithm. We used the FPGA signal acquisition and processing platform to implement ultrawide bandwidth signal sampling and designed the PFB algorithm to realize the digital channelization of multiple analog bandwidth signals. We also developed data encapsulation and multichannel parallel distribution firmware algorithms and realized the real-time parallel transmission of high-speed astronomical data streams based on the VLBI Data Interchange Format. We developed the Ultra Wide bandwidth Low-frequency pulsar data process PIPEline, which realized the real-time processing and data packaging of massive pulsar signals. Using the L-band (964–1732 MHz bandwidth) receiving system of the Nanshan 26 m radio telescope, we conducted a systematic test on the designed digital backend system and obtained high-quality observation data. By using the professional pulsar data processing software DSPSR to process the observation data, we obtained high signal-to-noise ratio pulse profiles.

FPGA-Based Design of a Ready-to-Use and Configurable Soft IP Core for Frame Blocking Time-Sampled Digital Speech Signals

‘Frame blocking’ or ‘Framing’ is a technique that divides a time-sampled speech or audio signal into consecutive and equi-sized short-time frames, either overlapped or non-overlapped, for analysis. The framing hardware architectures (FHA) in the literature support framing speech or audio samples of specific word size with specific frame size and frame overlap size. However, speech and audio applications often require framing signal samples of varied word sizes with varied frame sizes and frame overlap sizes. Therefore, the existing FHAs must be redesigned appropriately to keep up with the variability in word size, frame size and frame overlap size, as demanded across multiple applications. Redesigning the existing FHAs for each specific application is laborious, prompting the need for a configurable intellectual property (IP) core. The existing FHAs are inappropriate for creating configurable IP cores as they lack adaptability to accommodate variability in frame size and frame overlap size. Therefore, to address these issues, a novel FHA, adaptable to accommodate the desired variability, is proposed. Furthermore, the proposed FHA is transformed into a field-programmable gate array-based soft, ready-to-use and configurable frame blocking IP core using the Xilinx® Vivado™ tool. The resulting IP core is versatile, offering configurability for framing in numerous applications incorporating real-time digital speech and audio systems. This research article discusses the proposed FHA and frame blocking IP core in detail.

A technique for flank wear monitoring in milling machines based on the novel tool wear indicator

ABSTRACT This research investigates an innovative method for monitoring flank wear by analyzing these signal variations. By dissecting the signal bands into multiple frequency bands using Wavelet Packet Transform, a precise examination of the signal contents is enabled. Using the signal power spectrum to Shannon entropy (SPSSE) node power spectrum analysis metric, the specific frequency band that exhibits a stable rising trend in the proposed metric correlated with flank wear is identified. This frequency band serves as a basis for constructing a tool wear indicator that utilizes Mean Square Error (MSE) comparisons of a healthy baseline signal sample with the sample under test. The calculated indicator closely correlates with the actual measured levels of flank wear, allowing for effective evaluation of the tool condition and differentiation between the healthy tool and the tool exhibiting any levels of flank wear. The performance of the proposed method is evaluated using the publicly available benchmark NASA Ames Milling tool dataset. This innovative approach holds promise for real-time assessment of machining processes, facilitating timely intervention based on the tool wear estimation.

Load spectra of a light unmanned aircraft – data recording frequencies and resulting measurement variations

The article focuses on the load spectrum of a lightweight unmanned aircraft. It examines the impact of load signal sampling frequency on the load spectrum derived from the load signal. The flight sessions followed two scenarios: a maneuvering flight causing a wide range of load factor variations and a calm photogrammetric flight. The recorded load signals from two types of sensors were initially rescaled into load factor time series, with an optional signal smoothing process. These series were then downsampled by selecting every nth term, resulting in load factor sequences recorded at a lower frequency. Next, all sequences were transformed into sequences of local extremes of the Load Levels, resulting from quantifying the operational load variability into 32 intervals. Two options were considered: without filtering or with filtering of sequential pairs of terms generating small Load Level increments. Subsequently, the Rainflow Counting algorithm was applied, producing 32x32 load half-cycle arrays. Based on these arrays, incremental load spectra were calculated. The study provides a detailed comparison of load spectra for different load signal sources and recording frequencies. Additionally, calculations of the fatigue life of a structural element subjected to various load spectra were performed, leading to the formulated conclusions.

The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5−way5−shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments.

Leveraging Incremental Learning for Dynamic Modulation Recognition

Modulation recognition is an important technology used to correctly identify the modulation modes of wireless signals and is widely used in cooperative and confrontational scenarios. Traditional modulation-recognition algorithms require the assistance of expert experiences, which constrains their applications. With the rapid development of artificial intelligence in recent years, deep learning (DL) is widely advocated for intelligent modulation recognition. Typically, DL-based modulation-recognition algorithms implicitly assume a relatively static scenario in which the signal samples of all the modulation modes can be completely collected in advance. In practical situations, the radio environment is quite dynamic and the signal samples with new modulation modes may appear sequentially, in which the current DL-based modulation-recognition algorithms may require unacceptable time and computing resource consumption to re-train the optimal model from scratch. In this study, we leveraged incremental learning (IL) and designed a novel IL-based modulation-recognition algorithm that consists of an initial stage and multiple incremental stages. The main novelty of the proposed algorithm lies in the new loss function design in each incremental stage, which combines the distillation loss of recognizing old modulation modes and the cross-entropy loss of recognizing new modulation modes. With the proposed algorithm, the knowledge of the signal samples with new modulation modes can be efficiently learned in the current stage without forgetting the knowledge learned in the previous stages. The simulation results demonstrate that the proposed algorithm could achieve a recognition accuracy close to the upper bound with a much lower computing time and it outperformed the existing IL-based benchmarks.

Analysis of NR and LTE Target Signal Structure Based on Neural Network Approach and Deep Learning Methods

Purpose: the rapid development and consolidation of broadband wireless access networks of the fourth 4G LTE and fifth 5G NR generations determine the need to find ways to reuse frequencies. A well-known solution to this problem is the concept of cognitive radio. The use of artificial intelligence in radio networks in general and the use of a neural network approach with deep learning for recognizing signals of LTE and NR standards in particular have serious potential for the practical implementation of frequency resource reuse according to the concept of cognitive development. The aim of the work: is to analyze models, methods and algorithms that allow recognizing signals of the NR and LTE standards based on recording samples of radio signals obtained using pre-recorded NR and LTE signals in MatLab software. In this paper, the procedures for scanning and recognizing sections of the spectrum are considered. The operations of training a semantic segmentation network for the identification of these signals in a broadband spectrogram and the use of a trained network for subsequent spectrum sensing and recognition of signals of the NR and LTE standards are investigated. Methods: the method used to test the concept of cognitive radio is spectrum sensing, which results in information about the occupancy of frequency bands in a given location by primary users, and the identification of spectrum sections available for use by secondary users without interfering with the work of primary users in real time. The novelty of the work is the use of a neural network approach in the analysis of the target NR and LTE signal. Results: the model, trained on the basis of a neural network approach and deep learning methods, is able to distinguish between signals of the NR and LTE standards. Theoretical / Practical relevance: the software implementation of spectral sensing is implemented using an extension package in the MatLab environment and allows for experimental testing of a cognitive radio receiver when performing procedures for analyzing the structure of the target signal of the NR and LTE standards based on a neural network approach and deep learning methods.

Sensor placement method for water distribution networks based on sampling of non-bandlimited graph signals

Monitoring water distribution networks (WDNs) requires careful consideration of sensor placement, which is essential for obtaining comprehensive information about the network. A natural graphical structure underlies WDN, making graph sampling theory advantageous for selecting monitoring nodes. However, graph sampling theory is only applied only to restrictive band-limited signals, while the pressure data of WDN is a restrictive non-band-limited signal. To address this issue, this paper presents an approximate conversion method for transforming non-band-limited signals into band-limited signals, accompanied by an optimal spectrum threshold formula. This formula is used to perform spectral screening in the graph frequency domain, effectively converting non-band-limited signals into band-limited signals that preserve the major frequency components while ignoring smaller-value frequency components. By sampling band-limited signal, we identify sampling nodes that perfectly recover the signal. These sampling nodes act as monitoring nodes that can perform a comprehensive inspection of the WDN and accurately locate leaks. The accuracy of our method in recovering the signal and locating the leak is demonstrated by comparing it with two existing sensor placement optimization methods.

De-noising magnetotelluric data based on machine learning

The magnetotelluric (MT) sounding is a common geophysical exploration technique, but it is highly polluted by various types of cultural noise. In the realm of MT data processing, traditional techniques often rely on the quality of the measured MT data. Conventional MT time domain denoising methods tend to eliminate valuable signals, potentially leading to unreliable resistivity estimates. To address this concern, we propose employing machine learning to effectively suppress strong noise interference in MT data, thereby preventing the loss of valuable signals. We augment this approach with mathematical morphological filtering (MMF) to capture low-frequency signals, preserving their integrity. We constructed a signal sample library based on a substantial volume of signal samples. Through consistent training, we establish a support vector machine (SVM) classification model that distinguishes high-quality signal fragments from noisy signals. Subsequently, we use adaptive K-singular value decomposition (K-SVD) dictionary learning to extract noise profiles and suppress noisy signals. To validate the feasibility of our method, we apply machine learning to measured data from two distinct observation areas. The measured data were analyzed and processed, and the results were compared with the robust results. This method can effectively eliminate large-scale strong interference in time domain sequences and preserve more low-frequency slow change information and high-quality signals in the reconstructed signals. The apparent resistivity phase curve of synthetic data is smoother and more continuous, and the data quality in the low-frequency range is significantly improved. The results can more accurately and reliably reflect underground electrical structure information.

Derivative Sampling of Periodic and Nonperiodic Band-Limited Signals — Fourier Spectrum and Interpolation Formula

Derivative Sampling of Periodic and Nonperiodic Band-Limited Signals — Fourier Spectrum and Interpolation Formula

A threshold activation-based simplified Lv's transform algorithm for transient multi-component linear frequency modulation signals analysis.

The high sampling rate in modern digital systems generates a large scale of data. To address the computational burden, this paper proposes a threshold activation-based simplified Lv's transform (SLVT) algorithm to analyze the transient multi-component linear frequency modulation signals. Only the signal arrival can trigger the signal analysis. This mechanism alleviates the computation pressure because of the sparsity of signals. The threshold activation mechanism enables transient signal detection and sampling rate adjustment, thereby enhancing the efficiency and effectiveness of the analytical process. The simplified Lv's transform (LVT) removes redundant computations on stretch weighting and the Discrete Fourier Transform (DFT) in Lv's transform. SLVT uses the efficient Bluestein chirp-z algorithm to implement the stretch keystone transform. The comparison results show that SLVT reduces the computational complexity of the original LVT by at least 30.8%. This algorithm exhibits superior performance compared to other advanced signal processing methods, such as discrete chirp Fourier transform, fractional Fourier transform, and Radon Wigner transform algorithms, in terms of parameter extraction accuracy, computational complexity, and execution time. Moreover, the implementation of a field programmable gate array accelerates SLVT computing by a factor of 116 in comparison to the CPU (Central Processing Unit) platform.

Impact of Harmonic Voltage on the Partial Discharge Properties of Eco-Friendly Nanofluid

The Insulation is crucial in transformers to maintain electrical isolation between components and windings, ensuring safe and efficient operation. Mineral oil acts as both insulation and coolant in transformers. It is a substance that does not naturally degrade and can persist in the environment for an extended period if accidentally released. Therefore, Sunflower oil is used as a substitute for mineral oil. However, harmonic distortion is a common issue in power systems, leading to increased dielectric loss and partial discharge activity, ultimately causing insulation failure and equipment damage. Hence, it is vital to analyze the partial discharge performance of pure Sunflower oil under varying harmonic AC voltages to ensure the reliable operation of electrical devices. The study examines the partial discharge characteristics of a sunflower oil-based nanofluid under harmonic AC voltages, which is a blend of pure sunflower oil and SiO2 nanoparticles. Two nanofluid mixtures were created, one with a 0.01% mass ratio and the other with 0.05% sunflower oil. Various PD characteristics were evaluated, including Partial Discharge significance, starting voltage, duration of increase, maximum amplitude, Phase-Resolved PD sample, and frequency and time graphs of the Partial Discharge signal. All tests were carried out under IEC regulations. A comparison was made between the outcomes of Nano blended sunflower oil and natural sunflower edible oil. Experiments were conducted on partial discharges in Sunflower oil using specific harmonic superimposed AC Voltages, utilizing the third and fifth harmonics. The findings reveal that: a) PDIV value decreases with increasing harmonic order when using AC voltage with superimposed harmonics, b) PD characteristics are closely related to the voltage waveform, and the addition of Silica to sunflower oil results in reduced PD amplitude, pulse duration, and time duration compared to pure sunflower oil.

Conducted interference and voltage ripple suppression study in LLC resonant converter based on symmetric chaotic spread spectrum

ABSTRACT Spread-spectrum modulation technique can effectively reduce the peak value of conducted interference in automotive LLC DC/DC converters, but it will bring more serious ripple problems to Class E resonant converters. In this paper, we investigate the voltage ripple trend of LLC resonant converter when applying spread-spectrum modulation, and propose a chaotic signal sampling rule under the minimum output voltage ripple. A symmetric chaotic spreading method is proposed to reduce the output voltage ripple from the source by changing the distribution sequence of the chaotic signal to make it centrosymmetric. The symmetric chaotic spread spectrum is simulated and analysed to be able to suppress the conduction interference while reducing the voltage ripple, and is verified by experiments. This modulation strategy does not require the addition of auxiliary circuits and additional control links, which saves cost and reduces the difficulty of designing control links.

Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals.

Post-mechanotherapy differences in postural control in patients with Achilles tendinopathy - A randomized controlled trial

BackgroundNeuromuscular deficits affecting functional ability can occur in patients with Achilles tendinopathy during difficult balance activities. This study aimed to assess postural control in patients with Achilles tendinopathy after shockwave and sonotherapy, using advanced analytical methods, including rambling-trembling signal decomposition and sample entropy. Research questionWhat are the differences in postural control between patients with Achilles tendinopathy after shockwave therapy, ultrasound therapy, and placebo ultrasound? MethodsThirty-nine patients were included in the study, and randomly assigned to 3 groups, i.e., shockwave therapy, ultrasound therapy and placebo ultrasound. Postural sway was assessed during quiet standing with eyes open and closed, with two force platforms, one for the affected and the other for the non-affected limb, at baseline and at weeks 1 and 6 after treatment. Rambling-trembling trajectories and sample entropy were calculated for the antero-posterior and medio-lateral directions. ResultsThe parameters of trembling trajectory in both directions were significantly smaller for the affected compared to non-affected limb. The ultrasound group had significantly larger rambling-trembling trajectories in the antero-posterior and medio-lateral sway directions than the shockwave therapy group. Also, all patients had more difficulty controlling their postural sway while standing with eyes closed compared to eyes open. Sample entropy was not significantly affected by the therapy type, timepoint and limb condition. SignificanceAs opposed to sample entropy, rambling-trembling decomposition can complement or replace traditional linear measures of COP time series in functional assessment of the Achilles tendon.

A Bearing Fault Diagnosis Method in Scenarios of Imbalanced Samples and Insufficient Labeled Samples

In practical working environments, rolling bearings are one of the components that are prone to failure. Their vibration signal samples are faced with challenges, mainly including the imbalance between normal and fault samples as well as an insufficient number of labeled samples. This study proposes a sample-expansion method based on generative adversarial networks (GANs) and a fault diagnosis method based on a transformer to solve the above issues. First, selective kernel networks (SKNets) and a genetic algorithm (GA) were introduced to construct a conditional variational autoencoder–evolutionary generative adversarial network with a selective kernel (CVAE-SKEGAN) to achieve a balance between the proportion of normal and faulty samples. Then, a semi-supervised learning–variational convolutional Swin transformer (SSL-VCST) network was built for the fault classification, specifically introducing variational attention and semi-supervised mechanisms to reduce the overfitting risk of the model and solve the problem of a shortage of labeled samples. Three typical operating conditions were designed for the multi-case applicability verification. The results show that the method proposed in this study had good application effects when solving both sample imbalances and labeled-sample deficiencies and improved the accuracy of fault diagnosis in the above scenarios.