Signal Frame Research Articles

Research on the Technology of Improving the Compatibility of DC Measurement Devices Based on the Operation Experience of Siemens Hardware Solutions

The aim of this study was to manufacture alternative Siemens DC measurement system equipment to solve the problems of increased failure rates of foreign DC measurement equipment and the stopped production of key parts. In this paper, the overall structure and common faults of the Siemens DC measurement system are introduced, the difference between TDM signal frame structure and FT3 frame structure is detailed, and the related functional structure, chip type, and function of the Siemens distal module and merging unit (including laser function) are studied. The remote module, merging unit (including laser function), and related hardware design and software design to replace the Siemens DC measurement system were developed, and the test environment and control protection, fault recording wave for communication, and other functional tests were built. The test results show that the domestic remote module, merging unit (laser function), and other technologies communicate normally with the existing Siemens and the domestic mainstream manufacturers, and the accuracy meets the requirements of the 0.2 level.

LC dynamic signal modelling and analysis of resonant frequency measurement error under rapid eye movements interference

Abstract LC resonant wireless passive intraocular pressure (IOP) sensors measure IOPs by mapping them to the LC resonance frequencies. However, during the sweeping acquisition process of every LC signal frame, possible rapid eye movement (REM) can interfere with the wireless mutual inductance coupling, leading to the signal distortion and resulting in the error of resonance frequency measuring. Currently, there is a lack of modelling analysis of the error generated by REM effect, resulting in absence of guidance for reducing the impact. Therefore here, we start from the perspective of the signal model and establish the LC dynamic signal model of sweeping acquisition. By means of the limit state analysis and Monte Carlo simulation, we analyze the influence of external REM parameters (including REM range and velocity) and internal parameters of the LC sensing system (including the quality coefficient of LC sensor, diameter, and number of turns of the reader coil, signal sweep acquisition speed and period) on the errors. We demonstrated theoretical relationship between the extreme errors with these parameters and verify it through a computer simulation. Based on the results, we propose to optimize the internal parameters of the LC sensing system to reduce the REMs effect on errors, safeguarding the quality of signal acquisition and improving the measurement accuracy.

Motor Bearing Failure Identification Using Multiple Long Short-Term Memory Training Strategies

In the context of condition-based maintenance of rotating machines in manufacturing systems, the early diagnosis of possible faults related to rolling elements of the bearing is mainly based on techniques from artificial intelligence, namely, Machine Learning (ML) and Deep Learning (DL). Approaches based on using Deep Learning methods have been the most coveted in recent years. Among a variety of models, the type of architecture known as Long-Short-Term Memory (LSTM) of Recurrent Neural Network (RNN) has both the ability to capture long-term dependencies and to adapt to sequential data modeling. It is therefore able to work on data without any preprocessing. This paper studies using four types of LSTM networks to diagnose bearing faults in a classification approach. It aims to intervene on both the input parameters and the network architecture, to achieve high performance. The proposed method is carried out in two different ways. In the first case, the data inputs are raw frames of vibration signals. However, in the second case, the network inputs are pre-computed time-frequency features. The results clearly showed that LSTMs are more accurate with the latter.

Intrinsic mode ensembled statistical cepstral coefficients for feature extraction of ship-radiated noise

Effective analysis of ship underwater acoustic signals requires accurately capturing and distinguishing subtle differences between various types of signal features. This paper introduces a multi-objective feature extraction method based on intrinsic mode decomposition and statistical parameterized cepstral coefficients, aimed at identifying different ship signals. Firstly, the original sample signals are preprocessed and converted into multiple frame signals. Each acoustic signal frame is then decomposed into intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Cepstral coefficients are extracted from each IMF, and the statistical parameter features of each IMF are integrated to enhance the differentiation of various types of ship-radiated noise. These features also form unique “fingerprints” for each ship type, facilitating identity accurate authentication. The performance of the proposed method is evaluated using both K-nearest neighbors (KNN) and support vector machine (SVM) classification models. Experimental results demonstrate that the synergy between the proposed method and SVM significantly outperforms KNN, effectively distinguishing between 12 types of signals, including 11 ship-radiated signals and background noise, achieving an accuracy rate exceeding 89% across 1000 random tests. This method significantly increases the number of classifiable ship targets, demonstrating its considerable potential in distinguishing various underwater acoustic signals.

RPCA-based thermoacoustic imaging for microwave ablation monitoring

Microwave ablation (MWA) is a potent cancer treatment tool, but its effectiveness can be hindered by the lack of visual feedback. This paper validates the feasibility of using microwave-induced thermoacoustic imaging (TAI) technique to monitor the MWA process. A feasibility analysis was conducted at the principle level and a high-performance real-time TAI system was introduced. To address the interference caused by MWA, a robust principal component analysis (RPCA)-based method for TAI was proposed. This method leverages the correlation between multiple signal frames to eliminate interference. RPCA’s effectiveness in TAI was demonstrated through three sets of different experiments. Experiments demonstrated that TAI can effectively monitors the MWA process. This work represents the first application of RPCA-related matrix decomposition methods in TAI, paving the way for the application of TAI in more complex clinical scenarios. By providing rapid and accurate visual feedback, this research advances MWA technology.

A Segmented Sliding Window Reference Signal Reconstruction Method Based on Fuzzy C-Means

Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented sliding window reference signal reconstruction method based on Fuzzy C-Means (FCM). By partitioning the reference signals based on the structure of DTMB signal frames, this approach compensates for frequency offset and sample rate deviation individually for each segment. Additionally, FCM clustering is utilized for symbol mapping reconstruction. Both simulation and experimental results show that the proposed method significantly suppresses constellation diagram divergence and phase rotation, increases the adaptive cancellation gain and signal-to-noise ratio (SNR), and in the meantime reduces the computation cost.

Spike timing-based coding in neuromimetic tactile system enables dynamic object classification.

Rapid processing of tactile information is essential to human haptic exploration and dexterous object manipulation. Conventional electronic skins generate frames of tactile signals upon interaction with objects. Unfortunately, they are generally ill-suited for efficient coding of temporal information and rapid feature extraction. In this work, we report a neuromorphic tactile system that uses spike timing, especially the first-spike timing, to code dynamic tactile information about touch and grasp. This strategy enables the system to seamlessly code highly dynamic information with millisecond temporal resolution on par with the biological nervous system, yielding dynamic extraction of tactile features. Upon interaction with objects, the system rapidly classifies them in the initial phase of touch and grasp, thus paving the way to fast tactile feedback desired for neuro-robotics and neuro-prosthetics.

Speech emotion recognition using a combination of variational mode decomposition and Hilbert transform

The objective of automated speech emotion recognition (SER) is to recognize each emotion of the speech signal uniquely and efficiently using machines like computers, mobile devices, etc. The popularity of SER has been widely acknowledged among researchers due to its extensive applicability in practical contexts. The use of SER has shown to be advantageous in several domains, including medical treatment, enhancement of security systems, surveillance operations, online marketing strategies, online educational platforms, online search engines, personal communication, customer relationship management, reinforcement of machine and human connection, and numerous other areas. Numerous authors have used various techniques, including the combination of multiple features (acoustic, non-acoustic, or both) and classifiers (machine learning, deep learning, or both), in order to enhance the efficacy of emotion categorization. In the present work, our objective is to enhance the performance of emotion classification (PECL) by using a combined approach of the variational mode decomposition (VMD) and Hilbert transform (HT) techniques. Using the VMD method, we decomposed the speech signal frame into many sub-signals, or intrinsic mode functions (IMFs). Then HT is applied to each VMD-based IMF signal to find the mode instantaneous amplitude (MIA) and mode instantaneous frequency (MIF) signal vectors. We extracted proposed features such as HT-based approximate entropy (HTAE), HT-based permutation entropy (HTPE), HT-based increment entropy (HTIE), and HT-based sample entropy (HTSE) using each MIA and MIF signal vector. The combination of the proposed HT-based features is called HT-based entropy (HTE) features. Then, we accessed the PECL using the HTE and MFCC features alone and in conjunction with a deep neural network (DNN) classifier. The experiment's results showed that the combinations of the proposed feature (MFCC + HTE) using a DNN classifier outperformed the individual features and obtained a SER accuracy of 86.92% for the EMOVO dataset and 91.63% for the EMO-DB dataset.

Signal frame reception synchronization system with LoRa-modulated

Signal frame reception synchronization system with LoRa-modulated

A Novel Watermarking Scheme for Audio Data Stored in Third Party Servers

To improve the security and privacy of audio data stored in third party servers, a novel watermarking scheme is proposed. Firstly, the authors split the host signal into frames and scramble each frame to get the encrypted signal. Secondly, they generate watermark bits by using the frame number and embed them into each frame of the encrypted signal, which is the data that will be uploaded to the third party servers. For the users, they can download the encrypted data and verify the data is intact or not. If the data is intact, the users decrypt the data to get the audio signal. If the audio signal is attacked in the process of transmission, they can also locate the location of the attacked frame. The experimental results show that the method proposed is effective not only for encrypted signals, but also for the encrypted signals after decryption.

Аналитический обзор методов автоматического анализа экстралингвистических компонентов спонтанной речи

The accuracy of automatic spontaneous speech recognition systems is far from that of trained speech recognition systems. This is due to the fact that spontaneous speech is not as smooth and failure-free as spontaneous speech. Spontaneous speech varies from speaker to speaker: the quality of phonemes’ pronunciation, the presence of pauses, speech disruptions and extralinguistic items (laughing, coughing, sneezing, and chuckling when expressing emotions of irritation, etc.) interrupt the fluency of verbal speech. However, it is worth noting that extralinguistic items very often carry important paralinguistic information, so it is crucial for automatic spontaneous speech recognition systems not only to identify such phenomena and distinguish them from the verbal components of speech but also to classify them. This review presents an analysis of works on the topic of automatic detection and analysis of extralinguistic items in spontaneous speech. Both individual methods and approaches to the recognition of extralinguistic items in a speech stream, and works related to the multiclass classification of isolatedly recorded extralinguistic units are considered and described. The most popular methods of extralinguistic units’ analysis are neural networks, such as deep neural networks and networks based on transformer models. The basic concepts related to the term extralinguistic items are given, the original systematization of extralinguistic items in the Russian language is proposed, the corpus and databases of audio spoken speech both in Russian and in other languages are described, the data sets of extralinguistic items recorded isolatedly are also given. The accuracy of extralinguistic items recognition increases with the following conditions of work with the speech signal: pre-processing of audio signals of items has shown an increase in the accuracy of separately recorded extralinguistic items classification; consideration of context (analysis of several frames of speech signal) and use of filters for smoothing the time series after extraction of feature vectors showed an increase in accuracy in frame-by-frame analysis of the speech signal with spontaneous speech.

Improvement of emotion classification performance using multi-resolution variational mode decomposition method

Automated speech emotion recognition (SER) has been gaining popularity among researchers for three decades because of its vast number of applications in the real world. It is helpful to improve the relationship between humans and machines, online marketing and education, customer relations, medical treatment, safe driving, online search, etc. Researchers adopt various methods to improve emotion recognition performance from speech signals, like using different combinations of features (acoustic, non-acoustic, or both), classifiers (machine learning, deep learning, or both). Our study tried to improve emotion classification performance using features based on the multi-resolution variational mode decomposition (MRVMD) method. We first decompose each signal frame into several sub-signals known as modes or intrinsic mode functions (IMFs) using the MRVMD method. Then the proposed features, multi-resolution variational mode mel-frequency cepstral coefficient (MRVMMFCC), multi-resolution variational mode approximate entropy (MRVMAE), and multi-resolution variational mode permutation entropy (MRVMPE), were extracted using the MRVMD-decomposed IMF signals. Finally, different combinations of the proposed features are used to classify the emotion using a deep neural network (DNN) classifier. From the experimental results, we found that combination of the proposed feature (MRVMMFCC + MRVMAE + MRVMPE) performs better than the other combination in recognizing emotion using speech signals. The proposed feature combination with a DNN classifier achieved an emotion classification accuracy of 83.4%, 85.01%, and 90.51% for the SAVEE, EMOVO, and EMO-DB datasets, respectively. We found that the proposed MRVMD method performed better than the state-of-the-art methods.

Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems

Optical wireless communications (OWCs) are power-efficient for providing short-range and high-speed data transmission. In this paper, we propose an interference degradation algorithm for power-constrained OWC systems when using orthogonal frequency division multiplexing (OFDM). In this algorithm, we introduce a decision tolerance at the receiver that can be optimally designed to reduce interference caused by peak power clipping distortion and decrease additive noise collected by the photodetectors. Although the recently proposed clipping-enhanced optical OFDM (CEO-OFDM) can transmit clipped information through extra signal frames, reconstructing the received signal introduces more noise. Using the proposed decision tolerance, we can determine whether the signals in the extra frame are required for data reconstruction. By optimally choosing the decision tolerance, the bit error rate (BER) performance is enhanced compared to those of DCO, ACO, and CEO-OFDM. Additionally, the proposed algorithm offers a wider dynamic modulation index range than DCO, ACO, and CEO-OFDM at the same BER. Using the tested parameters, the proposed algorithm with 64-QAM achieves a similar best BER performance as DCO, ACO, and CEO-OFDM using 32-QAM. Therefore, the proposed algorithm achieves a higher data rate than those of the other compared techniques.

Time-frequency network combining batch attention and spatial attention for speech bandwidth extension

The field of speech bandwidth extension (BWE) has witnessed remarkable advancements through the implementation of convolutional neural networks (CNNs). However, one limitation of CNNs is the lack of consideration for the strong correlation between different frames of speech signals. Effectively utilizing the inter-frame features and sample points has become an urgent challenge that needs to be addressed in this context. To address this problem, we propose an improved time–frequency network that combines spatial attention module and batch attention module. Our proposed approach involves a spatial attention module that uses multi-layer dilated convolutions to capture spatial dependencies in feature maps. Additionally, a batch attention module utilizes pooling and fully connected operations to leverage the correlation between speech frames. We introduce a time–frequency (T-F) loss to optimize the amplitude and phase of reconstructed speech signals, reducing artificial artifacts. Experimental results demonstrate the superiority of our network compared to other baseline BWE networks on four datasets, as measured by SNR, LSD, PESQ, and MOS metrics. Notably, on the TIMIT dataset, our network with the T-F loss achieves superior LSD and SNR values over other loss functions.

MBR-Net: A multi-branch residual network based on ultrasound backscattered signals for characterizing pediatric hepatic steatosis

The evaluation of pediatric hepatic steatosis and early detection of fatty liver in children are of critical importance. In this paper, a deep learning model based on the convolutional neural network (CNN) of ultrasound backscattered signals, multi-branch residual network (MBR-Net), was proposed for characterizing pediatric hepatic steatosis. The MBR-Net was composed of three convolutional branches. Each branch used different sizes of convolution blocks to enhance the capability of local feature acquisition, and leveraged the residual mechanism with skip connections to guide the network to effectively capture features. A total of 393 frames of ultrasound backscattered signals collected from 131 children were included in the experiments. The hepatic steatosis index was used as the reference standard for diagnosing the steatosis grade, G0–G3. The ultrasound backscattered signals within the liver region of interests (ROIs) were normalized and augmented using a sliding gate method. The gated ROI signals were randomly divided into training, validation, and test sets with the ratio of 8:1:1. The area under the operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were used as the evaluation metrics. Experimental results showed that the MBR-Net yields AUCs for diagnosing pediatric hepatic steatosis grade ≥G1, ≥G2, and ≥G3 of 0.94 (ACC: 93.65%; SEN: 89.79%; SPE: 84.48%), 0.93 (ACC: 90.48%; SEN: 87.75%; SPE: 82.65%), and 0.93 (ACC: 87.76%; SEN: 84.84%; SPE: 86.55%), respectively, which were superior to the conventional one-branch CNNs without residual mechanisms. The proposed MBR-Net can be used as a new deep learning method for ultrasound backscattered signal analysis to characterize pediatric hepatic steatosis.

Overt speech decoding from cortical activity: a comparison of different linear methods.

Speech BCIs aim at reconstructing speech in real time from ongoing cortical activity. Ideal BCIs would need to reconstruct speech audio signal frame by frame on a millisecond-timescale. Such approaches require fast computation. In this respect, linear decoder are good candidates and have been widely used in motor BCIs. Yet, they have been very seldomly studied for speech reconstruction, and never for reconstruction of articulatory movements from intracranial activity. Here, we compared vanilla linear regression, ridge-regularized linear regressions, and partial least squares regressions for offline decoding of overt speech from cortical activity. Two decoding paradigms were investigated: (1) direct decoding of acoustic vocoder features of speech, and (2) indirect decoding of vocoder features through an intermediate articulatory representation chained with a real-time-compatible DNN-based articulatory-to-acoustic synthesizer. Participant's articulatory trajectories were estimated from an electromagnetic-articulography dataset using dynamic time warping. The accuracy of the decoders was evaluated by computing correlations between original and reconstructed features. We found that similar performance was achieved by all linear methods well above chance levels, albeit without reaching intelligibility. Direct and indirect methods achieved comparable performance, with an advantage for direct decoding. Future work will address the development of an improved neural speech decoder compatible with fast frame-by-frame speech reconstruction from ongoing activity at a millisecond timescale.

Coriolis mass flowmeter signal processing system based on time-varying signal model

In order to improve the signal processing effect and efficiency of Coriolis mass flowmeter, a signal processing system of Coriolis mass flowmeter based on time-varying signal model is designed. Based on the analysis of the structure and working principle of Coriolis mass flowmeter, the environmental factors affecting the performance of Coriolis mass flowmeter are discussed. Based on the above analysis, the signal processing environment is designed, including serial signal processing environment and dual core parallel signal processing environment. Then the effective signal processing of Coriolis mass flowmeter is completed from three aspects: initial processing, phase calculation and frequency tracking. The experimental results show that after the time-varying signal model is applied, a complete signal frame can be collected in less than 50 ms in the pre-processing process, and the signal processing frame can be completed in less than 80 ms in the post-processing process. This shows that the signal processing time of the system is short, and the phase difference detection accuracy of the signal processing system based on the time-varying signal model is high, which is of great value to further improve the application effect of Coriolis mass flowmeter.

Partial discharge signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold

AbstractPartial discharge (PD) signals have a large amount of data and a low energy proportion of pulse signals, resulting in difficult data transmission and poor reconstruction efficiency. To this end, a PD signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold (TSR‐DRRT) is proposed. TSR‐DRRT is centred on the sparse representation (SR) and accurate reconstruction of noisy signals. The intrinsic pulse of PD signals is extracted by signal decomposition, and jointly trained with different types of signals to establish the transfer SR dictionary. The compressed signal accurately retains the essential characteristics of the pulse information by improving the match between the dictionary atoms and the polymorphic PD pulses. To match the transfer SR dictionary, the inner and outer DRRT iteration termination conditions are set adaptively during the reconstruction process based on the correlation difference between the dictionary and signal frames. Independent control of PD pulse recognition and reconstruction accuracy is achieved, and its performance under noisy signals is improved. The results show that the method can achieve high ratio compression and efficient reconstruction of noisy signals. Different types of PD signals can also have high matching accuracy. This method can meet the demand for PD signals compression and transmission to the terminal for accurate reconstruction.

Fluorescein videoangiography data analysis protocol for mapping retinal vascular permeability in humans.

Fluorescein video angiographies (FVAs) are a diagnostic tool for eye diseases, such as diabetic retinopathy (DR). Currently, kinetic tracer model methods based on indicator-dilutions theory use FVAs to extract biomarkers (e.g., volumetric blood flow and retinal vascular permeability) via pixel mapping using two-step non-linear least square fitting. Prior to biomarker extraction, the FVAs must attain optimal quality. The objective of this research is to create a program to remove all frames experiencing signal drops (causes include blinking, squinting, and head movement). 15 FVAs (6 healthy control subjects, 6 diabetes mellitus no DR (DMnoDR) subjects, and 3 mild non-proliferative DR (NPDR) subjects) were analyzed for low quality frames. The average signal of each frame was analyzed as top, middle, and bottom thirds. The frame with maximum average signal up to the final frame of a created "Gold Standard" was compared with the raw AVI's frame with maximum average signal and subsequent frames. All frames before maximum average signal and any remaining frames were compared with the previous good-quality raw frame to determine if the frame of interest was of good quality. All remaining frames were subsequently re-evaluated and flagged if they had a local minimum prominence of 10% of the maximum average signal. The flagged frames', as well as former and subsequent frames', quality were subjectively determined. The AVI quality was subsequently tested via pre-DTKM processing and biomarker extraction via DTKM methods. Results displayed that the semi-automated frame removal process provides sufficient quality AVIs.

Efficient Video Watermarking Algorithm Based on Convolutional Neural Networks with Entropy-Based Information Mapper.

This paper presents a method for the transparent, robust, and highly capacitive watermarking of video signals using an information mapper. The proposed architecture is based on the use of deep neural networks to embed the watermark in the luminance channel in the YUV color space. An information mapper was used to enable the transformation of a multi-bit binary signature of varying capacitance reflecting the entropy measure of the system into a watermark embedded in the signal frame. To confirm the effectiveness of the method, tests were carried out for video frames with a resolution of 256 × 256 pixels, with a watermark capacity of 4 to 16,384 bits. Transparency metrics (SSIM and PSNR) and a robustness metric-the bit error rate (BER)-were used to assess the performance of the algorithms.