Time Frequency Research Articles

Impact of Ultra-Rapid Insulin on Boost and Ease-Off in the Cambridge Hybrid Closed-Loop System for Individuals With Type 1 Diabetes.

The objective was to evaluate the safety and efficacy of ultra-rapid-acting insulin with the Boost and Ease-off features of the Cambridge hybrid closed-loop system. A secondary analysis of Boost and Ease-off from two double-blind, randomized, crossover hybrid closed-loop studies comparing (1) Fiasp to insulin aspart (n = 25), and (2) Lyumjev to insulin lispro (n = 26) was carried out. Mean glucose on initialization of Boost and Ease-off, change in glucose 60 and 120 minutes after initialization, duration and frequency of use, mean glucose, and time in, above, and below target glucose range were calculated for periods of Boost use, Ease-off use, or neither. Participants used Boost for longer with Fiasp than insulin aspart (median [interquartile range, IQR] = 75 [53-125] minutes vs 60 [49-75] minutes; P = .01). Mean glucose on Boost initialization with Fiasp was 238 ± 62 mg/dL compared with 218 ± 45 mg/dL with insulin aspart (P = .08). Fiasp use resulted in a greater glucose reduction 120 minutes after Boost initialization [-59 ± 34 mg/dL vs -43 ± 31 mg/dL; P = .02]. There were no statistically significant differences in sensor glucose endpoints during Boost or Ease-off periods between Fiasp and aspart. There were no statistically significant differences during Boost or Ease-off periods when comparing Lyumjev with insulin lispro. There were no safety issues when using Boost and Ease-off with ultra-rapid insulins. The use of Fiasp and Lyumjev during Boost or Ease-off resulted in comparable safety and efficacy to using insulin aspart and lispro.

Adaptive feature fusion and disturbance correction for accurate remaining useful life prediction of rolling bearings

As a key component in the transmission system of high-speed trains, bearings need to withstand certain loads while rotating at high speeds. Once a failure occurs, it can directly affect the safety of train operations. Therefore, it is of great significance to establish a reliable remaining useful life model to ensure train operation safety. Addressing the problems of redundant features in the existing multi-feature fusion process, which affects diagnostic performance, and the spurious fluctuations in the fusion features, which cause inaccurate determination of the start fault time and lead to low prediction accuracy of remaining useful life, we propose a method based on adaptive feature fusion and the autoregressive integrated moving average model for rolling bearing prediction. Firstly, features from the time domain, frequency domain, and entropy are extracted. A feature selection mechanism with minimum redundancy is constructed to screen the optimal sensitive feature set. Secondly, based on adaptive feature fusion, the optimal sensitive feature set is dynamically fused, and the spurious fluctuations of the health index are corrected using linear regression and the 3σ principle. Next, a bottom-up time series segmentation method is employed to divide the health status of the Improved Health Indicators. Finally, a remaining useful life prediction model based on the autoregressive integrated moving average model is established. This study demonstrates that the proposed method effectively identifies features that are most sensitive to degradation trends, accurately determines the initial fault moment of bearings, and achieves effective prediction of the remaining useful life of bearings.

Study on wear state of ultrasonic-assisted grinding of glass-ceramics with diamond tools based on DBO-BiLSTM

With the development of precision manufacturing technology, diamond tools play a vital role in industrial machining because of their high hardness and excellent wear resistance. However, tool wear can seriously affect machining accuracy and efficiency. In order to ensure the efficient and high-quality machining, it is of paramount importance to monitor the state of the tools used with the utmost accuracy. First, the fixed area of the diamond tool after machining was observed. The ratio of the number of abrasive particles falling off and the presence of scratches in the matrix were used as the evaluation indexes of the wear state of the diamond tool. The vibration signal was used as a monitoring signal to identify the wear state of the diamond tools. To obtain a feature set sensitive to the tool wear state, the vibration signal was analyzed in the time domain, frequency domain, and wavelet packet. The dimensionality of the sensitive feature set is reduced through the application of principal component analysis (PCA). The tool wear state was identified using the dung beetle optimizer bidirectional long short-term memory (DBO-BiLSTM) model and the bidirectional long short-term memory (BiLSTM) model. The results demonstrate that the DBO-BiLSTM has a higher accuracy in wear state identification, showing an improvement of 10.05% over the BiLSTM. Moreover, optimizing feature dimension reduction can enhance the model’s identification accuracy.

Time–Frequency Co-Movement of South African Asset Markets: Evidence from an MGARCH-ADCC Wavelet Analysis

The growing prominence of generating a well-diversified portfolio by holding securities from multi-asset markets has, over the years, drawn criticism. Various financial market events have caused asset markets to co-move, especially in emerging markets, which reduces portfolio diversification and enhances return losses. Consequently, this study examines the time–frequency co-movement of multi-asset classes in South Africa by using the Multivariate Generalized Autoregressive Conditional Heteroscedastic–Asymmetrical Dynamic Conditional Correlation (MGARCH-DCC) model, Maximal Overlap Discrete Wavelet Transformation (MODWT), and the Continuous Wavelet Transform (WTC) for the period 2007 to 2024. The findings demonstrate that the equity–bond, equity–property, equity–gold, bond–property, bond–gold, and property–gold markets depict asymmetrical time-varying correlations. Moreover, correlation in these asset pairs varies at investment periods (short-term, medium-term, and long-term), with historical events such as the 2007/2008 Global Financial Crisis (GFC) and the COVID-19 pandemic causing these asset pairs to co-move at different investment periods, which reduces diversification properties. The findings suggest that South African multi-asset markets co-move, affecting the diversification properties of holding multi-asset classes in a portfolio at different investment periods. Consequently, investors should consider the holding periods of each asset market pair in a portfolio as they dictate the level of portfolio diversification. Investors should also remember that there are lead–lag relationships and risk transmission between asset market pairs, enhancing portfolio volatility. This study assists investors in making more informed investment decisions and identifying optimal entry or exit points within South African multi-asset markets.

Analysis of the Response Relationship Between PWV and Meteorological Parameters Using Combined GNSS and ERA5 Data: A Case Study of Typhoon Lekima

Precipitable water vapor (PWV) is a crucial parameter of Earth’s atmosphere, with its spatial and temporal variations significantly impacting Earth’s energy balance and weather patterns. Particularly during meteorological disasters such as typhoons, PWV and other meteorological parameters exhibit dramatic changes. Studying the response relationship between PWV and typhoon events, alongside other meteorological parameters, is essential for meteorological and climate analysis and research. To this end, this paper proposes a method for analyzing the response relationship between PWV and meteorological parameters based on Wavelet Coherence (WTC). Specifically, PWV and relevant meteorological parameters were obtained using GNSS and ERA5 data, and the response relationships between PWV and different meteorological parameters before and after typhoon events were studied in time–frequency domain. Considering that many GNSS stations are not equipped with meteorological monitoring equipment, this study interpolated meteorological parameters based on ERA5 data for PWV retrieval. In the experimental section, the accuracy of ERA5 meteorological parameters and the accuracy of PWV retrieval based on ERA5 were first analyzed, verifying the feasibility and effectiveness of this approach. Subsequently, using typhoon Lekima as a case study, data from six GNSS stations affected by the typhoon were selected, and the corresponding PWV was retrieved using ERA5. The WTC method was then employed to analyze the response relationship between PWV and meteorological parameters before and after the typhoon’s arrival. The results show that the correlation characteristics between PWV and pressure can reveal different stages before and after the typhoon passes, while the local characteristics between PWV and temperature better reflect regional precipitation trends.

Lightweight Multi-Domain Fusion Model for Through-Wall Human Activity Recognition Using IR-UWB Radar

Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the cost of a substantial computational overhead. In response, this paper proposes a lightweight model named TG2-CAFNet. First, clutter suppression and time–frequency analysis are used to obtain range–time and micro-Doppler feature maps of human activities. Then, leveraging GhostV2 convolution, a lightweight feature extraction module, TG2, suitable for radar spectrograms is constructed. Using a parallel structure, the features of the two spectrograms are extracted separately. Finally, to further explore the correlation between the two spectrograms and enhance the feature representation capabilities, an improved nonlinear fusion method called coordinate attention fusion (CAF) is proposed based on attention feature fusion (AFF). This method extends the adaptive weighting fusion of AFF to a spatial distribution, effectively capturing the subtle spatial relationships between the two radar spectrograms. Experiments showed that the proposed method achieved a high degree of model lightweightness, while also achieving a recognition accuracy of 99.1%.

Agile manipulation of the time-frequency distribution of high-speed electromagnetic waves

Controlling the temporal evolution of an electromagnetic (EM) wave’s frequency components, the so-called time-frequency (TF) distribution, in a versatile and real-time fashion remains very challenging, especially at the high speeds (> GHz regime) required in contemporary communication, imaging, and sensing applications. We propose a general framework for manipulating the TF properties of high-speed EM waves. Specifically, the TF distribution is continuously mapped along the time domain through phase-only processing, enabling its user-defined manipulation via widely-available temporal modulation techniques. The time-mapping operations can then be inverted to reconstruct the TF-processed signal. Using off-the-shelf telecommunication components, we demonstrate arbitrary control of the TF distribution of EM waves over a full bandwidth approaching 100 GHz with nanosecond-scale programmability and MHz-level frequency resolution. We further demonstrate applications for mitigating rapidly changing frequency interference terms and the direct synthesis of fast waveforms with customized TF distributions. The reported method represents a significant advancement in TF processing of EM waves and it fulfills the stringent requirements for many modern and emerging applications.

Multiple-Access Time and Frequency Transfer over Fiber and Free-Space Link Based on Optical Frequency Comb

We have demonstrated a multiple-access transfer of time and frequency signal over a fiber and free-space link based on an optical frequency comb (OFC). With this transfer technique, two time–frequency signals were disseminated separately from a master site to two slave sites over a 3.9 km fiber and 100 m free-space link for 10,000 s. The timing fluctuations and instabilities of the time and frequency transfer were measured, estimated, and discussed. The experimental results show that the total root-mean-square (RMS) timing fluctuation of the transfer from site A to B is about 119 ps, with a fractional frequency instability on the order of 3.3 × 10−11 at 1 s and 2.8 × 10−14 at 2000 s. The RMS timing fluctuation of the transfer from site A to C is about 59.5 ps, with a fractional frequency instability on the order of 3.0 × 10−11 at 1 s and 2.6 × 10−14 at 2000 s. These results indicate that the multiple-access transfer technique proposed in this paper can provide important support for the application of a large-scale time–frequency synchronization network.

Maneuvering Trajectory Synthetic Aperture Radar Processing Based on the Decomposition of Transfer Functions in the Frequency Domain Using Average Blurred Edge Width Assessment

With the rapid development of synthetic aperture radar (SAR), delivery platforms are gradually becoming diversified and miniaturized. The SAR flight process is susceptible to external influences, resulting in unsatisfactory imaging results, so it is necessary to optimize imaging processing in combination with the SAR imaging quality assessment (IQA) index. Based on the principle of SAR imaging, this paper analyzes the impact of defocusing on imaging results caused by mismatched filters and draws on the assessment algorithm of motion blur, proposing a SAR IQA index based on average blurred edge width (ABEW) in the salient area. In addition, the idea of decomposing the transfer function in the frequency domain and fitting the matched filter with a polynomial is also proposed. The estimation of the flight trajectory is changed to a correction of the matched filter, avoiding the precise estimation of Doppler parameters and complex calculations during the time–frequency conversion process. The effectiveness of ABEW was verified by using SAR images of real scenes, and the results were highly consistent with the actual image quality. The imaging processing was tested using the echo signals generated by the errors introduced during the flight process, and more satisfactory imaging results were obtained by using ABEW with the filter for correction. The imaging process was tested using the echo signal generated by introducing errors during the flight, and the filter was corrected using ABEW as an index, obtaining a comparatively ideal imaging result.

Time–frequency domain machine learning for detection of epilepsy using wearable EEG sensor signals recorded during physical activities

Epilepsy is a neurological ailment in which there is a disturbance in the nerve cell activity of the brain, causing recurrent seizures. The electroencephalogram (EEG) signal is widely used as a diagnostic modality to detect epilepsy ailment. The automated detection of epilepsy using wearable EEG sensor data recorded during various physical activities is interesting for continuous monitoring of brain health. This paper proposes a time–frequency (TF) domain machine learning (ML) approach for the automated detection of epilepsy using wearable sensor-based EEG signals. The Gaussian window-based Stockwell transform (GWST) is employed to evaluate the TF matrix from the EEG signal. The features such as the L1-norm and the Shannon entropy are extracted from the TF matrix of the EEG signal. The ML and deep learning (DL) models are employed to detect epilepsy using the TF domain features of EEG signals. The publicly available database containing wearable sensor-based EEG signals recorded from the subjects while performing different physical activities is used to evaluate the performance of the proposed approach. The results show that the random forest (RF) classifier coupled with GWST domain features of EEG signals has obtained an overall accuracy value of 90.74% for detecting epilepsy with hold-out validation using the EEG signals from different physical activity cases. For the 10-fold cross-validation (CV) case, the GWST domain features of EEG signal and multi-layer long short-term memory (LSTM) classifier have produced the average accuracy value of 74.44%. For jogging, running, and idle sitting activities, the GWST-based TF domain entropy features coupled with the multi-layer LSTM model have obtained accuracy values of 82.72%, 82.41%, and 87.30%, respectively. The proposed approach has achieved higher classification accuracy than existing methods to detect epilepsy using wearable sensor-based EEG signals using a 10-fold CV strategy. The suggested approach is compared with existing methods to classify seizure and seizure-free classes using resting-state EEG signals.

Time-frequency analysis of speech signals using the Stockwell transform for the detection of upper respiratory tract infection

The acoustic properties of speech demonstrate modifications in the presence of different health states. Biomedical engineering has great promise for creating non-invasive diagnostic processes that use speech as a biomarker. The use of speech indications to screen for upper respiratory tract infections (URTIs), such as the common cold, may have potential advantages in terms of limiting transmission. In this study, we have employed the Stockwell transform -based time-frequency (TF) analysis of speech signals for URTI detection. The Stockwell transform is applied on speech signals to derive their TF representation. Using a TF matrix, the various statistics of magnitude and phase are calculated and used as features for classifying speech of healthy speakers and speakers with URTI. The URTIC database is employed for evaluating the proposed features. The utilization of an ensemble of support vector machines (SVM) is proposed as a classification approach to address the issue of class imbalance. The results show that the proposed method produces comparable outcomes to state-of-the-art approaches. The proposed features obtain 66.53% and 64.65% UARs on the development and test partitions of the URTIC database.

Local maximum synchrosqueezing adaptive transformation for cross-instantaneous frequencies analysis

Abstract To overcome the shortcomings of existing time-frequency (TF) analysis (TFA) methods in analyzing signals containing cross-instantaneous frequencies (IFs), this paper proposes an adaptive TFA technique combined with image processing methods based on local maximum synchrosqueezing transform. The core idea of the proposed algorithm is to localize the filtering of signals containing several different IF components using kernel functions containing several different directions, respectively, to achieve energy separation at the crossing frequencies. In turn, the local maximum synchrosqueezing transform is used to rearrange the TF energy to the true IF ridges of the signal to improve the TF energy concentration. Simulation data demonstrates that the proposed algorithm has higher energy aggregation and better noise immunity, especially for signals with cross-IFs. Applying the proposed method to animal acoustic and radar wave signals of pedestrians can accurately describe the differences in the frequency change patterns and the temporal distribution of energy in the signals, thereby providing a judgment basis for effectively identifying and classifying the signals.

A study on the time–frequency variation law of electromagnetic radiation signals during concrete cracking

A study on the time–frequency variation law of electromagnetic radiation signals during concrete cracking

Optimization of Turnaround Time and Insufficient Quantity Frequency for Molecular Testing on FFPE Material

Abstract Introduction/Objective With clinical decisions relying progressively more on molecular data extracted from formalin- fixed, paraffin-embedded (FFPE) tissue, it is pivotal to optimize the use of tissue for diagnostic and prognostic purposes. This optimization can be reached by identifying actionable instances that can decrease the turnaround time (TAT) and the rate of “quantity not sufficient” (QNS) results Methods/Case Report From January 2023 to December 2023, the total TAT from the time a molecular study is ordered to its delivery to the molecular lab at LL200A (Tomsich Laboratories), as well as the TAT from the time between the ordering pathologist circling the region of interest to its delivery to LL200A were broken down by subspecialty and site. In addition, the QNS results of Caris genomic profiling were evaluated considering subspecialty and site, amount of material present in the initial sample, and number of levels cut for other ancillary testing from the same material. Results (if a Case Study enter NA) 83% of subspecialties took longer than 48 hours to have material ordered for molecular studies delivered to LL200A. The median TAT for all specialties was 58 hours (average: 71.6 hours). When examining TAT from circling to LL200A, the median time was 19 hours (average: 28.4 hours). Regarding the QNS rate of the Caris assay, most QNS cases were gastrointestinal tract specimens (27%) or specimens obtained from either CT-guided core needle biopsies (30%) or endoscopic biopsies (32%). The longest dimension of QNS cases ranged from 7.5 cm to 0.1 cm (median: 1.3cm)—the average number of slides ordered before Caris ranged from 1 to 52 (median: 12). Conclusion The initial results pointed towards the time from circling to LL200A as one actionable item that could improve the TAT of molecular tests. Possible solutions include enacting a dedicated tumor circling service. In addition, to decrease the rate of Caris QNS results, we propose working together with proceduralists to increase biopsy material yield and to work with pathologists to preserve limited specimens for molecular studies.

Detection of Early Browning in Pears Based on Time–Frequency Images of Vibro-Acoustic Signals and Improved MobileNetV3

Detection of Early Browning in Pears Based on Time–Frequency Images of Vibro-Acoustic Signals and Improved MobileNetV3

Instability flow mechanism of ultra-high head pumped-storage units during turbine runaway process

Ultra-high head pumped-storage units (PSUs) have longer upstream and downstream pipelines and more complex internal flows than those of conventional head units. This complexity increases the difficulty of simulating transient flows in ultra-high head pump-turbines (PTs). When numerically studying the transition process of an ultra-high head PSU, determining accurate time-varying boundary conditions and analyzing complex pressure pulsations is crucial. In this study, one- and three-dimensional (1D-3D) coupled computational approach was employed to simulate the turbine runaway process (TRP). The short-time Fourier transform (STFT) method was used to analyze the time–frequency characteristics of the transient pressure, revealing the formation mechanism of each pressure pulsation component. In addition to the pressure pulsation components that occur during the TRP in conventional head PTs, a novel pressure pulsation component was revealed. This component, extended from low frequency to high frequency and had a higher amplitude at 25 ∼ 27 times the rotational frequency, was induced by transient flowrate pulsations. This could significantly exacerbate the pulsation of the axial force. The findings provide a reference for the subsequent research and development of ultra-high head PSUs.

Train Bearing Fault Diagnosis Based on Time–Frequency Signal Contrastive Domain Share CNN

Train Bearing Fault Diagnosis Based on Time–Frequency Signal Contrastive Domain Share CNN

High‐speed algorithm for fault detection and location in DC microgrids based on a novel time–frequency analysis

AbstractProtecting DC microgrids (DCMGs) from faults is critical due to the rapid current changes that occur in milliseconds. However, ensuring fast and accurate protection in DCMGs is more challenging than in AC systems. This study proposes a novel protection algorithm using traveling waves (TWs) for fault detection and localization. The high‐order synchrosqueezing transform (FSSTH) is applied to precisely identify TWs at the relay location. FSSTH offers a sharp time–frequency representation, enhancing the accuracy and speed of fault detection. This method can accurately detect transient phenomena like TWs in DCMGs, even with noise and variable fault resistance. By using the spectral envelope with FSSTH, ridges in time–frequency representations are extracted, improving fault diagnosis. The approach differentiates external from internal faults and recognizes fault direction by assessing TW polarity. Testing on two different DCMGs showed this algorithm's high efficiency and accuracy, with fault location errors ranging from 1 to 50 meters in low‐voltage and 13 to 64 meters in medium‐voltage DCMGs, even under challenging conditions like high resistance (up to 500 Ω) and low signal‐to‐noise ratio (5 dB). These results demonstrate the method's superior accuracy and robustness compared to existing techniques.

Signal modulation waveform recognition method based on STF-Net

Signal modulation waveform recognition is one of the key technologies in the field of spatial spectrum cognition and an important means to realize the monitoring and control of spectrum resources of low-orbit satellites. In view of the problems of large number of parameters and high computational complexity in the current modulation waveform recognition methods based on deep learning, a lightweight signal modulation waveform recognition method based on space-time fusion network (STF-Net) is proposed. The signal is preprocessed into dual-channel data in the form of time domain and frequency domain, and the signal spatial features are extracted and feature redundancy is reduced by convolutional neural network (CNN). Then, the long short-term memory network (LSTM) is used to extract the timing information and output the recognition result. The experimental results show that when the signal-to-noise ratio is greater than 0 dB, the average recognition accuracy of the modulation waveform of the proposed method reaches 91.79%; compared with the same method, the number of parameters of the proposed method is reduced 96%and the efficiency is increased by 2.7 times.

Full-scale test of disc cutter rotary cutting in TBM tunnelling: a case study of Mawan granite in Shenzhen, China

Understanding the distribution of cutting force provides a foundation for the design and optimization of cutterhead layout. The simulation of rotary cutting with a large cutting radius remains a challenge for laboratory tests. In this study, taking the driving condition of the Mawan Tunnel located in the Guangdong-Hong Kong-Macao Greater Bay Area as a case study, a full-scale rotary cutting platform with large cutting radius was used to investigate the rock-breaking effects. Rotary cutting with cutting depths of 1–8 mm and cutting radii of 200–1400 mm were carried out on the granite testing platform using a 19-in disc cutter. The normal force, rolling force, and side force during the cutting process were systematically monitored. Moreover, the time–frequency characteristics of the rock breaking process and the effects of cutting depth, cutting radius and stress state on the average and peak loads were investigated. The results show that the average normal force and rolling force increased with increasing cutting depth, while the side force was less affected by cutting depth through the Boltzman model analysis. The average normal force and rolling force were not significantly affected by the cutting radius, but the average side force decreased as a power function with increasing cutting radius, and showed a stable side force when the installation radius exceeded 1000 mm. The dominant frequency of the cutting force was 0–1.2 Hz, exhibiting notable low-frequency characteristics. Additionally, the ratio of peak to average (RPA) with respect to triaxial cutting force demonstrated an increase with the growth of cutting depth, while RPA in terms of side force exhibited a corresponding growth with the increasing cutting radius. In particular, the cutting force characteristics of the cutterhead obtained in this study can inform the layout of disc cutters and real-life TBM operations, thereby avoiding severe overloading of internal disc cutters.