Finite element method-based study for spinal vibration characteristics of the scoliosis and kyphosis lumbar spine to whole-body vibration under a compressive follower preload

PURPOSE

To analyze the dynamic response of the lumbosacral vertebrae structure of a scoliosis spine and a kyphosis spine under whole-body vibration.

METHODS

Typical Lenke4 (kyphosis) and Lenke3 (scoliosis) spinal columns were used as research objects. A finite element model of the lumbosacral vertebrae segment was established and validated based on CT scanning images. Modal, harmonic response, and transient dynamic analyses were performed on the lumbar-sacral scoliosis model using the finite element software abaqus.

RESULTS

The first four resonance frequencies of kyphosis spine extracted from modal analysis were 0.86, 1.45, 8.51, and 55.71 Hz. The first four resonance frequencies of scoliosis spine extracted from modal analysis were 0.76, 1.45, 10.51, and 63.82 Hz. The scoliosis spine had the maximum resonance amplitude in the transverse direction, while the kyphosis spine had the maximum resonance amplitude in the anteroposterior direction. The dynamic response in transient analysis exhibited periodic response over time at all levels.

CONCLUSION

The scoliosis and kyphosis deformity of the spine significantly complicates the vibration response in the scoliosis and kyphosis areas at the top of the spine. Scoliosis and kyphosis patients are more likely to experience vibrational spinal diseases than healthy people. Besides, applying vertical cyclic loads on a malformed spine may cause further rotation of scoliosis and kyphosis deformities.

Federated Learning Backdoor Attack Based on Frequency Domain Injection

Federated learning (FL) is a distributed machine learning framework that enables scattered participants to collaboratively train machine learning models without revealing information to other participants. Due to its distributed nature, FL is susceptible to being manipulated by malicious clients. These malicious clients can launch backdoor attacks by contaminating local data or tampering with local model gradients, thereby damaging the global model. However, existing backdoor attacks in distributed scenarios have several vulnerabilities. For example, (1) the triggers in distributed backdoor attacks are mostly visible and easily perceivable by humans; (2) these triggers are mostly applied in the spatial domain, inevitably corrupting the semantic information of the contaminated pixels. To address these issues, this paper introduces a frequency-domain injection-based backdoor attack in FL. Specifically, by performing a Fourier transform, the trigger and the clean image are linearly mixed in the frequency domain, injecting the low-frequency information of the trigger into the clean image while preserving its semantic information. Experiments on multiple image classification datasets demonstrate that the attack method proposed in this paper is stealthier and more effective in FL scenarios compared to existing attack methods.

An association between heart rate variability and incident heart failure in an elderly cohort

BACKGROUND

Early identification of individuals at risk of developing heart failure (HF) may improve poor prognosis. A dominant sympathetic activity is common in HF and associated with worse outcomes; however, less is known about the autonomic balance before HF.

HYPOTHESIS

A low frequency/high frequency (L-F/H-F) ratio, index of heart rate variability, and marker of the autonomic balance predict the development of HF and may improve the performance of the HF prediction model when added to traditional cardiovascular (CV) risk factors.

METHODS

Individuals in the PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (n = 1016, all aged 70 years) were included. Exclusion criteria were prevalent HF, electrocardiographic QRS duration ≥130 millisecond, major arrhythmias, or conduction blocks at baseline. The association between the L-F/H-F ratio and incident HF was assessed using Cox proportional hazard analysis. The C-statistic evaluated whether adding the L-F/H-F-ratio to traditional CV risk factors improved the discrimination of incident HF.

RESULTS

HF developed in 107/836 study participants during 15 years of follow-up. A nonlinear, inverse association between the L-F/H-F ratio and incident HF was mainly driven by an L-F/H-F ratio of <30. The association curve was flat for higher values (hazard ratio, HR for the total curve = 0.78 [95% confidence interval, CI: 0.69-0.88, p < .001]; HR = 2 for L-F/H-F ratio = 10). The traditional prediction model improved by 3.3% (p < .03) when the L-F/H-F ratio was added.

CONCLUSIONS

An L-F/H-F ratio of <30 was related to incident HF and improved HF prediction when added to traditional CV risk factors.

Modifications of long-term heart rate variability produced in an experimental model of diet-induced metabolic syndrome

Metabolic syndrome (MetS) has been linked to a higher prevalence of cardiac arrhythmias, the most frequent being atrial fibrillation, but the mechanisms are not well understood. One possible underlying mechanism may be an abnormal modulation of autonomic nervous system activity, which can be quantified by analysing heart rate variability (HRV). Our aim was to investigate the modifications of long-term HRV in an experimental model of diet-induced MetS to identify the early changes in HRV and the link between autonomic dysregulation and MetS components. NZW rabbits were randomly assigned to control (n = 10) or MetS (n = 10) groups, fed 28 weeks with high-fat, high-sucrose diet. 24-hour recordings were used to analyse HRV at week 28 using time-domain, frequency-domain and nonlinear analyses. Time-domain analysis showed a decrease in RR interval and triangular index (Ti). In the frequency domain, we found a decrease in the low frequency band. Nonlinear analyses showed a decrease in DFA-α1 and DFA-α2 (detrended fluctuations analysis) and maximum multiscale entropy. The strongest association between HRV parameters and markers of MetS was found between Ti and mean arterial pressure, and Ti and left atrial diameter, which could point towards the initial changes induced by the autonomic imbalance in MetS.

Heart rate variability and haemodynamic function in individuals with hypertrophic cardiomyopathy

OBJECTIVES

Heart rate variability (HRV) is a measure of cardiac autonomic function. This study: (1) evaluated the differences in HRV and haemodynamic function between individuals with hypertrophic cardiomyopathy (HCM) and healthy controls, and (2) determined the relationship between HRV and haemodynamic variables in individuals with HCM.

METHODS

Twenty-eight individuals with HCM (n = 7, females; age 54 ± 15 years; body mass index: 29 ± 5 kg/m2 ) and 28 matched healthy individuals (n = 7 females; age 54 ± 16 years; body mass index: 29 ± 5 kg/m2 ) completed 5-min HRV and haemodynamic measurements under resting (supine) conditions using bioimpedance technology. Frequency domain HRV measures (absolute and normalized low-frequency power (LF), high-frequency power (HF) and LF/HF ratio) and RR interval were recorded.

RESULTS

Individuals with HCM demonstrated higher vagal activity (i.e., absolute unit of HF power (7.40 ± 2.50 vs. 6.03 ± 1.35 ms2 , p = 0.01) but lower RR interval (914 ± 178 vs. 1014 ± 168 ms, p = 0.03) compared to controls. Stroke volume (SV) index and cardiac index were lower in HCM compared with healthy individuals (SV, 33 ± 9 vs. 43 ± 7 ml‎/beat‎/m², p < 0.01; cardiac index,2.33 ± 0.42 vs. 3.57 ± 0.82 L/min/m2 , p < 0.01), but total peripheral resistance (TPR) was higher in HCM (3468 ± 1027 vs. 2953 ± 1050 dyn·s·m2 cm-5 , p = 0.03). HF power was significantly related to SV (r = -0.46, p < 0.01) and TPR (r = 0.28, p < 0.05) in HCM.

CONCLUSIONS

Short-term frequency domain indices of HRV provide a feasible approach to assess autonomic function in individuals with HCM. Vagal activity, represented by HF power, is increased, and associated with peripheral resistance in individuals with HCM.

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory

Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

Altered Heart Rate Variability During Rest in Schizophrenia: A State Marker

BACKGROUND

Autonomic nervous system (ANS) imbalance has been reported in a number of psychiatric disorders such as depression, schizophrenia, panic disorder, etc. Autonomic dysfunction in schizophrenia has been associated with the symptoms and manifestation of psychosis. Heart rate variability (HRV) as a tool has been widely used to assess ANS activity and the effect of disease on the sympathovagal balance. Therefore, in the present study, HRV derived from electrocardiogram (ECG) lead II at rest was investigated in order to understand the changes in frequency domain measures in patients with schizophrenia and their first-degree relatives compared to healthy controls.

METHODS

Twenty-five patients with schizophrenia, 24 first-degree relatives of patients, and 24 healthy controls (Diagnostic and Statistical Manual of Mental Disorders (DSM)-5; 18-45 years) were included in the study. HRV of the subjects was measured after five minutes of rest. ECG lead II was recorded for five minutes and HRV was analysed in the frequency domain: low frequency (LF), high frequency (HF), total power, and LF/HF ratio. HRV parameters and heart rate were statistically analysed for group comparisons using general linear model multivariate analysis.

RESULTS

Patients had significantly higher minimum heart rate and lower HF (normalized units (nu)) compared to their first-degree relatives. A trend was observed in HF (nu) with the lowest in patients followed by healthy controls and first-degree relatives and LF/HF ratio was the highest in patients followed by healthy controls and first-degree relatives, although not statistically significant. No significant difference was found between first-degree relatives and healthy controls.

CONCLUSION

The alteration of HRV in schizophrenia could be attributed to reduction in vagal tone and sympathetic dominance, which in turn could serve as state markers of schizophrenia.

Real-time dense small object detection algorithm based on multi-modal tea shoots

Introduction

The difficulties in tea shoot recognition are that the recognition is affected by lighting conditions, it is challenging to segment images with similar backgrounds to the shoot color, and the occlusion and overlap between leaves.

Methods

To solve the problem of low accuracy of dense small object detection of tea shoots, this paper proposes a real-time dense small object detection algorithm based on multimodal optimization. First, RGB, depth, and infrared images are collected form a multimodal image set, and a complete shoot object labeling is performed. Then, the YOLOv5 model is improved and applied to dense and tiny tea shoot detection. Secondly, based on the improved YOLOv5 model, this paper designs two data layer-based multimodal image fusion methods and a feature layerbased multimodal image fusion method; meanwhile, a cross-modal fusion module (FFA) based on frequency domain and attention mechanisms is designed for the feature layer fusion method to adaptively align and focus critical regions in intra- and inter-modal channel and frequency domain dimensions. Finally, an objective-based scale matching method is developed to further improve the detection performance of small dense objects in natural environments with the assistance of transfer learning techniques.

Results and discussion

The experimental results indicate that the improved YOLOv5 model increases the mAP50 value by 1.7% compared to the benchmark model with fewer parameters and less computational effort. Compared with the single modality, the multimodal image fusion method increases the mAP50 value in all cases, with the method introducing the FFA module obtaining the highest mAP50 value of 0.827. After the pre-training strategy is used after scale matching, the mAP values can be improved by 1% and 1.4% on the two datasets. The research idea of multimodal optimization in this paper can provide a basis and technical support for dense small object detection.

Frequency Domain Analysis of Endocardial Electrograms for Detection of Nontransmural Myocardial Fibrosis in Nonischemic Cardiomyopathy

BACKGROUND

Voltage mapping in nonischemic cardiomyopathy can fail to identify midmyocardial substrate for ventricular arrhythmias, an important cause of ablation failure.

OBJECTIVES

The aim of this study was to assess whether frequency domain analysis of endocardial left ventricular electrograms (EGMs) can better predict the presence of midmyocardial fibrosis (MMF) compared with voltage amplitude.

METHODS

Nonischemic cardiomyopathy patients undergoing ventricular tachycardia ablation with registered preprocedural cardiac computed tomography and late iodine enhancement were included. Presence of fibrosis at each EGM site was assessed. Bipolar and unipolar EGMs were transformed to the frequency domain using multitaper spectral analysis. Singular value decomposition of the EGM frequency spectrum was used within a supervised machine learning process to select features to predict the presence of MMF and compare against predictions using voltage amplitude.

RESULTS

Thirteen patients were included (median age 57 years [IQR: 28-73 years], median ejection fraction 40% [IQR: 15%-57%]). A total of 6,015 EGM pairs were processed: 2,459 EGM pairs in MMF areas and 3,556 EGM pairs in non-MMF areas. Supervised classifiers were trained with stratified k-fold cross-validation within patients. The distribution of mean area under the curve metrics using frequency features, f, was significantly greater than voltage feature area under the curve metrics, v, (mean f = 0.841 [95% CI: 0.789-0.884] vs mean v = 0.591 [95% CI: 0.530-0.658]; P < 0.001), indicating that frequency-trained classifiers better predicted the presence of MMF.

CONCLUSIONS

These data indicate the promising discriminatory value of endocardial EGM frequency content in the assessment of concealed myocardial substrate. Further studies are needed to investigate the importance of the specific frequency features identified.

Heart Rate Variability as a Translational Dynamic Biomarker of Altered Autonomic Function in Health and Psychiatric Disease

The autonomic nervous system (ANS) is responsible for the precise regulation of tissue functions and organs and, thus, is crucial for optimal stress reactivity, adaptive responses and health in basic and challenged states (survival). The fine-tuning of central ANS activity relies on the internal central autonomic regulation system of the central autonomic network (CAN), while the peripheral activity relies mainly on the two main and interdependent peripheral ANS tracts, the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). In disease, autonomic imbalance is associated with decreased dynamic adaptability and increased morbidity and mortality. Acute or prolonged autonomic dysregulation, as observed in stress-related disorders, affects CAN core centers, thereby altering downstream peripheral ANS function. One of the best established and most widely used non-invasive methods for the quantitative assessment of ANS activity is the computerized analysis of heart rate variability (HRV). HRV, which is determined by different methods from those used to determine the fluctuation of instantaneous heart rate (HR), has been used in many studies as a powerful index of autonomic (re)activity and an indicator of cardiac risk and ageing. Psychiatric patients regularly show altered autonomic function with increased HR, reduced HRV and blunted diurnal/circadian changes compared to the healthy state. The aim of this article is to provide basic knowledge on ANS function and (re)activity assessment and, thus, to support a much broader use of HRV as a valid, transdiagnostic and fully translational dynamic biomarker of stress system sensitivity and vulnerability to stress-related disorders in neuroscience research and clinical psychiatric practice. In particular, we review the functional levels of central and peripheral ANS control, the main neurobiophysiologic theoretical models (e.g., polyvagal theory, neurovisceral integration model), the precise autonomic influence on cardiac function and the definition and main aspects of HRV and its different measures (i.e., time, frequency and nonlinear domains). We also provide recommendations for the proper use of electrocardiogram recordings for HRV assessment in clinical and research settings and highlight pathophysiological, clinical and research implications for a better functional understanding of the neural and molecular mechanisms underlying healthy and malfunctioning brain-heart interactions in individual stress reactivity and psychiatric disorders.

Imaging the rotational mobility of carbon dot-gold nanoparticle conjugates using frequency domain wide-field time-resolved fluorescence anisotropy

Significance

Wide-field measurements of time-resolved fluorescence anisotropy (TR-FA) provide pixel-by-pixel information about the rotational mobility of fluorophores, reflecting changes in the local microviscosity and other factors influencing the fluorophore's diffusional motion. These features offer promising potential in many research fields, including cellular imaging and biochemical sensing, as demonstrated by previous works. Nevertheless, θ imaging is still rarely investigated in general and in carbon dots (CDs) in particular.

Aim

To extend existing frequency domain (FD) fluorescence lifetime (FLT) imaging microscopy (FLIM) to FD TR-FA imaging (TR-FAIM), which produces visual maps of the FLT and θ, together with the steady-state images of fluorescence intensity (FI) and FA (r).

Approach

The proof of concept of the combined FD FLIM/ FD TR-FAIM was validated on seven fluorescein solutions with increasing viscosities and was applied for comprehensive study of two types of CD-gold nano conjugates.

Results

The FLT of fluorescein samples was found to decrease from 4.01±0.01 to 3.56±0.02  ns, whereas both r and θ were significantly increased from 0.053±0.012 to 0.252±0.003 and 0.15±0.05 to 11.25±1.87  ns, respectively. In addition, the attachment of gold to the two CDs resulted in an increase in the FI due to metal-enhanced fluorescence. Moreover, it resulted in an increase of r from 0.100±0.011 to 0.150±0.013 and θ from 0.98±0.13 to 1.65±0.20  ns for the first CDs and from 0.280±0.008 to 0.310±0.004 and 5.55±1.08 to 7.95±0.97  ns for the second CDs. These trends are due to the size increase of the CDs-gold compared to CDs alone. The FLT presented relatively modest changes in CDs.

Conclusions

Through the combined FD FLIM/ FD TR-FAIM, a large variety of information can be probed (FI, FLT, r, and θ). Nevertheless, θ was the most beneficial, either by probing the spatial changes in viscosity or by evident variations in the peak and full width half maximum.

Dual-Band Infrared Scheimpflug Lidar Reveals Insect Activity in a Tropical Cloud Forest

We describe an entomological dual-band 808 and 980 nm lidar system which has been implemented in a tropical cloud forest (Ecuador). The system was successfully tested at a sample rate of 5 kHz in a cloud forest during challenging foggy conditions (extinction coefficients up to 20 km^-1). At times, the backscattered signal could be retrieved from a distance of 2.929 km. We present insect and bat observations up to 200 m during a single night with an emphasis on fog aspects, potentials, and benefits of such dual-band systems. We demonstrate that the modulation contrast between insects and fog is high in the frequency domain compared to intensity in the time domain, thus allowing for better identification and quantification in misty forests. Oscillatory lidar extinction effects are shown in this work for the first time, caused by the combination of dense fog and large moths partially obstructing the beam. We demonstrate here an interesting case of a moth where left- and right-wing movements induced oscillations in both intensity and pixel spread. In addition, we were able to identify the dorsal and ventral sides of the wings by estimating the corresponding melanization with the dual-band lidar. We demonstrate that the wing beat trajectories in the dual-band parameter space are complementary rather than covarying or redundant, thus a dual-band entomological lidar approach to biodiversity studies is feasible in situ and endows species specificity differentiation. Future improvements are discussed. The introduction of these methodologies opens the door to a wealth of possible experiments to monitor, understand, and safeguard the biological resources of one of the most biodiverse countries on Earth.

Evaluating the Importance of Monetary Policy Uncertainty: The Long- and Short-Term Effects and Responses

Monetary policy changes have an irreplaceable impact on economic activity. Considering the close linkage among economic policies, we employ a bi-directional Granger causality test to investigate the potential linkages between monetary policy uncertainty (MPU) and other categorical economic policy uncertainty (CEPU) in the time and frequency domains. We consider all news-based U.S. categorical economic policy uncertainty indices (CEPU). All monthly CEPU indicators, covering January 1986 to January 2022, can be obtained from the website of Economic Policy Uncertainty. On an average, causality running from each CEPU to MPU is not apparent, while MPU can significantly affect six policy-related uncertainties: taxes, government spending, health care, national security, entitlement programs and regulation. A further frequency-domain study showed the dynamic changes in the relationship between them. For instance, we capture mid- and long-run causality running from tax uncertainty to MPU, while MPU has an impact on taxes in the medium run. Our findings provide policymakers with a better understanding of the nexus between MPU and other CEPU for formulating appropriate economic policies. Particularly, if a sectional government considers the long- and short-term effects of different policies when formulating strategies, risk transmission may be curbed to some extent.

Transfer function approach to understanding periodic forcing of signal transduction networks

Signal transduction networks are responsible for transferring information from the extracellular to the intracellular environment. This process facilitates biochemical changes in the cell which, in turn, dictates physiological changes such as differentiation and growth. Information can be stored in a variety of physical modes, including signal frequency. Different signal frequencies impart different information onto the cell surface, driving different physiological outcomes via signal transduction networks. Frequency-dependent cell behaviour is well documented in experimental literature. Understanding this frequency dependence allows us to control cells, opening avenues into therapeutics and bridges between theory and experiment. The transfer function provides one approach to understanding this. In this tutorial, we show how to obtain the transfer function of an arbitrary signal transduction network and use it to explain how the network responds to periodic forcing. We discuss advantages and limitations to this method.

Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data

SIGNIFICANCE

The machine learning (ML) approach plays a critical role in assessing biomedical imaging processes especially optical imaging (OI) including segmentation, classification, and reconstruction, intending to achieve higher accuracy efficiently.

AIM

This research aims to develop an end-to-end deep learning framework for diffuse optical imaging (DOI) with multiple datasets to detect breast cancer and reconstruct its optical properties in the early stages.

APPROACH

The proposed Periodic-net is a nondestructive deep learning (DL) algorithm for the reconstruction and evaluation of inhomogeneities in an inverse model with high accuracy, while boundary measurements are calculated by solving a forward problem with sources/detectors arranged uniformly around a circular domain in various combinations, including 16 × 15 , 20 × 19 , and 36 × 35 boundary measurement setups.

RESULTS

The results of image reconstruction on numerical and phantom datasets demonstrate that the proposed network provides higher-quality images with a greater amount of small details, superior immunity to noise, and sharper edges with a reduction in image artifacts than other state-of-the-art competitors.

CONCLUSIONS

The network is highly effective at the simultaneous reconstruction of optical properties, i.e., absorption and reduced scattering coefficients, by optimizing the imaging time without degrading inclusions localization and image quality.

Optimization of Dominant Frequency and Bandwidth Analysis in Multi-Frequency 3D GPR Signals to Identify Contaminated Areas

Ground-penetrating radar (GPR) has been widely used in investigations of contaminated areas because of its sensitivity to variations associated with the nature of pore fluids. However, most of the studies were usually based on the visual interpretation of radargrams or on a time domain amplitude analysis. In this work, we propose a methodology that consists of analyzing the spectral content of the signal recorded in multi-frequency 3D GPR profiles. A remarkable advantage of this type of antenna is its step-frequency system, which provides a much wider emission spectrum than the one corresponding to conventional single-frequency antennas. From the data in the frequency domain, the dominant frequency and bandwidth were calculated as parameters whose variation could be related to the presence of light non-aqueous phase liquid (LNAPL) in the subsurface. By analyzing the variations of these two parameters simultaneously, we were able to delimit the contaminated zones in a case study, associating them with a significant shift of the frequency spectrum with respect to the average of the study area. Finally, as a validation method of the proposed methodology, the results of the frequency analysis were compared with resistivity data obtained with an electromagnetic conductivity meter, showing a very good correlation between the results.

Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity

Bio-signals are being increasingly used for the assessment of pathophysiological conditions including pain, stress, fatigue, and anxiety. For some approaches, a single signal is not sufficient to provide a comprehensive diagnosis; however, there is a growing consensus that multimodal approaches allow higher sensitivity and specificity. For instance, in visceral pain subjects, the autonomic activation can be inferred using electrodermal activity (EDA) and heart rate variability derived from the electrocardiogram (ECG), but including the muscle activation detected from the surface electromyogram (sEMG) can better differentiate the disease that causes the pain. There is no wearable device commercially capable of collecting these three signals simultaneously. This paper presents the validation of a novel multimodal low profile wearable data acquisition device for the simultaneous collection of EDA, ECG, and sEMG signals. The device was validated by comparing its performance to laboratory-scale reference devices. N = 20 healthy subjects were recruited to participate in a four-stage study that exposed them to an array of cognitive, orthostatic, and muscular stimuli, ensuring the device is sensitive to a range of stressors. Time and frequency domain analyses for all three signals showed significant similarities between our device and the reference devices. Correlation of sEMG metrics ranged from 0.81 to 0.95 and EDA/ECG metrics showed few instances of significant difference in trends between our device and the references. With only minor observed differences, we demonstrated the ability of our device to collect EDA, sEMG, and ECG signals. This device will enable future practical and impactful advances in the field of chronic pain and stress measurement and can confidently be implemented in related studies.

Learning Moiré Pattern Elimination in Both Frequency and Spatial Domains for Image Demoiréing

Recently, with the rapid development of mobile sensing technology, capturing scene information by mobile sensing devices in the form of images or videos has become a prevalent recording method. However, the moiré pattern phenomenon may occur when the scene contains digital screens or regular strips, which greatly degrade the visual performance and image quality. In this paper, considering the complexity and diversity of moiré patterns, we propose a novel end-to-end image demoiré method, which can learn moiré pattern elimination in both the frequency and spatial domains. To be specific, in the frequency domain, considering the signal energy of moiré pattern is widely distributed in the frequency, we introduce a wavelet transform to decompose the multi-scale image features, which can help the model identify the moiré features more precisely to suppress them effectively. On the other hand, we also design a spatial domain demoiré block (SDDB). The SDDB module can extract moiré features from the mixed features, then subtract them to obtain clean image features. The combination of the frequency domain and the spatial domain enhances the model's ability in terms of moiré feature recognition and elimination. Finally, extensive experiments demonstrate the superior performance of our proposed method to other state-of-the-art methods. The Grad-CAM results in our ablation study fully indicate the effectiveness of the two proposed blocks in our method.

Few-Shot Learning for Plant-Disease Recognition in the Frequency Domain

Few-shot learning (FSL) is suitable for plant-disease recognition due to the shortage of data. However, the limitations of feature representation and the demanding generalization requirements are still pressing issues that need to be addressed. The recent studies reveal that the frequency representation contains rich patterns for image understanding. Given that most existing studies based on image classification have been conducted in the spatial domain, we introduce frequency representation into the FSL paradigm for plant-disease recognition. A discrete cosine transform module is designed for converting RGB color images to the frequency domain, and a learning-based frequency selection method is proposed to select informative frequencies. As a post-processing of feature vectors, a Gaussian-like calibration module is proposed to improve the generalization by aligning a skewed distribution with a Gaussian-like distribution. The two modules can be independent components ported to other networks. Extensive experiments are carried out to explore the configurations of the two modules. Our results show that the performance is much better in the frequency domain than in the spatial domain, and the Gaussian-like calibrator further improves the performance. The disease identification of the same plant and the cross-domain problem, which are critical to bring FSL to agricultural industry, are the research directions in the future.

Acoustic Denoising Using Artificial Intelligence for Wood-Boring Pests Semanotus bifasciatus Larvae Early Monitoring

Acoustic detection technology is a new method for early monitoring of wood-boring pests, and the effective denoising methods are the premise of acoustic detection in forests. This paper used sensors to record Semanotus bifasciatus larval feeding sounds and various environmental noises, and two kinds of sounds were mixed to obtain the noisy feeding sounds with controllable noise intensity. Then, the time domain denoising models and frequency domain denoising models were designed, and the denoising effects were compared using the metrics of a signal-to-noise ratio (SNR), a segment signal-noise ratio (SegSNR), and log spectral distance (LSD). In the experiments, the average SNR increment could achieve 17.53 dB and 11.10 dB using the in the test data using the time domain features and frequency domain features, respectively. The average SegSNR increment achieved 18.59 dB and 12.04 dB, respectively, and the average LSD between pure feeding sounds and denoised feeding sounds were 0.85 dB and 0.84 dB, respectively. The experimental results demonstrated that the denoising models based on artificial intelligence were effective methods for S. bifasciatus larval feeding sounds, and the overall denoising effect was more significant, especially at low SNRs. In view of that, the denoising models using time domain features were more suitable for the forest area and quarantine environment with complex noise types and large noise interference.

Frequency-Domain sEMG Classification Using a Single Sensor

Working towards the development of robust motion recognition systems for assistive technology control, the widespread approach has been to use a plethora of, often times, multi-modal sensors. In this paper, we develop single-sensor motion recognition systems. Utilising the peripheral nature of surface electromyography (sEMG) data acquisition, we optimise the information extracted from sEMG sensors. This allows the reduction in sEMG sensors or provision of contingencies in a system with redundancies. In particular, we process the sEMG readings captured at the trapezius descendens and platysma muscles. We demonstrate that sEMG readings captured at one muscle contain distinct information on movements or contractions of other agonists. We used the trapezius and platysma muscle sEMG data captured in able-bodied participants and participants with tetraplegia to classify shoulder movements and platysma contractions using white-box supervised learning algorithms. Using the trapezius sensor, shoulder raise is classified with an accuracy of 99%. Implementing subject-specific multi-class classification, shoulder raise, shoulder forward and shoulder backward are classified with a 94% accuracy amongst object raise and shoulder raise-and-hold data in able bodied adults. A three-way classification of the platysma sensor data captured with participants with tetraplegia achieves a 95% accuracy on platysma contraction and shoulder raise detection.

Impaired phase synchronization of motor-evoked potentials reflects the degree of motor dysfunction in the lesioned human brain

The functional corticospinal integrity (CSI) can be indexed by motor-evoked potentials (MEP) following transcranial magnetic stimulation of the motor cortex. Glial brain tumors in motor-eloquent areas are frequently disturbing CSI resulting in different degrees of motor dysfunction. However, this is unreliably mirrored by MEP characteristics. In 59 consecutive patients with diffuse glial tumors and 21 healthy controls (CTRL), we investigated the conventional MEP features, that is, resting motor threshold (RMT), amplitudes and latencies. In addition, frequency-domain MEP features were analyzed to estimate the event-related spectral perturbation (ERSP), and the induced phase synchronization by intertrial coherence (ITC). The clinical motor status was captured including the Medical Research Council Scale (MRCS), the Grooved Pegboard Test (GPT), and the intake of antiepileptic drugs (AED). Motor function was classified according to MRCS and GPT as no motor deficit (NMD), fine motor deficits (FMD) and gross motor deficits (GMD). CSI was assessed by diffusion-tensor imaging (DTI). Motor competent subjects (CTRL and NMD) had similar ERSP and ITC values. The presence of a motor deficit (FMD and GMD) was associated with an impairment of high-frequency ITC (150-300 Hz). GMD and damage to the CSI demonstrated an additional reduction of high-frequency ERSP (150-300 Hz). GABAergic AED increased ERSP but not ITC. Notably, groups were indistinguishable based on conventional MEP features. Estimating MEP phase synchronization provides information about the corticospinal transmission after transcranial magnetic stimulation and reflects the degree of motor impairment that is not captured by conventional measures.

Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction

Sparse-view CT reconstruction is a fundamental task in computed tomography to overcome undesired artifacts and recover the details of textual structure in degraded CT images. Recently, many deep learning-based networks have achieved desirable performances compared to iterative reconstruction algorithms. However, the performance of these methods may severely deteriorate when the degradation strength of the test image is not consistent with that of the training dataset. In addition, these methods do not pay enough attention to the characteristics of different degradation levels, so solely extending the training dataset with multiple degraded images is also not effective. Although training plentiful models in terms of each degradation level can mitigate this problem, extensive parameter storage is involved. Accordingly, in this paper, we focused on sparse-view CT reconstruction for multiple degradation levels. We propose a single degradation-aware deep learning framework to predict clear CT images by understanding the disparity of degradation in both the frequency domain and image domain. The dual-domain procedure can perform particular operations at different degradation levels in frequency component recovery and spatial details reconstruction. The peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and visual results demonstrate that our method outperformed the classical deep learning-based reconstruction methods in terms of effectiveness and scalability.

A Highly Sensitive and Miniature Optical Fiber Sensor for Electromagnetic Pulse Fields

The detection of an electromagnetic pulse (EMP) field is of great significance in determining the field environment of tested equipment in small spaces. Finger-shaped miniature optical fiber sensors for electromagnetic pulse field measurement were designed. The antenna of a weak field sensor was integrated with a shielding shell, and the wire welded at the direct electro-optic converting circuit connected to an optical fiber through special structure and circuit design was taken as the antenna of a strong field sensor. Measurements in the time domain and frequency domain had been carried out for the two sensors. Experiment results demonstrate that the weak field sensor and the strong field sensor have flat responses from 100 kHz to 1 GHz with a variation of 2.3 dB and 2.9 dB, respectively, and the EMP waveform detected by the sensors agrees well with the applied standard square wave. Moreover, the strong field sensor exhibits linear responses from 645 V/m to 83 kV/m. The resolution of the weak field sensor is as low as 13 V/m. The result indicated that the designed sensors had good performance.

Predicting Post-operative Blood Inflammatory Biomarkers Using Pre-operative Heart Rate Variability in Patients With Cervical Cancer

Blood inflammatory biomarkers, including the neutrophil-to-lymphocyte ratio (NLR), the lymphocyte-to-monocyte ratio (LMR), and the platelet-to-lymphocyte ratio (PLR), play a significant role in determining the prognosis of patients with cervical cancer (CC). Currently, no methods are available to predict these indexes pre-operatively. Cardiac autonomic function is determined based on the heart rate variability (HRV), which is also associated with a progressive inflammatory response and cancer. Thus, the main aim of this study was to evaluate the feasibility of using pre-operative HRV parameters in CC patients to predict post-operative blood inflammation biomarkers as a means of determining prognosis. Between 2020 and 2021, 56 patients who were diagnosed with CC and then underwent hysterectomy surgery at the Department of Gynecologic Oncology, First Affiliated Hospital, Bengbu Medical College were enrolled in this study. Five-minute electrocardiogram data were collected 1 day before the operation for analysis of HRV parameters, including frequency domain parameters (LF, HF, and LF/HF) and Poincaré plot parameters (SD1, SD2, and SD2/SD1). Venous blood was collected 2 days post-operatively and inflammatory biomarkers were evaluated, with the NLR, LMR, and PLR determined. Pre-operative SD2 was significantly associated with post-operative PLR, with each 1-unit increase in SD2 decreasing the PLR value by 2.4 ± 0.9 (P < 0.05). Besides, LF/HF was significantly correlated with NLR, with each 1-unit increase in LF/HF increasing the NLR value by 1.1 ± 0.5 (P < 0.05). This association was independent of patient age and body mass index. These results suggest that the pre-operative autonomic nervous system plays a role in the regulation of post-operative cancer inflammation and that pre-operative HRV parameters can potentially predict post-operative inflammation and facilitate clinical treatment decisions.

Improving dermal level images from reflectance confocal microscopy using wavelet-based transformations and adaptive histogram equalization

OBJECTIVES

Reflectance confocal microscopy (RCM) generates scalar image data from serial depths in the skin, allowing in vivo examination of cellular features. The maximum imaging depth of RCM is approximately 250 µm, to the papillary dermis, or upper reticular dermis. Frequently, important diagnostic features are present in the dermis, hence improved visualization of deeper levels is advantageous.

METHODS

Low contrast and noise in dermal images were improved by employing a combination of wavelet-based transformations and contrast-limited adaptive histogram equalization.

RESULTS

Preserved details, noise reduction, increased contrast, and feature enhancement were observed in the resulting processed images.

CONCLUSIONS

Complex and combined wavelet-based enhancement approaches for dermal level images yielded reconstructions of higher quality than less sophisticated histogram-based strategies. Image optimization may improve the diagnostic accuracy of RCM, especially for entities with dermal findings.

Low-temperature high-frequency dynamic magnetic susceptibility of classical spin-ice Dy2Ti2O7

Radio-frequency (14.6 MHz) AC magnetic susceptibility,χAC', of Dy₂Ti₂O₇was measured using self-oscillating tunnel-diode resonator. Measurements were made with the excitation AC field parallel to the superimposed DC magnetic field up to 5 T in a wide temperature range from 50 mK to 100 K. At 14.6 MHz, a known broad peak ofχAC'(T)from kHz-range audio-frequency measurements around 15 K for both [111] and [110] directions shifts to 45 K, continuing the Arrhenius activated behavior with the same activation energy barrier ofE_a≈ 230 K. Magnetic field dependence ofχAC'along [111] reproduces previously reported low-temperature two-in-two-out to three-in-one-out spin configuration transition at about 1 T, and an intermediate phase between 1 and 1.5 T. The boundaries of the intermediate phase show reasonable overlap with the literature data and connect at a critical endpoint of the first order transition line, suggesting that these features are frequency independent. An unusual upturn of the magnetic susceptibility atT→ 0 was observed in magnetic fields between 1.5 T and 2 T for both magnetic field directions, before fully polarized configuration sets in above 2 T.

Dynamic Systems Approach in Sensorimotor Synchronization: Adaptation to Tempo Step-Change

This paper presents a dynamic systems model of a sensorimotor synchronization (SMS) task. An SMS task typically gives temporally discrete human responses to some temporally discrete stimuli. Here, a dynamic systems modeling approach is applied after converting the discrete events to regularly sampled time signals. To collect data for model parameter fitting, a previously published pilot study was expanded. Three human participants took part in an experiment: to tap a finger on a keyboard, following a metronome which changed tempo in steps. System identification was used to estimate the transfer function that represented the relationship between the stimulus and the step response signals, assuming a separate linear, time-invariant system for each tempo step. Different versions of model complexity were investigated. As a minimum, a second-order linear system with delay, two poles, and one zero was needed to model the most important features of the tempo step response by humans, while an additional third pole could give a somewhat better fit to the response data. The modeling results revealed the behavior of the system in two distinct regimes: tempo steps below and above the conscious awareness of tempo change, i.e., around 12% of the base tempo. For the tempo steps above this value, model parameters were derived as linear functions of step size for the group of three participants. The results were interpreted in the light of known facts from other fields like SMS, psychoacoustics and behavioral neuroscience.

Spectral features of cortical auditory evoked potentials inform hearing threshold and intensity percepts in acoustic and electric hearing

Objective. Stimulus-elicited changes in electroencephalography (EEG) recordings can be represented using Fourier magnitude and phase features (Makeiget al(2004Trends Cogn. Sci.8204-10)). The present study aimed to quantify how much information about hearing responses are contained in the magnitude, quantified by event-related spectral perturbations (ERSPs); and the phase, quantified by inter-trial coherence (ITC). By testing if one feature contained more information and whether this information was mutually exclusive to the features, we aimed to relate specific EEG magnitude and phase features to hearing perception.Approach.EEG responses were recorded from 20 adults who were presented with acoustic stimuli, and 20 adult cochlear implant users with electrical stimuli. Both groups were presented with short, 50 ms stimuli at varying intensity levels relative to their hearing thresholds. Extracted ERSP and ITC features were inputs for a linear discriminant analysis classifier (Wonget al(2016J. Neural. Eng.13036003)). The classifier then predicted whether the EEG signal contained information about the sound stimuli based on the input features. Classifier decoding accuracy was quantified with the mutual information measure (Cottaris and Elfar (2009J. Neural. Eng.6026007), Hawelleket al(2016Proc. Natl Acad. Sci.11313492-7)), and compared across the two feature sets, and to when both feature sets were combined.Main results. We found that classifiers using either ITC or ERSP feature sets were both able to decode hearing perception, but ITC-feature classifiers were able to decode responses to a lower but still audible stimulation intensity, making ITC more useful than ERSP for hearing threshold estimation. We also found that combining the information from both feature sets did not improve decoding significantly, implying that ERSP brain dynamics has a limited contribution to the EEG response, possibly due to the stimuli used in this study.Significance.We successfully related hearing perception to an EEG measure, which does not require behavioral feedback from the listener; an objective measure is important in both neuroscience research and clinical audiology.

Quantitative evaluation of frequency domain measurements in high density diffuse optical tomography

SIGNIFICANCE

High density diffuse optical tomography (HD-DOT) as applied in functional near-infrared spectroscopy (fNIRS) is largely limited to continuous wave (CW) data. Using a single modulation frequency, frequency domain (FD) HD-DOT has recently demonstrated better localization of focal activation as compared to CW data. We show that combining CW and FD measurements and multiple modulation frequencies increases imaging performance in fNIRS.

AIM

We evaluate the benefits of multiple modulation frequencies, combining different frequencies as well as CW data in fNIRS HD-DOT.

APPROACH

A layered model was used, with activation occurring within a cortex layer. CW and FD measurements were simulated at 78, 141, and 203 MHz with and without noise. The localization error, full width half maximum, and effective resolution were evaluated.

RESULTS

Across the average of the three metrics, at 141 MHz, FD performed 8.4% better than CW, and the combination of CW and FD was 21.7% better than CW. FD measurements at 203 MHz performed 5% better than 78 MHz. Moreover, the three combined modulation frequencies of FD and CW performed up to 3.92% better than 141 MHz alone.

CONCLUSIONS

We show that combining CW and FD measurements offers better performance than FD alone, with higher modulation frequencies increasing accuracy. Combining CW and FD measurements at multiple modulation frequencies yields the best overall performance.

Development of Impact-Echo Multitransducer Device for Automated Concrete Homogeneity Assessment

A combination of multiple nondestructive testing (NDT) methods speeds up the assessment of concrete and increases the precision. This is why the UIR-Scanner was developed at Warsaw University of Technology. The scanner uses an Impact-Echo (IE) method with a unique arrangement of multiple transducers. This paper presents the development of the IE module using numerical models validated with experimental testing. It was found that rectangular arrangement of four transducers with the impactor in the middle is optimal for quick scanning of area for faults and discontinuities, changing the method from punctual to volumetric. A numerical study of void detectability depending on its position with respect to the IE module is discussed as well. After confirmation of the findings of models using experimental tests, the module was implemented into the scanner.

A Review of New High-Throughput Methods Designed for Fluorescence Lifetime Sensing From Cells and Tissues

Though much of the interest in fluorescence in the past has been on measuring spectral qualities such as wavelength and intensity, there are two other highly useful intrinsic properties of fluorescence: lifetime (or decay) and anisotropy (or polarization). Each has its own set of unique advantages, limitations, and challenges in detection when it comes to use in biological studies. This review will focus on the property of fluorescence lifetime, providing a brief background on instrumentation and theory, and examine the recent advancements and applications of measuring lifetime in the fields of high-throughput fluorescence lifetime imaging microscopy (HT-FLIM) and time-resolved flow cytometry (TRFC). In addition, the crossover of these two methods and their outlooks will be discussed.

Analyses of Countermovement Jump Performance in Time and Frequency Domains

This study aimed to analyze counter-movement jump (CMJ) performance in time and frequency domains. Fortyfour Division I American football players participated in the study. Kinetic variables were collected from both dominant and non-dominant legs using two force plates. Normalized peak power, normalized net impulse, and normalized peak force significantly correlated with jump height (r = .960, r = .998, r = .725, respectively with p < .05). The mean frequency component was significantly correlated with CMJ performance (r = .355 with p < .05). The reliability of the frequency variables was higher than the time domain variables. Frequency domain variables showed weaker correlations with jump height compared with time domain variables. Frequency domain analysis provides frequency components, which represent the rate of energy transmission from the eccentric phase to the end of the push-off phase. Frequency component information may provide additional information for the analyses of CMJ performance for athletes.

Frequency Domain Analysis of Partial-Tensor Rotating Accelerometer Gravity Gradiometer

The output model of a rotating accelerometer gravity gradiometer (RAGG) established by the inertial dynamics method cannot reflect the change of signal frequency, and calibration sensitivity and self-gradient compensation effect for the RAGG is a very important stage in the development process that cannot be omitted. In this study, a model based on the outputs of accelerometers on the disc of RGAA is established to calculate the gravity gradient corresponding to the distance, through the study of the RAGG output influenced by a surrounding mass in the frequency domain. Taking particle, sphere, and cuboid as examples, the input-output models of gravity gradiometer are established based on the center gradient and four accelerometers, respectively. Simulation results show that, if the scale factors of the four accelerometers on the disk are the same, the output signal of the RAGG only contains (4k+2)ω (ω is the spin frequency of disc for RAGG) harmonic components, and its amplitude is related to the orientation of the surrounding mass. Based on the results of numerical simulation of the three models, if the surrounding mass is close to the RAGG, the input-output models of gravity gradiometer are more accurate based on the four accelerometers. Finally, some advantages and disadvantages of cuboid and sphere are compared and some suggestions related to calibration and self-gradient compensation are given.

Modulation frequency selection and efficient look-up table inversion for frequency domain diffuse optical spectroscopy

SIGNIFICANCE

Frequency domain diffuse optical spectroscopy (FD-DOS) uses intensity modulated light to measure the absorption and reduced scattering coefficients of turbid media such as biological tissue. Some FD-DOS instruments utilize a single modulation frequency, whereas others use hundreds of frequencies. The effect of modulation frequency choice and measurement bandwidth on optical property (OP) extraction accuracy has not yet been fully characterized.

AIM

We aim to assess the effect of modulation frequency selection on OP extraction error and develop a high-speed look-up table (LUT) approach for OP estimation.

APPROACH

We first used noise-free simulations of light transport in homogeneous media to determine optimized iterative inversion model parameters and developed a new multi-frequency LUT method to increase the speed of inversion. We then used experimentally derived noise models for two FD-DOS instruments to generate realistic simulated data for a broad range of OPs and modulation frequencies to test OP extraction accuracy.

RESULTS

We found that repeated measurements at a single low-frequency (110 MHz) yielded essentially identical OP errors as a broadband frequency sweep (35 evenly spaced frequencies between 50 and 253 MHz) for these noise models. The inclusion of modulation frequencies >300  MHz diminished overall performance for one of the instruments. Additionally, we developed a LUT inversion algorithm capable of increasing inversion speeds by up to 6  ×  , with 1000  inversions  /  s and ∼1  %   error when a single modulation frequency was used.

CONCLUSION

These results suggest that simpler single-frequency systems are likely sufficient for many applications and pave the way for a new generation of simpler digital FD-DOS systems capable of rapid, large-volume measurements with real-time feedback.

Time-Frequency Representation of Motor Evoked Potentials in Brain Tumor Patients

Background: The integrity of the motor system can be examined by applying navigated transcranial magnetic stimulation (nTMS) to the cortex. The corresponding motor-evoked potentials (MEPs) in the target muscles are mirroring the status of the human motor system, far beyond corticospinal integrity. Commonly used time domain features of MEPs (e.g., peak-to-peak amplitudes and onset latencies) exert a high inter-subject and intra-subject variability. Frequency domain analysis might help to resolve or quantify disease-related MEP changes, e.g., in brain tumor patients. The aim of the present study was to describe the time-frequency representation of MEPs in brain tumor patients, its relation to clinical and imaging findings, and the differences to healthy subject.

Methods: This prospective study compared 12 healthy subjects with 12 consecutive brain tumor patients (with and without a paresis) applying nTMS mapping. Resulting MEPs were evaluated in the time series domain (i.e., amplitudes and latencies). After transformation into the frequency domain using a Morlet wavelet approach, event-related spectral perturbation (ERSP), and inter-trial coherence (ITC) were calculated and compared to diffusion tensor imaging (DTI) results.

Results: There were no significant differences in the time series characteristics between groups. MEPs were projecting to a frequency band between 30 and 300 Hz with a local maximum around 100 Hz for both healthy subjects and patients. However, there was ERSP reduction for higher frequencies (>100 Hz) in patients in contrast to healthy subjects. This deceleration was mirrored in an increase of the inter-peak MEP latencies. Patients with a paresis showed an additional disturbance in ITC in these frequencies. There was no correlation between the CST integrity (as measured by DTI) and the MEP parameters.

Conclusion: Time-frequency analysis may provide additional information above and beyond classical MEP time domain features and the status of the corticospinal system in brain tumor patients. This first evaluation indicates that brain tumors might affect cortical physiology and the responsiveness of the cortex to TMS resulting in a temporal dispersion of the corticospinal transmission.

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the frequency domain. In this paper, we propose a unified convolutional neural network architecture from a frequency domain perspective for a domain adaptation named Frequency-domain Fusing Convolutional Neural Network (FFCNN). The method of FFCNN contains two parts, frequency-domain fusing layer and feature extractor. The frequency-domain fusing layer uses convolution operations to filter signals at different frequency bands and combines them into new input signals. These signals are input to the feature extractor to extract features and make domain adaptation. We apply FFCNN for three domain adaptation methods, and the diagnosis accuracy is improved compared to the typical CNN.

Signal regression in frequency-domain diffuse optical tomography to remove superficial signal contamination

Significance: Signal contamination is a major hurdle in functional near-infrared spectroscopy (fNIRS) of the human head as the NIR signal is contaminated with the changes corresponding to superficial tissue, therefore occluding the functional information originating from the cerebral region. For continuous wave, this is generally handled through linear regression of the shortest source-detector (SD) distance intensity measurement from all of the signals. Although phase measurements utilizing frequency domain (FD) provide deeper tissue sampling, the use of the shortest SD distance phase measurement for regression of superficial signal contamination can lead to misleading results, therefore suppressing cortical signals. Aim: An approach for FD fNIRS that utilizes a short-separation intensity signal directly to regress both intensity and phase measurements, providing a better regression of superficial signal contamination from both data-types, is proposed. Approach: Simulated data from realistic models of the human head are used, and signal regression using both intensity and phase-based components of the FD fNIRS is evaluated. Results: Intensity-based phase regression achieves a suppression of superficial signal contamination by 68% whereas phase-based phase regression is only by 13%. Phase-based phase regression is also shown to generate false-positive signals from the cortex, which are not desirable. Conclusions: Intensity-based phase regression provides a better methodology for minimizing superficial signal contamination in FD fNIRS.

Frequency domain analysis to extract dynamic response characteristics for oxygen uptake during transitions to moderate- and heavy-intensity exercises

At the onset of an exercise transition, exponential modeling to calculate a time constant (τ) is the conventional method to analyze pulmonary oxygen uptake (V̇O_2p) kinetics for moderate and heavy exercises. A new frequency domain analysis technique, mean normalized gain (MNG), has been used to analyze V̇O_2p kinetics during moderate exercise, but has not been evaluated for its ability to detect differences in kinetics between moderate and heavy exercises. This study tested the hypothesis that MNG would detect smaller amplitude V̇O_2p responses in the heavy-exercise domain compared with moderate-exercise domain. Eight young healthy adults (3 female; age: 27 ± 6 yr; peak V̇O_2p: 43 ± 6 mL·min^-1·kg^-1; means ± SD) performed three bouts of pseudorandom binary sequence (PRBS) exercise for frequency analysis, with the work rate (WR) changing between 25 W and 90% ventilatory threshold (VT; L → M_PRBS), 25 W and 50% of the difference between VT and peak V̇O_2p (Δ50%; L → H_PRBS), and VT to Δ50% (VT → H_PRBS). Step exercise tests with equivalent changes in WR to the PRBS tests were performed to facilitate the comparison between MNG and τ. MNG was the highest for L → M_PRBS (59 ± 7%), then L → H_PRBS (52 ± 6%), and the lowest for VT → H_PRBS (38 ± 7%, F_(2,14) = 129.755, P < 0.001) exercise conditions indicating slower kinetics with increasing exercise intensity that correlated strongly in repeated measures with τ from step transitions (r_rm = -0.893). These results indicate that frequency domain analysis and MNG reliably detect differences in V̇O_2p kinetics observed across exercise intensity domains.NEW & NOTEWORTHY Mean normalized gain is able to detect differences in V̇O_2p kinetics between moderate-, heavy-, and heavy-intensity exercises from a raised WR within the same individuals. This new method of kinetic analysis may be advantageous compared with conventional V̇O_2p curve fitting, as it is less sensitive to breath-by-breath noise, it can provide useful information from a single exercise testing session, and it can be applied to nonconstant work rate exercise situations.

Evaluation of Electrochemical Process Improvement Using the Computer-Aided Nonlinear Frequency Response Method: Oxygen Reduction Reaction in Alkaline Media

The intensification of an electrochemical process by forced periodic operation was studied for the first time using the computer-aided Nonlinear Frequency Response method. This method enabled the automatic generation of frequency response functions and the DC components (Faradaic rectification) of the cost (overpotential) and benefit (current density) indicators. The case study, oxygen reduction reaction, was investigated both experimentally and theoretically. The results of the cost-benefit indicator analysis show that forced periodic change of electrode potential can be superior when compared to the steady-state regime for specific operational parameters. When the electrode rotation rate is changed periodically, the process will always deteriorate as the dynamic operation will inevitably lead to the thickening of the diffusion layer. This phenomenon is explained both from a mathematical and a physical point of view.

Heart rate variability during cardiovascular reflex testing: the importance of underlying heart rate

OBJECTIVES

Heart rate variability (HRV) is often measured during clinical and experimental cardiovascular reflex tests (CRT), as a reflection of cardiac autonomic modulation, despite limited characterization of the rapid responses that occur. Therefore, we evaluated the responsiveness of HRV indices in 20 healthy young adults (age, 27 ± 6 y; mass, 76.9 ± 16.8 kg; height, 1.79 ± 0.12 m) during four separate established CRT.

METHODS

These included the [I] orthostatic challenge, [II] isometric handgrip, [III] cold pressor and [IV] cold diving reflex tests. Electrocardiogram was recorded throughout, with HRV derived from RR intervals at rest and from each CRT. On a separate day, a subgroup of participants (n=9) completed the same protocol for a second time.

RESULTS

The maximal slope of heart rate change (dTdt) was significantly different between all CRT, with the orthostatic challenge producing the fastest increase (2.56 ± 0.48) and the cold pressor the fastest reduction (-1.93 ± 0.68) in heart rate. Overall HRV, reflected by Poincaré plot ratio (SD1:SD2), was significantly reduced during all CRT ([I], -0.41 ± 0.12; [II], -0.19 ± 0.05; [III], -0.36 ± 0.12; [IV], -0.44 ± 0.11; p<0.05) relative to baseline and this was reproducible in time-series. However, when HRV indices were correlated to mean-RR an exponential growth-like relationship was evident (R² ranging from: 0.52-0.62).

CONCLUSIONS

These unique outcomes demonstrate that short-term alterations in HRV are evident during CRT, while indicating the importance of adjusting for, or at least reporting, underlying heart rate when interpreting such measures.

Information-Theoretic Radar Waveform Design under the SINR Constraint

This study investigates the information-theoretic waveform design problem to improve radar performance in the presence of signal-dependent clutter environments. The goal was to study the waveform energy allocation strategies and provide guidance for radar waveform design through the trade-off relationship between the information theory criterion and the signal-to-interference-plus-noise ratio (SINR) criterion. To this end, a model of the constraint relationship among the mutual information (MI), the Kullback-Leibler divergence (KLD), and the SINR is established in the frequency domain. The effects of the SINR value range on maximizing the MI and KLD under the energy constraint are derived. Under the constraints of energy and the SINR, the optimal radar waveform method based on maximizing the MI is proposed for radar estimation, with another method based on maximizing the KLD proposed for radar detection. The maximum MI value range is bounded by SINR and the maximum KLD value range is between 0 and the Jenson-Shannon divergence (J-divergence) value. Simulation results show that under the SINR constraint, the MI-based optimal signal waveform can make full use of the transmitted energy to target information extraction and put the signal energy in the frequency bin where the target spectrum is larger than the clutter spectrum. The KLD-based optimal signal waveform can therefore make full use of the transmitted energy to detect the target and put the signal energy in the frequency bin with the maximum target spectrum.

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images

Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify stress status is crude. Building a person-specific model leads to a reliable classification, but it requires the collection of new data to train a new model for every individual and needs periodic upgrades because stress is dynamic. In this paper, a new binary classification (called stressed and non-stressed) approach is proposed for a subject’s stress state in which the inter-beat intervals extracted from a photoplethysomogram (PPG) were transferred to spatial images and then to frequency domain images according to the number of consecutive. Then, the convolution neural network (CNN) was used to train and validate the classification accuracy of the person’s stress state. Three types of classification models were built: person-specific models, generic classification models, and calibrated-generic classification models. The average classification accuracies achieved by person-specific models using spatial images and frequency domain images were 99.9%, 100%, and 99.8%, and 99.68%, 98.97%, and 96.4% for the training, validation, and test, respectively. By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models’ accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models.

Association Between Disease Severity, Heart Rate Variability (HRV) and Serum Cortisol Concentrations in Horses with Acute Abdominal Pain

Simple Summary

Acute abdominal pain is a major cause for emergency treatment in horses and associated with a high stress level leading to an increased serum cortisol concentration. Stress can also be assessed by analyzing the heart rate variability (HRV). We investigated whether the stress level was different between horses with different causes of abdominal pain and, therefore, demanding a different treatment strategy. Heart rate, its variability in the time domain analyses, and cortisol level indicated a decrease in the stress level the day after admission and the day of discharge from the hospital in comparison to admission for both conservatively and surgically treated patients. However, such changes, over time, were not seen in horses that were euthanized during the hospitalization. Furthermore, the difference in the parameters measured between horses that were eventually euthanized and those that survived was best visible the day after admission. Therefore, we concluded that HRV can give further important information on the stress level in horses with colic and might be helpful in assessing possible outcome. However, further studies are required to assess the validity of HRV analyses in horses with colic.

Abstract

Heart rate variability (HRV) is a noninvasive technique to detect changes in the autonomous nervous system. It has rarely been investigated in horses with colic. Therefore, the objective was to assess the evolution of HRV parameters and cortisol concentrations in horses with colic. The 43 horses included in this study were categorized into three groups according to the treatment (1, surgical; 2, conservative; 3, euthanized). The HRV and laboratory variables were measured at admission (T1), the day after admission (T2), and at discharge (T3) and compared between groups and over time with an ANOVA with Bonferroni correction. Relationships between the HRV parameters themselves and the laboratory variables was assessed by Pearson correlation coefficients. Evolution of the heart rate (HR) over time, mean normal to normal R intervals (meanNN) and cortisol concentrations indicate a decreased sympathetic stimulation over time in group 1 and 2, in contrast to group 3. For group 3, the meanNN and HR differed significantly to group 2 at T1 and to group 1 and 2 at T2. Treatment induced a change in the HRV and cortisol response in horses managed conservatively or surgically but not in horses that required euthanasia. However, further studies are required to assess the validity of HRV analyses in horses with colic.

Evaluation of Impulse Attenuation by Football Helmets in the Frequency Domain

Design of helmets used in contact sports has been driven by the necessity of preventing severe head injuries. Manufacturing standards and pass or fail grading systems ensure protective headgear built to withstand large impacts, but design standards do no account for impacts resulting in subconcussive episodes and the effects of cumulative impacts on its user. Thus, it is important to explore new design parameters, such as the frequency-domain measures of transmissibility and mechanical impedance that are based on energy absorption from a range of impact loads. Within the experimentally determined frequency range of interest (FROI), transmissibilities above unity were found in the 0-40 Hz range with the magnitude characteristics varying considerably with impact location. A similar variability with location was observed for the mechanical impedance, which ranged from 9 N/m to 50 N/m. Additional research is required to further understand how changes in the components or materials of the components will affect the performance of helmets, and how they may be used to reduce both transmissibility and dynamic impedance.

Wireless Body Area Networks: UWB Wearable Textile Antenna for Telemedicine and Mobile Health Systems

A compact textile ultra-wideband (UWB) antenna with an electrical dimension of 0.24λ_o × 0.24λ_o × 0.009λ_o with microstrip line feed at lower edge and a frequency of operation of 2.96 GHz is proposed for UWB application. The analytical investigation using circuit theory concepts and the cavity model of the antenna is presented to validate the design. The main contribution of this paper is to propose a wearable antenna with wide impedance bandwidth of 118.68 % (2.96–11.6 GHz) applicable for UWB range of 3.1 to 10.6 GHz. The results present a maximum gain of 5.47 dBi at 7.3 GHz frequency. Moreover, this antenna exhibits Omni and quasi-Omni radiation patterns at various frequencies (4 GHz, 7 GHz and 10 GHz) for short-distance communication. The cutting notch and slot on the patch, and its effect on the antenna impedance to increase performance through current distribution is also presented. The time-domain characteristic of the proposed antenna is also discussed for the analysis of the pulse distortion phenomena. A constant group delay less than 1 ns is obtained over the entire operating impedance bandwidth (2.96–11.6 GHz) of the textile antenna in both situations, i.e., side by side and front to front. Linear phase consideration is also presented for both situations, as well as configurations of reception and transmission. An assessment of the effects of bending and humidity has been demonstrated by placing the antenna on the human body. The specific absorption rate (SAR) value was tested to show the radiation effect on the human body, and it was found that its impact on the human body SAR value is 1.68 W/kg, which indicates the safer limit to avoid radiation effects. Therefore, the proposed method is promising for telemedicine and mobile health systems.

Frequency-Domain Techniques for Cerebral and Functional Near-Infrared Spectroscopy

This article reviews the basic principles of frequency-domain near-infrared spectroscopy (FD-NIRS), which relies on intensity-modulated light sources and phase-sensitive optical detection, and its non-invasive applications to the brain. The simpler instrumentation and more straightforward data analysis of continuous-wave NIRS (CW-NIRS) accounts for the fact that almost all the current commercial instruments for cerebral NIRS have embraced the CW technique. However, FD-NIRS provides data with richer information content, which complements or exceeds the capabilities of CW-NIRS. One example is the ability of FD-NIRS to measure the absolute optical properties (absorption and reduced scattering coefficients) of tissue, and thus the absolute concentrations of oxyhemoglobin and deoxyhemoglobin in brain tissue. This article reviews the measured values of such optical properties and hemoglobin concentrations reported in the literature for animal models and for the human brain in newborns, infants, children, and adults. We also review the application of FD-NIRS to functional brain studies that focused on slower hemodynamic responses to brain activity (time scale of seconds) and faster optical signals that have been linked to neuronal activation (time scale of 100 ms). Another example of the power of FD-NIRS data is related to the different regions of sensitivity featured by intensity and phase data. We report recent developments that take advantage of this feature to maximize the sensitivity of non-invasive optical signals to brain tissue relative to more superficial extracerebral tissue (scalp, skull, etc.). We contend that this latter capability is a highly appealing quality of FD-NIRS, which complements absolute optical measurements and may result in significant advances in the field of non-invasive optical sensing of the brain.

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

Polarimetric dehazing methods can significantly enhance the quality of hazy images. However, current methods are not robust enough under different imaging conditions. In this paper, we propose a generalized polarimetric dehazing method based on low-pass filtering in the frequency domain. This method can accurately estimate the polarized state of the scattering light automatically without adjusting bias parameters. Experimental results show the effectiveness and robustness of our proposed method in different hazy weather and scattering underwater environments with different densities. Furthermore, computational efficiency is enhanced more than 70% compared to the polarimetric dehazing method we proposed previously.

Experimental Study on Vibration Characteristics of Unit-Plate Ballastless Track Systems Laid on Long-Span Bridges Using Full-Scale Test Rigs

In this work, we present a series of hammering tests on full-scale unit-plate ballastless tracks used for long-span bridges. There is no denying that it is a new attempt to pave ballastless tracks on high-speed railway long-span bridges; the related issues deserve to be studied, and especially the vibration characteristics. Hence, the vibration characteristics and transmission rules of the ballastless track with geotextile or rubber isolation layers are explored, and the vibration reduction effect of the rubber isolation layer is analyzed. The main conclusions are as follows: the isolation layers change vibration modes and transmission characteristics of ballastless tracks; the introduction of the rubber isolation layer makes the excited vibration frequency range of the ballastless track concentrated; and the vibrations of the ballastless track with the rubber isolation layers are stable. Moreover, the rubber isolation layer has an obvious attenuation effect on vibration transmission in ballastless track structures. When the vibration is transmitted from the rail to the bridge deck, the vibration level differences of the ballastless track with rubber isolation layers are 20 dB larger than that of the ballastless track with the geotextile isolation layers. The vibration attenuation rate of the rubber isolation layer is about ten times larger than that of geotextile isolation layer.

Short-term Heart-rate Variability in Healthy Small and Medium-sized Dogs Over a Five-minute Measuring Period

Introduction

Five-minute heart-rate variability (HRV) measurement is a useful tool for assessing the autonomic nervous system (ANS) balance in humans, but there are no studies on healthy dogs. The aim of the study was, therefore, to provide the reference ranges in small and medium-sized breeds for short-term HRV time and frequency domain (TFD) analyses.

Material and Methods

A total of 79 healthy dogs were included in the study between 2015 and 2019. Grouping by age with the breakpoint at six years and subgrouping by reproductive status and sex was imposed. All the dogs were included after physical and cardiological examinations and blood analyses. The TFD of HRV were analysed from a five-minute-long digital ECG recording after removal of non-sinus complexes.

Results

There were no statistically significant differences in any TFD parameters between age, reproductive status or sex groups. A mild increase in all time domain parameters and the high-frequency (HF) band was observed in older dogs, and the low frequency (LF):HF ratio decreased in these dogs. In males, the time domain parameters and HF band increased slightly.

Conclusion

The normal ranges for HRV derived from short-term ECG recording in the usual clinical environment now have proposed reference ranges. Our findings suggest that accommodation time, age, sex, or reproductive status do not influence the results derived from these recordings, indicating that this method is reliable for assessing the ANS function in small and medium-sized dog breeds.