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Retinitis pigmentosa is a hereditary retinal disease that affects rod and cone photoreceptors, leading to progressive photoreceptor loss. Previous research supports the beneficial effect of electrical stimulation on photoreceptor survival. This study aims to identify the most effective electrical stimulation parameters and functional advantages of transcorneal electrical stimulation (tcES) in mice affected by inherited retinal degeneration. Additionally, the study seeked to analyze the electric field that reaches the retina in both eyes in mice and post-mortem humans. In this study, we recorded waveforms and voltages directed to the retina during transcorneal electrical stimulation in C57BL/6J mice using an intraocular needle probe with rectangular, sine, and ramp waveforms. To investigate the functional effects of electrical stimulation on photoreceptors, we used human retinal explant cultures and rhodopsin knockout (Rho–/–) mice, demonstrating progressive photoreceptor degeneration with age. Human retinal explants isolated from the donors' eyes were then subjected to electrical stimulation and cultured for 48 hours to simulate the neurodegenerative environment in vitro. Photoreceptor density was evaluated by rhodopsin immunolabeling. In vivo Rho–/– mice were subjected to two 5-day series of daily transcorneal electrical stimulation using rectangular and ramp waveforms. Retinal function and visual perception of mice were evaluated by electroretinography and optomotor response (OMR), respectively. Immunolabeling was used to assess the morphological and biochemical changes of the photoreceptor and bipolar cells in mouse retinas. Oscilloscope recordings indicated effective delivery of rectangular, sine, and ramp waveforms to the retina by transcorneal electrical stimulation, of which the ramp waveform required the lowest voltage. Evaluation of the total conductive resistance of the post-mortem human compared to the mouse eyes indicated higher cornea-to-retina resistance in human eyes. The temperature recordings during and after electrical stimulation indicated no significant temperature change in vivo and only a subtle temperature increase in vitro (~0.5–1.5°C). Electrical stimulation increased photoreceptor survival in human retinal explant cultures, particularly at the ramp waveform. Transcorneal electrical stimulation (rectangular + ramp) waveforms significantly improved the survival and function of S and M-cones and enhanced visual acuity based on the optomotor response results. Histology and immunolabeling demonstrated increased photoreceptor survival, improved outer nuclear layer thickness, and increased bipolar cell sprouting in Rho–/– mice. These results indicate that transcorneal electrical stimulation effectively delivers the electrical field to the retina, improves photoreceptor survival in both human and mouse retinas, and increases visual function in Rho–/– mice. Combined rectangular and ramp waveform stimulation can promote photoreceptor survival in a minimally invasive fashion. [ABSTRACT FROM AUTHOR]

Audio-based classification techniques for body sounds have long been studied to aid in the diagnosis of respiratory diseases. While most research is centered on the use of coughs as the main acoustic biomarker, other body sounds also have the potential to detect respiratory diseases. Recent studies on the coronavirus disease 2019 (COVID-19) have suggested that breath and speech sounds, in addition to cough, correlate with the disease. Our study proposes fused audio instance and representation (FAIR) as a method for respiratory disease detection. FAIR relies on constructing a joint feature vector from various body sounds represented in waveform and spectrogram form. We conduct experiments on the use case of COVID-19 detection by combining waveform and spectrogram representation of body sounds. Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an area under the receiver operating characteristic curve (AUC) score of 0.8658, a sensitivity of 0.8057, and a specificity of 0.7958. Compared to models trained solely on spectrograms or waveforms, the use of both representations results in an improved AUC score, demonstrating that combining spectrogram and waveform representation helps to enrich the extracted features and outperforms the models that use only one representation. While this study focuses on COVID-19, FAIR's flexibility allows it to combine various multi-modal and multi-instance features in many other diagnostic applications, potentially leading to more accurate diagnoses across a wider range of diseases. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated the acceleration signal presents a typical running signature, which allows for the extraction of reliable information. However, few studies have focused on the exhaustion-induced variability of the acceleration signature during running. The present study included 10 participants who ran at a constant speed on a treadmill until exhaustion. The participants were equipped with three accelerometers, located at the lumbar spine, tibia, and foot. The results showed that all the participants kept a constant pace throughout the test (coefficient of variation <5%). Similarities between acceleration signatures were observed using the coefficient of multiple correlation. For the longitudinal axis of the lumbar spine, the longitudinal axis of the tibia, and the anteroposterior axis of the tibia, running signatures were not affected by exhaustion (coefficient of multiple correlation >0.8). For all the other axes, the signature was impacted within and between the states of exhaustion. Signatures were particularly different for the foot sensors, which makes it difficult to use to extract reliable information. The results showed that the coefficient of multiple correlation allowed the quantification of the variability of the running signature, and that each axis and measuring point varied in how they were influenced by exhaustion. [ABSTRACT FROM AUTHOR]

Anomalies displaying significant deviations between terrain elevation products acquired from spaceborne full-waveform LiDAR and reference elevations are frequently observed in assessment studies. While the predominant focus is on "normal" data, recognizing anomalies within datasets obtained from the Geoscience Laser Altimeter System (GLAS) and the Global Ecosystem Dynamics Investigation (GEDI) is essential for a comprehensive understanding of widely used spaceborne full-waveform data, which not only facilitates optimal data utilization but also enhances the exploration of potential applications. Nevertheless, our comprehension of anomalies remains limited as they have received scant specific attention. Diverging from prevalent practices of directly eliminating outliers, we conducted a targeted exploration of anomalies in forested areas using both transmitted and return waveforms from the GLAS and the GEDI in conjunction with airborne LiDAR point cloud data. We unveiled that elevation anomalies stem not from the transmitted pulses or product algorithms, but rather from scattering sources. We further observed similarities between the GLAS and the GEDI despite their considerable disparities in sensor parameters, with the waveforms characterized by a low signal-to-noise ratio and a near exponential decay in return energy; specifically, return signals of anomalies originated from clouds rather than the land surface. This discovery underscores the potential of deriving cloud-top height from spaceborne full-waveform LiDAR missions, particularly the GEDI, suggesting promising prospects for applying GEDI data in atmospheric science—an area that has received scant attention thus far. To mitigate the impact of abnormal return waveforms on diverse land surface studies, we strongly recommend incorporating spaceborne LiDAR-offered terrain elevation in data filtering by establishing an elevation-difference threshold against a reference elevation. This is especially vital for studies concerning forest parameters due to potential cloud interference, yet a consensus has not been reached within the community. [ABSTRACT FROM AUTHOR]

Boat acceleration profiles can provide valuable information for coaches and practitioners to make meaningful technical interventions and monitor the determinants of success in rowing. Previous studies have used simple feature detection methods to identify key phases within individual strokes, such as drive onset, drive time, drive offset and stroke time. However, based on skill level, technique or boat class, the hull acceleration profile can differ, making robust feature detection more challenging. The current study's purpose is to employ the undecimated wavelet transform (UWT) technique to detect individual features in the stroke acceleration profile from a single rowing hull-mounted accelerometer. In this investigation, the temporal and kinematic values obtained using the AdMosTM sensor in conjunction with the UWT processing approach were strongly correlated with the comparative measures of the Peach™ instrumented oarlock system. The measures for stroke time displayed very strong agreeability between the systems for all boat classes, with ICC values of 0.993, 0.963 and 0.954 for the W8+, W4− and W1x boats, respectively. Similarly, the drive time was also very consistent, with strong to very strong agreeability, producing ICC values of 0.937, 0.901 and 0.881 for the W8+, W4− and W1x boat classes. Further, a Bland–Altman analysis displayed little to no bias between the AdMosTM-derived and Peach™ measures, indicating that there were no systematic discrepancies between signals. This single-sensor solution could form the basis for a simple, cost-effective and accessible alternative to multi-sensor instrumented systems for the determination of sub-stroke kinematic phases. [ABSTRACT FROM AUTHOR]

The polyethylene naphthalate (PEN) film has been widely applied as a heat‐resistant thin insulation film and sensor film. For the safe and long application of the film in various products, one significant characteristic is the partial discharge (PD) resistivity. In this study, a well‐known IEC(b) electrode is used to measure PD characteristics such as the maximum partial discharge, qmax and fast PD current waveforms at AC peak voltages of 1–3 kVp and 50–1000 Hz over PEN films with thicknesses of 75, 50, or 25 μm. All experiments are conducted at room temperature of ~20 °C. The positive PD current is defined as the current flowing from a high‐voltage electrode to a ground electrode. The positive and negative qmax increased rapidly with the applied voltage increase but remained almost the same for the applied voltage frequency changes. The PD current duration time was less than 40 ns for the positive current and 30 ns for the negative current at all voltages, frequencies, and film thicknesses. It was deduced that the positive current peak magnitude was approximately twice of the negative one at all applied voltages, frequencies, and film thicknesses. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain–computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI). [ABSTRACT FROM AUTHOR]

Seismic oceanography has been widely used in the study of internal solitary waves (ISWs) in recent years, and has achieved remarkable results. In this paper, we analyzed the multi-channel seismic reflection data in the Canterbury Basin offshore New Zealand from January 9 to January 29, 2000, collected by R/V Maurice Ewing. We observed 4 groups of ISWs (labeled ISW1s to ISW4s) on 4 seismic survey lines. We studied their waveforms and propagation speeds in detail. There are two theoretical structures used to describe the vertical waveform of ISWs: the first-order nonlinear vertical structure and the linear vertical structure. We found that ISW1s fit the nonlinear structure well, ISW3s and ISW4s fit the linear structure, and ISW2 does not fit either one. As the water depth increases, the waveforms of all ISWs gradually widen. Two satellite SAR images reveal that all ISWs generally travel shoreward across the isobaths. However, the propagation direction of ISW1s is about 354°-360° (clockwise from due north), different from the propagation directions of other ISWs (about 22°-26°), which explains why ISW1s have the largest characteristic half-height width. The estimated propagation speeds are close to the theoretical speeds, confirming our speed correction method. In the end, we also discuss the interaction of ISWs and eddies. [ABSTRACT FROM AUTHOR]

In order to ensure the stability of the surrounding rock of underground mining excavation tunnels and identify unfavorable geological bodies ahead, geological radar was used to detect the empty area of the surrounding rock and floor of the excavation tunnel, and stability analysis was conducted on the surrounding rock of the tunnel under different geostress effects. The axial force of anchor rods with different lengths of reinforcement was compared. The results indicate that both areas detect by the radar exhibit strong amplitude reflection zones at different depths. Under the action of 1. 5 m anchor rod support, the maximum displacement of the surrounding rock decreases by 39. 3% and 10. 0% respectively, and the X-effective stress increases by 42. 8% and 37. 5% respectively. The axial force changes are basically the same when supported by 1. 5 m and 1. 8 m anchor rods. Based on the continuity of the same phase axis, the abnormal area is judged, and the abnormal signal is the unfilled area and cavity, which are supported by anchor bolts, anchor mesh, and shotcrete. The research results can provide reference significance for the stability and support of surrounding rocks in similar mine tunnels. [ABSTRACT FROM AUTHOR]

With the advancement of manufacturing equipment and the development of sensing technology, the measurement cycle of data is becoming very short, and the characteristics of data observed at the same time are also diversifying. A Fault Detection and Classification (FDC) system is in operation to collect and analyze measurement data in real time in the display manufacturing factory. However, the anomaly detection function provided by the FDC system is based on a threshold comparison method for data in seconds, so there is a limit to accuracy in processing measurement data in milliseconds. In particular, if the process for one panel is divided into several steps and each of them shows various characteristics, it is difficult to manage because it takes a lot of airlift to set search conditions and thresholds for anomaly detection. To overcome this, a waveform analysis system is implemented to assist the FDC system. This system extracts waveforms from milliseconds of measurement data during the processing time of one panel in the equipment, and diagnoses whether the equipment processing is normal through GAN‐based waveform pattern analysis. Generative models are used in the anomaly detection process in consideration of the manufacturing environment in which the normal data is overwhelmingly larger than the abnormal data. The DCGAN‐based model that is excellent in image processing and the TadGAN‐based model that combines Auto encoder and GAN were implemented and used. In this paper, the anomaly detection performance of models is compared and evaluated for the display photo process Coater Pressure sensor using the waveform analysis system implemented in this way. [ABSTRACT FROM AUTHOR]

Electrorefining in molten salts is used for purifying actinides. Optimizing electrorefining is key to minimizing processing time and radiological waste. One possible way of improving electrorefining efficiency is using an AC superimposed DC waveform. This waveform has demonstrated potential benefits in aqueous solutions but has never been utilized in a molten metal, molten salt application. This work investigates the effects of using an AC superimposed DC waveform on molten bismuth electrorefining in a molten LiCl–KCl–CaCl2 eutectic. Bismuth has been identified as a potential surrogate for plutonium electrorefining and a potential cathode in electrorefining used nuclear fuel (UNF). All electrorefining runs resulted in a high purity cathode ring and high yield with exception of the run using a low-frequency, high-amplitude superimposed AC waveform, which experienced some contamination and a lower yield. The other three AC superimposed DC runs experienced an average yield 6.7 % higher than the average yield of the DC runs. The electrorefining run using the high-frequency, high-amplitude superimposed AC signal had the highest yield. It is recommended in future studies to investigate the statistical variability of electrorefining yield and current efficiency and the impact of AC superimposed DC waveforms on solidified bismuth anodes.

Introduction: Postoperative hyperperfusion syndrome (PHS) is a well-known complication following superficial temporal artery (STA)-middle cerebral artery (MCA) bypass for moyamoya disease (MMD). The early detection of postoperative radiological hyperperfusion (PRH), characterized by a transient increase in local cerebral blood flow (CBF), is crucial for the early diagnosis of PHS. This study aimed to investigate the effectiveness of waveform analysis for early PRH detection. Methods: We reviewed 52 consecutive patients who underwent STA-MCA bypass for MMD. Patients were divided into PRH and non-PRH groups based on the postoperative/preoperative CBF ratio. We collected the intraoperative bypass graft waveform and bypass flow data using a flowmeter. The pulsatile index (PI), an indicator of peripheral vascular resistance (PVR), was calculated from bypass flow data. Next, the newly proposed index of PVR, the ratio of the time from peak to 50% decay and to 100% decay (RT50), was calculated through waveform analysis. The values were then compared between the PRH and non-PRH groups. Results: Twenty-seven of the 52 patients met the inclusion criteria. Fourteen of these 27 patients showed PRH. The RT50, but not the PI, was significantly higher in the PRH group. Linear regression analysis revealed a significant correlation between the RT50 and PI. In the receiver operating characteristic for predicting PRH, the area under the curve of RT50 was 0.750, with a cutoff value of 0.255, a sensitivity of 0.928, and a specificity of 0.500. Conclusion: The RT50 obtained from waveform analysis is associated with PVR and can be useful for the early detection of PRH in patients with MMD.

Spinal cord stimulation (SCS) is a therapeutic modality for the treatment of various chronic pain conditions that has rapidly evolved over the past 50 years. Unfortunately, over time, patients implanted with SCS undergo a habituation phenomenon leading to decreased pain relief. Consequently, the discovery of new stimulation waveforms and SCS applications has been shown to prolong efficacy and reduce explantation rates. This article explores various SCS waveforms, their applications, and proposes a graded approach to habituation mitigation. We suspect the neural habituation phenomenon parallels that seen in pharmacology. Consequently, we urge further exploration of the early introduction of these stimulation strategies to abate spinal cord stimulation habituation. [ABSTRACT FROM AUTHOR]

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Abstract Despite a sharp increase in the use of the waterpipe (WP) has been noted recently in Iran, no information is available for the smoking behavior and topography parameters. The present study is intended to obtain the inhalation and smoking topography parameters for the Iranian WP smokers. The smoking data collected from 122 smoking sessions, including 192 WP smokers in the Iranian Fars province have been used to perform smoking topography assessments. The influence of demographic and smoking parameters on puffing data is obtained. Results have indicated that gender and tobacco type strongly affect puff volume and duration. Women smokers inhale smaller volume of smoke than men and puff duration is significantly increased for regular smokers than occasional smokers. However, the results of the present study have not revealed a major effect of age, residence and setting on the puffing behavior.

Retinitis pigmentosa is a hereditary retinal disease that affects rod and cone photoreceptors, leading to progressive photoreceptor loss. Previous research supports the beneficial effect of electrical stimulation on photoreceptor survival. This study aims to identify the most effective electrical stimulation parameters and functional advantages of transcorneal electrical stimulation (tcES) in mice affected by inherited retinal degeneration. Additionally, the study seeked to analyze the electric field that reaches the retina in both eyes in mice and post-mortem humans. In this study, we recorded waveforms and voltages directed to the retina during transcorneal electrical stimulation in C57BL/6J mice using an intraocular needle probe with rectangular, sine, and ramp waveforms. To investigate the functional effects of electrical stimulation on photoreceptors, we used human retinal explant cultures and rhodopsin knockout (Rho–/–) mice, demonstrating progressive photoreceptor degeneration with age. Human retinal explants isolated from the donors’ eyes were then subjected to electrical stimulation and cultured for 48 hours to simulate the neurodegenerative environment in vitro. Photoreceptor density was evaluated by rhodopsin immunolabeling. In vivo Rho–/– mice were subjected to two 5-day series of daily transcorneal electrical stimulation using rectangular and ramp waveforms. Retinal function and visual perception of mice were evaluated by electroretinography and optomotor response (OMR), respectively. Immunolabeling was used to assess the morphological and biochemical changes of the photoreceptor and bipolar cells in mouse retinas. Oscilloscope recordings indicated effective delivery of rectangular, sine, and ramp waveforms to the retina by transcorneal electrical stimulation, of which the ramp waveform required the lowest voltage. Evaluation of the total conductive resistance of the post-mortem human compared to the mouse eyes indicated higher cornea-to-retina resistance in human eyes. The temperature recordings during and after electrical stimulation indicated no significant temperature change in vivo and only a subtle temperature increase in vitro (~0.5–1.5°C). Electrical stimulation increased photoreceptor survival in human retinal explant cultures, particularly at the ramp waveform. Transcorneal electrical stimulation (rectangular + ramp) waveforms significantly improved the survival and function of S and M-cones and enhanced visual acuity based on the optomotor response results. Histology and immunolabeling demonstrated increased photoreceptor survival, improved outer nuclear layer thickness, and increased bipolar cell sprouting in Rho–/– mice. These results indicate that transcorneal electrical stimulation effectively delivers the electrical field to the retina, improves photoreceptor survival in both human and mouse retinas, and increases visual function in Rho–/– mice. Combined rectangular and ramp waveform stimulation can promote photoreceptor survival in a minimally invasive fashion.

Audio-based classification techniques for body sounds have long been studied to aid in the diagnosis of respiratory diseases. While most research is centered on the use of coughs as the main acoustic biomarker, other body sounds also have the potential to detect respiratory diseases. Recent studies on the coronavirus disease 2019 (COVID-19) have suggested that breath and speech sounds, in addition to cough, correlate with the disease. Our study proposes fused audio instance and representation (FAIR) as a method for respiratory disease detection. FAIR relies on constructing a joint feature vector from various body sounds represented in waveform and spectrogram form. We conduct experiments on the use case of COVID-19 detection by combining waveform and spectrogram representation of body sounds. Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an area under the receiver operating characteristic curve (AUC) score of 0.8658, a sensitivity of 0.8057, and a specificity of 0.7958. Compared to models trained solely on spectrograms or waveforms, the use of both representations results in an improved AUC score, demonstrating that combining spectrogram and waveform representation helps to enrich the extracted features and outperforms the models that use only one representation. While this study focuses on COVID-19, FAIR’s flexibility allows it to combine various multi-modal and multi-instance features in many other diagnostic applications, potentially leading to more accurate diagnoses across a wider range of diseases.

Boat acceleration profiles can provide valuable information for coaches and practitioners to make meaningful technical interventions and monitor the determinants of success in rowing. Previous studies have used simple feature detection methods to identify key phases within individual strokes, such as drive onset, drive time, drive offset and stroke time. However, based on skill level, technique or boat class, the hull acceleration profile can differ, making robust feature detection more challenging. The current study’s purpose is to employ the undecimated wavelet transform (UWT) technique to detect individual features in the stroke acceleration profile from a single rowing hull-mounted accelerometer. In this investigation, the temporal and kinematic values obtained using the AdMosTM sensor in conjunction with the UWT processing approach were strongly correlated with the comparative measures of the Peach™ instrumented oarlock system. The measures for stroke time displayed very strong agreeability between the systems for all boat classes, with ICC values of 0.993, 0.963 and 0.954 for the W8+, W4− and W1x boats, respectively. Similarly, the drive time was also very consistent, with strong to very strong agreeability, producing ICC values of 0.937, 0.901 and 0.881 for the W8+, W4− and W1x boat classes. Further, a Bland–Altman analysis displayed little to no bias between the AdMosTM-derived and Peach™ measures, indicating that there were no systematic discrepancies between signals. This single-sensor solution could form the basis for a simple, cost-effective and accessible alternative to multi-sensor instrumented systems for the determination of sub-stroke kinematic phases.

We develop an analytical solution to the problem of one‐dimensional consolidation of unsaturated soil subjected to cyclic loads with arbitrary waveforms. The solution predicts the excess pore water and pore air pressures and the accompanying vertical compression in a poroelastic, unsaturated soil material. Cyclic loading occurs in a variety of engineering applications and often generates higher excess pore fluid pressures and larger vertical compression than does a time‐invariant load. In the present study, the loading function is allowed to take on any arbitrary waveform represented by a Fourier trigonometric series. Analytical solution to the boundary‐value problem in one dimension is given in closed form describing the frequency‐independent and frequency‐dependent components of the poroelastic response. We verify the analytical solution through representative examples involving cyclic loads with square and triangular patterns. Apart from the shape of the forcing function, we also investigate the effects of initial water saturation, soil texture, and excitation frequency on the system response. [ABSTRACT FROM AUTHOR]

Despite a sharp increase in the use of the waterpipe (WP) has been noted recently in Iran, no information is available for the smoking behavior and topography parameters. The present study is intended to obtain the inhalation and smoking topography parameters for the Iranian WP smokers. The smoking data collected from 122 smoking sessions, including 192 WP smokers in the Iranian Fars province have been used to perform smoking topography assessments. The influence of demographic and smoking parameters on puffing data is obtained. Results have indicated that gender and tobacco type strongly affect puff volume and duration. Women smokers inhale smaller volume of smoke than men and puff duration is significantly increased for regular smokers than occasional smokers. However, the results of the present study have not revealed a major effect of age, residence and setting on the puffing behavior. [ABSTRACT FROM AUTHOR]

Ground‐penetrating radar (GPR) technology is widely used in tunnel engineering detection; however, various factors, such as environmental interference and low signal‐to‐noise ratio characteristics of the echo data, limit the detection accuracy. A noise and interference suppression algorithm based on improved singular value decomposition is proposed in this paper. Compared with traditional filtering methods, the proposed method has the advantages of thorough denoising, no clutter, efficient improvement of profile resolution and less dependence on parameters. The main features of the proposed algorithm are as follows. (1) Given the global characteristics of the noise disturbance on the signal space, the minimum mean square error estimation is employed to approximate the effective signal, introducing the correction factor to suppress the larger singular value from the noise output in the reconstructing process of the effective signal subspace and to eliminate the strong direct wave interference to avoid producing false signals. (2) A positive difference sequence search algorithm based on rank order variance as well as the method of selecting correction factors are proposed to improve the processing accuracy. In order to verify the design, the tunnel lining simulation model and the actual tunnel lining detection data are used. The results show good performance for noise and interference suppression, providing technical support for improving GPR data quality and tunnel detection accuracy. [ABSTRACT FROM AUTHOR]

Over-the-horizon (OTH) radar is a technology that propagates radar signals beyond the Earth's curvature along the horizon to detect and track a wide range of distant targets. This technology is crucial in addressing potential threats from neighboring countries; however, it has not been implemented domestically for practical use. The OTH radar system utilizes high frequencies (3∼30 MHz) to detect distant targets, resulting in a limited bandwidth and, consequently, reduced range resolution. To overcome these limitations, this study focused on enhancing the range resolution of narrow-bandwidth transmission pulse waveforms by constructing a hybrid least-squares reception filter. This filter combines the outputs processed using matched filters and the least-squares method to effectively maintain a high resolution while controlling noise. Finally, simulation results confirmed the improvement in the range resolution of the OTH radar through the application of this hybrid reception filter. [ABSTRACT FROM AUTHOR]

Mechanical components often experience fatigue loading from various waveform conditions during their operational lifespan. However, the underlying mechanisms through which variations in loading waveform affect the fatigue life of components remain unclear. Thus, this study conducted tension–compression fatigue experiments on 34CrNi3MoVA steel specimens under the same stress amplitude with different waveforms (cosine, triangular, sawtooth, and reverse sawtooth) to investigate the effects of loading waveform variations on the cyclic strain hardening behaviors, the fatigue fracture failure, and the fatigue life. The results indicated that specimens under different waveforms all exhibited cyclic strain hardening. The fatigue cyclic hardening level progressively increased in the order of cosine, triangular, and sawtooth waveforms, resulting in a continuous increase in cyclic saturation strain amplitude. The analysis of fatigue fractures demonstrated a consistent increase in both the initiation and propagation zone areas in the order of cosine, triangular, and sawtooth waveforms, and the boundary between the propagation and final fracture zones gradually shifted from a straight to a curved shape. The influence mechanisms of cyclic loading waveforms on the fatigue life of specimens were analyzed based on the energy dissipation, leading to the development of a universal fatigue life prediction model applicable to different waveform conditions, the model was then verified with the reverse sawtooth wave specimens and resulted in a prediction error less than 15%. The study is expected to serve as a significant guide for predicting and evaluating the fatigue life of mechanical components under various fatigue loading conditions. [ABSTRACT FROM AUTHOR]

Cerebrospinal fluid disorders have a wide-ranging impact on vision, headache, cognition and a person's quality of life. Due to advances in technology and accessibility, intracranial pressure measurement and monitoring, usually managed by neurosurgeons, are being employed more widely in clinical practice. These developments are of direct importance for Ophthalmologists and Neurologists because the ability to readily measure intracranial pressure can aide management decisions. The aim of this review is to present the emerging evidence for intracranial pressure measurement methods and interpretation that is relevant to Neuro-ophthalmologists. [ABSTRACT FROM AUTHOR]

Speech coding is a technique that compresses speech signals into a smaller digital form, making it easier to transmit or store, while still maintaining the quality and intelligibility of the speech. The review aimed to identify and analyses the most effective waveform-based nonlinear speech coding prediction techniques, including the use of neural networks and polynomial filters. The study analyzed 29 publications from 2000 to 2023 and found that neural network-based models are widely used for speech compression, with RNN topologies being favored due to their ability to introduce nonlinearity and nonstationary. While nonlinear adaptive speech prediction techniques have been explored for speech coding, further research is needed to optimize the adaptive algorithms used in these models. The review also identified a need for future research to address quality performance and computational cost, and suggested further exploration of RNN predictor models. The methodology used in this study involved a computer science approach that follows three main phases: planning, conducting, and reporting. Six different stages were followed, including determining research questions, defining research approach, study selection criteria, quality measurement criteria, data extraction strategy, and synthesizing extracted data. Overall, this study highlights the need for continued research in the development and improvement of neural network-based speech compression models. [ABSTRACT FROM AUTHOR]

End-tidal capnography can provide useful clinical information displayed on the ventilator screen or bedside monitor. It is important that clinicians can assess and utilise this information to assist in identifying underlying complications and pulmonary pathology. Sudden change or loss of the CO2 waveform can act as a safety measure in alerting clinicians of a dislodged or blocked endotracheal tube, considering the concurrent flow and volume waveforms. Visual pattern recognition by the clinicians of commonly seen waveform traces may act as an adjunct to other modes of ventilatory monitoring techniques. Waveforms traces can aid clinical management, help identify cases of ventilation asynchrony between the infant and the ventilator. We present some common clinical scenarios where tidal capnography can be useful in the timely identification of pulmonary complication and for practical troubleshooting at the cot-side.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain–computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI).

To optimally assess oscillatory phenomena within physiological variables, spectral domain transforms are used. A discrete Fourier transform (DFT) is one of the most common methods used to attain this spectral change. In traumatic brain injury (TBI), a DFT is used to derive more complicated methods of physiological assessment, particularly that of cerebrovascular reactivity (CVR). However, a practical application of a DFT will introduce various errors that need to be considered. This study will evaluate the pulse amplitude DFT derivation of intracranial pressure (AMP) to highlight how slight differences in DFT methodologies can impact calculations. Utilizing a high-frequency prospectively maintained data set of TBI patients with recorded arterial and intracranial blood pressure, various cerebral physiological aspects of interest were assessed using the DFT windowing methods of rectangular, Hanning, and Chebyshev. These included AMP, CVR indices (including the pressure reactivity and pulse amplitude index), and the optimal cerebral perfusion pressure (with all methods of CVR). The results of the different DFT-derived windowing methods were compared using the Wilcoxon signed-ranked test and histogram plots between individual patients and over the whole 100-patient cohort. The results for this analysis demonstrate that, overall and for grand average values, there were limited differences between the different DFT windowing techniques. However, there were individual patient outliers to whom the different methods resulted in noticeably different overall values. From this information, for derived indices utilizing a DFT in the assessment of AMP, there are limited differences within the resulting calculations for larger aggregates of data. However, when the amplitude of spectrally resolved response is important and needs to be robust in smaller moments in time, it is recommended to use a window that has amplitude accuracy (such as Chebyshev or flat-top).

Background. Modern requirements for passenger cars include a number of main directions: efficient use of energy, improvement of environmental indicators, improvement of operational characteristics. The use of variator gearboxes has significantly improved the energy efficiency and environmental performance of the car due to optimal engine performance. However, the disadvantage of variators in comparison with manual transmissions is still insufficient efficiency, which does not allow competing with “mechanics” due to low performance characteristics. Since the variator is a friction mechanism, a way to increase the efficiency of its operation is to change the coefficient of friction in friction pairs. One of the options for such modernization is the creation of a regular microprofile on the surface of friction pulleys, namely– undulations. The actual contact spots formed by the undulation provide sufficient traction of the variator transmission and minimize power loss. The object of the study is the relationship between power indicators and the quality of the surface layer of friction elements of a continuously variable transmission. The subject of the study is an adequate assessment and provision of the power parameters of a continuously variable transmission, by ensuring the surface quality parameters of the drive pulley of the variator. The purpose of the study is to develop a technique for providing power indicators of a variator transmission by creating a wavy microprofile based on the mechanics of contact interaction. Materials and methods. The theoretical and experimental research carried out in the work is based on the study of literature, conducted research and the experience of practicing technologists. Results. The empirical dependence of the prediction of the coefficient of friction at normal wear depending on the parameters of surface quality is analyzed. It is shown that the parameters of the undulation of the surface have a greater influence on the coefficient of friction, and as a consequence, the traction ability of the transmission in the frictional interaction of parts, than the roughness parameters. Analytical dependences of determining the power on the output shaft of the variator and the efficiency of the variator depending on the height parameter of the waviness are proposed. Conclusions. Based on the conducted research and experiments, a mathematical model is proposed to ensure the power parameters of variators by forming a high-altitude waviness index. This will make it possible to predict and ensure the coefficient of friction of the friction pair at the stage of development work and drawing up the technological process of processing parts.

Photopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literature, it is common to classify DVPs into four classes based on the dicrotic notch position. However, when working with real data, labelling waveforms with one of these four classes is no longer straightforward and may be challenging. The correct identification of the DVP shape could enhance the precision and the reliability of the extracted bio markers. In this work we proposed unsupervised machine learning and deep learning approaches to overcome the data labelling limitations. Concretely we performed a K-medoids based clustering that takes as input 1) DVP handcrafted features, 2) similarity matrix computed with the Derivative Dynamic Time Warping and 3) DVP features extracted from a CNN AutoEncoder. All the cited methods have been tested first by imposing four medoids representative of the Dawber classes, and after by automatically searching four clusters. We then searched the optimal number of clusters for each method using silhouette score, the prediction strength and inertia. To validate the proposed approaches we analyse the dissimilarities in the clinical data related to obtained clusters. [ABSTRACT FROM AUTHOR]

In George Wald's Nobel Prize acceptance speech for "discoveries concerning the primary physiological and chemical visual processes in the eye", he noted that events after the activation of rhodopsin are too slow to explain visual reception. Photoreceptor membrane phosphoglycerides contain near-saturation amounts of the omega-3 fatty acid docosahexaenoic acid (DHA). The visual response to a photon is a retinal cis–trans isomerization. The trans-state is lower in energy; hence, a quantum of energy is released equivalent to the sum of the photon and cis–trans difference. We hypothesize that DHA traps this energy, and the resulting hyperpolarization extracts the energized electron, which depolarizes the membrane and carries a function of the photon's energy (wavelength) to the brain. There, it contributes to the creation of the vivid images of our world that we see in our consciousness. This proposed revision to the visual process provides an explanation for these previously unresolved issues around the speed of information transfer and the purity of conservation of a photon's wavelength and supports observations of the unique and indispensable role of DHA in the visual process. [ABSTRACT FROM AUTHOR]

Introduction and Objectives: Advanced analysis of the morphological features of the photoplethysmographic (PPG) waveform may provide greater understanding of mechanisms of action of photobiomodulation (PBM). Photobiomodulation is a non-ionizing, red to near-infrared irradiation shown to induce peripheral vasodilatation, promote wound healing, and reduce pain. Using laser Doppler flowmetry combined with thermal imaging we found previously in a clinical study that PBM stimulates microcirculatory blood flow and that baseline palm skin temperature determines, at least in part, why some individuals respond favorably to PBM while others do not. "Responders" (n = 12) had a skin temperature range of 33°C-37.5°C, while "non-responders" (n = 8) had "cold" or "hot" skin temperature (<33°C or >37.5°C respectively). The continuous PPG signals recorded from the index fingers of both hands in the original clinical study were subjected to advanced post-acquisitional analysis in the current study, aiming to identify morphological features that may improve the accuracy of discrimination between potential responders and non-responders to PBM. Methods: The PPG signals were detrended by subtracting the lower envelope from the raw signal. The Root Mean Square (RMS) and Entropy features were extracted as were two additional morphological features -- Smoothness and number of local extrema per PPG beat (#Extrema). These describe the signal jaggedness and were developed specifically for this study. The Wilcoxon test was used for paired comparisons. Correlations were determined by the Spearman correlation test (rs). Results: The PPG waveforms of responders to PBM had increased amplitude and decreased jaggedness (Baseline vs. 10' post-irradiation: Entropy, 5.0 ± 1.3 vs. 3.9 ± 1.1, p = 0.012; #Extrema, 4.0 ± 1.1 vs. 3.0 ± 1.6, p = 0.009; RMS, 1.6 ± 0.9 vs. 2.3 ± 1.2, p = 0.004; Smoothness, 0.10 ± 0.05 vs. 0.19 ± 0.16, p = 0.016). In addition, unilateral irradiation resulted in a bilateral response, although the response of the contralateral, non-irradiated hand was shorter in duration and lower in magnitude. Although subjects with 'cold,' or 'hot,' baseline skin temperature appeared to have morphologically distinct PPG waveforms, representing vasoconstriction and vasodilatation, these were not affected by PBM irradiation. Conclusion: This pilot study indicates that post-acquisitional analysis of morphological features of the PPG waveform provides new measures for the exploration of microcirculation responsiveness to PBM. [ABSTRACT FROM AUTHOR]

Chronosequence changes among Tropical Dry Forests (TDFs) are essential for understanding this unique ecosystem, which is characterized by its seasonality (wet and dry) and a high diversity of deciduous trees and shrubs. From 2005 to 2021, we used two different airborne LiDAR systems to quantify structural changes in the forest at Santa Rosa National Park. Line- and shape-based waveform metrics were used to record the overall changes in the TDF structure. Based on a 16-year growth analysis, notable variations in height-related profiles were observed, particularly for RH50, RH100, and waveform-produced canopy heights. The results showed that Cy and RG have increased since the forests have been growing, whereas Cx has decreased. The decrease in Cx is because ground returns are lower when the canopy density i and canopy height increase. A positive relationship was observed between Cy and CH, RG, and RH100, particularly for the wet season data collected in 2021. These findings provide important insights into the growth dynamics of TDFs in Santa Rosa National Park and could inform future conservation efforts. [ABSTRACT FROM AUTHOR]

Using the pulse-width modulation (PWM) is a very convenient and efficient way to generate the approximation of the AC, in the simplest case sinewaves in the embedded test & measurement solutions. Partly, because the on-chip PWM modulator is available in most of the microcontroller and DSP chips. In the current paper improved (compared with conventional PWM) implementation of the binary waveforms is proposed. The idea is based first on the conversion of the desired AC (e.g., sinewave) to quantize in the amplitude domain waveform, to be further converted to the binary pulses with a duty cycle, corresponding to each quantization level. Such an approach is described and analyzed, with several examples of the implementation and application for impedance measurement solutions. The results show the significant improvements of the approximated binary signals both in the frequency and time domain, by optional simple low- or bandpass filtering of the binary signal, over classical PWM, while still preserving simplicity (e.g., number of needed binary signal segments) of the generated waveforms (e.g., compared with delta-sigma bitstreams). The beneficial usage of the proposed approach for the impedance measurement applications is described, as well as to generate excitation waveforms, as well as to be used as reference waveforms for synchronous demodulation of the response signal. It is shown, that for the frequency range of 10 to 70 kHz total harmonic distortion below 2.5% can be achieved by the proposed improved PWM solution. [ABSTRACT FROM AUTHOR]

As the existing Blade Tip Timing (BTT) vibration measurement methods have serious under-sampling problems, where the blade resonance frequency is usually higher than the sampling frequency of the data acquisition system of the BTT method, resulting in large errors in the identification of blade vibration parameters, new solutions are needed to extend the capability of BTT to nonlinear and multimodal vibration analysis. Therefore, it is the current research direction to pursue new and more accurate measurement and signal processing methods. By analyzing the waveform data from the BTT sensor and using it for vibration analysis, it significantly extends the BTT database. To avoid the current problems of under-sampling and low recognition accuracy, this paper conducts a study on the recognition of rotating blade vibration parameters based on the Radial Basis Function (RBF) model by establishing a RBF neural network prediction model to analyze the static calibration experimental data and predict the waveform of the BTT sensor, and comparing the prediction curves of various models. As the results show, for the RBF model, the prediction accuracy is closely related to the source data of the sampling point data, when the source data predicted by the RBF model is close to the center of the samples, the prediction accuracy is high, meanwhile, the prediction accuracy decreases as it is far away from the center of these data. At the same time, the number of samples is too small to affect the prediction ability of the RBF model. By using this method, more waveforms under the Blade Tip Clearance (BTC) can be predicted with the available sample point data, and the errors in the experimental measurement process can be corrected. [ABSTRACT FROM AUTHOR]

Spinal cord stimulation (SCS) is a therapeutic modality for the treatment of various chronic pain conditions that has rapidly evolved over the past 50 years. Unfortunately, over time, patients implanted with SCS undergo a habituation phenomenon leading to decreased pain relief. Consequently, the discovery of new stimulation waveforms and SCS applications has been shown to prolong efficacy and reduce explantation rates. This article explores various SCS waveforms, their applications, and proposes a graded approach to habituation mitigation. We suspect the neural habituation phenomenon parallels that seen in pharmacology. Consequently, we urge further exploration of the early introduction of these stimulation strategies to abate spinal cord stimulation habituation.

Photopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literature, it is common to classify DVPs into four classes based on the dicrotic notch position. However, when working with real data, labelling waveforms with one of these four classes is no longer straightforward and may be challenging. The correct identification of the DVP shape could enhance the precision and the reliability of the extracted bio markers. In this work we proposed unsupervised machine learning and deep learning approaches to overcome the data labelling limitations. Concretely we performed a K-medoids based clustering that takes as input 1) DVP handcrafted features, 2) similarity matrix computed with the Derivative Dynamic Time Warping and 3) DVP features extracted from a CNN AutoEncoder. All the cited methods have been tested first by imposing four medoids representative of the Dawber classes, and after by automatically searching four clusters. We then searched the optimal number of clusters for each method using silhouette score, the prediction strength and inertia. To validate the proposed approaches we analyse the dissimilarities in the clinical data related to obtained clusters.

Introduction and Objectives: Advanced analysis of the morphological features of the photoplethysmographic (PPG) waveform may provide greater understanding of mechanisms of action of photobiomodulation (PBM). Photobiomodulation is a non-ionizing, red to near-infrared irradiation shown to induce peripheral vasodilatation, promote wound healing, and reduce pain. Using laser Doppler flowmetry combined with thermal imaging we found previously in a clinical study that PBM stimulates microcirculatory blood flow and that baseline palm skin temperature determines, at least in part, why some individuals respond favorably to PBM while others do not. “Responders” (n = 12) had a skin temperature range of 33°C–37.5°C, while “non-responders” (n = 8) had “cold” or “hot” skin temperature (37.5°C respectively). The continuous PPG signals recorded from the index fingers of both hands in the original clinical study were subjected to advanced post-acquisitional analysis in the current study, aiming to identify morphological features that may improve the accuracy of discrimination between potential responders and non-responders to PBM.Methods: The PPG signals were detrended by subtracting the lower envelope from the raw signal. The Root Mean Square (RMS) and Entropy features were extracted as were two additional morphological features -- Smoothness and number of local extrema per PPG beat (#Extrema). These describe the signal jaggedness and were developed specifically for this study. The Wilcoxon test was used for paired comparisons. Correlations were determined by the Spearman correlation test (rs).Results: The PPG waveforms of responders to PBM had increased amplitude and decreased jaggedness (Baseline vs. 10’ post-irradiation: Entropy, 5.0 ± 1.3 vs. 3.9 ± 1.1, p = 0.012; #Extrema, 4.0 ± 1.1 vs. 3.0 ± 1.6, p = 0.009; RMS, 1.6 ± 0.9 vs. 2.3 ± 1.2, p = 0.004; Smoothness, 0.10 ± 0.05 vs. 0.19 ± 0.16, p = 0.016). In addition, unilateral irradiation resulted in a bilateral response, although the response of the contralateral, non-irradiated hand was shorter in duration and lower in magnitude. Although subjects with ‘cold,’ or ‘hot,’ baseline skin temperature appeared to have morphologically distinct PPG waveforms, representing vasoconstriction and vasodilatation, these were not affected by PBM irradiation.Conclusion: This pilot study indicates that post-acquisitional analysis of morphological features of the PPG waveform provides new measures for the exploration of microcirculation responsiveness to PBM.

Accurate spike sorting to the appropriate neuron is crucial for neural activity analysis. To improve spike sorting performance, it is essential to fully leverage each processing step, including filtering, spike detection, feature extraction, and clustering. However, compared to the latter two steps that were widely studied and optimized, the filtering process was largely neglected. In this study, we proposed a fast and effective spike sorting method (MultiFq) based on multi-frequency composite waveform shapes acquired through an optimized filtering process. When combined with the classical PCA-Km spiking sorting algorithm, our proposed MultiFq significantly improved its sorting performance and achieved as high performance as the complex Wave-clus did in both the simulated and in vivo datasets. But, the combined method was about 10 times faster than Wave-clus (0.16 s vs. 2.06 s in simulated datasets; 0.46 s vs. 2.03 s in in vivo datasets). Furthermore, we demonstrated the compatibility of our MultiFq by combining it with other sorting algorithms, which consistently resulted in significant improvement in sorting accuracy with the maximum improvement at 35.04%. The above results demonstrated that our proposed method could significantly improve the sorting performance with low computation cost and good compatibility by leveraging the multi-frequency composite waveform shapes. [ABSTRACT FROM AUTHOR]

This study aimed to characterize and correlate the stylet penetration behaviors of nymphs of the Neotropical brown stink bug, Euschistus heros (F.), on immature soybean pods. Waveforms were obtained using electropenetrography (EPG). The findings revealed that the nymphs exploited the xylem vessels and the seed tegument or endosperm. Primarily 4 phases were characterized: nonfeeding, pathway, salivation, and ingestion. The waveforms of each phase were similar in appearance across instars. The biological meanings of waveforms were based on visual observations, comparison with waveforms of adults, and histological studies. Np represents the insect resting or walking on soybean pod surface. Eh1 represents the first contact between the mouthparts (stylets) and plant tissue. Eh2 represents xylem sap ingestion, and Eh3 represents seed activities (including tegument and endosperm). The number of waveform events did not differ among instars for all waveforms. However, for Eh3, fifth instars performed more activities than other instars. The second instars had the smallest value, and third and fourth instars had intermediate values. For total duration, all waveforms differed among instars. Np duration was shorter for third compared with second and fourth instars and intermediate for fifth instar. For Eh1, second and third instars had the longest duration (1.5× to 2× greater) compared with fourth and fifth instars. For Eh2 and Eh3, the second-instar showed the longest (~2× greater) and shortest durations, respectively. Overall, this study provides important insights into the feeding behavior of E. heros nymphs so that effective pest management programs can be developed to contain this pest. [ABSTRACT FROM AUTHOR]

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