Pulse Signal Research Articles

Propagation Features of Pseudorandom Pulse Signals from an Extended Shelf into the Deep Sea upon Reception at Different Depths

Propagation Features of Pseudorandom Pulse Signals from an Extended Shelf into the Deep Sea upon Reception at Different Depths

Радиолокационный метод оценивания углового положения летательного аппарата на основе использования векторных свойств зондирующих сигналов

The radar monopulse method of determining the bearing and roll angles of an aircraft using a passive radio beacon with specified polarization scattering characteristics is considered, in which the reflected radio pulse contains complete information about the navigation elements of the aircraft flight. The passive beacon is two spatially separated in the horizontal plane radar polarization-anisotropic trihedral corner reflectors with horizontal and vertical natural polarizations, respectively. To determine the bearing and roll angles of an aircraft, probing pulse signals are used, the linear plane of polarization of which makes an angle π/4 with the horizontal plane. The bearing of the aircraft is estimated in this plane by a single reflected radio pulse at the output of a two-channel receiving device according to the measured phase difference between the horizontal and vertical polarized components of the received signal. The roll angle is estimated from the measured ratio of the amplitudes of the orthogonally linearly polarized components. Analytical expressions for determining the bearing and roll angles of the aircraft are obtained.

Quantitative evaluation of oncogenic KRAS signaling and therapeutic effects of molecular-targeted drugs in living cells by single-molecule imaging.

76 Background: Oncogenic KRAS mutations pose challenges due to their constitutive activation and resistance to drugs in colorectal cancer. However, several aspects remain unresolved. Single-molecule imaging has revealed distinct behaviors of KRAS. Inactive KRAS wild-type (WT) molecules exhibit continuous lateral diffusion on the plasma membrane, while activated or oncogenic KRAS molecules display slower diffusion and transient trapping due to interactions with signaling molecules. These observations suggest that the overall signal intensity of KRAS within a cell is finely regulated by integrating short-term pulse signals during transient trapping. Methods: We conducted single-molecule observations of KRAS (WT, G13D, and G12D/C/V) in living colon cancer cells, both with and without molecular-targeted drugs, before and after EGF stimulation. We quantitatively analyzed their diffusional behavior to evaluate KRAS signaling and the therapeutic effects of molecular-targeted drugs. Results: The temporal fraction of trapped KRAS WT was highest at 2 minutes (6.4%) and decreased to pre-activation level (3.0%) at 5 minutes after EGF stimulation. Although the temporal fractions of trapped KRAS mutants were higher than those of KRAS WT both before stimulation (5%) and after stimulation (10%), their diffusional behaviors were mutation-specific. The temporal fraction of trapped KRAS G13D increased sharply, and those of KRAS G12D and G12C increased at a moderate rate, while that of KRAS G12V did not elevate after EGF stimulation. Cetuximab treatment significantly reduced the temporal fraction of trapped KRAS WT after stimulation, but only moderately decreased those of KRAS mutants. Notably, transient trappings of KRAS G12D and G12C were abrogated by KRAS inhibitors, and the combined therapy of KRAS inhibitors and cetuximab demonstrated additive effects. Conclusions: Our findings indicate that the mutation-specific diffusional behavior of oncogenic KRAS can be quantitatively assessed by single-molecule imaging. This method serves as a powerful tool to uncover the mechanisms underlying oncogenic KRAS signaling and evaluate the therapeutic efficacy of molecular-targeted drugs in living cells.

The Diffusion Exchange Ratio (DEXR): A minimal sampling of diffusion exchange spectroscopy to probe exchange, restriction, and time-dependence.

Water exchange is increasingly recognized as an important biological process that can affect the study of biological tissue using diffusion MR. Methods to measure exchange, however, remain immature as opposed to those used to characterize restriction, with no consensus on the optimal pulse sequence(s) or signal model(s). In general, the trend has been towards data-intensive fitting of highly parameterized models. We take the opposite approach and show that a judicious sub-sample of diffusion exchange spectroscopy (DEXSY) data can be used to robustly quantify exchange, as well as restriction, in a data-efficient manner. This sampling produces a ratio of two points per mixing time: (i) one point with equal diffusion weighting in both encoding periods, which gives maximal exchange contrast, and (ii) one point with the same total diffusion weighting in just the first encoding period, for normalization. We call this quotient the Diffusion EXchange Ratio (DEXR). Furthermore, we show that it can be used to probe time-dependent diffusion by estimating the velocity autocorrelation function (VACF) over intermediate to long times (~ 2-500 ms). We provide a comprehensive theoretical framework for the design of DEXR experiments in the case of static or constant gradients. Data from Monte Carlo simulations and experiments acquired in fixed and viable ex vivo neonatal mouse spinal cord using a permanent magnet system are presented to test and validate this approach. In viable spinal cord, we report the following apparent parameters from just 6 data points: , , , and , which correspond to the exchange time, restricted or non-Gaussian signal fraction, an effective spherical radius, and permeability, respectively. For the VACF, we report a long-time, power-law scaling with , which is approximately consistent with disordered domains in 3-D. Overall, the DEXR method is shown to be highly efficient, capable of providing valuable quantitative diffusion metrics using minimal MR data.

Digital technologies for signal radio vision and radio monitoring

Objectives. Radiophysical processes involving the electrodynamic formation of signal radio images diffusely scattered by the signature of small-sized objects or induced by the near field of radio devices are relevant for identifying radiogenomic (cumulant) features of objects in the microwave range in the development of neuroimaging ultra-short pulse (USP) signal radio vision systems, telemonitoring, and near-radio detection. The paper sets out to develop methods and algorithms for vector analysis of radio wave deformation of nonstationary fields forming a signal radio image based on radiophysical and topological characteristics of small-sized objects; to develop software and hardware for registration and neural network recognition of signal radio images, including methods for the synthesis and extraction of signal radiogenomes using digital twins of objects obtained through vector electrodynamic modeling; and to analyze signal radio images induced by elements of printed topology of electronic devices.Methods. The study is based on statistical radiophysics methods, time-frequency approaches for wavelet transformation of USP radio images, numerical electrodynamic methods for creating digital twins of small-sized objects, as well as neural network authentication algorithms based on the cumulant theory of pole-genetic and resonant physically unclonable functions used in identifying signal radio images.Results. The results of fundamental research on electrodynamic effects of vector-wave deformation of nonstationary fields of sub-nanosecond configuration are presented as a means of identifying and authenticating signal radio images. Neural network techniques for cumulant formation of radio genomes of signal radio images are proposed on the basis of pole-genetic and resonant functions.Conclusions. A radiogenome, representing the unique authenticator of a radio image, is shown to be formed on the basis of physically unclonable functions determined by the structure and set of radiophysical parameters of the image. Cumulant features of signal radio images identified on the basis of pole-genetic and physically unclonable resonant functions of small-sized objects are revealed.

A biodegradable ZnFe2O4/PLLA electrospun fibers based longitudinal mode piezoelectric membrane for health monitoring

Recently, multifunctional sensors based on piezoelectric active film fabricated with environmental friendly materials is a nascent field which can address the growing concern of electronic waste. Nevertheless, frequently utilized poly(vinylidene fluoride) (PVDF) and poly(L-lactic acid) (PLLA) encounter challenges of limited biocompatibility and weak piezoelectricity with low shear piezoelectric coefficient (d14), respectively. Accordingly, a biodegradable piezoelectric sensor comprising of PLLA nanofiber blended with ZnFe2O4 nanoparticles was developed in this work. The ZnFe2O4 nanoparticles served as heterogeneous nucleation center leading to improved piezoelectric coefficient and output than pure PLLA due to more consistent orientation of CO dipoles. The ZnFe2O4/PLLA film exhibited significant performance improvement featured with 1.7-fold higher longitudinal piezoelectric coefficient (d33 ∼ 6.5 ± 0.3 pm/V), nearly 250 % boosting on the output voltage. The ZnFe2O4/PLLA piezoelectric sensor induced high sensitivity (2–10 N:0.55 V/N; 10–32 N:0.29 V/N) and excellent mechanical stability for 27,000 cycles. The results of the soil burial experiment demonstrate that the film can degrade by over 75 % within 90 days, confirming its excellent biodegradability. Moreover, the ZnFe2O4/PLLA nanofibers based piezoelectric sensor could serve as effective detection evidenced by different output signals of head motion, pulse and artery beating demonstrating its feasible application for health monitoring. Considering the characteristics of straightforward fabrication and readily accessible sustainable materials, the ZnFe2O4/PLLA sensor provides an appealing eco-friendly solution for self-powered flexible personal devices disregarding the need to dispose of electronic waste.

Deep Learning for Dynamic Modeling and Coded Information Storage of Vector‐Soliton Pulsations in Mode‐Locked Fiber Lasers

AbstractSoliton pulsations are ubiquitous feature of non‐stationary soliton dynamics in mode‐locked lasers and many other physical systems. To overcome difficulties related to a huge amount of necessary computations and low efficiency of traditional numerical methods in modeling the evolution of non‐stationary solitons, a two‐parallel bidirectional long short‐term memory recurrent neural network (TP‐Bi_LSTM RNN) is proposed, with the main objective to predict dynamics of vector‐soliton pulsations (VSPs) in various complex states, whose real‐time dynamics is verified by experiments. For two examples, viz., single‐ and bi‐periodic VSPs, with period‐21 and a combination of period‐3 and period‐43, the prediction results are better than provided by direct simulations – namely, deviations produced by the TP‐Bi_LSTM RNN results are 36% and 18% less than those provided by the simulations, respectively. This means that predicted results provided by the neural network are better than numerical simulations. Moreover, the prediction results for unstable VSP state with period‐9 indicate that the optimization of training sets and the number of training iterations are particularly important for the predictability. Besides, the scheme of coded information storage based on the TP‐Bi_LSTM RNN, instead of actual pulse signals, is realized too. The findings offer new applications of deep learning to ultrafast optics and information storage.

Sub-THz Characterization of Technical Surfaces for Particle Accelerator Vacuum Chambers.

Coatings play a crucial role in the functionality of vacuum chambers in particle accelerators, serving a dual goal by efficiently facilitating pumping and mitigating electron cloud effects. However, their impact on the surface impedance of the chamber walls raises concerns, potentially affecting the machine performance and imposing limitations on achievable energies and currents. Therefore, an electromagnetic characterization is essential for a comprehensive study of accelerator structures, particularly in the context of the next-generation machines where the demand for extremely short particle bunches accentuates the importance of evaluating material responses in the very-high-frequency region. We present a technique for probing the sub-THz response of coating materials by measuring pulsed signals passing through a specifically designed waveguide, in which is placed a slab with the deposited material under test. The proposed methodology allows for a comprehensive exploration of the electromagnetic properties of the most used technical surfaces (substrate plus coatings) in accelerators under realistic conditions, providing valuable insights into their behavior in the sub-THz frequency range. The experimental data of three different Non-Evaporable Getter coating samples, prepared on a copper substrate at the CERN deposition facilities under different sputtering conditions, are discussed. The findings contribute to a deeper understanding of the complex interactions between coatings and accelerator structures, with the aim of optimizing performance and efficiency in the evolving landscape of particle acceleration technologies. The limitations and advantages of the technique are also reported.

Local color display technology

AbstractThe local color display technology utilizes a high refresh rate LCD open‐cell without a color filter and enables color adjustment through independent RGB backlight zones isolated by an anti‐scatter grid (ASG). Synchronized calibration between the open‐cell and the backlight zones is achieved by aligning the backlight controller with the vertical clock pulse (CPV)/vertical start pulse (STV) signals in the open‐cell gate driver, thereby determining the open‐cell scan line position. Once synchronous calibration is completed, the backlight controller refreshes the corresponding RGB backlight zones whenever the open‐cell scan line passes through them. The local color display technology can reduce the cost of open‐cell by 20%, improve resolution by three times, enhance brightness by eight times, expand the color gamut to BT2020, and improve grayscale to 32‐bits.

An accurate tidal peak localization method in radial arterial pulse signals based on hybrid neural networks

Background: cardiovascular diseases (CVDs) have become the leading causes of death worldwide. Arterial stiffness and elasticity are important indicators of cardiovascular health. Pulse wave analysis (PWA) is essential for analyzing arterial stiffness and elasticity, which are highly dependent on the tidal peak (P 2). P 2 is one of the four key physiological points, which also include percussion peaks (P 1), diastolic notches (P 3), and diastolic peaks (P 4). P 1, P 3, and P 4 are often local maxima or minima, facilitating their identification via the second derivatives method, a classic localization method for key physiological points. Classic methods such as the second derivative method, Empirical Mode Decomposition (EMD), and Wavelet Transform (WT), have been employed for the extraction and analysis of the P 2. Due to individual variation and arterial stiffness, locating the P 2 using classic methods is particularly challenging. Methods: we propose a hybrid neural network based on Residual Networks (ResNet) and bidirectional Long Short-Term Memory Networks (Bi-LSTM), successfully achieving high-precision localization of the P 2 in radial artery pulse signals. Meanwhile, we compared our method with the second derivative method, EMD, WT, Convolutional Neural Networks (CNN) and the hybrid model with ResNet and LSTM. Results: the results indicate that our proposed model exhibits significantly higher accuracy compared to other algorithms. Overall, MAEs and RMSEs for our proposed method are 62.60% and 58.84% on average less than those for other algorithms. The average R Adj 2 is 29.20% higher. The outcomes of the efficiency evaluation suggest that the hybrid model performs more balancedly without any significant shortcomings, which indicates that the Bi-LSTM structure upgrades the performances of LSTM. Significance: our hybrid model can provide the medical field with improved diagnostic tools and promote the development of clinical practice and research.

An extraction method for early fault features of rolling bearings based on resonance sparse signal decomposition and non-convex second-order total variation denoising

A primary challenge in the field of fault diagnosis is extracting weak fault characteristics of bearings under large background noise and non-stationary conditions. Due to significant noise interference in the signals of rolling bearings, resonance sparse signal decomposition (RSSD) cannot efficiently extract the transient impact components in the early failure stage and the total variation denoising (TVD) method distorts signal waveforms. In this study, a combined extraction method for early fault features of rolling bearings based on RSSD and non-convex second-order total variation denoising (NCSOTVD) is proposed. Firstly, a non-convex function is introduced to define the regularisation term in the second-order TVD method, and the regularisation parameter and the convexity parameter in the NCSOTVD method are respectively screened using the noise standard deviation and the permutation entropy value to enhance the impact characteristics of signals and induce signal sparsity. The NCSOTVD model is solved using the optimisation-minimisation algorithm, so as to achieve noise reduction and feature enhancement of the vibration signals. Then, the low-resonance components of RSSD are denoised using the NCSOTVD method to highlight periodic pulse signals and extract the fault features of the rolling bearings. The simulation results and experimental data show that the method largely suppresses the noise interference, highlights the fault characteristics and reduces the problems of waveform distortion and poor sparsity of the TVD method in the denoising process.

Research on electromagnetic pulse signal detection method based on intelligent compressed sensing technology

Abstract Electromagnetic pulse signal measurement is crucial for national security, scientific research, technological advancement, and emergency preparedness, playing a vital role in various aspects of modern society. In this work, an electromagnetic pulse signal detection approach utilizing compressed sensing technology is suggested. By unitizing three parallel low-frequency analog-to-digital converters, the high-frequency acquisition waveform of the measured electromagnetic pulse signal can be recovered. The compressed sensing effect of the proposed system on electromagnetic pulse signal is tested by simulation, which verifies the feasibility of this method.

Fourier Transform Ion Trap Mass Analysis by Deciphering Ion Ejection Signals in the Frequency Domain.

Mass analysis in an ion trap is conventionally realized through time domain analysis of the ejected ion current collected from an electron multiplier (EM), in which the ion ejection time is found to have a correlation with the mass-to-charge (m/z) ratio of the ion. In this study, we investigated a new method for mass analysis by examining ion ejection signals in the frequency domain. Theoretical analysis and ion trajectory simulations show that ions of the same m/z ratio are ejected from an ion trap at regular intervals, producing a periodic pulsed signal on the EM. The period of this pulsed ejection signal is directly linked to the m/z values of the ions. To realize this method experimentally, a broadband preamplifier was built and integrated on a miniature ion trap mass spectrometer (the "Brick" series from Nier Inc.) to capture this pulsed ion ejection signal collected from the EM. Experimental results were in good agreement with theoretical and simulation analyses. This method has the potential to improve the mass resolution of an ion trap mass analyzer. As a proof-of-concept demonstration, a peak width of 0.1 Da at a m/z value of 281 was achieved in experiments.

Design of digital frequency meter based on FPGA

Frequency measurement is one of the key technicals of the electronic measurement field, and the digital frequency meter is an indispensable measuring tool for measuring technology engineering personnel. Nowadays, digital frequency meters are indispensable components in daily appliances such as televisions, refrigerators, washing machines, and other smart home appliances. Therefore, the measurement range of digital frequency meters is becoming wider, and their design structures are becoming increasingly complex. Digital frequency meters play a crucial role in developing electronic products as a whole. Use the Very High-speed Hardware Description Language (VHDL) in Electronic Design Automation(EDA)technology to simulate and verify the digital frequency meter. Through simulation analysis, design the measurement circuit of the digital frequency meter and verify its accuracy. In the design process of a digital frequency meter, its function is to measure SIN signals, unit pulse signals, and the frequency range of various signals, achieved by collecting physical quantities within a unit of time.

A Multielectrode Layout for High Reliability of Flexible Piezoresistive Sensor: One Stimulus Signal to Three Sensing Signals.

Flexible sensors have developed rapidly due to their great application potential in the intelligent era. However, the frequent bending work requirements pose a serious challenge to the mechanical reliability of flexible sensors. Herein, a strategy of using a new multielectrode layout to achieve multiple sensing signals based on one external signal is proposed for the first time to improve the reliability of flexible piezoresistive sensors. The multielectrode layout consists of a pair of interdigital electrodes and a bottom electrode. The interdigitated electrodes are used to sense the change in the surface resistance of the sensor, and the interdigital electrodes and the bottom electrode are used to sense the change in the bulk resistance of the sensor. As a result, without increasing the sensing unit area, the electrode layout allows the sensor to generate three response electrical signals when sensing an external pressure, thus improving the reliability of the sensor. Based on the electrode layout, a highly reliable flexible piezoresistive sensor with a multilevel porous structure is obtained by a microwave foaming method with a template. In the working state of sensing surface resistance, the sensor has a 22.12 kPa-1 sensitivity. Meanwhile, in the working state of sensing bulk resistance, the sensor shows a 55.17 kPa-1 sensitivity. Furthermore, the sensor is applied to monitor human pulse and speech signals, demonstrating its multisignal output characteristics and potential applications in flexible electronics. In conclusion, the new strategy of using the proposed electrode layout to improve the reliability of flexible sensors is expected to greatly promote the practical application of flexible electronics.

Modelling of long-wave mid-infrared ultrashort pulse generation via difference frequency generation

In this paper, a model of generating mid-infrared (MIR) ultrashort laser pulse through difference frequency generation (DFG) is established. The pulse evolution relationship among the pump, signal, and idler pulses during the DFG process, as well as the effects of crystal length, pulse energy of the pump and signal lights, pulse width, and other factors on the characteristics of the MIR pulse are explored. Furthermore, through simulations from the time domain to the frequency domain, the spectral characteristics and angular distribution of MIR were analyzed. DFG experimental data are also presented to support the model.

Continuous blood pressure prediction system using Conv-LSTM network on hybrid latent features of photoplethysmogram (PPG) and electrocardiogram (ECG) signals

Continuous blood pressure (BP) monitoring is essential for managing cardiovascular disease. However, existing devices often require expert handling, highlighting the need for alternative methods to simplify the process. Researchers have developed various methods using physiological signals to address this issue. Yet, many of these methods either fall short in accuracy according to the BHS, AAMI, and IEEE standards for BP measurement devices or suffer from low computational efficiency due to the complexity of their models. To solve this problem, we developed a BP prediction system that merges extracted features of PPG and ECG from two pulses of both signals using convolutional and LSTM layers, followed by incorporating the R-to-R interval durations as additional features for predicting systolic (SBP) and diastolic (DBP) blood pressure. Our findings indicate that the prediction accuracies for SBP and DBP were 5.306 ± 7.248 mmHg with a 0.877 correlation coefficient and 3.296 ± 4.764 mmHg with a 0.918 correlation coefficient, respectively. We found that our proposed model achieved a robust performance on the MIMIC III dataset with a minimum architectural design and high-level accuracy compared to existing methods. Thus, our method not only meets the passing category for BHS, AAMI, and IEEE guidelines but also stands out as the most rapidly accurate deep-learning-based BP measurement device currently available.

Ultrasensitive Wearable Pressure Sensors with Stress-Concentrated Tip-Array Design for Long-Term Bimodal Identification.

The great challenges for existing wearable pressure sensors are the degradation of sensing performance and weak interfacial adhesion owing to the low mechanical transfer efficiency and interfacial differences at the skin-sensor interface. Here, an ultrasensitive wearable pressure sensor is reported by introducing a stress-concentrated tip-array design and self-adhesive interface for improving the detection limit. A bipyramidal microstructure with various Young's moduli is designed to improve mechanical transfer efficiency from 72.6% to 98.4%. By increasing the difference in modulus, it also mechanically amplifies the sensitivity to 8.5V kPa-1 with a detection limit of 0.14Pa. The self-adhesive hydrogel is developed to strengthen the sensor-skin interface, which allows stable signals for long-term and real-time monitoring. It enables generating high signal-to-noise ratios and multifeatures when wirelessly monitoring weak pulse signals and eye muscle movements. Finally, combined with a deep learning bimodal fused network, the accuracy of fatigued driving identification is significantly increased to 95.6%.

Minimization of Parasitic Capacitance between Skin and Ag/AgCl Dry Electrodes.

Conventional dry electrodes often yield unstable results due to the presence of parasitic capacitance between the flat electrode surface and the non-uniform skin interface. To address this issue, a gel is typically placed between the electrodes to minimize parasitic capacitance. However, this approach has the drawbacks of being unsuitable for repeated use, limited lifetime due to gel evaporation, and the possibility of developing skin irritation. This is particularly problematic in underserved areas since, due to the cost of disposable wet electrodes, they often sterilize and reuse dry electrodes. In this study, we propose a method to neutralize the effects of parasitic capacitance by attaching high-value capacitors to the electrodes in parallel, specifically when applied to pulse wave monitoring through bioimpedance. Skin capacitance can also be mitigated due to the serial connection, enabling stable reception of arterial pulse signals through bioimpedance circuits. A high-frequency structure simulator (HFSS) was first used to simulate the capacitance when injection currents flow into the arteries through the bioimpedance circuits. We also used the simulation to investigate the effects of add-on capacitors. Lastly, we conducted preliminary comparative analyses between wet electrodes and dry electrodes in vivo with added capacitance values ranging from 100 pF to 1 μF, altering capacitance magnitudes by factors of 100. As a result, we obtained a signal-to-noise ratio (SNR) that was 8.2 dB higher than that of dry electrodes. Performance was also shown to be comparable to wet electrodes, with a reduction of only 0.4 dB using 1 μF. The comparative results demonstrate that the addition of capacitors to the electrodes has the potential to allow for performance similar to that of wet electrodes for bioimpedance pulse rate monitoring and could potentially be used for other applications of dry electrodes.

Low Costs Electrical Calibration System of SLM with the Uncertainty Measurements Compared with Primary System Platform Brūel & Kjær type 3630

Sound level meter (SLM) calibration according to IEC61672-1, 2, 3: 2013 is an essential step in the measurement process. It ensures that the SLM meets its specifications. There is a strong need for an efficient and cheap calibration system device for the electrical calibration of SLM since it avoids the body effect and angle sensitivity of the microphone membrane to the sound incidence angle for free field calibration. As well as, it avoids leakage errors due to the microphone fitting in the coupler and the coupler effect in pressure field calibration. This work gives an alternative system device used in the electrical calibration of SLM. This system consists of individual tools; PULSE signal generator, universal frequency counter, HP Dynamic signal analyzer, and DMM voltmeter. All system tools are complying with the IEC17025. The calibration procedures follow the second edition of IEC61672-2. Calibration measurements of frequency, time-weightings, and linearity of range parameters as the major features of SLMs are tested in the frequency range of 31.5 Hz to 16 k Hz. The calibration was repeated 5 times on different days and different warm-up times for devices to estimate the associated uncertainty measurement values in accordance with Part 2 and Part 3 of IEC 61672. The obtained results show that, the calibration measurement deviations are in the allowable tolerance of the IEC 61672-1 and they are comparable with that obtained from the primary Brüel & Kjær (B & K) platform 3630. The computed uncertainty of measurements is in accepted limits that are required by IEC 61672-1 which is determined to the level of confidence of 95%, using coverage factor 2.