Time Delay Research Articles

Statistical analysis of vertically stacked nanosheet complementary FET based on polycrystalline silicon with multiple grain boundaries

In this study, a complementary field-effect transistor (CFET) based on a polycrystalline silicon (poly-Si) stacked-nanosheet (NS) structure with a grain boundary (GB) is designed and analyzed using a technology computer-aided design (TCAD) simulation. The advantage of the CFET using a poly-Si is that the active cell region can be reduced by 50 % because the p-type transistors are vertically stacked on the n-type transistors. In addition, it is possible to replace the conventional CMOS process and fabricate a three-dimensional vertically stacked structure at a low cost. To consider the effect of the GB in poly-Si, we assumed that there is a single GB at the center of the body region. By optimizing the geometrical parameters of the proposed CFET, the transfer characteristics of the NMOS and PMOS transistors, such as the threshold voltage (Vth), on current (Ion), off current (Ioff), and subthreshold swing (SS), are symmetrical. The proposed CFET demonstrated a superior logic performance of NML = 0.299 V and NMH = 0.297 V at an operation voltage of VDD = 0.65 V. To consider random GBs in the poly-Si, we assumed that five GBs existed in the proposed CFET, and the variation in CMOS logic inverter characteristics are statistically analyzed. As a result, the RSDs for Vm of the NMOS and PMOS transistors are 2.067 % and 0.293 %, and the RSDs of the pull-down delay time (tpHL) and pull-up delay time (tpLH) are 5.320 % and 3.244 %, respectively. An acceptor-like traps with high trap density are dominant in the proposed CFET, and this trend is similar across the 1024 samples. Nevertheless, most of the differences are within 5 % and the proposed CFET exhibited stability against GBs and the potential to replace the conventional CMOS logic inverters.

Meander Structure Analysis Techniques Using Artificial Neural Networks

Typically, analyses of meander structures (MSs) for transfer characteristics are conducted using specialized commercial software based on numerical methods. However, these methods can be time-consuming, particularly when a researcher is seeking to perform a preliminary study of the designed structures. This study aims to explore the application of neural networks in the design and analysis of meander structures. Three different feedforward neural network (FFNN), time delay neural network (TDNN), and convolutional neural network (CNN) techniques were investigated for the analysis and design of the meander structures in this article. The geometric dimensions or top-view images of 369 different meander structures were used for training an FFNN, TDNN, and CNN. The investigated networks were designed to predict such electrodynamic parameters as the delay time (td), reflection coefficient (S11), and transmission coefficient (S21) in the 0–10 GHz frequency band. A sufficiently low mean absolute error (MAE) was achieved with all three methods for the analysis of MSs. Using an FFNN, the characteristic td was predicted with a 3.3 ps average MAE. The characteristic S21 was predicted with a 0.64 dB average MAE, and S11 was predicted with a 2.47 dB average MAE. The TDNN allowed the average MAEs to be reduced to 0.9 ps, 0.11 dB, and 1.63 dB, respectively. Using a CNN, the average MAEs were 27.5 ps, 0.44 dB, and 1.36 dB, respectively. The use of neural networks has allowed accelerating the analysis procedure from approximately 120 min on average to less than 5 min.

NOx concentration prediction based on multi-channel fused spectral temporal graph neural network in coal-fired power plants

Establishing an accurate and stable NOx concentration prediction model is the foundation for achieving environmental protection in coal-fired power plant denitrification. In this research, a data-driven hybrid prediction model (BMFE-MFST-GNN) is proposed to improve the prediction accuracy of inlet NOx concentrations. First, we develop the adaptive variational modal decomposition method (FEVMD) to decompose historical NOx concentration data into simple and smooth subsequences and extract time-frequency features. Second, we propose the boosting mutual information feature selection algorithm (BMIFS) to determine the best set of auxiliary variables. The delay time is calculated based on the maximal information coefficient (MIC) to reconstruct the datasets. Then, the multi-channel fused spectral temporal graph neural network (MFST-GNN) is created to build the graph feature information of decomposed subsequences and reconstructed auxiliary variables to predict the concentration subsequences. Finally, we integrate the subsequence prediction results to obtain the future NOx concentrations. The experimental results demonstrate that the proposed method outperforms several comparative models in predicting NOx concentrations.

Multi‐Frequency SuperDARN Interferometer Calibration

AbstractThe ground‐based, high‐frequency radars of the Super Dual Auroral Radar Network (SuperDARN) observe backscatter from ionospheric field‐aligned plasma irregularities and features on the Earth's surface out to ranges of several thousand kilometers via over‐the‐horizon propagation of transmitted radio waves. Interferometric techniques can be applied to the received signals at the primary and secondary antenna arrays to measure the vertical angle of arrival, or elevation angle, for more accurate geolocation of SuperDARN observations. However, the calibration of SuperDARN interferometer measurements remains challenging for several reasons, including a 2π phase ambiguity when solving for the time delay correction factor needed to account for differences in the electrical path lengths between signals received at the two antenna arrays. We present a new technique using multi‐frequency ionospheric and ground backscatter observations for the calibration of SuperDARN interferometer data, and demonstrate its application to both historical and recent data.

Ultimate boundedness and stability of highly nonlinear neutral stochastic delay differential equations with semi-Markovian switching signals

The ultimate boundedness and stability of highly nonlinear neutral stochastic delay differential equations(NSDDEs) are investigated in this paper. Different from many previous works, the highly nonlinear NSDDEs with semi-Markov switching signals are considered for the first time in this paper. Meanwhile, the time delay function in this paper is only required to meet much more relaxed restrictions, which can invalidate plentiful methods with requirements on its derivatives for the stability of NSDDEs. To overcome this difficulty, several novel techniques to tackle NSDDEs with such a delay are developed. Furthermore, a crucial property on the ergodic semi-Markov process is established, which plays a key role in the proof on the existence and boundedness of global solution. The generalized Khasmiskii-type theorems are established for the existence and uniqueness of global solution by applying new methods and this property, and the criteria for boundedness and stability are also provided. In particular, feasible measures are provided to deal with the neutral term in our stability criteria. Finally, the effectiveness is verified by a numerical example.

Quantitative determination of oxides in cement raw meal based on near-infrared spectroscopy and hybrid feature selection strategy

Determining the content of components in cement raw meal is vital in Portland cement manufacturing. Near-infrared spectroscopy (NIRS)enables rapid, safe, and accurate quantitative analysis of the primary constituents of cement raw meal (CaO, SiO2, Al2O3, and Fe2O3), thereby assisting the cement industry in reducing the time delay in raw meal proportioning control and enhancing the quality of the raw meal. This study employed a hybrid feature selection strategy to identify effective feature points in the NIRS of raw meal samples. Effective feature points from the raw meal were integrated with partial least squares (PLS) to develop a regression model. Initially, NIRS data from raw meal samples were obtained using a Fourier NIR spectrometer. Subsequently, sample set partitioning based on joint X-Y distance (SPXY) was utilized to divide the samples into a calibration set (70 samples) and a validation set (22 samples). Next, A backward interval partial least squares (BiPLS) approach, along with various preprocessing algorithms, was then employed to initially screen the optimal waveband combinations of the NIRS. Within these combinations, effective feature points were further identified using competitive adaptive reweighted sampling (CARS), variable combined population analysis (VCPA) and weighted fusion variable space shrinkage approach (WFVSSA). Finally, we developed and compared PLS models based on the full spectrum and three hybrid feature selection methods. The results demonstrated that.WFVSSA offers a comprehensive assessment of wavelength importance, effectively preserving feature points while eliminating redundant information. BiPLS-WFVSSA-PLS outperformed BiPLS-CARS-PLS and BiPLS-VCPAPLS on the validation set, with R2, RMSEP, and RPD values of 0.8676, 0.1431, and 2.8129 for CaO; 0.9212, 0.1216, and 3.6461 for SiO2; 0.9198, 0.0607, and 3.5618 for Al2O3; and 0.8717, 0.0166, and 2.8614 for Fe2O3, respectively. These findings underscore the superior accuracy and stability of BiPLS-WFVSSA-PLS in determining cement raw meal component contents.

Crystallization and microwave dielectric properties of CaO-B2O3-SiO2 glass-ceramic/Al2O3 for LTCC applications

High-frequency electronic devices with low dielectric loss and electronic components with high integration densities require high-performance electronic packaging materials. In this study, materials primarily composed of CaO-B2O3-SiO2 (CBS) with an elevated boron content were fabricated through solid-phase sintering using Al2O3 as the ceramic phase. The phase compositions, densification process, microstructures, thermal conductivity, thermal expansion, and dielectric properties of the LTCC were investigated. Remarkably, the CBS-1 LTCC composite sintered at 850 °C for 4h, exhibited a dense and nonporous microstructure. It demonstrated exceptional thermoelectric properties, including a thermal shrinkage of 5.61%, coefficients of thermal expansion of 4.86 ppm/°C, thermal conductivity of 2.36W/m·K, dielectric constant (εr) of 4.88, and tan δ of 1.05× 10-3 at 15GHz. These characteristics are conducive to minimizing the signal propagation time delay and filtering noisy signals. Consequently, the CBS-1 LTCC composite emerged as a promising substrate for LTCC applications.

Neural Adaptive Optimal Control of Inequality-Constrained Nonlinear System With Partial Uncertain Time Delay

Neural Adaptive Optimal Control of Inequality-Constrained Nonlinear System With Partial Uncertain Time Delay

An analytical criterion for stability testing of delayed Bode’s transfer function with unity feedback

The unity feedback where delayed Bode’s transfer function is placed in forward path is used as reference transfer function in controller design for fractional time delay systems. In this study, bounded input-bounded output stability of this reference transfer function is investigated. This investigation is based on a theorem for fractional order time delay systems using Hassard’s techniques. Sufficient and necessary conditions are obtained for testing the stability of this reference transfer function. Moreover, an explicit formula is given for calculating the number of unstable roots in case of instability. The illustrative examples show that this stability criterion works efficiently.

AT2021lwx: Another Neutrino-coincident Tidal Disruption Event with a Strong Dust Echo?

We discuss the possible association of an astrophysical neutrino (IC220405B) with the recently reported, extremely energetic tidal disruption event (TDE) candidate AT2021lwx (ZTF20abrbeie, aka “Scary Barbie”) at redshift z = 0.995. Although the TDE is about 2.°6 off the direction of the reconstructed neutrino event (outside the 90% confidence level localization region), the TDE candidate shares some important characteristics with so-far-reported neutrino–TDE associations: a strong infrared dust echo, high bolometric luminosity, a neutrino time delay with respect to the peak mass accretion rate of the order of a hundred days, and a high observed X-ray luminosity. We interpret this new association using an isotropic emission model, where neutrinos are produced by the collision of accelerated protons with infrared photons. After accounting for the high redshift of AT2021lwx (by interpreting the data in the supermassive black hole (SMBH) frame), we find that the expected neutrino fluences and neutrino time delays are qualitatively comparable to the other TDEs. Since data are only available up to 300 days postpeak in the SMBH frame, significant uncertainties exist in the dust echo interpretation, and therefore in the predicted number of neutrinos detected, Nν≃3.0×10−3−0.012 . We recommend further follow-up of this object for an extended period and suggest refining the reconstruction of the neutrino arrival direction in this particular case.

Predicted Trajectory Accuracy Requirements to Reduce Aviation Impact of Space Launch Operations

Thousands of aircraft flight plans are affected by space launch and reentry operations each year, increasing the distances flown, causing flight delays, and increasing air traffic controllers workload. Due to regulations and procedures, predefined Aircraft Hazard Areas (AHAs) are used to protect aircraft from the risks of space launch and reentry debris, thus constraining the available airspace for other airspace users. While disruptive, there is no less impactful approach at present that adequately protects the flying public. In this paper, we explore the application of trajectory-based operations to evaluate the impact of an AHA on commercial aircraft, with the aim of reducing the number of flights that must be rerouted or rescheduled. This approach relies on precise trajectory predictions to the AHA boundary to determine which flights are expected to clear the AHA before its activation or remain clear of the AHA until after its deactivation. This paper derives the required predicted trajectory accuracy for air traffic automation systems to effectively predict flights impacted by an AHA. The required accuracy is derived based on a model for managing flights relative to the AHA using speed changes alone (as opposed to reroutes or holding) in the context of operational uncertainties like departure time delays and flight characteristics. Additionally, we derived a model to relate scheduling buffers to the AHA activation time, delivery accuracy at the AHA boundary, and AHA violation probability.

AT2021lwx: Another Neutrino-coincident Tidal Disruption Event with a Strong Dust Echo?

We discuss the possible association of an astrophysical neutrino (IC220405B) with the recently reported, extremely energetic tidal disruption event (TDE) candidate AT2021lwx (ZTF20abrbeie, aka “Scary Barbie”) at redshift z = 0.995. Although the TDE is about 2.°6 off the direction of the reconstructed neutrino event (outside the 90% confidence level localization region), the TDE candidate shares some important characteristics with so-far-reported neutrino–TDE associations: a strong infrared dust echo, high bolometric luminosity, a neutrino time delay with respect to the peak mass accretion rate of the order of a hundred days, and a high observed X-ray luminosity. We interpret this new association using an isotropic emission model, where neutrinos are produced by the collision of accelerated protons with infrared photons. After accounting for the high redshift of AT2021lwx (by interpreting the data in the supermassive black hole (SMBH) frame), we find that the expected neutrino fluences and neutrino time delays are qualitatively comparable to the other TDEs. Since data are only available up to 300 days postpeak in the SMBH frame, significant uncertainties exist in the dust echo interpretation, and therefore in the predicted number of neutrinos detected, Nν≃3.0×10−3−0.012 . We recommend further follow-up of this object for an extended period and suggest refining the reconstruction of the neutrino arrival direction in this particular case.

Real-time hardware-in-the-loop approach for adaptive centralized protection schemes using clustering algorithms

The main objectives of adaptive protection are isolating only the faulty network section and operating with varying network topologies and levels of generation. To accomplish this, the settings of protective devices may need to modify if, for example, the network topology is changed. Protection relays can have several predefined setting groups (SGs) depending on the number of possible configurations. However, centralized protection and control devices can store a limited number of setting groups, in many cases by far less than the number of possible network topologies. Therefore, in adaptive centralized protection schemes where online setting calculation is not feasible, a proper method should be used to cluster the different possible configurations and reduce their number to match or be fewer than the number of available SGs in relays. The offline calculated settings for each cluster can be stored in the centralized protection and control devices, which continuously monitor the status of circuit breakers (CBs) online. Then, upon detecting a change in the CB status, the corresponding SG will be activated. This paper presents three new methods for clustering network topologies, considering N-1 contingency in adaptive relay coordination. These methods are compared to the one in the literature and each other in terms of operation time delay of relays. Moreover, to mitigate false operations of relays due to unavailability of communication networks, a setting group is dedicated to the robust setting group. Finally, a practical implementation of the proposed method using IEC 61850 standard is illustrated using two schemes step by step. IEEE 14-bus is selected as a test system to evaluate the applicability of the proposed methods. The results, obtained by using MATLAB, DIgSILENT Programming Language, and hardware-in-the-loop setup show the effectiveness of the proposed methods in minimizing operating time delays of overcurrent relays.

A Single-Shot Technique for Measuring Broadband Two-Photon Absorption Spectra in Solution.

Applications involving two-photon activation, including two-photon fluorescence imaging, photodynamic therapy, and 3D data storage, require precise knowledge of the two-photon absorption (2PA) spectra of target chromophores. Broadband pump-probe spectroscopy using femtosecond laser pulses provides wavelength-dependent 2PA spectra with absolute cross sections, but the measurements are sometimes complicated by cross-phase modulation effects and dispersion of the broadband probe. Here, we introduce a single-shot approach that eliminates artifacts from cross-phase modulation and enables more rapid measurements by avoiding the need to scan the time delay between the pump and the probe pulses. The approach uses counterpropagating beams to automatically integrate over the full interaction between the two pulses as they cross. We demonstrate this single-shot approach for a common 2PA reference, coumarin 153 (C153), in three different solvents using the output from a Yb:KGW laser. This approach provides accurate 2PA cross sections that are more reliable and easier to obtain compared with scanning pump-probe methods using copropagating laser beams. The single-shot method for broadband two-photon absorption (BB-2PA) spectroscopy also has significant advantages compared with single-wavelength measurements, such as z-scan and two-photon fluorescence.

Multi‐Frequency SuperDARN Interferometer Calibration

AbstractThe ground‐based, high‐frequency radars of the Super Dual Auroral Radar Network (SuperDARN) observe backscatter from ionospheric field‐aligned plasma irregularities and features on the Earth's surface out to ranges of several thousand kilometers via over‐the‐horizon propagation of transmitted radio waves. Interferometric techniques can be applied to the received signals at the primary and secondary antenna arrays to measure the vertical angle of arrival, or elevation angle, for more accurate geolocation of SuperDARN observations. However, the calibration of SuperDARN interferometer measurements remains challenging for several reasons, including a 2π phase ambiguity when solving for the time delay correction factor needed to account for differences in the electrical path lengths between signals received at the two antenna arrays. We present a new technique using multi‐frequency ionospheric and ground backscatter observations for the calibration of SuperDARN interferometer data, and demonstrate its application to both historical and recent data.

Robust Methods for Assessing the Characteristics of Locomotor Activity Based on Markerless Video Capture Data

Introduction. Analysis of locomotor activity is essential in a number of biomedical and pharmacological research designs, as well as environmental monitoring. The movement trajectories of biological objects can be represented by time series exhibiting a complex multicomponent structure and non-stationary dynamics, thus limiting the effectiveness of conventional correlation and spectral time series analysis methods. Recordings obtained using markerless technologies are typically characterized by enhanced noise levels, including both instrumental noise and anomalous errors associated with false estimates of the location of the points of interest, as well as gaps in the trajectories, promoting an urgent need in the development of robust methods to assess the characteristics of locomotor activity.Aim. Development of robust methods for assessing the characteristics of locomotor activity capable of efficient processing of noisy recordings obtained by markerless video-based motion capture systems.Materials and methods. In order to assess the characteristics of locomotor activity, the relative movements of body parts of laboratory animals were analyzed using the stability metrics of the mutual dynamics of their trajectories, their relative delays, as well as the relative duration of the recording fragments when relatively stable mutual dynamics could be observed. The local maxima of the cross-correlation function of two body fragments, the minima of the standard deviation of the difference between their Hilbert phases, as well as their relative delays, were used as the metrics of mutual dynamics.Results. The considered phase metrics were shown to explicitly reflect changes in locomotor activity, while the assessment of time delays using phase metric was shown to be prone to periodic error. The above limitation could be largely overcome using the correlation metrics, assuming that phase and correlation metrics could be combined.Conclusion. The proposed robust methods provide stable estimates of the characteristics of locomotor activity based on markerless video capture recordings, altogether increasing the efficiency of diagnostic procedures and assessment of the therapeutic effect during rehabilitation.

Ultimate boundedness and stability of highly nonlinear neutral stochastic delay differential equations with semi-Markovian switching signals

The ultimate boundedness and stability of highly nonlinear neutral stochastic delay differential equations(NSDDEs) are investigated in this paper. Different from many previous works, the highly nonlinear NSDDEs with semi-Markov switching signals are considered for the first time in this paper. Meanwhile, the time delay function in this paper is only required to meet much more relaxed restrictions, which can invalidate plentiful methods with requirements on its derivatives for the stability of NSDDEs. To overcome this difficulty, several novel techniques to tackle NSDDEs with such a delay are developed. Furthermore, a crucial property on the ergodic semi-Markov process is established, which plays a key role in the proof on the existence and boundedness of global solution. The generalized Khasmiskii-type theorems are established for the existence and uniqueness of global solution by applying new methods and this property, and the criteria for boundedness and stability are also provided. In particular, feasible measures are provided to deal with the neutral term in our stability criteria. Finally, the effectiveness is verified by a numerical example.

Drones can be used to provide dispatch centres with on-site photos before arrival of EMS in time critical incidents

BackgroundDrones are able to deliver automated external defibrillators in cases of out-of-hospital cardiac arrest (OHCA) but can be deployed for other purposes. Our aim was to evaluate the feasibility of sending live photos to dispatch centres before arrival of other units during time-critical incidents. MethodsIn this retrospective observational study, the regional dispatch centre implemented a new service using five existing AED-drone systems covering an estimated 200000 inhabitants in Sweden. Drones were deployed automatically over a 4-month study period (December 2022–April 2023) in emergency calls involving suspected OHCAs, traffic accidents and fires in buildings. Upon arrival at the scene, an overhead photo was taken and transmitted to the dispatch centre. Feasibility of providing photos in real time, and time delays intervals were examined. ResultsOverall, drones were deployed in 59/440 (13%) of all emergency calls: 26/59 (44%) of suspected OHCAs, 20/59 (34%) of traffic accidents, and 13/59 (22%) of fires in buildings.The main reasons for non-deployment were closed airspace and unfavourable weather conditions (68%). Drones arrived safely at the exact location in 58/59 cases (98%). Their overall median response time was 3:49 min, (IQR 3:18–4:26) vs. emergency medical services (EMS), 05:51 (IQR: 04:29–08:04) p-value for time difference between drone and EMS = 0,05. Drones arrived first on scene in 47/52 cases (90%) and the largest median time difference was found in suspected OHCAs 4:10 min, (IQR: 02:57–05:28). The time difference in the 5/52 (10%) cases when EMS arrived first the time difference was 5:18 min (IQR 2:19–7:38), p = NA. Photos were transmitted correctly in all 59 alerts. No adverse events occurred. ConclusionIn a newly implemented drone dispatch service, drones were dispatched to 13% of relevant EMS calls. When drones were dispatched, they arrived at scene earlier than EMS services in 90% of cases. Drones were able to relay photos to the dispatch centre in all cases.Although severely affected by closed airspace and weather conditions, this novel method may facilitate additional decision-making information during time-critical incidents.

Spiking neural networks in the Alexiewicz topology: A new perspective on analysis and error bounds

In order to ease the analysis of error propagation in neuromorphic computing and to get a better understanding of spiking neural networks (SNN), we address the problem of mathematical analysis of SNNs as endomorphisms that map spike trains to spike trains. A central question is the adequate structure for a space of spike trains and its implication for the design of error measurements of SNNs including time delay, threshold deviations, and the design of the reinitialization mode of the leaky-integrate-and-fire (LIF) neuron model. First, we identify the underlying topology by analyzing the closure of all sub-threshold signals of a LIF model. For zero leakage this approach yields the Alexiewicz topology, which we adopt to LIF neurons with arbitrary positive leakage. As a result, LIF can be understood as spike train quantization in the corresponding norm. This way we obtain various error bounds and inequalities such as a quasi-isometry relation between incoming and outgoing spike trains. Another result is a Lipschitz-style global upper bound for the error propagation and a related resonance-type phenomenon.

A shock-tube experimental and kinetic simulation study on the autoignition of methane at ultra-lean and lean conditions

Coalbed methane represents an important kind of natural gas resource in many countries. However, the low-concentration property of coalbed methane limits its applications. To gain insight into the combustion kinetics of coalbed methane and facilitate its combustion utilization, this work reports an experimental and kinetic simulation study on the autoignition properties of methane at ultra-lean and lean conditions. A shock tube (ST) facility is used for ignition delay time (IDT) measurements with equivalence ratios at 0.5, 0.1, and 0.05 with pressure at 2 and 10 bar under the temperature ranging from 1320-1850 K. The measured IDTs can be correlated into a general Arrhenius expression, and the equivalence ratio effect on IDTs is then analyzed. Seven detailed chemical kinetic mechanisms are employed to predict the IDTs and statistical error indicators are used to evaluate their performance. Detailed kinetic analysis via sensitivity and reaction path analysis is performed to uncover the kinetic differences among the seven mechanisms. It is shown that some of the reaction paths only exist in the NUIGMech 1.3 mechanism, while the other detailed mechanisms do not consider them. Reaction path analysis indicates that the reactions related to O2, OH and O species become more important compared to the reactions involving CH3 and H radicals as the equivalence ratio decreases from lean to ultra-lean conditions. Detailed chemical kinetics analysis is also conducted to demonstrate the uncertainty of key reactions. The present work should be valuable to gain insight into the methane ignition characteristics and to facilitate kinetic mechanism optimization of methane combustion.