Wide Range Of Delays Research Articles

Prototype development of a three-stage, high-precision, low-jitter, wide-range digital delay generator fully utilizing on-chip resources.

This paper introduces the design and implementation of a prototype Digital Delay Generator (DDG) characterized by high precision, low jitter, and a wide delay range, fully realized within a Field Programmable Gate Array (FPGA). The DDG's architecture is based on an innovative combination of an embedded time-to-digital converter (TDC) and Multi-stage Time Interpolation (MTI) delay logic. The paper explores the factors influencing delay jitter during external trigger mode and carefully selects the optimal design approach for each element. The embedded TDC, which undergoes automatic calibration, accurately measures the time difference between the arrival of an external trigger and the FPGA's internal clock signal. When paired with the MTI delay logic, this allows for highly precise control over delay durations. A key aspect of this design is its sole dependence on the FPGA's built-in resources, ensuring simplicity in implementation and adaptability to various applications. Evaluation of the prototype has shown promising results, demonstrating a delay resolution as fine as 20ps and maintaining a low jitter of 105ps peak-to-peak (20ps rms) when operated in the externally triggered mode.

The Origin Along the Cochlea of Otoacoustic Emissions Evoked by Mid-Frequency Tone Pips.

Tone-pip-evoked otoacoustic emissions (PEOAEs) are transient-evoked otoacoustic emissions (OAEs) that are hypothesized to originate from reflection of energy near the best-frequency (BF) cochlear place of the stimulus frequency. However, individual PEOAEs have energy with a wide range of delays. We sought to determine whether some PEOAE energy is consistent with having been generated far from BF. PEOAEs from 35 and 47dB SPL tone pips were obtained by removing pip-stimulus energy by subtracting the ear-canal sound pressure from scaled-down 59dB SPL tone pips (which evoke relatively small OAEs). PEOAE delays were measured at each peak in the PEOAE absolute-value waveforms. While measuring PEOAEs and auditory-nerve compound action potentials (CAPs), amplification was blocked sequentially from apex to base by cochlear salicylate perfusion. The perfusion time when a CAP was reduced identified when the perfusion reached the tone-pip BF place. The perfusion times when each PEOAE peak was reduced identified where along the cochlea it received cochlear amplification. PEOAEs and CAPs were measured simultaneously using one pip frequency in each ear (1.4 to 4kHz across 16 ears). Most PEOAE peaks received amplification primarily between the BF place and 1-2 octaves basal of the BF place. PEOAE peaks with short delays received amplification basal of BF place. PEOAE peaks with longer delays sometimes received amplification apical of BF place, consistent with previous stimulus-frequency-OAE results. PEOAEs provide information about cochlear amplification primarily within ~ 1.5 octave of the tone-pip BF place, not about regions > 3 octaves basal of BF.

Highly stable fiber-based photonic delay line with large and tunable delay.

A stable photonic delay line with large and tunable delay is essential for large-distance simulation, beamforming, and diverse photonic signal processing applications. Here, we demonstrate a fiber-based tunable photonic delay line (TPDL) with a maximum delay of 905 µs. Its environmental-related delay jitter is compensated for by a homodyne phase-locked loop (PLL). Precise delay tuning is realized by changing the phase of the reference with a minimum tuning step of 0.5 ps without breaking its locking state. The demonstrated delay line shows exceptional stability, as indicated by an overlapping Allan deviation (ADEV) of 2.06 × 10-17 at the averaging time of 1000 s and the delay jitter below 20 fs. Its high stability, wide delay range, wideband characteristics, and precise tunability make the TPDL an ideal photonic delay line for the above-mentioned applications.

The fusion point of temporal binding: Promises and perils of multisensory accounts

Performing an action to initiate a consequence in the environment triggers the perceptual illusion of temporal binding. This phenomenon entails that actions and following effects are perceived to occur closer in time than they do outside the action-effect relationship. Here we ask whether temporal binding can be explained in terms of multisensory integration, by assuming either multisensory fusion or partial integration of the two events. We gathered two datasets featuring a wide range of action-effect delays as a key factor influencing integration. We then tested the fit of a computational model for multisensory integration, the statistically optimal cue integration (SOCI) model. Indeed, qualitative aspects of the data on a group-level followed the principles of a multisensory account. By contrast, quantitative evidence from a comprehensive model evaluation indicated that temporal binding cannot be reduced to multisensory integration. Rather, multisensory integration should be seen as one of several component processes underlying temporal binding on an individual level.

A multi-mode free-space delay interferometer with no refractive compensation elements for phase-encoded QKD protocols

We demonstrate a compensation-free approach to the realization of multi-mode delay interferometers, mainly for use in phase-encoded quantum key distribution (QKD). High interference visibility of spatially multi-mode beams in unbalanced Michelson or Mach–Zehnder interferometers with a relatively wide range of delays is achieved by the appropriate choice of the transverse size of the beam. We provide a simple theoretical model that gives a direct connection between the visibility of interference, the delay and the beam parameters. The performed experimental study confirms our theoretical findings and demonstrates measured visibility of up to 0.95 for a delay of 2 ns. Our approach’s simplicity and robust performance make it a practical choice for the implementation of QKD systems, where a quantum signal is received over a multi-mode fiber. The important application of such a configuration is an intermodal QKD system, where the free-space atmospheric communication channel is coupled into a span of the multi-mode fiber, delivering the spatially distorted beam to the remote receiver with minimal coupling loss.

Excitation and Inhibition Delays within a Feedforward Inhibitory Pathway Modulate Cerebellar Purkinje Cell Output in Mice.

The cerebellar cortex computes sensorimotor information from many brain areas through a feedforward inhibitory (FFI) microcircuit between the input stage, the granule cell (GC) layer, and the output stage, the Purkinje cells (PCs). Although in other brain areas FFI underlies a precise excitation versus inhibition temporal correlation, recent findings in the cerebellum highlighted more complex behaviors at GC-molecular layer interneuron (MLI)-PC pathway. To dissect the temporal organization of this cerebellar FFI pathway, we combined ex vivo patch-clamp recordings of PCs in male mice with a viral-based strategy to express Channelrhodopsin2 in a subset of mossy fibers (MFs), the major excitatory inputs to GCs. We show that although light-mediated MF activation elicited pairs of excitatory and inhibitory postsynaptic currents in PCs, excitation (E) from GCs and inhibition (I) from MLIs reached PCs with a wide range of different temporal delays. However, when GCs were directly stimulated, a low variability in E/I delays was observed. Our results demonstrate that in many recordings MF stimulation recruited different groups of GCs that trigger E and/or I, and expanded PC temporal synaptic integration. Finally, using a computational model of the FFI pathway, we showed that this temporal expansion could strongly influence how PCs integrate GC inputs. Our findings show that specific E/I delays may help PCs encoding specific MF inputs.SIGNIFICANCE STATEMENT Sensorimotor information is conveyed to the cerebellar cortex by mossy fibers. Mossy fiber inputs activate granule cells that excite molecular interneurons and Purkinje cells, the sole output of the cerebellar cortex, leading to a sequence of synaptic excitation and inhibition in Purkinje cells, thus defining a feedforward inhibitory pathway. Using electrophysiological recordings, optogenetic stimulation, and mathematical modeling, we demonstrated that different groups of granule cells can elicit synaptic excitation and inhibition with various latencies onto Purkinje cells. This temporal variability controls how granule cells influence Purkinje cell discharge and may support temporal coding in the cerebellar cortex.

A Wide-Range Low-Power Thyristor-Based Delay Element With Improved Temperature Sensitivity

This brief describes an improved CMOS thyristor-based delay element (THDE) in which two current sources, digitally-controlled (DC) and voltage-controlled (VC), are implemented to demonstrate the design choice trade-offs and offer a wide range of delays. Both THDE designs show reduced temperature sensitivity while allowing an extended tuning range. Prototypes fabricated in a 0.35-μm CMOS process exhibit measured delay range of 26 ns-4.5 μs for DC-based delay element and 16 μs-5 ms for VC-based delay element. At their maximum delay amounts, the integrated THDEs consume 4.1 and 1.58 μW, respectively, from a 3.3-V supply. Also, the fabricated delay cells show a temperature sensitivity of 900 and 2710 ppm/∘C, respectively, when operating at maximum delay.

Flight Delay Propagation Prediction Based on Deep Learning

The current flight delay not only affects the normal operation of the current flight, but also spreads to the downstream flights through the flights schedule, resulting in a wide range of flight delays. The analysis and prediction of flight delay propagation in advance can help civil aviation departments control the flight delay rate and reduce the economic loss caused by flight delays. Due to the small number of data samples that can constitute flight chains, it is difficult to construct flight chain data. In recent years, the analysis of the flight delay propagation problem is generally based on traditional machine learning methods with a small sample size. After obtaining a large amount of raw data from the China Air Traffic Management Bureau, we have constructed 36,287 pieces of three-level flight chain data. Based on these data, we tried to use a deep learning method to analyze and forecast flight delays. In the field of deep learning, there are CNN models and RNN models that deal with classification problems well. Based on these two classes of models, we modify and innovate the study of the problem of flight delay propagation and prediction. Firstly, the CNN-based CondenseNet algorithm is used to predict the delay level of the three-level flight chain data. Based on this, the CondenseNet network is improved by inserting CBAM modules and named CBAM-CondenseNet. The experimental results show that the improved algorithm can effectively improve the network performance, and the prediction accuracy can reach 89.8%. Compared with the traditional machine learning method, the average prediction accuracy increased by 8.7 percentage points. On the basis of the CNN model, we also considered the superiority of the LSTM (Long Short-Term Memory network) considering the processing time sequence information, and then constructed the CNN-MLSTM network and injected the SimAM module to enhance the attention of flight chain data. In the experiment of flight delay propagation prediction, the accuracy rate is 91.36%, which is a significant improvement compared to using the CNN or LSTM alone.

Assessing the Effects of Delay to NMP via Audio Analysis.

For network music performance (NMP), end-to-end delay is the most critical factor affecting the quality of experience (QoE) of the musicians, as longer delays prevent the musicians from synchronizing. To analyze the sensitivity of QoE to delay, we performed a controlled NMP experiment, where 11 pairs of musicians performed under a wide range of delays. The analysis of the QoE questionnaires answered by the participants produced results with wide variances, making the extraction of solid conclusions quite difficult. In this paper, we complement the subjective study with an analysis of the performance tempo of the NMP sessions. Specifically, we used signal processing techniques to analyze the audio recordings of the experiments, to recover the performance tempo of the musicians, assess its evolution during each session, and correlate it with the underlying delay. The results of the analysis indicate that musicians in real NMP settings are more tolerant to delay than previously thought, managing to reach and maintain a steady tempo even with one-way delays of 40 ms. We also study how the performance tempo is related to delay, finding that the exact relationship between the two depends on the musicians.

MSP designing with optimal fractional PI–PD controller for IPTD processes

Abstract An effective tuning methodology of modified Smith predictor (MSP) based fractional controller designing for purely integrating time delayed (IPTD) processes is reported here. IPTD processes with pole at the origin are truly difficult to control; exhibit large oscillations once get disturbed from their steady state. Proposed MSP design consists of fractional PI (proportional-integral) and fractional PD (proportional-derivative) controllers together with P (proportional) controller. Fractional controllers are competent to provide improved closed loop responses due to flexibility of additional tuning parameters. Fractional tuning parameters of PI and PD controllers are derived through optimization algorithms where integral absolute error (IAE) is considered as cost function. Efficacy of the proposed methodology is validated for IPTD processes having wide range of time delay. Stability and robustness issues are explored under process model uncertainties with small gain theorem. Performance of the proposed MSP-FO(PI–PD) controller is validated through simulation study relating five IPTD process models. Overall satisfactory closed loop responses are observed for each case during transient as well as steady state operational phases.

Development of a Semiconductor Time Relay for Locomotives

Purpose. The need to replace the outdated element base of locomotive electrical apparatuses is urgent under conditions of locomotive overhaul to extend their service life. The main aim of the work is to reduce costs for the operation and repair of rolling stock by increasing the reliability of electrical apparatuses, the introduction of unified technical solutions. Methodology. Using the system-historical approach, the advantages and disadvantages of the main design principles of electrical apparatuses of locomotives are found out. The improvement directions of the elemental base of the time relay have been determined. The need to develop unified technical solutions for the implementation of locomotive time relays is substantiated. Using the methods of applied theory of digital machines and circuit engineering, a semiconductor time relay for locomotives was developed, which provides the possibility of implementing 23 options for time delay without changing the relay design. The proposed methodology allows designing universal electronic relays with any ranges of time delays. A combination of binary counters and a multiplexer is used as the main elements of the time delay control. The absence of elements of control capacitance and active resistance (RC circuits) in the scheme of the proposed semiconductor time relay allows ensuring the stability of the time relay characteristics over the entire operating interval, reducing the maintenance and repair costs of electrical apparatuses. Findings. A unified time relay with a wide range of time delay and the possibility of connecting electrical circuits of locomotives with a supply voltage of 50 and 110 V has been developed. The obtained results make it possible to predict the further development of semiconductor time relays and to apply the developed semiconductor (electronic) time relay for all types of railway rolling stock. Originality. In the work, the development of technologies and the element base of locomotive electrical apparatuses were analysed. The need to improve the element base of electrical apparatuses is substantiated using a time relay example. Practical value. Based on the proposed technical solutions, it is possible to produce a universal semiconductor time relay for locomotives.

A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables

By conserving natural resources and reducing the consumption of fossil fuels, sustainable energy development plays a crucial role in energy planning. Specifically, demand-side planning must be researched and anticipated based on electricity consumption at the grounded level. Due to the global warming crisis, atmospheric conditions are among the most influential components that have altered electricity consumption patterns. In this study, 66 climate variables from the ERA5 reanalysis and the observed power demand at four grid substations (GSs) in Cambodia were examined using recurrent neural networks (RNNs). Using the cross-correlation function between power demand and each climate variable, statistically significant climate variables were sorted out. In addition, a wide range of feedback delays (FDs) was generated from the data on power demand and defined using 95% confidence intervals. The combination of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique with a nonlinear autoregressive neural network with exogenous inputs (NARX) and a nonlinear autoregressive neural network (NAR) produced a hybrid electricity forecasting model. The data were decomposed into the intrinsic mode functions (IMFs) and were then used as inputs in optimized NARX and NAR models. The performance of the various benchmarked models was analyzed and compared using mainly statistical indicators such as the normalized root mean square error (NMSE) and the coefficient of determination (R2). The hybrid models perform exceptionally well in predicting electricity demand, and the ICEEMDAN-NARX hybrid model with correlated climate variables performs the best among the tested experiments as a useful prediction tool.

Electrical dyssynchrony mapping and cardiac resynchronization therapy

PurposeThere is no clinical methodology for quantification or display of electrical dyssynchrony over a wide range of atrial-ventricular delays (AVD) and ventricular-ventricular delays (VVD) in patients with cardiac resynchronization therapy (CRT). This study aimed to develop a new methodology, based on wavefront fusion, for mapping electrical synchrony. MethodsA cardiac resynchronization index (CRI) was measured at multiple device settings in 90 patients. Electrical dyssynchrony maps (EDM) were constructed for each patient to display CRI at any combination of AVD and VVD. An optimal synchrony line (OSL) depicted the AVD/VVD combinations producing the highest CRIs. Fusion of right ventricular paced (RVp), left ventricular paced (LVp), and native wavefront offsets were calculated. ResultsCRI significantly increased (p < 0.0001) from 58.0 ± 28.1% at baseline to 98.3 ± 1.7% at optimized settings. EDMs in patients with high-grade heart block (n = 20) had an OSL parallel to the simultaneous biventricular pacing (BiVPVV-SIM) line with leftward shift across all AVDs (RVp-LVpOFFSET = 50.5 ± 29.8 ms). EDMs in patients with intact AV node conduction (n = 64) had an OSL parallel to the BiVPVV-SIM line with leftward shift at short AVDs (RVp-LVpOFFSET = 33.4 ± 23.3 ms), curvilinear at intermediate AVDs (triple fusion), and vertical at long AVDs (native-LVpOFFSET = 85.2 ± 22.8 ms) in all patients except those with poor LV lead position (n = 6). ConclusionA new methodology is described for quantifying and graphing electrical dyssynchrony over a physiologic range of AVDs/VVDs. This methodology offers a noninvasive, practical, clinical approach for measuring electrical synchrony that could be applied to optimization of CRT devices.

Super-Resolution Channel Sounding Method of Time Delay for Millimeter-Wave Channel

To satisfy the dynamic requirements of the millimeter-waveband, the higher time delay resolution is required. Time delay resolution of pseudo-noise (PN) cross-correlation channel sounding method can be limited by chip rate and sampling rate of analog to digital converter (A/D). In this letter, the multipath information in the chip is approximated using equivalent sequential sampling at a lower sampling rate, followed by the deconvolution method to derive the channel impulse response (CIR). Compared with the PN cross-correlation method, the time delay resolution of new method is much smaller than the chip width with a low sample rate of A/D. To improve time delay resolution over a wide range of time delays, we integrate the novel method with the PN cross-correlation method. Moreover, we have designed a novel channel simulator in order to verify the effectiveness of super-resolution approach of time delay.

Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness

Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.

PO-643-01 ELECTRICAL DYSSYNCHRONY MAPPING IN CARDIAC RESYNCHRONIZATION THERAPY

There is no clinical method for measuring electrical dyssynchrony over a wide range of atrial-ventricular delays (AVD) and ventricular-ventricular delays (VVD) in cardiac resynchronization therapy (CRT) patients.

Delayed Reward Bernoulli Bandits: Optimal Policy and Predictive Meta-Algorithm PARDI

Bernoulli multi-armed bandits are a reinforcement learning model used to optimize the sequences of decisions with binary outcomes. Well-known bandit algorithms, including the optimal policy, assume that before a decision is made the outcomes of previous decisions are known. This assumption is often not satisfied in real-life scenarios. As demonstrated in this article, if decision outcomes are affected by delays, the performance of existing algorithms can be severely affected. We present the first practically applicable method to compute statistically optimal decisions in the presence of outcome delays. Our method has a predictive component abstracted out into a meta-algorithm, predictive algorithm reducing delay impact (PARDI), which significantly reduces the impact of delays on commonly used algorithms. We demonstrate empirically that PARDI-enhanced Whittle index is nearly optimal for a wide range of Bernoulli bandit parameters and delays. In a wide spectrum of experiments, it performed better than any other suboptimal algorithm, e.g., UCB1-tuned and Thompson sampling. PARDI-enhanced Whittle index can be used when computational requirements of the optimal policy are too high.

Electricity demand prediction for sustainable development in Cambodia using recurrent neural networks with ERA5 reanalysis climate variables

Sustainable energy development plays a prominent role in energy planning to maintain natural resources and mitigate the usage of fossil fuels. The atmospheric factor is one of the main influencing factors that changed the electricity consumption’s behavior due to global warming. In this study, the recurrent neural network (RNN) models were developed to examine the effects of 66 climate variables, collected from the European Center for Medium-Range Weather Forecast (ECMWF) ERA5 reanalysis, on power demand in Cambodia. The statistically significant climate variables were filtered by considering the cross-correlation between power demand and each climate variable. Moreover, the wide range of feedback delays was computed from the power demand dataset and was defined using the 95% confidence intervals. The comparison between a nonlinear autoregressive neural network with exogenous inputs (NARX) using historical power demand with the correlated climate variables and a nonlinear autoregressive neural network (NAR) using only historical power demand dataset was made. The various benchmarked models were evaluated and compared for their performances using statistical indices such as normalized root-mean-square error (NMSE) and coefficient of determination (R2). The results showed the NARX model could perform better than the NAR model for predicting electricity demand time-series.

The Temporal Dynamics of Functional Networks are Modulated by Acupuncture: an EEG Microstates Study

Information transition delay is an inherent factor in neuronal systems and has great influences on their dynamical behaviors. For theoretical studies about time delay in neuronal systems, researchers usually set all connections between neurons to be delayed. However, in real neural systems, communication between some connected neurons maybe instantaneous which make the connection be delay-free. Thus, we will discuss effects of partial time delay on detecting subthreshold signal of neuronal systems in this paper from the viewpoint of stochastic resonance. Partial time delay is introduced by assuming that time delay between two connected neurons to be τ (which is not zero) with a probability pdelay. With the obtained numerical results, we show that the ability of detecting subthreshold signal could be reduced or enhanced strongly depending on the values of pdelay and τ. Especially, the detecting ability could be enhanced by a wide range of time delay τ when pdelay is small. It means that partial time delay could have quite different influences on the ability of detecting subthreshold signal as comparing with the full time delay case (all connections are delayed). Meanwhile, we also exhibit that network topology, noise intensity and coupling strength could have great influences on the effects of partial time delay on detecting subthreshold signal of the neuronal systems.

Superthermal Proton and Electron Fluxes in the Plasma Sheet Transition Region and Their Dependence on Solar Wind Parameters

AbstractTo study further the factors and mechanisms controlling 10–150 keV particle fluxes in the inner magnetosphere, we investigate empirically their behavior in the nightside transition region (6–14 Re) depending on solar wind parameters taken at different time lags. We aim to establish the hierarchy of predictors (V, N, Pd, Ekl = VByz sin2(θ/2), etc.) and the optimal range of their time delays, both depending on the distance and local time. We use THEMIS 5‐min averaged observations of energetic proton and electron fluxes in 2007–2018 near the plasma sheet midplane and build regression models exploring the combination of predictors, taken at time delays up to 24 h. The model obtained shows that protons and electrons are controlled differently by solar wind parameters: electrons are influenced equally by Vsw and Ekl, whereas protons are controlled mostly by Vsw and Pd and less by Ekl. We found that a wide range of time delays is involved depending on distance and particle energy. Specifically, the Ekl affects the energetic fluxes with time delays up to 24 h (or more), exhibiting the long delays in the innermost regions. As regards the mechanism of Vsw influence, the Vsw‐related flux changes are large and, to a large extent, established on the route of the energy flow from solar wind to the plasma sheet and, eventually, the inner magnetosphere. We also identified a new parameter, NBL = VByz cos2(θ/2), which helps to reveal the loss processes in the plasma sheet transition region.