Precise Timing Research Articles

Time Scale in Alternative Positioning, Navigation, and Timing: New Dynamic Radio Resource Assignments and Clock Steering Strategies

Terrestrial and satellite communications, tactical data links, positioning, navigation, and timing (PNT), as well as distributed sensing will continue to require precise timing and the ability to synchronize and disseminate time effectively. However, the supply of space-qualified clocks that meet Global Navigation Satellite Systems (GNSS)-level performance standards is limited. As the awareness of potential disruptions to GNSS due to adversarial actions grows, the current reliance on GNSS-level timing appears costly and outdated. This is especially relevant given the benefits of developing robust and stable time scale references in orbit, especially as various alternatives to GNSS are being explored. The onboard realization of clock ensembles is particularly promising for applications such as those providing the on-demand dissemination of a reference time scale for navigation services via a proliferated Low-Earth Orbit (pLEO) constellation. This article investigates potential inter-satellite network architectures for coordinating time and frequency across pLEO platforms. These architectures dynamically allocate radio resources for clock data transport based on the requirements for pLEO time scale formations. Additionally, this work proposes a model-based control system for wireless networked timekeeping systems. It envisions the optimal placement of critical information concerning the implicit ensemble mean (IEM) estimation across a multi-platform clock ensemble, which can offer better stability than relying on any single ensemble member. This approach aims to reduce data traffic flexibly. By making the IEM estimation sensor more intelligent and running it on the anchor platform while also optimizing the steering of remote frequency standards on participating platforms, the networked control system can better predict the future behavior of local reference clocks paired with low-noise oscillators. This system would then send precise IEM estimation information at critical moments to ensure a common pLEO time scale is realized across all participating platforms. Clock steering is essential for establishing these time scales, and the effectiveness of the realization depends on the selected control intervals and steering techniques. To enhance performance reliability beyond what the existing Linear Quadratic Gaussian (LQG) control technique can provide, the minimal-cost-variance (MCV) control theory is proposed for clock steering operations. The steering process enabled by the MCV control technique significantly impacts the overall performance reliability of the time scale, which is generated by the onboard ensemble of compact, lightweight, and low-power clocks. This is achieved by minimizing the variance of the chi-squared random performance of LQG control while maintaining a constraint on its mean.

A Comparative Analysis of Compression and Transfer Learning Techniques in DeepFake Detection Models

DeepFake detection models play a crucial role in ambient intelligence and smart environments, where systems rely on authentic information for accurate decisions. These environments, integrating interconnected IoT devices and AI-driven systems, face significant threats from DeepFakes, potentially leading to compromised trust, erroneous decisions, and security breaches. To mitigate these risks, neural-network-based DeepFake detection models have been developed. However, their substantial computational requirements and long training times hinder deployment on resource-constrained edge devices. This paper investigates compression and transfer learning techniques to reduce the computational demands of training and deploying DeepFake detection models, while preserving performance. Pruning, knowledge distillation, quantization, and adapter modules are explored to enable efficient real-time DeepFake detection. An evaluation was conducted on four benchmark datasets: “SynthBuster”, “140k Real and Fake Faces”, “DeepFake and Real Images”, and “ForenSynths”. It compared compressed models with uncompressed baselines using widely recognized metrics such as accuracy, precision, recall, F1-score, model size, and training time. The results showed that a compressed model at 10% of the original size retained only 56% of the baseline accuracy, but fine-tuning in similar scenarios increased this to nearly 98%. In some cases, the accuracy even surpassed the original’s performance by up to 12%. These findings highlight the feasibility of deploying DeepFake detection models in edge computing scenarios.

Non-obtrusive Imposter Detection During Online Assessments

Cheating during online assessments is a major challenge faced by test administering institutions. Impersonation is the most common fraudulent act. Since impersonation can be resorted to at any stage of an online test, conventional access control at initial login is ineffective. Hence, the need for continuous authentication. Although technology is available to monitor candidates continuously while they take online tests, there are several constraints that limit their deployment. This paper presents a non-obtrusive, easily deployable, and effective continuous authentication system that could detect impersonation during online assessments. It uses a popular class of behavioural biometrics called ‘keystroke dynamics’ which are basically precision time measurements that can be made latently without the aid of any additional accessories. Authentication is performed using a novel parameter called the ‘angle of departure’ together with the ‘pause durations’, both computed from the biometrics captured during the online test and those captured during a mock test conducted as part of the candidate registration process. The underlying concept is presented in detail. Details of experiments conducted to establish proof of the concept are given. These include roleplays conducted for the acquisition of biometrics and simulations of genuine test taking as well as impersonation. The performance of the system is evaluated using both traditional metrics as well as recently established metrics that are considered as being more suitable for assessing continuous authentication systems. Analysis of the results indicates the effectiveness of the use of the angle of departure and pause durations for continuous authentication of candidates during online tests.

An Analog Sensor Signal Processing Method Susceptible to Anthropogenic Noise Based on Improved Adaptive Singular Spectrum Analysis

Sensor measurements are often affected by complex ambient noise and complicating signal processing tasks. The singular spectrum decomposition (SSA) algorithm, while widely used, faces challenges such as the difficulty of determining the number of decomposition layers, requiring iterative adjustments that reduce precision and increase processing time. This paper proposes an improved adaptive singular spectrum analysis (ASSA) algorithm that integrates a deep residual network (Res-Net) for automatic recognition. A comprehensive interference signal database was constructed to train the Deep Res-Net, and common interferences were restored through the combination of different signals, enabling greater frequency resolution performance. Meanwhile, a novel correlation detection reconstruction method based on a clustering algorithm for adaptive signal classification was developed to suppress background noise and extract meaningful signals. ASSA addresses the challenge of determining the optimal number of decomposition layers, eliminating the parameter adjusting process and enhancing the measurement efficiency of sensor systems. Through experiments, magnetotelluric (MT) observation data with complex interferences were applied to demonstrate the performance of ASSA, and promising results with an RMSE of 0.2 were obtained. The experiments also showed that the accuracy of ASSA was improved by 14% compared to other signal extraction algorithms, proving that ASSA can achieve excellent results when applied to other data processing fields.

Integrating quantum synchronization in future generation networks

The advent of Beyond 5G (emerging 6G) technologies represents a significant step forward in telecommunications, offering unprecedented data speeds and connectivity. These advances enable a wide range of applications, from enhanced mobile broadband and the Internet of Things to ultra-reliable low-latency communication and the tactical Internet. Thus, having accurate and dependable time synchronization is of utmost importance and plays a critical role in ensuring that all processes function smoothly and effectively. However, existing standards, such as the precision time protocol, are unreliable due to jitters, datagram losses, and complexity. Increasing the synchronization error from the ideal tens of nanoseconds to hundreds of microseconds is unacceptable in future-generation networks. This work provides a novel way to establish ultraprecise synchronization, which is critical for the growth of converged optical communication networks and the 6G era. We investigate quantum non-linear synchronization (QNS), which explores the interaction between the non-linear dynamics of atomic systems and dissipation to establish a stable limit-cycle state. In this process, atoms confined within optical resonators are subjected to potential fields, and their spatial motion is synchronized by achieving a stable, phase-locked configuration. By introducing photons into the optical resonators and precisely managing the dissipation effects, it is possible to synchronize multiple optical resonators (referred to as nodes), even in systems with more than three interconnected resonators containing non-linear atoms. To transcend the synchronization signal from the optical setup to communication networks, we propose a distinct mechanism that utilizes the exceptional precision of QNS in the optical lattice setup and frequency down-conversion using frequency combs. In addition, it is combined with electronic components such as analog-to-digital converters and field-programmable gate arrays (FPGAs) to create synchronized digital signals that are understandable to communication networks. Our method transforms optical pulses into precisely timed electrical signals that can be analyzed and used in sophisticated network systems. We demonstrated that QNS and dissipation can synchronize a tri-node clock network to the highest precision of thulium atom-based optical lattice clocks. Our work also highlights the practicality of these applications through MATLAB simulations, bridging theoretical principles and real-world solutions with current technology. In our simulations, we utilized an optical signal with a frequency of 263 THz, downconverted to a lower microwave frequency of 100 GHz to achieve subnanosecond-level synchronized signals. The down-converted signal was subjected to white noise and subsequently digitized. The digital signal was then simulated by sampling rate of GHz or GSa/s (gigasample per second) and limiting the resolution to bits. Finally, high-frequency noise was removed by implementing low-pass filtration using FPGAs. This study takes an essential step toward meeting the rising demands for rapid and efficient data transfer in the ever-evolving digital communications landscape, enabling faster and more reliable connectivity for future communication networks and the quantum Internet.

Neural network insights: explainable artificial intelligence (XAI) and hyper-parameter dynamics for precise travel time predictions

Urban transportation systems face increasing challenges due to population growth, urbanization, and dynamic mobility patterns. Accurate travel time prediction is critical for efficient traffic management, urban planning, and transportation infrastructure optimization. This study investigates travel time prediction in urban environments through a two-phase approach combining advanced Artificial Neural Network (ANN) architectures and interpretability techniques. In the first phase, we systematically analyze the impact of ANN architectural depth, width, and key hyper-parameters on predictive accuracy, highlighting the interplay between model configurations and performance. In the second phase, we employ SHapley Additive exPlanations (SHAP) to investigate hyper-parameter-feature interaction, and enhance model transparency. Additionally, an in-depth error and uncertainty analysis for the best-performing model provides insights into residual behavior, prediction intervals, and areas for refinement. The results demonstrate the robustness and adaptability of ANN models in capturing complex spatiotemporal dependencies, while SHAP-based analysis offers actionable insights into feature contributions. Ultimately, this study advances travel time prediction understanding and offers a comprehensive AI framework for urban mobility studies, facilitating the development of precise and adaptive transportation systems.

Multivariate Piecewise Rand Divergencive Cockroach Swarm Optimization for Agriculture Crops Recommendation

Multivariate Piecewise Rand Divergencive Cockroach Swarm Optimization for Agriculture Crops Recommendation

Self-Rectifying Dynamic Memristor Circuits for Periodic LIF Refractory Period Emulation and TTFS/Rate Signal Encoding.

Spiking Neural Networks (SNNs) have gained attention due to their potential to improve computational efficiency compared to traditional artificial neural networks. This study investigates the use of dynamic memristors Ta/IGZO/TaOx/Pt combined with peripheral circuits to emulate the leaky integrate-and-fire behavior of neurons, focusing on incorporating a refractory period. The refractory period is crucial as it prevents neurons from becoming overactive and ensures precise timing in signal processing. This improvement allows the memristor to mimic biological neuron behavior more accurately. The memristor's transient resistance exhibits nonlinear I-V hysteresis and changes in response to pulses, enabling functions of integration, leakage, and firing. Additionally, the memristor is configured as an encoder, converting external signals into voltage pulse sequences. Using coding methods, including rate coding and time-to-first-spike (TTFS) coding, the encoder demonstrates improved signal processing, with TTFS occurring within 21 to 62ms and encoder frequencies from 2500 to 9500Hz. Experimental results show that this approach enhances SNN performance, making it more suitable for real-time applications and complex temporal signal processing tasks. This research highlights the potential of dynamic memristors to bridge the gap between neurons and artificial neurons, paving the way for more efficient neuromorphic computing systems.

SETD1B-mediated broad H3K4me3 controls proper temporal patterns of gene expression critical for spermatid development.

Epigenetic programming governs cell fate determination during development through intricately controlling sequential gene activation and repression. Although H3K4me3 is widely recognized as a hallmark of gene activation, its role in modulating transcription output and timing within a continuously developing system remains poorly understood. In this study, we provide a detailed characterization of the epigenomic landscapes in developing male germ cells. We identified thousands of spermatid-specific broad H3K4me3 domains regulated by the SETD1B-RFX2 axis, representing a previously underappreciated form of H3K4me3. These domains, overlapping with H3K27ac-marked enhancers and promoters, play critical roles in orchestrating robust transcription and accurate temporal control of gene expression. Mechanistically, these broad H3K4me3 compete effectively with regular H3K4me3 for transcriptional machinery, thereby ensuring robust levels and precise timing of master gene expression in mouse spermiogenesis. Disruption of this mechanism compromises the accuracy of transcription dosage and timing, ultimately impairing spermiogenesis. Additionally, we unveil remarkable changes in the distribution of heterochromatin marks, including H3K27me3 and H3K9me2, during the mitosis-to-meiosis transition and completion of meiotic recombination, which closely correlates with gene silencing. This work underscores the highly orchestrated epigenetic regulation in spermatogenesis, highlighting the previously unrecognized role of Setd1b in the formation of broad H3K4me3 domains and transcriptional control, and provides an invaluable resource for future studies toward the elucidation of spermatogenesis.

Precision Timing of Beta-Lactam Administration in Very Low Birth Weight Infants with Suspected Sepsis: A Multi-National Cohort Study

Background: Timely administration of antibiotics is critical in the management of sepsis, but evidence regarding optimal timing of beta-lactam administration in very low birth weight (VLBW) infants remains limited. This study investigated the association between precision timing of beta-lactam administration and clinical outcomes in VLBW infants with suspected sepsis. Methods: We conducted a retrospective multi-national cohort study across 4 neonatal intensive care units in 4 countries from January 2021 to December 2023. VLBW infants (birth weight <1500 g) with clinically suspected sepsis who received beta-lactam antibiotics were included. The primary outcome was all-cause 30-day mortality. Secondary outcomes included duration of mechanical ventilation, length of hospital stay, and development of antibiotic-resistant infections. Time-to-antibiotic was defined as the interval between clinical recognition of sepsis and administration of the first beta-lactam dose. Results: Among 58 VLBW infants with suspected sepsis (median gestational age 27.3 weeks [IQR 25.6-29.1], median birth weight 980 g [IQR 780-1250]), 14 (24.1%) had culture-confirmed sepsis. Median time-to-antibiotic was 65 minutes (IQR 42-95). Each hour delay in beta-lactam administration was associated with an increased risk of 30-day mortality (adjusted hazard ratio [aHR] 1.16, 95% CI 1.02-1.32, p=0.024), particularly in infants with culture-confirmed sepsis (aHR 1.28, 95% CI 1.05-1.55, p=0.012). Administration within 60 minutes was achieved in 46.6% (n=27) of cases and was associated with lower mortality compared to administration after 60 minutes (7.4% vs. 16.1%, p=0.042). Unit-level barriers to timely administration included delays in recognition, central line access difficulties, and medication preparation processes. Conclusions: Delayed beta-lactam administration in VLBW infants with suspected sepsis is associated with increased mortality, with each hour of delay conferring additional risk. Achieving administration within 60 minutes of sepsis recognition may improve outcomes in this vulnerable population. Quality improvement initiatives targeting identified barriers to timely antibiotic delivery are warranted.

Signal transmission through excitable media by an oscillatory source

Living organisms transmit signals through local interactions with their neighbors. However, the precise timing of signals is fundamentally challenged by the presence of numerous stochastic reactions within interacting cells. Here we develop a minimal model to investigate the strength of signal propagation in stochastic environments. We uncover a multimodal frequency dependency with a few local optima, which is robust in both one and two dimensions. Through mathematical analysis we explain the frequency dependency and uncover the interplay between stochasticity and external frequency in order to optimize the signaling strength of the system. Finally we investigate the possibilities of bidirectional reactions in the oscillating source, allowing us to define thermodynamic properties of the system and investigate the relationship between free-energy dissipation and precision of signaling, showing nontrivial frequency dependency in coupled cells. Published by the American Physical Society 2025

Microscopic origin of the spatial and temporal precision in biological systems.

All living systems display remarkable spatial and temporal precision, despite operating in intrinsically fluctuating environments. It is even more surprising given that biological phenomena are regulated by multiple chemical reactions that are also random. Although the underlying molecular mechanisms of surprisingly high precision in biology remain not well understood, a novel theoretical picture that relies on the coupling of relevant stochastic processes has recently been proposed and applied to explain different phenomena. To illustrate this approach, in this review, we discuss two systems that exhibit precision control: spatial regulation in bacterial cell size and temporal regulation in the timing of cell lysis by λ bacteriophage. In cell-size regulation, it is argued that a balance between stochastic cell growth and cell division processes leads to a narrow distribution of cell sizes. In cell lysis, it is shown that precise timing is due to the coupling of holin protein accumulation and the breakage of the cellular membrane. The stochastic coupling framework also allows us to explicitly evaluate dynamic properties for both biological systems, eliminating the need to utilize the phenomenological concept of thresholds. Excellent agreement with experimental observations is observed, supporting the proposed theoretical ideas. These observations also suggest that the stochastic coupling method captures the important aspects of molecular mechanisms of precise cellular regulation, providing a powerful new tool for more advanced investigations of complex biological phenomena.

Design and development of humanoid robotic arm

This paper presents the design, development, and evaluation of a 5-degrees of freedom (5-DoF) humanoid robotic arm featuring a sophisticated 5-finger gripper. The five degrees of freedom include the base, shoulder, elbow, wrist, and gripper, all controlled by MG996R servo motors to enhance grasping, positioning, flexibility, and mobility. The arm is constructed from laser-cut aluminum sheets. It effectively picks and places objects such as bottles and bags. A high-speed portable computing system is used to control robotics hand operations. A webcam is used for object detection and to acquire information about the surroundings. The system uses a convolutional neural network-based MobileNet architecture for object detection. The robotic hand is used as an assistive aid for amputees. It mimics finger movements based on detected objects. The system achieved a precision of 0.97 for bags and 0.93 for bottles, with accuracies of 96.83% and 92.42%, respectively. The system employs advanced computer vision algorithms and real-time strategies, ensuring adaptability across various tasks. It integrates advanced visual systems and improved feedback to enhance user interaction and overall usability. It addresses trade-offs between detection precision and processing time.

Ensuring clock phase repeatability by preventing loss of the 40.078 MHz clock in time-critical detectors: a non-disruptive clock switching approach

For the High Luminosity phase of the LHC (HL-LHC), the CMS detector electronics require precise and accurate timing to discriminate pileup events. The upgraded Back-End electronics of the Barrel Electromagnetic Calorimeter supply the required high-precision clock (Time Interval Error σ < 5 ps) to the Front-End. However, after a reset is applied on the timing distribution system, the resulting phase of the clock is not accurately repetitive but it gives an up to 10 ps offset for each FPGA node of the timing distribution chain. That is resulting to the uncertainty on the total clock skew with the maximum being N x 10 ps between neighbor channels of the system (where N is the number of FPGA-nodes of the subdetector branch). We have studied a solution minimizing the need to reset the timing distribution system by switching on-the-fly and seamlessly the source clock at the top of the tree to a local LHC-frequency clock. This approach prevents the loss of the links towards the on-detector electronics ensuring clock phase repeatability and offers stability to the on-detectors electronics.

Complete Decomposition of Symmetric Tensors in Linear Time and Polylogarithmic Precision

Complete Decomposition of Symmetric Tensors in Linear Time and Polylogarithmic Precision

Precision timing measurements at the CMS ETL with Low-Gain Avalanche Diodes

Precision timing measurements at the CMS ETL with Low-Gain Avalanche Diodes

VmPTP: Precise Time Protocol for Inter-VM Communication in Embedded Virtualized Systems

vmPTP: Precise Time Protocol for Inter-VM Communication in Embedded Virtualized Systems

PICOSEC Micromegas precise-timing detectors: development towards large-area application and integration

PICOSEC Micromegas (MM) is a precise timing gaseous detector based on a Cherenkov radiator coupled with a semi-transparent photocathode and an MM amplifying structure. The detectorconceptwas successfully demonstrated through a single-channel prototype, achieving sub-25 ps time resolution with Minimum Ionizing Particles (MIPs). A series of studies followed, aimed at developing robust, large-area, and scalable detectors with high time resolution, complemented by specialized fast-response readout electronics. This work presents recent advancements towards large-area resistive PICOSEC MM, including 10 × 10 cm2 area prototypes and a 20 × 20 cm2 prototype, which features the jointing of four photocathodes. The time resolution of these detector prototypes was tested during the test beam, achieved a timing performance of around 25 ps for individual pads in MIPs. Meanwhile, customized electronics have been developed dedicated to the high-precision time measurement of the large-area PICOSEC MM. The performance of the entire system was evaluated during the test beam, demonstrating its capability for large-area integration. These advancements highlight the potential of PICOSEC MM to meet the stringent requirements of future particle physics experiments.

Distinct roles of PV and Sst interneurons in visually induced gamma oscillations.

Distinct roles of PV and Sst interneurons in visually induced gamma oscillations.

STSimM: A new tool for evaluating neuron model performance and detecting spike trains similarity.

STSimM: A new tool for evaluating neuron model performance and detecting spike trains similarity.