Timing Estimation Research Articles

Cell ID and Timing Estimation Techniques for Underwater Acoustic Cellular Systems in High-Doppler Environments.

In an underwater acoustic cellular (UAC) system, underwater equipment or sensor nodes need to detect the identity of an underwater base station (UBS) and synchronise it with a serving UBS. It is known that, in an underwater acoustic channel, the temporal variability of the ocean coupled with the low speed of sound in water may induce a significant Doppler shift. In this paper, two different types of cell search techniques (CSTs) are proposed to detect the cell ID and correct timing of the UBS in UAC systems with a Doppler shift: CST based on linear frequency modulation with full bandwidth in the time domain (LFM-FT) and CST based on linear frequency modulation in the frequency domain (LFM-FF). The performances (auto-correlation, cross-correlation, ambiguity function, and cross ambiguity function) of the proposed techniques are analysed and compared with simulation results. It is demonstrated by simulation that the proposed techniques perform better than previous techniques in both AWGN and multipath channels when a Doppler shift exists. It is also shown that the LFM-FF-CST achieves the best performance in the presence of a Doppler shift and is suitable for mobile UAC systems.

Continuous dynamic sliding mode control of converter-fed DC motor system with high order mismatched disturbance compensation

The combination of DC-DC buck power converters with DC motors for generating the so-called smooth start of drives has many advantages in engineering practice. Achieving high performance of such systems is, however, limited by the influence of disturbances/uncertainties of multiple sources. Some of the disturbances are mismatched, which makes them even more difficult to handle. Furthermore, the relatively high order of system dynamics makes the control design challenging. In this paper, a control structure with continuous dynamic sliding mode controller with a finite-time disturbance observer is proposed to address these practical issues. First, a special state transformation is applied, aggregating the acting disturbances/uncertainties in a sole perturbing term of the system expressed in new coordinates. Then, the observer estimates in real time the information about the lumped disturbances based on already available input/output signals and the obtained estimated signals (and their high order time-derivatives) are used to construct a sliding surface. Finally, the sliding mode controller is applied to achieve high performance of the resultant plant dynamics and to robustify the governing scheme against modelling discrepancies. The stability of the closed-loop system is proved here using Lyapunov stability theory and the efficiency of the proposed control method is validated through a multi-criteria numerical simulation.

Bounds on Phase, Frequency, and Timing Synchronization in Fully Digital Receivers With 1-bit Quantization and Oversampling

Digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth promise lower energy consumption. However, since 1-bit quantization is a highly non-linear operation, standard receiver algorithms cannot be applied. Thus, we derive performance bounds for phase, timing, and frequency estimation in order to gain a deeper insight into the impact of 1-bit quantization and oversampling. We identify uniform phase and sample dithering as crucial to combat the effect of the non-linearity introduced by 1-bit quantization. Since oversampling results in noise correlation, a closed form of the likelihood function is not available. Thus, we study a system model with white noise by adapting the receive filter bandwidth to the sampling rate. Considering the aforementioned dithering, we obtain very tight closed form lower bounds on the Cramér-Rao lower bound (CRLB) in the large sample regime. We show that with uniform phase and sample dithering, all large sample properties of the CRLB of the unquantized receiver are preserved under 1-bit quantization, except for an signal-to-noise ratio (SNR) dependent performance loss that can be decreased by oversampling. Numerical computations show that the properties of the CRLB for white noise still hold for colored noise except that the performance loss due to 1-bit quantization is reduced.

Ignoring Fossil Age Uncertainty Leads to Inaccurate Topology and Divergence Time Estimates in Time Calibrated Tree Inference

Time calibrated trees are challenging to estimate for many extinct groups of species due to the incompleteness of the rock and fossil records. Additionally, the precise age of a sample is typically not known as it may have occurred at any time during the time interval spanned by the rock layer. Bayesian phylogenetic approaches provide a coherent framework for incorporating multiple sources of evidence and uncertainty. In this study, we simulate datasets with characteristics typical of Palaeozoic marine invertebrates, in terms of character and taxon sampling. We use these datasets to examine the impact of different age handling methods on estimated topologies and divergence times obtained using the fossilized birth-death process. Our results reiterate the importance of modeling fossil age uncertainty, although we find that the relative impact of fossil age uncertainty depends on both fossil taxon sampling and character sampling. Sampling the fossil ages as part of the inference gives topology and divergence time estimates that are as good as those obtained by fixing ages to the truth, whereas fixing fossil ages to incorrect values results in higher error and lower coverage. The relative effect increases with increased fossil and character sampling. Modeling fossil age uncertainty is thus critical, as fixing incorrect fossil ages will negate the benefits of improved fossil and character sampling.

Time-stepping DPG formulations for the heat equation

For a wide range of PDEs, the discontinuous Petrov–Galerkin (DPG) methodology of Demkowicz and Gopalakrishnan provides discrete stability starting from a coarse mesh and minimization of the residual in a user-controlled norm, among other appealing features. Research on DPG for transient problems has mainly focused on spacetime discretizations, which has theoretical advantages, but practical costs for computations and software implementations. The sole examination of time-stepping DPG formulations was performed by Führer, Heuer, and Gupta, who applied Rothe’s method to an ultraweak formulation of the heat equation to develop an implicit time-stepping scheme; their work emphasized theoretical results, including error estimates in time and space.In the present work, we follow Führer, Heuer, and Gupta in examining the heat equation; our focus is on numerical experiments, examining the stability and accuracy of several formulations, including primal as well as ultraweak, and explicit as well as implicit and Crank–Nicolson time-stepping schemes. We are additionally interested in communication-avoiding algorithms, and we therefore include a highly experimental formulation that places all the trace terms on the right-hand side of the equation.

Wheat Area Mapping in Afghanistan Based on Optical and SAR Time-Series Images in Google Earth Engine Cloud Environment

Wheat is cultivated on more than 2.7 million hectares in Afghanistan annually, yet the country is dependent on imports to meet domestic demand. The timely estimation of domestic wheat production is highly critical to address any potential food security issues and has been identified as a priority by the Ministry of Agriculture Irrigation and Livestock (MAIL). In this study, we developed a system for in-season mapping of wheat crop area based on both optical (Sentinel-2) and synthetic aperture radar (SAR, Sentinel-1) data to support estimation of wheat cultivated area for management and food security planning. Utilizing a 2010 Food and Agriculture Organization(FAO) cropland mask, wheat sown area for 2017 was mapped integrating decision trees and machine learning algorithms in the Google Earth Engine cloud platform. Information from provincial crop calendars in addition to training and validation data from field-based surveys, and high-resolution Digitalglobe and Airbus Pleiades images were used for classification and validation. The total irrigated and rainfed wheat area were estimated as 912,525 ha and 562,611 ha respectively for 2017. Province-wise accuracy assessments show the maximum accuracy of irrigated (IR) and rainfed (RF) wheat across provinces was 98.76 % and 99% respectively, whereas the minimum accuracy was found to be 48% (IR) and 73% (RF). The lower accuracy is attributed to the unavailability of reference data, cloud cover in the satellite images and overlap of spectral reflectance of wheat with other crops, especially in the opium poppy growing provinces. While the method is designed to provide estimation at different stages of the growing season, the best accuracy is achieved at the end of harvest using time-series satellite data for the whole season. The approach followed in the study can be used to generate wheat area maps for other years to aid in food security planning and policy decisions.

Associations Between Peaks of Foodborne Infections and Food Recalls

Abstract Objectives Food delivery and supply chains represent complex dynamic systems susceptible to pathogen introduction. However, seasonal infections are rarely compared with the timing of food recalls. Gaps between peak infections and recalls illustrate inefficient food inspection and monitoring. We use seasonality features (peak timing and amplitude) to examine associations between peaks of pathogen infectiousness and food recalls. Methods Monthly counts of 3 foodborne infections (Salmonella, Listeria, and Shiga toxin-producing E.coli) were abstracted from the CDC's public database, FoodNet Fast, for 10 representative states (CA, CO, CT, GA, MD, MN, NM, NY, OR, TN) from 1996–2018. We applied Negative Binomial harmonic regression models with the δ-method to derive peak timing and amplitude estimates (with confidence intervals). Food recalls were extracted using the United States Department of Agriculture's (USDA) Food Safety and Inspection Service (FSIS) Recall Case Archive. We extracted recall dates and durations for each infection in each state. We compared lagged associations between infection peaks and recall dates using Spearman correlations. Results Seasonal peaks of Salmonella and Shiga Toxin-producing E.Coli (STEC) are tightly clustered (7.79 [7.71, 7.87], and 7.80 [7.65, 7.95], respectively) while Listeria falls later in August (8.35 [8.05, 8.65]). Peak timing estimates were positively synchronized between New York and Connecticut for Listeria (ρ = 0.780, P = 0.001), and between Georgia and Tennessee for STEC (ρ = 0.666, P = 0.009) indicating possible regional hotspots. From 1996–2018, inspection recalls tended to lag behind peaks of confirmed infections for all infections by 1–2 months, with most recalls occurring during August and September annually. Conclusions Measurements of infection seasonality coupled with food recall information shows lagged associations between population infectiousness and supply chain reporting. This suggests the need for improved food inspection scheduling and monitoring to minimize fiscal and food waste losses. Funding Sources The Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2017–17,072,100,002.

Exponential integrability and exit times of diffusions on sub-Riemannian and metric measure spaces

In this article, we derive moment estimates, exponential integrability, concentration inequalities and exit times estimates for canonical diffusions firstly on sub-Riemannian limits of Riemannian foliations and secondly in the nonsmooth setting of $\operatorname{RCD}^{*}(K,N)$ spaces. In each case, the necessary ingredients are Itô’s formula and a comparison theorem for the Laplacian, for which we refer to the recent literature. As an application, we derive pointwise Carmona-type estimates on eigenfunctions of Schrödinger operators.

A Novel Artificial Intelligence Based Timing Synchronization Scheme for Smart Grid Applications

The smart grid control applications necessitate real-time communication systems with time efficiency for real-time monitoring, measurement, and control. Time-efficient communication systems should have the ability to function in severe propagation conditions in smart grid applications. The data/packet communications need to be maintained by synchronized timing and reliability through equally considering the signal deterioration occurrences, which are propagation delay, phase errors and channel conditions. Phase synchronization plays a vital part in the digital smart grid to get precise and real-time control measurement information. IEEE C37.118 and IEC 61850 had implemented for the synchronization communication to measure as well as control the smart grid applications. Both IEEE C37.118 and IEC 61850 experienced a huge propagation and packet delays due to synchronization precision issues. Because of these delays and errors, measurement and monitoring of the smart grid application in real-time is not accurate. Therefore, it has been investigated that the time synchronization in real-time is a critical challenge in smart grid applications, and for this issue, other errors raised consequently. The existing communication systems are designed with the phasor measurement unit (PMU) along with communication protocol IEEE C37.118 and uses the GPS timestamps as the reference clock stamps. The absence of GPS increases the clock offsets, which surely can hamper the synchronization process and the full control measurement system that can be imprecise. Therefore, to reduce this clock offsets, a new algorithm is needed which may consider any alternative reference timestamps rather than GPS. The revolutionary Artificial Intelligence (AI) enables the industrial revolution to provide a significant performance to engineering solutions. Therefore, this article proposed the AI-based Synchronization scheme to mitigate smart grid timing issues. The backpropagation neural network is applied as the AI method that employs the timing estimations and error corrections for the precise performances. The novel AIFS scheme is considered the radio communication functionalities in order to connect the external timing server. The performance of the proposed AIFS scheme is evaluated using a MATLAB-based simulation approach. Simulation results show that the proposed scheme performs better than the existing system.

Nonparametric and semiparametric estimators of restricted mean survival time under length-biased sampling.

Restricted mean survival time is often of direct interest in epidemiologic studies involving censored survival time. In this article, we propose the nonparametric and semiparametric estimators of the mean restricted to the preassigned interval with censored length-biased data. Based on the peculiarity of length-biased data, the auxiliary information that truncation time and residual time have the same distribution is taken into account for improving estimation efficiency. For two-sample comparison, we construct two tests which are easy to implement. We also derive the asymptotic properties for the proposed estimators and test statistics. In simulation studies, some simulations are conducted to compare the performances of several approaches to estimate restricted mean and to assess the test statistics. In addition, our methods are applied to a real data example and some interesting results are presented.

Mobile geolocation techniques for indoor environment monitoring

Advances in localization-based technologies and the increase in ubiquitous computing have led to a growing interest in location-based applications and services. High accuracy of the position of a wireless device is still a crucial requirement to be satisfied. Firstly, the rapid development of wireless communication technologies has affected the location accuracy of radio monitoring systems employed locally and globally. Secondly, the location is determined using standard complex computing methods and needs a relatively long execution time. In this paper, two geolocalization techniques, based on trigonometric and CORDIC computing processes, are proposed and implemented for Bluetooth-based indoor monitoring applications. Theoretical analysis and simulation results are investigated in terms of accuracy, scalability, and responsiveness. They show that the proposed techniques can locate a target wireless device accurately and are well suited for timing estimation.

Hydrocarbon migration and accumulation in the Lower Cambrian to Neoproterozoic reservoirs in the Micangshan tectonic zone, China: New evidence of fluid inclusions

The Lower Paleozoic reservoir in the Micangshan tectonic zone is a new shale gas exploration area with excellent potential. However, the hydrocarbon migration and accumulation histories of this reservoir have not been thoroughly elucidated and urgently require further research. Fluid inclusions offer a unique and useful method to test for hydrocarbon migration, accumulation, composition and timing. This study integrated the hydrocarbon charge history using fluid inclusions in the Lower Cambrian to Neoproterozoic reservoir from the Micangshan tectonic zone. This work involves the delineation and analysis of fluid inclusions using the petrography, spectroscopy and microthermometry of fluid inclusions. Based on the fluid inclusion analyses combined with reservoir thermal history, timing estimates and charge models of the hydrocarbons were obtained. The formation of natural gas was multifactorial in the Lower Cambrian to Neoproterozoic petroleum system, and the gas in the Niutitang (ϵ1n) and Dengying (Z2d) reservoirs was mainly from oil cracking and dry gas, and gas in the Xiannvdong (ϵ1x) and Canglangpu (ϵ1c) reservoirs was abiogenic and formed in hydrothermal fluids by regional tectonothermal events. Three well-defined stages of hydrocarbon charge were identified in the petroleum system in the Micangshan tectonic zone. The stage of oil charge first began before the Early Permian (∼277 Ma); the stage of gas charge from oil cracking occurred in the Late Triassic to Early Jurassic (212 Ma-198 Ma); and the stage of gas charge from dry gas occurred in the Middle Jurassic (173 Ma-166 Ma). The gas of mixed origin from the Niutitang reservoir was stored in the nanoscale pores as a self-reservoir, rather than as an effective gas source for the overlying reservoirs.

Complexity and time

For any quantum algorithm given by a path in the space of unitary operators we define the computational complexity as the typical computational time associated with the path. This time is defined using a quantum time estimator associated with the path. This quantum time estimator is fully characterized by the Lyapunov generator of the path and the corresponding quantum Fisher information. The computational metric associated with this definition of computational complexity leads to a natural characterization of cost factors on the Lie algebra generators. Operator complexity growth in time is analyzed from this perspective leading to a simple characterization of Lyapunov exponent in case of chaotic Hamiltonians. The connection between complexity and entropy is expressed using the relation between quantum Fisher information about quantum time estimation and von Neumann entropy. This relation suggest a natural bound on computational complexity that generalizes the standard time energy quantum uncertainty. The connection between Lyapunov and modular Hamiltonian is briefly discussed. In the case of theories with holographic duals and for those reduced density matrix defined by tracing over a bounded region of the bulk, quantum estimation theory is crucial to estimate quantum mechanically the geometry of the tracing region. It is suggested that the corresponding quantum Fisher information associated with this estimation problem is at the root of the holographic bulk geometry.

A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework

Accurate estimation of the fine-resolution fraction of absorbed photosynthetically active radiation (FPAR) across broad spatial extents and long time periods requires efficient and applicable methods. The existing methods can hardly provide a balance between accuracy, simplicity, and transferability through space and time. Within the remote-sensing trend-surface conceptual framework, this article proposes a scaling-based method to efficiently retrieve FPAR from fine-resolution satellite data using coarse-resolution FPAR products as a reference. The method was particularly developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR product and Landsat imagery. First, necessary prior knowledge related to FPAR retrieval and scaling theories was used to explicitly linearize the complex relationship between MODIS FPAR and Landsat surface reflectance. Second, the explicit linear model for FPAR estimation was trained through one-pair image learning for each date to estimate FPAR from Landsat imagery in real time. Both homogeneous and heterogeneous cases were considered. The method was validated at ten selected worldwide sites from the Validation of Land European Remote Sensing Instruments (VALERI) program and derived an overall root mean squared error (RMSE) of 0.133. A long time series of FPAR data set at the 30-m resolution was generated at the regional scale (approximately 2000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for 13 years (2000–2012). The results were accurate (RMSE = 0.072) and MODIS-consistent, which were significantly better than those of the normalized difference vegetation index (NDVI) downscaling-based and regression tree methods. The scaling-based method provides accurate, MODIS-consistent and spatially consistent FPAR estimates in real time, is highly transferrable through space and time, and allows for future extension of FPAR estimates to the era of the Landsat series satellites.

A Timed-Value Stream Based ESL Timing and Power Estimation and Simulation Framework for Heterogeneous MPSoCs

Consideration of an embedded system’s timing behavior and power consumption at system-level is an ambitious task. Sophisticated tools and techniques exist for power and timing estimations of individual components such as custom hard- and software as well as IP components. In this article we present an ESL framework for timing and power aware virtual system prototyping of heterogeneous MPSoCs consisting of software, custom hardware and 3rd party IP components. In virtual platform, previously only used for functional software verification, our proposed timed value streams enable a hierarchical and composable power model. Our proposed ESL framework supports the integration of a broad range of system-level timing and power models into virtual platform. Power and timing models can either be generated from a functional C/C++ description or include state-machine based power models to existing functional and timed virtual platform (black-box) components. Our timed value stream based power model supports the run-time analysis of different platform power management strategies with configurable temporal abstraction, supporting simulation speed and accuracy trade-offs. This work evaluates timing and power back-annotation and power state machine based approaches with timed value streams in two use-cases: An MP3 decoder, compared to a power-aware ISS and gate-level simulation, and an FPGA based many-core architecture against measurements. Finally, the simulation time overhead of the proposed stream based power model is analyzed and discussed.

Cross-projects software defect prediction using spotted hyena optimizer algorithm

Cross-projects software defect prediction improves the quality of new software projects or projects with a shortage of historical data. Therefore, various data mining techniques are recommended in this field. The classification accuracy issue is considered one of the most significant problems due to the shortage and heterogeneous in historical data. To address this challenge, this research utilizes a spotted hyena optimizer algorithm as a classifier to predict defects through cross-projects. Confidence and Support are utilized as a multi-objective fitness function to look for the best classification rules. These classification rules are used to predict defects for new projects or other projects with insufficient data. The datasets of NASA such as JM1, KC1, and KC2 are used. By applying spotted hyena optimizer algorithm as a classifier on one dataset and predicting defects in the other two datasets, accuracy is reported 84.6, 92.0, 82.4, 90.7, 86.6 and 81.8 for JM1, KC1, and KC2 respectively. These accuracy values are better than the most significant data mining techniques in the field such as Support Vector Machine, Naive Bayes, Boosting, C4.5, and Bagging. Also, the proposed research discusses other performance measures such as precision, recall, and f-measure. The conclusion proves that there are many features of McCabe and Halstead that have a strong impact to generate highly accurate predictors for defects such as McCabe’s line count of code, McCabe’s cyclomatic complexity, McCabe’s essential complexity, McCabe’s design complexity iv, Halstead’s effort, Halstead’s time estimator, Halstead’s line count, Halstead’s count of line of comments and total operators.

Startup Timing Estimation of Temperature Control System and Industrial Applicability Evaluation

Startup Timing Estimation of Temperature Control System and Industrial Applicability Evaluation

Investigation of the rigidity and sensitivity dependence of neutron monitors for cosmic ray modulation using algorithm-selected Forbush decreases

ABSTRACTWe emphasize the need for a careful and rigorous timing of Forbush decreases (FDs) as well as a correct calculation of FD magnitudes in studies related to cosmic ray (CR) modulation. We have employed Fourier and R-based algorithms for FD event selection, timing and magnitude estimation. The large number of Forbush events that have been identified were employed in correlation and regression analyses to investigate the rigidity and sensitivity dependence of neutron monitors (NMs). It was found that there is a significant difference between the number of FDs identified manually and those selected by the automated method. While the minimum number (238) of FDs occurred at Irkutsk NM, the Novosibirsk CR station observed the largest number (386) of Forbush events. However, within the north high-latitude band (39°N ≤ latitude ≤ 90°N), only 29 FDs have been simultaneously identified using the data from some NMs in the region, including Irkutsk and Novosibirsk. The result obtained using a large number of FDs differs significantly from those employing manual identification of Forbush events. We conclude, among other things, that the automation of FD event selection is essential for understanding the dependence of CR modulation on NM rigidity and altitude, as well as on the contribution from terrestrial modulation agents.

Fine time synchronization and residual ISI influence in SC-FDE systems

We show a novel method for a fine time synchronization in a presence of residual intersymbol interferences (ISIs). The proposed method is compared with a (1) Cramér-Rao lower bound (CRLB) for data-aided timing estimators and with a (2) maximum likelihood (ML) estimator both in (a) ISI-free additive white Gaussian noise (AWGN) channel and (b) in a residual ISI channel. The effect of the residual ISI is introduced via a power-delay profile (PDP) with a delay spread spanning over several samples. We consider a single-carrier system with a frequency domain channel estimation. The proposed method almost reaches the performance of the ML estimator in AWGN, while in the presence of the residual ISI, the proposed method shows superior performance evaluated in terms of error vector magnitude improvement of up to 10%.

3-D Human Pose Estimation Using Iterative Conditional Squeeze and Excitation Networks.

We propose a new method for single-camera real-world 3-D human pose estimation. Our method uses multitask training together with iterative pose refinement using a novel conditional attention mechanism. For iterative pose refinement, the output of each convolutional layer is conditioned on the latest pose estimate, using a conditioned squeeze-and-excitation network architecture that incorporates novel feedback connections. Multitask training on both an in-the-wild 2-D pose dataset and a controlled 3-D pose dataset allows for real-world 3-D pose estimation without the need for a large-scale in-the-wild 3-D pose dataset, which is unavailable. Experiments are performed on several real-world datasets, as well as the Human 3.6 Million and HumanEva-I datasets, to show that the combined attention mechanism, iterative refinement scheme, and multitask training allow us to achieve robust and competitive performance with only a simple network architecture. In addition, we show that our method is efficient enough to run on commodity hardware, producing pose estimates in real time.