Estimate Of Location Research Articles

CASTELO: Convex Approximation based Solution To Elliptic Localization with Outliers

This short communication considers mitigating the negative effects of possibly unreliable path delay measurements acquired in non-line-of-sight (NLOS) environments on the positioning performance, a problem deserving further investigation within the expanding research area of elliptic localization. We present CASTELO, a Convex Approximation based Solution To Elliptic Localization with Outliers, to achieve such a goal. Our proposal corresponds to a mixed semidefinite (SD)/second-order cone (SOC) programming formulation derived from an error-mitigated nonlinear least squares (LS) location estimator, presenting itself as a remedy for the neglect of positivity of NLOS biases suffered by the majority of currently fashionable outlier-handling approaches. In terms of analytical discussions, we provide rationales supporting the incorporation of the SOC constraints, which serve to tighten the problem obtained after SD relaxation, and conduct a complexity analysis for the ultimate mixed SD/SOC programming formulation. Simulations are carried out to confirm the strong ability of CASTELO to attain reliable elliptic localization in the presence of NLOS outliers.

Deep learning aided multi-source passive 3D AOA wireless positioning using a moving receiver: A low complexity approach

Passive localization of multiple sources while the receiver is in motion is a fundamental problem in wireless communication with practical applications in various fields such as acoustic event localization and object tracking. In this study, we propose a novel method for three dimensional (3D) angle of arrival (AOA) localization using a moving receiver with a limited number of sensors. In the proposed method, we record signals from sources in a series of time windows. Then we estimate the elevation and azimuth AOAs of sources along with their pairing by using the proposed three low-complexity deep learning (DL)-based AOA estimators. Following the initial AOA estimation, a low-complexity off-grid based refinement technique is employed to further enhance the accuracy of the estimated elevation and azimuth AOAs based on the maximum likelihood (ML) criterion. Next, a multi-source 3D-localization algorithm is proposed to estimate the positions of sources across the recorded time windows. The simulation results validate the effectiveness and efficiency of the proposed method. The results highlight that the DL-based AOA and location estimators outperform recent studies, while also demonstrating robustness in the face of practical imperfections, such as situations where the receiver’s position or direction is uncertain during movement. Our complexity analysis verifies that the proposed method outperforms existing methods in terms of speed and the number of computations. This enhanced computational efficiency makes the proposed method highly attractive and practically significant for application in various fields.

Conversational Localization

We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that can intelligently strategize and guide the interaction to elicit localizationally valuable information from users. Finally, we employ visibility catchment area and line-of-sight heuristics to generate spatial estimates for the user's location. We conduct two user studies in designing and testing the system. We collect 800 natural language descriptions of unfamiliar indoor spaces in an online crowdsourcing study to learn the feasibility of extracting localizationally useful entities from user utterances. We then conduct a field study with 10 participants at 10 locations to evaluate the feasibility and performance of conversational localization. The results show that conversational localization can achieve within-10 meter localization accuracy at eight out of the ten study sites, showing the technique's utility for classes of indoor location-based services.

Role of activation signatures in re-entrant ventricular tachycardia circuits.

Development of a rapid means to verify the ventricular tachycardia (VT) isthmus location from heart surface electrogram recordings would be a helpful tool for the electrophysiologist. Myocardial infarction was induced in 22 canines by left anterior descending coronary artery ligation under general anesthesia. After3-5 days, VT was inducible via programmed electrical stimulation at the anterior left ventricular epicardial surface. Bipolar VT electrograms were acquired from 196 to 312 recording sites using a multielectrode array. Electrograms were marked for activation time, and activation maps were constructed. The activation signal, or signature, is defined as the cumulative number of recording sites that have activated per millisecond, and it was utilized to segment each circuit into inner and outer circuit pathways, and as an estimate of best ablation lesion location to prevent VT. VT circuit components were differentiable by activation signals as: inner pathway (mean: 0.30 sites activating/ms) and outer pathway (mean: 2.68 sites activating/ms). These variables were linearly related (p < .001). Activation signal characteristics were dependent in part upon the isthmus exit site. The inner circuit pathway determined by the activation signal overlappedand often extended beyondthe activation map isthmus location for each circuit. The best lesion location estimated by the activation signal would likely block an electrical impulse traveling through the isthmus, to prevent VT in all circuits. The activation signal algorithm, simple to implement for real-time computer display, approximates the VT isthmus location and shape as determined from activation marking, and best ablation lesion location to prevent reinduction.

Estimating the Location of Acoustic Sources with Oceanic Internal Gravity Waves via Frequency-Difference Source Localization

Frequency-difference source localization (FDSL) is a relatively new method for locating sources emitting acoustic or electromagnetic waves from recorded data. Estimates of location are produced by matching a nonlinear function of the received data with the same nonlinear function of modeled data. This so-called matched-field approach depends on the accuracy of the modeled field. FDSL exhibits less sensitivity to model errors in some cases of interest. Fluctuations of the speed of sound in the ocean are affected by a wide variety of phenomena and internal gravity waves is one where their spectrum is usually well known. There is no analytical theory for predicting how these fluctuations of sound speed affect the accuracy of FDSL, leaving a numerical evaluation as the alternative for the focus of this paper. Experimental application of FDSL was previously made for an acoustic source of 100 Hz bandwidth surrounding 250 Hz for a 129 km section in the Philippine Sea with data recorded on a long vertical array [Geroski and Dowling, Long-range frequency-difference source localization in the Philippine Sea, J. Acoust. Soc. Am. 146 (2019) 4727–4739.]. The role of internal waves is quantified with a realistic FDSL simulation using models based on the parabolic and ray approximations of the linear acoustic wave equation. The modeled vertical variation of FDSL error is similar to observed while the modeled horizontal error is too small. The complicated software model is automatic but is computationally intensive, with our version needing about 1.5 mo to compute on a PC with 16 cpu cores.

A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning.

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave "picking" directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.

Robust confidence intervals for the process capability index Cpk with bootstrap improvement

The process capability index (PCI), C pk , one of the widely used tools for assessing the capability of a manufacturing process, expresses the deviation of the process mean from the midpoint of the specification limits. The C pk is known to perform well under the general assumption that the experimental data are normally distributed without contamination. Under this assumption, the sample mean and sample standard deviation are used for the estimation of the PCI. However, the sample mean and sample standard deviation are quite sensitive to data contamination and this will result in underperformance of C pk . Therefore, in this article, we propose alternatives to the conventional method by replacing the sample mean and sample standard deviation with robust location and scale estimators. We also propose a method for constructing a robust PCI C pk confidence interval which lends itself to robust statistical hypothesis testing. The robust hypothesis testing methods based on this confidence interval are shown to be quite efficient when the data are normally distributed yet also outperform the conventional method when data contamination exists.

Suspended and deposited microplastics in the coastal atmosphere of southwest England

Atmospheric microplastics (MPs) have been sampled from coastal southwest England during twelve periods over a 42-day timeframe in late autumn. MPs were dominated by fibres, with foams, fragments and pellets also observed. The majority of fibres were identified as the semisynthetic polymer, rayon, while other shapes were dominated by various petroleum-based thermoplastics (including polyvinyl acetate, polyvinyl alcohol, polyamide and polyester) and paints. MP concentrations suspended in air ranged from 0.016 to 0.238 items per m3 but displayed no clear dependence on wind speed or direction. Total depositional fluxes ranged from 0.47 to 3.30 m−2 h−1 and showed no clear dependence on wind conditions or electrical conductivity of precipitation (as a measure of maritime influence). However, the concentration of deposited MPs in rainwater was inversely related to rainfall volume, suggesting that incipient precipitation acts to efficiently washout microplastics. A comparison of deposited and suspended MPs by size, shape and polymer type suggests that larger fibres constructed of rayon, polyamide and acrylic are preferentially removed from the atmosphere relative to smaller, non-fibrous MPs and particles constructed of polyester. A quantitative comparison of deposited and suspended MPs provided estimates of location- and environment-specific net settling velocities of between about 7 and 180 m h−1 and corresponding residence times for an air column of 5000 m of between about 30 and 700 h. The findings of the study contribute to an improved understanding of the occurrence, transport and deposition of MPs in the atmosphere more generally.

Solving a Seismic Mystery With the Audio From a Diver's Camera: A Case of Shallow Water T‐Waves in the Persian Gulf

AbstractSparse offshore coverage of seismic networks has hindered detailed studies of submarine earthquakes and their associated seismic hazard. We present results of our analysis of a diver's recording of acoustic signals from an ML = 5.6 earthquake in the Persian Gulf. We model the signals as a set of several shallow water T phases the frequency and group velocity of which are determined by bathymetry. We show that the audio track from this recording can provide reliable estimates of earthquake location and seismic moment. We also show that the reported shaking in the southern Persian Gulf, &gt;170 km from the source of this small earthquake could result from T waves traveling through the entire width of the basin. Our results point to rudimentary and affordable underwater microphones similar to those used in the divers' cameras as tools to build efficient, low‐cost networks for the study of offshore events and early warning.

Change-Point Detection in the Volatility of Conditional Heteroscedastic Autoregressive Nonlinear Models

This paper studies single change-point detection in the volatility of a class of parametric conditional heteroscedastic autoregressive nonlinear (CHARN) models. The conditional least-squares (CLS) estimators of the parameters are defined and are proved to be consistent. A Kolmogorov–Smirnov type-test for change-point detection is constructed and its null distribution is provided. An estimator of the change-point location is defined. Its consistency and its limiting distribution are studied in detail. A simulation experiment is carried out to assess the performance of the results, which are compared to recent results and applied to two sets of real data.

Solving Fuzzy Rayleigh Model with Application

في هذا البحث اشتققنا وقدرنا معلمتي توزيع رالي باستخدام طريقة تعظيم دالة الامكان المقدرة وطريقة المربعات الصغرى المقدرة وطريقة العزوم المقدرة . تم استخدام اسلوب المحاكاة للعديد من أحجام العينات وللعديد من القيم الابتدائية وتم التوصل بأن طريقة العزوم المقدرة هي الأفضل من بقية الطرق الأخرى باستخدام اسلوب متوسط مربعات الخطأ. ومن ثم تم استخدام تقدير الفترات لإيجاد المقدر لمعلمة الموقع ومعلمة القياس لتوزيع رالي. بعد ذلك استخدمت دالة العضوية المثلثية لإيجاد المعلمات الضبابية المقدرة لطريقة العزوم لتوزيع رالي. وفي النهاية استخدمنا الدالة الزمنية الترتيبية لتحويل المعلمات الضبابية المقدرة الى معلمات تقليدية وتم التوصل بأن المعلمات الضبابية المقدرة هي الافضل.

Enhanced performance of mixed HWMA-CUSUM charts using auxiliary information.

Quality control (QC) is a systematic approach to ensuring that products and services meet customer requirements. It is an essential part of manufacturing and industry, as it helps to improve product quality, customer satisfaction, and profitability. Quality practitioners generally apply control charts to monitor the industrial process, among many other statistical process control tools, and to detect changes. New developments in control charting schemes for high-quality monitoring are the need of the hour. In this paper, we have enhanced the performance of the mixed homogeneously weighted moving average (HWMA)-cumulative sum (CUSUM) control chart by using the auxiliary information-based (AIB) regression estimator and named it MHCAIB. The proposed MHCAIB chart provided an unbiased and more efficient estimator of the process location. The various measures of the run length are used to judge the performance of the proposed MHCAIB and to compare it with existing AIB charts like CUSUMAIB, EWMAAIB, MECAIB (mixed AIB EWMA-CUSUM), and HWMAAIB. The Run length (RL) based performance comparisons indicate that the MHCAIB chart performs relatively better in monitoring small to moderate shifts over its competitor's charts. It is shown that the chart's performance improves with the increase in correlation between the study variable and the auxiliary variable. An illustrative application of the proposed MHCAIB chart is also provided to show its implementation in practical situations.

A Low-Complexity Iterative Message Passing Algorithm for Robust RSS-TOA IoT Localization

This contribution considers the problem of robust target localization using the possibly unreliable hybrid received signal strength and time-of-arrival measurements, in the Internet of Things (IoT) context. Traditional positioning approaches relying on either extra a priori error information for robustification or the computationally intensive convex programming techniques for optimization do not fit well into the IoT applications with limited computing resources. Such concerns, however, will jeopardize the straightforward applicability of many ready-made solutions to the IoT positioning services if left untreated. In this article, the problem is resolved in a different manner. We adopt here a Geman-McClure like loss function, which is much less sensitive to the biased sensor observations, in order to statistically robustify the ℓ2-sapce based location estimator. A computationally attractive iterative message passing algorithm is then developed to conduct efficient optimization. Simulation results demonstrate the performance superiority of the proposed scheme over its competitors in various localization environments.

The association of Sirtuin1 (SIRT1) polymorphism and downregulation of STAT4 gene expression with increased susceptibility to COVID-19 infection

ABSTRACT Sirtuin1 (SIRT1) is an epigenetic modulator that belongs to sirtuins family and participates in many physiologic reactions, as genomic stabilization, apoptosis, proliferation, and inflammation. STAT4 (signal transducer and activator of transcription 4) gene is a component of JAK-STAT signaling pathway, which plays key role in activating cellular-mediated immune responses. The present study aimed to investigate the association between SIRT1 rs12778366 SNP, expression level of STAT4 gene and susceptibility to COVID-19 infections as well as their correlation to clinicopathological data. The present study included 100 ICU patients with severe COVID-19 infection and 100 age- and sex-matched healthy controls. DNA was extracted. Genotyping of SNP in SIRT1 (rs12778366) was performed, Total RNA was extracted from PBMCs. Reverse transcription was done. STAT4 gene expression levels were evaluated with GAPDH as internal control using real-time PCR. We found a significantly higher frequency of ‘C’ allele and C/T genotype in case vs. control. The association was of low strength (φ = 0.105 for alleles, and 0.154 for genotypes). This was associated with higher expression of STAT4 gene (P < 0.001) and increased tendency for lower respiratory complications. Median STAT4 (FC) (Median = 0.18, 95% CI = 0.15–0.27) was lower than normal control value of 1.0. This difference was statistically significant (Hodges-Lehmann location estimator = 0.217, P < 0.001). SIRT1 polymorphism and decreased expression of STAT4 gene are associated with increased susceptibility to COVID-19 infection and correlate to its phenotypic manifestations.

On the use of robust estimators of multivariate location for heterogeneous data

The properties of orthogonal equivariance and componentwise increasingness are often discussed for estimators of multivariate location. The former property is linked to a coordinate-free nature of the data (as in spatial data), whereas the latter property is linked to an ordered nature of the data (typically formed by several univariate variables). Since both properties do not get along well with each other, if robustness in the presence of outliers is to be pursued, one must choose between them. Admittedly, most of the literature on multivariate estimation of location, in which affine equivariance is pursued, typically abandons the latter property. Unfortunately, in the more and more common presence of heterogeneous data (as in spatio-temporal data), both orthogonal equivariance (therefore, affine equivariance) and componentwise increasingness might bring very disappointing results. For this very reason, both properties should be forfeited and only required for some of the components.

Broad-Learning-Based Localization for Underwater Sensor Networks With Stratification Compensation

Localization is an indispensable service for underwater sensor networks (USNs). Generally, the convex optimization method is adopted to solve the localization problem. However, the acoustic ray in water medium does not propagate along a straight line, which makes it difficult or impossible to transform the non-convex optimization problem into a convex optimization problem. This paper develops a broad learning (BL) based localization solution for USNs with isogradient sound speed profile. We first employ the ray tracing model to compensate the range bias caused by straight-line propagation. On the basis of collected range information from anchor nodes, the localization optimization problem is transformed into supervised, unsupervised and semisupervised learning frameworks. Correspondingly, three BL-based location estimators are developed to seek the position information of sensor nodes, where the incremental learning schemes are conducted for fast parameter tuning and remodeling. In addition, the Cramer-Rao Lower Bound (CRLB) of positioning error and the convergence to global optimality are both analysed. Finally, simulation and experiment results are presented to show the effectiveness of our approach. It is demonstrated that the proposed solution in this paper has the following nice features: 1) relax the dependence of convex relaxation over convex optimization-based location estimators; 2) reduce the training time and improve the localization efficiency over deep learning-based location estimators.

Convex Relaxation Approaches to Robust RSS-TOA Based Source Localization in NLOS Environments

For handling unreliable datasets contaminated by the non-line-of-sight (NLOS) bias errors, this correspondence statistically robustifies the traditional least squares type hybrid received signal strength and time-of-arrival location estimator using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> and Huber loss functions. The two robust formulations are then tackled via different convex relaxation approaches. Numerical results are presented to make fair comparisons with the commonly adopted balancing parameter based scheme, demonstrating the positioning accuracy superiority of the proposed methods in various NLOS environments.

An ultra-precise Fast Fourier Transform

The Fast Fourier Transform (FFT) is a cornerstone of digital signal processing, generating a computationally efficient estimate of the frequency content of a time series. Its limitations include: (1) information is only provided at discrete frequency steps, so further calculation, for example interpolation, may be required to obtain improved estimates of peak frequencies, amplitudes and phases; (2) ‘energy’ from spectral peaks may ‘leak’ into adjacent frequencies, potentially causing lower amplitude peaks to be distorted or hidden; (3) the FFT is a discrete time approximation of continuous time mathematics. A new FFT calculation addresses each of these issues through the use of two windowing functions, derived from Prism Signal Processing. Separate FFT results are obtained by applying each windowing function to the data set. Calculations based on the two FFT results yields high precision estimates of spectral peak location (frequency), amplitude and phase while suppressing spectral leakage.

Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method’s robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.

Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation

Pose estimation is crucial for automating assembly tasks, yet achieving sufficient accuracy for assembly automation remains challenging and part-specific. This paper presents a novel, streamlined approach to pose estimation that facilitates automation of assembly tasks. Our proposed method employs deep learning on a limited number of annotated images to identify a set of keypoints on the parts of interest. To compensate for network shortcomings and enhance accuracy we incorporated a Bayesian updating stage that leverages our detailed knowledge of the assembly part design. This Bayesian updating step refines the network output, significantly improving pose estimation accuracy. For this purpose, we utilized a subset of network-generated keypoint positions with higher quality as measurements, while for the remaining keypoints, the network outputs only serve as priors. The geometry data aid in constructing likelihood functions, which in turn result in enhanced posterior distributions of keypoint pixel positions. We then employed the maximum a posteriori (MAP) estimates of keypoint locations to obtain a final pose, allowing for an update to the nominal assembly trajectory. We evaluated our method on a 14-point snap-fit dash trim assembly for a Ford Mustang dashboard, demonstrating promising results. Our approach does not require tailoring to new applications, nor does it rely on extensive machine learning expertise or large amounts of training data. This makes our method a scalable and adaptable solution for the production floors.