Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter

The likelihood calculation of a vast number of particles is the computational bottleneck for the particle filter in applications where the observation information is rich. For fast computing the likelihood of particles, a numerical fitting approach is proposed to construct the Likelihood Probability Density Function (Li-PDF) by using a comparably small number of so-called fulcrums. The likelihood of particles is thereby analytically inferred, explicitly or implicitly, based on the Li-PDF instead of directly computed by utilizing the observation, which can significantly reduce the computation and enables real time filtering. The proposed approach guarantees the estimation quality when an appropriate fitting function and properly distributed fulcrums are used. The details for construction of the fitting function and fulcrums are addressed respectively in detail. In particular, to deal with multivariate fitting, the nonparametric kernel density estimator is presented which is flexible and convenient for implicit Li-PDF implementation. Simulation comparison with a variety of existing approaches on a benchmark 1-dimensional model and multi-dimensional robot localization and visual tracking demonstrate the validity of our approach.Comment: 42 pages, 17 figures, 4 tables and 1 appendix. This paper is a draft/preprint of one paper submitted to the IEEE Transaction

Numerical fitting-based likelihood calculation to speed up the particle filter

The likelihood calculation of a vast number of particles forms the computational bottleneck for the particle filter in applications where the observation model is complicated, especially when map or image processing is involved. In this paper, a numerical fitting approach is proposed to speed up the particle filter in which the likelihood of particles is analytically inferred/fitted, explicitly or implicitly, based on that of a small number of so-called fulcrums. It is demonstrated to be of fairly good estimation accuracy when an appropriate fitting function and properly distributed fulcrums are used. The construction of the fitting function and fulcrums are addressed respectively in detail. To avoid intractable multivariate fitting in multi-dimensional models, a nonparametric kernel density estimator such as the nearest neighbor smoother or the uniform kernel average smoother can be employed for implicit likelihood fitting. Simulations based on a benchmark one-dimensional model and multi-dimensional mobile robot localization are provided

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Wireless Communication with Mobile Inspection Robots Operating While Submerged Inside Oil Storage Tanks

Data acquisition during storage tank inspection is one of the most important aspects for petrochemical storage tank owners. Mobile inspection robots designed to enter inside a storage tanks without taking the tank out of service are required to enter through manholes on the roof of the tank with openings as small as 300-millimetre diameter. These robots are controlled via an umbilical cable which supplies power to the robot, sends and receives signals to control robot motion, and transfers inspection data acquired by non-destructive testing (NDT) sensors back to NDT inspectors. It is important to localize a robot inside the tank so that NDT data indicating a defect such as corrosion pitting in tank floors or weld cracks can be mapped for subsequent monitoring and repair. Wireless communication with the robot for NDT data acquisition and localization would enable the minimization of umbilical size and its effective management which is important when a small mobile robot is supplied with a very long umbilical. This paper presents results of a study to develop a wireless communication system that uses radio frequency (RF) signals with low power (< 1W) sent by a transceiver on a robot operating inside an oil storage tank which travel through an oil medium, are transmitted through steel tank walls and are captured by receivers placed in air outside the tank. Simulations using Feko software have been performed to assess the feasibility of using RF for communication in oil storage tanks with laboratory experiments conducted using vegetable oil to validate the simulations. RF signals transmitted by a robot operating inside an oil tank and received by a number of receivers placed in air around the tank has potential application as a robot localization system

Indoor localization of mobile robots with wireless sensor network based on ultra wideband using experimental measurements of time difference of arrival

This paper presents investigations into wireless localization techniques for mobile robots operat-ing in indoor environments. Localization systems can guide robots to perform different tasks such as monitoring children or elderly people, aid mobility of the visually impaired and localize mobile objects or packages in warehouses. They are essential for localization of robots operating in re-mote places that are inaccessible or hazardous to humans. Currently, ultra wide band (UWB) in indoor environments provides an accuracy of 24 mm under line of sight (LOS) or non-line of sight (NLOS) conditions in a working range of 160 m indoors. The work presented in this paper carries out experimental validation of localization algorithms using mobile robots and UWB signals. These are measured in LOS and NLOS environments. The measurements are performed with the UWB radio PulsON 410 (P410) and mobile robots (AmigoBot) with maximum travel-ling speed of 1 m/s and equipped with an on-board computer, sonar, odometer, camera and inertial navigation system. Experimental results obtained for the system show positioning errors of less than 55 mm