Auditory model-based estimation of the effect of head-worn devices on frontal horizontal localisation

Auditory localisation accuracy may be degraded when a head-worn device (HWD), such as a helmet or hearing protector, is used. A computational method is proposed in this study for estimating how horizontal plane localisation is impaired by a HWD through distortions of interaural cues. Head-related impulse responses (HRIRs) of different HWDs were measured with a KEMAR and a binaural auditory model was used to compute interaural cues from HRIR-convolved noise bursts. A shallow neural network (NN) was trained with data from a subjective listening experiment, where horizontal plane localisation was assessed while wearing different HWDs. Interaural cues were used as features to estimate perceived direction and position uncertainty (standard deviation) of a sound source in the horizontal plane with the NN. The NN predicted the position uncertainty of localisation among subjects for a given HWD with an average estimation error of 1°. The obtained results suggest that it is possible to predict the degradation of localisation ability for specific HWDs in the frontal horizontal plane using the method.

Research on Measurement Method of Yak Body Size and Weight Based on Convolutional Neural Network and Binocular Vision

In order to solve the labor-intensive and time-consuming problem in the process of measuring yak body size and weight in yak breeding industry in Qinghai Province, a non-contact method for measuring yak body size and weight was proposed in this experiment, and key technologies based on semantic segmentation, binocular ranging and neural network algorithm were studied to boost the development of yak breeding industry in Qinghai Province. Main conclusions: (1) Study yak foreground image extraction, and implement yak foreground image extraction model based on U-net algorithm; select 2263 yak images for experiment, and verify that the accuracy of the model in yak image extraction is over 97%. (2) Develop an algorithm for estimating yak body size based on binocular vision, and use the extraction algorithm of yak body size related measurement points combined with depth image to estimate yak body size. The final test shows that the average estimation error of body height and body oblique length is 2.6%, and the average estimation error of chest depth is 5.94%. (3) Study the yak weight prediction model; select the body height, body oblique length and chest depth obtained by binocular vision to estimate the yak weight; use two algorithms to establish the yak weight prediction model, and verify that the average estimation error of the model for yak weight is 10.78% and 13.01% respectively.

RF Vital Sign Sensing under Free Body Movement

Radio frequency (RF) sensors such as radar are instrumental for continuous, contactless sensing of vital signs, especially heart rate (HR) and respiration rate (RR). However, decades of related research mainly focused on static subjects, because the motion artifacts from other body parts may easily overwhelm the weak reflections from vital signs. This paper marks a first step in enabling RF vital sign sensing under ambulant daily living conditions. Our solution is inspired by existing physiological research that revealed the correlation between vital signs and body movement. Specifically, we propose to combine direct RF sensing for static instances and indirect vital sign prediction based on movement power estimation. We design customized machine learning models to capture the sophisticated correlation between RF signal pattern, movement power, and vital signs. We further design an instant calibration and adaptive training scheme to enable cross-subjects generalization, without any explicit data labeling from unknown subjects. We prototype and evaluate the framework using a commodity radar sensor. Under a variety of moving conditions, our solution demonstrates an average estimation error of 5.57 bpm for HR and 3.32 bpm for RR across multiple subjects, which largely outperforms state-of-the-art systems.

Evaluación de un protocolo automatizado para la obtención de medidas morfométricas de huevos de aves a partir de fotografías digitales

Evaluation of a protocol for automated extraction of morphometric measurements from avian eggs using digital photography As many ecomorphological studies are limited by the time required to gather manual measurement data, automatizing the process is an important focus of methodological innovations. We developed, implemented and validated a protocol for the semi–automated extraction of a set of morphometric variables of egg size and shape from digital pictures. The protocol was implemented in R language as a web app called OvometriK. After binarizing and calibrating images, this protocol uses geometric and trigonometric functions to calculate eleven egg variables. We tested calculations in several ways, assuming contour continuity or using voxel counts. Application was validated with geometric shapes and 30 manually–measured chicken eggs. Mathematical validation with spheres showed that the algorithm provided high precision diameter measures, with a correlation of 99.9 %. Average estimation error was 1.4 %. The mathematical volume estimation was underestimated by 27 %, while voxels were underestimated by only 6 %. Differences between manual egg measurements of diameters and those obtained from images was less than 3 mm (4 %). Correlation between estimated volume and measured by silica gel filling was higher than 90 % using the voxel count method. Neither inclination angle or picture resolution had significant effects on precision (3.2 % maximum difference). Measures showed high repeatability and represent a significant saving in processing time. This new protocol represents an improvement on previous programs regarding limitations of platform, accessibility and number of variables. Furthermore, its flexibility and openness means it can be adapted to other specific applications.

Comparison of Three Location Estimation Methods of an Autonomous Driving Robot for Underground Mines

In this study, we compared the accuracy of three location estimation methods of an autonomous driving robot for underground mines: an inertial measurement unit with encoder (IMU + encoder) sensors, Light Detecting and Ranging with encoder (LiDAR + encoder) sensors, and IMU with LiDAR and encoder (IMU + LiDAR + encoder) sensors. An accuracy comparison experiment was conducted in an indoor laboratory composed of four sections (X-change, X-Y change, X-Z change, and Y-change sections) that simulated an underground mine. The robot’s location was estimated using each of the three location estimation methods as the autonomous driving robot moved, and the results accuracy was analyzed by comparing the estimated location with the robot’s actual location. From the results of the indoor experiments, the average estimation error of the IMU + LiDAR + encoder sensors was approximately 0.09 m, that of the IMU + encoder was 0.19 m, and that of the LiDAR + encoder was 0.81 m. In a field experiment, the average error of the IMU + LiDAR + encoder was approximately 0.11 m, that of the IMU + encoder was 0.17 m, and that of the LiDAR + encoder was 0.70 m. In conclusion, the IMU + LiDAR + encoder method, which uses three types of sensors, showed the highest accuracy in estimating the location of autonomous robots in an underground mine.

Monitoring 10-m LST from the Combination MODIS/Sentinel-2, Validation in a High Contrast Semi-Arid Agroecosystem

Downscaling techniques offer a solution to the lack of high-resolution satellite Thermal InfraRed (TIR) data and can bridge the gap until operational TIR missions accomplishing spatio-temporal requirements are available. These techniques are generally based on the Visible Near InfraRed (VNIR)-TIR variable relations at a coarse spatial resolution, and the assumption that the relationship between spectral bands is independent of the spatial resolution. In this work, we adopted a previous downscaling method and introduced some adjustments to the original formulation to improve the model performance. Maps of Land Surface Temperature (LST) with 10-m spatial resolution were obtained as output from the combination of MODIS/Sentinel-2 images. An experiment was conducted in an agricultural area located in the Barrax test site, Spain (39°03′35″ N, 2°06′ W), for the summer of 2018. Ground measurements of LST transects collocated with the MODIS overpasses were used for a robust local validation of the downscaling approach. Data from 6 different dates were available, covering a variety of croplands and surface conditions, with LST values ranging 300–325 K. Differences within ±4.0 K were observed between measured and modeled temperatures, with an average estimation error of ±2.2 K and a systematic deviation of 0.2 K for the full ground dataset. A further cross-validation of the disaggregated 10-m LST products was conducted using an additional set of Landsat-7/ETM+ images. A similar uncertainty of ±2.0 K was obtained as an average. These results are encouraging for the adaptation of this methodology to the tandem Sentinel-3/Sentinel-2, and are promising since the 10-m pixel size, together with the 3–5 days revisit frequency of Sentinel-2 satellites can fulfill the LST input requirements of the surface energy balance methods for a variety of hydrological, climatological or agricultural applications. However, certain limitations to capture the variability of extreme LST, or in recently sprinkler irrigated fields, claim the necessity to explore the implementation of soil moisture or vegetation indices sensitive to soil water content as inputs in the downscaling approach. The ground LST dataset introduced in this paper will be of great value for further refinements and assessments.

Adaptive Neuro-fuzzy Inference System Trained for Sizing Semi-elliptical Notches Scanned by Eddy Currents

The present study explores the capability of COMSOL Multiphysics, as a finite element modelling (FEM) tool, to model the interaction between a split-D differential surface eddy current (ECT) probe and semi-elliptical surface electrical discharge machined (EDM) notches. The effect of the small probe’s lift-off and tilt on its signal is investigated through modelling and subsequently, the simulation outcomes are validated using the probe’s impedance measurements. In the next stage, an adaptive neuro-fuzzy inference system (ANFIS) is designed to take the signal features as inputs and consequently, provide the length of the scanned notch as the system’s output. The system is trained by extracted features of thirty model-generated signals obtained from scanning of the same number of semi-elliptical notches by means of the split-D probe. The trained ANFIS is tested afterwards using the measured signals of 3 calibration EDM notches together with 5 model-based ones. A very low average estimation error is observed with regard to the length estimation of the test notches and the accuracy of the length estimation is found to be quite reasonable.

What’s the penalty for using the Square Root Law of inventory centralisation?

Purpose The square root law (SRL) is a popular model for assessing inventory levels when changing the number of warehouses. Previous empirical research, however, has shown that mostly its underlying assumptions do not hold in practice. This sparks the question how inaccurate the SRL’s results are. The paper aims to discuss this issue. Design/methodology/approach In 26 company cases of reducing the number of warehouses, the estimation error of the SRL is analysed irrespective of its underlying assumptions. Findings The analysis reveals an average estimation error for total inventory of 27.85 per cent (median=27.84 per cent), but a high variability across the cases. The SRL seems to mostly overestimate inventory savings from centralisation and inventory increases from decentralisation. Managers should only use the SRL if inventory depends on the number of warehouses in their situation, i.e. if they use the economic order or production quantity policy and safety factor approach. Suggestions for coping with the SRL’s estimation error are given. Research limitations/implications This paper is based on the 26 cases that could be found in a thorough literature review in the ten most widely spoken languages and that contained or allowed to deduce the necessary information. In order to enable wider generalisations, this sample could be extended. Originality/value Most past research has been more theoretical in nature. This research is the first to investigate the SRL’s estimation error using a variety of company cases and how to cope with this error.

A NOVEL MICROWAVE SENSOR WITH HIGH-Q SYMMETRICAL SPLIT RING RESONATOR FOR MATERIAL PROPERTIES MEASUREMENT

A new sensor based on symmetrical split ring resonator (SSRR) functioning at microwave frequencies has been proposed in order to detect and characterize the properties of the materials. This sensor is based on perturbation theory, in which the dielectric properties of the material affect the quality factor and resonance frequency of the microwave resonator. Conventionally, coaxial cavity, waveguide, dielectric resonator techniques have been used for characterizing materials. However, these techniques are often large, and expensive to build, which restricts their use in many important applications. Thus, the proposed bio-sensing technique presents advantages such as high measurement sensitivity (around 400 Q-factor) with the capability of suppressing undesired harmonic spurious and permits potentially material characterization and determination. Hence, using a specific experimental methodology, tests performed have demonstrated the biosensor ability to characterize at least four references materials with known permittivity (Air, Roger Duriod RT 5880, Roger Duriod RT 4530, FR4) and one material with unknown permittivity (Beef). Accordingly, the numerically established relations are experimentally verified for these reference materials and the results indicated that the average estimation error of measuring the permittivity was 2.56 % at resonant of around 2.2 GHz. The proposed design is useful for various applications such as food industry, medicine, pharmacy, bio-sensing and quality control

Pressure-Based Soft Sensors for Fast Volume Estimation

To acquire a fast method for better volume estimation, a novel soft sensor technique is proposed in this paper. Based on the principle of energy conservation as well as the ideal gas law, mathematical models of charging and discharging process are set up. Afterwards, simulations are carried out to explore the relationship between the estimated volume and the pressure stable time. Finally, experiments with different pneumatic cylinders are conducted to select the suitable estimation mode and also optimize the parameters. Results show that in discharging mode, the average estimation error for volume estimation can be less than 4% with both low pressure and high pressure, making this method quite suitable for industrial application.