Self-Mixing Laser Distance-Sensor Enhanced by Multiple Modulation Waveforms

Optical rangefinders based on Self-Mixing Interferometry are widely described in literature, but not yet on the market as commercial instruments. The main reason is that it is relatively easy to propose new elaboration techniques and get results in controlled conditions, while it is very difficult to develop a reliable instrument. In this paper, we propose a laser distance sensor with improved reliability, realized through a wavelength modulation at a different frequency, able to decorrelate single measurement errors and obtain improvement by averages. A dedicated software is implemented to automatically calculate the modulation pre-emphasis, needed to linearize the wavelength modulation. Finally, data selection algorithms allow to overcome signal fading problems due to the speckle effect. A prototype demonstrates the approach with about 0.1 mm accuracy up to 2 m of distance at 200 measurements per second

Noise Decrease in a Balanced Self-Mixing Interferometer: Theory and Experiments

In a self-mixing interferometer built around a laser diode, the signals at the outputs of the two mirrors are in phase opposition, whereas noise fluctuations are partially correlated. Thus, on making the difference between the two outputs, the useful signal is doubled in amplitude and the signal-to-noise ratio is even more enhanced. Through a second-quantization model, the improvement is theoretically predicted to be dependent on laser facets reflectivity. The results are then validated by experimental measurements with different laser types that show very good agreement with theoretical results. The new technique is applicable to a number of already existent self-mixing sensors, potentially improving significantly their measurement performances

Toxicity and LC50 determination of phenol and 1-Naphtol in Caspian Kutum and bream fingerlings

In this investigation acute toxicity of phenol and 1-naphthol were determined based on OECD guideline in the laboratory. Experimental fishes were Caspian kutum (Rutilus frisii kutum) and bream (Abramis brama orientalis). Static bioassays were used for acute toxicity tests during the period of 96 hours and all of important physicochemical parameters of water including pH, dissolved oxygen, hardness, temperature and conductivity were monitored continuously and maintained at a constant value. Five treatments were used and three replicates run for each treatment. The 96h LCSO values of phenol and 1-naphthol for Caspian kutum and bream were 21.5928 and 2.1544 mg/lit and 25.1880 and 2.8490 mg/lit, respectively. The Maximum Allowable Concentration (MAC) of phenol in Caspian kutum and bream were 2.1593 and 2.5188 mg/lit, respectively. The MAC value of 1-naphthol in Caspian kutum and bream were 0.2154 and 0.2849 mg/lit, respectively. It is evident from the results of the present study that Caspian kutum is more sensitive comparing to bream and the toxicity of I-naphthol is higher than phenol

Model-driven decision support system for estimating number of ambulances required during earthquake disaster relief operation

Most of human life has been encountered danger due to natural disasters nowadays. One of these natural disasters that endanger human lives and which causes lot of damages is earthquake. A proper emergency response after an earthquake happening is important and has high priority in earthquake emergency management to reduce number of damages. Decision making for critical resources in the phase of response, is one of the main concerns for managers. Ambulance, as one of the critical resource that can help to reduce earthquake losses and costs, needs to be planned. Confusion in the number of victims in the early stages of earthquake, access complexity to the required data of different organizations by the pressing time, complicated nature of estimation, diversity of models and limitation of time for decision making are the main problems associated with estimating ambulances during earthquake disaster which makes estimation too difficult. In addition, there is a call for research in determining the number of required ambulances during earthquake emergency management, due to high error in estimating the number of ambulances in the current methods, which leads to unnecessary expenses and thereby helping to ensure that disaster sites are not overcrowded with emergency workers impeding each other's effectiveness. Such complexity suggests the introduction of Decision Support System (DSS). More accurate estimation of the number of required ambulances using a decision support system can help managers to speed up the process of decision making and thus reducing error and costs. Since the number of ambulances needed during a disaster is directly proportional to the number of victims requiring hospital treatment and in order to reach the first objective of this study, factors determining the number of human casualties in earthquake disaster i.e. population, modified Mercalli, age, time, building occupancy and gender are selected as the most relevant factors which have high probability in creating human casualties. The collected data from various relevant sources is used in proposing the model of this research. After testing different approaches, Fuzzy rule-based approach is being used, after defining the rules for each aforementioned factors and optimization is conducted in order to minimize the error for estimating the number of human casualties. Finally, by using de Boer formula and obtained number of human casualties, the number of required ambulances is estimated accurately. The results indicate that the error is decreased by more than 50% in the proposed method. A prototype of Model-Driven Decision Support System was developed based on the proposed model that can be used to aid emergency response planners for their decision making process prior to take any action during earthquake emergency management

Symmetric Conditions for Strain Analysis in a Long Thick Cylinder under Internal Pressure Using NASIR Unstructured GFVM Solver

Utilization of symmetric condition in NASIR Galerkin Finite Volume Method for linear triangular element unstructured meshes is introduced for numerical solution of two dimensional strain and stress fields in a long thick cylinder section. The developed shape function free Galerkin Finite Volume structural solver explicitly computes stresses and displacements in Cartesian coordinate directions for the two- dimensional solid mechanic problems under either static or dynamic loads. The accuracy of the introduced algorithm is assessed by comparison of computed results of a thick cylinder under internal fluid pressure load with analytical solutions. The performance of the solver for taking advantage of symmetric conditions is presented by computation of stress and strain contours on a half and a quarter of the cylinder section

Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models

© 2018 Esmaili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background Transport injuries commonly result in significant disease burden, leading to physical disability, mental health deterioration and reduced quality of life. Analyzing the patterns of healthcare service utilization after transport injuries can provide an insight into the health of the affected parties, allow improved health system resource planning, and provide a baseline against which any future system-level interventions can be evaluated. Therefore, this research aims to use time series of service utilization provided by a compensation agency to identify groups of claimants with similar utilization patterns, describe such patterns, and characterize the groups in terms of demographic, accident type and injury type. Methods To achieve this aim, we have proposed an analytical framework that utilizes latent variables to describe the utilization patterns over time and group the claimants into clusters based on their service utilization time series. To perform the clustering without dismissing the temporal dimension of the time series, we have used a well-established statistical approach known as the mixture of hidden Markov models (MHMM). Ensuing the clustering, we have applied multinomial logistic regression to provide a description of the clusters against demographic, injury and accident covariates. Results We have tested our model with data on psychology service utilization from one of the main compensation agencies for transport accidents in Australia, and found that three clear clusters of service utilization can be evinced from the data. These three clusters correspond to claimants who have tended to use the services 1) only briefly after the accident; 2) for an intermediate period of time and in moderate amounts; and 3) for a sustained period of time, and intensely. The size of these clusters is approximately 67%, 27% and 6% of the number of claimants, respectively. The multinomial logistic regression analysis has showed that claimants who were 30 to 60-year-old at the time of accident, were witnesses, and who suffered a soft tissue injury were more likely to be part of the intermediate cluster than the majority cluster. Conversely, claimants who suffered more severe injuries such as a brain head injury or anon-limb fracture injury and who started their service utilization later were more likely to be part of the sustained cluster