Multiwavelength fiber laser based on bidirectional lyot filter in conjunction with intensity dependent loss mechanism

We experimentally demonstrate a multiwavelength fiber laser (MWFL) based on bidirectional Lyot filter. A semiconductor optical amplifier (SOA) is used as the gain medium, while its combination with polarization controllers (PCs) and polarization beam combiner (PBC) induces intensity dependent loss (IDL) mechanism. The IDL mechanism acts as an intensity equalizer to flatten the multiwavelength spectrum, which can be obtained at a certain polarization state. Using different ratio of optical splitter has affected to multiwavelength flatness degradation. Subsequently, when we removed a polarizer in the setup, the extinction ratio (ER) is decreased. Ultimately, with two segments of polarization maintaining fiber (PMF), two channel spacings can be achieved due to splicing shift of 0° and 90°

Analysis Of A Neuro-Fuzzy Approach Of Air Pollution: Building A Case Study

This work illustrates the necessity of an Artificial Intelligence (AI)-based approach of air quality in urban and industrial areas. Some related results of Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) for environmental data are considered: ANNs are proposed to the problem of short-term predicting of air pollutant concentrations in urban/industrial areas, with a special focus in the south-eastern Romania. The problems of designing a database about air quality in an urban/industrial area are discussed. First results confirm ANNs as an improvement of classical models and show the utility of ANNs in a well built air monitoring center

Towards an accurate Ground-Level Ozone Prediction

This paper motivation is to find the most accurate technique to predict the ground level ozone at Al Jahra station, Kuwait. The data on the meteorological variables (air temperature, relative humidity, solar radiation, direction and speed of wind) and concentration of seven pollutants of environment (SO2, NO2, NO, CO2, CO, NMHC, and CH4) were applied to forecast the ozone concentration in atmosphere. In this report, three methods (PLS regression, support vector machine (SVM), and multiple least-square regression) were used to predict ground-level ozone. We used Fifteen parameters to evaluate the performance of methods. Multiple least-square regression, partial least square regression (PLS regression), and SVM using linear and radial kernels were the best performers with MAE (mean absolute error) of 9.17x 10-03, 9.72 x 10-03, 9.64 x 10-03, and 9.12 x 10-03, respectively. SVM with polynomial kernel had MAE of 5.46 x 10-02. These results show that these methods could be used to predict ground-level ozone concentrations at Al Jahra station in Kuwait

Use of neural networks for tropospheric ozone time series approximation and forecasting ? a review

International audienceThe use of artificial neural networks in atmospheric science expands constantly. During the last years, many papers were published dealing with air pollution modeling. A number of papers deals with the time series approximation and forecasting of tropospheric ozone concentration. Neural networks have been found to outperform other statistical techniques like multiple regression etc. This paper reviews and discusses some practical aspects of the proposed neural network models applied to ozone concentration approximation and forecasting

A review of urban air pollution monitoring and exposure assessment methods

The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data