Implementation of Neural Networks in FPGA

57-63Artificial Intelligence (AI) refers to the recreation of human intelligence in machines that have been designed to think like humans and mimic their actions. AI has been used in many fields such as image processing, health care, education, and marketing. Machine Learning (ML) has been the sub-division of AI, and deep learning has been the subdivision of ML. Artificial Neural Network has been the most predominantly used deep learning technique. While implementing the ANN technique, knowing whether the implementation could have been done in hardware or software becomes necessary, which is essential to achieve the expected performance. This paper gives a survey on the available methods in which the ANN architecture has been implemented to achieve efficient output with minimal resources. It is vital to study and analyze various strategies for implementation and their functionality. This paper has also explained the advantages and disadvantages of different implementation techniques that allow selecting the most appropriate hardware and respective methodology for optimizing the hardware

Segmentation of satellite images using machine learning algorithms for cloud classification

12-18Clouds play a significant role in determining the state of a changing weather. Clouds offer useful information for forecasting precipitation and provide measurement for showcasing solar irradiance variability. The influence of specific types of clouds on rainfall prediction and solar radiance has been discussed in this paper. Various segmentation algorithms, clustering algorithms and supervised machine learning algorithms such as K Nearest Neighbors and Random forest have been used to segment/classify the clouds using the dataset obtained from INSAT-3DR satellite. Clouds have been classified into high level clouds (Cirrus clouds), medium level clouds (Alto clouds) and low level clouds (Stratus clouds) in accordance with the altitude and cloud densities. The performance metrics has been found for the segmented images. Parameters that provide optimum results for supervised machine learning algorithms have been explored. On the images, different machine learning algorithms have been compared

Aerosol classification using machine learning algorithms

217-223Aerosols are particles that are omnipresent in the atmosphere. They vary in size, shape and composition. They can be naturally occurring or might be produced artificially. However, proper classification and characterization of aerosols have been still in progress and this creates uncertainty in climatological studies. In this paper, an aerosol classification scheme has been presented based on the measurements done using a CIMEL sunphotometer in Thessaloniki, Greece from 1998 to 2017. The study has been mainly upheld by the direct measurements of Single Scattering Albedo (SSA) at 440nm and Fine Mode Fraction (FMF) at 500nm. These parameters have been used to establish testing and training datasets. Machine learning algorithms have been used to validate the classified data. Various performance metrics have been evaluated. Also, the best-fit algorithm for classifying aerosol data has been found out

Accurate rain drop size distribution models for the tropical region

In this thesis, a detail study on the modeling of rain drop size distribution is undertaken. The rain drop size data measured using the Joss distrometer during the years 1994 to 1995 and 1997 to 1998 and the RADAR data during the year 1998 are used in this study. Gamma model is found to be the preferred model for drop size distribution modeling in the Singapore climatic zone. The method of moments is used to retrieve the parameters of the gamma distribution. By studying the contribution of individual bins on rain rate estimation, it was found that the contributions of lower drop diameters are small as compared to the central drop diameters. Therefore, the lower drop diameters are removed from the drop size data before the gamma model is redesigned for its moments. The effects of this removal on the specific rain attenuation (in dB) and the slant-path rain attenuation calculations using ITU-R P.838-3 model and using forward scattering coefficients for vertical polarization are analyzed at Ku-band, Ka-band and Q-band frequencies. It is concluded that the sensitivity of the Joss distrometer although affects the rain rate estimation at low rain rates, does not affect the slant path rain attenuation on microwave links. Therefore, the small drop diameters can be ignored for slant path rain attenuation calculations in the tropical region. The research work continues to find the suitable reflectivity to rain rate (Z-R) relations using a data set which consists of nine rain events selected from Singapore’s drop size distribution. The variability of the rain integral parameters R, Z, Nw, D0 and gamma model parameter μ are used for the classification of rain into convective, stratiform and transition. Z-R relations are derived for each type of rain after classification. The Z-R relations for different rain types for the Singapore climatic zone are compared and analyzed. Reflectivities are extracted from RADAR data above NTU site for rain events and compared with the reflectivities derived from the distrometer data. Rain rates retrieved from RADAR data using the proposed Singapore Z-R relations are compared with the distrometer rain rates. It was found that the Singapore Z-R relations is able to extract the rain rate from RADAR data well although they are found to be constantly lower than the distrometer derived rain rates. Finally the thesis examines the possibility of using a two parameter gamma models to retrieve the rain rates from dual polarized RADAR data. A two parameter gamma model can be found either by fixing μ or by deriving an appropriate shape-slope (μ-Λ) relation for the tropical region. In order to find an appropriate μ value, observed DSDs are fitted with different μ values to estimate the rain rates. In order to find an appropriate μ-Λ relation, different μ-Λ relations are fitted for different categories according to the rain rate and the number of drops. The derived μ-Λ relationships for the Singapore region are compared to published results from Gadanki and India. Two parameter gamma models are compared by retrieving the rain rate using the polarimetric RADAR variables found from the T-Matrix code. The use of the μ-Λ relation for rain retrieval is recommended for the tropical region.DOCTOR OF PHILOSOPHY (EEE

Comparison of S-band radar attenuation prediction with beacon measurements

A comparative analysis of the rain attenuation evaluated from beacon measurements and from a single polarization S-band Radar is performed. The beacon measurements are obtained for two slant satellite paths with different elevation angles in the Ku- and Ka-band. The single polarization S-band Radar reflectivity data is used to predict the attenuation along the satellite propagation paths. This is done by first converting the reflectivity into rain rate using the Z-R relations suitable for the tropical region and, afterwards, by evaluating the slant path attenuation through the integration of the specific rain attenuation derived from the point rainfall rate. An empirical calibration factor for the Radar reflectivity is provided. A comparison of the rain fade suffered from the two satellite paths is presented both on event basis and in terms of cumulative distribution functions. The empirical calibration factor and a single Z-R relation suitable for the tropical region are used for rain attenuation evaluation from single polarization Radar data

Optimal energy transfer pipe arrangement for acoustic drill string telemetry

Drill string acoustic telemetry is an effective transmission method to retrieve downhole data. Finite-difference simulations produce the comb-filter-like channel response (patterns of pass bands and stop bands) due to the presence of coupling joints in the metallic drill string. Practical pipes used for drilling deep wells have slight variation in length. The selection and arrangement of downhole pipes is important for improving the transmission efficiency of extensional waves transmitted through the drill string. Downhole drill string channel is studied using the transmission coefficients calculated from the transmission matrix method, and the resultant transfer function produces identical results similar to the finite-difference simulations. Reciprocity of the drill string structure is proved by comparing the pass band responses using the ascend-only (AO) and descend-only pipe arrangements. Transferred energies calculated up to 180 pipes of random length at the end of the drill strings using transmission coefficients for the three different pipe arrangements, namely, AO, descend-then-ascend, and ascend-then-descend (ATD), are compared to find the optimal pipe arrangement for single measurement. For the situations when pipes are distributed in sets, multiple measurements are required. In this paper, two sets of AO and two sets of ATD arrangements are analyzed for multiple measurements. ATD and nxATD arrangements are proposed as optimal pipe arrangements to produce the best possible telemetry performance in terms of optimal acoustic energy transfer via one- and two-way acoustic communication for single and multiple measurements, respectivelyASTAR (Agency for Sci., Tech. and Research, S’pore)Accepted versio