Underwater Localization in a Confined Space Using Acoustic Positioning and Machine Learning

Localization is a critical step in any navigation system. Through localization, the vehicle can estimate its position in the surrounding environment and plan how to reach its goal without any collision. This thesis focuses on underwater source localization, using sound signals for position estimation. We propose a novel underwater localization method based on machine learning techniques in which source position is directly estimated from collected acoustic data. The position of the sound source is estimated by training Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FNN), and Convolutional Neural Network (CNN). To train these data-driven methods, data are collected inside a confined test tank with dimensions of 6m x 4.5m x 1.7m. The transmission unit, which includes Xilinx LX45 FPGA and transducer, generates acoustic signal. The receiver unit collects and prepares propagated sound signals and transmit them to a computer. It consists of 4 hydrophones, Red Pitay analog front-end board, and NI 9234 data acquisition board. We used MATLAB 2018 to extract pitch, Mel-Frequency Cepstrum Coefficients (MFCC), and spectrogram from the sound signals. These features are used by MATLAB Toolboxes to train RF, SVM, FNN, and CNN. Experimental results show that CNN archives 4% of Mean Absolute Percentage Error (MAPE) in the test tank. The finding of this research can pave the way for Autonomous Underwater Vehicle (AUV) and Remotely Operated Vehicle (ROV) navigation in underwater open spaces

A Fuzzy ANP-SWOT Approach for Analyzing the IT Problems Based on Capabilities in Iran

Determining strategy, as a very effective tool for policy (and especially science and technology policy), helps governments to be able in the challenges such as globalization and increased competition, and have an appropriate respond in the world today. Although some scattered researches about strategy determining have been conducted in Iran, the purpose of them is to recognize the future of IT and determine a framework for strategy determining. This paper examines the situation of Iran in IT. First, according to the techniques of the strategy formulation, the most important internal and external factors have been identified, then using matrix strengths, weaknesses, opportunities and threats, the organizational strategies have been developed. Fuzzy ANP Methods were used for quantitative analysis. The results were calculated using the Expert Choice Software. Statistical community of this study will be including the experts in the field collected among 400 experts. According to the results, the best strategy for implementation ST Strategy. Therefore, some strategies should be considered such as: Explicit decisions to expand or change of research program, selecting an independent organization as trustee of futures studies projects, explicit decisions to expand or change of research program, definition and implementation of future studies for appropriate infrastructure