Ambient-noise Free Generation of Clean Underwater Ship Engine Audios from Hydrophones using Generative Adversarial Networks

Generative adversarial networks (GANs) have been extensively used in image domain showing promising results in generating and learning data distributions in the absence of clean data. However, the audio domain, specially underwater acoustics are not yet fully explored in reporting the efficiency and applicability of GANs. We propose an audio GAN framework called ambient noise-free GAN (AN-GAN) to address the underwater acoustic signal denoising problem by removing the background ambient noise. The proposed AN-GAN can learn a clean audio generation with improved signal-to-noise ratio (SNR) given only the noisy samples from the underwater audio dataset. The simulated and real-time data collected from online available source ShipsEar, is used for the analysis and validation purpose. The comparative analysis shows an average percentage improvement of proposed AN-GAN with GAN-based and conventional statistical underwater denoising methods as 6.27% for UWAR-GAN, 227% for Wavelet denoising, 247% for EMD and 65% for Wiener technique

Antimicrobial Activity of Pinus wallachiana Leaf Extracts against Fusarium oxysporum f. sp. cubense and Analysis of Its Fractions by HPLC

Fusarium wilt has ruined banana production and poses a major threat to its industry because of highly virulent Fusarium oxysporum f. sp. cubense (Foc) race 4. The present study focused on the efficacy of Pinus wallachiana leaf extracts and its organic fractions against Foc in in vitro and greenhouse experiments. The presence of polyphenols in the fractions was also investigated using high performance liquid chromatography (HPLC). The in vitro tests carried out for the leaf extract of P. wallachiana showed its inhibitory effect on the mycelial growth and, based on this evidence, further characterization of fractions were done. Complete mycelial inhibition and the highest zone of inhibition against Foc was observed for the n-butanol fraction in vitro, while the n-hexane and dichloromethane fractions showed lower disease severity index (DSI) in greenhouse experiments. The fractions were further analysed by HPLC using nine polyphenolic standards, namely quercitin, myrecitin, kaempferol, rutin, gallic acid, trans-ferulic acid, coumeric acid, epicatechin and catechin. The highest content of polyphenols, based on standards used, was quantified in the n-butanol fraction followed by the ethyl acetate fraction of the leaf extract. This is the first report of antimicrobial activity of Pinus wallachiana extracts against Foc to the best of our knowledge

IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%

Applications of CEDA (catch effort data analysis) computer software in estimation of Maximum Sustainable Yieldof the Black Pomfret <i>Parastromateus niger</i>Fishery from Pakistani Waters

576-581The surplus production models of Fox, Schaefer and Pell-Tomlinson with three error assumptions of normal, log-normal and gamma were used to analyze the catch and effort data of Black PomfretParastromateus niger fishery from Pakistani waters using the computer software package CEDA (catch effort data analysis).When using initial proportion of 0.4 (because the initial catch was roughly 40% of the maximum catch) the estimated maximum sustainable yield (MSY) were about 2000-2300t, the coefficient determinationR2 were about 0.20-0.31. Gamma error assumption often showed minimization failure. The estimated MSY from CEDA is smaller than most recent catch of the fishery which indicates that the fishery of P.niger in Pakistani waters may not be in a sustainable condition