首页> 外文会议>2018 International Conference on Advancement in Electrical and Electronic Engineering >Biomass Estimation of a Popular Aquarium Fish Using an Acoustic Signal Processing Technique with Three Acoustic Sensors
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Biomass Estimation of a Popular Aquarium Fish Using an Acoustic Signal Processing Technique with Three Acoustic Sensors

机译:带有三个声传感器的声信号处理技术对流行水族馆鱼的生物量估计

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The popular aquarium fish called damselfish, sometimes called demoiselle also, is a small, tropical, marine fish of the family Pomacentridae (order Perciformes). It is seen in the Atlantic and Indo-Pacific oceans and is composed of about 250 species. Beside aquarium uses, these types of fish are precious because of their ecological and economic worth. Therefore, a proper estimation of their biomass is a significant task. However, the conventional techniques for estimating fish-biomass are visual sampling techniques, raft and floating radio frequency identification tag systems, minnow traps, removal method of population estimation, etc., which are complex, costly and human-interactive. To get rid of these problems, in this paper, we propose a passive acoustic technique for estimating damselfish biomass. The estimation process is based on the production of an acoustic signal called “chirp signal” by damselfish species during swimming within their territory. In this article, we have presented a theoretical method of biomass estimation that is verified by simulation and the performance of the estimation is evaluated for different fish distributions. We have found that Exponential distribution of damselfish produce best results among the three distributions, i.e., Exponential, Normal, and Rayleigh, of damselfish.
机译:流行的水族馆鱼被称为雀鲷,有时也被称为闺目鱼,是Pomacentridae(Perciformes)科的小型热带热带鱼。在大西洋和印度洋-太平洋中都可以看到它,它约有250种。除了用于水族馆之外,这些鱼类还具有珍贵的生态和经济价值。因此,对其生物量的正确估算是一项重要的任务。然而,用于估计鱼类生物量的常规技术是视觉采样技术,筏式和浮动式射频识别标签系统,min鱼陷阱,种群估计的去除方法等,它们复杂,昂贵且与人互动。为了解决这些问题,在本文中,我们提出了一种用于估计雀鲷生物量的无源声学技术。估计过程基于雀鲷物种在其区域内游泳期间产生的称为“线性调频信号”的声信号。在本文中,我们提出了一种生物量估算的理论方法,该方法已通过仿真验证,并针对不同鱼类分布评估了估算的性能。我们发现,雀鲷的指数分布在雀鲷的三种分布中,即指数分布,正态分布和瑞利分布中,产生了最好的结果。

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