Marine Fish Calendar. 2. Visakhapatnam

Gear wise landing details of the fish species in the important families (e.g. Clupeidae,Scombridae, Sciaenidae, etc.), groups (e.g. sardines,mackerel, croakers, etc.) in the Vishakapatnam landing centre during 1981-1986 was provided in the article

An assessment of the bottom-trawl fishery resources of the northeast coast of India

An overview of the bottom trawl fishery resources of the continental shelf of the northeast coast (lat. 15° N-21° N and long. 80^ E-83° El has been attempted based on data collected from the exploratory fishery surveys conducted by the Govt. of India fishing vessels during 1961-1985. The 'swapt-area' method has been employed to estimate the standing stock, and 60% of this has bean reckoned as the potential yield The catch rates in the shelf region ranged between 1 l<g/hr in square 17-33 CI and 377 kg/hr in squares that yielded 150kg/hr or more are distributed widely both in the inshore and offshore grounds- The potential yield estimates for the area explored varied between 083 t/km2 in 20° H-Sl" E and 3 37 t/km^ in 19° N-35°

Chemical Additives for Corrosion Control in Desalination Plants

The addition of chemical additives has been considered as a standard operation in water treatment systems. This chapter discusses the chemical additives used for the control of corrosion in desalination systems. Specifically, corrosion inhibitors for various metallurgies, biocides, and oxygen scavengers are covered. The pros and cons of the additive chemicals have been highlighted. The need to utilize green corrosion inhibitors based on plants and ionic liquids materials have been emphasized. This class of materials are environmentally friendly, cheap, and readily available

VLSI Architecture Design Methodology for Deep learning based Upper Limb and Lower Limb Movement Classification for Rehabilitation Application

Recently, many works have proposed an highly accurate deep learning based movement classification algorithms for the assistive technology applications. But very less importance is given for it's corresponding hardward implementation. In this paper we proposed an VLSI architecture design methodology for deep learning based movement classification for assistive technology applications. LoCoMo-Net and MyoNet are the two Deep learning based networks proposed by Gautam et al [1] [2] for upper limb and lower limb for assistive technology. The proposed architecture is capable enough to adapt both the networks. We have implemented the architecture on ZYNQ ultra-textscale + textMPSoC textzcu102 textFPGA. LoCoMo-Net consumes 3.5 Watts of on chip power and MyoNet consumes 5 Watts of on chip power on the FPGA. LoCoMo-Net takes 1.876ms of time to classify the task and MyoNet takes 61.988ms of time to classify the task on FPGA. © 2022 IEEE