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Ichthyofauna diversity acts as a bio-indicator and is playing a significant role in the sustainability of an aquatic environment. The present study explores the ichthyofauna assemblage in one of the largest wetlands of Bangladesh known as Asura beel located in Dinajpur district. Fortnightly fish samplings were done from November 2017 to October 2018 considering three seasons as winter,summer, and rainy. Fish biodiversity was assessed with their threatened status and analyzed seasonal abundance with the diversity indices. Findings revealed a total of 35 identified species of fish belonging to 7 orders, 17 families, and 26 genera. According to IUCN- Bangladesh 2015, the current research recorded 6 fish species as vulnerable (17%). While only 1 species is data deficient (3%), 6 species are endangered (17%), and the other 22 species are not threatened (63%). Among the fish species, Amblypharyngodon mola of Cypriniformes order contributed the highest abundance (12%). On the contrary, Ostreobrama cotio and Macrognathus armatus both represented the lowest abundance by contributing 0.35%. The survey also reported that the seasonal diversity indices and richness values did not vary significantly according to seasons although the number of catches was found higher in the winter season followedby summer and rainy seasons.

Custom low power hardware for real-time network security and anomaly detection are in great demand, as these would allow for efficient security in battery-powered network devices. This paper presents a memristor based system for real-time intrusion detection, as well as an anomaly detection based on autoencoders. Intrusion detection is based on a single autoencoder, and the overall detection accuracy of this system is 92.91% with a malicious packet detection accuracy of 98.89%. The system described in this paper is also capable of using two autoencoders to perform anomaly detection using real-time online learning. Using this system, we show that anomalous data is flagged by the system, but over time the system stops flagging a particular datatype if its presence is abundant. Utilizing memristors in these designs allows us to present extreme low power systems for intrusion and anomaly detection, while sacrificing little accuracy. CCS CONCEPTSCCS → Hardware → Emerging technologies → Analysis and design of emerging devices and systems → Emerging architectures

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