Canadian machine learning researchers from the University of Victoria have teamed up with government marine biologists and private remote sensing specialists to develop a system for improved detection and classification of schools of herring. The world's oceans are home to some 200,000 species of sea animals, including over 18,000 species of fish, more than 1,800 sea stars, 816 squids, 93 whales and dolphins and 8,900 clams and other bivalves, according to a 2015 report from the World Register of Marine Species. Ocean fishes come in a variety of shapes, sizes, and colors and live in many different depth and temperature environments. This diverse marine world is however under threat. A 2016 United Nations Food and Agriculture Organization's World Fisheries and Aquaculture report reveals that 89.5 percent of the world's fish stocks are either fully fished (catches are close to the maximum sustainable yield) or overfished (catches are unsustainable).

Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and prone to inconsistencies. This paper proposes a deep learning framework for the automatic detection of schools of herring from echograms. Experiments demonstrated that our approach outperforms a traditional machine learning algorithm using hand-crafted features. Our framework could easily be expanded to detect more species of interest to sustainable fisheries.

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learningbased communication methods are presented along with the research opportunities and challenges. H. Huang, G. Gui, Z. Yang, and H. Sari are with Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education, Nanjing 210003, China. S. Guo is with Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (Email: song.guo@polyu.edu.hk). J. Zhang is with Beijing University of Posts and Telecommunication (BUPT), Beijing 100876, China (Email: jhzhang@bupt.edu.cn). F. Adachi is with Wireless Signal Processing Research Group, Research Organization of Electrical Communication (ROEC), Tohoku University, Sendai 980-8577, Japan (Email: adachi@ecei.tohoku.ac.jp).