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Frost & Sullivan views Axilion as an excellent opportunity for those seeking to invest in sustainable and smart cities

Frost & Sullivan views Axilion as an excellent opportunity for those seeking to invest in sustainable and smart cities USA - English - Axilion target stock price rose 50% to NIS 52.7 after Frost & Sullivan recommendation - The AI-based Digital Twin technology developed by Axilion has already been implemented in cities in Israel and around the world - Axilion s unique technology is proven to reduce air pollution, save energy consumption, and shorten journey times up to 47% News provided by Share this article TEL AVIV, Israel, March 3, 2021 /PRNewswire/ Axilion Smart Mobility  (TASE: AILN), the Israeli AI company leading the digital transformation of the mobility industry, published an analysis conducted by Frost & Sullivan.

Packt Free eBook

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IIT Madras Robert Bosch Centre presents Distinguished Fellowship to world-renowned researchers

IIT Madras Robert Bosch Centre presents Distinguished Fellowship to world-renowned researchers IIT Madras Robert Bosch Centre presents Distinguished Fellowship to world-renowned researchers The IIT Madras Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) has awarded the RBCDSAI Distinguished Fellowship to three world-renowned researchers from International Institutions for their valuable contributions to Data Science and AI. The Fellows will engage in long-term collaborations with the Centre, by inspiring, mentoring and initiating meaningful collaborations. As part of welcoming the Distinguished Fellows, RBCDSAI is holding a series of events interacting with them on topical issues in Data Science and AI. In the first of these events, a fireside chat with Prof. Srinivasan Parthasarathy on the topic AI Ethics: Separating Perception from Truth has been scheduled for January 21 and you can check out the official website to register for the same.

Learning to Bond in Dense WLANs with Random Traffic Demands by Yizhou Luo and Kwan-Wu Chin

Publication Details Y. Luo & K. Chin, Learning to Bond in Dense WLANs with Random Traffic Demands, IEEE Transactions on Vehicular Technology, vol. 69, (10) pp. 11868-11879, 2020. Abstract © 1967-2012 IEEE. The Access Points (APs) in a Wireless Local Area Network (WLAN) must be assigned one or more channels to meet traffic demands from users. To date, prior works on channel assignment assume APs have a fixed traffic demand, meaning they do not consider or adapt to spatio-temporal changes in traffic demands. To this end, we leverage Deep Reinforcement Learning (DRL), where we equip APs with a DRL-based channel assignment solution, to maximize the average number of slots in which an AP has sufficient bandwidth to meet user demands. Our APs learn from their historical traffic loads and assign themselves partially overlapping channels with minimal interference. Simulation results show that our DRL solution leads to APs satisfying 60% more user demands as compared to fixed and greedy

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