If your phone takes amazing photos, chances are its camera has been augmented by artificial intelligence embedded in the operating system. Now videos are getting the same treatment. In recent years, smartphone makers have been gradually transforming their cameras into devices that capture data for AI processing beyond what the lens and sensor pick up in a single shot. That effectively turns a smartphone into a professional camera on auto mode and lowers the bar of capturing compelling images and videos. In an era of TikTok and vlogging, there's a huge demand to easily produce professional-looking videos on the go.

An illuminated 5G sign hangs behind a weave of electronic cables on the opening day of the MWC ... [ ] Barcelona in Barcelona, Spain, on Monday, Feb. 25, 2019. At the wireless industry's biggest conference, over 100,000 people are set to see the latest innovations in smartphones, artificial intelligence devices and autonomous drones exhibited by more than 2,400 companies. At the core of this evolutionary step is the use of machine learning algorithms. The ability to be more dynamic with real-time network optimization capabilities such as resource loading, power budget balancing and interference detection is what made networks "smart" in the 4G era. While there are many uses of machine learning across all layers of a 5G network from the physical layer through to the application layer, the base station is emerging as a key application for machine learning.

The unprecedented growth of mobile devices, applications and services have placed the utmost demand on mobile and wireless networking infrastructure. Rapid research and development of 5G systems have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover, inference from heterogeneous mobile data from distributed devices experiences challenges due to computational and battery power limitations. As a result, models employed in the edge-based scenario are constrained to light-weight to achieve a trade-off between model complexity and accuracy. Also, model compression, pruning, and quantization are largely in place.

At the core of this evolutionary step is the use of machine learning algorithms. The ability to be more dynamic with real-time network optimization capabilities such as resource loading, power budget balancing and interference detection is what made networks "smart" in the 4G era. While there are many uses of machine learning across all layers of a 5G network from the physical layer through to the application layer, the base station is emerging as a key application for machine learning. One of the hallmarks of a next generation 5G base station is the use of advanced antenna capabilities These capabilities include but are not limited to massive multiple-input multiple-output (MIMO) antenna arrays, beamforming, and beam steering. Massive MIMO is the use of antenna arrays with a large number of active elements.

This book presents selected papers from the 3rd International Conference on Micro-Electronics and Telecommunication Engineering, held at SRM Institute of Science and Technology, Ghaziabad, India, on 30-31 August 2019. It covers a wide variety of topics in micro-electronics and telecommunication engineering, including micro-electronic engineering, computational remote sensing, computer science and intelligent systems, signal and image processing, and information and communication technology.

Galway start-up Chatspace has developed an artificial intelligence answers and insights platform that prevents projects on track and prevents costly failures. Chatspace works with the world's largest companies unleashing new insights for company strategy that traditional teams can't reach, automating repeatable tasks and scaling capabilities across the enterprise. The company believes that the future of work is engaged and connected employees taking advantage of the capabilities that technology provides. Its clients to date include ATOS, Nestle and Medtronic. "Project Management is integral to Enterprise," explains Chatspace CEO and founder John Clancy.

China Mobile is stepping up efforts to promote its 5G and artificial intelligence services by targeting "Generation Z" consumers. M-zone, a popular brand under China Mobile, has teamed up with pop idol Zhang Yixing to popularize new services, including co-branded Xback smartphone phone SIM cards, virtual photo-taking services supported by augmented reality and virtual reality technologies, as well as AI-enabled avatar services. Zhang has also become a partner with M-zone's 5G services and a promoter of its AI services. M-zone's Xback SIM cards are part of the company's broader efforts to better commercialize 5G technologies to create new value. Given Chinese pop idols' growing appeal to tech-savvy, young subscribers, China Mobile hopes pop idols can help boost the popularity of its 5G services.

This paper studies the problem of information freshness-aware task offloading in an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). A third-party real-time application service provider provides computing services to the subscribed mobile users (MUs) with the limited communication and computation resources from the InP based on a long-term business agreement. Due to the dynamic characteristics, the interactions among the MUs are modelled by a non-cooperative stochastic game, in which the control policies are coupled and each MU aims to selfishly maximize its own expected long-term payoff. To address the Nash equilibrium solutions, we propose that each MU behaves in accordance with the local system states and conjectures, based on which the stochastic game is transformed into a single-agent Markov decision process. Moreover, we derive a novel online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks for each MU to approximate the Q-factor and the post-decision Q-factor. Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics. Numerical experiments examine the potentials of the proposed scheme in balancing the age of information and the energy consumption.

Library sales to streaming companies have helped to keep revenue flowing for content owners during lockdown. Now, they can take advantage of a new technology that can digitally insert branded products and promotional items into finished content. Its platform uses AI to identify the most natural and meaningful placement opportunities, and then employs VFX technology to insert real-world objects that weren't in the original shoot, like a vehicle or a bag of potato chips, or overlays existing brand imagery with new product shots. Mirriad can boast an impressive list of media clients it has worked with, including Tencent Video, 20th Century Fox, RTL, Channel 4, France TV and ABC. Brands they have helped include Pepsi, Sherwin Williams, P&G, Huawei and T-Mobile.

Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralized server. Performance of FL in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. A recent work, Coded Federated Learning (CFL), proposes to mitigate stragglers and speed up training for linear regression tasks by assigning redundant computations at the MEC server. Coding redundancy in CFL is computed by exploiting statistical properties of compute and communication delays. We develop CodedFedL that addresses the difficult task of extending CFL to distributed non-linear regression and classification problems with multioutput labels. The key innovation of our work is to exploit distributed kernel embedding using random Fourier features that transforms the training task into distributed linear regression. We provide an analytical solution for load allocation, and demonstrate significant performance gains for CodedFedL through experiments over benchmark datasets using practical network parameters.