The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring, mobile-health systems, intelligent transportation systems and so on. The {information and communication technology }(ICT) sector is experiencing a significant growth in { data} traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. {It} is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy {via} providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS {require} cybersecurity to protect {them} against malicious attacks and unauthorized intrusion, which {become} a challenge with the enormous amount of data that is continuously being generated in the network. {Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

Editor's note: The use of AI in LinkedIn products has been the subject of multiple press articles and research papers (some highlighted on this blog). With the release of a new LinkedIn Learning course about AI at LinkedIn, we asked our Head of AI, Deepak Agarwal, for a brief overview of what AI is and how it works, geared towards people who are interested in this growing field. In this post, we discuss AI as a broad topic and look at a few ways that it influences product design at LinkedIn. Back in 2005, I was working in my first job at AT&T Bell Labs. The telecommunications industry was struggling due to price wars and increased competition from wireless carriers.

Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: avoiding causing harmful interference to primary users (PUs), and conducting effective interference coordination with other secondary users. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network (RNN), called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large.

You're going to hear a lot of claims about "5G firsts" as the leading telecom companies slug it out to deliver the next generation of wireless. And one just came from AT&T. The company says it has successfully completed the "world's first millimeter wave" 5G connection over a live network to what will be commercially available "standards-based" commercial mobile 5G device. Did your eyes just cross? Let's break this down a bit first.

Apple, Google, and Samsung all have smart speakers. Not to be left behind, fellow smartphone titan Huawei is playing catch up with another budget contender, following the reveal of its AI Cube (a speaker, 4G modem, WiFi router hybrid and Google Home clone, all rolled into one). The company teased the new gadget -- the Chinese name of which translates as "Huawei AI Speaker" -- at its Mate 20 series event in Shanghai, China, earlier today. As first impressions go, there's the glaringly obvious: this device looks like a HomePod doppelgänger, complete with a stout, cylindrical design with control buttons at the top. It also comes in black and white.

IDEAS 278,119 views SolidWorks Tutorial #181: Cola Bottle - Duration: 27:34. SolidWorks Tutorial 1,589,749 views Inside a Google data center - Duration: 5:28.

Machine learning made a big splash in 2018, and companies are expected to continue or increase their investments in this technology in the coming year. IDC forecasts that machine learning and AI spending will increase from $12 billion in 2017 to $57.6 billion by 2021. Data science platforms that support machine learning are predicted to grow at a 13% CAGR through 2021. Numerous industries have felt an enormous shift due to machine learning, and large tech giants continue to vie for top data science talent. Manufacturing, for example, saw the implementation of smart factories, where machines can essentially talk to each other and predictive analytics can forecast any potential problems.

Abstract--Handling the tremendous amount of network data, produced by the explosive growth of mobile traffic volume, is becoming of main priority to achieve desired performance targets efficiently. Opportunistic communication such as Floating Content (FC), can be used to offload part of the cellular traffic volume to vehicular-to-vehicular communication (V2V), leaving to the infrastructure the task of coordinating the communication. Existing FC dimensioning approaches have limitations, mainly due to unrealistic assumptions and on a coarse partitioning of users, which results in over-dimensioning. Shaping the opportunistic communication area is a crucial task to achieve desired application performance efficiently. In this work, we propose a solution for this open challenge. In particular, the broadcasting areas called Anchor Zone (AZ), are selected via a deep learning approach to minimize communication resources achieving desired message availability. No assumption required to fit the classifier in both synthetic and real mobility. A numerical study is made to validate the effectiveness and efficiency of the proposed method. The predicted AZ configuration can achieve an accuracy of 89.7% within 98% of confidence level. By cause of the learning approach, the method performs even better in real scenarios, saving up to 27% of resources compared to previous work analytically modeled. I NTRODUCTION New offloading techniques to cope with the explosive growth in mobile traffic volumes, are a fundamental component of the next generation radio access network (5G). Part of the cellular traffic volume can be offloaded to vehicular-to- vehicular communication (V2V), leaving to the infrastructure the task of managing and coordinating the communication. In this context, of special interest are communication paradigms such as Floating Content (FC), an opportunistic communication scheme for the local dissemination of information [1]. FC as an infrastructure-less communication model, enables probabilistic contents storing in geographically constrained locations - denoted as Anchor Zone (AZ) - and over a limited amount of time based on the application requirements.

It's only been almost a quarter since Qualcomm launched its Snapdragon 670 mid-range chipset, but today, the company is already bringing out a slightly beefier Snapdragon 675, which is clearly designed with three smartphone trends in mind: Gaming optimization, multiple cameras and AI-enhanced features (including face unlock). Announced at the 4G/5G Summit in Hong Kong, the Snapdragon 675 features Qualcomm's brand new fourth-generation Kryo CPU -- the Kryo 460 with two high-power 2GHz cores and six low-power 1.7GHz cores -- which apparently gives a notable boost to its everyday performance and gaming performance. For instance, compared to the Snapdragon 670, the new Snapdragon 675 is claimed to launch games 30 percent faster and offers 35 percent faster web browsing. In the case of gaming, the Snapdragon 675 also leverages from further software optimization for smoother gameplay, along with prioritized connectivity to ensure minimal interruption. Qualcomm listed PUBG and King of Honor being two of the handful of games that already benefit from these features, with the latter title even able to run at up to 60 fps -- not bad given that the GPU is just a mid-range Adreno 612, though we shall see whether it runs just as well outside of the lab, especially when this chipset is fabricated using an 11nm LPP process instead of the finer 10nm.

When it comes to adding a voice assistant to a speaker or a pair of headphones, Amazon's Alexa has become the default choice for many OEMs, likely due to the openness and high adoption rate of the platform. Never one to miss a money-making opportunity, Qualcomm has decided to lend these manufacturers a hand by building a smart headset reference design, which features its very own QCC5100-series Bluetooth audio chip. With the Alexa app installed on your Android phone, once it's paired with these earbuds, you can toggle Alexa with a simple push of a button on one of the buds. Thanks to Qualcomm's radio know-how, this reference design apparently boasts a long battery life (as do many Bluetooth headphones these days, anyway) along with active noise cancellation, aptX HD audio and TrueWireless Stereo. Since this development kit supports Alexa Mobile Accessory Kit protocol out of the box, manufacturers can focus more on the design and other features, thus saving time and cost in the long run.