Your next smartphone might just throw you a curve. Picture this: You pull your phone out of your pocket and unfold it like a napkin into a tablet. You press your finger on the screen, and it unlocks. You switch to the camera app, and a spider-like array of lenses shoot simultaneously to capture one giant photo. These are all things I've seen phones do -- some in prototype form, others in models you can get only in China.

Have you started working with the Qualcomm Snapdragon Neural Processing Engine (NPE) SDK yet? I posted last time about deep learning at the edge on IoT devices and described how you can use the SDK to execute artificial intelligence (AI) workloads on GPU, DSP and CPU. Since then, we've added to the number and strength of deep neural networks (DNN) we support in the SNPE SDK. To help get the wheels of innovation turning for you, in this post I'll describe ways you can apply AI using those DNNs to various IoT devices and use cases, including context-aware AI. How You Can Use AI in the Internet of Things First, we've found that although many developers want to use AI in IoT, there is no comprehensive resource telling them how they can use it.

The existing spectrum management paradigm treats frequency spectrum as a fixed commodity, which leads to spectrum under utilization. Cognitive radio has emerged as a useful strategy to increase spectrum utilization. The existing literature on cognitive radio has largely been focused on the primary/secondary user paradigm, where secondary users need to detect vacant spectrum when available and vacate the occupied spectrum when a primary user wants to transmit. We focus on a different type of spectrum sharing system in which there is no distinction between users, and in which there is no coordination among the users. The collective performance across all users is more important than that of individual users. This is in contrast to the typical primary/secondary user paradigm in which secondary users bear the responsibility for ensuring priority-based spectrum sharing.

Qualcomm Technologies, Inc. today announced three new additions to the Qualcomm Snapdragon 600 and 400 tiers - the Snapdragon 632, 439 and 429 Mobile Platforms. These platforms are engineered to bring higher performance, better battery life, more efficient designs, impressive graphics and artificial intelligence (AI) capabilities into the highest-selling Snapdragon tiers. Qualcomm debuts Snapdragon 429, 439 and 632 Mobile Platforms Qualcomm continues to bring more premium technology advancements to lower Snapdragon tiers, which is helping to transform the mass-market consumer experience. Snapdragon 632 The new Snapdragon 632 brings some of the most sought-after mobile experiences, including mainstream gaming, 4K video capture, artificial intelligence, and fast LTE speeds-all for an affordable price. Built on advanced FinFET process technology, the Snapdragon 632 boasts up to 40% higher performance thanks to the combination of the Qualcomm Kryo 250 CPU and Qualcomm Adreno 506 GPU.

We consider the problem of detecting norm violations in open multi-agent systems (MAS). We show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations. The cost of providing the incentives is not borne by the MAS and does not come from fines charged for norm violations (fines may be impossible to levy in a system where agents are free to leave and rejoin again under a different identity). Instead, monitoring incentives come from (scrip) fees for accessing the services provided by the MAS. In some cases, perfect monitoring (and hence enforcement) can be achieved: no norms will be violated in equilibrium. In other cases, we show that, while it is impossible to achieve perfect enforcement, we can get arbitrarily close; we can make the probability of a norm violation in equilibrium arbitrarily small. We show using simulations that our theoretical results, which apply to systems with a large number of agents, hold for multi-agent systems with as few as 1000 agents--the system rapidly converges to the steady-state distribution of scrip tokens necessary to ensure monitoring and then remains close to the steady state.

Whether it is facial recognition tech that is (allegedly) able to pick a wanted criminal out of a crowd of thousands or aerial drones which use image recognition smarts to predict fights before they take place, there is no doubt that we are living through a major paradigm shift for automated surveillance technology. But this kind of tech can have more grounded, everyday applications, too -- like helping prevent shoplifters stealing goods from their local mom-and-pop corner store. That is something seemingly demonstrated by a new artificial intelligence security camera called the "A.I. Guardman," built by Japanese telecommunication company NTT East and startup Earth Eyes Corp. The camera uses a special pose detection system to identify behavior it deems to be suspicious.

Unlike the previous wireless networks – 3G, 4G, and LTE – that have all successfully launched and done their part in creating faster, more reliable cellular connections, 5G networks that wireless companies are trying their hardest to roll out to customers are having a bit more trouble. The radio access networks that 5G connections work off of rely on two emerging technologies that haven't worked out all the kinks yet. One of these technologies, referred to as millimeter waves, which are spectrums that broadcast at frequencies between 30 and 300 gigahertz, compared to the bands below 6 GHz that were used for mobile devices in the past. Their name comes from their size, which is much smaller (about 1-10 mm) than the current radio waves that cater to smartphones that measure tens of centimeters in length. The other new tech that 5G networks need to work to their potential is known as massive MIMO.

This paper discusses technology challenges and opportunities to embrace artificial intelligence (AI) era in the design of wireless networks. We aim to provide readers with motivation and general methodology for adoption of AI in the context of next-generation networks. First, we discuss the rise of network intelligence and then, we introduce a brief overview of AI with machine learning (ML) and their relationship to self-organization designs. Finally, we discuss design of intelligent agent and it's functions to enable knowledge-driven wireless networks with AI.

The Samsung Galaxy S10 is heavily rumored to arrive with an in-display fingerprint scanner. Now a new report from Korea claims that this could be true and that the Galaxy S10 won't have an iris scanner, but could come with an iPhone X-inspired feature. Samsung has not requested any of its suppliers to make iris scanners for the Galaxy S10, according to The Bell. The Korean news outlet claims that the upcoming Samsung flagship phone will have an in-display fingerprint scanner and 3D facial recognition technology, which will function a lot like Apple's Face ID on the iPhone X. Samsung is still lagging behind Apple when it comes to 3D facial recognition. The South Korean phone maker did introduce an Animoji competitor called AR Emoji on the Galaxy S9, as pointed out by The Verge. However, Apple already has an answer to AR Emoji called Memoji, which will arrive with the iOS 12 update later this fall.

Wireless carriers around the world are pushing to bring 5G service to their customers as quickly as possible, but the new radio access networks--which will rely on emerging technologies including millimeter waves and huge antenna arrays known as massive MIMO--will be a lot more complicated than what came before. Nokia is applying machine learning to some of the problems that result from this complexity, hoping that artificial intelligence can boost network performance and cut costs, Rajeev Agrawal said recently during a 5G summit at the Computex trade show in Taipei, Taiwan. Agrawal, who is in charge of Nokia's radio access network offerings, presented three possibilities for machine learning and 5G that Nokia has studied internally but not yet published in academic research papers. In a MIMO (multiple-input multiple-output) network, cellular base stations send and receive radio frequency signals in parallel through many more antennas than are normally used on a base station. This means the base station can transmit and receive more data, but these signals also interfere with one another.