KS&R's Digital Pulse

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KS&R’s Digital Pulse Social conversation across all digital topics has dipped (unsurprisingly) as we enter the final weeks of 2018; however, Internet of Things (IoT) continues to have steady social conversation relative to the other topics. One element of IoT that has seen increased conversation across different channels is the emerging technology called Smart Dust. What is Smart Dust? Smart Dust is a network of many tiny systems that can detect light, temperature, vibration, magnetism, or chemicals. These miniaturized devices have sensors, cameras and communication mechanisms to transmit the data they collect back to a base in order to process.1 What are they saying about it? Social conversation related to Smart Dust is still ramping up however there has been a large spike starting around October 2018. 1https://www.forbes.com/sites/bernardmarr/2018/09/16/smart-dust-is-coming-are-you-ready/#3d0ea8a5e41a Visit our website www.ksrinc.com The emerging technology has caught the attention of several industry giants including Gartner, Forbes, and the Wall Street Journal. 5 Trends Emerge in the Gartner Hype Cycle for Emerging Technologies, 2018 (httpsi://gtnr.it/2vTTphv) Smart Dust Is Coming. Are You Ready? (https://bit.ly/2Q3xtMK) Here Comes ‘Smart Dust,’ the Tiny Computers That Pull Power from the Air (https://on.wsj.com/2DcQqFs) Much of the current social conversation is sourced from news and industry channels. The sentiment tends to be positive with many saying they are excited to see the new applications of this system. There are several applications, which appear to be on the horizon and span across industries: telecom (powering 5G), healthcare (diagnostic procedures without surgery), and technology (identify weaknesses and corrosion prior to a system failure). “Is this the next step in IoT? A sensor that never needs to be need to be charged and can run forever?” “A revolution is nigh. Get ready for tiny, always-on devices that will never need to be charged.” “A world covered in sensors – no batteries or wires required? Meet the first wave of 'smart dust’.” What’s next? We are interested to see if the social conversation related to Smart Dust continues to climb and the market reaction to this new technology. Did you miss last month’s digital pulse? Take a couple of minutes and read it here! 1https://www.forbes.com/sites/bernardmarr/2018/09/16/smart-dust-is-coming-are-you-ready/#3d0ea8a5e41a Visit our website www.ksrinc.com .

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Internet of Things Meets Brain-Computer Interface: a Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Internet of Things Meets Brain-Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity Xiang Zhang, Student Member, IEEE, Lina Yao, Member, IEEE, Shuai Zhang, Student Member, IEEE, Salil Kanhere, Member, IEEE, Michael Sheng, Member, IEEE, and Yunhao Liu, Fellow, IEEE Abstract—A Brain-Computer Interface (BCI) acquires brain signals, analyzes and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), BCI can empower individuals to directly control objects such as smart home appliances or assistive robots, directly via their thoughts. However, realization of this vision is faced with a number of challenges, most importantly being the issue of accurately interpreting the intent of the individual from the raw brain signals that are often of low fidelity and subject to noise. Moreover, pre-processing brain signals and the subsequent feature engineering are both time-consuming and highly reliant on human domain expertise. To address the aforementioned issues, in this paper, we propose a unified deep learning based framework that enables effective human-thing cognitive interactivity in order to bridge individuals and IoT objects. We design a reinforcement learning based Selective Attention Mechanism (SAM) to discover the distinctive features from the input brain signals. In addition, we propose a modified Long Short-Term Memory (LSTM) to distinguish the inter-dimensional information forwarded from the SAM. To evaluate the efficiency of the proposed framework, we conduct extensive real-world experiments and demonstrate that our model outperforms a number of competitive state-of-the-art baselines.
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A Prototype of EEG System for Iot
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