Simultaneous Material Identification and Target Imaging with Commodity RFID Devices

Ju, Wang; Xiong, Jie; Liu, Chen; Jiang, Hongbo; Balan, Rajesh Krishna; Fang, Dingyi

doi:10.1109/tmc.2019.2946072

Cited by 41 publications

(40 citation statements)

References 30 publications

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“…Wireless and battery-free sensors can support lightweight and robust monitoring. How to extend the capabilities of wireless signals [27][28][29][30] and how to increase the battery life have triggered numerous research motivations over the past few years [31][32][33]; (2) Secondly, the processing of massive networked sensors requires the upgrade of computing architecture to reduce elaboration time, e.g., collaborative edge computing [34][35][36], and in order to reduce the effort of cloud architecture and save the bandwidth, it is important to enable a resource-constrained IoT device with modern analysis techniques, e.g., deep learning [37][38][39][40];…”

Section: Industrial Internet Of Things and Digital Twinmentioning

confidence: 99%

Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants

et al. 2020

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In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms.

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Section: Industrial Internet Of Things and Digital Twinmentioning

confidence: 99%

Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants

et al. 2020

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“…AIM [44] is a smartphone-based acoustic imaging system, but its imaging mechanism also applies echoes of acoustic signals and the way it deals with background noise is enlightening to acoustic localization. The rationale of AIM is based on Synthetic-Aperture Radar (SAR) which is widely used in RF imaging systems [2,30,68,88]. The main idea is moving the transmitter along a distance to simulate a large aperture that helps produce high-resolution images.…”

Section: Round-trip Time-of-flightmentioning

confidence: 99%

Indoor acoustic localization: a survey

Liu

Cheng

Qian

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Applications of localization range from body tracking, gesture capturing, indoor plan construction to mobile health sensing. Technologies such as inertial sensors, radio frequency signals and cameras have been deeply excavated to locate targets. Among all the technologies, the acoustic signal gains enormous favor considering its comparatively high accuracy with common infrastructure and low time latency. Rangebased localization falls into two categories: absolute range and relative range. Different mechanisms, such as Time of Flight, Doppler effect and phase shift, are widely studied to achieve the two genres of localization. The subcategories show distinguishing features but also face diverse challenges. In this survey, we present a comprehensive overview on various indoor localization systems derived from the various mechanisms. We also discuss the remaining issues and the future work.

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“…Investigations in realistic environments are carried out in a followup work [15]. Recently, researchers have also been able to track multiple objects passively with existing radio signals [1,17], and can even identify the material type and image the horizontal cut of targeted object with RFID signal [21]. Motivated by these works, researchers are analyzing the unique properties of visible light waves for passive sensing, as discussed in this work.…”

Section: Related Techniquesmentioning

confidence: 99%

Passive visible light networks

Wang

Zúñiga

2020

Proceedings of the Workshop on Light Up the IoT

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Artificial lighting has been used mainly for illumination for more than a century. Only recently, we have started to transform our lighting infrastructure to provide new services such as sensing and communication. These advancements have two key requirements: the ability to modulate light sources (for data transmission) and the presence of photodetectors on objects (for data reception). These requirements assume that the system has direct control over the transmitter and receiver, as in any traditional communication system. But not all lights can be modulated, and most objects do not have photodetectors. To overcome these limitations, researchers are developing novel networks that (i) exploit passive light sources that cannot be directly modulated, such as the sun, and (ii) leverage reflections from the external surfaces of objects to create a new generation of sensing and communication networks with visible light that is sustainable and does not require active control over the system. In this survey, we propose a taxonomy to analyze state-of-the-art contributions. We also identify the overarching principles, challenges, and opportunities of this new rising area.

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Simultaneous Material Identification and Target Imaging with Commodity RFID Devices

Cited by 41 publications

References 30 publications

Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants

Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants

Indoor acoustic localization: a survey

Passive visible light networks

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