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Intelligent systems commonly employ vision sensors like cameras to analyze a scene. Recent work has proposed a wireless sensing technique, wireless vibrometry, to enrich the scene analysis generated by vision sensors. Wireless vibrometry employs wireless signals to sense subtle vibrations from the objects and infer their internal states. However, it is difficult for pure Radio-Frequency (RF) sensing systems to obtain objects' visual appearances (e.g., object types and locations), especially when an object is inactive. Thus, most existing wireless vibrometry systems assume that the number and the types of objects in the scene are known. The key to getting rid of these presumptions is to build a connection between wireless sensor time series and vision sensor images. We present Capricorn, a vision-guided wireless vibrometry system. In Capricorn, the object type information from vision sensors guides the wireless vibrometry system to select the most appropriate signal processing pipeline. The object tracking capability in computer vision also helps wireless systems efficiently detect and separate vibrations from multiple objects in real time.
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Dispersed passive RF-sensing for 3D structural health monitoring
We propose a sensing system comprising a large network of tiny, battery-less, Radio Frequency (RF)-powered sensors that use backscatter communication. The sensors use an entirely passive technique to 'sense' the parameters of the wireless channel between themselves. Since the material properties influence RF channels, this fine-grain sensing can uncover multiple material properties both at a large scale and fine spatial resolution. In this paper, we study the feasibility of the proposed passive technique for monitoring parameters of material in which the sensors are embedded. We performed a set of experiments where the sensor-to-sensor wireless channel parameters are well-defined using physics-based modeling, and we compared the theoretical and experimentally obtained values. For some material parameters of interest, like humidity or strain, the relationship with the observed wireless channel parameters have to be modeled relying on data-driven approaches. The initial experiments show an observable difference in the sensor-to-sensor channel phase with variation in the applied weights.
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- PAR ID:
- 10466328
- Date Published:
- Journal Name:
- ITU Journal on Future and Evolving Technologies
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2616-8375
- Page Range / eLocation ID:
- 535 to 544
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.52953/ZQYZ8264
