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1. Capricorn: Towards Real-Time Rich Scene Analysis Using RF-Vision Sensor Fusion

   https://doi.org/10.1145/3560905.3568504  

   Wang, Ziqi; Sarker, Ankur; Wu, Jason; Hua, Derek; Dong, Gaofeng; Singh, Akash Deep; Srivastava, Mani (November 2022, Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems)

   Video scene analysis is a well-investigated area where researchers have devoted efforts to detect and classify people and objects in the scene. However, real-life scenes are more complex: the intrinsic states of the objects (e.g., machine operating states or human vital signals) are often overlooked by vision-based scene analysis. Recent work has proposed a radio frequency (RF) sensing technique, wireless vibrometry, that employs wireless signals to sense subtle vibrations from the objects and infer their internal states. We envision that the combination of video scene analysis with wireless vibrometry form a more comprehensive understanding of the scene, namely "rich scene analysis". However, the RF sensors used in wireless vibrometry only provide time series, and it is challenging to associate these time series data with multiple real-world objects. We propose a real-time RF-vision sensor fusion system, Capricorn, that efficiently builds a cross-modal correspondence between visual pixels and RF time series to better understand the complex natures of a scene. The vision sensors in Capricorn model the surrounding environment in 3D and obtain the distances of different objects. In the RF domain, the distance is proportional to the signal time-of-flight (ToF), and we can leverage the ToF to separate the RF time series corresponding to each object. The RF-vision sensor fusion in Capricorn brings multiple benefits. The vision sensors provide environmental contexts to guide the processing of RF data, which helps us select the most appropriate algorithms and models. Meanwhile, the RF sensor yields additional information that is originally invisible to vision sensors, providing insight into objects' intrinsic states. Our extensive evaluations show that Capricorn real-timely monitors multiple appliances' operating status with an accuracy of 97%+ and recovers vital signals like respirations from multiple people. A video (https://youtu.be/b-5nav3Fi78) demonstrates the capability of Capricorn. 

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2. Multi-Perspective, Simultaneous Embedding

   https://doi.org/10.1109/TVCG.2020.3030373  

   Hossain, I; Huroyan, V; Kobourov, S; Navarrete, R (October 2020, IEEE Symposium on Information Visualization)

   We describe MPSE: a Multi-Perspective Simultaneous Embedding method for visualizing high-dimensional data, based on multiple pairwise distances between the data points. Specifically, MPSE computes positions for the points in 3D and provides different views into the data by means of 2D projections (planes) that preserve each of the given distance matrices. We consider two versions of the problem: fixed projections and variable projections. MPSE with fixed projections takes as input a set of pairwise distance matrices defined on the data points, along with the same number of projections and embeds the points in 3D so that the pairwise distances are preserved in the given projections. MPSE with variable projections takes as input a set of pairwise distance matrices and embeds the points in 3D while also computing the appropriate projections that preserve the pairwise distances. The proposed approach can be useful in multiple scenarios: from creating simultaneous embedding of multiple graphs on the same set of vertices, to reconstructing a 3D object from multiple 2D snapshots, to analyzing data from multiple points of view. We provide a functional prototype of MPSE that is based on an adaptive and stochastic generalization of multi-dimensional scaling to multiple distances and multiple variable projections. We provide an extensive quantitative evaluation with datasets of different sizes and using different number of projections, as well as several examples that illustrate the quality of the resulting solutions. 

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3. Performance Evaluation of Magnetic Resonance Coupling Method for Intra-Body Network (IBNet)

   https://doi.org/10.1109/TBME.2021.3130408  

   Islam, Sayemul; Gulati, Rajpreet Kaur; Domic, Michael; Pal, Amitangshu; Kant, Krishna; Kim, Albert (November 2021, IEEE Transactions on Biomedical Engineering)

   Effective management of emerging medical devices can lead to new insights in healthcare. Thus, a human body communication (HBC) is becoming increasingly important. In this paper, we present magnetic resonance (MR) coupling as a promising method for intra-body network (IBNet). The study reveals that MR coupling can effectively send or receive signals in biological tissue, with a maximum path loss of PL 33 dB (i.e. at 13.56 MHz), which is lower than other methods (e.g., galvanic, capacitive, or RF) for the same distance. The angular orientation of the transmitter and receiver coils at short and long distances also show a minor variation of the path loss (0.19 PL 0.62 dB), but more dependency on the distance (0.0547 dB/cm). Additionally, different postures during the MR coupling essentially do not affect path loss (PL 0.21 dB). In the multi-nodal transmission scenario, the MR coupling demonstrates that two nodes can simultaneously receive signals with -16.77 dBm loss at 60 cm and 100 cm distances, respectively. Such multi-node MR transmission can be utilized for communication, sensing, and powering wearable and implantable devices. 

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4. A Wideband Sliding Correlation Channel Sounder in 65 nm CMOS: Evaluation Board Performance

   Dipankar Shakya, Ting Wu (September 2021, IEEE transactions on circuits and systems)

   Abstract—Emerging applications such as wireless sensing, position location, robotics, and many more are driven by the ultra-wide bandwidths available at millimeter-wave (mmWave) and Terahertz (THz) frequencies. The characterization and effi- cient utilization of wireless channels at these extremely high frequencies require detailed knowledge of the radio propaga- tion characteristics of the channels. Such knowledge is developed through empirical observations of operating conditions using wireless transceivers that measure the impulse response through channel sounding. Today, cutting-edge channel sounders rely on several bulky RF hardware components with complicated interconnections, large parasitics, and sub-GHz RF bandwidth. This brief presents a compact sliding correlation-based chan- nel sounder baseband built on a monolithic integrated circuit (IC) using 65 nm CMOS, implemented as an evaluation board achieving a 2 GHz RF bandwidth. The IC is the world’s first gigabit-per-second channel sounder baseband implemented in low-cost CMOS. The presented single-board system can be employed at both the transmit and receive baseband to study multipath characteristics and path loss. Thus, the single-board implementation provides an inexpensive and compact solution for sliding correlation-based channel sounding with 1 ns multipath delay resolution. Index Terms—142 GHz, channel sounder, mmWave, on-chip baseband, PN sequence, RF hardware, sliding correlation, THz, XPD 

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5. Proceedings of MOBICOM 2021

   Soltanaghaei, Elahe; Prabhakara, Akarsh; Balanuta, Artur; Anderson, Matthew; Rabaey, Jan M.; Kumar, Swarun; Rowe, Anthony (February 2022, Proceedings of the annual International Conference on Mobile Computing and Networking)

   null (Ed.)

   This paper presents Millimetro, an ultra-low-power tag that can be localized at high accuracy over extended distances. We develop Mil-limetro in the context of autonomous driving to efficiently localize roadside infrastructure such as lane markers and road signs, even if obscured from view, where visual sensing fails. While RF-based localization offers a natural solution, current ultra-low-power local-ization systems struggle to operate accurately at extended ranges under strict latency requirements. Millimetro addresses this challenge by re-using existing automotive radars that operate at mmWave fre-quency where plentiful bandwidth is available to ensure high accuracy and low latency. We address the crucial free space path loss problem experienced by signals from the tag at mmWave bands by building upon Van Atta Arrays that retro-reflect incident energy back towards the transmitting radar with minimal loss and low power consumption. Our experimental results indoors and outdoors demonstrate a scal-able system that operates at a desirable range (over 100 m), accuracy (centimeter-level), and ultra-low-power (< 3 uW). 

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