8,573 research outputs found
Walking Speed Detection from 5G prototype System
While most RF-sensing approaches proposed in the literature rely on short-distance indoor point-to-point instrumentation, actual large-scale installation of RF sensing suggests the use of ubiquitously available cellular systems. In particular, the 5th generation of the wireless communication standard (5G) is envisioned as a universal communication means also for Internet of Things devices.
This thesis presents an investigation of device-free environmental perception capabilities in a 5G prototype system in two cases; walking speed and human presence detection, and elaborate a comparison with the former case and acceleration sensing analysis. This thesis attempts to analyze the perception capabilities of 5G system in order to recognize human mostly common activities and presence detection near transceiver devices which the instrumentation exploits a device-free system capable of detect activities without carrying devices capitalizing on environmental RF-noise. This is done via the study of existing and related literature. After that, the implementation and evaluation of walking speed and presence detection is described in details. In addition, evaluation consists of utilizing a prototypical 5G system with 52 OFDM carriers over 12.48 MHz bandwidth at 3.45 GHz, which we consider the impact of the number and choice of channels and compare the recognition performance with acceleration-based sensing. It was concluded that in realistic settings with five subjects, accurate recognition of activities and environmental situations can be a reliable implicit service of future 5G installations
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool
Accelerated by the increasing attention drawn by 5G, 6G, and Internet of
Things applications, communication and sensing technologies have rapidly
evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years.
Enabled by significant advancements in electromagnetic (EM) hardware, mmWave
and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz,
respectively, can be employed for a host of applications. The main feature of
THz systems is high-bandwidth transmission, enabling ultra-high-resolution
imaging and high-throughput communications; however, challenges in both the
hardware and algorithmic arenas remain for the ubiquitous adoption of THz
technology. Spectra comprising mmWave and THz frequencies are well-suited for
synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide
spectrum of tasks like material characterization and nondestructive testing
(NDT). This article provides a tutorial review of systems and algorithms for
THz SAR in the near-field with an emphasis on emerging algorithms that combine
signal processing and machine learning techniques. As part of this study, an
overview of classical and data-driven THz SAR algorithms is provided, focusing
on object detection for security applications and SAR image super-resolution.
We also discuss relevant issues, challenges, and future research directions for
emerging algorithms and THz SAR, including standardization of system and
algorithm benchmarking, adoption of state-of-the-art deep learning techniques,
signal processing-optimized machine learning, and hybrid data-driven signal
processing algorithms...Comment: Submitted to Proceedings of IEE
Flexible and scalable software defined radio based testbed for large scale body movement
Human activity (HA) sensing is becoming one of the key component in future healthcare system. The prevailing detection techniques for IHA uses ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concerns as well. This paper proposes a novel, non-invasive, easily-deployable, flexible and scalable test-bed for identifying large-scale body movements based on Software Defined Radios (SDRs). Two Universal Software Radio Peripheral (USRP) models, working as SDR based transceivers, are used to extract the Channel State Information (CSI) from continuous stream of multiple frequency subcarriers. The variances of amplitude information obtained from CSI data stream are used to infer daily life activities. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis and Naïve Bayes are used to evaluate the overall performance of the test-bed. The training, validation and testing processes are performed by considering the time-domain statistical features obtained from CSI data. The K-nearest neighbour outperformed all aforementioned classifiers, providing an accuracy of 89.73%. This preliminary non-invasive work will open a new direction for design of scalable framework for future healthcare systems
Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm
In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g.,
software-defined radios (SDR)-based methods have emerged as promising
candidates for intelligent remote sensing of human vitals, and could help in
containment of contagious viruses like covid19. To this end, this work utilizes
the universal software radio peripherals (USRP)-based SDRs along with classical
machine learning (ML) methods to design a non-contact method to monitor
different breathing abnormalities. Under our proposed method, a subject rests
his/her hand on a table in between the transmit and receive antennas, while an
orthogonal frequency division multiplexing (OFDM) signal passes through the
hand. Subsequently, the receiver extracts the channel frequency response
(basically, fine-grained wireless channel state information), and feeds it to
various ML algorithms which eventually classify between different breathing
abnormalities. Among all classifiers, linear SVM classifier resulted in a
maximum accuracy of 88.1\%. To train the ML classifiers in a supervised manner,
data was collected by doing real-time experiments on 4 subjects in a lab
environment. For label generation purpose, the breathing of the subjects was
classified into three classes: normal, fast, and slow breathing. Furthermore,
in addition to our proposed method (where only a hand is exposed to RF
signals), we also implemented and tested the state-of-the-art method (where
full chest is exposed to RF radiation). The performance comparison of the two
methods reveals a trade-off, i.e., the accuracy of our proposed method is
slightly inferior but our method results in minimal body exposure to RF
radiation, compared to the benchmark method
MIMOCrypt: Multi-User Privacy-Preserving Wi-Fi Sensing via MIMO Encryption
Wi-Fi signals may help realize low-cost and non-invasive human sensing, yet
it can also be exploited by eavesdroppers to capture private information. Very
few studies rise to handle this privacy concern so far; they either jam all
sensing attempts or rely on sophisticated technologies to support only a single
sensing user, rendering them impractical for multi-user scenarios. Moreover,
these proposals all fail to exploit Wi-Fi's multiple-in multiple-out (MIMO)
capability. To this end, we propose MIMOCrypt, a privacy-preserving Wi-Fi
sensing framework to support realistic multi-user scenarios. To thwart
unauthorized eavesdropping while retaining the sensing and communication
capabilities for legitimate users, MIMOCrypt innovates in exploiting MIMO to
physically encrypt Wi-Fi channels, treating the sensed human activities as
physical plaintexts. The encryption scheme is further enhanced via an
optimization framework, aiming to strike a balance among i) risk of
eavesdropping, ii) sensing accuracy, and iii) communication quality, upon
securely conveying decryption keys to legitimate users. We implement a
prototype of MIMOCrypt on an SDR platform and perform extensive experiments to
evaluate its effectiveness in common application scenarios, especially
privacy-sensitive human gesture recognition.Comment: IEEE S&P 2024, 19 pages, 22 figures, including meta reviews and
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