Reflection and Transmission Properties of Common Construction Materials at 2.4 GHz Frequency

Koppel, Tarmo; Shishkin, Andrei; Haldre, Heldur; Toropovs, Nikolajs; Vilcane, Inese; Tint, Piia

doi:10.1016/j.egypro.2017.04.045

Cited by 33 publications

(9 citation statements)

References 14 publications

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“…The question is how transparent are various materials within the operational frequency range of a mm-wave radar sensor? Even though a few tabulations of material properties are available in the literature [ 62 , 63 ], to the best of our knowledge, none exist for a large variety of everyday materials we can find in our homes, offices and other environments. Future research should provide such tabulation values and explore how they could be applied to the on-object gesture detection scenarios, enabling more informed choices of object selection and perhaps, through distortion modelling, enhance system performance.…”

Section: Discussionmentioning

confidence: 99%

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing

Attygalle

Leiva

Kljun

et al. 2021

Sensors

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Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational capacity into objects, we can simply detect users’ gestures on them. However, gesture detection on such unmodified objects has to date been limited in the spatial resolution and detection fidelity. To address this gap, we conducted research on micro-gesture detection on physical objects based on Google Soli’s radar sensor. We introduced two novel deep learning architectures to process range Doppler images, namely a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet. The results show that our architectures enable robust on-object gesture detection, achieving an accuracy of approximately 94% for a five-gesture set, surpassing previous state-of-the-art performance results by up to 39%. We also showed that the decibel (dB) Doppler range setting has a significant effect on system performance, as accuracy can vary up to 20% across the dB range. As a result, we provide guidelines on how to best calibrate the radar sensor.

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Section: Discussionmentioning

confidence: 99%

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing

Attygalle

Leiva

Kljun

et al. 2021

Sensors

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“…To validate and properly map the RSS, the actual MS position has been calculated on two grids, consisting of 21 and 361 points, distributed in (3 × 7) and (19 × 19) grids, respectively. Furthermore, solid concrete material was used as walls material with a reflection coefficient of 0.35 at 2.4 GHz [39] during the RSS data analysis and calculations. The theoretical received power is calculated in this study, which is based on the dimensions and properties of the office of Advanced Communication Engineering (ACE) Centre at UniMAP, which was used for the measurement campaign.…”

Section: Rss Based Indoor Localizationmentioning

confidence: 99%

RSS-Based Indoor Localization System with Single Base Station

Al‐Bawri¹,

Islam²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

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The paper proposes an Indoor Localization System (ILS) which uses only one fixed Base Station (BS) with simple non-reconfigurable antennas. The proposed algorithm measures Received Signal Strength (RSS) and maps it to the location in the room by estimating signal strength of a direct line of sight (LOS) signal and signal of the first order reflection from the wall. The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office, sampling 21 different locations in the room. It is demonstrated the system can identify user's real-time location with a maximum estimation error below 0.7 m for 80% confidence Cumulative Distribution Function (CDF) user level, demonstrating the ability to accurately estimate the receiver's location within the room. The system is intended as a cost-efficient indoor localization technique, offering simplicity and easy integration with existing wireless communication systems. Unlike comparable single base station localization techniques, the proposed system does not require beam scanning, offering stable communication capacity while performing the localization process.

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“…where Γ i represents the reflection coefficient of the ith reflexive surface, which depends not only on the the material that composes the surface, but also on the carrier frequency [17]. Unless it is otherwise stated, we consider that Γ i = 1 ∀ i for the I active multipath components.…”

Section: Stretchermentioning

confidence: 99%

On the Achievable Capacity of MIMO-OFDM Systems in the CathLab Environment

Guerreiro

Dinis

Campos

2020

Sensors

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In the last years, the evolution of digital communications has been harnessed by medical applications. In that context, wireless communications are preferable over wired communications, as they facilitate the work of health technicians by reducing cabling on the stretchers. However, the use of wireless communications is challenging, especially when high data rates and low latencies are required. In those scenarios, multiple-input multiple-output (MIMO) techniques might have an important role, thanks to the high capacity gains that they can exhibit, which ideally increase with the MIMO size. In this work, we study the propagation scenario of a typical medical laboratory through ray-tracing techniques. By taking into account the derived channel model, we study the potential of MIMO techniques in an IEEE 802.11ax environment. Through a set of performance results regarding the system capacity, we show that the MIMO gains might not be as high as supposed in the medical laboratory, being far from the ideal scenario. Therefore, the large data rates required by the modern medical imaging applications might only be achieved with a combination of MIMO systems and large bandwidths.

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Reflection and Transmission Properties of Common Construction Materials at 2.4 GHz Frequency

Cited by 33 publications

References 14 publications

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing

RSS-Based Indoor Localization System with Single Base Station

On the Achievable Capacity of MIMO-OFDM Systems in the CathLab Environment

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