Indoor Localization System Using Uwb

Gnaś, Dominik; Adamkiewicz, Przemysław

doi:10.35784/iapgos.2895

Cited by 3 publications

(1 citation statement)

References 7 publications

(7 reference statements)

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“…It is readily available, and common, and is widely used in diagnosis and inspection. Of the numerous computed tomography methods based on physical phenomena such as electrical resistivity tomography (ERT) [4], electrical capacitance tomography (ECT) [3], electrical impedance tomography (EIT) [2], radio wave tomography and localization [5], X-ray tomography [6] or magnetism tomography [7], ultrasound tomography (UST) [8] is a commonly used method in medicine and industry [9,10]. UST is an imaging technique that exploits the properties of acoustic wave transmittance through tissues and materials [11].…”

Section: Introductionmentioning

confidence: 99%

Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography

et al. 2023

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This study addresses the issue of energy optimization by investigating solutions for the reduction of energy consumption in the diagnostics and monitoring of technological processes. The implementation of advanced process control is identified as a key approach for achieving energy savings and improving product quality, process efficiency, and production flexibility. The goal of this research is to develop a cost-effective system with a minimal number of ultrasound sensors, thus reducing the energy consumption of the overall system. To accomplish this, a novel method for obtaining high-resolution reconstruction in transmission ultrasound tomography (t-UST) is proposed. The method involves utilizing a convolutional neural network to take low-resolution measurements as input and output high-resolution sinograms that are used for tomography image reconstruction. This approach allows for the construction of a super-resolution sinogram by utilizing information hidden in the low-resolution measurement. The model is trained on simulation data and validated on real measurement data. The results of this technique demonstrate significant improvement compared to state-of-the-art methods. The study also highlights that UST measurements contain more information than previously thought, and this hidden information can be extracted and utilized with the use of machine learning techniques to further improve image quality and object recognition.

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Section: Introductionmentioning

confidence: 99%

Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography

et al. 2023

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CAZAC-based adaptative modulation scheme for the MB-OFDM UWBoF system

Xiang,

Zhou,

Tan

et al. 2024

Appl. Opt.

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A scheme that combines 256/128/64 quadrantal amplitude modulation (QAM) adaptive modulation with constant amplitude zero autocorrelation sequence (CAZAC) precoding in a multi-band orthogonal frequency division multiplexing (MB-OFDM) ultra-wideband over fiber (UWBoF) system is proposed in this paper. CAZAC precoding enables a relatively balanced signal-to-noise ratio (SNR) within each band, and the use of adaptive modulation optimizes the upper limit of transmission performance in each subband. Simulation results show that CAZAC precoding combined with 256/128/64QAM and conventional 64QAM reduces the peak-to-average power ratio (PAPR) by 1 dB and 0.5 dB, respectively. The proposed scheme achieves PAPR performance comparable to conventional 64QAM. It confirms that CAZAC precoding performs better in the reduction of PAPR and bit error rate (BER) when combined with the 256/128/64QAM scheme.

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Machine Learning-Enhanced Radio Tomographic Device for Energy Optimization in Smart Buildings

Styła¹,

Kiczek

Kłosowski

et al. 2022

Energies

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Smart buildings are becoming a new standard in construction, which allows for many possibilities to introduce ergonomics and energy savings. These contain simple improvements, such as controlling lights and optimizing heating or air conditioning systems in the building, but also more complex ones, such as indoor movement tracking of building users. One of the necessary components is an indoor localization system, especially without any device worn by the person being located. These types of solutions are important in locating people inside smart buildings, managing hospitals of the future and other similar institutions. The article presents a prototype of an innovative energy-efficient device for radio tomography, in which the hardware and software layers of the solution are presented. The presented example consists of 32 radio sensors based on a Bluetooth 5 protocol controlled by a central unit. The preciseness of the system was verified both visually and quantitatively by the image reconstruction as a result of solving the inverse tomographic problem using three neural networks.

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Indoor Localization System Using Uwb

Cited by 3 publications

References 7 publications

Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography

Energy Reduction with Super-Resolution Convolutional Neural Network for Ultrasound Tomography

CAZAC-based adaptative modulation scheme for the MB-OFDM UWBoF system

Machine Learning-Enhanced Radio Tomographic Device for Energy Optimization in Smart Buildings

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