Browsing by Author "Konings D"

Now showing 1 - 4 of 4
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    Estimation of the Rod Velocity in Wood using Multi-frequency Guided Wave Measurements
    (Elsevier Ltd, 2023-01) Bakar AHA; Legg M; Konings D; Alam F
    This study presents a new approach for measuring the acoustic “rod velocity” in wood using guided wave measurements. The approach fits the acoustic guided wave longitudinal L(0,1) wave mode dispersion curve, through experimental guided wave phase velocity measurements taken over a range of frequencies. The rod velocity is obtained by measuring the phase velocity of the fitted L(0,1) wave mode dispersion curve at zero frequency. This technique is used to obtain rod velocity measurements for cylindrical wood and aluminium samples. The same approach was also performed on resonance measurements at a wide range of harmonics. These rod velocities are then compared to acoustic velocities obtained using the traditional time of flight and resonance methods.
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    Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks
    (MDPI AG, 23/09/2022) Konings D; Alam F; Faulkner N; de Jong C
    In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.
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    SpringLoc: A device-free localization technique for indoor positioning and tracking using adaptive RSSI spring relaxation
    (Institute of Electrical and Electronics Engineers (IEEE), 5/05/2019) Konings D; Alam F; Noble F; Lai E
    Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density.
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    The effects of dispersion on time-of-flight acoustic velocity measurements in a wooden rod
    (Elsevier BV, 2023-03) Bakar AHA; Legg M; Konings D; Alam F
    The stiffness of wood can be estimated from the acoustic velocity in the longitudinal direction. Studies have reported that stiffness measurements obtained using time-of-flight acoustic velocity measurements are overestimated compared to those obtained using the acoustic resonance and bending test methods. More research is needed to understand what is causing this phenomenon. In this work, amplitude threshold time-of-flight, resonance, and guided wave measurements are performed on wooden and aluminium rods. Using guided wave theory, it is shown through simulations and experimental results that dispersion causes an overestimation of time-of-flight measurements. This overestimation was able to be mitigated using dispersion compensation. However, other guided wave techniques could potentially be used to obtain improved measurements.