None-invasive blood glucose measurement enables effective diagnosis and treatment of diabetic patients. The existing microwave sensors suffer from low sensitivity and large size. This article presents a highly sensitive and compact size non-invasive microwave sensor for real-time blood glucose measurements. We have developed a new sensing technique using directly connected Branch Line Coupler and Split Ring Resonators. This technique significantly improves the sensitivity by mitigating the limited coupling between transmission lines and resonators. In addition, using Split Ring Resonators reduces the sensor size due to capacitive and inductive effects, which are loaded to the Branch Line Coupler. The proposed sensing function is based on shifting the transmission notch frequency in coupled arm of the Branch Line Coupler, which is caused by loading the Split Ring Resonators with varying glucose concentration. A prototype sensor is fabricated and successfully tested using several glucose concentrations in deionized water. Experimental results demonstrate 0.72 MHz/mgdL-1 measurement sensitivity, which is higher in comparison with available sensors in PCB technology. The prototype sensor size is 3.5×3.5×0.16 cm3.

Recently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals’ energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals’ transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks.

Radio frequency fingerprinting (RFF) is a promising physical layer protection technique which can be used to defend wireless networks from malicious attacks. It is based on the use of the distinctive features of the physical waveforms (signals) transmitted from wireless devices in order to classify authorized users. The most important requirement to develop an RFF method is the existence of a precise, robust, and extensive database of the emitted signals. In this context, this paper introduces a database consisting of Bluetooth (BT) signals collected at different sampling rates from 27 different smartphones (six manufacturers with several models for each). Firstly, the data acquisition system to create the database is described in detail. Then, the two well-known methods based on transient BT signals are experimentally tested by using the provided data to check their solidity. The results show that the created database may be useful for many researchers working on the development of the RFF of BT devices.

A novel dual band, highly sensitive Substrate Integrated Waveguide (SIW) sensor for permittivity measurements is presented. A pair of modified Complementary Split Ring Resonators (CSRRs) is etched on SIW surface. CSRRs are located in the center of SIW, where the electric field distribution is high so that the coupling be maximized. The coupling between the SIW and the CSRRs as well as the adjacent CSRRs results in two notches in transmission coefficient. These notches vary with the dielectric loading on the sensor. The ratio of a notch variation to the load permittivity variation determines the sensitivity of proposed sensor. Two sensitivities proportional to two notches are provided. Normalized sensitivities from both notches show identical values. Therefore, any environmental effect have the same variation on the TZs. This demonstrates the potential of the proposed sensor for differential operation that can mitigate the effect of environmental condition. The size of the proposed sensor is small as the inductive and the capacitive effects of CSRRs forced the SIW to operate below the cut off frequency at negative-order-resonance mode. All design steps including SIW design, CSRRs design and modified CSRRs effects are presented in details. The sensor operation principle is described through an equivalent circuit model as well as simulation results. The experimental results indicates that the normalized sensitivity is 3.4% which is much higher than similar sensors. The prototype sensor size (27.8×18.4×0.508 mm3) is smaller than those reported in the literature.

In this study, a novel method is proposed to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems. In the study, phase-sensitive optical time-domain reflectometry (phase-OTDR) is realized for the sensing system. The proposed method is consisted of three main stages. In the first stage, Wavelet denoising method is applied for noise reduction in the measured signal, and difference in time domain approach is used to perform high-pass filtering. Autocorrelation is then used for comparing the signal with itself over time in each bin to remove uncorrelated signals. Next, the power of the correlated signals at each bin is calculated and sorted where maximum valued bins are considered as the event signal. In the second stage, Variational Mode Decomposition (VMD) technique is used to decompose the detected event signals into a series of band-limited modes from which the event signals are reconstructed. From the reconstructed event signals, higher order statistical (HOS) features including variance, skewness, and kurtosis are extracted. In the last stage, the threats are discriminated by implementing Linear Support Vector Machine (LSVM)-based classification approach to the extracted features. In order to evaluate the effects of proposed method on the classification performance, different types of activities such as digging with hammer, pickaxe, and shovel collected from various points of a buried fiber optic cable have been used under different Signal-to-Noise Ratio (SNR) levels (−4 to −18 dB). It has observed that the classification accuracy at high/moderate (−4 to −8 dB) and low (−8 to −18 dB) SNR levels are 79.5% and 75.2%, respectively. To the best of authors’ knowledge, this research study is the first report to use VMD technique for threat classification in phase-OTDR-based DAS systems.

Radio frequency fingerprinting (RFF) is based on identification of unique features of RF transient signals emitted by radio devices. RF transient signals of radio devices are short in duration, non-stationary and nonlinear time series. This paper evaluates the performance of RF fingerprinting method based on variational mode decomposition (VMD). For this purpose, VMD is used to decompose Bluetooth (BT) transient signals into a series of band-limited modes, and then, the transient signal is reconstructed from the modes. Higher order statistical (HOS) features are extracted from the complex form of reconstructed transients. Then, Linear Support Vector Machine (LVM) classifier is used to identify BT devices. The method has been tested experimentally with BT devices of different brands, models and series. The classification performance shows that VMD based RF fingerprinting method achieves better performance (at least 8% higher) than time-frequency-energy (TFED) distribution based methods such as Hilbert-Huang Transform. This is demonstrated with the same dataset but with smaller number of features (nine features) and slightly lower (2-3 dB) SNR levels.