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Nuclear radiation shielding capabilities for a glass series 20Bi 2 O 3  − xPbO − (80 − 2x)B 2 O 3  − xGeO 2 (where x = 5, 10, 20, and 30 mol%) have been investigated using the Phy-X/PSD software and Monte Carlo N-Particle transport code. The mass attenuation coefficients (μ m ) of selected samples have been estimated through XCOM dependent Phy-X/PSD program and MCNP-5 code in the photon-energy range 0.015–15 MeV. So obtained μ m values are used to calculate other γ-ray shielding parameters such as half-value layer (HVL), mean-free-path (MFP), etc. The calculated μ m values were found to be 71.20 cm 2 /g, 76.03 cm 2 /g, 84.24 cm 2 /g, and 90.94 cm 2 /g for four glasses S 1 to S 4 , respectively. The effective atomic number (Z eff )values vary between 69.87 and 17.11 for S 1 or 75.66 and 29.11 for S 4 over 0.05–15 MeV of photon-energy. Sample S4, which has a larger PbO/GeO 2 of 30 mol% in the bismuth-borate glass, possesses the lowest MFP and HVL, providing higher radiation protection efficiency compared to all other combinations. It shows outperformance while compared the calculated parameters (HVL and MFP) with the commercial shielding glasses, different alloys, polymers, standard shielding concretes, and ceramics. Geometric Progression (G-P) was applied for evaluating the energy absorption and exposure buildup factors at energies 0.015–15 MeV with penetration depths up to 40 mfp. The buildup factors showed dependence on the MFP and photon-energy as well. The studied samples' neutron shielding behavior was also evaluated by calculating the fast neutron removal cross-section (Σ R ), i.e. found to be 0.139 cm −1 for S 1 , 0.133 cm −1 for S 2 , 0.128 cm −1 for S 3, and 0.12 cm −1 for S 4 . The results reveal a great potential for using a glass composite sample S4 in radiation protection applications.

Autonomous vehicles are regarded as future transport mechanisms that drive the vehicles without the need of drivers. The photonic-based radar technology is a promising candidate for delivering attractive applications to autonomous vehicles such as self-parking assistance, navigation, recognition of traffic environment, etc. Alternatively, microwave radars are not able to meet the demand of next-generation autonomous vehicles due to its limited bandwidth availability. Moreover, the performance of microwave radars is limited by atmospheric fluctuation which causes severe attenuation at higher frequencies. In this work, we have developed coherent-based frequency-modulated photonic radar to detect target locations with longer distance. Furthermore, the performance of the proposed photonic radar is investigated under the impact of various atmospheric weather conditions, particularly fog and rain. The reported results show the achievement of significant signal to noise ratio (SNR) and received power of reflected echoes from the target for the proposed photonic radar under the influence of bad weather conditions. Moreover, a conventional radar is designed to establish the effectiveness of the proposed photonic radar by considering similar parameters such as frequency and sweep time.

Inter-satellite optical wireless communication (Is-OWC) links can become promising solutions to realize the next-generation high-speed communication services. The operation of Global Navigation Satellite Systems can be improved with the use of Is-OWC links through ranging and communication services. However, the key challenge in Inter-satellite link (ISL) is its effective range which is limited due to pointing errors. In this work, we propose to develop a high-capacity and long-reach Is-OWC link by incorporating hybrid mode division multiplexing (MDM) and wavelength division multiplexing (WDM) schemes to transmit ten independent channels over 40000kms Is-OWC link. Each channel is capable of carrying 400Gbps data which is encoded by the dual polarization quadrature phase shift key technique with required signal to noise ratio (SNR) and received power. The proposed Is-OWC link satisfies the enhanced communication within Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites. The proposed Is-OWC is further evaluated under the impact of space turbulences, particularly transmitter and receiver pointing errors. The result reported that the proposed Is-OWC link can transmit 4Tbps data over 16000kms with the transmitter pointing error of 2μrad and receiver pointing error of 1μrad.

In this article, a new method is developed to design a three-band miniaturized bandpass filter (BPF) that uses two asymmetrically coupled resonators with one step discontinuity and open-circuited uniform impedance resonator (UIR) to achieve Global Interoperability with Microwave Access (WiMAX) and Radio Frequency Identification (RFID) applications. First, a pair of asymmetrical step impedance resonators (ASIR) is used to implement a dual band filter, then a half wavelength uniform impedance resonator is added below to the transmission line to achieve a triple band response. The proposed filter resonates at frequencies of 3.7 GHz, 6.6 GHz, and 9 GHz with the fractional bandwidth of 7.52%, 5.1%, and 4.44%, respectively. By adjusting the physical length ratio (α) and the impedance ratio (R) of the asymmetric SIR, the proposed fundamental frequencies of the triple BPF are obtained. Moreover, the coupling coefficient (K e ) and external quality factor (Q e ) are investigated between the resonators and the input/output ports of the transmission line and are calculated using full-wave EM simulator HFSS. In addition, five transmission zeros are introduced near the passbands to increase the filter selectivity. Finally, the proposed filter is designed and fabricated with a size of 13.69 × 25 mm (0.02 λ g × 0.03 λ g ), where λ g represents the guiding wavelength in the first passband. The simulated and measured results have a good correspondence, thus confirming the design concept.

Unmanned aerial vehicle (UAV) antenna tracking system is an electromechanical component designed to track and steer the signal beams from the ground control station (GCS) to the airborne platform for optimum signal alignment. In a tracking system, an antenna continuously tracks a moving target and records their position. A UAV tracking antenna system is susceptible to signal loss if omnidirectional antenna is deployed as the preferred design. Therefore, to achieve longer UAV distance communication, there is a need for directional high gain antenna. From design principle, directional antennas are known to focus their signal energy in a particular direction viewed from their radiation pattern which is concentrated in a particular azimuth direction. Unfortunately, a directional antenna is limited by angle, thus, it must always be directed to the target. The other limitation of a UAV mechanical beam steering system is that the system is expensive to maintain and with low reliability. To solve this problem, we are proposing the use of MIMO technology as a readily available technology for UAV beyond line of sight technology. Although UAV antenna tracking is domiciled in the mechanical beam steering arrangement, this study shows that this native technology could be usurped by MIMO beam forming.

This paper presents a proposed low-noise and high-sensitivity Internet of Thing (IoT) system based on an M&NEMS microphone. The IoT device consists of an M&NEMS resistive accelerometer associated with an electronic readout circuit, which is a silicon nanowire and a Continuous-Time (CT) ΔΣ ADC. The first integrator of the ΔΣ ADC is based on a positive feedback DC-gain enhancement two-stage amplifier due to its high linearity and low-noise operations. To mitigate both the offset and 1/f noise, a suggested delay-time chopper negative-R stabilization technique is applied around the first integrator. A 65-nm CMOS process implements the CT ΔΣ ADC. The supply voltage of the CMOS circuit is 1.2-V while 0.96-mW is the power consumption and 0.1-mm 2 is the silicon area. The M&NEMS microphone and ΔΣ ADC complete circuit are fabricated and measured. Over a working frequency bandwidth of 20-kHz, the measurement results of the proposed IoT system reach a signal to noise ratio (SNR) of 102.8-dB. Moreover, it has a measured dynamic range (DR) of 108-dB and a measured signal to noise and distortion ratio (SNDR) of 101.3-dB.

In this article, a new method is developed to design a three-band miniaturized bandpass filter (BPF) that uses two asymmetrically coupled resonators with one step discontinuity and open-circuited uniform impedance resonator (UIR) to achieve Global Interoperability with Microwave Access (WiMAX) and Radio Frequency Identification (RFID) applications. First, a pair of asymmetrical step impedance resonators (ASIR) is used to implement a dual band filter, then a half wavelength uniform impedance resonator is added below to the transmission line to achieve a triple band response. The proposed filter resonates at frequencies of 3.7 GHz, 6.6 GHz, and 9 GHz with the fractional bandwidth of 7.52%, 5.1%, and 4.44%, respectively. By adjusting the physical length ratio (α) and the impedance ratio (R) of the asymmetric SIR, the proposed fundamental frequencies of the triple BPF are obtained. Moreover, the coupling coefficient (Ke) and external quality factor (Qe) are investigated between the resonators and the input/output ports of the transmission line and are calculated using full-wave EM simulator HFSS. In addition, five transmission zeros are introduced near the passbands to increase the filter selectivity. Finally, the proposed filter is designed and fabricated with a size of 13.69 × 25 mm (0.02 λg × 0.03 λg), where λg represents the guiding wavelength in the first passband. The simulated and measured results have a good correspondence, thus confirming the design concept.

Diabetes is one of the most common metabolic diseases that cause high blood sugar. Early diagnosis of such a condition is challenging due to its complex interdependence on various factors. There is a need to develop critical decision support systems to assist medical practitioners in the diagnosis process. This research proposes developing a predictive model that can achieve a high classification accuracy of type 2 diabetes. The study consisted of two fundamental parts. Firstly, the study investigated handling missing data adopting data imputation, namely, median value imputation, K-nearest neighbor imputation, and iterative imputation. Consequently, the study validated the implications of these imputations using various classification algorithms, i.e., linear, tree-based, and ensemble algorithms, to see how each method affected classification accuracy. Secondly, Artificial Neural Network was employed to model the best performing imputed data, balanced with SMOTETomek ensuring each class is represented fairly. This approach provided the best accuracy of 98% on the test data, outperforming accuracies achieved in prior studies using the same dataset. The dataset used in this study is concerned with gender and population. As a prospect, the study recommends adopting a larger population sample without geographic boundaries. Additionally, as the developed Artificial Neural Network model did not undergo any specific hyperparameter tuning, it would be interesting to explore tuning on top of normalized data to optimize accuracy further.

Millimetre-wave frequencies are promising for sensitive detection of glucose levels in the blood, where the temperature effect is insignificant. All these features provide the feasibility of continuous, portable, and accurate monitoring of glucose levels. This paper presents a metamaterial-inspired resonator comprising five split-rings to detect glucose levels at 24.9 GHz. The plexiglass case containing blood is modelled on the sensor’s surface and the structure is simulated for the glucose levels in blood from 50 mg/dl to 120 mg/dl. The novelty of the sensor is demonstrated by the capability to sense the normal glucose levels at millimetre-wave frequencies. The dielectric characteristics of the blood are modelled by using the Debye parameters. The proposed design can detect small changes in the dielectric properties of blood caused by varying glucose levels. The variation in the transmission coefficient for each glucose level tested in this study is determined by the quality factor and resonant frequency. The sensor presented can detect the change in the quality factor of transmission response up to 2.71/mg/dl. The sensor’s performance has also been tested to detect diabetic hyperosmolar syndrome. The sensor showed a linear shift in resonant frequency with the change in glucose levels, and an R 2 of 0.9976 was obtained by applying regression analysis. Thus, the sensor can be used to monitor glucose in a normal range as well as at extreme levels.

In present times, a massive quantity of big data has been generated by the Internet of Things (IoT) devices for a wide range of applications. The IoT devices generate an enormous data quantity that is troublesome for data processing and analytics functionalities, which is effortlessly managed by the cloud before the explosive development of the IoT. Specifically, the big IoT data analytics by mobile edge computing (MEC) becomes a hot research topic and needs comprehensive research works for intelligent decision making. This paper introduces a new generative adversarial network (GAN) with a quantum elephant herd optimization (QEHO) algorithm for MEC in IoT enabled big data environment called GAN-QEHO. The presented GAN-QEHO algorithm follows two-stage processes, namely feature selection (FS) and data classification. The QEHO algorithm is used to elect an optimal feature subset for the FS process. By the quantization of elephant individuals, the search scope of feature space can be enhanced, and an optimal tradeoff has been attained among exploration and exploitation. Then, the GAN model is employed for the classification process to identify different class labels. In order to validate the experimental results analysis of the GAN-QEHO algorithm, a series of simulations take place in terms of diverse aspects.