Interconnected microwave dielectric sensing nodes have the potential to revolutionize microwave material processing and design, where microwave dielectric materials characterization (MDMC) with high precision and rapid circuit design are crucial. This research presents an Internet of Things (IoT)-enabled automated MDMC system designed to tackle the non-linearity challenges in the extended cavity perturbation regime. Utilizing a cylindrical cavity operating in TE111 mode at 5 GHz, the proposed MDMC system is extensively trained on a diverse range of materials through numerous full-wave 3D electromagnetic simulations. The outputs, i.e., relative permittivity and loss tangent, are derived using advanced machine learning models, including Decision Tree (DT) and Ensemble Learning (EL). A comparative analysis that incorporates simulation, measurement, and predicted permittivity values across varying sample dimensions demonstrates the robustness and accuracy of the DT and EL model. This validates the effectiveness of our high-quality sensor node and sophisticated data processing techniques within an IoT-centric framework.

This paper presents the design of the near-field wireless power transfer (WPT) system capable of charging two independent loads simultaneously. It is imperative to note that it becomes possible by developing single-input multiple-output WPT, i.e., a single transmitter (Tx) and two receivers (Rxs). The developed Tx and Rx resonators possess small areas of 30-by-30 sq. mm and 30-by-13 sq. mm, respectively. The concurrent powering is achieved by coupling Tx and two Rxs at 27 mm. As a consequence, the obtained measurement results demonstrate power transfer efficiencies of 50% and 25%.

A millimeter-wave antenna with enhanced gain and efficiency is presented in this work. A decagonal-shaped patch antenna for the desired 28 GHz frequency is initially utilized, the gain of which is enhanced by the Fabry-Perot cavity technique. The metasurface used in the Fabry-Pérot cavity was placed at different points. The antenna gain and efficiency were initially measured as 4.45 dBi and 0.69, respectively. However, introducing a metasurface at a distance of 5.4 mm enhanced the gain and efficiency to 5.16 dBi and 0.24, respectively. As a result, the total maximum gain and efficiency achieved are 9.61 dBi and 0.93. A bandwidth of 1.45 GHz is also attained, which covers the 28 GHz frequency band. The antenna and metasurface are successfully fabricated and tested. The results validate that the proposed antenna can be used in 5G applications.

A systematic optimization of parameters related with Artificial Neural Network (ANN) is absolutely necessary to extract the best possible optimized ANN, therefore gaining traction for the development of behavioral models for advanced Radio Frequency (RF) and microwave components in the wireless industry. This paper develops and demonstrates a hybrid Slime Mould Algorithm (SMA)-ANN based modelling approach for fully printed Vanadium Dioxide (VO2) RF switches, which constitute a pivotal part for next-generation reconfigurable components. At first, ANN using cascade-forward neural network architecture is exploited to develop behavioral model for VO2switch. Thereafter, parameters of ANN are tuned with SMA optimization algorithm. Finally, both ANN and hybrid SMA-ANN approaches are compared with conventional regression-based metrics namely mean squared error, mean absolute error, coefficient of determination, simulation time, parameters' tuning time, complexity of the models and ability of the models to predict on untrained data to establish the pros and cons of each approach.

This paper develops and demonstrates an accurate and effective approach for small-signal modeling of Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT) using Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The basic working principle of ANFIS is elucidated. Here, ANFIS is applied on a GaN HEMT device characterized for a broad frequency range of 0.1 GHz - 20 GHz and drain voltage up to 30 V. The developed ANFIS based models have shown extremely good accuracy in terms of common regression metrics such as mean squared error, mean absolute error, coefficient of regression ($R^{2}$) etc., where$R^{2}$is achieved around %99.9 for both training and testing datasets. To verify the relevance of this paper with the state-of-the-art, the results of several artificial neural networks based small-signal models of GaN HEMTs under similar operating conditions is compared with the obtained results.

The accuracy and convergence of Artificial Neural Network (ANN) based models are contingent on initial weights as they employ backpropagation algorithm. To address these issues, this paper investigates and develops accurate Global Optimization (GO) assisted ANN based small-signal behavioral models for Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). Two potent explorer-exploiter frameworks-based optimization algorithms namely Grey Wolf Optimization-Particle Swarm Optimization (GWO-PSO) and Whale Optimization Algorithm (WOA) are utilized to fine-tune the initial weights of ANN. Thereafter, ANN, GWO-PSO- and WOA-assisted ANN based behavioral models are thoroughly examined and evaluated for various regression metrics. A strong correlation between the predicted and measured scattering parameters across the entire frequency spectrum is observed. We found both GO assisted ANN based models produced accurate models. However, GWO-PSO-ANN based models have shown better convergence behavior and at the same time the most accurate among all the tested models in this paper.

This article presents a thorough analysis of two distinct Physically Unclonable Functions (PUF), namely RO-PUF (Ring oscillator-based PUF) and RS-LPUF (RS Latch-based PUF), prototyped on FPGA. It is shown that the implemented PUFs possess significantly enhanced performance when compared to the state of the art. It is also identified that the enhancements are achieved through the incorporation of Programmable Delay Lines of FPGA Lookup Tables, the Temporal Majority Voting (TMV) scheme, and placed macro techniques for routing and placements of PUF units. The prototypes developed on Xilinx Artix-7 FPGAs are used for validation over the rated temperature range of 0-85° C with ±5% variation in the supply voltage. The proposed schemes when evaluated experimentally also achieve good uniformity, bit-aliasing, uniqueness, and reliability. Finally, it is shown that the proposed designs outperform the existing conventional PUFs in the area and speed tradeoff.

This paper develops and presents a CAD deployability of small-signal model of Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). First, a Gaussian Process Regression (GPR), a non-parametric probability-based method, is utilized to develop a small-signal model to effectively describe the behavior of the device. The model is developed to captures the bias and frequency dependence of GaN HEMT. The performance of the model is supplemented by advanced preprocessing and tuning of the hyperparameters of GPR approach. The tuning is done using 10-fold cross-validation error loss function. To examine the model's generalization ability the Mean squared error (MSE) metrics is used for both training and testing sets. Thereafter, the developed GPR based model is incorporated into CAD environment. Then the performance of the model is evaluated for stability and is also investigated for the usefulness of the model in class-F power amplifier design. The amplifier achieves excellent maximum available gain and small-signal voltage gain.

In this paper, we analyze the system performance of a large intelligent surface (LIS) enabled wireless system over Nakagami-m channels. We derive closed-form expressions of the outage probability, ergodic capacity, and average bit error rate using different approximation methods, namely, central limit theorem (CLT), Gamma and generalized-K$(K_{\mathrm{G}})$. The effects of a number of passive LIS pixels$(N)$and fading parameters on the system performance are examined. Results show that the Gamma and$K_{\mathrm{G}}$approximations are precise given different values of$N$, while the CLT approximation's accuracy depends on the number of LIS elements implemented. Finally, analytical findings are validated by thorough Monte Carlo simulations.

In this paper, the outage probability (OP) of the two-hop cooperative non-orthogonal multiple access network with an energy-constrained cooperative agent is evaluated over generalized α-µ and κ-µ fading models. The power splitting relaying protocol is implemented at the cooperative agent, acting as a relay in amplify-and-forward mode. The effect of hardware impairments (HIs) at the radio frequency front-ends of transceivers are incorporated during the performance evaluation. The results based on the derived analytical expressions suggest the importance of considering HIs for given network architecture and allow one to evaluate the OP over various statistical models.

This paper provides a performance evaluation of two-hop non-orthogonal multiple access (NOMA) architecture with energy harvesting (EH) cooperative agent and channel gains following the k-m fading, through analysis of ergodic capacity with respect to hardware impairments and channel conditions. The performance results of the distant user over two EH protocols, namely power splitting and time-switching relaying, are obtained and compared with simulation outcomes. The developed framework allows to evaluate the network under a range of external conditions and infers the importance of considering the hardware impairments.

Hardware-based security primitives play important roles in protecting and securing a system in Internet of Things (IoT) applications. The main primitives are physical unclonable functions (PUF) and true random number generator (TRNG) studied in this paper. Efficient FPGA implementation are proposed in the work along with relevant security analysis using prevalent metrics. Finally, an application of designed TRNG and PUF is proposed for implementing an authenticated key agreement protocol,

In this article, we present a high-performance hardware architecture for Elliptic curve based (authenticated) key agreement protocol “Elliptic Curve Menezes, Qu and Vanstone” (ECMQV) over Binary Edwards Curve (BEC). We begin by analyzing inversion module on a 251-bit binary field. Subsequently, we present Field Programmable Gate Array (FPGA) implementations of the unified formula for computing elliptic curve point addition on BEC in affine and projective coordinates and investigate the relative performance of these two coordinates. Then, we implement the w -coordinate based differential addition formulae suitable for usage in Montgomery ladder. Next, we present a novel hardware architecture of BEC point multiplication using mixed w -coordinates of the Montgomery laddering algorithm and analyze it in terms of resistance to Simple Power Analysis (SPA) attack. In order to improve the performance, the architecture utilizes registers efficiently and uses efficient scheduling mechanisms for the BEC arithmetic implementations. Our implementation results show that the proposed architecture is resistant against SPA attack and yields a better performance when compared to the existing state-of-the-art BEC designs for computing point multiplication (PM). Finally, we present an FPGA design of ECMQV key agreement protocol using BEC defined over GF(2 251 ). The execution of ECMQV protocol takes 66.47μs using 32,479 slices on Virtex-4 FPGA and 52.34μs using 15,988 slices on Virtex-5 FPGA. To the best of our knowledge, this is the first FPGA design of the ECMQV protocol using BEC.

In advanced technology nodes, the process variations deteriorate SRAM performance and greatly affect yield. It is necessary to formulate yield estimation models to optimize SRAMs and effectively trade-off area, performance and robustness. We propose models that in addition to enabling yield estimates also enable evaluation of lowering minimum operational voltage (VDDMIN). We present a quantitative analysis for SNM limited SRAM yield using Design of Experiments (DOE) method. The proposed framework for yield based design can also utilize recovery techniques like Error Correcting Codes (ECC) and redundancy and quantifies yield, area, and VDDMIN improvements. We also present a case study that trades-off ECC recovery budget, VDDMIN and area gain. We show 25% improvement in area and VDDMIN lowering by 300mV at constant yield levels by using 50% of ECC recovery budget.

Efficient spectrum usage is an issue that has been inviting a lot of interest and research in recent times, due to the omnipresence of wireless technologies all around us. Spectrum sensing is a key step towards efficient spectrum usage. Energy detection is a fast and simple method for spectrum sensing, but the sensing precision is limited by the dependency on a threshold value. This paper reports a novel real time energy detection based spectrum sensing technique using a logistic regression classifier. The implementation is done using USRP and GNU-Radio, and achieves a classification accuracy of 98.6% on a dataset that was collected over commercial FM band.