The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal multiple access (NOMA) is one of the fundamental pieces of technology for solving the problem of limited bandwidth, and it is expected to be beneficial for many modern wireless underwater acoustic applications. NOMA downlink underwater acoustic communication (UWA) is accomplished by broadcasting data symbols from a source station to several users, which uses superimposed coding with variable power levels to enable detection through successive interference cancellation (SIC) receivers. Nevertheless, comprehensive information of the channel condition and channel state information (CSI) are both essential for SIC receivers, but they can be difficult to obtain, particularly in an underwater environment. To address this critical issue, this research proposes downlink underwater acoustic communication using a deep neural network utilizing a 1D convolution neural network (CNN). Two cases are considered for the proposed system in the first case: in the first case, two users with different power levels and distances from the transmitter employ BPSK and QPSK modulations to support multi-user communication, while, in the second case, three users employ BPSK modulation. Users far from the base station receive the most power. The base station uses superimposed coding. The BELLHOP ray-tracing algorithm is utilized to generate the training dataset with user depth and range modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. The DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in terms of BER in simulation results for all the modulation orders.

The early detection and diagnosis of cancer presents significant challenges in today’s healthcare. So, this research, suggests an original experimental biosensor for cell cancer detection using a corona-shaped metamaterial resonator. This resonator is designed to detect cancer markers with high sensitivity, selectivity, and linearity properties. By exploiting the unique properties of the corona metamaterial structure in the GHz regime, the resonator provides enhanced interaction of electromagnetic waves and improved detection skills. Through careful experimental, simulation, and optimization studies, we accurately demonstrate the resonator’s ability to detect cancer. The proposed detection system is capable of real-time non-invasive cancer detection, allowing for rapid intervention and better patient outcomes. The sensitivity value was confirmed through simulation, estimated at 0.1825 GHz/RIU. The results of two different simulation methods are used: the simulation software CST Studio Suite (version 2017) based on the finite element method (FEM), and the simulation software ADS (version 2019) based on the equivalent circuit method, thereby increasing confidence in the convergence of simulation and measurement results. This work opens new avenues for developing advanced detection technologies in the field of oncology, and paves the way for more effective cancer diagnosis. The experimental study verified that this realized sensor has very small frequency shifts, significantly small electrical dimension and miniaturization, high sensitivity, and good linearity. The suggested configurations showed a capacity for sensing cancer cells in the GHz regime.

This article presents a miniaturized antenna operating on ultra-wideband (UWB) spectrum having two reconfigurable notch bands. Initially, an antenna is designed for UWB applications, offering a 3.3–14.5 GHz impedance bandwidth. Subsequently, a semicircular stub and an arc-shaped slot are etched to achieve two distinguish notch bands at 3.7 and 8.1 GHz. PIN diodes are loaded onto the antenna to enable on-demand switching between notch bands. The final geometry of the antenna offers compact size of 15 mm × 20 mm × 1.6 mm and is designed using commercially available substrate material FR-4. The structure of antenna consists of a rectangular stub loaded into a circular patch, while an additional semicircular stub and arc-shaped slot are utilized to achieve notch bands. The antenna provides a moderate value of gain surpassing 2.5 dBi in the operational region. The antenna has the capability to operate between the UWB mode or the UWB mode having single or dual notch bands using two PIN diodes. To validate the simulated results, a hardware prototype of the design is fabricated and tested, yielding similar results obtained from software-based antenna. Finally, the antenna performance is compared with recently reported similar work in literature, demonstrating advantages such as miniaturization, wideband, moderate gain, and high efficiency along with on-demand switching characteristics while maintaining simple geometrical configuration. These characteristics highlight the scientific contribution of the proposed work along with making it a potential candidate for targeted applications.

Lattice-based post-quantum cryptography (PQC) algorithms demand number theoretic transform (NTT)-based polynomial multiplications. NTT-based polynomials’ multiplication relies on the computation of forward number theoretic transform (FNTT) and inverse number theoretic transform (INTT), respectively. Therefore, this work presents a unified NTT hardware accelerator architecture to facilitate the polynomial multiplications of the CRYSTALS-Kyber PQC algorithm. Moreover, a unified butterfly unit design of Cooley–Tukey and Gentleman–Sande configurations is proposed to implement the FNTT and INTT operations using one adder, one multiplier, and one subtractor, sharing four routing multiplexers and one Barrett-based modular reduction unit. The critical path of the proposed butterfly unit is minimized using pipelining. An efficient controller is implemented for control functionalities. The simulation results after the post-place and -route step are provided on Xilinx Virtex-6 and Virtex-7 field-programmable gate array devices. Also, the proposed design is physically implemented for validation on Virtex-7 FPGA. The number of slices utilized on Virtex-6 and Virtex-7 devices is 398 and 312, the required number of clock cycles for one set of FNTT and INTT computations is 1410 and 1540, and the maximum operating frequency is 256 and 290 MHz, respectively. The average figure of merit (FoM), where FoM is the ratio of throughput to slices, illustrates 62% better performance than the most relevant NTT design from the literature.

In this paper, a sensor with a multi-layered structure based on MXene and chalcogenide which is based on germanium (Ge)-antimony (Sb) telluride (Te) (GST) as an active plasmonic material is designed. The design is simulated in 2D. The design is a general sensor to detect refractive index from 1 to 1.15. Here, the design structure is arranged by placing the analyte on top. The middle metal layer is considered Ag/Au/Al/Cu to identify the effect of the metal layer on overall absorption and reflector. The GST layer is modeled with two distinct states: aGST and cGST, allowing for the analysis of the structure's tunable behavior based on the varying properties of these states. Optimisation and validation for parameters like height, width, and other metals, such as Ag/Au/Cu/Al processes, were conducted to ensure optimal device performance. The proposed refractive index sensor offers a high sensitivity variation ranging from 80 deg RIU⁻¹ to 770 deg/RIU. The bandwidth and resonant area show significant variation between the two states of GST material. In other words, the proposed refractive index sensor can be used as a lab-on-chip biosensor. This proposed sensor provides potential application in the research of biomolecular detection and chemical testing.