This paper presents the design of a novel low-profile ultra-wideband textile antenna for wearable microwave medical imaging systems. The proposed antenna is based on a monopole structure, where two triangles and a few parallel slots are cut at the bottom corners and top edge of the radiation patch, respectively, to achieve an optimized ultra-wide bandwidth and a reduced size of the antenna. Polyester fabrics and conductive copper taffeta, which are universally available on the market, are selected to construct the antenna. Thus, the fabricated prototypes well suit the needs of wearable applications, where flexible components are required to conform to the curved human bodies. The measured results show that the antenna prototype can realize a bandwidth of 109% from 1.198–4.055 GHz with a realized gain of 2.9 dBi. Furthermore, the on-phantom measurements show that there is limited effect on the operating bandwidth and realized gain of the antenna when it is working in the proximity to human bodies. The antenna is used to monitor the recovery process of a bone fracture that is emulated by a body-mimicking phantom with a size-varying blood strip. The time-domain reflection coefficient of the antenna varies significantly with the size of the fracture introduced, which demonstrates the applicability of the antenna for such use scenarios in microwave medical imaging.

Meta-surfaces, also known as reconfigurable intelligent surfaces (RISs), have emerged as a cost-effective, low-power consumption, and flexible solution for enabling multiple applications in Internet of Things (IoT). However, in the context of meta-surface-assisted multipair IoT communications, significant interference issues often arise among multiple channels. This issue is particularly pronounced in scenarios characterized by Line-of-Sight (LoS) conditions, where the channels exhibit low rank due to the significant correlation in propagation paths. These challenges pose a considerable threat to the quality of communication when multiplexing data streams. In this article, we introduce a meta-surface-aided communication scheme for multipair interactions in IoT environments. Inspired by holographic technology, a novel compensation method on the whole meta-surface has been proposed, which allows for independent multipair direct data streams transmission with low interference. To further reduce correlations under LoS channel conditions, we propose a vortex beam-based solution that leverages the low-correlation property between distinct topological modes. We use different vortex beams to carry distinct data streams, thereby enabling distinct receivers to capture their intended signal with low interference, aided by holographic meta-surfaces. Moreover, a prototype has been performed successfully to demonstrate a two-pair multinode communication scenario operating at 10 GHz with quadrature phase shift keying (QPSK)/16-QAM modulation. The experiment results demonstrate that, even under LoS conditions, the isolation between the two-pair channels exceeds 21 dB. This allows receiving users to undertake simultaneous, same-frequency multiplexed data transmission under extremely low-interference conditions, with a real-time demodulation bit error rate (BER) remaining below $3.8 \times 10^{-3}$ at achievable signal-to-noise ratio (SNR) conditions. Through the convergence of holographic meta-surfaces and vortex beams, we present a fresh perspective on achieving efficient, low-interference multipair IoT communications.

Wireless backhaul offers a more cost-effective, time-efficient, and reconfigurable solution than wired backhaul to connect the edge-computing cells to the core network. As the amount of transmitted data increases, the low-rank characteristic of Line-of-Sight (LoS) channel severely limits the growth of channel capacity in the point-to-point backhaul transmission scenario. Orbital Angular Momentum (OAM), also known as vortex beam, is considered a potentially effective solution for high-capacity LoS wireless transmission. However, due to the shortcomings of its energy divergence and the specificity of multi-mode divergence angles, OAM beams have been difficult to apply in practical communication systems for a long time. In this work, a novel multi-mode convergent transmission with co-scale reception scheme is proposed. OAM beams of different modes can be transmitted with the same beam divergent angle, while the wavefronts are tailored by the ring-shaped Airy compensation lens during propagation, so that the energy will converge to the same spatial area for receiving. Based on this scheme, not only is the Signal-to-Noise Ratio (SNR) greatly improved, but it is also possible to simultaneously receive and demodulate OAM channels multiplexed with different modes in a limited space area. Through prototype experiments, we demonstrated that 3 kinds of OAM modes are tunable, and different channels can be separated simultaneously with receiving power increasing. The measurement isolations between channels are over 11 dB, which ensures a reliable 16-QAM multiplexing wireless transmission demo system. This work may explore the potential applications of OAM-based multi-mode convergent transmission in LoS wireless communications.

The non-local-based model for hyperspectral image (HSI) denoising first uses non-local self-similarity (NSS) prior to group similar full-band patches into three-dimensional non-local full-band groups (tensors) using a block matching (BM) operation, and then a low-rank (LR) penalty is typically applied to each non-local full-band group to reduce noise. While non-local-based methods have shown promising performance in HSI denoising, most existing methods have only considered the LR property of the non-local full-band group while ignoring the strong correlation between sparse coefficients. Moreover, such methods often result in unsatisfactory visual artifacts due to the noise sensitivity of BM operations, while requiring expensive computations. To address these limitations, this paper proposes a novel non-local structured sparsity regularization (NLSSR) approach for HSI denoising. First, to mitigate the noise sensitivity of the BM operation, we propose a graph-based domain distance scheme to index similar full-band patches to form the non-local full-band group. Second, we design an adaptive unidirectional low-rank (LR) dictionary with low complexity that takes into account the differences in intrinsic structure correlation among different modes of the non-local full-band tensor. Third, we utilize a global spectral LR prior to reduce spectral redundancy. Fourth, we develop a generalized soft-thresholding (GST) algorithm based on the alternating minimization framework to solve the NLSSR-based HSI denoising problem. We perform extensive experiments on both simulated and real data to show that the proposed NLSSR algorithm outperforms many popular or state-of-the-art HSI denoising methods in both quantitative and visual evaluations.

Adaptive Digital Beamforming (DBF) Array is vital in advanced wireless systems. Beamforming problems in DBF arrays could be time consuming and inflexible as most of them are only solvable by optimization algorithms. In this work, we propose an improvement to this issue in solving wide nulling problems of an adaptive DBF array by building a data based wide nulling model using a powerful optimization algorithm - the Bat Algorithm (BA) with the General Regression Neural Network (GRNN). BA efficiently generates the necessary full complex weights as training samples to train the model. GRNN estimates the data in the gaps of training samples and gives an efficient and flexible estimation. A 32-element uniform linear array is used an example to demonstrate the proposed algorithm. Numerical experimental results show that the data based model is functional with a acceptable performance for all test cases and achieves a much better time efficiency in comparison to using solely BA in determining the complex weights to wide nulling problems.