Enhancing OFDM with index modulation using heuristic geometric constellation shaping and generalized interleaving for underwater VLC.

In this paper, we propose and demonstrate enhanced orthogonal frequency division multiplexing with index modulation (OFDM-IM) schemes for bandlimited underwater visible light communication (UVLC) systems via geometric constellation shaping (GCS) and subblock interleaving. Specifically, two heuristic GCS approaches based on particle swarm optimization (PSO) and hybrid genetic algorithm-PSO (GA-PSO) algorithms are proposed to generate IM-preferable constellations. Moreover, a generalized interleaving technique is further proposed to overcome the low-pass effect of bandlimited UVLC systems, where an optimal step size can be obtained to perform subblock interleaving. Simulation and experiments are conducted to evaluate the performance of the proposed enhanced OFDM-IM schemes in bandlimited UVLC systems, where both OFDM with single-mode index modulation (OFDM-SM) and OFDM with dual-mode index modulation (OFDM-DM) schemes are considered. The experimental results demonstrate remarkable signal-to-noise ratio (SNR) gains of 1.3 and 1.9 dB for OFDM-SM and OFDM-DM in comparison to the benchmark schemes, respectively.

Graphene-based Pancharatnam-Berry phase metasurface in the terahertz domain for dynamically independent amplitude and phase manipulation.

Dynamic and independent amplitude and phase manipulation are the paramount demand for many advanced wavefronts engineering applications. Currently, the coupling issue between the amplitude and phase hinders the efficient modulation wavefront's further implementation. This paper proposes and numerically demonstrates the bi-layer stacked graphene Pancharatnam-Berry (P-B) phase metasurface and mono-layer graphene P-B phase metasurface to address the above problem. The simulation results show that the proposed models can achieve the independent control amplitude and phase and significantly reduce their coupling strength. Our findings offer a flexible and straightforward method for precise wave reconstruction applications such as holography, optical tweezers, and high-resolution imaging.

Ultra-broadband Pancharatnam-Berry phase metasurface for arbitrary rotation of linear polarization and beam splitter.

A systematic study of a robust angular tolerance ultra-broadband metasurface for arbitrary rotation of linear polarization is demonstrated. The proposed method combines the spin-dependent Pancharatnam-Berry phase and the generalized Snell's law to achieve an arbitrary angle linear polarization rotator and beam splitter. Numerical results of one terahertz example show that a 90° polarization rotator has a polarization conversion ratio of more than 90% from 1.3 to 2.3 THz in the ultra-broadband range. This method represents a significant advance in versatile, flexible design and performance compared to previously reported birefringent material wave plates, grating structures, and multi-resonance-based polarization rotators.

Terahertz quasi non-diffraction Bessel vortex beam generation using three lattice types reflective metasurface.

Bandwidth, orbital-angular momentum (OAM) divergence, and mode purity are the three critical issues for the practical terahertz orbital angular momentum manipulation, especially in the next sixth-generation (6G) communication system. Here we propose the broadband high-order Bessel vortex beam carrying multiple OAM modes reflective metasurface in the terahertz domain. The simulation results agree with the theoretical expectation, and the diffracting divergence of OAM vortex beam characteristics has been alleviated. The research on the relationship between the varieties of lattice type and mode purity is also relatively scarce. Henceforth, a comparison study has been conducted between three lattice types, i.e., square lattice, triangular lattice, and concentric ring lattice. And corresponding results of the relationship of mode purity with those lattice types show that the concentric ring lattice has the best performance.

Unravelling the origin of long-term stability for Cs+ and Sr2+ solidification inside sodalite.

Cesium (Cs+) and strontium (Sr2+) ions are the main fission byproducts in the reprocessing of spent nuclear fuels for nuclear power plants. Their long half-live period (30.17 years for 137Cs and 28.80 years for 90Sr) makes them very dangerous radionuclides. Hence the solidification of Cs+ and Sr2+ is of paramount importance for preventing them from entering the human food chain through water. Despite tremendous efforts for solidification, the long-term stability remains a great challenge due to the experimental limitation and lack of good evaluation indicators for such long half-life radionuclides. Using density functional theory (DFT), we investigate the origin of long-term stability for the solidification of Cs+ and Sr2+ inside sodalite and establish that the exchange energy and the diffusion barrier play an important role in gaining the long-term stability both thermodynamically and kinetically. The acidity/basicity, solvation, temperature, and diffusion effect are comprehensively studied. It is found that solidification of Cs+ and Sr2+ is mainly attributed to the solvation effect, zeolitic adsorption ability, and diffusion barriers. The present study provides theoretical evidence to use geopolymers to adsorb Cs+ and Sr2+ and convert the adsorbed geopolymers to zeolites to achieve solidification of Cs+ and Sr2+ with long-term stability.

Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications.

Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite's precoding matrix and IRS's phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of IRS due to the high mobility of LEO and the passive nature of reflective elements. Moreover, most conventional solution algorithms suffer from high computational complexity and are not applicable to these dynamic scenarios. A robust beamforming design based on graph attention networks (RBF-GAT) is proposed to establish a direct mapping from the received pilots and dynamic network topology to the satellite and IRS's beamforming, which is trained offline using the unsupervised learning approach. The simulation results corroborate that the proposed RBF-GAT approach can achieve more than 95% of the performance provided by the upper bound with low complexity.

Discretization of two-dimensional Luneburg lens based on the correctional effective medium theory.

The Luneburg lens is widely applied in both the optical and microwave regimes because it offers high gain and a wide beam-scanning range. However, Luneburg lens typically suffer from low efficiency which is caused by the dielectric loss of medium employed. To address this issue, we propose herein a general method for discretization of two-dimensional Luneburg lens based on correctional effective-medium theory. In discrete Luneburg, the efficiency is not dependent on the employed medium roughly because that the main component in the lens is air, resulting into a significant improvement of efficiency. Subsequently, a systemic study of lens discretization is presented, which is validated by a discrete Luneburg lens easily fabricated by using 3D printing. In addition, a novel wave-patch reduction feature allows the discrete lens to function as well. This work presents a fundamental theory for lens discretization, which is valid not only for the Luneburg lens but also for other types of lenses. It can be applied in imaging, antennas, or phase manipulation in both the optical and microwave bands.

An Electrically Tunable Dual-Wavelength Refractive Index Sensor Based on a Metagrating Structure Integrating Epsilon-Near-Zero Materials.

In this paper, a reconfigurable sensing platform based on an asymmetrical metal-insulator-metal stacked structure integrating an indium tin oxide (ITO) ultrathin film is proposed and investigated numerically. The epsilon-near-zero (ENZ) mode and antisymmetric mode can be resonantly excited, generating near-perfect absorption of over 99.7% at 1144 and 1404 nm, respectively. The absorptivity for the ENZ mode can be modulated from 90.2% to 98.0% by varying the ENZ wavelength of ITO by applying different voltages. To obtain a highly sensitive biosensor, we show that the proposed structure has a full-width at half-maximum (FWHM) of 8.65 nm and a figure-of-merit (FOM) of 24.7 with a sensitivity of 213.3 nm/RI (refractive index) for the glucose solution. Our proposed device has potential for developing tunable biosensors for real-time health monitoring.

Dual-Axis Metasurface Strain Sensor Based on Polarization-Phase-Deformation Relationship.

Herein, we propose an approach for sensitivity improvement of dual-axis strain sensing using the property of a metasurface (MS) that the phase response shifts sharply with the MS deformation. A feasible approach for phase measurement is first demonstrated by calculating multi-polarized reception when the incident electromagnetic (EM) wave has anisotropic phase values. A flexible MS consisting of periodically arranged lantern-shaped elements is designed and fabricated for dual-axis strain sensing and evaluation based on the proposed method. The simulation and measurement results demonstrated a high sensitivity of the proposed MS for strain sensing in the microwave band. The method can be used potentially in both pressure and tensile sensing. Moreover, the operational frequency can be extended to the THz range and even to the optical band.

[Human activity recognition based on the inertial information and convolutional neural network].

With the rapid improvement of the perception and computing capacity of mobile devices such as smart phones, human activity recognition using mobile devices as the carrier has been a new research hot-spot. The inertial information collected by the acceleration sensor in the smart mobile device is used for human activity recognition. Compared with the common computer vision recognition, it has the following advantages: convenience, low cost, and better reflection of the essence of human motion. Based on the WISDM data set collected by smart phones, the inertial navigation information and the deep learning algorithm-convolutional neural network (CNN) were adopted to build a human activity recognition model in this paper. The K nearest neighbor algorithm (KNN) and the random forest algorithm were compared with the CNN network in the recognition accuracy to evaluate the performance of the CNN network. The classification accuracy of CNN model reached 92.73%, which was much higher than KNN and random forest. Experimental results show that the CNN algorithm model can achieve more accurate human activity recognition and has broad application prospects in predicting and promoting human health.

Large Scale Triboelectric Nanogenerator and Self-Powered Flexible Sensor for Human Sleep Monitoring.

The triboelectric nanogenerator (TENG) and its application as a sensor is a popular research subject. There is demand for self-powered, flexible sensors with high sensitivity and high power-output for the next generation of consumer electronics. In this study, a 300 mm × 300 mm carbon nanotube (CNT)-doped porous PDMS film was successfully fabricated wherein the CNT influenced the micropore structure. A self-powered TENG tactile sensor was established according to triboelectric theory. The CNT-doped porous TENG showed a voltage output seven times higher than undoped porous TENG and 16 times higher than TENG with pure PDMS, respectively. The TENG successfully acquired human motion signals, breath signals, and heartbeat signals during a sleep monitoring experiment. The results presented here may provide an effective approach for fabricating large-scale and low-cost flexible TENG sensors.

Fast 2D DOA Estimation Algorithm by an Array Manifold Matching Method with Parallel Linear Arrays.

In this paper, the problem of two-dimensional (2D) direction-of-arrival (DOA) estimation with parallel linear arrays is addressed. Two array manifold matching (AMM) approaches, in this work, are developed for the incoherent and coherent signals, respectively. The proposed AMM methods estimate the azimuth angle only with the assumption that the elevation angles are known or estimated. The proposed methods are time efficient since they do not require eigenvalue decomposition (EVD) or peak searching. In addition, the complexity analysis shows the proposed AMM approaches have lower computational complexity than many current state-of-the-art algorithms. The estimated azimuth angles produced by the AMM approaches are automatically paired with the elevation angles. More importantly, for estimating the azimuth angles of coherent signals, the aperture loss issue is avoided since a decorrelation procedure is not required for the proposed AMM method. Numerical studies demonstrate the effectiveness of the proposed approaches.

A CSRR-Fed SIW Cavity-Backed Fractal Patch Antenna for Wireless Energy Harvesting and Communication.

This paper presents a novel compact dual-band and dual-polarized complementary split-ring resonator (CSRR)-fed substrate-integrated waveguide (SIW) cavity-backed fractal patch antenna for wireless energy harvesting and communication. The proposed antenna is composed of a Giuseppe Peano fractal radiation patch with a backed SIW cavity. To enhance the bandwidth and minimize the dimensions, the CSRR structure is designed to feed the Giuseppe Peano fractal patch orthogonally. A prototype of the proposed antenna is simulated, fabricated and measured. The proposed antenna exhibits good directionality and high cross-polarization level with especially compact size.

ARR-GCN: Anatomy-Relation Reasoning Graph Convolutional Network for Automatic Fine-Grained Segmentation of Organ's Surgical Anatomy.

Anatomical resection (AR) based on anatomical sub-regions is a promising method of precise surgical resection, which has been proven to improve long-term survival by reducing local recurrence. The fine-grained segmentation of an organ's surgical anatomy (FGS-OSA), i.e., segmenting an organ into multiple anatomic regions, is critical for localizing tumors in AR surgical planning. However, automatically obtaining FGS-OSA results in computer-aided methods faces the challenges of appearance ambiguities among sub-regions (i.e., inter-sub-region appearance ambiguities) caused by similar HU distributions in different sub-regions of an organ's surgical anatomy, invisible boundaries, and similarities between anatomical landmarks and other anatomical information. In this paper, we propose a novel fine-grained segmentation framework termed the "anatomic relation reasoning graph convolutional network" (ARR-GCN), which incorporates prior anatomic relations into the framework learning. In ARR-GCN, a graph is constructed based on the sub-regions to model the class and their relations. Further, to obtain discriminative initial node representations of graph space, a sub-region center module is designed. Most importantly, to explicitly learn the anatomic relations, the prior anatomic-relations among the sub-regions are encoded in the form of an adjacency matrix and embedded into the intermediate node representations to guide framework learning. The ARR-GCN was validated on two FGS-OSA tasks: i) liver segments segmentation, and ii) lung lobes segmentation. Experimental results on both tasks outperformed other state-of-the-art segmentation methods and yielded promising performances by ARR-GCN for suppressing ambiguities among sub-regions.

Fully-automated functional region annotation of liver via a 2.5D class-aware deep neural network with spatial adaptation.

BACKGROUND AND OBJECTIVE: Automatic functional region annotation of liver should be very useful for preoperative planning of liver resection in the clinical domain. However, many traditional computer-aided annotation methods based on anatomical landmarks or the vascular tree often fail to extract accurate liver segments. Furthermore, these methods are difficult to fully automate and thus remain time-consuming. To address these issues, in this study we aim to develop a fully-automated approach for functional region annotation of liver using deep learning based on 2.5D class-aware deep neural networks with spatial adaptation. METHODS: 112 CT scans were fed into our 2.5D class-aware deep neural network with spatial adaptation for automatic functional region annotation of liver. The proposed model was built upon the ResU-net architecture, which adaptively selected a stack of adjacent CT slices as input and, generating masks corresponding to the center slice, automatically annotated the liver functional region from abdominal CT images. Furthermore, to minimize the problem of class-level ambiguity between different slices, the anatomy class-specific information was used. RESULTS: The final algorithm performance for automatic functional region annotation of liver showed large overlap with that of manual reference segmentation. The dice similarity coefficient of hepatic segments achieved high scores and an average dice score of 0.882. The entire calculation time was quite fast (~5 s) compared to manual annotation (~2.5 hours). CONCLUSION: The proposed models described in this paper offer a feasible solution for fully-automated functional region annotation of liver from CT images. The experimental results demonstrated that the proposed method can attain a high average dice score and low computational time. Therefore, this work should allow for improved liver surgical resection planning by our precise segmentation and simple fully-automated method.

Alterations in patients with major depressive disorder before and after electroconvulsive therapy measured by fractional amplitude of low-frequency fluctuations (fALFF).

BACKGROUND: Electroconvulsive therapy (ECT) is an important treatment option for patients with major depressive disorder (MDD). However, the mechanisms of ECT in MDD are still unclear. METHODS: Twenty-four patients with severe MDD and 14 healthy controls were enrolled in this study. Eight ECT sessions were conducted for MDD patients using brief-pulse square-wave signal at bitemporal locations. To investigate the regional cerebral blood flow in MDD patients before and after ECT treatments by resting-state functional magnetic resonance imaging (rs-fMRI), the patients were scanned twice (before the first ECT and after the eighth ECT) for data acquisition. Afterward, we adopted fractional amplitude of low-frequency fluctuations (fALFF) to assess the alterations of regional brain activity. RESULTS: Compared with healthy controls, the fALFF in the cerebellum lobe, parahippocampal gyrus, fusiform gyrus, anterior cingulate gyrus, and thalamus in MDD patients before ECT (pre-ECT) was significantly increased. In another comparison, the fALFF in the cerebellum anterior lobe, fusiform gyrus, insula, parahippocampal gyrus, middle frontal gyrus, and inferior frontal gyrus in pre-ECT patients was significantly greater than the post-ECT fALFF. LIMITATIONS: Only two rs-fMRI scans were conducted at predefined times: before the first and after the eighth ECT treatment. More scans during the ECT sessions would yield more information. In addition, the sample size in this study was limited. The number of control subjects was relatively small. A larger number of subjects would produce more robust findings. CONCLUSIONS: The fALFF of both healthy controls and post-ECT patients in cerebellum anterior lobe, fusiform gyrus, and parahippocampal gyrus is significantly lower than the fALFF of pre-ECT patients. This finding demonstrates that ECT treatment is effective on these brain areas in MDD patients.