Spatial Modulator Research Articles

An innovative method for improving the performance of unmanned aerial vehicle‐based generalized spatial modulation and non‐orthogonal multiple access in 6G networks

AbstractThe rapid evolution of wireless networks is leading to the emergence of 6G, promising unprecedented performance. We propose a nonorthogonal multiple access (NOMA)‐based multiuser generalized spatial modulation (GSM) system that allows various transmitting antennas. Integrating unmanned aerial vehicles (UAVs) with NOMA and GSM enhances overall network performance. This paper proposes optimizing UAV‐GSM‐NOMA systems in 6G using the butterfly optimization algorithm (BOA) in the two‐wave with diffuse power (TWDP) channel. Results are compared with those obtained from Nakagami and Rayleigh channels. Objectives include enhancing the sumrate, outage probability (OP), and energy efficiency (EE) in perfect and imperfect channel state information (CSI). Simulation results yield significant improvements in the TWDP channel compared with the Nakagami and Rayleigh channels. The OP is reduced, the sumrate is enhanced, and the EE is notably improved. These findings underscore the BOA's potential for 6G UAV‐GSM‐NOMA networks in challenging channel environments, ensuring reliable and efficient communication.

RDA-Net: A multi-cascade network for DAS background noise attenuation

The increasing attention to seismic data collection under complex geological conditions is attributed to the utilization of distributed acoustic sensing (DAS), which has gained significant prominence due to the rapid development of exploration techniques. However, the presence of background noise in DAS has adverse effects on the quality of DAS data and significantly affects the accuracy of the subsequent migration and imaging processing. In addition, these interferences have a different generation mechanism from random noise in geophone-acquired seismic data, resulting in the degradation of traditional denoising techniques. Recent advancements have introduced convolutional neural networks (CNNs) for DAS background noise attenuation, showcasing their superiority over conventional techniques in terms of denoising capability and processing accuracy. Unfortunately, most CNN-based methods only have a single-scale architecture and cannot precisely accomplish denoising tasks when confronted with weak reflection signals in DAS records. In this research, we introduce a residual dense-connection attention network called RDA-Net to further augment the denoising ability. More precisely, the multi-scale structure in the backbone of RDA-Net is employed to capture informative features from the DAS data. Simultaneously, we employ a self-enhanced spatial attention module to refine and strengthen the primary features, thereby positively impacting feature representation and denoising performance. Furthermore, dense connections are also leveraged to reinforce the feature-interaction capability and effective features. Compared with typical conventional methods and CNN-based frameworks, RDA-Net can improve the signal-to-noise ratio over 24 dB and show superiority in weak signal recovery and intense DAS noise attenuation, both for synthetic and field records.

SDGSA: a lightweight shallow dual-group symmetric attention network for micro-expression recognition

Recognizing micro-expressions (MEs) as subtle and transient forms of human emotional expressions is critical for accurately judging human feelings. However, recognizing MEs is challenging due to their transient and low-intensity characteristics. This study develops a lightweight shallow dual-group symmetric attention network (SDGSA) to address the limitations of existing methods in capturing the subtle features of MEs. This network takes the optical flow features as inputs, extracting ME features through a shallow network and performing finer feature segmentation in the channel dimension through a dual-group strategy. The goal is to focus on different types of facial information without disrupting facial symmetry. Moreover, this study implements a spatial symmetry attention module, focusing on extracting facial symmetry features to emphasize further the symmetric information of the left and right sides of the face. Additionally, we introduce the channel blending technique to optimize the information fusion between different channel features. Extensive experiments on SMIC, CASME II, SAMM, and 3DB-combined mainstream ME datasets demonstrate that the proposed SDGSA method outperforms the metrics of current state-of-the-art methods. As shown by ablation experimental results, the proposed dual-group symmetric attention module outperforms classical attention modules, such as the convolutional block attention module, squeeze-and-excitation, efficient channel attention, spatial group-wise enhancement, and multi-head self-attention. Importantly, SDGSA maintained excellent performance while having only 0.278 million parameters. The code and model are publicly available at https://github.com/YZY980123/SDGSA.

ST-TDCN: A two-channel tree-structure spatial–temporal convolutional network model for traffic velocity prediction

In the development of intelligent transport systems which aims to provide safe, convenient, and comfortable transport and effectively addresses issues with traffic congestion and pollution, accurate collection of spatial–temporal data is of great significance. Based on the study of the topology and spatial characteristics of the transportation network, data with spatial–temporal attributed can be predicted by relevant models. Thus, the purpose of providing additional information for decision making and labor cost saving is achieved. This study proposed a more accurate traffic velocity prediction model, named Spatial-Temporal Tree-structure Dual-channel Convolution Network. The model designs a spatial tree convolution module for capturing the spatial features of nodes in the traffic network represented by the tree structure and applies a dual-channel temporal convolutional network module to capture the temporal information of velocity data more accurately. Comparative tests have demonstrated that the model proposed in this study has the advantages of higher accuracy, better convergence and more flexibility in responding to dynamic changes of traffic velocity.

Computer-aided Detection and Diagnosis of Cancer Regions in Mammogram Images Using Resource-Efficient CNN Architecture.

The automatic computer-assisted mammogram classification system is important for women patients to detect and diagnose the cancer regions. In this work, the mammogram images are classified into three cases: healthy, benign and cancer, using the proposed Resource Efficient Convolutional Neural Network (RECNN architecture). The proposed mammogram image classification system consists of Data Augmentation (DA) module and Spatial transformation module and CNN architecture with a segmentation module. The DA methods are used to increase the mammogram image count and Spatial Gabor Transform is used as the spatial transformation module for transforming the spatial pixels into spatial-frequency pixels. Then, the proposed RECNN architecture is used to perform the classification of mammogram images into healthy, benign and cancer cases. Further, the cancer mammogram images are diagnosed as either 'Early' or 'Severe' using the proposed RECNN architecture in this work. The proposed MCDS obtains 98.65% SeDR, 98.93% SpDR and 98.84% ADR for benign case mammogram images on DDSM dataset and also obtains 98.84% SeDR, 98.7% SpDR and 98.92% ADR for benign case mammogram images on DDSM dataset. The proposed MCDS obtains 98.94% SeDR, 98.86% SpDR and 98.96% ADR for benign case mammogram images on DDSM dataset and also obtains 98.89% SeDR, 98.88% SpDR and 99.03% ADR for benign case mammogram images on MIAS dataset. This proposed method is tested on the mammogram images from DDSM and MIAS datasets and the experimental results are compared with other similar mammogram classification works in this paper. Based on several performance evaluation measures, the experimental results show that MCDS outperforms the state-of-the-art methods currently used for the diagnosis and detection of mammography cancer.

Ship Detection in Synthetic Aperture Radar Images Based on BiLevel Spatial Attention and Deep Poly Kernel Network

Synthetic aperture radar (SAR) is a technique widely used in the field of ship detection. However, due to the high ship density, fore-ground-background imbalance, and varying target sizes, achieving lightweight and high-precision multiscale ship object detection remains a significant challenge. In response to these challenges, this research presents YOLO-MSD, a multiscale SAR ship detection method. Firstly, we propose a Deep Poly Kernel Backbone Network (DPK-Net) that utilizes the Optimized Convolution (OC) Module to reduce data redundancy and the Poly Kernel (PK) Module to improve the feature extraction capability and scale adaptability. Secondly, we design a BiLevel Spatial Attention Module (BSAM), which consists of the BiLevel Routing Attention (BRA) and the Spatial Attention Module. The BRA is first utilized to capture global information. Then, the Spatial Attention Module is used to improve the network’s ability to localize the target and capture high-quality detailed information. Finally, we adopt a Powerful-IoU (P-IoU) loss function, which can adjust to the ship size adaptively, effectively guiding the anchor box to achieve faster and more accurate detection. Using HRSID and SSDD as experimental datasets, mAP of 90.2% and 98.8% are achieved, respectively, outperforming the baseline by 5.9% and 6.2% with a model size of 12.3 M. Furthermore, the network exhibits excellent performance across various ship scales.

The nonlinear optical properties of meso-substituted porphyrins using spatial self-phase modulation

Herein, we studied the nonlinear optical properties of four meso-substituted porphyrins (H2MHTPP, H2THTPP, H2DETPP, and H2TETPP) by the spatial self-phase modulation (SSPM) method, using CW lasers with wavelengths of 532 nm and 780 nm. All samples exhibit strong nonlinear response due to the thermal optical nonlinear effect at 532 nm, which corresponds to the strong absorption band. In addition, four porphyrin samples were calculated that have nonlinear refractive index coefficients of the same order of magnitude (10−10 m2/W) at 532 nm. However, four porphyrins exhibited different optical nonlinear responses affected by the type and numbers of peripheral substituents at 780 nm. This work is of significance for reasonably adjusting the structure of compounds and improving their third-order nonlinear optical coefficients.

A dual-instrument Kalman-based tracker to enhance robustness of microsurgical tools tracking.

The integration of a surgical robotic instrument tracking module within optical microscopes holds the potential to advance microsurgery practices, as it facilitates automated camera movements, thereby augmenting the surgeon's capability in executing surgical procedures. In the present work, an innovative detection backbone based on spatial attention module is implemented to enhance the detection accuracy of small objects within the image. Additionally, we have introduced a robust data association technique, capable to re-track surgical instrument, mainly based on the knowledge of the dual-instrument robotics system, Intersection over Union metric and Kalman filter. The effectiveness of this pipeline was evaluated through testing on a dataset comprising ten manually annotated videos of anastomosis procedures involving either animal or phantom vessels, exploiting the Symani®Surgical System-a dedicated robotic platform designed for microsurgery. The multiple object tracking precision (MOTP) and the multiple object tracking accuracy (MOTA) are used to evaluate the performance of the proposed approach, and a new metric is computed to demonstrate the efficacy in stabilizing the tracking result along the video frames. An average MOTP of 74±0.06% and a MOTA of 99±0.03% over the test videos were found. These results confirm the potential of the proposed approach in enhancing precision and reliability in microsurgical instrument tracking. Thus, the integration of attention mechanisms and a tailored data association module could be a solid base for automatizing the motion of optical microscopes.

Driver fatigue detection method based on temporal–spatial adaptive networks and adaptive temporal fusion module

The reduction of traffic accidents by determining the driver’s state through fatigue detection is a worthy research issue. Most of the current fatigue driving detection method fails to fully utilize the temporal features of fatigue. To address this problem, this paper proposes a driver fatigue detection method combined with temporal–spatial adaptive networks (TSNet) and adaptive temporal fusion module (ATFM). First, a frame sequence of T frames is obtained by strided sampling of the input video and data enhancement. Subsequently, the temporal–spatial adaptive module (TSAM) is used as the core module and incorporated into Efficientnet-v2 to construct TSNet, which adaptively extracts temporal features according to different videos, adds attention weights to discriminative spatial and channel features, and fully extracts fatiguing temporal–spatial features of videos. Finally, ATFM is utilized to learn the weights between the fatigue classification scores of each frame in the frame sequence and adaptively fuses the fatigue classification scores of individual frames to obtain fatigue prediction results, increasing the extent of the influence of keyframes on the fatigue prediction results. In this paper, the proposed method achieves an accuracy of 89.42% on the NTHU-DDD dataset, which is better than other state-of-the-art methods, and the number of parameters of the proposed method is 24.70M, which is smaller than most of the methods. Through a series of comparative experiments, TSNet and ATFM alone also outperform models and modules with similar functionality.

BMSeNet: Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network for Real-Time Semantic Segmentation.

Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network's receptive field. Concurrently, feature information extraction is restricted to a single scale, which reduces the network's ability to generalize and maintain robustness. Furthermore, loss of image spatial details negatively impacts segmentation accuracy. To address these limitations, this paper proposes a Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network (BMSeNet). First, to address the limitation of singular semantic feature scales, a Multiscale Context Pyramid Pooling Module (MSCPPM) is introduced. By leveraging various pooling operations, this module efficiently enlarges the receptive field and better aggregates multiscale contextual information. Moreover, a Spatial Detail Enhancement Module (SDEM) is designed, to effectively compensate for lost spatial detail information and significantly enhance the perception of spatial details. Finally, a Bilateral Attention Fusion Module (BAFM) is proposed. This module leverages pixel positional correlations to guide the network in assigning appropriate weights to the features extracted from the two branches, effectively merging the feature information of both branches. Extensive experiments were conducted on the Cityscapes and CamVid datasets. Experimental results show that the proposed BMSeNet achieves a good balance between inference speed and segmentation accuracy, outperforming some state-of-the-art real-time semantic segmentation methods.

Ultrafine Spatial Modulation of Diazapyrene-Based Two-Dimensional Conjugated Covalent Organic Frameworks.

Tailormade bottom-up synthesis of covalent organic frameworks (COFs) from various functional building blocks offer not only tunable topology and pore size but also multidimensional properties. High crystallinity is one of the prerequisites for their structures and associated physicochemical properties. Among different π-conjugated motifs for constructing COFs, pyrene-based tetragonal structures are effective in achieving highly ordered and crystalline states. In the present research, we demonstrated that the substitution of pyrene with 2,7-diazapyrene produces nearly "flat" structures of two-dimensional (2D) COF layers by controlling the torsional angle of linker molecules. Featuring finite pore diameters and excellent thermodynamic stability of ∼500 °C, ordered face-to-face (slipped AA) stacking arrangements were produced. Extended electrical conjugation spanning 2D frames with modest optical bandgaps (Eg) of ∼2.1 eV shows the planar character of diazapyrene-based COFs. The stacking of the conjugated 2D frames with small Eg values is also beneficial for the formation of highly stable conducting pathways in the crystalline state, which was confirmed by the results of the microwave conductivity measurements. Nitrogen centers in diazapyrene units also play a key role as the active sites for proton transfer, and the maximum proton conductivity of σ = 10-2 S cm-1 was achieved along the cocontinuous nanopore structures surrounded by the active sites. Results show that tetragonal COFs based on diazapyrene can be used as a highly crystalline two-dimensional material with special electrical and proton-conducting capabilities.

Optical nonlinearity and all-optical switching in pumpkin seed oil based on the spatial cross-phase modulation (SXPM) technique

Nonlinear optics (NLO) and its applications have attracted increasing research interest in recent years owing to their contribution to the development of photonic technology. Accordingly, in this study, we investigated the NLO response of pumpkin seed oil using the spatial self-phase modulation (SSPM) method. Significant NLO characteristics have been experimentally studied at 405nm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$405\\,{\ ext{nm}}$$\\end{document} and 532nm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$532\\,{\ ext{nm}}$$\\end{document} continuous wave (CW) laser wavelengths, yielding second-order nonlinear refractive index (n2,th\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n_{2,\\,th}$$\\end{document}) values of 6.54×10-5cm2/W\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.54 \ imes 10^{ - 5} \\,{{{\ ext{cm}}^{2} } \\mathord{\\left/ {\\vphantom {{{\ ext{cm}}^{2} } W}} \\right. \\kern-0pt} W}$$\\end{document} and 2.73×10-5cm2/W\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.73 \ imes 10^{ - 5} \\,{{{\ ext{cm}}^{2} } \\mathord{\\left/ {\\vphantom {{{\ ext{cm}}^{2} } W}} \\right. \\kern-0pt} W}$$\\end{document}, respectively. The findings suggest that the absorption of the material leads to higher optical nonlinearity at shorter wavelengths owing to higher thermal effects. Furthermore, we implemented a light-controlled-light system based on the spatial cross-phase modulation (SXPM) technique employing pumpkin seed oil. We successfully achieved all-optical switching by designing the 'ON' and 'OFF' modes. The results of this study can be considered for the future development of NLO applications. Moreover, our work investigates the potential of pumpkin seed oil for designing low-cost and high-efficiency NLO devices, and this contribution opens up a novel practical avenue for oil-based optical devices.

Spatial and Bidirectional Work Function Modulation of Monolayer Graphene with Patterned Polymer "Fluorozwitterists".

Understanding the electronic properties resulting from soft-hard material interfacial contact has elevated the utility of functional polymers in advanced materials and nanoscale structures, such as in work function engineering of two-dimensional (2D) materials to produce new types of high-performance devices. In this paper, we describe the electronic impact of functional polymers, containing both zwitterionic and fluorocarbon components in their side chains, on the work function of monolayer graphene through the preparation of negative-tone photoresists, which we term "fluorozwitterists." The zwitterionic and fluorinated groups each represent dipole-containing moieties capable of producing distinct surface energies as thin films. Kelvin probe force microscopy revealed these polymers to have a p-doping effect on graphene, which contrasts the work function decrease typically associated with polymer-to-graphene contact. Copolymerization of fluorinated zwitterionic monomers with methyl methacrylate and a benzophenone-substituted methacrylate produced copolymers that were amenable to photolithographic fabrication of fluorozwitterist structures. Consequently, spatial alteration of zwitterion coverage across graphene yielded stripes that resemble a lateral p-i-n diode configuration, with local increase or decrease of work function. Overall, this polymeric fluorozwitterist design is suitable for enabling simple, solution-based surface patterning and is anticipated to be useful for spatial work function modulation of 2D materials integrated into electronic devices.

FFA-BiGRU: Attention-Based Spatial-Temporal Feature Extraction Model for Music Emotion Classification

Music emotion recognition is becoming an important research direction due to its great significance for music information retrieval, music recommendation, and so on. In the task of music emotion recognition, the key to achieving accurate emotion recognition lies in how to extract the affect-salient features fully. In this paper, we propose an end-to-end spatial-temporal feature extraction method named FFA-BiGRU for music emotion classification. Taking the log Mel-spectrogram of music audio as the input, this method employs an attention-based convolutional residual module named FFA, which serves as a spatial feature learning module to obtain multi-scale spatial features. In the FFA module, three group architecture blocks extract multi-level spatial features, each of which consists of a stack of multiple channel-spatial attention-based residual blocks. Then, the output features from FFA are fed into the bidirectional gated recurrent units (BiGRU) module to capture the temporal features of music further. In order to make full use of the extracted spatial and temporal features, the output feature maps of FFA and those of the BiGRU are concatenated in the channel dimension. Finally, the concatenated features are passed through fully connected layers to predict the emotion classification results. The experimental results of the EMOPIA dataset show that the proposed model achieves better classification accuracy than the existing baselines. Meanwhile, the ablation experiments also demonstrate the effectiveness of each part of the proposed method.

Freeform-surface-based optical design of a broadband compressive spectral imager with co-aperture coding

Compressive spectral imaging (CSI) is an advanced computational imaging approach to reconstruct the three-dimensional (3D) spatio-spectral data cube of a target scene through a single or a few snapshots. However, limited by the response range of the image detector, the existing CSI systems mostly work within narrow spectral bands, such as the visible or shortwave-infrared (SWIR) spectral band. The work band of the CSI system constrains the detection capacity for the targets under complex environments (such as rain, snow, haze, etc.). In addition, most of the current CSI prototypes lack engineering design for practical applications. This paper develops a novel, to the best of our knowledge, optical design scheme of a broadband CSI system with co-aperture coding to simultaneously realize visible multi-spectral imaging (10 channels) and SWIR super-resolution imaging (16×). The freeform surfaces are used to design the front-end reflective objective lens, thus significantly improving the image quality and spatial modulation precision of the system. By means of performance evaluation and tolerance analysis, excellent image quality and manufacturability of the proposed system are demonstrated.

STELLM: Spatio-temporal enhanced pre-trained large language model for wind speed forecasting

As a renewable and clean energy source, wind energy has caught worldwide attention. To ensure the reliability and stability of wind energy production, wind speed forecasting is of great importance. Although existing forecasting models have shown promising results, they remain relatively small in scale and are incapable of learning comprehensive sequential representations from large-scale data to reason intricate spatio-temporal patterns obscured in entangled wind series. Recently, pre-trained large models have demonstrated impressive performance in natural language processing (NLP) and computer vision (CV). However, large models are rarely applied to wind speed forecasting due to the lack of large-scale wind data for training. To resolve this issue, we propose Spatio-Temporal Enhanced Pre-Trained Large Language Model, namely STELLM, innovatively leveraging the powerful reasoning ability of Large Language Models (LLMs) for accurate wind speed forecasting. Specifically, we first perform the series decomposition to separate wind series data into seasonal and trend components, capturing the distinct properties from disentangled patterns. Then, to empower LLMs with the spatio-temporal modeling capability, we introduce the spatial and temporal prompts, which fuse the geographic distribution information and short-term temporal patterns of wind turbines to enrich the input wind series. Moreover, we leverage an autoregressive fine-tuning strategy to align LLMs with wind series data and learn patch-level representations. Afterwards these representations are fed into a localized spatial module to capture the spatial dependencies between wind turbines. Extensive experiments on four public wind datasets demonstrate the performance superiority of STELLM.

Self‐location in perceptual experience: A top‐down account

Perceptual experience is self‐locating. This claim aligns with our intuitions and is the dominant view in philosophy. To defend the claim, some philosophers have advanced perspectival accounts and others have advanced agentive accounts. Here, I explore tensions between the two accounts and propose a novel, integrative account: the top‐down view, which defends that visual experience is self‐locating in virtue of cognitive maps that modulate visual processing in a top‐down fashion. I assess recent neuroscientific evidence of spatial modulation in the visual cortex and show how it turns existing notions of self‐location upside‐down, shifting the focus from bottom‐up to top‐down processes.

SSANet-BS: Spectral–Spatial Cross-Dimensional Attention Network for Hyperspectral Band Selection

Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this end, we propose an unsupervised BS method based on a spectral–spatial cross-dimensional attention network, named SSANet-BS. This network is comprised of three stages: a band attention module (BAM) that employs an attention mechanism to adaptively identify and select highly significant bands; two parallel spectral–spatial attention modules (SSAMs), which fuse complex spectral–spatial structural information across dimensions in HSI; a multi-scale reconstruction network that learns spectral–spatial nonlinear dependencies in the SSAM-fusion image at various scales and guides the BAM weights to automatically converge to the target bands via backpropagation. The three-stage structure of SSANet-BS enables the BAM weights to fully represent the saliency of the bands, thereby valuable bands are obtained automatically. Experimental results on four real hyperspectral datasets demonstrate the effectiveness of SSANet-BS.

NOMA‐based precoded quadrature spatial modulation in multiuser MIMO downlink transmission over correlated channel

SummaryIn this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme.

Superhydrophobic quasi-periodic concave microlens array on fused silica prepared by spatially modulated picosecond laser parallel processing

Fused-silica-based microlens array (MLA) has excellent stability in the harsh environment. However, it is still a challenge to efficiently prepare superhydrophobic quasi-periodic MLA and keep the optical performance of the microlens. In this paper, we proposed spatial modulated picosecond laser parallel processing method to prepare a superhydrophobic quasi-periodic MLA on fused silica. By using a spatial light modulator, the picosecond laser was shaped to multi-focal spots and line-shape beam, respectively, which improved the fabrication efficiency. The line-shape beam was the key to induce micro-nanostructures on the smooth MLA and keep the optical performance of the microlens. The results showed that the surface roughness of the microlens region was less than 100 nm. Combing with shaping picosecond laser processing and chemical treatment, the superhydrophobic MLAs showed the excellent anti-fogging and self-cleaning properties. The average contact angle of the superhydrophobic MLAs was 162.3°. The superhydrophobic MLA showed low adhesion to water droplet (<10 μN) and low contact angle hysteresis (2.1° ± 0.7°). We believe shaping picosecond laser parallel processing method is a flexible and low-cost approach to prepare superhydrophobic micro-optical elements on fused silica.