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In this paper, a new preamble waveform design and a corresponding minimum-point processing algorithm at receiver are proposed to perform radar sensing of communication signal echoes. In our design, we consider the millimeter wave (mmWave) 802.11ad/802.11ay communication frame format as a reference. Our proposed new preamble replaces the second pair of single-carrier (SC) Golay complementary code (GCC) used in the preamble of IEEE 802.11ad/802.11ay by orthogonal frequency-division multiplexing (OFDM) GCC waveform, resulting in a SC-OFDM GCC preamble waveform. At the receiver, a point-wise minimum operation is performed on the separate radar ambiguity functions (AF) of the SC-GCC and OFDM-GCC to improve the overall quality of the preamble AF. Furthermore, point-wise minimum processing is applied to fuse the communication data AF and the preamble AF, so as to further enhance the radar sensing quality of the entire communication frame. In addition, theoretical analysis of the performance gain of such radar AF fusion is provided. Both theoretical analysis and simulation results demonstrate that the newly proposed SC-OFDM GCC preamble design and the minimum-point fusion of data AF and preamble AF provide a significant performance improvement for automotive radar sensing. Specifically, the proposed SC-OFDM GCC preamble with minimum-point processing algorithm improves the peak-to-max-sidelobe ratio (PMSR) of radar AF over the conventional 802.11ad/802.11ay preamble. Moreover, the proposed preamble-data AF fusion technique further enhances the PMSR as well as the Doppler resolution over the existing joint radar communication systems (which process either the preamble echo or the data echo only). Finally, the proposed minimum-point processing and data AF fusion are extended from single-frame to multi-frame processing to further improve the Doppler resolution. Submitted/Accepted version The work of Pingzhi Fan was supported by NSFC Project No. 62020106001.

Yilong Lu,1 Zengrong Jia2 1School of Stomatology, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 2Thyroid Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of ChinaCorrespondence: Zengrong Jia, Thyroid Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325015, People’s Republic of China, Tel +86 135 874 22709, Fax +86 577 55578033, Email immunity2@163.comPurpose: The inflammatory response was associated with the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to perform a novel prognostic signature based on inflammation-related genes (IRGs) for a better understanding of the prognosis of HNSCC.Patients and Methods: IRGs were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed to explore potential pathways. Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation. The nomogram model was constructed based on independent prognostic factors after univariate and multivariate Cox regression. The immune cell infiltration level was analyzed via the Tumor Immune Estimation Resource (TIMER) database.Results: In this study, we confirmed that 60% IRGs were abnormally expressed in HNSCC samples, and these were associated with important oncobiology. Then, a prognostic signature comprising 7 hub genes was generated based on TCGA database. The results were validated in 97 patients from GSE41613. A nomogram comprising risk score, age, M stage and N stage was generated to improve the accuracy of prognosis evaluation. The immune cell infiltration analysis suggested that 5 hub genes (ADGRE1, OLR1, TIMP1, GPR132 and CCR7) were negatively correlated with tumor purity and positively correlated with the infiltration of immune cells.Conclusion: Our study established a novel signature consisting of 7 hub genes for the prognostic prediction in patients with HNSCC.Keywords: TCGA, inflammation-related gene, prognosis, HNSCC

The nonlocal-based model for hyperspectral image (HSI) denoising first uses nonlocal self-similarity (NSS) prior to group similar full-band patches into 3-D nonlocal full-band groups (tensors) using a block matching (BM) operation, and then a low-rank (LR) penalty is typically applied to each nonlocal full-band group to reduce noise. While nonlocal-based methods have shown promising performance in HSI denoising, most existing methods have only considered the LR property of the nonlocal 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 article proposes a novel nonlocal 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 nonlocal full-band group. Second, we design an adaptive unidirectional LR dictionary with low complexity that takes into account the differences in intrinsic structure correlation among different modes of the nonlocal 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. Ministry of Education (MOE) Submitted/Accepted version This work was supported in part by the National Natural Science Foundation of China under Grant U19A2052, Grant 62020106011, Grant 62271414, Grant 61971476, Grant 62002160, and Grant 62072238; in part by the Ministry of Education, Republic of Singapore, under its Academic Research Fund Tier 1 under Project RG61/22 and Startup Grant; in part by the Westlake Foundation and the Research Center for Industries of the Future (RCIF) at Westlake University under Grant 2021B1501-2; and in part by the Macau Science and Technology Development Fund, Macau, under File SKLIOTSC-2021- 2023, File 0022/2022/A1, and File 0072/2020/AMJ.

This is an experiment about decision under risk. In CPT theory the Utility function and probability weighting function are independent, but we assume that the shape of utility and weighting function are not independent, and can be different under different gambles.

The relationship between doctors and patients has always been one of the hot topics that researchers have paid attention to. In recent years, there have been endless cases of doctor-patient disputes, and even several violent injuries have occurred, resulting in double losses for patients and doctors, causing all sectors of society The widespread concern has set off a great discussion on how to form a benign and healthy doctor-patient relationship. The problem of doctor-patient relationship is essentially a category of psychology and an aspect of interpersonal relationship. Therefore, using the relevant theories and methodological principles of psychology to explore the factors that affect the relationship between doctors and patients, in order to find practical means to improve the relationship between doctors and patients, has far-reaching theoretical and practical significance. Doctor-patient trust is an important dimension in the relationship between doctors and patients. The interpersonal basis for establishing, maintaining, and maintaining a good relationship between doctors and patients is to establish, maintain, and maintain a good relationship between doctors and patients. The lack of the patient's trust in the doctor is the main reason for the doctor-patient relationship, and enhancing the trust of the doctor and the patient in each other is of great significance for improving the doctor-patient relationship. Opinion selection is the process by which an individual starts from the perspective of others and imagines or speculates about the opinions and attitudes of others (Zhao Xian, Liu Li, Zhang Xiaoxiao, Xiang Zhendong, Fu Hongling, 2012). Previous studies have found that opinion selection is an effective way to increase trust (Erle, Ruessmann, & Sascha, 2018). Since the doctor-patient trust comes from both the doctor and the patient, it is of certain research significance and value to explore methods that can increase the trust of the other party from any one party. Improve the impact of doctors' trust in patients and the degree to which doctors consider patients in the process of treating patients (Benjamin, 2010). However, the research on how to improve the patient's trust in doctors through opinion selection intervention needs to be improved. At the same time, most of the existing relevant studies used real patient subjects and doctor subjects, but did not include healthy individuals in the society. However, intervention measures for healthy individuals are also necessary. Discovering methods that can increase the trust of healthy individuals in the group of doctors can solve the conditions for the contradiction between doctors and patients at the source and prevent possible future conflicts between doctors and patients. . Therefore, from the perspective of patients, this study will include existing patients and healthy individuals into the study at the same time, and explore the intervention training based on the views of patients and healthy individuals to improve the trust of doctor-patient relationship (specifically, patients to doctors) On the role. This study hopes to explore whether the intervention training adopted by the perspective can significantly improve the patient's trust in doctors through randomized controlled experimental design. This result can provide methodological guidance for improving the doctor-patient relationship, help to form a benign doctor-patient interactive relationship, and provide corresponding psychological advice and theoretical guidance for preventing doctor-patient conflict from the source.

Crop classification is one of the focused topics in remote sensing study nowadays. Optical imageries, although providing much information, are often contaminated with cloud or other weather effects while SAR imageries are more resilient to those. Temporal series data are often used to improve classification accuracy, especially in crop classification. This article investigates the usage of temporal series SAR imagery on crop classification in vast rural areas of China. The selected area of interest has a complicated, heavily mixed agriculture as well as lots of nonagricultural landcovers. Total ten classes are considered, six of them being crop types. A pixel-based classifier using subspace k th nearest neighbor (KNN) algorithm is applied to open source Sentinel-1 polarized SAR data. Discussion includes analyzing temporal features of the SAR observation, time domain smoothing, feature engineering, different classification algorithms, and selection of temporal series. The study results in an overall accuracy of 98.2% for the ten classes in fivefold cross validation, indicating a propitious application for agricultural monitoring using SAR data.

This article presents the design of a novel low-profile ultrawideband 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 ultrawide 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 to 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.

Due to the beam divergence of the Orbital Angular Momentum (OAM) Radio Frequency (RF) waves, the transmission distance is quite limited. Considering actual engineering constraints, it is difficult and costly to deploy conventional coplanar arrays on the station towers to receive OAM beams. In this paper, a flexible self-alignment reception scheme with General Non-uniform Array (GNA) is proposed to solve this problem. The antennas are distributed around the propagation axis without strict coplanar and uniform requirement, so that the phase plane covered by this distributed array can be large enough to receive the divergent vortex beams. With credible analysis and experiments, it can be confirmed that the OAM-based long-distance wireless backhaul links can be acquired. Agency for Science, Technology and Research (A*STAR) Submitted/Accepted version This research was supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - Pre Positioning (IAF-PP) under Grant No. A19D6a0053

An 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 letter, 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). The BA efficiently generates the necessary full complex weights as training samples to train the model. The 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 an acceptable performance for all test cases and achieves a much better time efficiency in comparison to using solely the BA in determining the complex weights to wide nulling problems.

A dielectric-loaded low-profile monocone antenna with vertical polarization and omnidirectional radiation is proposed to cover a superwide frequency bandwidth for some emerging wireless communication applications. The extended bandwidth is achieved by using four quasi-TM0 N modes ( $N$ from 1 to 4) and introducing the dielectric loading (DL) with optimal design. The DL can effectively change the impedance response of the monocone antenna and provide additional degree of freedom for increasing the bandwidth. The proposed antenna is designed, fabricated, and tested to verify the antenna’s performance. The designed and fabricated antenna with a thickness of only $0.057~\lambda _{\mathrm{low}}$ can achieve a superwide impedance bandwidth of 7.3:1 (from 1.6 to 11.7 GHz).

A wideband circular patch antenna with enhanced omnidirectional gains in the horizontal plane is presented. The gain enhancement over 1 GHz bandwidth is achieved by loading a low-profile ‘I-shaped’ structure (ISS) without increasing antenna's thickness. The effective refractive index of the ISS is larger, over a wide bandwidth than that of the conventional substrate with the same material. Therefore, the artificial structure behaves as a lens to refract the electromagnetic wave which is parallelly incident to the ISS and thus is able to concentrate the energy and enhance the antenna's horizontal radiation performance. In addition, the proposed antenna achieves a relative bandwidth of 19% through combining the resonant frequency for the TM 01 mode and another resonant frequency excited by the coupling between the circular patch and the unit cells for verifying the wideband gain enhancement of the ISS. Both simulation and measurement results indicate that the proposed antenna achieves 0.5–2.0 dB gain increment within the operating bandwidth compared with the unloaded antenna on the same ground.

Sign language is the primary communication medium for the deaf-mute community. However, the literacy of understanding and using sign language is hard to gain without professional training. In this paper, we explore the use of low power frequency modulated continuous wave radar for automatic sign language recognition. The proposed system is composed of a radar, a sound cluster and a computer for transforming signals to spectrograms. Furthermore, as the time-frequency spectrograms are high-dimensional data with redundant information, we then perform dimensionality reduction by extracting the histogram of oriented gradients features from these spectrograms. The features are finally classified by the k-Nearest Neighbour algorithm and a classification result of 95.8% is achieved on the five testing signs. The impact of the k value in the k-Nearest Neighbour is also investigated.

For polarimetric synthetic aperture radar (PolSAR) images, building extraction has been a challenging topic for long time in applications of land-use and land-cover analysis. Due to similar structures of buildings and such vegetation as forest, they often exhibit similar PolSAR scattering characteristics that are often difficult to distinguishing. Recently, deep Convolutional Neural Network (CNN) has been widely investigated for image processing with many promising results. This paper proposes a method that combines polarimetric features with the CNN network to realize the comprehensive utilization of polarimetric and contextual information of PolSAR data for the extraction of building areas in PolSAR images. Comparison experiments on both ESAR and EMISAR L-band PolSAR datasets show that the proposed method can generate better results for building extraction.

Object-Based Image Analysis (OBIA) is one of the cutting edge image processing technologies attracting significant attention in remote sensing industry recent years and Synthetic Aperture Radar (SAR) possess a high potential for classification of earth surface. In this paper, a novel classification method, integrating OBIA and majority voting strategy, is proposed for land-cover classification based on SAR data. The main purposes of OBIA are delineating objects, extracting feature and measuring similarity by using superpixel segmentation technology and cosine similarity algorithm. The color histogram is utilized as feature in this study. Majority voting strategy provides an effective way for class determination. At last, the experimental result is presented and it demonstrates the proposed classification scheme is an appropriate method for land cover classification of SAR data.