High-Precision Doppler Frequency Estimation Based Positioning Using OTFS Modulations by Red and Blue Frequency Shift Discriminator

Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.

Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.

LociScan,a tool for screening genetic marker combinations for plant variety discrimination

To reduce the cost and increase the efficiency of plant genetic marker fingerprinting for variety discrimination,it is desirable to identify the optimal marker combinations.We describe a marker combination screening model based on the genetic algorithm(GA)and implemented in a software tool,Loci Scan.Ratio-based variety discrimination power provided the largest optimization space among multiple fitness functions.Among GA parameters,an increase in population size and generation number enlarged optimization depth but also calculation workload.Exhaustive algorithm afforded the same optimization depth as GA but vastly increased calculation time.In comparison with two other software tools,Loci Scan accommodated missing data,reduced calculation time,and offered more fitness functions.In large datasets,the sample size of training data exerted the strongest influence on calculation time,whereas the marker size of training data showed no effect,and target marker number had limited effect on analysis speed.

Discrimination of polysorbate 20 by high-performance liquid chromatography-charged aerosol detection and characterization for components by expanding compound database and library

Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.

The Analysis of the Correlation between SPT and CPT Based on CNN-GA and Liquefaction Discrimination Research

The objective of this study is to investigate themethods for soil liquefaction discrimination. Typically, predicting soilliquefaction potential involves conducting the standard penetration test (SPT), which requires field testing and canbe time-consuming and labor-intensive. In contrast, the cone penetration test (CPT) provides a more convenientmethod and offers detailed and continuous information about soil layers. In this study, the feature matrix based onCPT data is proposed to predict the standard penetration test blow count N. The featurematrix comprises the CPTcharacteristic parameters at specific depths, such as tip resistance qc, sleeve resistance f s, and depth H. To fuse thefeatures on the matrix, the convolutional neural network (CNN) is employed for feature extraction. Additionally,Genetic Algorithm (GA) is utilized to obtain the best combination of convolutional kernels and the number ofneurons. The study evaluated the robustness of the proposed model using multiple engineering field data sets.Results demonstrated that the proposed model outperformed conventional methods in predicting N values forvarious soil categories, including sandy silt, silty sand, and clayey silt. Finally, the proposed model was employedfor liquefaction discrimination. The liquefaction discrimination based on the predicted N values was comparedwith the measured N values, and the results showed that the discrimination results were in 75% agreement. Thestudy has important practical application value for foundation liquefaction engineering. Also, the novel methodadopted in this research provides new ideas and methods for research in related fields, which is of great academicsignificance.

EDU-GAN:Edge Enhancement Generative Adversarial Networks with Dual-Domain Discriminators for Inscription Images Denoising

Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.However,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character damage.To solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,EDU-GAN.Unlike existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription image.Moreover,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising tasks.The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact.Due to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising.The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.

Balancing the minimum error rate and minimum copy consumption in quantum state discrimination

Extracting more information and saving quantum resources are two main aims for quantum measurements.However,the optimization of strategies for these two objectives varies when discriminating between quantum states |ψ_(0)> and |ψ_(1)> through multiple measurements.In this study,we introduce a novel state discrimination model that reveals the intricate relationship between the average error rate and average copy consumption.By integrating these two crucial metrics and minimizing their weighted sum for any given weight value,our research underscores the infeasibility of simultaneously minimizing these metrics through local measurements with one-way communication.Our findings present a compelling trade-off curve,highlighting the advantages of achieving a balance between error rate and copy consumption in quantum discrimination tasks,offering valuable insights into the optimization of quantum resources while ensuring the accuracy of quantum state discrimination.

FPGA implementation of 500-MHz high-count-rate high-time-resolution real-time digital neutron-gamma discrimination for fast liquid detectors

Fast neutron flux measurements with high count rates and high time resolution have important applications in equipment such as tokamaks.In this study,real-time neutron and gamma discrimination was implemented on a self-developed 500-Msps,12-bit digitizer,and the neutron and gamma spectra were calculated directly on an FPGA.A fast neutron flux measurement system with BC-501A and EJ-309 liquid scintillator detectors was developed and a fast neutron measurement experiment was successfully performed on the HL-2 M tokamak at the Southwestern Institute of Physics,China.The experimental results demonstrated that the system obtained the neutron and gamma spectra with a time accuracy of 1 ms.At count rates of up to 1 Mcps,the figure of merit was greater than 1.05 for energies between 50 keV and 2.8 MeV.

Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.

A Dual Discriminator Method for Generalized Zero-Shot Learning

Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.

Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer

To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.

The Influence of Price Discrimination from Airports on the Route Development Behavior of Airlines

Given the prominence and magnitude of airport incentive schemes,it is surprising that literature hitherto remains silent as to their effectiveness.In this paper,the relationship between airport incentive schemes and the route development behavior of airlines is analyzed.Because of rare and often controversial findings in the extant literature regarding relevant influencing variables for attracting airlines at an airport,expert interviews are used as a complement to formulate testable hypotheses in this regard.A fixed effects regression model is used to test the hypotheses with a dataset that covers all seat capacity offered at the 22 largest German commercial airports in the week 46 from 2004 to 2011.It is found that incentives from primary choice,as well as secondary choice airports,have a significant influence on Low Cost Carriers.Furthermore,Low Cost Carriers,in general,do not leave any of both types of airports when the incentives cease.In the case of Network Carriers,no case is found where one joins a primary choice airport and receives an incentive.Insufficient data between Network Carriers and secondary choice airports in the time when incentives have ceased means that no statement can be given.

莫来石-Ti(C,N)复合材料的制备及其力学性能研究

以TiO_(2)粉、石墨粉、Al/Si/Al_(2)O_(3)复合粉体为原料,在氮气气氛下经1400℃~1600℃保温2 h,采用碳热还原氮化法制备得到莫来石-Ti(C,N)复合材料,研究了烧成温度对复合材料物相组成、微观结构与力学性能的影响。结果表明:经1400℃热处理后,试样的物相组成为Ti(C,N)、SiO_(2)和硅线石。随热处理温度升高至1450℃~1600℃,硅线石消失的同时,试样出现了短柱状莫来石,并与无定形SiO_(2)紧密连接,形成有效的化学结合。当烧成温度为1500℃时,大量开口气孔随颗粒重排、界面移动而消失,材料颗粒间结合较为紧密,气孔数量明显减少,该烧成温度下试样具有最佳综合性能,其体积密度、弹性模量、抗折强度和维氏硬度分别为(3.48±0.02) g·cm-3、(138.5±0.1) GPa、(158.0±0.03) MPa和(21.01±0.01) GPa。

探讨基于核心指标集构建中医特色的射血分数保留心力衰竭1+N模式疗效评价体系

核心指标集是就某一特定研究领域达成的所有临床研究都应该测量和报告的最少的临床结局,核心指标集可以提高临床疗效评价的规范性和可比性,是循证医学发展的必然、是中医药临床疗效评价的重要抓手、是推动中医药疗效评价与国际接轨的关键。如何运用核心指标集实现临床疗效国际化、标准化的同时,又能遵循中医诊疗特点体现自身优势是中医临床疗效评价的难点。针对这一难点,项目组创新性地提出了1+N模式的射血分数保留心衰(heart failure with preserved ejection fraction, HFpEF)疗效评价体系,即通用的核心指标集“1”+个性化指标集“N”,以满足循证医学和中医学疗效评价的双重需求。本文初探HFpEF疗效评价构建模式,主张在规范化基础上体现多样化和个性化的研究目的需求。结合HFpEF临床疗效评价的现状,综合业内专家意见,探讨了HFpEF 1+N模式疗效评价构建中核心指标集“1”的取舍以及“N”体系的构建思路,以期最大程度的形成符合中医药特色的临床评价体系新范式,推动中医药国际化进程。

基于改进COF-YOLO v8n的油茶果静态与动态检测计数方法

针对自然环境下油茶果存在严重遮挡、近景色、小目标等现象,使用YOLO网络存在检测精度低、漏检现象严重等问题,提出对YOLO v8n网络进行改进。首先使用MPDIOU作为YOLO v8n的损失函数,有效解决因为果实重叠导致的漏检问题;其次调整网络,向其中加入小目标检测层,使网络能够关注小目标油茶以及被树叶遮挡的油茶;最后使用SCConv作为特征提取网络,既能兼顾检测精度又能兼顾检测速度。改进COF-YOLO v8n网络精确率、召回率、平均精度均值分别达到97.7%、97%、99%,比未改进的YOLO v8n分别提高3.2、4.8、2.4个百分点,其中严重遮挡情况下油茶检测精确率、召回率、平均精度均值分别达到95.9%、95%、98.5%,分别比YOLO v8n提高4.0、9.1、4.6个百分点。因此改进后COF-YOLO v8n网络能够明显提高油茶在严重遮挡、近景色、小目标均存在情况下的识别精度,减小油茶的漏检。此外,模型能够实现动、静态输入条件下油茶果计数。动态计数借鉴DeepSORT算法的多目标跟踪思想,将改进后COF-YOLO v8n的识别输出作为DeepSORT的输入,实现油茶果实的追踪计数。所得改进模型具有很好的鲁棒性,且模型简单可以嵌入到边缘设备中,不仅可用于指导自动化采收,还可用于果园产量估计,为果园物流分配提供可靠借鉴。

基于新型“1+X+N”人才培养模式的一流本科课程建设探索

为了适应时代的发展需求,响应国务院提出的在应用型本科高校启动“学历证书+若干职业技能等级证书”制度(即“1+X”证书制度)的试点工作,民办本科院校积极探索“1+X+N”人才培养新模式。在人才培养新模式下,省级一流本科课程光纤通信技术课程组从明确课程定位、推动教学改革、加大资源建设、优化教学内容与实施过程、推进课程思政、改善课程成绩评定等方面开展工作,努力提升学生在光纤通信方面的综合应用能力,培养出满足新时代通信发展需求的高质量人才。

天红2号苹果花芽分化期枝条和叶片碳水化合物含量和C/N变化

以天红2号苹果为试材,研究不同砧木短枝和中枝在花芽分化期间叶片和枝条中碳水化合物含量和C/N变化情况。结果表明,稳产期天红2号苹果成花率不受砧木影响。整个花芽分化期,无论嫁接在SH40中间砧还是八棱海棠乔砧上,在同一枝类中,天红2号苹果叶片可溶性糖、蔗糖、果糖和氮含量高于枝条,淀粉含量和C/N低于枝条。嫁接在SH40中间砧上的天红2号苹果成花率高的短枝叶片淀粉和果糖含量低于成花率低的中枝叶片,可溶性糖、蔗糖含量和C/N于花芽分化初期至花瓣原基分化期高于中枝叶片,短枝枝条可溶性糖、果糖含量和C/N低于中枝枝条,短枝枝条淀粉含量于花瓣原基分化期前高于中枝枝条;嫁接在八棱海棠乔砧上的天红2号苹果成花率高的短枝叶片可溶性糖、淀粉、果糖含量和C/N低于成花率低的中枝叶片,短枝枝条可溶性糖、果糖含量和C/N低于中枝枝条,短枝枝条淀粉含量于转化期低于中枝枝条。说明碳水化合物含量和C/N的高低对苹果成花不起决定性作用。在整个花芽分化期,叶片中可溶性糖含量呈上升趋势,枝条中淀粉含量和C/N呈波动性上升趋势,说明天红2号苹果花芽形态分化需要碳水化合物和C/N的积累。

指数有界双连续n阶α次积分C半群的扰动

利用经典算子半群理论中的研究方法,在指数有界双连续α次积分C半群的基础上,讨论了指数有界双连续n阶α次积分C半群的扰动定理。设A为次生成元的指数有界双连续n阶α次积分C半群{T(t)}_(t≥0),B为界线性算子,A、B、C可交换,则在一定条件下,C^(-1)(A+B)C_(B)生成双连续n阶α次积分C半群{T_(B)(t)}_(t≥0),并给出T_(B)(t)的表达式,从而推广了n阶α次积分C半群相关的扰动定理。

N6-甲基腺苷相关调节因子与骨关节炎:生物信息学和实验验证分析

背景:越来越多证据表明N6-甲基腺苷(N6-methyladenosine,m6A)调节因子与骨关节炎密切相关,被认为是防治骨关节炎新方向,但具体作用机制不明。目的:通过对骨关节炎基因芯片数据集进行生物信息学分析,探讨m6A对骨关节炎的作用,解析骨关节炎发病机制。方法:首先利用R软件提取GEO数据库中GSE1919数据集中骨关节炎相关m6A调节因子及其表达量,进而对提取结果行基因差异分析及GO、KEGG富集分析;接着对PPI网络拓扑学分析结果和机器学习结果取交集得到m6A关键调节因子,并通过体外细胞实验验证。结果与结论:①提取得到16个骨关节炎相关m6A调节因子表达量,通过差异分析获得ZC3H13、YTHDC1、YTHDF3、HNRNPC等11个m6A差异调节因子;②GO富集分析显示,骨关节炎相关m6A差异调节因子在生物过程中主要于mRNA转运、RNA分解代谢、胰岛素样生长因子受体信号通路调控等发挥作用;③KEGG富集分析显示,差异调节因子主要参与p53、白细胞介素17和AMPK信号通路;④综合PPI网络拓扑学分析和机器学习结果获得m6A关键调节因子——YTHDC1;⑤体外细胞实验结果表明,m6A关键调节因子——YTHDC1在对照组与骨关节炎组中表达存在显著差异(P<0.05);⑥结果显示,YTHDC1与骨关节炎发生发展密切相关,有望成为m6A治疗骨关节炎的分子靶点。

两种咔唑基-吡啶-N-氧化物内盐荧光极性探针研究

合成了4-(9H-咔唑-9-基)吡啶1-氧化物(CPNO)和4-(4-(9H-咔唑-9-基)苯基)吡啶1-氧化物(CPPNO)两种咔唑基-吡啶-N-氧化物内盐,测定了它们在不同溶剂中的紫外-可见吸收和荧光光谱,均表现出对溶剂极性较好的敏感性。计算表明,两个化合物都具有较大的激发态偶极矩,是化合物溶剂极性敏感性的原因。研究为开发新型的荧光极性探针提供了一种新思路。