An innovative detection method of high frequency BPSK signal with low signal-to-noise ratio

Based on chaotic oscillator system, this paper proposes a novel method on high frequency low signal-to-noise ratio BPSK(Binary Phase Shift Keying) signal detection. Chaotic oscillator system is a typical non-linear system which is sensitive to periodic signals and immune to noise at the same time. Those properties make it possible to detect low signal-to-noise ratio signals. The BPSK signal is a common signal type which is widely used in modern communication. Starting from the analysis of advantages of chaotic oscillator system and signal features of the BPSK signal, we put forward a unique method that can detect low signal-to-noise ratio BPSK signals with high frequency. The simulation results show that the novel method can detect low signal-to-noise ratio BPSK signals with frequency in an order of magnitude of 108 Hz, and the input Signal-to-Noise Ratio threshold can be -20 dB.

Adaptive Bistable Stochastic Resonance Based Weak Signal Reception in Additive Laplacian Noise

Weak signal reception is a very important and challenging problem for communication systems especially in the presence of non-Gaussian noise,and in which case the performance of optimal linear correlated receiver degrades dramatically.Aiming at this,a novel uncorrelated reception scheme based on adaptive bistable stochastic resonance(ABSR)for a weak signal in additive Laplacian noise is investigated.By analyzing the key issue that the quantitative cooperative resonance matching relationship between the characteristics of the noisy signal and the nonlinear bistable system,an analytical expression of the bistable system parameters is derived.On this basis,by means of bistable system parameters self-adaptive adjustment,the counterintuitive stochastic resonance(SR)phenomenon can be easily generated at which the random noise is changed into a benefit to assist signal transmission.Finally,it is demonstrated that approximately 8dB bit error ratio(BER)performance improvement for the ABSR-based uncorrelated receiver when compared with the traditional uncorrelated receiver at low signal to noise ratio(SNR)conditions varying from-30dB to-5dB.

Low-Temperature Signaling Pathways and Their Signaling Factors in Plant

Low temperature as abiotic stress adversely impacts plant growth and development, and limits the ecological distribution of plants as well. Throughout their long evolutionary history, plants have developed a range of complicated and precise molecular regulatory mechanisms to deal with low-temperature stress, involving the activation of signal transduction pathways and the regulation of related genes. In this review, we provide a systematic summary of the most recent research findings regarding three hypotheses of cellular perception of low-temperature signals and two major intracellular low-temperature signaling pathways, including CBF-dependent signaling pathways and CBF-independent signaling pathways. Focus is placed on the functions of each component of the ICE-CBF-COR signaling cascade as well as their interrelationships. This review concludes that although some progress has been made in the identification, function, and mechanism of low-temperature response genes, their roles in the low-temperature regulatory network and molecular mechanisms still need to be studied in detail, which will be of great significance for improving the low-temperature tolerance of plants and adapting to climate change.

Research on Feature Extraction Method for Low-Speed Reciprocating Bearings Based on Segmented Short Signal Modulation Signal Bispectrum Slicing

Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.

Combining Radon-ambiguity transform with second-order difference to improve detection probability of LFM signals in low SNR

The Radon-ambiguity transform （RAT）, although efficient for detecting the linear frequency modulated signals （LFMs）, is troubled by the energy accumulation of noise in low signal-to-noise ratio （SNR）. A secondorder difference （SOD） method is proposed to treat with this problem. In the SOD method, the optimal search step and difference step are derived from the LFM rate resolution formula. The sharpness of the peaks of RAT is measured by curvature, and the sharpness, but not the magnitude of the peaks, is used to detect the LFMs. The SOD method removes the noise energy accumulation and reserves the drastically changing components integrally; thus, it improves the detection probability of LFMs in low SNR. The expected performance of the new method is verified by 100 Monte Carlo simulations.

Bistable Stochastic Resonance Enhanced 4-ary PAM Signal Detection under Low SNR

To boost the performance of 4-ary pulse amplitude modulated(PAM) at low signal-to-noise ratio(SNR), bistable stochastic resonance(BSR) system is introduced into digital communications system and get a reliable signal detection scheme. In this paper, we first analyse BSR system for different amplitudes of 4-ary PAM signals. The steadystate of the bistable system will be statistically distinct, and the feasibility of the proposed detection scheme is confirmed. On this basis, we present a detailed study on steady-state transitions of the BSR system, and an explicit expression of the bistable system parameters is derived. By setting the bistable system parameters, bistable system, 4-ary PAM signal, and noise reach the resonance state, and the BSR-based detection scheme is implemented. Moreover, we derive an analytical expression to calculate the symbol error rate(SER) of 4-ary PAM signals with the BSR-based detection under additive white Gaussian noise(AWGN). Finally, the simulation results validate that BSR-based detection scheme can improve the detection performance while efficiently reducing the symbol error rate.

Tracking algorithm of BPSK signal in low bit SNR and high dynamic scenarios

A new tracking algorithm is proposed aiming at the tracking problem in low bit signal-to- noise ratio （i. e. , Eb/N0 ） scenarios, in which the bit clock regenerated by bit synchronization loop decides loop update moment. The double frequency processing and non-coherent accumulation tech- nologies are applied to eliminate the impact of data polarity inversion, and then long time accumula- tion improves the input signal-to-noise ratio of discriminator. The frequency locked loop and phase locked loop constitute a carrier loop in parallel, which can meet the high dynamic demands. The ef- fectiveness of this algorithm has been corroborated by theoretical analysis, simulation and measure- ments, and the new tracking algorithm has been used in an aerospace engineering project successfully.

Research on the Seismic Wave Characteristics of Low Frequency Signals before the Alxa Left Banner MS5.8 Earthquake in Inner Mongolia,China

In order to search for the seismic wave characteristics of low frequency signals in the Alxa Left Banner region,Inner Mongolia,the low frequency signals of seismic wave data are extracted from the earthquakes of MS5. 8 in 2015 and MS5. 0 in 2016 in this area. The results show that:① Before the MS5. 8 earthquake,the seismic stations located near the epicenter in Wuhai,Dongshengmiao,and Shizuishan recorded seismic waves that showed the phenomenon of spectrum shift from high to low frequency.② The low frequency signals recorded by different stations have obvious difference.③ According to the data recorded by the station closest to the epicenter,low-frequency signals were recorded about120 hours before the earthquake and had obvious anomalies. This may reflect slow slip before the earthquake.

Study on System of Low Frequency Signal′s Measurement and Extraction of its Characteristic Quantity

The essay mainly studies a system of low frequency signal′s measurement and the extraction of its characteristic quantity,introduces how matlab analyzes and extracts low frequency signal.MATLAB,a visual programming software,designs testing program for mechanic vibration signal,analyzes and identifies vibration signal.The collected data shows the effectiveness of the system.Based on XM121 modules,the testing system collects and analyzes single channel signal.

Low-temperature Plasma Promotes Fibroblast Proliferation in Wound Healing by ROS-activated NF-κB Signaling Pathway

Low-temperature plasma（LTP） has shown great promise in wound healing,although the underlying mechanism remains poorly understood.In the present study,an argon atmospheric pressure plasma jet was employed to treat L929 murine fibroblasts cultured in vitro and skin wounds in BALB/c mice.The in vitro analysis revealed that treatment of fibroblasts with LTP for 15 s resulted in a significant increase in cell proliferation,secretion of epidermal growth factor（EGF） and transforming growth factor-β1（TGF-β1）,production of intracellular reactive oxygen species（ROS）,and the percentage of cells in S phase,protein expression of phosphorylated p65（P-p65） and cyclin D1,but a noted decrease in the protein expression of inhibitor kappa B（IκB）.The in vivo experiments demonstrated that 30-s LTP treatment enhanced the number of fibroblasts and the ability of collagen synthesis,while 50-s treatment led to the opposite outcomes.These results suggested that LTP treatment promotes the fibroblast proliferation in wound healing by inducing the generation of ROS,upregulating the expression of P-p65,downregulating the expression of IκB,and activating the NF-κB signaling pathway and consequently altering cell cycle progression（increased DNA synthesis in S phage）.

Low-Power Operational Amplifier for Real-Time Signal Processing System of Micro Air Vehicle

A low-power complementary metal oxide semiconductor（CMOS） operational amplifier （op-amp） for real-time signal processing of micro air vehicle （MAV） is designed in this paper.Traditional folded cascode architecture with positive channel metal oxide semiconductor（PMOS） differential input transistors and sub-threshold technology are applied under the low supply voltage.Simulation results show that this amplifier has significantly low power,while maintaining almost the same gain,bandwidth and other key performances.The power required is only 0.12 mW,which is applicable to low-power and low-voltage real-time signal acquisition and processing system.

Metal magnetic memory signals from surface of low-carbon steel and low-carbon alloyed steel

In order to investigate the regularity of metal magnetic signals of ferromagnetic materials under the effect of applied load, the static tensile test of Q235 steel and 18CrNiWA steel plate specimens were conducted and metal magnetic memory signals of specimens were measured during the test process. The influencing factors of metal magnetic memory signals and the relationship between axial applied load and signals were analyzed. The fracture and microstructure of the specimens were observed. The results show that the magnetic signals corresponding to the measured points change linearly approximately with increasing axial load. The microstructure of Q235 steel is ferrite and perlite, whereas that of 18CrNiWA steel is bainite and low-carbon martensite. The fracture of these two kinds of specimens is ductile rupture; carbon content of specimen materials and dislocation glide give much contribution to the characteristics of magnetic curves.

不同粗糙度煤岩界面超低摩擦效应与声发射特征试验研究

为了揭示动载扰动作用下煤岩界面粗糙度对超低摩擦型冲击地压影响机制,采用自行研制的煤岩界面超低摩擦试验装置,以沈阳红阳三矿1082 m采深煤岩体为研究对象,通过改变煤块与砂岩块体表面粗糙度来模拟煤岩界面不同粗糙特性,以粗糙度系数表征煤岩界面粗糙程度,工作块体水平位移表征冲击过程中超低摩擦效应强度,声发射能量为工作块体摩擦滑动过程中的信号参数,进行应力波扰动下不同粗糙度煤岩界面超低摩擦试验.研究结果表明:(1)超低摩擦滑动过程中,工作块体水平位移、声发射能量计数以及累计能量曲线呈现出孕育阶段、激发阶段、稳定阶段变化特征;(2)煤岩界面粗糙度越小,工作块体水平位移和声发射能量峰值越大,煤岩界面越易发生超低摩擦效应;(3)不同煤岩界面粗糙度下,相比于其他扰动频率, 2 Hz时更易发生超低摩擦效应;(4)给出了声发射能量峰值与煤岩界面粗糙度系数对应关系.声发射能量峰值可以有效表征超低摩擦效应强度,可以用声发射能量峰值预测超低摩擦效应强度.

高速低载频比传感信号下的逆变器有源阻尼自适应控制

逆变器工作在高频运行和控制载波比低的情况下,存在谐振尖峰抑制,提出了高速低载频比传感信号下的逆变器有源阻尼自适应控制技术。在高速低载频比传感信号下,构建逆变器数学模型。输出电流到电容电压与定子电流之间的传递函数,将逆变器的有源阻尼等效成一个控制电压环。调整有源阻尼自适应控制回路的参数,制定逆变器有源阻尼自适应控制策略。实验结果表明:文中技术的稳定裕度由2.46 dB下降至1.18 dB,可以保证逆变器的稳定运行;在不同采样频率下,可以控制逆变器输出与电网电压相同相位和频率的并网电流,其中在采样频率为5.9f时,输出并网电流和电压均表现为正弦状态,波动范围分别为[35 A,58 A]和[185 V,225 V],具有较好的控制效果。

基于MEEMD算法的二冲程柴油发动机机体振动分析

二冲程低速柴油机具有复杂的振动特性,传统的经验模态分解(Empirical Mode Decomposition,EMD)算法对其振动信号处理效果不理想。为此,采用修正多元集合经验模态分解(Modified Ensemble Empirical Mode Decomposition,MEEMD)算法对低速柴油机机体振动信号进行分解。首先,采用三轴加速度计测量发动机机体振动。然后利用均方根(Root Mean Square,RMS)对三轴振动强度进行分析。最后,对x轴上的信号进行MEEMD分析。结果表明:砌块在x轴方向的振动强度最大;与EMD算法相比,MEEMD算法可以抑制模态混合,有助于更好地识别块振动激励。

雷达信号与遥感地图融合的深度学习低慢小目标检测算法

雷达复杂环境低慢小目标检测是一项具有挑战性的任务,而利用深度学习以及数据特征融合等方法是解决这一难题的有效手段。本文在雷达地图融合检测网络(Radar Map fusion Detection Network,RMDN)的基础上进行了优化,主要优化方向为将雷达与地图信息在检测过程中进行重要性程度区分,具体优化内容为减少地图特征提取模块的网络深度,加入通道注意力机制,让神经网络自主学习雷达信息与地图信息特征的权重,使神经网能够更好地利用地图信息对雷达目标进行辅助检测。在此优化基础上,本文重新设计出了雷达地图融合检测网络RMDN-V2。算法的主要思想为利用卫星遥感地图来提供背景环境信息,作为雷达信号检测的辅助,通过将目标背景中的特征信息融入检测决策中,提高目标检测的准确性和鲁棒性,减少对强杂波和移动物体的干扰敏感性,改善目标检测算法在复杂环境下的表现。最后的无人机雷达实测数据实验结果表明,本文所做的针对性优化是有效的,RMDN-V2的检测性能优于原始的RMDN,同时本文算法检测性能远超传统的雷达检测算法,同时也优于目前主流的一些深度学习雷达目标检测算法。本文为解决当下低慢小目标检测的难题提出了新的算法。

LAMOST的“Unknown”光谱分类研究:ODS-YOLOv7模型

天体识别是天文新发现和深入研究天体的基础。在LAMOST DR8 v1.0发布的低分辨率光谱数据中有约53万条因没有类别标签而被命名为“Unknown”的光谱,其中有88.56%的光谱信噪比在0~10之间,对这批光谱的研究分析将增加LAMOST的有效数据产出。该研究为“Unknown”光谱的分类设计了一种ODS-YOLOv7模型。它是一种端到端的类别预测模型,通过添加一维卷积注意力模块以提高光谱识别能力。在经过一批信噪比在0~10之间的已知类别光谱训练后,ODS-YOLOv7可以学习到低信噪比光谱的有效特征,进而实现对“Unknown”光谱的类别预测。实验表明,该模型在已知类别标记的低信噪比恒星、星系、类星体的光谱识别中,F1-score分别为0.98、0.95、0.95;同时在与传统算法KNN、RF、DT、SVM和深度学习算法1D CNN、1DSSCNN、ResNet、DenseNet、VIT对比实验中取得相对最好的效果。实验结果还给出了ODS-YOLOv7模型对DR8 v1.0中信噪比在0~10的“Unknown”光谱预测置信度分布,在预测类别为恒星、星系、类星体任务中,有92%的分类置信度在60%以上。为保证模型输出质量,本文只选取分类置信度大于99%的光谱类别作为输出结果。以此为依据,在DR8 v1.0和DR9 v0发布的全部“Unknown”光谱中分别有37.19%和47.03%被ODS-YOLOv7模型预测出类别。此外,还增加人工认证以检验该模型预测的正确性。为提升模型的可解释性,参照了二维图像特征可视化的Grad-CAM方法,将其改进为适合于可视化一维光谱数据特征的算法。其结果表明该模型可自动关注到不同的分类特征,使得该模型非常适用于低信噪比“Unknown”光谱的类别预测。

银杏素调节TGF⁃β1/Smad通路对ox⁃LDL诱导血管内皮细胞损伤的影响

目的探讨银杏素调节转化生长因子⁃β1(TGF⁃β1)/Smad信号通路对ox⁃LDL诱导血管内皮细胞损伤的影响。方法将人脐静脉血管内皮细胞HUVEC分为对照组(未处理的HUVEC细胞)、模型组(50μg/mL ox⁃LDL处理的HUVEC细胞)、银杏素组(50μg/mL ox⁃LDL+12.5μmol/L银杏素处理HUVEC细胞)、SRI⁃011381组(50μg/mL ox⁃LDL+10μmol/L的TGF⁃β1/Smad激活剂SRI⁃011381处理HUVEC细胞)、银杏素+SRI⁃011381组(50μg/mL ox⁃LDL+12.5μmol/L银杏素+10μmol/L的SRI⁃011381共同处理HUVEC细胞)。ELISA试剂盒检测HUVEC细胞丙二醛(MDA)、谷胱甘肽(GSH)、超氧化物歧化酶(SOD)、IL⁃6、IL⁃1β、TNF⁃α水平;CCK⁃8法检测HUVEC细胞增殖;流式细胞术检测HUVEC细胞凋亡率;Western blot检测细胞上皮间质转化(EMT)、TGF⁃β1/Smad通路相关蛋白表达。结果与对照组相比,模型组IL⁃1β、IL⁃6、TNF⁃α、MDA水平、细胞凋亡率、TGF⁃β1、Smad3、神经型钙黏附蛋白(N⁃cadherin)、波形蛋白(Vimentin)蛋白水平显著增加(P<0.05),A值、GSH、SOD水平、上皮钙黏附素(E⁃cadherin)蛋白水平显著降低(P<0.05);与模型组相比,银杏素组IL⁃1β、IL⁃6、TNF⁃α、MDA水平、细胞凋亡率、TGF⁃β1、Smad3、N⁃cadherin、Vimentin蛋白水平显著下降(P<0.05),A值、GSH、SOD水平、E⁃cadherin蛋白水平显著升高(P<0.05),而SRI⁃011381组趋势相反,SRI⁃011381减弱了银杏素对ox⁃LDL诱导的HUVEC细胞损伤的抑制作用。结论银杏素可能通过下调TGF⁃β1/Smad信号通路对ox⁃LDL诱导的血管内皮细胞损伤起到改善作用。

利用双通道激光雷达验证低信噪比反演算法

针对相干测风激光雷达(LiDAR)在低信噪比(SNR)下反演算法的可靠性和精度难以验证的问题,搭建了一种双通道的脉冲相干测风LiDAR系统,可以同时获取高、低SNR的2组数据,并使用高SNR通道的结果作为真值,比较利用不同的算法在低SNR通道进行矢量风速估计的结果。该系统避免了使用其他观测设备作为真值时难以配准或观测目标不统一的问题。本文研究利用雷达获取的观测结果验证了各个算法的有效性,并在各个算法间进行了横向对比。最终,依据各个算法的性能表现和计算复杂度,指明了各个算法具有优势的使用场景。该结果对于充分利用现有系统、使用新算法提高低SNR下的数据获取效率,或在指定探测指标的前提下降低激光能量等硬件要求、实现系统小型化有一定意义。

基于小波奇异特征约束的期望最大时延估计算法

针对低信噪比条件下非平稳信号时延估计精度低的问题,提出基于小波奇异特征约束的期望最大时延估计算法。设计小波奇异性特征尺度广义互相关矩阵,构建多尺度小波奇异特征约束下的期望最大化模型。推导参数更新公式,利用期望最大化算法并行迭代,求取奇异性特征显著性最大条件下信号的自适应尺度以及该尺度下声源信号的最优时延估计值。仿真和实验结果表明,所提算法在低信噪比条件下,相较于传统广义互相关时延估计算法以及改进算法具有较高的时延估计精度,并且有效提高了误差约束范围内的有效估计成功率。