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认知无线电是为了提高频谱利用率而提出的一种智能频谱共享技术,而频谱感知则是实现认知无线电的关键技术.分析了三种最基本的单用户频谱检测技术,通过仿真可以看出,相比能量检测,循环平稳特征检测能够有效地提高认知用户对主用户的检测概率,具有更加可靠的检测性能. 相似文献
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频谱检测技术是无线电提高频谱利用率的重要环节,认知无线电在杂波融合条件下频谱感知检测受到杂波阴影衰落的影响,降低频谱检测概率。传统方法采用AD转换网络拓扑结构进行频谱感知,受到动态门限检测的影响,频谱感知检测效果不佳。提出一种基于动态阈值加权能量检测模型的无线电频谱感知检测算法,引入衰落传输融合拓扑结构,设计杂波背景下的信道融合准则,定义信道衰落因子,动态调整各认知用户的检测门限和传输可信度,基于动态阈值和可信度实现协作频谱感知检测,仿真实验证明采用该算法能大幅提高对无线电感知频谱的检测概率,克服传输信道中存在的阴影衰落的影响,检测性能优于传统算法。 相似文献
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为了解决频谱稀缺带来的困扰,以及提高链路的可靠性和系统的吞吐量,建立了基于Nakagami—m衰落的多天线认知中继协作模型,重最分析将多天线加入该模型的中断性能,更进一步考虑了认知中继节点间的协作检测。针对认知中继节点获得频谱的能力,分三种情形讨论了该模型中断率性能：完全获得频谱,不完全获得频谱及基于内部合作获得频谱。依据推出的相应中断概率精确闭合表达式,可以看到多天线的应用大大提高了中断性能,认知用户内部协作进一步提高了网络中断性能。Matlab仿真结果验证了理论分析。 相似文献
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为了提升无人机机群间的通信频谱利用效率,认知无线电技术被应用在无人机机群中。由于传统频谱感知算法易受到噪声干扰、受通信时延干扰、需要累积信号长,无法适应无人机机群间通信,本文提出了一种基于长短期记忆神经网络(LSTM)的双门限无人机机群频谱感知算法。首先利用所有次级用户对频谱信号进行感知收集,随后将所有次级用户信号传入无人机融合节点,最后利用长短期记忆(LSTM)神经网络进行感知判断。在仿真实验条件下与传统单门限能量协作算法、双门限能量协作算法比较,仿真结果表明在不同信噪比和虚警概率下,所提方法的检测性能均优于传统算法。 相似文献
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针对图着色理论模型下的认知无线电频谱分配问题,提出了基于改进教与学算法的认知无线电频谱分配算法。利用改进教与学算法寻优精度高和收敛速度快等特点,实现了认知用户在平均系统效益最大化的准则下获得最优的频谱分配方案的目的。仿真结果表明,提出的算法使用户能获得更高的系统效益,提高了频谱分配效率,扩大了教与学算法的应用范围。 相似文献
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在认知无线电网络中,多个用户协作频谱感知能有效提高系统感知性能。但是这种协同方式会使得认知无线电网络的整体灵敏度降低,带来了新的安全隐患。而且当恶意节点或是障碍节点出现时,就会影响决策中心的最终判决。针对这一问题,本文提出了一种基于信任度的协同频谱感知技术,该技术通过认知用户的本地感知结果与认知无线电网络基站最终判决结果的比较,计算认知用户信任度,进而协同信任度参与频谱检测。仿真结果表明当认知用户中存在恶意节点或是故障节点时,本文提出的技术相比较于传统的检测方法,能够得到更好更稳定的检测性能。 相似文献
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认知无线电作为一种革命性智能频谱共享技术,可显著提高频谱的使用效率。认知网络中的用户通过感知所处的频谱环境,实现对已分配给主用户但未被占用的频谱空穴的接入,认知用户必须及时检测到主用户的出现。本系统针对主用户检测方法及实现进行了优化设计,采用了一种简化的数字信道化实现方案,以较少的硬件资源实现了主用户的实时检测： 相似文献
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对网络攻击信号检测中,需要对网络威胁态势预测的抗体虚警概率阈值估计,提高攻击信号检测性能。传统方法采用伪随机时频跳变的信息容量估计方法实现阈值估计和攻击信号检测,当攻击信号为非线性谐振信号时,检测性能不好。提出一种改进的网络威胁态势预测的抗体虚警概率阈值估计算法。构建多路复用器输入输出网络威胁态势预测算法,通过病毒信息特征预处理为免疫性分析提供信息特征数据基础。得到网络跳变向量和观测向量的标准正态阈值,将网络接收端和发送端数据的信息熵作为信道传递向量函数,在人工免疫进化过程中实现对免疫节点的抗体的虚警概率阈值估计,提高网络攻击信号的检测性能,降低网络威胁态势抗体虚警概率。仿真实验得出,该算法得到的估计结果精确,有效提高了攻击信号的检测性能,确保网络安全。 相似文献
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在异质同构自组网网络中,链路概率路由层具有随机分布特性,导致路由层数据完整性起伏不定。需要对概率路由层动态数据进行完整性检测,提高数据分发的Qo S服务质量。提出一种基于热点循迹的概率路由层动态数据完整性检测算法。进行异质同构自组网路概率路由层设计,通过热点循迹处理,取得数据的验证信息,输出数据信息动态更新策略,得到概率路由层数据动态更新和完整性确认模型。采用热点循迹随机分布预测控制计算动态数据分组转发控制开销,实现数据完整性检测改进。仿真实验结果表明,采用该算法进行异质同构自组网网络的链路概率路由层的动态数据完整性检测,有效降低数据丢包率,降低数据包的碰撞概率,网络负载大幅降低。算法在网络路由设计和通信中具有较好的应用性能。 相似文献
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Emad S. Hassan 《Journal of The Franklin Institute》2019,356(3):1640-1657
The multi-taper spectrum (MTS) estimator enjoys a relatively low computational complexity and high estimation accuracy making it an attractive method for spectrum sensing in cognitive radio (CR) networks. However, it is difficult to guarantee both detection and false alarm probabilities when its design is based on fixed threshold, especially when the noise power fluctuates due to channel conditions. In this paper, a new adaptive threshold method to guarantee both detection and false alarm probabilities for MTS based spectrum sensing is proposed. By means of estimating noise power and signal power, the decision of adaptive threshold is able to adapt the noise fluctuation and achieve efficient trade-off between detection and false alarm probabilities. A closed form expression for the adaptive threshold is derived for both additive white Gaussian noise (AWGN) channel and multipath fading channel. Several metrics are employed to compare the performance of the proposed adaptive threshold method with that of the fixed threshold methods such as: error decision probability, detection probability, false alarm probability and throughput. The obtained results show that the proposed method achieves better spectrum efficiency and high throughput in comparison with the conventional fixed and adaptive threshold methods presented in the literature. 相似文献
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在自行构建的超声频谱分析系统上,研究表面缺陷对声波调制的声学机理,提出一种基于单缺陷回波的局部频谱分析而对表面缺陷类型进行检测的方法,给出了用超声频域分析法对工件表面缺陷特征分析的实例,并用计算机检测系统得出了相应的试验结果,从而提高了检测可靠性.此检测方法可成为计算机辅助对缺陷进行分析的依据. 相似文献
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论文以低轨卫星信道为例，给出一种适用于大频偏条件下突发通信系统直接扩频序列混合并行捕获结构，分析了预检测积分时间和判决门限对平均捕获时间的影响，提出了在门限信噪比下以平均捕获时间最小为原则的预检测积分时间和判决门限参数最优设置方案。 相似文献
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射频频谱分析仪作为一种通用测试仪器,可用于通信系统或终端的测试与维护。小型化、标准化与模块化的特点,使其在快速组建多功能、智能化、低成本的自动测试系统中得到广泛应用。PXI总线接口是射频频谱分析仪的重要组成部分,也是其设计的重难点之一。本课题采用了PCI9056加可编程逻辑器件的方法,来完成PXI总线的数据传输以及PXI总线的触发功能。本项目中PCI9056工作在C模式,并利用DMA进行了频谱数据采取、控制命令下发以及对FPGA芯片的程序的动态配置。详细介绍了9056的驱动层工作。 相似文献
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《Information processing & management》2022,59(2):102845
Cognitive impairments like memory disorder and depressive disorders lead to fatal consequences if proper attention is not given to such health hazards. Their impact is extended to the socioeconomic status of the developed and low or middle-income countries in terms of loss of talented and skilled population. Additionally, financial burden is borne by the countries in terms of additional health budget allotment. This paper presents a novel strategy for early detection of cognitive deficiency to eliminate the economic repercussions caused by memory disorder and depressive disorders. In this work, Electroencephalogram (EEG) and a word learning neuropsychological test, i.e. California Verbal Learning Task (CVLT), are conjunctively used for memory assessment. The features of EEG and scores of CVLT are modeled by applying different machine learning techniques, namely K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). Comparatively, experimental results have better classification accuracy than the existing schemes that considered EEG for estimating cognitive heuristics. More specifically, SVM attains the highest accuracy score of 81.56% among all machine learning algorithms, which can assist in the early detection of cognitive impairments. The proposed strategy can be helpful in clinical diagnosis of psychological health and improving quality of life as a whole. 相似文献
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作为数字水印领域的新的研究方向,非对称性水印因其嵌入水印信息使用的私钥与检测时使用的公钥的不同,在许多领域更加符合人们的要求。本文对非对称性水印的特征进行了分析,并对扩频非对称性水印算法进行了分析。 相似文献
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《Information processing & management》2023,60(2):103191
Conceptual metaphor detection is a well-researched topic in Natural Language Processing. At the same time, conceptual metaphor use analysis produces unique insight into individual psychological processes and characteristics, as demonstrated by research in cognitive psychology. Despite the fact that state-of-the-art language models allow for highly effective automatic detection of conceptual metaphor in benchmark datasets, the models have never been applied to psychological tasks. The benchmark datasets differ a lot from experimental texts recorded or produced in a psychological setting, in their domain, genre, and the scope of metaphoric expressions covered.We present the first experiment to apply NLP metaphor detection methods to a psychological task, specifically, analyzing individual differences. For that, we annotate MetPersonality, a dataset of Russian texts written in a psychological experiment setting, with conceptual metaphor. With a widely used conceptual metaphor annotation procedure, we obtain low annotation quality, which arises from the dataset characteristics uncommon in typical automatic metaphor detection tasks. We suggest a novel conceptual metaphor annotation procedure to mitigate issues in annotation quality, increasing the inter-annotator agreement to a moderately high level. We leverage the annotated dataset and existing metaphor datasets in Russian to select, train and evaluate state-of-the-art metaphor detection models, obtaining acceptable results in the metaphor detection task. In turn, the most effective model is used to detect conceptual metaphor automatically in RusPersonality, a larger dataset containing meta-information on psychological traits of the participant authors. Finally, we analyze correlations of automatically detected metaphor use with psychological traits encoded in the Freiburg Personality Inventory (FPI).Our pioneering work on automatically-detected metaphor use and individual differences demonstrates the possibility of unprecedented large-scale research on the relation between of metaphor use and personality traits and dispositions, cognitive and emotional processing. 相似文献
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《Journal of The Franklin Institute》2022,359(8):3831-3856
Aiming at early detection of faults in dynamic systems subject to external periodic disturbances, this paper proposes a new generalized proportional-integral observer (GPIO) fault detection scheme with zero-pole joint optimization and novel complex coefficient gain (CCG) of residual evaluation. The focus of the proposed scheme is to reduce the adverse impacts caused by the semi-stationary periodic disturbance whose spectrum is uneven, with most energy being at some dominant frequencies. The proposed GPIO with a complex coefficient gain is designed in a two-stage procedure. In the first stage of zero assignment and pole optimization, the additional zeros introduced by the GPIO’s integration action are allocated to near the disturbance frequency. The gain of the transfer function matrix relating from the disturbances to the fault indicator signals is minimized by pole optimization. In the second stage of designing complex coefficient gain in residual evaluation, the unique feature of rank-deficient caused by the additional zeros assigned in stage one is further exploited to cancel the disturbances in the fault indicator signals (which is also referred to as the fault detection residual in this article). It is proved that, for an arbitrary periodic disturbance with a specific spectrum, the remnant components of the disturbance in the indicator signals generated by the GPIO can cancel each other by a complex gain vector, which can be determined by the zero eigenvalue’s left eigenvector of the rank-deficient of the disturbance transfer function matrix. The sufficient conditions for the convergence of the proposed fault detection filter are also given. Numerical examples illustrate the proposed method’s better performance in detecting minor faults. 相似文献