Wideband spectrum sensing in unknown white Gaussian noise

The spectrum sensing of a wideband frequency range is studied by dividing it into multiple subbands. It is assumed that in each subband either a primary user (PU) is active or absent in a additive white Gaussian noise environment with an unknown variance. It is also assumed that at least a minimum given number of subbands are vacant of PUs. In this multiple interrelated hypothesis testing problem, the noise variance is estimated and a generalised likelihood ratio detector is proposed to identify possible spectrum holes at a secondary user (SU). Provided that it is known that a specific PU can occupy a subset of subbands simultaneously, a grouping algorithm which allows faster spectrum sensing is proposed. The collaboration of multiple SUs can also be considered in order to enhance the detection performance. The collaborative algorithms are compared in terms of the required exchange information among SUs in some collaboration methods. The simulation results show that the proposed detector outperforms the energy detector in the presence of noise variance mismatch above 2.3 dB. Some involved trade-offs in the spectrum sensing using the proposed detector are discussed.     

Reconfigurable Sensing Time in Cooperative Cognitive Network Using Machine Learning

A cognitive radio network (CRN) intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization. In CRNs, the secondary users (SUs) opportunistically access the primary users (PUs) spectrum. Therefore, unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs. Cooperative spectrum sensing (CSS) is rated as the best choice for making reliable sensing decisions. This paper employs machine-learning tools to sense the PU channels reliably in CSS. The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved channel throughput. The fine-k-nearest neighbor algorithm (FKNN), employed in this paper, estimates the number of samples based on the nature of the channel under-specific detection and false alarm probability demands. The simulation results reveal that the sensing cost is suppressed by reducing the sensing time and exploiting the traditional fusion rules, validating the effectiveness of the proposed scheme. Furthermore, the global decision made at the fusion center (FC) based on the modified sensing samples, results low energy consumption, higher throughput, and improved detection with low error probabilities.     

Information theoretic criterion-based spectrum sensing for cognitive radio

Energy detection is a promising candidate for spectrum sensing of cognitive radio systems due to its merits of simplicity and easy implementation. However, energy detection needs to subjectively pre-configure a threshold and requires the knowledge of noise power. Furthermore, energy detection is vulnerable to noise uncertainty, which inevitably occurs in practical implementations. To solve the above problems, two spectrum sensing methods requiring no knowledge of noise power and no subjective threshold settings are proposed based on the information theoretic criterion. Additionally, the most important advantage of these methods is that their performance is totally unaffected by noise uncertainty. Finally, simulation results are provided to demonstrate the benefits of these methods.     

Optimisation of cooperative spectrum sensing in cognitive radio network

Spectrum sensing is a key problem in cognitive radio (CR). Because of a low SNR, fading and sensing time constraints, a single CR may not be able to reliably sense the presence of primary radios, which motivates the study of sensing by multiple cognitive users. Here, the authors consider cooperative spectrum sensing (CSS) using a counting rule where several cognitive users sense whether primary users exist or not and send their decisions to the centre where the final decision is made. Optimal strategies under both the Neyman-Pearson criterion and the Bayesian criterion for CSS are derived using a counting rule. In addition, the authors present simple methods to calculate the optimal settings. Another contribution here is the analysis of a randomised rule at the centre, which is a long-existing problem in the field of distributed detection systems.     

Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA

Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by allocating optimal transmission power. First, for the situation of multiple sub-users, an adaptive optimization method is proposed to reduce the complexity of the optimization solution. Secondly, for the optimization problem of nonlinear programming, a maximization throughput optimization algorithm based on Chebyshev and convex (MTCC) for CRN-NOMA is proposed, which converts multi-objective optimization problem into single-objective optimization problem to solve. At the same time, the convergence and time complexity of the algorithm are verified. Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput. In terms of interference and throughput, the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access (OFDMA). This paper provides important insights for the research and application of NOMA in future communications.     

基于小波奇异性理论的水轮机空化检测

空化噪声谱分析中常采用的功率谱分析在研究空化噪声这种具有很强突变性的信号时具有明显的缺陷,文中提出了一种基于小波奇异性理论的水轮机空化检测方法,并提出了小波基选择方案、突变点检测条件和最佳检测阈值。进行了混流式水轮机的模型转轮试验,观测了模型转轮额定工况下的涡带形态和空化发展情况,并采集存储了大量空化噪声数据。对照观测结果分析实际的检测数据,结果表明该方法的有效性,并很好的检测出空化初生和空化形态转变。     

EMAT noise suppression using information fusion in stationary wavelet packets

An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering, and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for electromagnetic acoustic transducer (EMAT) pulse-echo signals, and therefore do not exploit their unique impulse nature. The approach to ultrasonic signal filtering proposed in this paper is based on stationary wavelet packet denoising with a threshold influenced by several information sources: a statistical echo detection, the amplitude distribution of the wavelet transform coefficients, and a priori known system frequency characteristics. The proposed method was evaluated on signals measured with EMAT probes and under various SNR conditions; it outperforms the wavelet transform with the Stein unbiased risk estimate (SURE) threshold estimation method and split-spectrum processing (SSP). The results indicate SNR enhancement of 19 dB with real EMAT data.     

Performance analysis of low average reporting bits cognitive radio schemes in bandwidth constraint control channels

Collaborative spectrum sensing is regarded as a key technology for tackling the challenges of the practical implementation of cognitive radio (CR). However, most of the proposed solutions require infinite bits or several bits exchanging per each CR to make the final decision to indicate the absence or presence of a primary user. It is well known that if N CRs employ energy detector with the same threshold and then send individual decision bits to a fusion centre, the OR-rule (1-out-of-N rule) will be optimal under Neyman-Pearson criterion. To decrease the average number of exchanging bits, we propose novel schemes while they perform as well as the OR-rule, our analytical and simulation results show that the proposed schemes has two important advantages over previously proposed schemes: (1) the average number of exchanging bits for N CRs always is 1/N bits per CR in perfect control channels; however, it is much higher than 1/N for the other proposed methods, e.g. it is 1 bit for the OR-rule and (2) the proposed method is very robust against the errors of imperfect control channels, specially, in low signal-to-noise ratio (SNR) regimes. Furthermore, the average number of reporting bits do not change considerably under the imperfect control channels.     

扩频信号的声光谱相关检测

传统检测扩频信号的声光技术,是将扩频信号建模为随机平稳过程,而扩频信号是被伪随机码调制的周期信号,应建模为循环平稳随机过程。基于声光技术与循环谱相关技术,提出了扩频信号的声光谱相关检测方法。建立了基于声光时间积分的声光谱相关检测模型,分析了当干扰瞄准扩频接收机时,扩频信号在循环频率α=1/Tc的循环谱,给出了循环谱峰的检测公式TcRs1/。仿真实验结果表明,与传统的声光技术检测扩频信号的功率谱相比,在光电检测阵列上检测的循环谱峰,谱峰旁瓣几乎为零,并可多获得约3dB的增益,表明声光谱相关检测方法具有更优异的检测和抗干扰性能。     

Code-aided quasi-maximum SINR algorithms for blind linear multiuser detection

The problem of blind linear data-symbol estimation and data detection for the air- interface adopting the wide-band direct sequence code division multiple access multiuser multiplexing technique with the promising short-code configurations has been addressed. The superior interference-suppression ability of the code-constrained minimum output energy multipath- component estimation is utilised to develop three code-aided quasi-maximum signal-to-interference and noise ratio (SINR) algorithms. These algorithms operate in such a way as to maximise approximate measures of the output SINR, each having variations, especially in the adaptive implementation, because of different criteria employed. The 'quasi-maximum SINR' nature is because of the approximations employed. Extensive simulations indicate that all of these algorithms significantly outperform the existing code-aided blind linear algorithms at considerably low computational complexity. Moreover their adaptive versions exhibit very high level of desirable trade-off between convergence speed and steady-state performance at further reduced computational complexity.     

Bridging semantic gap between high-level and low-level features in content-based video retrieval using multi-stage ESN–SVM classifier

Content-based video retrieval system aims at assisting a user to retrieve targeted video sequence in a large database. Most of the search engines use textual annotations to retrieve videos. These types of engines offer a low-level abstraction while the user seeks high-level semantics. Bridging this type of semantic gap in video retrieval remains an important challenge. In this paper, colour, texture and shapes are considered to be low-level features and motion is a high-level feature. Colour histograms convert the RGB colour space into YcbCr and extract hue and saturation values from frames. After colour extraction, filter mask is applied and gradient value is computed. Gradient and threshold values are compared to draw the edge map. Edges are smoothed for sharpening to remove the unnecessary connected components. These diverse shapes are then extracted and stored in shape feature vectors. Finally, an SVM classifier is used for classification of low-level features. For high-level features, depth images are extracted for motion feature identification and classification is done via echo state neural networks (ESN). ESN are a supervised learning technique and follow the principle of recurrent neural networks. ESN are well known for time series classification and also proved their effective performance in gesture detection. By combining the existing algorithms, a high-performance multimedia event detection system is constructed. The effectiveness and efficiency of proposed event detection mechanism is validated using MSR 3D action pair dataset. Experimental results show that the detection accuracy of proposed combination is better than those of other algorithms     

一种V型坡口焊缝图像处理方法及特征提取算法研究

为了提高V型坡口焊缝特征提取算法的效率和准确性,对V型坡口光条图像预处理,在图像感兴趣区域(Region of Interest,ROI)开窗基础上构建差异化卷积模板,经图像差分后实现光条纹增强及噪声颗粒化.通过形态学开运算和小连通域去除提取出二值化光条,并采用几何中心法完成光条骨架细化.通过对光条形态学特征分析,初步定位角点,分区域提取光条中心线,最终获得焊缝的精确特征点.实验结果表明,采用该方法能够有效去除噪声,准确提取出亚像素级特征点,相较传统角点检测算法效率提升60%,满足工业应用的高精度和实时性要求.     

Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for the researchers to safeguard the fusion center (FC) against abnormal sensing reports initiated by the malicious users (MUs). In this paper, butterfly optimization algorithm (BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications. The coefficient vector is utilized in the soft decision rule at the FC before making any global decision. The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results. The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’ environment and when lower and higher SNRs information is carried by the different categories of MUs.     

基于EEMD和分层阈值的磁记忆信号降噪方法研究

针对现有缺陷管道的磁记忆信号降噪效果不佳及信号完整性鲜有考虑等问题,提出了基于总体平均经验模态分解（ensemble empirical mode decomposition,EEMD）和分层阈值的磁记忆信号降噪方法。首先,设计了以STM32F407为控制核心的金属磁记忆检测系统,用于采集缺陷管道的磁信号;然后,对磁信号进行EEMD预处理,得到其本征模函数（intrinsic mode function, IMF）分量,并根据频谱分析和相似度计算选择最佳分解层数;最后,利用分层阈值降噪算法重构在最佳分解层数下的IMF分量,得到降噪后的信号。通过仿真分析和实验测试,对EEMD分层阈值降噪方法进行定量评价。结果表明：该方法适用于信噪比较小的含噪信号;与小波阈值降噪方法相比,其降噪后信号的信噪比和平滑度较高,均方根误差较小,缺陷特征信号完整,可更直观地显示缺陷位置。研究结果为金属管道磁信号降噪提供了一种切实可行的方法,为管道缺陷的在线检测奠定了基础。     

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Wireless Sensor Networks (WSNs) can be termed as an auto-configured and infrastructure-less wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure and motion etc. WSNs may comprise thousands of Internet of Things (IoT) devices to sense and collect data from its surrounding, process the data and take an automated and mechanized decision. On the other side the proliferation of these devices will soon cause radio spectrum shortage. So, to facilitate these networks, we integrate Cognitive Radio (CR) functionality in these networks. CR can sense the unutilized spectrum of licensed users and then use these empty bands when required. In order to keep the IoT nodes functional all time, continuous energy is required. For this reason the energy harvested techniques are preferred in IoT networks. Mainly it is preferred to harvest Radio Frequency (RF) energy in the network. In this paper a region based multi-channel architecture is proposed. In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region. The Secondary User (SU) that are the licensed user is IoT enabled with Cognitive Radio (CR) techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy. To harvest energy efficiently and to reduce the energy consumption during sensing, the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel. From the experimental analysis, it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel. We also show that channel load can be highly reduced and channel utilization is proved to be more proficient. Thus, this proves the proposed architecture cost-effective and energy-efficient.     

基于LSM-Kalman滤波器的湍流噪声抑制

针对大气湍流效应所引起的光无线通信系统中的衰落问题,建立了与大气湍流噪声及其它加性噪声有关的信号模型。根据最大似然比准则,推导了强度调制/直接探测(IM/DD)的光无线通信系统的最佳判决门限。采用线性状态模型的自适应Kalman滤波器和同态滤波器实现了对探测门限中所含信号和湍流的统计值的预测,可以在不同的信噪比条件下实现自适应门限检测,对大气湍流噪声有很好的抑制作用,信噪比在10~20dB时,可以降低系统的误码率到10-5以下。     

基于偶树复小波和改进型阈值函数的降噪方法 及其应用

为更好地保留原有用信号信息,有效恢复强噪声背景下微弱故障信号,提出了一种基于对偶树复小波和改进型阈值函数的降噪方法,将其应用于机械故障诊断,取得了较好效果。运用对偶树复小波变换滤波器设计方法和改进型阈值函数,以实施降噪的具体步骤。该法充分利用了对偶树复小波变换的平移不变的优良特性,同时,改进型阈值函数与传统软、硬阈值降噪算法相比,克服了软阈值信号失真和硬阈值信号不连续、振荡等缺点。实验表明：此法有效去除了噪声,是一种较好的提取微弱故障信号的方法。     

Robust chaotic communications exploiting waveform diversity. part 2: Complexity reduction and equalisation

The symbolic dynamics method of communication using chaotic systems has previously been shown to offer performance better than conventional binary phase shift keying (BPSK), while also providing similar spectrum efficiency. This is achieved by exploiting diversity in the waveform, through a mechanism similar to partial response signalling. In order to achieve this performance, a correlation method for detection has been proposed, but complexity was high and only performance in additive white Gaussian noise (AWGN) channels has been considered. It is demonstrated that the complexity can be reduced from requiring 1024 correlations per symbol down to only 32, while degrading performance only by 0.6 dB. The ability to further reduce the occupied bandwidth is investigated. Spectrum occupancy equivalent to a root-raised cosine-filtered BPSK signal is demonstrated, and performance is maintained when the reference waveforms are similarly filtered to maintain the signal match. Finally, a novel equalisation technique that incorporates a decision feedback structure into the correlation detector is proposed. Performance in multipath channels is investigated and shown to be effective.     

Harris算子耦合强度特征的图像复制-粘贴篡改检测算法

目的针对当前较多图像复制-粘贴篡改检测算法主要依靠度量图像的结构特征来实现篡改检测,忽略了图像的强度特征,使其在各种几何变换下难以准确检测出伪造内容,导致检测结果中存在漏检和误检等问题,设计一种基于Harris算子耦合强度特征的图像复制-粘贴篡改检测算法。方法利用Harris算子对图像的特征点进行精确的提取。通过特征点构造圆形特征区域,求取该区域的Zernike矩,通过Zernike矩的大小实现对特征点的描述。随后,利用不同阶数的Zernike矩来描述图像的强度特征和纹理特征,从而构造匹配模型,对图像特征进行粗匹配,并引入RANSAC方法对粗匹配结果进行优化。最后,利用形态学腐蚀与膨胀操作将特征区域进行连通,以确定篡改区域。结果实验结果表明,与已有的图像伪造检测方案相比,所提算法具备更高的检测精度和鲁棒性,在噪声和旋转等变换下仍有更好的检测效果。结论所提技术拥有较高的伪造检测准确性,在图像水印、信息安全领域具有一定的参考价值。     

抗频移声谱特征提取及目标分类应用研究

针对目标运动等导致的辐射噪声频谱特征的时变性对目标分类稳定性的影响,提出一种基于时频图像累积变换的抗频移声谱特征提取方法,不仅能够提取淹没在强噪声中的线谱信号,还能够实时给出谱线的参数信息,同时结合听觉特征识别原理,采用抗频移的仿倍频程的三角滤波法提取目标特征。仿真和实际数据处理表明,所提出的特征有助于探测设备克服目标未知的复杂运动带来的频谱时变影响,提高了分类特征的稳定性。