Line spectrum detection algorithm based on the phase feature of target radiated noise

In order to improve the performance of line spectrum detection,according to the feature that the underwater target radiated noise containing stable line spectrum,the differences of the phase difference between line spectrum and background noise,a weighted line spectrum detection algorithm based on the phase variance is proposed in frequency domain.After phase difference alignment,the phase variance of line spectrum and the phase of background noise,respectively,are small and big in frequency domain,this method utilizes the weighted statistical algorithm to cumulate the frequency spectrum based on the phase variance,which can restrain the background noise disturbance,and enhance the signal to noise ratio（SNR）.The theory analysis and experimental results both verify that the proposed method can well enhance the energy of line spectrum,restrain the energy of background noise,and have better detection performance under lower SNR.

AN ADAPTIVE MEASUREMENT SCHEME BASED ON COMPRESSED SENSING FOR WIDEBAND SPECTRUM DETECTION IN COGNITIVE WSN

An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.

Statistics Modeling of Shallow Sea Ambient Noise and Its Applications in Low-frequency Line Spectrum Detection

The noise's statistical characteristics are very important for signal detection.In this paper,the ambient noise statistical characteristics are investigated by using the recorded noise data in sea trials first,and the results show that the generalized Gaussian distribution is a suitable model for the ambient noise modeling.Thereafter,the optimal detector based on maximum likelihood ratio can be deduced,and the asymptotic detector is also derived under weak signal assumption.The detector's performance is verified by using numerical simulation,and the results showthat the optimal and asymptotic detectors outperform the conventional correlation-integration system due to accuracy modeling of ambient noise.

Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network

In view of the uncertainty of the status of primary users in cognitive networks and the fact that the random detection strategy cannot guarantee cognitive users to accurately find available channels,this paper proposes a joint random detection strategy using the idle cognitive users in cognitive wireless networks.After adding idle cognitive users for detection,the compressed sensing model is employed to describe the number of available channels obtained by the cognitive base station to derive the detection performance of the cognitive network at this time.Both theoretical analysis and simulation results show that using idle cognitive users can reduce service delay and improve the throughput of cognitive networks.After considering the time occupied by cognitive users to report detection information,the optimal participation number of idle cognitive users in joint detection is obtained through the optimization algorithm.

Femtosecond laser processing stainless steel foil and its Fourier spectrum detection

In this paper, we use femtosecond laser pulse to scribe 304 stainless steel foil, detect the Fourier transform infrared spectrum of the sample before and after processing, confirm the "cold processing" and "thermal processing" and their mutual conversion, and determine the "cold processing" parameter window. The ablation threshold and incubation coefficient of 304 stainless steel foil are calculated, and the effects of scanning speed and effective pulse number on the ablation threshold are analyzed. The ANSYS software is used to simulate the radial and axial temperature distributions of the surface on 304 stainless steel foil sample and the heat-affected zone with a femtosecond laser fluence of 10 J/cm2 and an effective number of pulses of 1 200 are obtained. In the aspect of spectral detection, the Fourier transform infrared spectra of the sample before and after processing are measured and two processing mechanisms of "cold processing" and "hot processing" are confirmed, which proves that we can achieve the conversion between "cold processing" and "hot processing" by changing the laser fluence and determine the "cold processing" laser fluence range.

Improving coherent averaging line spectrum detection with phase interpolation and compensation

For the purpose of resolving the problem of performance deterioration introduced by inaccurate phase compensation in existing coherent averaging line spectrum detectors, a modified coherent detector is proposed. The three point interpolation in frequency domain is applied to obtain accurate estimate of phase difference between segments when the segmented length is not an integral multiple of the signal period. Then the segmented data are multiplied by a complex coefficient to remove the phase difference and synchronize the phases of all the segments before coherent averaging. Theoretical analysis shows that there will be a gain of 3.9 dB at most by using the modified detector. The detection performance of the incoher- ent averaging power spectrum detector （AVGPR）, the phase coherent averaging detector, the modified coherent averaging detector are compared with each other by computer simulations. The results coincide basically with the theoretical analysis, which show the superiority of the modified detector to the former two detectors.

Design of a Weak Signal Detecting Circuit in the Infrared Spectrum Absorbed Type Gas Sensor

In the infrared spectrum absorbed type gas concentration sensor,voltage signal obtained from the two-channel thermopile infrared detector TPS2534 is very weak.In order to solve this problem,the authors have established the structure of the sensor and designed weak signal detecting circuit of the sensor based on infrared spectrum absorption principle,differential de-noising principle and weak signal detecting principle.The authors have made experiments using CH4 gas.The results show that the circuit can remove noise effectively and detect weak electrical signal obtained from the detector.

Collaborative Spectrum Sensing for Illegal Drone Detection: A Deep Learning-Based Image Classification Perspective

Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.

Cooperative detection algorithm of spectrum holes in cognitive radio

To improve the detection performance of sensing users for primary users in the cognitive radio, an optimal cooperative detection algorithm for many sensing users is proposed. In this paper, optimal decision thresholds of each sensing user are discussed. Theoretical analysis and simulation results indicate that the detection probability of optimal decision threshold rules is better than that of determined threshold rules when the false alarm of the fusion center is constant. The proposed optimal cooperative detection algorithm improves the detection performance of primary users as the attendees grow. The 2 dB gain of detection probability can be obtained when a new sensing user joins in, and there is a 17 dB improvement when the accumulation number increases from 1 to 50.

A Diffusion-Based Distributed Collaborative Energy Detection Algorithm for Spectrum Sensing in Cognitive Radio

Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.

Results on Pathogen Detection of Foot and Mouth Disease in Guangxi China and Analysis on Its Popular Spectrum

Objective: The study aims to understand the characteristics and epidemic trend of the pathogen of hand, foot and mouth disease (HFMD) in Guangxi regions, China. Besides, it aims to analyze the differences of intestinal virus detection rate between anal swab and pharyngeal swab samples. Methods: Anal swab and pharyngeal swabs of suspected HFMD children were collected in our hospital from 2012 to 2015. Real-time fluorescent PCR (Polymerase Chain Reaction) was used to detect enterovirus 71 (EV71), coxsackie virus type 16 (CA16), and universal intestinal virus nucleic acid (EV). Composition and conversion of predominant pathogens were analyzed, and paired samples’ test results of swabs anal and pharyngeal swab were statistically analyzed. Results: There are 681 cases with enterovirus in 2351 cases of patients. Among those who got enterovirus, there are 501 cases of EV71, 102 cases of CA16 and 79 cases of EV. From 2012 to 2015, the total proportion of the virus detection is 46.47%, 16.23%, 41.02% and 15.33% respectively in each year, while the proportion of predominant epidemic virus is 93.93% of EV71, 66.12% of CA16, 89.30% of EV71 and 98.73% of EV, non-EV71, non-CA16 EV (from October to December in 2015). It’s obvious that the total virus detection rate in 2012 and 2014 is significantly higher than that in 2013 and 2015. There is statistical significance. Conclusion: The main HFMD pathogens are EV71 from 2012 to 2015 in Guangxi regions. In 2012 and 2014, the predominant epidemic pathogens were EV71, while in 2013 and 2015, the predominant epidemic pathogens turn to be CA16 and non-EV71, non-CA16 EV respectively. What’s more, collecting anal swab and pharyngeal swab virus at the same time for nucleic acid detection is of great significance to improve the HFMD laboratory diagnostic.

Automatic Anomaly Detection of Respiratory Motion Based on Singular Spectrum Analysis

The realization of automatic anomaly detection of respiratory motion could be very useful to prevent accidental damage during radiation therapy. In this paper, we proposed an automatic anomaly detection method using singular value decomposition analysis. Before applying this method, the investigator needs a normal respiratory motion data of a patient. From these data, a trajectory matrix representing normal time-series feature is created. Decomposing the matrix, we obtained the feature of normal time series. Then, we applied the same procedure to real-time data and obtained real-time features. Calculating the similarity of those feature matrixes, an anomaly score was obtained. Patient motion was observed by a depth camera. In our simulation, two types of motion e.g. cough and sudden stop of breathing were successfully detected, while gradual change of respiratory cycle frequency was not detected clearly.

An Intelligent Fuzzy Based Energy Detection Approach for Cooperative Spectrum Sensing

Cognitive radio systems are helpful to access the unused spectrum using the popular technique, referred to as spectrum sensing. Spectrum sensing involves the detection of primary user (PU) signal using dynamic spectrum access. Cooperative spectrum sensing takes advantage of the spatial diversity in multiple cognitive radio user networks to improve the sensing accuracy. Though the cooperative spectrum sensing schemes significantly improve the sensing accuracy, it requires the noise variance and channel state information which may lead to transmission overhead. To overcome the drawbacks in conventional cooperative spectrum sensing, this paper proposes a fuzzy system based cooperative spectrum sensing. Selection combining (SC) and maximum ratio combining (MRC) are used at fuzzy based fusion center to obtain the value of the sensing energy. These energy values are utilized in finding the presence of PU, results in improved sensing accuracy. In addition, an intelligent fuzzy fusion algorithm determines the PU presence without the channel state information based on multiple threshold values. Simulation results show that the proposed scheme outperforms the existing schemes in terms of sensing accuracy.

近红外光谱联合化学计量学在柑橘类水果质量无损检测方面的最新研究及应用进展

柑橘类水果以其诱人的风味和高保健作用而备受消费者青睐。面对日益增长的市场份额,以及对口感、质量的双重需求,高品质柑橘类水果成为消费偏爱。快速检测技术的研发应用可为柑橘类水果的安全生产和消费保驾护航。近红外(near-infrared,NIR)光谱技术作为一种无损、便捷、高效的绿色检测技术,已被广泛用于水果质量检测研究。文章全面归纳综述了近5年来NIR光谱技术结合化学计量学在柑橘类水果内部成分、外部缺陷、活性物质、病害、品种、分类、产地等方面的检测研究及应用进展,阐明了NIR光谱技术在柑橘类水果质量检测应用方面的发展趋势并提出了相关建议,为完善光谱技术检测柑橘类水果理论和研发专用水果检测设备提供方法借鉴。

HBV感染孕妇早期胆汁酸谱检测在妊娠期肝内胆汁淤积症诊断中的应用

目的探讨HBV感染孕妇早期胆汁酸谱检测在妊娠期肝内胆汁淤积症(ICP)诊断中的应用价值。方法回顾性分析2020年1月—2022年12月无锡市妇幼保健院收治的186例HBV感染孕妇,根据ICP诊断标准将其分为单纯HBV组(124例)和HBV并发ICP组(62例),收集两组孕妇一般资料及妊娠早期围保肝生化指标和胆汁酸谱检测结果,采用单因素分析、多因素logistic回归分析HBV感染孕妇并发ICP的主要影响因素,ROC曲线分析预测诊断效能。结果与HBV组比,HBV并发ICP组AST[(46.60±38.98)U/L比(30.97±31.49)U/L,P=0.004]、ALT[(50.80±36.81)U/L比(40.32±29.45)U/L,P=0.037]、DBil[(6.07±2.34)μmol/L比(4.73±1.83)μmol/L,P<0.001]、TBA[(16.98±2.48)μmol/L比(6.01±2.34)μmol/L,P=0.010]明显升高,CA[(0.59±0.49)μmol/L比(0.40±0.34)μmol/L,P=0.007]、GCA[(2.41±1.04)μmol/L比(1.52±0.70)μmol/L,P<0.001]、GDCA[(0.92±0.35)μmol/L比(0.67±0.37)μmol/L,P<0.001]、GCDCA[(2.14±0.89)μmol/L比(1.67±0.56)μmol/L,P<0.001]也升高,且AST、DBil、GCA、GDCA、GCDCA为HBV并发ICP的主要危险因素(P<0.05);AST、DBil、GCA对HBV并发ICP诊断价值较高(分别AUC 0.747、0.725、0.761);GCDCA、GDCA的诊断价值一般(AUC 0.667、0.688)。结论胆汁酸谱GCA、GCDCA、GDCA亚型升高及AST、DBil升高均是HBV感染孕妇并发ICP发生的主要危险因素,且GCA的预测诊断价值最高,临床应结合肝功能指标综合诊断。

海面船舶探测谱段优选与效能分析

船舶等海面目标的探测对于海面管理、国防安全、搜索救援等领域有着重要意义。星载红外遥感覆盖范围广,是实现船舶目标广域探测的有效手段。受太阳天顶角昼夜循环的影响,船舶与海面的温差周期性变化,导致海面船舶探测场景存在一天两次的热交叉时期,此时使用单一探测谱段难以满足船舶目标全天时探测需求。文章以中纬度夏季和冬季海面环境为例,建立海面船舶探测场景的24小时红外辐射特征,提出多谱段优选组合方案,通过3.50~4.10μm和10.25~10.75μm双探测谱段探测实现信噪比优于16.52(夏季)和17.64(冬季)的昼夜连续观测。所提出的双谱段探测方法为实现广域全天时海面目标探测应用提供了技术支撑。

Harmonicity Spectrum

Perceiving harmonic information (especially weak harmonic information) in time series has important scientific and engineering significance. Fourier spectrum and time-frequency spectrum are commonly used tools for perceiving harmonic information, but they are often ineffective in perceiving weak harmonic signals because they are based on energy or amplitude analysis. Based on the theory of Normal time-frequency transform (NTFT) and complex correlation coefficient, a new type of spectrum, the Harmonicity Spectrum (HS), is developed to perceive harmonic information in time series. HS is based on the degree of signal harmony rather than energy or amplitude analysis, and can therefore perceive very weak harmonic information in signals sensitively. Simulation examples show that HS can detect harmonic information that cannot be detected by Fourier spectrum or time-frequency spectrum. Acoustic data analysis shows that HS has better resolution than traditional LOFAR spectrum.

Inter-Data Commonality Detection for Spectrum Monitoring in Wireless Sensor Networks

Cooperative spectrum monitoring with multiple sensors has been deemed as an efficient mechanism for improving the monitoring accuracy and enlarging the monitoring area in wireless sensor networks.However,there exists redundancy among the spectrum data collected by a sensor node within a data collection period,which may reduce the data uploading efficiency.In this paper,we investigate the inter-data commonality detection which describes how much two data have in common.We define common segment set and divide it into six categories firstly,then a method to measure a common segment set is conducted by extracting commonality between two files.Moreover,the existing algorithms fail in finding a good common segment set,so Common Data Measurement(CDM)algorithm that can identify a good common segment set based on inter-data commonality detection is proposed.Theoretical analysis proves that CDM algorithm achieves a good measurement for the commonality between two strings.In addition,we conduct an synthetic dataset which are produced randomly.Numerical results shows that CDM algorithm can get better performance in measuring commonality between two binary files compared with Greedy-String-Tiling(GST)algorithm and simple greedy algorithm.

超声传感器检测乳状液质量的方法研究

超声波检测技术又称无损检测技术,具有无污染、无损伤、低成本、高精度、高效率等特点。不仅可以应用于实验室分析,而且适用于现场的快速检测和实时在线分析。牛奶是人们日常生活中不可缺少的营养食品,通过对牛奶浓度及质量变化所引起的超声频谱衰减进行数据分析,进而快速检测牛奶品质。实验证明超声检测乳状液技术是可行的,在食品质检中具有广阔的应用前景。