Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe(Yellow) River Estuary wetland

This paper develops a deep learning classification method with fully-connected 8-layers characteristics to classification of coastal wetland based on CHRIS hyperspectral image. The method combined spectral feature and multi-spatial texture feature information has been applied in the Huanghe(Yellow) River Estuary coastal wetland.The results show that:(1) Based on testing samples, the DCNN model combined spectral feature and texture feature after K-L transformation appear high classification accuracy, which is up to 99%.(2) The accuracy by using spectral feature with all the texture feature is lower than that using spectral only and combing spectral and texture feature after K-L transformation. The DCNN classification accuracy using spectral feature and texture feature after K-L transformation was up to 99.38%, and the outperformed that of all the texture feature by 4.15%.(3) The classification accuracy of the DCNN method achieves better performance than other methods based on the whole validation image, with an overall accuracy of 84.64% and the Kappa coefficient of 0.80.(4) The developed DCNN model classification algorithm ensured the accuracy of all types is more balanced, and it also greatly improved the accuracy of tidal flat and farmland, while kept the classification accuracy of main types almost invariant compared to the shallow algorithms. The classification accuracy of tidal flat and farmland is up to 79.26% and 56.72%respectively based on the DCNN model. And it improves by about 2.51% and 10.6% compared with that of the other shallow classification methods.

Dynamic Positioning System Based on Active Disturbance Rejection Technology

A dynamically positioned vessel, by the International Maritime Organization(IMO) and the certifying class societies(DNV, ABS, LR, etc.), is defined as a vessel that maintains its position and heading(fixed location or pre-determined track) exclusively by means of active thrusters. The development of control technology promotes the upgrading of dynamic positioning(DP) systems. Today there are two different DP systems solutions available on the market: DP system based on PID regulator and that based on model-based control. Both systems have limited disturbance rejection capability due to their design principle. In this paper, a new DP system solution is proposed based on Active Disturbance Rejection Control(ADRC) technology. This technology is composed of Tracking-Differentiator(TD), Extended State Observer(ESO) and Nonlinear Feedback Combination. On one hand, both TD and ESO can act as filters and can be used in place of conventional filters; on the other hand, the total disturbance of the system can be estimated and compensated by ESO, which therefore enhances the system's disturbance rejection capability. This technology's advantages over other methods lie in two aspects: 1) This method itself can not only achieve control objectives but also filter noisy measurements without other specialized filters; 2) This method offers a new useful approach to suppress the ocean disturbance. The simulation results demonstrate the effectiveness of the proposed method.

Tunable ultra-wideband terahertz filter based on three-dimensional arrays of H-shaped plasmonic crystals

A face-to-face system of double-layer three-dimensional arrays of H-shaped plasmonic crystals is proposed, and its transmission and filtering properties are investigated in the terahertz regime. Simulation results show that our design has excellent filtering properties. It has an ultra-wide bandgap and passband with steep band-edges, and the transmittance of the passband and the forbidden band are very close to I and 0, respectively. As the distance between the two face-to-face plates increases, the resonance frequency exhibits a gradual blueshift from 0.88 THz to 1.30 THz. Therefore, we can dynamically control the bandwidths of bandgap and passband by adding a piezoelectric ceramic plate between the two crystal plates. Furthermore, the dispersion relations of modes and electric field distributions are presented to analyze the generation mechanisms of bandgaps and to explain the location of bandgaps and the frequency shift phenomenon. Due to the fact that our design can provide many resonant modes, the bandwidth of the bandgaps can be greatly broadened. This paper can serve as a valuable reference for the design of terahertz functional devices and three-dimensional terahertz metamaterials.

Numerical simulation of four-wave mixing efficiency and its induced relative intensity noise

Four-wave mixing, as well as its induced intensity noise, is harmful to wavelength division multiplexing systems. The efficiency and the relative intensity noise of four-wave mixing are numerically simulated for the two-wave and the three-wave fiber transmissions. It is found that the efficiency decreases with the increase of both the frequency spacing and the fiber length, which can be explained using the quasi-phase-matching condition. Furthermore, the relative intensity noise decreases with the increase of frequency spacing, while it increases with the increase of fiber length, which is due to the considerable power loss of the pump light. This investigation presents a good reference for the practical application of wavelength division multiplexing systems.

Combining Multiple Factors of LightGBM and XGBoost Algorithms to Predict the Morbidity of Double-High Disease

In recent years, cardiovascular and cerebrovascular diseases have seriously jeopardized people’s health. Double-high (hyperlipidemia and hypertension) is one of the main causes of cardiovascular and cerebrovascular diseases in clinical practice. To diagnose cardiovascular and cerebrovascular diseases early, a reliable prediction system should be developed to assist doctors. In this paper, the different elements and evaluate the weight of these elements on double-high diseases are analyzed by machine learning method. The LightGBM algorithm and XGBoost algorithm were employed to construct the prediction models, respectively. Significantly, the proposed model was trained by real physical examination data and five meaningful and useful biochemical indicators were selected to encoding the raw physical examination data to numerical vector. The selected features are systolic blood pressure, diastolic blood pressure, serum triglyceride, serum high-density lipoprotein and serum low-density lipoprotein. The mean square error (MSE) after calculating the logarithm of the predicted value and the true value was introduced to assess the prediction model. Results show that this model can effectively predict cardiovascular and cerebrovascular diseases in advance.

An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

Autism Spectrum Disorder(ASD)is a complicated neurodevelopmen-tal disorder that is often identified in toddlers.The microarray data is used as a diagnostic tool to identify the genetics of the disorder.However,microarray data is large and has a high volume.Consequently,it suffers from the problem of dimensionality.In microarray data,the sample size and variance of the gene expression will lead to overfitting and misclassification.Identifying the autism gene(feature)subset from microarray data is an important and challenging research area.It has to be efficiently addressed to improve gene feature selection and classification.To overcome the challenges,a novel Intelligent Hybrid Ensem-ble Gene Selection(IHEGS)model is proposed in this paper.The proposed model integrates the intelligence of different feature selection techniques over the data partitions.In this model,the initial gene selection is carried out by data perturba-tion,and thefinal autism gene subset is obtained by functional perturbation,which reduces the problem of dimensionality in microarray data.The functional perturbation module employs three meta-heuristic swarm intelligence-based tech-niques for gene selection.The obtained gene subset is validated by the Deep Neural Network(DNN)model.The proposed model is implemented using python with six National Center for Biotechnology Information(NCBI)gene expression datasets.From the comparative study with other existing state-of-the-art systems,the proposed model provides stable results in terms of feature selection and clas-sification accuracy.

Vehicle Density Prediction in Low Quality Videos with Transformer Timeseries Prediction Model(TTPM)

Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India.The video obtained from such surveillance are of low quality.Still counting vehicles from such videos are necessity to avoid traf-fic congestion and allows drivers to plan their routes more precisely.On the other hand,detecting vehicles from such low quality videos are highly challenging with vision based methodologies.In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India,which is mostly an un-attempted entity by most available sources.In this work profound Detection Transformer(DETR)model is used for object(vehicle)detection.Here vehicles are anticipated in a rush-hour traffic video using a set of loss functions that carry out bipartite coordinating among estimated and information acquired on real attributes.Every frame in the traffic footage has its date and time which is detected and retrieved using Tesseract Optical Character Recognition.The date and time extricated and perceived from the input image are incorporated with the length of the recognized objects acquired from the DETR model.This furnishes the vehicles report with timestamp.Transformer Timeseries Prediction Model(TTPM)is proposed to predict the density of the vehicle for future prediction,here the regular NLP layers have been removed and the encoding temporal layer has been modified.The proposed TTPM error rate outperforms the existing models with RMSE of 4.313 and MAE of 3.812.

Design of active disturbance rejection internal model control strategy for SISO system with time delay process

A novel control scheme of active disturbance rejection internal model control(ADRIMC) is proposed to improve the anti-interference ability and robustness for the dead-time process. The active anti-interference concept is introduced into the internal model control(IMC) by analyzing the relationship between IMC and disturbance observer control(DOB). Further, a design process of disturbance filter is presented to realize the active anti-interference ability for ADRIMC scheme. The disturbance filter is used to estimate an equivalent disturbance consisting of both external disturbances and internal disturbances caused by model mismatches.Simulation results demonstrate that the proposed method possesses a good disturbance rejection performance, though losing some partial dynamic performance. In other words, the proposed method shows a tradeoff between the dynamic performance and the system robust.

Stimulated Brillouin scattering-induced phase noise in an interferometric fiber sensing system

Stimulated Brillouin scattering-induced phase noise is harmful to interferometric fiber sensing systems. The localized fluctuating model is used to study the intensity noise caused by the stimulated Brillouin scattering in a single-mode fiber. The phase noise structure is analyzed for an interferometric fiber sensing system, and an unbalanced Michelson interferometer with an optical path difference of 1 m, as well as the phase-generated carrier technique, is used to measure the phase noise. It is found that the phase noise is small when the input power is below the stimulated Brillouin scattering threshold, increases dramatically at first and then gradually becomes flat when the input power is above the threshold, which is similar to the variation in relative intensity noise. It can be inferred that the increase in phase noise is mainly due to the broadening of the laser linewidth caused by stimulated Brillouin scattering, which is verified through linewidth measurements in the absence and presence of the stimulated Brillouin scattering.

Phase-matching analysis of four-wave mixing induced by modulation instability in a single-mode fiber

Four-wave mixing induced by modulation instability in a single-mode fiber is analyzed from the phase-matching point of view. For the two-channel transmission, a method is proposed to select the four-wave-mixing-induced sidebands, which is based on the proper use of a continuous-wave and a pulse as light sources. We find that a mass of sidebands are generated in the modulation instability resonance region, and the power of the sideband increases with not only the peak power of the pump pulse but also the continuous-wave power which acts as a seed. The research will provide guidance for fiber communication and sensing systems using wavelength division multiplexing technology.

A spatial resolution effect analysis of remote sensing bathymetry

A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including Quick Bird and World View-2 remote sensing images with their original spatial resolution（2.4/2.0 m）and four kinds of reducing spatial resolution（4, 8, 16 and 32 m）, and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors（MRE） of Quick Bird and World View-2 bathymetry are 21.2% and 13.1%,compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors（MAE） are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.

A simple model of suppressing stimulated Brillouin scattering in optical fiber with frequency-modulated laser

A simple model is developed to study the mechanism of stimulated Brillouin scattering（SBS） suppression with frequency-modulated laser in optical fiber. By taking into account the laser frequency distribution along the fiber induced by frequency modulation, the average effective Brillouin gain is calculated to determine the SBS threshold. Experimental results show agreement with the numerical analysis. The application for SBS suppression in interferometric fiber sensing system is also discussed in this paper. The results show that the maximum input power can be increased effectively by frequency modulation method.

Hierarchical Stream Clustering Based NEWS Summarization System

News feed is one of the potential information providing sources which give updates on various topics of different domains.These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources.In this paper,the news summarization system is proposed for the news data streams from RSS feeds and Google news.Since news stream analysis requires live content,the news data are continuously collected for our experimentation.Themajor contributions of thiswork involve domain corpus based news collection,news content extraction,hierarchical clustering of the news and summarization of news.Many of the existing news summarization systems lack in providing dynamic content with domain wise representation.This is alleviated in our proposed systemby tagging the news feed with domain corpuses and organizing the news streams with the hierarchical structure with topic wise representation.Further,the news streams are summarized for the users with a novel summarization algorithm.The proposed summarization system generates topic wise summaries effectively for the user and no system in the literature has handled the news summarization by collecting the data dynamically and organizing the content hierarchically.The proposed system is compared with existing systems and achieves better results in generating news summaries.The Online news content editors are highly benefitted by this system for instantly getting the news summaries of their domain interest.

A new model analysis of the third harmonic voltage in inductive measurement for critical current density of superconducting films＊

The critical current density Jc is one of the most important parameters of high temperature superconducting films in superconducting applications, such as superconducting filter and superconducting Josephson devices. This paper presents a new model to describe inhomogeneous current distribution throughout the thickness of superconducting films applying magnetic field by solving the differential equation derived from Maxwell equation and the second London equation. Using this model, it accurately calculates the inductive third-harmonic voltage when the film applying magnetic field with the inductive measurement for Jc. The theoretic curve is consistent with the experimental results about measuring superconducting film, especially when the third-harmonic voltage just exceeds zero. The Jc value of superconducting films determined by the inductive method is also compared with results raeasured by four-probe transport method. The agreements between inductive method and transport method are very good.

FEATURE-EXTRACT ANALYSIS OF SERIAL ANALYSIS OF GENE EXPRESSION DATA

Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feature to improve the accuracy and efficiency when they are used for pattern recognition and clustering analysis. A Poisson Model-based Kernel (PMK) was proposed based on the Poisson distribution of the SAGE data. Kernel Principle Component Analysis (KPCA) with PMK was proposed and used in feature-extract analysis of mouse retinal SAGE data. The computa-tional results show that this algorithm can extract feature effectively and reduce dimensions of SAGE data.

Effects of Input Spectra on the Threshold of Modulation Instability in a Single-Mode Fiber

We investigate modulation instability (MI) in the presence of a monochromatic spectrum and on the background of a broadband amplified spontaneous emission spectrum induced by an erbium-doped fiber amplifier,respectively.For the former case,the MI threshold is ~110mW,which agrees well with the theoretical value.However,for the latter case,the threshold is as high as ~170mW,which indicates that the MI threshold depends on the shape of the input spectrum and that the broadband spectrum has a higher threshold than the monochromatic one.

Phonon Lifetime Measurement by Stimulated Brillouin Scattering Slow Light Technique in Optical Fiber

Phonon lifetime is a significant parameter in the process of stimulated Brillouin scattering(SBS).In the present study,SBS slow light technique is used to measure phonon lifetime.Brillouin bandwidth is divided into natural,spontaneous and stimulated bandwidth.Compared with the traditional heterodyne-detection and pump-probe techniques,the natural Brillouin bandwidth can be obtained by SBS slow light technique,which equals the reciprocal of phonon lifetime and has no relations with the pump power.Another advantage of this technique is that the effect of polarization can be excluded.The natural Brillouin bandwidth is measured to be~50 MHz and the phonon lifetime~3.2 ns in the conventional single-mode fiber(SMF)at room temperature and zero strain.The obtained results are guidable in applications where the phonon lifetime information is a requisite such as phase conjugation and pulse compression.

Quick Web Services Lookup Model Based on Hierarchical Registration

Quick Web Services Lookup (Q-WSL) is a new model to registration and lookup of complex services in the Internet. The model is designed to quickly find complex Web services by using hierarchical registration method. The basic concepts of Web services system are introduced and presented, and then the method of hierarchical registration of services is described. In particular, service query document description and service lookup procedure are concentrated, and it addresses how to lookup these services which are registered in the Web services system. Furthermore, an example design and an evaluation of its performance are presented.Specifically, it shows that the using of attributionbased service query document design and contentbased hierarchical registration in Q-WSL allows service requesters to discover needed services more flexibly and rapidly. It is confirmed that Q-WSL is very suitable for Web services system.

Multi-UAV Cooperative GPS Spoofing Based on YOLO Nano

In recent years,with the rapid development of the drone industry,drones have been widely used in many fields such as aerial photography,plant protection,performance,and monitoring.To effectively control the unauthorized flight of drones,using GPS spoofing attacks to interfere with the flight of drones is a relatively simple and highly feasible attack method.However,the current method uses ground equipment to carry out spoofing attacks.The attack range is limited and the flexibility is not high.Based on the existing methods,this paper proposes a multi-UAV coordinated GPS spoofing scheme based on YOLO Nano,which can launch effective attacks against target drones with autonomous movement:First,a single-attack drone based on YOLO Nano is proposed.The target tracking scheme achieves accurate tracking of the target direction on a single-attack drone;then,based on the single-UAV target tracking,a multi-attack drone coordinated target tracking scheme based on the weighted least squares method is proposed to realize the target drone Finally,a new calculation method for false GPS signals is proposed,which adaptively adjusts the flight trajectory of the attacking drone and the content of the false GPS signal according to the autonomous movement of the target drone.