Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

Frame Detection Based on Cyclic Autocorrelation and Constant False Alarm Rate in Burst Communication Systems

Frame detection is important in burst communication systems for its contributions in frame synchronization.It locates the information bits in the received data stream at receivers.To realize frame detection in the presence of additive white Gaussian noise(AWGN) and frequency offset,a constant false alarm rate(CFAR) detector is proposed through exploitation of cyclic autocorrelation feature implied in the preamble.The frame detection can be achieved prior to bit timing recovery.The threshold setting is independent of the signal level and noise level by utilizing CFAR method.Mathematical expressions is derived in AWGN channel by considering the probability of false alarm and probability of detection,separately.Given the probability of false alarm,the mathematical relationship between the frame detection performance and E_b/N_0 of received signals is established.Experimental results are also presented in accordance with analysis.

Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellation(SLC)and Sidelobe Blanking are two unique solutions to solve this problem(SLB).Aside from this approach,the probability of false alert and likelihood of detection are the most essential parameters in radar.The chance of a false alarm for any radar system should be minimal,and as a result,the probability of detection should be high.There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment.With the necessity for interference cancellation methods and the constant false alarm rate(CFAR),the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference.The received radar returns and interference are simulated as non-stationary in this approach,and side-lobe interference is cancelled using an adaptive algorithm.By comparing the performance of adaptive algorithms,simulation results are shown.In a severe clutter situation,the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique.

Multi-Attack Intrusion Detection System for Software-Defined Internet of Things Network

Currently,the Internet of Things(IoT)is revolutionizing communi-cation technology by facilitating the sharing of information between different physical devices connected to a network.To improve control,customization,flexibility,and reduce network maintenance costs,a new Software-Defined Network(SDN)technology must be used in this infrastructure.Despite the various advantages of combining SDN and IoT,this environment is more vulnerable to various attacks due to the centralization of control.Most methods to ensure IoT security are designed to detect Distributed Denial-of-Service(DDoS)attacks,but they often lack mechanisms to mitigate their severity.This paper proposes a Multi-Attack Intrusion Detection System(MAIDS)for Software-Defined IoT Networks(SDN-IoT).The proposed scheme uses two machine-learning algorithms to improve detection efficiency and provide a mechanism to prevent false alarms.First,a comparative analysis of the most commonly used machine-learning algorithms to secure the SDN was performed on two datasets:the Network Security Laboratory Knowledge Discovery in Databases(NSL-KDD)and the Canadian Institute for Cyberse-curity Intrusion Detection Systems(CICIDS2017),to select the most suitable algorithms for the proposed scheme and for securing SDN-IoT systems.The algorithms evaluated include Extreme Gradient Boosting(XGBoost),K-Nearest Neighbor(KNN),Random Forest(RF),Support Vector Machine(SVM),and Logistic Regression(LR).Second,an algorithm for selecting the best dataset for machine learning in Intrusion Detection Systems(IDS)was developed to enable effective comparison between the datasets used in the development of the security scheme.The results showed that XGBoost and RF are the best algorithms to ensure the security of SDN-IoT and to be applied in the proposed security system,with average accuracies of 99.88%and 99.89%,respectively.Furthermore,the proposed security scheme reduced the false alarm rate by 33.23%,which is a significant improvement over prevalent schemes.Finally,tests of the algorithm for dataset selection showed that the rates of false positives and false negatives were reduced when the XGBoost and RF algorithms were trained on the CICIDS2017 dataset,making it the best for IDS compared to the NSL-KDD dataset.

Preparation of polythiophene/WO_3 organic-inorganic hybrids and their gas sensing properties for NO_2 detection at low temperature

Polythiophene/WO3（PTP/WO3）organic-inorganic hybrids were synthesized by an in situ chemical oxidative polymerization method,and char- acterized by X-ray diffraction（XRD）,transmission electron microscopy（TEM）and thermo-gravimetric analysis（TGA）.The Polythiophene/ WO3 hybrids have higher thermal stability than pure polythiophene,which is beneficial to potential application as chemical sensors.Gas sensing measurements demonstrate that the gas sensor based on the Polythiophene/WO3 hybrids has high response and good selectivity for de- tecting NO2 of ppm level at low temperature.Both the operating temperature and PTP contents have an influence on the response of PTP/WO3 hybrids to NO2.The 10 wt%PTP/WO3 hybrid showed the highest response at low operating temperature of 70-C.It is expected that the PTP/WO3 hybrids can be potentially used as gas sensor material for detecting the low concentration of NO2 at low temperature.

Detection and analysis of gas tightness of continuous annealing furnaces

When a cold rolled strip is being treated in a continuous annealing furnace which is full of protective gas, the gas tightness of the furnace body, the connected facilities and the gas channels become an important indicator that directly affects the product＇s surface quality and shows the technical level of the design, the manufacture and the installation. By considering the problems of the gas tightness of a vertical annealing furnace in the installation and maintenance, this thesis evaluates the gas tightness indicator and gas tightness related level of the furnace body and the circulation duct, while studying and analyzing the technologies of negative-pressure leak detection and sealing.

Development and Application of Methane Leakage Monitoring System for Gas Transmission Pipeline

Oil and gas pipeline transportation,as a relatively safe way of oil and gas transportation,undertakes most of the transportation tasks of crude oil and natural gas.Oil and gas pipeline accidents affect a wide range of consequences.Therefore,the oil and gas pipeline leakage detection is paid more and more attention.In this paper,ultra-low power methane gas sensor is selected to collect methane gas concentration in the air,and wireless network technology is used to build a wireless network sensor system with 4G function.Through the sensor distribution along the pipeline,it can intuitively and accurately judge whether there is a micro-leakage in the pipeline,and understand the diffusion situation after the leakage.The sensor system has high reliability and stability,and has high value of popularization and application.

A Neural Based Experimental Fire-Outbreak Detection System for Urban Centres

Incessant fire-outbreak in urban settlements has remained intractable especially in developing countries like Nigeria. This is often characterized by grave socio-economic aftermath effects. Urban fire outbreak in Nigerian cities has been on increase in recent times. The major problem faced by fire fighters in Nigerian urban centres is that there are no mechanisms to detect fire outbreaks early enough to save lives and properties. They often rely on calls made by neighbours or occupants when an outbreak occurs and this accounts for the delay in fighting fire outbreaks. This work uses Artificial Neural Networks (ANN) with backpropagation method to detect the occurrence of urban fires. The method uses smoke density, room temperature and cooking gas concentration as inputs. The work was implemented using Java programming language and results showed that it detected the occurrence of urban fires with reasonable accuracy. The work is recommended for use to minimize the effect of urban fire outbreak.

Rock physics modeling of heterogeneous carbonatereservoirs： porosity estimation and hydrocarbon detection

In heterogeneous natural gas reservoirs, gas is generally present as small patch-like pockets embedded in the water-saturated host matrix. This type of heterogeneity, alsocalled ＂patchy saturation＂, causes significant seismic velocity dispersion and attenuation. Toestablish the relation between seismic response and type of fluids, we designed a rock physicsmodel for carbonates. First, we performed CT scanning and analysis of the fluid distributionin the partially saturated rocks. Then, we predicted the quantitative relation between the waveresponse at different frequency ranges and the basic lithological properties and pore fluids.A rock physics template was constructed based on thin section analysis of pore structuresand seismic inversion. This approach was applied to the limestone gas reservoirs of the rightbank block of the Amu Darya River. Based on poststack wave impedance and prestack elasticparameter inversions, the seismic data were used to estimate rock porosity and gas saturation.The model results were in ~ood a^reement with the production regime of the wells.

A Review on Graphene-Based Gas/Vapor Sensors with Unique Properties and Potential Applications

Graphene-based gas/vapor sensors have attracted much attention in recent years due to their variety of structures, unique sensing performances, room-temperature working conditions, and tremendous application prospects, etc.Herein, we summarize recent advantages in graphene preparation, sensor construction, and sensing properties of various graphene-based gas/vapor sensors, such as NH_3, NO_2, H_2, CO, SO_2, H_2S, as well as vapor of volatile organic compounds.The detection mechanisms pertaining to various gases are also discussed. In conclusion part, some existing problems which may hinder the sensor applications are presented. Several possible methods to solve these problems are proposed, for example, conceived solutions, hybrid nanostructures, multiple sensor arrays, and new recognition algorithm.

Combining TDLAS and multi-fusion algorithms for methane gas concentration detection

High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration information is susceptible to noise, which leads to its useful signal being drowned by noise. A fusion algorithm of variational modal decomposition(VMD) and improved wavelet threshold filtering is proposed, which is used in combination with tunable diode laser absorption spectroscopy(TDLAS) to implement a non-contact, high-resolution methane gas concentration detection. The fusion algorithm can perform noise reduction and further segmentation of the methane gas detection signal. And the simulation and experiment verify the effectiveness of the fusion algorithm, and the experimental results show that for the detection of air containing 10 ppm, 30 ppm, 60 ppm, 80 ppm, and 99 ppm methane, the errors are 12.75%, 8.18%, 3.37%, 2.46%, and 1.78%, respectively.

Experimental simulations and methods for natural gas hydrate analysis in China

This paper provides an overview of the developments in analytical and testing methods and experimental simulations on gas hydrate in China.In the laboratory,the analyses and experiments of gas hydrate can provide useful parameters for hydrate exploration and exploitation.In recent years,modem analytical instruments and techniques,including Laser Raman spectroscopy (Raman),X-ray diffraction (XRD),X-ray computed tomography (X-CT),scanning electron microscope (SEM),nuclear magnetic resonance (NMR) and high pressure differential scanning calorimetry (DSC),were applied in the study of structure,formation mechanisms,phase equilibrium,thermal physical properties and so forth of gas hydrates.The detection technology and time-domain reflectometry (TDR)technique are integrated to the experimental devices to study the physical parameters of gas hydrates,such as the acoustics,resistivity,thermal,and mechanical properties.It is believed that the various analytical techniques together with the experimental simulations from large-scale to micro-scale on gas hydrate will play a significant role and provide a powerful support for future gas hydrate researches.

Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis(FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder,a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.

A Robust Fault Detection Algorithm for the GNSS/INS Integrated Navigation Systems

Integrity is an important index for GNSS-based navigation and positioning, and the receiver autonomous integrity monitoring (RAIM) algorithm has been presented for integrity applications. In the integrated navigation systems of a global navigation satellite system (GNSS) and inertial navigation system (INS),the conventional RAIM algorithm has been developed to extended receiver autonomous integrity monitoring (ERAIM). However, the ERAIM algorithm may fail and a false alarm may generate once the measurements are contaminated by significant outliers, and this problem is rarely discussed in the existing literatures. In this paper, a robust fault detection and the corresponding data processing algorithm are proposed based on the ERAIM algorithm and the robust estimation. In the proposed algorithm, weights of the measurements are adjusted with the equivalent weight function, and the efficiency of the outlier detection and identification is improved, therefore, the estimates become more reliable, and the probability of the false alarm is decreased. Experiments with the data collected under actual environments are implemented, and results indicate that the proposed algorithm is more efficient than the conventional ERAIM algorithm for multiple outliers and a better filtering performance is achieved.

Hypothesis testing analysis and unknown parameter estimation of GPS signal detection

Hypothesis testing analysis and unknown parameter estimation of both the intermediate frequency(IF) and baseband GPS signal detection are given by using the generalized likelihood ratio test(GLRT) approach,applying the model of GPS signal in white Gaussian noise,It is proved that the test statistic follows central or noncentral F distribution,It is also pointed out that the test statistic is nearly identical to central or noncentral chi-squared distribution because the processing samples are large enough to be considered as infinite in GPS acquisition problem.It is also proved that the probability of false alarm,the probability of detection and the threshold are affected largely when the hypothesis testing refers to the full pseudorandom noise(PRN) code phase and Doppler frequency search space cells instead of each individual cell.The performance of the test statistic is also given with combining the noncoherent integration.

Robust and Low-Complexity Synchronization for Energy Detection UWB Receiver

A simple method using aided sliding rectangular windows for synchronization in energy detector （ED） receiver is proposed for impulse-based ultra wideband radios （IR-UWB） under binary pulse position modulation （PPM）, therefore grants an attractive solution for gaining low complexity while the accompanying performance loss in terms of UWB signal reception is comparatively low. Also, a method is developed to sup- press noise through accumulation of integrated results before synchronization point is reached. This proposed method can effectively reduce the impact of one of the major performance-degrading factors in ED receivers, i. e., noise caused by heightened noise floor due to large bandwidth product. Our theoretic work on this im- proved synchronization performance and relevant simulations are conducted on IEEE 802.15.4a channel mod- els, and results show that the proposed design scheme can effectively decrease both the probability of false alarm and probability of missed detection.

A Double-threshold Constant False Alarm Rate Detector And Its Performance Analysis

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Intra-Cavity Absorption Gas Sensors Using Wavelength Modulation and Wavelength Sweep

Wavelength modulation technique （WMT） and wavelength sweep technique （WST） are introduced into intra-cavity absorption gas sensors （ICAGS） for low concentration gas detection. The optimized parameters of the system maximizing the signal-to-noise ratio （SNR） are found. Calibration of acetylene concentration and gas recognition are both realized.

MOF/Polymer-Integrated Multi-Hotspot Mid-Infrared Nanoantennas for Sensitive Detection of CO_(2) Gas

Metal-organic frameworks(MOFs)have been extensively used for gas sorption,storage and separation owing to ultrahigh porosity,exceptional thermal stability,and wide structural diversity.However,when it comes to ultra-low concentration gas detection,technical bottlenecks of MOFs appear due to the poor adsorption capacity at ppm-/ppblevel concentration and the limited sensitivity for signal transduction.Here,we present hybrid MOF-polymer physi-chemisorption mechanisms integrated with infrared(IR)nanoantennas for highly selective and ultrasensitive CO_(2) detection.To improve the adsorption capacity for trace amounts of gas molecules,MOFs are decorated with amino groups to introduce the chemisorption while maintaining the structural integrity for physisorption.Additionally,leveraging all major optimization methods,a multi-hotspot strategy is proposed to improve the sensitivity of nanoantennas by enhancing the near field and engineering the radiative and absorptive loss.As a benefit,we demonstrate the competitive advantages of our strategy against the state-of-the-art miniaturized IR CO_(2) sensors,including low detection limit,high sensitivity(0.18%/ppm),excellent reversibility(variation within 2%),and high selectivity(against C_(2)H_(5)OH,CH_(3)OH,N_(2)).This work provides valuable insights into the integration of advanced porous materials and nanophotonic devices,which can be further adopted in ultra-low concentration gas monitoring in industry and environmental applications.

SELECTIVE SULFUR DIOXIDE DETERMINATION BY GAS DIFFUSION FLOW INJECTION ANALYSIS WITH CHEMILUMINESCENT DETECTION

Sulfur dioxide has been found to decrease the chemiluminescence of luminol-iodine system.A new determination method for sulfur dioxide in atmosphere is developed by applying this reaction to a flow injection gas diffusion separation system.This permits the determination of sulfur dioxide selectively and rapidly.