Research and Application of Log Defect Detection and Visualization System Based on Dry Coupling Ultrasonic Method

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.

Identification of thermal front dynamics in the northern Malacca Strait using ROMS 3D-model

The thermal front in the oceanic system is believed to have a significant effect on biological activity.During an era of climate change,changes in heat regulation between the atmosphere and oceanic interior can alter the characteristics of this important feature.Using the simulation results of the 3D Regional Ocean Modelling System(ROMS),we identified the location of thermal fronts and determined their dynamic variability in the area between the southern Andaman Sea and northern Malacca Strait.The Single Image Edge Detection(SIED)algorithm was used to detect the thermal front from model-derived temperature.Results show that a thermal front occurred every year from 2002 to 2012 with the temperature gradient at the location of the front was 0.3°C/km.Compared to the years affected by El Ni?o and negative Indian Ocean Dipole(IOD),the normal years(e.g.,May 2003)show the presence of the thermal front at every selected depth(10,25,50,and 75 m),whereas El Ni?o and negative IOD during 2010 show the presence of the thermal front only at depth of 75 m due to greater warming,leading to the thermocline deepening and enhanced stratification.During May 2003,the thermal front was separated by cooler SST in the southern Andaman Sea and warmer SST in the northern Malacca Strait.The higher SST in the northern Malacca Strait was believed due to the besieged Malacca Strait,which trapped the heat and make it difficult to release while higher chlorophyll a in Malacca Strait is due to the freshwater conduit from nearby rivers(Klang,Langat,Perak,and Selangor).Furthermore,compared to the southern Andaman Sea,the chlorophyll a in the northern Malacca Strait is easier to reach the surface area due to the shallower thermocline,which allows nutrients in the area to reach the surface faster.

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.

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource supervision.Almost all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords etc.Therefore,automatic weapons detection is a vital requirement now a day.The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net.Real time datasets,from local surveillance department’s test sessions are used for model training and testing.Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension.The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution.Efficient-Net algorithm gives better results than existing algorithms.By using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

A Practical Comparison between Signature Approach and Other Existing Approaches in Error Detection over TCP

Error coding is suited when the transmission channel is noisy. This is the case of wireless communication. So to provide a reliable digital data transmission, we should use error detection and correction algorithms. In this paper, we constructed a simulation study for four detection algorithms. The first three methods—hamming, LRC, and parity are common techniques in networking while the fourth is a proposed one called Signature. The results show that, the hamming code is the best one in term of detection but the worst one in term of execution time. Parity, LRC and signature have the same ability in detecting error, while the signature has a preference than all others methods in term of execution time.

A Parameter-Detection Algorithm for Moving Ships

In traffic-monitoring systems, numerous vision-based approaches have been used to detect vehicle parameters. However, few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object. In this paper, we present an approach to detecting the parameters of a moving ship in an inland river. This approach involves interactive calibration without a calibration reference. We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios, and we detect ship length, width, speed, and flow. We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90% for all parameters.

Tire Defect Detection Using Local and Global Features

In this paper, we present a tire defect detection algorithm based on sparse representation. The dictionary learned from reference images can efficiently represent the test image. As the representation coefficients of normal images have a specific distribution, the local feature can be estimate by comparing representation coefficient distribution. Meanwhile, a coding length is used to measure the global features of representation coefficients. The tire defect is located by both these local and global features. Experimental results demonstrate that the proposed method can accurately detect and locate the tire defects.

Analysis and Design of Surgical Instrument Localization Algorithm

With the help of surgical navigation system,doctors can operate on patients more intuitively and accurately.The positioning accuracy and real-time performance of surgical instruments are very important to the whole system.In this paper,we analyze and design the detection algorithm of surgical instrument location mark,and estimate the posture of surgical instrument.In addition,we optimized the pose by remapping.Finally,the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments.The final result shows a high accuracy.

Delivery Invoice Information Classification System for Joint Courier Logistics Infrastructure

With the growth of the online market,demand for logistics and courier cargo is increasing rapidly.Accordingly,in the case of urban areas,road congestion and environmental problems due to cargo vehicles are mainly occurring.The joint courier logistics system,a plan to solve this problem,aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery companies.However,several courier companies use different types of courier invoices.Such a system has a problem of information data transmission interruption.Therefore,the data processing process was systematically analyzed,a practically feasible methodology was devised,and delivery invoice information processing standards were established for this.In addition,the importance of this paper can be emphasized in terms of data processing in the logistics sector,which is expected to grow rapidly in the future.The results of this study can be used as basic data for the implementation of the logistics joint delivery terminal system in the future.And it can be used as a basis for securing the operational reliability of the joint courier logistics system.

Targets detecting in the ocean using the cross-polarized channels of fully polarimetric SAR data

Azimuth ambiguities （ghost targets） discrimination is of great interest with the development of a synthet- ic aperture radar （SAR）. And the azimuth ambiguities are often mistaken as actual targets and cause false alarms. For actual targets, HV channel signals acquired by a fully polarimetric SAR are approximately equal to a VH channel in magnitude and phase, i.e., the reciprocity theorem applies, but shifted in phase about ±π for the first-order azimuth ambiguities. Exploiting this physical behavior, the real part of the product of the two cross-polarized channels, i.e. （SHVSVH）, hereafter called A12r, is employed as a new parameter for a target detection at sea. Compared with other parameters, the contrast of A12r image between a target and the surrounding sea surface will be obviously increased when A12r image is processed by mean filtering algo- rithm. Here, in order to detect target with constant false-alarm rates （CFARs）, an analytical expression for the probability density function （pdf） ofA12r is derived based on the complexWishart-distribution. Because a value of A12r is greater/less than 0 for real target/its azimuth ambiguities, the first-order azimuth ambiguities can be completely removed by this A12r-based CFAR technology. Experiments accomplished over C-band RADARSAT-2 fully polarimetric imageries confirm the validity.

Characteristics of cyclone climatology and variability in the Southern Ocean

A new climatology of cyclones in the Southern Ocean is generated by applying an automated cyclone detection and tracking algorithm （developed by Hodges at the Reading University） for an improved and relatively high- resolution European Centre for Medium-Range Weather Forecasts atmospheric reanalysis during 1979-2013. A validation shows that identified cyclone tracks are in good agreement with a available analyzed cyclone product. The climatological characteristics of the Southern Ocean cyclones are then analyzed, including track, number, density, intensity, deepening rate and explosive events. An analysis shows that the number of cyclones in the Southern Ocean has increased for 1979-2013, but only statistically significant in summer. Coincident with the circumpolar trough, a single high-density band of cyclones is observed in 55^-67~S, and cyclone density has generally increased in north of this band for 1979-2013, except summer. The intensity of up to 70% cyclones in the Southern Ocean is less than 980 hPa, and only a few cyclones with pressure less than 920 hPa are detected for 1979-2013. Further analysis shows that a high frequency of explosive cyclones is located in the band of 45^-55~S, and the Atlantic Ocean sector has much higher frequent occurrence of the explosive cyclones than that in the Pacific Ocean sector. Additionally, the relationship between cyclone activities in the Southern Ocean and the Southern Annular Mode is discussed.

Seasonal and inter-annual variations of Arctic cyclones and their linkage with Arctic sea ice and atmospheric teleconnections

The seasonal and inter-annual variations of Arctic cyclone are investigated. An automatic cyclone tracking algorithm developed by University of Reading was applied on the basis of European Center for Medium-range Weather Forecasts（ECMWF） ERA-interim mean sea level pressure field with 6 h interval for 34 a period. The maximum number of the Arctic cyclones is counted in winter, and the minimum is in spring not in summer.About 50% of Arctic cyclones in summer generated from south of 70°N, moving into the Arctic. The number of Arctic cyclones has large inter-annual and seasonal variabilities, but no significant linear trend is detected for the period 1979–2012. The spatial distribution and linear trends of the Arctic cyclones track density show that the cyclone activity extent is the widest in summer with significant increasing trend in CRU（central Russia）subregion, and the largest track density is in winter with decreasing trend in the same subregion. The linear regressions between the cyclone track density and large-scale indices for the same period and pre-period sea ice area indices show that Arctic cyclone activities are closely linked to large-scale atmospheric circulations, such as Arctic Oscillation（AO）, North Atlantic Oscillation（NAO） and Pacific-North American Pattern（PNA）. Moreover,the pre-period sea ice area is significantly associated with the cyclone activities in some regions.

Multisatellite observations of smaller mesoscale eddy generation in the Kuroshio Extension

Smaller mesoscale eddies(SMEs)have an important effect on the transmission of ocean temperatures,salinity,energy,and marine biochemical processes.However,traditional altimeters,the dominant sensors used to identify and track eddies,have made it challenging to observe SMEs accurately due to resolution limitations.Eddies drive local upwelling or downwelling,leaving signatures on sea surface temperatures(SSTs)and chlorophyll concentrations(Chls).SST can be observed by spaceborne infrared sensors,and Chl can be measured by ocean color remote sensing.Therefore,multisatellite observations provide an opportunity to obtain information to characterize SMEs.In this paper,an eddy detection algorithm based on SST and Chl images is proposed,which identifies eddies by characterizing the spatial and temporal distribution of SST and Chl data.The algorithm is applied to characterize and analyze SMEs in the Kuroshio Extension.Statistical results on their distribution and seasonal variability are shown,and the formation processes are preliminarily discussed.SMEs generation may be contributed by horizontal strain instability,the interaction of topographic obstacles and currents,and wind stress curl.

A Machine Learning-Based Botnet Malicious Domain Detection Technique for New Business

In the new network business,the danger of botnets should not be underestimated.Botnets often generatemalicious domain names through DGAs to enable communication with command and control servers(C&C)and then receive commands from the botmaster,carrying out further attack activities.Therefore,a system based onmachine learning to dichotomizeDNSdomain access is designed,which can instantly detectDGAdomain names and thus quickly dispose of infected computers to avoid spreading the virus and further damage.In the comparison,the bidirectional LSTM model slightly outperformed the unidirectional LSTM network and achieved 99%accuracy in the open dataset classification task.

Hyperspectral anomaly detection:a performance comparison of existing techniques

Anomaly detection in Hyperspectral Imagery(HSI)has received considerable attention because of its potential application in several areas.Numerous anomaly detection algorithms for HSI have been proposed in the literature;however,due to the use of different datasets in previous studies,an extensive performance comparison of these algorithms is missing.In this paper,an overview of the current state of research in hyperspectral anomaly detection is presented by broadly dividing all the previously proposed algorithms into eight different categories.In addition,this paper presents the most comprehensive comparative analysis to-date in hyperspectral anomaly detection by evaluating 22 algorithms on 17 different publicly available datasets.Results indicate that attribute and edge-preserving filtering-based detection(AED),local summation anomaly detection based on collaborative representation and inverse distance weight(LSAD-CR-IDW)and local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector(LSUNRSORAD)perform better as indicated by the mean and median values of area under the receiver operating characteristic(ROC)curves.Finally,this paper studies the effect of various dimensionality reduction techniques on anomaly detection.Results indicate that reducing the number of components to around 20 improves the performance;however,any further decrease deteriorates the performance.

An edge detection algorithm for imaging ladar

In this paper, the morphological filter based on parametric edge detection is presented and applied to imaging ladar image with speckle noise. This algorithm and Laplacian of Gaussian (LOG) operator are compared on edge detection. The experimental results indicate the superior performance of this kind of the edge detection.

Global detection of large lunar craters based on the CE-1 digital elevation model

Craters, one of the most significant features of the lunar surface, have been widely researched because they offer us the relative age of the surface unit as well as crucial geological information. Research on crater detec- tion algorithms （CDAs） of the Moon and other planetary bodies has concentrated on detecting them from imagery data, but the computational cost of detecting large craters using images makes these CDAs impractical. This paper presents a new approach to crater detection that utilizes a digital elevation model instead of images; this enables fully automatic global detection of large craters. Craters were delineated by terrain attributes, and then thresholding maps of terrain attributes were used to transform topographic data into a binary image, finally craters were detected by using the Hough Transform from the binary image. By using the proposed algorithm, we produced a catalog of all craters ≥ 10 km in diameter on the lunar surface and analyzed their distribution and population characteristics.

Sequential Similarity Detection Algorithm Based on Image Edge Feature

This paper proposes a new sequential similarity detection algorithm(SSDA), which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.

A Detection Method of WLAN Security Mechanisms Based on MAC Frame Resolution

Security mechanism detection is not only an important content of vulnerabilities evaluation but also the foundation of key strength test for wireless local area network （WLAN）. This paper analyzes the specifications of WLAN security mechanisms and points out the defects in design of security mechanisms detection algorithm based on the standards. By capturing and analyzing a large number of Beacon frames from different vendor＇s access points （APs）, we summarize the relevant fields and information elements in a Beacon frame, and present their values or status when an AP is set to every specific security mechanism. A detection algorithm of WLAN security mechanisms is proposed based on the experimental study result and the pseudo code of a reference implementation for the algorithm is designed. The validity of the algorithm is illustrated by examples, which shows it can detect every WLAN security mechanism accurately.

An echo detection algorithm for underwater continuous wave active detection

The model of linear frequency modulation continuous wave （LFMCW） applied in underwater detection and the method for the detection of echo signal and the estimation of target parameters were studied. By analyzing the heterodyne signal, an algorithm with the structure of heterodyne-Practional Fourier Transform （FRFT） was proposed. To reduce the computation of searching targets in a two-dimensional FRFT result, the heterodyne signal would be processed by FRFT at a specific order, after Radon-Ambiguity Transform （RAT） was applied to estimate the sweep rate of the signal. Simulations proved that the algorithm can eliminate the coupling phenomenon of distance and velocity of LFMCW, and estimate targets＇ parameters accurately. The lake trial results showed that the processing gain of LFMCW processed by the algorithm in this paper was 13 dB better than that of the LFM processed by matched filter. The research results indicated that the algorithm applied in LFMCW underwater detection was feasible and effective, and it could estimate targets＇ parameters accurately and obtain a good detection performance.