Abstract:Considering phase ambiguity problem of the pilot-symbol-assisted-modulated ( PSAM) based frequency offset estimation , this paper proposes a novel time-domain frequency offset estimation algorithm. First, a partial cross correlation ( PCC) algorithm is derived with the use of the idea of the autocorrelation algorithm , capable of eliminating complex multiplication operation. Then , a phase- unwrapping algorithm is proposed based on Monte-Carlo simulations, and by using it an improved PCC ( IPCC) algorithm is also derived , which is adaptive to larger frequency offsets. Finally, simulation results show that comparing with the existing classical algorithms(i.e. , theM&Mand ACalgorithms), the proposed IPCCalgorithm exhibits the highest accuracy while theM&Mis the worst； and the IPCCalgorithm has linearly computational complexity but the AChas the highest computational complexity.

Abstract:The physical layer key has the characteristics of reducing the difficulty of key distribution, updating and maintenance. This paper aims at the problem that the node transmission information is vulnerable to eavesdropping on wireless sensor networks. Based on the “one time secret” theory in physical layer security, a physical layer key generation method is proposed, which mainly includes channel estimation and characteristics. Extraction, key generation and negotiation. In the half duplex communication mode, according to the length of coherent time in the channel estimation stage, the length of the training sequence is dynamically adjusted to improve the consistency of channel estimation. The simulation test and comparison analysis show that the consistency of the eavesdropping node generation key and the legitimate node generation key remains low, it reduces the risk of eavesdropping attack.

Abstract:In view of the problem of low energy utilization and unbalanced task taken by multiMCs in wireless rechargeable sensor network(WRSN), this paper jointly considers maximizing the energy utilization and balancing the tasks of multiMCs for the first time, and builds the multiMCs charging planning problem into multiobjective optimization problem, a multiobjective fireworks algorithm based on decomposition (MOFWA/D) is proposed to solve the problem. The experimental results show that the ratio of charging energy to the total energy consumption of the multiMCs based on the MOFWA/D algorithm is up to 3349%, which is better than MOFWA, MOEA/D and Schedule algorithm, and the balance index of the multiMCs obtained by the proposed algorithm is lower than 1139% of MOFWA algorithm, 4367% of MOEA/D algorithm and 7929% of schedule algorithm.

Abstract:In order to effectively control the false alarm rate in the process of infrared dim small target detection and improve the accuracy of target detection in complex cloud background, A small target detection algorithm based on spatialfrequency domain mapping and false alarm suppression was proposed in this paper. A directional maximum median filter is constructed according to the intensity values in different directions of the infrared central pixel to effectively eliminate noise. And a background suppression filter is formed by using the intensity difference between the center pixel and its neighboring pixels to fully enhance the dim target. Considering the unique attributes of cloud region, a cloud region recognition mechanism was defined to extract spatial mapping by combining with nonlinear filtering. The Butterworth differential lowpass filter is introduced to complete the coarse saliency detection of the denoised image. And the fine saliency detection was completed according to the amplitude information of the coarse saliency detection result. Then, the threshold was calculated by using the coarse and fine saliency detection results, so that the frequency domain mapping of denoised infrared image was obtained by using the adaptive binary segmentation method. The candidate targets in infrared images were extracted by jointing spatial map and frequency domain map. According to the difference of motion characteristics between real moving target and false alarm, these false alarms in candidate targets were suppressed based on improved pipeline filtering to accurately detect the real infrared dim targets. Test results show that this algorithm can accurately identify the real target, which has a better ROC characteristic curve compared with current infrared dim and small target detection technology

Abstract:For an image with a large damaged area, in the existing image restoration method, a distorted structure or a blurred texture that does not coincide with the surrounding area tends to be generated. With the development and application of deep learning, this paper is based on the method of generative adversarial networks and generates missing content by adjusting the available data. For a data set, the samples in the data set are first parsed into sample points in a probability distribution, a large number of falsified images are quickly generated using the generative adversarial network, the code of the closest damaged image is searched for, and then the code is generated by generating model to infer missing content. On this basis, this paper combines the semantic loss function and the perceptual loss function, and the unsaturated region is enlarged by improving the activation function sigmoid function, and the problem that the gradient easily disappears is solved. Experiments show that the method successfully predicts the information of large areas missing in the image, and realizes the photorealism, producing clearer and more consistent results than previous methods.

Abstract:The harm of pantographcatenary arc seriously hinders the development of highspeed railway. The pantographcatenary arc occurs frequently under the poor environmental condition. The pantographcatenary arc aggravates the wear of the slide plate and catenary wire, and the quality of current reception is decreased. In order to improve the safety and stability of highspeed railway operation, the dynamic characteristics of pantographcatenary arc under rainfall conditions were studied. Based on the selfdeveloped pantograph arcing experimental system, the current and voltage waveforms were collected under normal, light rain (rainfall 30 g) and heavy rain (rainfall 150 g) conditions, respectively. The pantographcatenary arc image was captured by using high speed camera. When working condition is constant, the arc is stretched and both arc area and circumference increase under rainfall conditions. The arc circumference and area are the largest under light rain conditions. The rainfall makes the current ripple increase. It also makes arc zerocurrent time become longer, and arc peak voltage increases. Therefore, the rainfall shortens burning time of the arc and increases the arc incidence rate, especially in the light rain environment. The arc power in rainfall environment increases. The minimum arc resistance appears near the peak value of the arc current waveform.The minimum arc resistance value in rainfall environment increases compared with that in normal environment.

Abstract:Along with the emergence of convolutional neural networks (CNN), pedestrian detection has been largely improved. Although CNN models can learn different variations of objects, pedestrian detection in autonomous driving still faces various challenges, which mainly include large scale variation, illumination variation and occlusion of different levels. In this paper, based on the previous CNN models, a robust pedestrian detection method is proposed. The main idea lies in combining the semantic information into the original detection framework for further supervision. It firstly extracts feature maps of different scales in CNN, based on the paved anchor boxes with various scales, and an additional convolutional layer is appended to be responsible for classification and regression. Meantime, semantic segmentation maps are generated from these feature maps. Finally, two streams are utilized to supervise detection and segmentation. Experiments on the recent CityPersons pedestrian detection dataset show that the semantic segmentation can significantly improve the detection accuracy without taking extra time, and the processing time is only 03 second per image in 1 280×384 pixels images in the dataset.

Abstract:In order to maximize the lifetime of wireless sensor network（WSN）, a lifetime maximization strategy to ensure coverage and connectivity in WSN is proposed in this paper. First of all, the problem is considered as an energysaving coverage problem and formulated using integer linear programming technique, and a feasible set containing the minimum number of sensors is obtained. These feasible sets cover all the target points and have the largest sum of utility at the same time. Secondly, in order to ensure each feasible set has the coverage and connectivity of the network, the energysaving coverage problem is extended to a lifetime coverage and connectivity problem. A greedy iterative heuristic algorithm, which is composed of coverage phase, connectivity phase and redundant sensor reduction phase, is used to solve this problem in order to find the maximum number of disjoint active (feasible) sets and to activate them successively. Thus, the extended network lifetime R is obtained. Experimental results show that the proposed algorithm is very close to the optimal solution in terms of network lifetime and energy efficiency performance, and the network lifetime is linearly related to the number of sensor nodes in WSN.

Abstract:To solve the problem of relay node placement in hybrid wireless sensor networks, a minimum number of relay nodes placement algorithm is proposed in this paper to meet certain requirements of connectivity and survivability, where relay nodes can only be placed on a subset of candidate locations. In the connected relay node placement problem, an efficient STP approximation algorithm based on minimum spanning tree is proposed to ensure the connectivity between sensor nodes and base stations. In the survivable relay node placement problem, a polynomial time approximation algorithm based on {0,1,2}SNDP is proposed to ensure the biconnectivity between sensor nodes and base stations. Experimental results show that the proposed singletiered constrained relay node placement algorithm has a smaller running time and can almost achieve the performance comparable to the optimal solution.

Abstract:To implement the optimal noncoherent sequence detection of quadrature modulated signals in flat fading channels, a new algorithm that performs generalizedlikelihoodratiotest optimal incoherent sequence detection is proposed in this paper. Moreover, for Rayleigh fading channels, the proposed algorithm is equivalent to the maximumlikelihood noncoherent sequence detector. Firstly, the decision on the information symbol xn at the nth time slot is obtained by the algorithm, and a decision sequence is formed according to the change of the decision in a given phase interval. And then the phases where the decision sequence changes are sorted in order to identify the target sequences on the corresponding intervals. The theoretical analysis results show that the proposed BFSK optimal sequence detection can be applied to the detection of the optimal incoherent sequence of FM0 signals used in modern radio frequency identification systems. And the simulation results show that the proposed optimal sequence detection algorithm has nearly equal BER/SER performance to the traditional ML coherent sequence detection algorithm. Compared with the traditional exhaustive search algorithm, the proposed algorithm greatly reduces the computational complexity.

Abstract:A material tester of fiberreinforced composite is developed. Firstly, the excitation platform, laser scanning device, signal generator and data acquisition device are utilized to establish its hardware structure. Then, the functions as well as control and measurement advantages of each software module based on LabVIEW are described in detail. Finally, the elastic moduli, loss factors and Poisson's ratios of the two TC500 carbon fiber/resin composite beam specimens in the longitudinal, transverse and shear directions are identified, which are also compared with the material results provided by the manufacturer. The results show that their deviations are in the range of 275%～1182%, which is within an acceptable level. Therefore, the effectiveness of the developed composite material tester and its software algorithm has been verified. Compared with the commercial instruments available on the market, the tester developed in this paper adopts laser nondestructive scanning technology, has higher cost performance, and can effectively obtain the loss factors of the anisotropic composite in the shear direction.

Abstract:Aiming at the problems of color distortion and loss of detail caused by improper selection of image quality attributes in the current multiexposure image fusion process, a multiexposure image fusion scheme based on quality measurement coupled with color correction was designed. Firstly, three most prominent image quality attributes (contrast, saturation and brightness) were selected as measurement methods. Secondly, these three quality attributes were weighted by linear combination, and the power function was used to control the influence of each attribute. Low weight values are assigned to underexposed and overexposed pixels to eliminate pixels with poor visual effects, thus, effectively preserving exposure pixels, bright colors and details. Then, Laplacian pyramid decomposition is used to decompose the weighted combination features of different exposure images. After normalized weight mapping, multiresolution fusion of different coefficients was performed to achieve multiexposure image fusion. In addition, in order to avoid color distortion and detail loss, the postprocessing steps of color correction based on local saturation are adopted to improve image quality. Experimental results show that the proposed algorithm has higher fusion visual quality than current multiexposure image fusion scheme, and can better maintain image details and correct the color of the exposure fusion image.

Abstract:In order to solve the problem of blocking effect and blurring effect induced by ignoring the edge features of image region pixels t in current remote sensing image fusion algorithms. the remote sensing image fusion algorithm based on edge constraint model and nonsubsampled shearlet transform was proposed in this paper. Firstly, multispectral images are decomposed by luminance hue saturation decomposition to extract luminance components. Then, the panchromatic image and luminance components are decomposed by nonsubsampled Shearlet transform to obtain highfrequency coefficients and lowfrequency coefficients. Finally, the fusion function of lowfrequency coefficients is established through the spatial frequency characteristics of the image to fuse the lowfrequency coefficients. An edge constraint model is constructed to fuse the high frequency coefficients by using the average gradient feature and the edge energy feature of pixels in the image region. After the fusion, the lowfrequency coefficients and highfrequency coefficients are inversely transformed by nondownsampling Shearlet, then the fused images are obtained by inverse transform of IHS. The experimental results show that, compared with the current remote sensing image fusion methods, the fusion images designed in this paper not only have better clarity, but also have better spectral characteristics without blocking effect and blurring effect.

Abstract:Aiming at the problem that the temperature and humidity data of medicines in medical cold chain system are not easy to diagnose, an improved long shortterm memory（LSTM） method for predicting the temperature and humidity of drugs is proposed. The method first expands the humidity data set by interpolation expansion algorithm, and then proposes an LSTM structure containing multiple LSTM cell elements instead of the traditional iterative prediction. Then the Adam optimization algorithm adjusts the network parameters and changes the network layer to reduce the prediction error. Achieve early prediction of the temperature and humidity of the drug. Finally, the test was carried out on the temperature and humidity data set of the drug collected in the pharmacy refrigerator. The mean square error was 0036 9. Compared with the traditional BP neural network prediction method and the Gaussian process mixture model prediction method, the improved LSTM drug temperature and humidity prediction method is more accurate.

Abstract:The classical distance vectorhop(DVHop) method has the problem of error in distance estimation among nodes and low accuracy of node localization in nonuniform distributed networks. The calculation rule of similarity factor for multihop shortest path among nodes is introduced, which is used to correct the hopsize of a node in the distance estimation step, and to select the beacon node involved in localization calculation in the position calculation step. The initial position of a node is optimized using an improved simulated annealing (SA) algorithm. The simulation results show that under the condition of randomly generated network topology, the improved strategy can reduce the positioning error by about 20% on average compared with the classical DVHop algorithm, and about 4% on average compared with the existing methods, which shows good positioning performance.

Abstract:Aiming at the problem of multiple sensor fault detection and reconstruction in the electric forklift stability control system, a sliding mode observer with adaptive regulation law is proposed to realize sensor fault detection and reconstruction. Starting from the threedegreeoffreedom model of the forklift, a linear state equation with output disturbance is constructed as its equivalent forklift sensor fault model, and the sensor fault is converted into an actuator fault by defining an auxiliary state variable as an output signal filter. According to the fault model and sliding mode control theory, the sensor fault detection and reconstruction method based on sliding mode observer is given, based on the adaptive algorithm, the observer design has the advantage of not knowing the upper limit of unknown fault. Finally, the experimental results show that the method is effective.

Abstract:In this work, a reactive jamming detection scheme is proposed to solve the problem that reactive jamming attack disrupts the legal communication of ZigBee network. Multipleclass sequence distributing scheme and error bits detecting algorithm are proposed, which could obtain a set of abnormal received signal strength indication (RSSI) of error bits that would be subsequently identified by sequence probability ratio test (SPRT). An evaluation of the proposed scheme is validated experimentally and the results show that the solution is able to detect reactive jamming at success rate above 95% with relatively fewer communication and energy overheads. The measurement of packet delivery ratio (PDR) or topology changes is not considered in the scheme, which achieves effective detection against reactive jamming and enhancing security performance of ZigBee networks.

Abstract:Aiming at the accuracy of calibration results of robot tool coordinate system, a noncontact tool center frame （TCF） calibration method based on structured light was used to calibrate the tool coordinate system of selfmade serial robot. The related coordinate system, calibration principle and structured light vision imaging system involved in the calibration process are introduced, and the calibration algorithm of TCF is deduced in detail. The experimental steps of TCF calibration are elaborated. Kinova Mico2 robot is taken as the research object. By pasting the “ten” label on the tool, the center and laser projection of the "ten" label are restrained. The coordinates of the pixels in the image are the same after the shadow points coincide, and the average value is obtained as the result of calibration after multiple positioning constraints. The results show that the noncontact TCF calibration method based on structured light has stable calibration results for tool coordinate system, and the accuracy of data meets the needs of production.

Abstract:In order to improve the detection capability of power quality disturbances, a power quality disturbance detection and location algorithm based on improved wavelet threshold function and complementary ensemble empirical mode decomposition(CEEMD) are proposed. After using CEEMD to process the power quality disturbance, it calculated the random noise intensity of each IMF component by permutation entropy, and the noise intensity is higher than the permutation entropy value by the improved wavelet threshold denoise. The remaining components retain and reconstruct the signal. The HilbertHuang transform locates parameters such as the start point and end of the disturbance and frequency of the disturbance. Compared with CEEMD’s rejection of highfrequency component noise suppression and method based on wavelet threshold denoise, it proved that the proposed algorithm has higher noise immunity. Moreover, through the example of threephase short circuit and twophase short circuit in PSCAD/EMTC Doublefed wind power system, the simulation verified the effectiveness of the proposed algorithm. Finally, it built a platform of power quality disturbance based on PXI and LabVIEW platform. The algorithm laid the foundation for application in engineering practice.

Abstract:Aiming at the problem of threshold selection in image segmentation, a multi threshold segmentation algorithm based on grasshopper algorithm is proposed in this paper. In this algorithm the otsu method and Kapur’s entropy are considered. The fitness function which used are the maximum between class variance criterion (Otsu) and the Kapur’s Entropy. This method uses the grasshopper optimization algorithm to optimize threshold. In the end, the image is segmented with the best threshold. The algorithm is compared with the traditional Otsu algorithm, the maximum entropy method, the image segmentation method based on particle swarm, and the image segmentation method based on artificial bee colony. Experimental results show that the algorithm is better than other algorithms. When the number of thresholds is 4 and 5, the PSNR of the proposed algorithm is about 3% and 15% higher than that of particle swarm optimization algorithm and artificial bee colony algorithm. The running time of this algorithm is about 9% and 5% faster than that of particle swarm optimization algorithm and artificial bee colony algorithm.

Abstract:In this paper, perceptual agent and mobile control law based distributed target tracking scheme is proposed to solve the problem of target tracking in mobile sensor networks composed of unmanned automatic tracking robots. Firstly, a distributed target tracking algorithm is proposed on the basis of constructed mobile robot agent model and perceptualcoverage target tracking model. The algorithm consists of estimation phase and consensus phase. Through the execution of these two phases, all the agents in the network achieve an agreement on the maximum perception confidence value, as well as on the corresponding target state and a posteriori covariance matrix, so as to improve the individual prediction performance. Secondly, potential field based control law is proposed for the movement of the general mobile agent so as to ensure that the sensor network is connected at any time, such that each mobile agent approaches the target as close as possible to improve its perception confidence value and to avoid collision with the target, the environment walls and the other agents. This is useful for keeping the coverage of the area at a satisfactory level. Simulation results show that the proposed tracking scheme not only improves the tracking accuracy, but also has a better coverage perception performance.

Abstract:To improve the estimating precision and robustness of fault parameters, fault parameter joint estimation algorithm based on multiple fading factors strong tracking seventhdegree cubature Kalman filter (MST7thCKF) is proposed. The algorithmextends the fault parameter to state vector, and realizes joint filtering of state and fault parameters. Then, the algorithm introduces multiple fading factors strong tracking filter (MSTF) into the frame of seventhdegree cubature Kalman filter (7thCKF) to improve the robustness of 7thCKF when the fault parameters changing function is unknown or abruptly changed, and enhances estimating precision of fault parameters. Simulation results show that the proposed algorithm has better estimating precision than MSTF squarerootcubature Kalman filter (MSTSCKF) and 7thCKF.

Abstract:In order to monitor the biological tissue lesion when using HIFU to treat, a method that biological tissue lesion identification based on ultrasound image by Graylevelgradient cooccurrence matrix and FCM clustering is proposed. First, obtaining ultrasound images of fresh pork tissue which irradiated by high intensity focused ultrasound and extract parameters of Gray levelgradient cooccurrence matrix after preprocessing. The parameters of gray entropy and entropy of mixing are the characterization parameter of identification of biological tissue lesion. And then using FCM clustering method to analysis. The results show that the rate of identification is higher when combining Gray entropy with entropy of mixing to identify biological tissue lesion than signal parameter, which is 332 percentage more than that gray average and wavelet transform coefficient method. Thus,this method can better identify tissue lesion in the course of HIFU treatment. The method is helpful to improve the efficacy of HIFU treatment, relating the noninvasive temperature measurement with tissue lesion identification further.

Abstract:Interactive system is the basis for the interaction between service robots and people. Using RealSense 3D camera as a sensor, a multimode interactive system with the elderly assistance robot was developed. It mainly implements user registration and verification based on face recognition, news on demand and calendar reminder based on gesture recognition and speech recognition. Experimental tests show that the accuracy of face recognition is 93%, the accuracy of gesture recognition is 72% and the accuracy of speech recognition is 90%. The interactive system has the advantages of userfriendly interaction, network independence and high recognition speed, etc. It integrates with the Omnidirectional mobile robotics platform to design a userfriendly, multifunctional and lowcost elderly assistance robot for empty nesters.

Abstract:In order to improve the reliability and stability of data transmission of the temperature field monitoring system of blast furnace hearth, and reduce the construction cost and maintenance cost, a wireless temperature monitoring system for blast furnaces based on wireless mesh network (WMN) is proposed. The data acquisition node is installed on the outside wall of the blast furnace hearth, which converts the analog signal of the thermocouple into digital signal, greatly shortens the transmission distance of the analog signal of the thermocouple and reduces the external interference. Then the temperature data is transmitted to the monitoring center through the mesh wireless network for analysis modeling and blast furnace hearth condition monitoring. The mesh wireless data transmission network is constructed based on IEEE80211, and based on the optimized linkstate routing protocol, a dualpath backup adaptive routing protocol and an adaptive handoff mechanism for primary and alternate routing are designed. Finally, based on OpenWRT opensource platform, a prototype system is built and installed in a blast furnace in Laiwu Iron and Steel works. The system has been put into a long period of continuous and stable operation, which can meet the needs of blast furnace hearth temperature field monitoring.

Abstract:In order to enhance the ability to resist plain attack and better the management in current optical image encryption technology. An asymmetric color image encryption algorithm based on chaotic Gyrator transform and matrix decomposition was designed in this paper. Firstly, the R, G and B components were obtained from the color image. A twodimensional coupled chaotic system was designed to generate three random phase masks for modulating R, G and B components respectively. And the modulated R, G and B components were fused into a grayscale image. Then the rotation angle of the Gyrator mechanism was calculated by the random sequence, and the Gyrator transform was used to process the grayscale image for obtaining the Gyrator spectrum. The Gyrator spectrum was processed based on the phaseamplitude truncation mechanism to output two coded images. Finally, the phase truncated image was decomposed into a unitary matrix and a triangular matrix based on matrix decomposition mechanism, and image encryption was completed by using the Gyrator transform of different rotation angles to process the unitary matrix and triangulation matrix. In the process of image encryption, the decryption key of R, G and B components were generated by using its amplitudetruncate image information which make its encryption and decryption key completely different, and the image can be decrypted by only using the private key. The experimental results show that the proposed algorithm has higher security level compared with the current optical image encryption technology.

Abstract:As a current sampling device of singlephase intelligent watthour meter, manganesecopper shunt will produce inductive current under the interference of external magnetic field of power frequency, which will affect the accuracy of watthour meter measurement. In this paper, an optimal design method of induced current of manganese copper shunt is proposed based on robust design theory. Firstly, the basic physical principle of induced current produced by manganesecopper shunt under the interference of power frequency magnetic field is analyzed theoretically, then the simulation model of manganesecopper shunt is established based on Flux software. Afterwards, design of experiment (DOE) based signal noise ratio (SNR) analysis is carried out to rank the influence factor for induced current. Then, the optimal design of manganesecopper shunt is carried out based on Taguchi robust design method. Finally, the induced current before and after optimization is compared by Monte Carlo simulation consistency evaluation. It is proved that the robust design method is suitable for improving the magnetic field interference ability of the manganese copper shunt.