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With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by users in the era of big data, improve user experience, but also help merchants quickly find target users and achieve accurate marketing. Most of the works are based on users' check-in history and social network data to model users' personalized preferences for interest points, and recommend interest points through collaborative filtering and other recommendation technologies. However, in the check-in history, the multi-source heterogeneous information (including the position, category, popularity, social, reviews) describes user activity from different aspects which hides people's life style and personal preference. However, the above methods do not fully consider these factors' combined action. Considering the data privacy, it is difficult for individuals to share data with others with similar preferences. In this paper, we propose a privacy protection point of interest recommendation algorithm based on multi-exploring locality sensitive hashing (LSH). This algorithm studies the POI recommendation problem under distributed system. This paper introduces a multi-exploring method to improve the LSH algorithm. On the one hand, it reduces the number of hash tables to decrease the memory overhead; On the other hand, the retrieval range on each hash table is increased to reduce the time retrieval overhead. Meanwhile, the retrieval quality is similar to the original algorithm. The proposed method uses modified LSH and homomorphic encryption technology to assist POI recommendation which can ensure the accuracy, privacy and efficiency of the recommendation algorithm, and it verifies feasibility through experiments on real data sets. In terms of root mean square error (RMSE), mean absolute error (MAE) and running time, the proposed method has a competitive advantage.

Object detection in optical remote sensing imagery is being explored to deal with arbitrary orientations and complex appearance which is still a major issue in recent years. To perceive a better solution to the addressed problem, the post-processing of bounding boxes (BBs) has been evaluated and discussed for the applications of object detection. In this paper, the proposed method has divided into two stages; the first stage is based on thresholding of BBs with respect to the confidence values and the second stage is based on the area-based BB regression (BBR). In BBR, the area of each BB was estimated then the oversized and undersized BBs were removed with respect to the size of objects which are being detected. The widely known region-based approaches RCNN, Fast-RCNN and Faster-RCNN are used for evaluation and comparative analysis validates the proposed framework. The results show that the proposed post-processing is very effective for each kind of region-based detector.

The extensive acquisition and using of high-resolution remote sensing images have greatly promoted the development of airport detection. However, due to the complex shape, background and different scale of the airport location, the real-time and accuracy of airport detection are also facing major challenges. These factors can reduce the detection accuracy. To solve the above problems, we propose an improved faster region-based convolutional neural network (RCNN) detection method for airport detection in large scale remote sensing images. Multi-scale training is applied to faster RCNN to enhance the robustness of network for detecting airport with different sizes. Meanwhile, we adopt the modified multitask loss function to improve the accuracy of airport detection. Online hard example mining strategy is introduced to decrease the redundant negative samples in the training process. Then the non-maximum suppression method is used to remove the redundant boxes of the detected airport. Finally, we conduct sufficient experiments with the airport data obtained from Google Earth and make comparison with the state-of-the-art airport detection methods. The results show that the proposed method can accurately detect different airports under complex background with high detection rate, low false alarm rate and short running time.

Image fusion is an important part of image processing because it can collaboratively use image information from different sensors for the same scene. Image fusion outputs the images, which are more...

As we all know, The feature obtained by the convolutional neural network (CNN) is the expression of higher level for the input image, which can improve the discrimination of the input data. In this paper, we use an excellent convolutional neural network framework to design the pedestrian detection algorithm. Aiming at the background interference between the descendant and the occlusion between the pedestrians, the head of pedestrian is considered as the detection object, so the features of the object to be tested are reduced. To obtain a better detection effect and accelerate the calculation speed, the regional proposal network (RPN) in fast RCNN can be used as a better pedestrian detector, but the latter classifier reduces the performance. The object detection network is improved based on fast RCNN, and the features of the first layer are merged, so that the obtained features are richer. Experiments show that the new method can separate the objects from the background accurately.

The original BP neural network has some disadvantages, such as slow convergence speed, low precision, which is easy to fall into local minimum value. So this paper proposes an improved particle swarm optimization (PSO) algorithm to optimize BP neural network. In this new algorithm, PSO uses improved adaptive acceleration factor and improved adaptive inertia weight to improve the initial weight value and threshold value of BP neural network. And we give the detailed improved process. At the end, simulation results show that the new algorithm can improve convergence rate and precision of prediction of BP neural network, which reduces the error of prediction. At the end, we use multimedia evaluation model to verify the new method’s performance.

Recent research progress in shadow detection has leveraged the development of remote sensing and computer vision. Since shadows of buildings, trees, bridges in one image can provide useful information about the scene to help people understand the shape, feature or estimate their locations and orientations of original objects, especially for damaged objects. In this study, a novel shadow detection algorithm for remote sensing imagery, called self-supervised learning method is proposed. The aim of this work is to generate shadow ratio threshold automatically without human interaction. To alleviate the traditional drawbacks of shadow detection, we fully combine supervised and unsupervised shadow detection method to suggest a self-supervised learning method, which supports us a strongly clue with establishing the relation of shadow and its original object. Subsequently, we benefit from gray-scale histogram to extract shadow segments, then shadow outlines are obtained. Finally, we assess the shadow detection performance of the proposed approach by comparing our results with the state-of-the-art methods. The results reveal the applicability and precision of the proposed self-supervised learning shadow detection technique.

Region proposals are very important for several perfections of remote sensing applications such as vehicle detection, traffic surveillance and intelligent transport system. In this paper, an efficient region proposal approach has been proposed. The framework is organized into two key steps. The first step is based on extracting region proposals using Cascade system. The second step is based on the classification of extracted region proposals which is performed by transfer learning using Convolutional Neural Networks (CNN) and AlexNet architecture is utilized for transfer learning. The aim of this investigation is to evaluate the proposed method for vehicle detection. The selective search (SS) method is also briefly discussed for comparison. The results regarding vehicle detection are very promising.

Currently, big data is a new and hot issue. Particularly, the rapid growth of the Internet of Things causes a sharp growth of data. Enormous amounts of networking sensors are continuously collecting and transmitting data to be stored and processed in the cloud, including remote sensing data, environmental data, and geographical data. And region is regarded as the very important object in remote sensing data, which is mainly researched in this article. Region search is a crucial task in remote sensing process, especially for military area and civilian fields. It is difficult to fast search region accurately and achieve generalizability of the regions’ features due to the complex background information, as well as the smaller size. Especially, when processing region search in large-scale remote sensing image, detailed information as the feature can be extracted in inner region. To overcome the above difficulty region search task, we propose an accurate and fast region search in optical remote sensing images under cloud computing environment, which is based on hybrid convolutional neural network. The proposed region search method partitioned into four processes. First, fully convolutional network is adopted to produce all the candidate regions that contain the possible object regions. This process avoids exhaustive search for input images. Then, the features of all candidate regions are extracted by a fast region-based convolutional neural network structure. Third, we design a new difficult sample mining method for the training process. At the end, in order to improve the region search precision, we use an iterative bounding box regression algorithm to normalize the detected bounding boxes, in which the regions contain candidate objects. The proposed algorithm is evaluated on optical remote sensing images acquired from Google Earth. Finally, we conduct the experiments, and the obtained results show that the proposed region search method constantly achieves better results regardless of the type of images tested. Compared with traditional region search methods, such as region-based convolutional neural network and newest feature extraction frameworks, our proposed methods show better robustness with complex context semantic information and backgrounds.

Though many intelligence algorithms are used for travelling salesman problem (TSP), the main objective of this paper is to execute new approach to obtain significant improvements. This paper proposes an improved wolf colony search algorithm based on search strategy. First, we introduce interaction strategy into travel behaviour and calling behaviour to promote the communication between artificial wolves, which can improve the information acquirement for wolves and enhance the exploring ability of wolves. Second, we present adaptive siege strategy for siege behaviour, which guarantees that the new algorithm can obtain better collaborative search feature. Therefore, the range of wolf siege constantly decreases and the mining ability of wolf algorithm increases with the new strategy. Finally, experiments are carried out to verify the effectiveness of new method compared with other algorithms for TSP problems. The results show that the improved wolf colony search algorithm has higher solving accuracy, faster convergence speed.

User interface (UI) is an interaction and information exchange medium between systems and users, which is convenient to communicate and enable users to operate hardware easily and effectively to achieve the bidirectional interaction. Traditional UI has difficulty in satisfying requirements of users. Therefore, this paper proposes a new intelligence interface scheme based on K-means gaze tracking. First, it uniformly describes the user, interface and system in intelligent interface interaction framework based on visual attention selection mechanism. Second, it uses K-means method to calculate attention degree value of interface on the basis of mapping relation of user, interface and system. Third, it adopts visual attention allocation strategy to predict users' interest degree on interface. Finally, experiments are conducted to verify the performance of our new scheme. The results show that the accuracy of intelligent interface predicting user decision intention is very high. This new method is a kind of selectable solution for marking intelligent interface of user interest goal automatically. What's more, intelligence interface scheme based on K-means gaze tracking can effectively improve the quality of gaze tracking.

Antenna miniaturization plays an important role in modern ultrashort wave communication system. Monopole antenna has the advantages with simple construction, feed easily, small lateral size and omnidirectional radiation. Monopole antenna miniaturization means that it has smaller physical size, smaller wind resistance and lower cost. Antenna has a better concealment and strong durability. However, it ensures the bandwidth and gain of the antenna at the same time which is very difficult. So studying the monopole antenna miniaturization is necessary. The antenna miniaturization is beneficial to improve its concealment and stability, hence, it is very important to design the antenna miniaturization. In this paper, we propose time-domain integration method based on moments and apply it into ultra wideband response of monopole antenna. This numerical algorithm adopts Gail polynomial as expanding basis function in the whole time domain space to expand time domain coefficients of unknown current. Also, it makes numerical simulation for the transient state response of monopole antenna and makes comparison with testing data in experiments. The new method matches experimental data well. Finally, we make a analysis for our scheme to verify its effectiveness. The analysis method can provide input conditions for the response of electromagnetic interference by antenna or system on purpose. What's more, we will study more new methods to design better monopole antenna in the future.

Traditional distinction and recognition methods of electromagnetic radiation source have some shortcomings with highly error and long time. Therefore, Wilson algorithm, Lee algorithm and near field wave impedance theory have been proposed to analyze radiation source characteristics. And it presented common-mode radiation noise testing method of poor-average algorithm and differential mode noise testing method of variance algorithm respectively according to the different radiation source characteristics. Nevertheless, there are still some questions with imprecise location. By acquiring electric field intensity distribution around space of electromagnetic radiation sources, we can determine the direction characteristics of the radiation sources. Using the directional characteristic parameter with high differentiation degree, we make accurate classification and recognition for electromagnetic radiation sources based on support vector machine (SVM). In this paper, we analyze three basic antenna radiation models. It establishes cube reception array to obtain its filed strength value of far zone field in the same position, in addition, it adopts support vector machine method to deal with data and sets up a evaluation model to analyze recognition accuracy. Finally, experimental results show that the proposed method has high recognition accuracy. What's more, we illustrate the new scheme from three aspects with anti-noise performance, data normalization method and F1 value.

TEM horn antenna is a kind of ultra-wide band antennas. Ultra-wide band antenna can make a pulse signal have minimum distortion through radiation, namely the smaller the impulse response trailing is. TEM horn antenna radiate TEM spherical wave. It is limited by center of antenna port and the edge phase difference, so generally it requires a small horn flare angle. There are many loads in the TEM horn antenna to reduce low frequency reflection and absorb some low frequency energy. TEM horn antenna with a small field angle can be approximated as electrical dipole antenna in a low frequency. And loading low frequency compensation circuits can constitute a magnetic dipole, so electric dipole and magnetic dipole forms a combination oscillator antenna, which can improve the low frequency radiation performance of antenna. Taking power capacity and caliber match into consideration, we design a new TEM horn antenna based on ultra-wide band broken line. Compared to traditional constant impedance TEM horn antenna, the new design has better performance, on account for the fact that the new TEM horn antenna adopts transitional feed-in structure in coaxial panel, and the radiation plate is separated into two broken line forms. By theoretical simulation and experimental research, we compare and analyze the input, radiation characteristics of two kinds of antenna. Results indicate that when width of Gaussian pulse excitation is 960 ps, the transition peak power transmission efficiency of coaxial panel will reach at 85%. Peak electric field radiation of new TEM horn antenna improves by 30% than constant impedance horn. Main lobe width of H aside pattern decreases to 60° noticeably compared with original 80°. In addition, it perfects transmission radiation characteristics in 700MHz band, transient pulse peak power capacity comes to 15GW.

Searchable asymmetric encryption (SAE) can also be called Public Key Encryption with Keyword Search (PEKS), which allows us to search the keyword on the data of having been encrypted. The essence of Asymmetric searchable encryption is that users exchange the data of encryption, one party sends a ciphertext with key encryption, the other party with another key receives the ciphertext. Encryption key is not the same as the decryption key, and cannot deduce another key from any one of the key, thus it greatly enhances the information protection, and can prevent leakage the user's search criteria—Search Pattern. Secure schemes of SAE are practical, sometimes, however the speed of encryption/decryption in Public-key encryption is slower than private key. In order to get higher efficiency and security in information retrieval, in this paper we introduce the concept of distributed SAE, which is useful for security and can enable search operations on encrypted data. Moreover, we give the proof of security.

In this paper, we perfect the mutual coupling of compact microstrip array antenna by designing a new defected ground structure. When the resonant frequency is 2.45GHz, array element spacing is 0.1 times of free space wavelength, we introduce new defected ground structure into antenna array. Then we use HFSS to make simulation and compare the changing of antenna's parameters before and after adding defected ground structure. The results demonstrate that the parameters representing mutual coupling in new model can reduce by 30dB, which effectively perfects the mutual coupling of compact microstrip array antenna.

Wireless sensor network (WSN) is self-organizing network; it consists of a large number of sensor nodes with perception, calculation ability and communication ability. As we all know, the floor, walls or people moving has an effect on indoor localization, so it will result in multi-path phenomena and decrease signal strength, also the received signal strength indicator (RSSI) is unable to gain higher accuracy of positioning. When using multilateral measurement method to calculate the unknown node coordinates, it will generate big error in range-free distance vector-hop (DV-hop) localization algorithm of WSN. In order to improve the WSN positioning accuracy in indoor condition, more reasonable distribute network resources, in this paper, we firstly propose krill swarm algorithm used for WSN localization. First, we detailed analyze the multilateral measurement method in DV-hop localization algorithm. The position problem can be transformed into a global optimization problem. Then, we adequately utilize the advantage of calculating optimization problem. We apply the krill swarm algorithm into the stage of estimating unknown node coordinates in DV-hop algorithm to realize localization. Finally, the simulation experience results show that the localization with krill swarm algorithm has an obviously higher positioning precision and accuracy stability with different anchor node proportion and nodes. We also make comparison with DV-hop algorithm and the newest localization algorithm.

Biclustering algorithm is proposed for discovering matrix with biological significance in gene expression data matrix and it is used widely in machine learning which can cluster the row and column of matrix. In order to further improve the performance of biclustering algorithm, this paper proposes a semi-supervised clustering algorithm based on variational Bayesian. Firstly, it introduces supplementary information of row and column for biclustering process and represents corresponding joint distribution probability model. In addition, it estimates the parameter of joint distribution probability model based on variational Bayesian learning method. Finally, it estimates the performance of proposed algorithm through synthesized data and real gene expression data set. Experiments show that normalized mutual information of this paper ’ s new method is better than relevant biclustering algorithms for biclustering analysis.

Artificial Bee Colony(ABC) or its improved algorithms used in solving high dimensional complex function optimization issues has some disadvantages, such as lower convergence, lower solution precision, lots of control parameters of improved algorithms, easy to fall into a local optimum solution. In this letter, we propose an improved ABC of staged search. This new algorithm designs staged employed bee search strategy which makes that employed bee has different search characters in different stages. That reduces probability of falling into local extreme value. It defines the escape radius which can guide precocious individual to jump local extreme value and avoid the blindness of flight behavior. Meanwhile, we adopt initialization strategy combining uniform distribution and backward learning to prompt initial solution with uniform distribution and better quality. Finally, we make simulation experiments for eight typical high dimensional complex functions. Results show that the improved algorithm has a higher solution precision and faster convergence rate which is more suitable for solving high dimensional complex functions.

As we all know, traditional electromagnetism mechanism (EM) algorithm has the disadvantage with low solution precision, lack of mining ability and easily falling into precocity. This paper proposes a new chaos electromagnetism mechanism algorithm combining chaotic mapping with limited storage Quasi-Newton Method (EM-CMLSQN). Its main idea is that it adopts limit quasi-Newton operator to replace the local optimization operator in EM algorithm for local searching in the late of algorithm. In the process of algorithm, the chaos mapping is introduced into optimization processes, and it generates new individuals to jump out of local to maintain the population diversity according to characteristics of chaos mapping random traversal. Finally, the experiments show that the new algorithm can effectively jump out of local optimal solution through comparing three continuous space test functions. The new algorithm has obvious advantages in terms of convergence speed compared to traditional EM algorithm, in addition, it is more accuracy than particle swarm optimization (PSO) algorithm. We compare the new chaos electromagnetism mechanism algorithm with ant colony optimization (ACO) algorithm, PSO algorithm, the results represent that new scheme can obtain the optimal path in the path optimization process, which shows that the new method has better applicability in the discrete domain problem.

Maneuvering target tracking is a target motion estimation problem, which can describe the irregular target maneuvering motion. It has been widely used in the field of military and civilian applications. In the maneuvering target tracking, the performance of Kalman filter(KF) and its improved algorithms depend on the accuracy of process noise statistical properties. If there exists deviation between process noise model and the actual process, it will generate the phenomenon of estimation error increasing. Unbiased finite impulse response(UFIR) filter does not need priori knowledge of noise statistical properties in the filtering process. The existing UFIR filters have the problem that generalized noise power gain(GNPG) does not change with measurement of innovation. We propose an improved UFIR filter based on measurement of innovation with ratio dynamic adaptive adjustment at adjacent time. It perfects the maneuvering detect-ability. The simulation results show that the improved UFIR filter has the best filtering effect than KF when process noise is not accurate.

In this paper, we express arc furnace electrode regulator system as a time-variant nonlinear-linear-nonlinear model. On this basis, we propose an online identification method based on nonlinear-linear-nonlinear model system. This new scheme solves the problem of model variation and prediction precision decline causing by time-varying of arc characteristic. In order to dispose the difficulty of parameters separation in the online identification process, this new method adopts the mind of update the parameters of linear parts and nonlinear parts respectively. It realizes the parameters separation of system effectively. Simulation results show that this method can track the changes of arc characteristics effectively. That it achieves the aim of real-time monitoring and controlling system parameters.

We propose a new method based on discrete Artificial Bee Colony algorithm (DABC) for traveling salesman problem(TSP). We redefine the searching strategy and transforming mechanism of leading bees, following bees and scout bees according to discrete variables. The transition of swarm role is based on ratio factor of definition. leading bees use 2-Opt operator and learning operator to accelerate the convergence speed and to search the neighborhood. The searching of following bees introduce tabu table to improve the local refinement ability of the algorithm. Scouts bees define exclusive operation to maintain the diversity of population, so it is better to balance the exploration and exploitation ability of the algorithm. Finally, the experimental results show that the new algorithm can find relatively satisfactory solution in a short time, and improve the efficiency of solving the TSP.

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.