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According to the research works of the references on chaotic cryptanalysis, many recent chaotic image encryption algorithms cannot resist chosen-plaintext attacks. Although some chaotic image encryption algorithms introduce plain-image information, they still violate some design requirements of modern cryptosystems. The traditional image encryption algorithm has the disadvantages of low security and time-consuming. Therefore, after analyzing many literatures related to chaotic image encryption and aiming to improve the security of ciphertext and effectively resist plaintext attack, we propose a hybrid chaotic system-oriented artificial fish swarm neural network method for image encryption. Firstly, based on the nonlinear combination theory, the Logistic, Tent and Sine mappings are used to design the hybrid complex chaotic system. According to the pixel value of plaintext, it generates the initial value to output the chaotic sequence. Taking a chaotic sequence as the input layer, the artificial neural network is introduced to train and learn the chaotic sequence. It eliminates the chaotic periodicity and outputs the neural network sequence. The artificial fish swarm algorithm is used to optimize the initial weight and threshold of the Elman neural network. The set obfuscation method is defined to scramble the plaintext. A quantization method is constructed to quantify the neural network sequence and obtain the key flow. The mixed images are classified, the key flow is combined, the pixel value is changed and the ciphertext is output by designing the piecewise diffusion technology. Experimental results show that compared with the current state-of-the-art image encryption methods, the proposed algorithm has higher security and resist plaintext attack ability.

Anatomical maps depicted by medical images reflect the health of the human body. Understanding the detailed division of the regions in the anatomical maps is helpful for diagnosis and further treatment. For example, the lung window contains the lung parenchyma, mediastinum, and diseased tumor. Accurate tumor segmentation can be more comprehensive and accurate to determine the disease condition to select the appropriate radiotherapy means to improve the success rate of treatment. However, the traditional manual segmentation task is large, and the segmentation results vary with the experience level of the observer. Therefore, the development of automatic and reliable segmentation methods is a feasible and important task in clinical practice. Therefore, this paper proposes a multichannel convolutional neural network (CNN)-based fuzzy active contour model for medical image segmentation. Medical images are gridded and labeled using the superpixel SLIC method. The gridded images are used as data sets to train the multichannel CNN network to segment superpixels of organ boundaries, and the seed points of these superpixels are connected to form rough segmentation boundaries. The rough segmentation boundary is used as the initial contour, and the organ boundary in the medical image is obtained by fuzzy active contour segmentation. Finally, we conduct comparison experiments with the state-of-the-art segmentation methods, the results of the subjective evaluation and objective evaluation show that the proposed method has a better segmentation effect and robustness.

Network intrusion detection (NID) is an important method for network system administrators to detect various security holes. The performance of traditional NID methods can be affected when unknown or new attacks are detected. Compared with other machine learning methods, the intrusion detection method based on convolutional neural network (CNN) can significantly improve the accuracy of classification, but the convergence speed and generalization ability of CNN are not ideal in model training process resulting in a low true rate and a high false alarm rate. To solve the above problems, this paper proposes a deep multi-scale convolutional neural network (DMCNN) for network intrusion detection. Different levels of features in a large number of high-dimensional unlabeled original data are extracted by different scales convolution kernel. And the learning rate of network structure is optimized by batch normalization method to obtain the optimal feature representation of the raw data. We use NSL-KDD dataset as the benchmark thus we can compare the performance of our proposed method with other existing works. This dataset includes two testing sets which are the first one is KDDTest+ while the second one is $$\text {KDDTest}^{-21}$$ which is more difficult to be classified. The experimental results reveal that the AC and TPR are higher through our DMCNN model. Especially, in terms of DOS, the AC appropriately reaches to 98%. DMCNN has a high intrusion detection accuracy and a low false alarm rate, which overcomes the limitations of using the traditional detection methods and makes the new approach an attractive one for practical intrusion detection.

Currently, medical image segmentation has attracted more attention from researchers, which can assist in medical diagnosis. However, in the process of traditional medical image segmentation, it is sensitive to the initial contour and noise, which is difficult to deal with the weak edge image, complex iterative process. In this paper, we propose a new medical image segmentation method, which adopts density-oriented BIRCH (balanced iterative reducing and clustering using hierarchies) clustering method to modify active contour model and improve the robustness of noise. The BIRCH is a multi-stage clustering method using clustering feature tree. The improved model can effectively deal with the gray non-uniformity of real medical images. And we also introduce a new energy function in active contour model to make the contour curve approach to the edge, and finally stay at the edge of the image to complete the object segmentation. Experimental results show that this new model can overcome the influence of complex background on medical image segmentation and improve the speed and accuracy of medical segmentation results.

To address the low accuracy problem of playground detection under complex background, the accurate playground localization based on multi-feature extraction and cascade classifier is proposed in th...

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.

Image segmentation (Lin et al. 2018, Shoulin et al. 2018) is the basic link of image processing and machine vision, so it is very important to study this content. Because the image can be interfere...

Traditional image segment algorithms have some demerits: edge blur, discontinuous edge is susceptible to peripheral irrelevant information, Poor noise resistance. Therefore, we propose an improved ...

As the continuous development of mobile social networks, the structure of the mobile social network increasingly becomes complex. It not only speeds up information transmission between people but also expands the scope of information exchange, which has become an essential and important social media in people's social life. How to effectively identify and classify these online communities has important practical significance for the study of social networks. Correctly detecting the community structure of mobile social networks can not only improve the accuracy of friend recommendation, link prediction, service user positioning, product marketing, and other aspects but also provide an important basis for the monitoring of online public opinion. But the traditional social network cluster method based on the trust degree mainly calculates the user trust by analyzing the interactive feedback information between users. This method cannot effectively solve the “cold start” problem in the trust calculation process, that is, for the new network node, the trust value of this node cannot be accurately measured due to the lack of interaction with other nodes. Focusing on this problem, we propose a Gaussian pigeon-oriented graph clustering algorithm for social networks' cluster in this paper. A graph model is first built. Then, an efficient K-medoid algorithm is utilized to search the user center in all groups. The Gaussian pigeon algorithm is used to search the similarity between each user and the central user. Users that meet the similarity threshold are divided into the same user group. Finally, the simulation results show that the proposed method has better cluster effect than other state-of-the-art social networks' clustering approaches.

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...

Network intrusion can enter the network through informal channels. Some illegal users utilize Trojans and self-programmed attack to change the network security system, so that the system loses the defense and alarm function and the Hacker can steal the internal information. Network intrusion seriously harms the security of network information and the legitimate rights of users. Therefore, a bagging strategy-based kernel extreme learning machine for complex network intrusion detection is presented in this paper. This method adopts a bagging strategy to train several sub-kernel extreme learning machines independently. Then the integrated gain of above machines is measured based on the margin distance minimization (MDM) criterion. Selected machines with high gain degree are selected for selective integration to obtain selective integrated learners with strong generalization ability and high efficiency. Then an improved universal gravitation search algorithm is used to optimize the kernel parameters. Meanwhile, a sub-kernel extreme learning machine online update strategy based on incremental learning of batch samples is introduced to realize the online update of intrusion detection model, so that the proposed detection method can effectively be adapted to the changes of complex network environment. Finally, experiments illustrate that the proposed method has better effect on network intrusion detection in terms of detection accuracy and speed, especially for unknown network intrusion connection events, the response speed is fast, the false alarm rate is low.

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.

In order to solve the problem that the semantic gap cannot be alleviated effectively in the field of medical image retrieval, we propose a new graph semi-supervised learning method for medical image automatic annotation. Because the model adopts semi-supervised technology, it can learn from abundant unlabeled instances to avoid the decreasing of generalization ability which is induced by the relative lack of labeled data. Furthermore, by improving graph based semi-supervised learning technology with normalization and modification of decision boundary on its iterative results, the scoring model effectively reduces the bad impact of asymmetric dataset. In view of the relationship between word extraction and image in image learning model, we analyze image similarity calculation in detail. It effectively combines together into the physician's diagnosis information as high-level semantic feature of image, to calculate the similarity between images more effectively. Finally, the Toy data and clinical data sets gastroscope image sets are conducted with a series of experiments, the results show that the method is superior to traditional image annotation method in this paper.

The using of deep learning method belongs to the application and research of artificial intelligence technology in medical field for assisting medical image information processing. Due to the larger difference between the image underlying character features and high-level semantic concepts described, the existing image annotation algorithms perform not very ideal, therefore, the we propose an image annotation framework based on multimodule deep transfer learning method, aiming at the application of deep convolution neural network technology to optimize the neural network parameters, and improve the tagging accuracy in detail. The proposed multimodule deep learning annotation framework uses two stages to optimiz neural network parameters: (1) using deep neural network to optimize the single-mode condition parameters; (2) using the phase correlation, to realize the multi-mode state, the experiment of the optimal combination of public data sets shows that the scheme can effectively improve the performance of image annotation.

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.

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.

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.

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.