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The current paper verifies the event-triggered finite-time tracking control of a fully actuated unmanned surface vessel under unmodeled dynamics and external disturbances. Radial basis function neural networks (RBFNNs), nonlinear disturbance observers (NDO), and the event-triggered mechanism (ETM) are utilized to design a new type of finite time tracking controller (FFTC). The proposed controller utilizes the dynamic surface control (DSC) approach to resolve the “differential explosion” issue of virtual control laws. Prescribed performance functions (PPFs) and the finite-time control (FFC) technique are utilized to specify the efficiency of tracking errors. The designed control laws make the control system of USV semi-globally practically finite-time stable (SGPFS) and make the tracking errors tend to a narrow residual set involving the specified bound in a finite time. In the end, the simulations reflect the presented FFTC’s efficiency.

Federated learning (FL) is now considered a critical method for breaking down data silos. However, data encryption can significantly increase computing time, limiting its large-scale deployment. While hardware acceleration can be an effective solution, existing research has largely focused on a single hardware type, which hinders the acceleration of FL across the various heterogeneous hardware of the participants. In light of this challenge, this paper proposes a novel FL acceleration framework that supports diverse types of hardware. Firstly, we conduct an analysis of the key elements of FL to clarify our accelerator design goals. Secondly, a unified acceleration framework is proposed, which divides FL into four layers, providing a basis for the compatibility and implementation of heterogeneous hardware acceleration. After that, based on the physical properties of three mainstream acceleration hardware, i.e., GPU, ASIC and FPGA, the architecture design of corresponding heterogeneous accelerators under the framework is detailed. Finally, we validate the effectiveness of the proposed heterogeneous hardware acceleration framework through experiments. For specific algorithms, our implementation achieves a state of the art acceleration effect compared to previous work. For the end-to-end acceleration performance, we gain $12\times $ , $7.7\times $ and $2.2\times $ improvement on GPU, ASIC and FPGA respectively, compared to CPU in large-scale vertical linear regression training tasks.

High temperatures in unconventional oil and gas reservoir present a challenge to DC-DC converter design for downhole drilling equipment. In response to the high-temperature challenge, a design methodology consisting of controlling-junction temperature rise, derating component electrical parameters for more design margin and selecting high-temperature components was proposed. Based on the methodology, a converter board with a nominal input of 42-54V and output of three low voltages (+15V, −15V, and +5V), was designed for the 150°C application. The maximum temperature rise of the components in the converter prototype under a temperature environment of 22°C is 11.9°C. The performance of the converter was verified at the ambient temperature from 30°C to 150°C. With a 42~54V input voltage, the output voltages have less than 0.2V fluctuation; the three output voltages can achieve a ripple suppression ratio of approximately 40dB, and the average conversion efficiency was around 75% at 150°C, which meets the design specification.

During camera calibration, the plane target image is required to occupy most of the whole image, and the focused image is also needed. It is difficult to satisfy these conditions in large-scale structural vision measurement. Therefore, it is very important to explore a camera calibration method with a small calibration board for a large field of view. This paper proposed an out-of-focus image calibration method based on the accurate positioning of concentric circular projection center, which is not limited by the camera’s field of view and can overcome the influence of out-of-focus image on camera calibration. The proposed method consists of three parts: adaptive blurred circle image enhancement, accurate positioning of concentric circle projection center and camera calibration of concentric circle center point array. The simulation results show that under different Gaussian blur kernel radius conditions, the proposed accurate positioning method of the concentric circle projection center has better robustness. When Gaussian kernel radius K = 40, the positioning accuracy of the proposed method is improved by 41.88%. In the real image experiment, the internal parameters of the camera in the heavy defocus image have similar results to the Zhang’s calibration method in the focused image. Both simulation and real image experiments show that the proposed method can realize the calibration of large targets with a small plane calibration board.

The R&D (Research & Development) process of mobile application can be regarded as a complex system engineering that needs to take into account a variety of influencing elements, such as customer’s demands, engineering measures and business goals etc., as well as their interplay. A novel decision support platform is proposed to assist project managers in determining the optimal technical measures with implementation degrees and resource allocation scheme. Specially, the HDQ (House of Development Quality) is designed to collect essential data and information as well as reflect correlations among elements, which can effectively connect isolated information islands. Through the transform of HDQ, a multi-objective programming model is constructed to meet customers’ overall demands and specific requirements. A combination of specified technical measures and resource allocation can be obtained under ideal conditions. Additionally, the extended model considering resource substitution is designed to realize further improvement by effectively utilizing the surplus resource in the above optimal condition, demonstrating the advantage of proper managerial arrangement and resource deployment. Finally, the R&D case of family financial management application is conducted to illustrate the effectiveness of the suggested method, which offers a new perspective on mobile application R&D project management.

Stochastic resonance (SR) as an important nonlinear detection technique has been widely applied in weak signal diagnosis. In order to improve the weak signal processing capability of SR, a new asymmetric SR system with nonlinear jointed potential function is proposed in this paper. Firstly, the new joint potential stochastic resonance (JPSR) system is built by combined gaussian potential function and asymmetric bistable potential function. Secondly, the analytical expression of signal-to-noise ratio (SNR) is derived based on two-state theory and adiabatic approximation theory. Meanwhile, the influence of system parameters on SNR is discussed theoretically. Thirdly, based on proposed system, various types weak signals with noise are detected more accurately in simulation experiments. Then, by particle swarm optimization algorithm, the JPSR system is applied to detect actual bearing fault signals and the better effects are obtained. Finally, the simulation results show that the proposed system can detect weak signal characteristics more effectively.

Real-time dim space target detection is a significant challenge in space situation awareness. This paper proposes a single-frame space object segmentation and detection method based on deep learning. Firstly, Channel and Space Attention U-net (CSAU-Net) is presented based on space image features. We remove unnecessary feature layers and add attention modules in the traditional encoder and decoder structure to enhance feature fusion and better use original feature layers. The proposed network structure can achieve accurate segmentation of space objects with fewer data training. At the same time, we construct a space target dataset for training, which contains targets with different signal-to-noise ratios to enhance the generalization of convolutional neural networks. After obtaining the segmentation masks, a simple connected component labeling method is applied to extract the centroid of the space target. Experiments show that our approach can achieve an ideal segmentation effect when the signal-to-noise ratio of the space target in the simulated dataset is 0.3. In addition, the proposed algorithm can realize fast segmentation and achieve an accuracy of 98.5%, which is similar to the traditional multi-frame space target detection method in real space image detection.

In the field of port handling for bulk-cargo with heavy load and high speed, the differential gear box is usually used for towing wire ropes, and then realizing the Horizontal transport and vertical unloading of goods. However, the design and manufacture of gear device which is difficult to maintenance is of high cost. Thus, there is a kind of electric differential system to realize the loading and unloading of cargo at present. This article focus on the transmission mode and the process of loading-unloading of electric differential device. With the kinematic model based on the Lagrange's equation, the electrical differential system state model is established. Several AMESim simulations of the system is used to analyzing the energy transfer of combined action under the condition of electric differential. It helps to learn which path are used and the proportion on each line of energy. Finally, in this article, the difference of the energy transfer characteristics between the traditional mechanical transmission and the electrical differential drive system are analyzed. It provides guidance for design and use of equipment transmission system.

Aero-engine exhaust gas temperature margin (EGTM) is one of the main indexes of engine replacement; however, the application of existing methods in EGTM forecasting is restricted because of the limited prediction accuracy and many non-linearities. In this study, an adaptive-tunable-based hybrid radial basis function (RBF) network is proposed to improve the prediction accuracy of aero-engine EGTM. Firstly, a hybrid RBF network consisting of a RBF network and a linear regression model is built as a fundamental EGTM predictive algorithm. Secondly, to increase the network's adaptation capabilities, the structural parameters of the proposed network are adaptively modulated by Brownian motion modeling and particle filter without physics-based models. Finally, multiple sets of EGTM data from a certain type aero-engines in an airline company is selected for engine removal time prediction. Experiment results demonstrate that the proposed adaptive-tunable-based hybrid RBF network with a high prediction accuracy, and can reflect the characteristics of EGTM well and truly, which can capture the dynamic nature of EGTM in time during the forecasting process.

A concatenated coding scheme employing an irregular marker code as the inner code is designed to improve the ability of correcting insertions/deletions. In this scheme, bits associated with each marker symbol are allocated to the symbol of the LDPC code non-uniformly. Since the non-binary marker symbol at the irregular position provides reliable forward/backward quantities, significant amount of insertions and deletions can be detected and corrected by the presented method. Simulation results show that the proposed scheme has an improved performance with only a very small penalty in coding rate compared with the traditional regular marker code.

As the increasing of both users' demand and the number of communication satellites, spectrum resource is becoming increasingly scarce. Cognitive radio (CR) can effectively alleviate the scarcity of spectrum resource. By using CR technology in the satellite and terrestrial integrated network, the satellite can share the spectrum resource with the terrestrial communication equipment to improve the spectrum utilization. However, due to the satellite communication's inherent openness and the increasing intelligent level of devices, it makes the satellite communication vulnerable to spectrum misuse, which seriously threatens the reliability and efficiency of the satellite communication system. This paper studies the detection of spectrum misuse in the satellite-terrestrial spectrum sharing system. First, we model the problem as a ternary hypothesis test problem by using generalized multi-hypothesis Neyman-Pearson (GMNP). Then, we utilize a two-step method to solve the complex formula problem in the multiple authorized users scenario. Reasonable decision thresholds are derived by means of the generalized likelihood ratio test (GLRT) and maximum a posterior (MAP) criterion, respectively. Finally, the performance of detection is evaluated and analyzed comprehensively to verify the feasibility of the study.

The public key encryption with equality test (PKEET) allows the cloud server to judge whether two different ciphertexts are generated by the same message without decryption. Through this technique, PKEET provides an effective solution for building secure outsourced databases, and has made some rich achievements. This paper combines multivariate public key encryption and equality test, and proposes the first multivariate public key encryption scheme with equality test (MPKEET), which inherits the advantages of both primitives. Moreover, the equality test algorithm proposed in this paper is based on a straight line. Compared with the schemes based on bilinear pairing, it is simpler and easier to implement. And our MPKEET scheme achieves desirable security, which can resist linearization equation attacks, differential attacks, XL attacks, Gröbner basis attacks and the attack of quantum computer, when appropriate parameters are selected.

Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%.

In order to improve the reliability and real-time of the control system of aero-engine, an intelligent fault-tolerant control system based on the online sequential extreme learning machine (OS-ELM) is proposed against the sensor faults. This system can realize the online fault diagnosis and signal reconstruction without a system model. And while considering the traditional PID control robustness and poor anti-interference ability and other shortcomings, an improved global fast non-singular terminal sliding mode control method is used to obtain better control effects, effectively solve the uncertainty problem in aero-engine, and give full play to aero-engine performance. To verify the feasibility and effectiveness of this system based on the above method, a turbofan engine is taken as the research object and semi-physical simulation experiments on fault-tolerant control are conducted on a semi-physical simulation test platform. Results show that the controller of this system can safely and reliably control the aero-engine without losing its control performance under the circumstance that there are faults in engine sensors. The purpose of fault-tolerant control is reached.

A random marker code is inserted into the information sequences periodically, and a novel symbol-level decoding algorithm considering the weighted Levenshtein distance (WLD) is designed for correcting insertions, deletions, as well as substitutions in the received sequences. In this method, branch quantities in the decoding trellis are calculated by measuring the WLD, which is done using the dynamic programming. A simulation study is performed to demonstrate the effectiveness of the presented scheme in the practical system, especially for channels with weak synchronization problems.

It is an effective method to analyze the maritime traffic risk using near miss model, but there are no works from the temporal perspective. The duration of near miss shows the maritime traffic risk. The longer the duration is, the higher the risk is. The influence of the ship maneuvering capability, speed, size, and Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) on the safety of ship navigation are considered in this article, the navigation safety domain is used to detect near misses, and the duration of a single near miss is defined, which is the time from target ship sailing into the navigation safety domain of own ship to leaving, and then an algorithm for calculating duration of near misses is proposed. To measure the similarity of two ships' lengths, the similarity coefficient model of ships' lengths is defined. Based on the k -means algorithm, different parameters k are selected to cluster the duration of near misses. According to the clustering results, the connections between the duration of near misses and the ship type, length, and speed were analyzed to identify a series of time characteristics of near misses, that is high frequencies of short-duration near misses and the strong connections between the long-duration near misses, ship length, and ship speed. The method is first proposed for doing research of near misses in temporal dimension. The findings of this article help researchers and maritime traffic management agencies to better understand the importance of maritime traffic risk from the temporal perspective.

Aiming at the problem that the traditional collaborative filtering algorithm using shallow models cannot learn the deep features of users and items, and the recommendation model is very susceptible to the counter-interference of its parameters; this paper proposes a matrix-factorization recommendation model that combines adversarial learning and attention-gated recurrent units (AGAMF). Firstly, the gated recurrent unit based on the attention mechanism is used to extract the user's latent vector from the user's auxiliary side information. Secondly, the convolutional neural network is used to extract the item's latent vector from the item's auxiliary side information. Finally, adversarial disturbances are introduced on the latent factors of users and items to quantify the loss of the model under parameter disturbances, and the latent vectors of users and items are integrated into the probability matrix factorization to predict the user's rating of the item. Experiments were performed on two real data sets MovieLens-1M and MovieLens-10M, and the RMSE, MAE and Recall indicators were used for evaluation. Experiments prove that the model proposed in this paper is robust and can effectively alleviate the problem of data sparsity. Compared with other related recommendation algorithms, our model has a significant improvement in recommendation performance.

In recent years, with the continuous growth of multimedia data on the Internet, multimodal hashing has attracted increasing attention for its efficiency in large-scale cross-modal retrieval. Typically, most existing multimodal hashing methods are batch-based methods that cannot deal with the growing streaming data. Online multimodal hashing adopts online learning strategy to learn hash models incrementally, which can process large-scale streaming data. However, existing supervised online multimodal hashing methods still suffer from several limitations: (1) most methods cannot update hash codes of old data when hash model changes, which will hinder the retrieval accuracy. (2) the discrete optimizations of most methods for learning binary hash codes are less efficient or less effective. To address the above limitations, an efficient supervised online multimodal hashing method termed Online Adaptive Supervised Hashing (OASH) is proposed in this paper. OASH regresses class labels and training data to binary hash codes to learn discrete hash codes and hash functions with an efficient online optimization scheme. It learns hash functions incrementally with newly arriving data and updates hash codes with the latest hash model. By adaptively updating hash functions and hash codes with new data rather than accessing to old data, it gains much performance enhancement and computation savings. Extensive experiments on three benchmark data sets validate the superiority of OASH over state-of-the-art methods in both accuracy and efficiency.

Aiming at the problem of insufficient sentiment word extraction ability in existing text sentiment analysis methods and OOV (out-of-vocabulary) problem of pre-training word vectors, a neural network model combining multi-head self-attention and character-level embedding is proposed. An encoder-decoder (Seq2Seq) model is used for sentiment analysis of text. By adding character-level embedding to the word embedding layer, the OOV problem of pre-trained word vectors is solved. At the same time, the self-attention mechanism is used to perform attention calculations within the input sequence to find intra-sequence connections. The model uses bidirectional LSTM (Long Short-Term Memory) independently encodes the context semantic information of the input sequence to obtain deeper emotional features, to more effectively identify the emotional polarity of short text. The effectiveness of our method was verified on the public Twitter dataset and the movie review dataset published by ACL. Experimental results show that the accuracy of the model on the three categories of Twitter, movie reviews and IMDB datasets reaches 66.45%, 79.48% and 90.34%, respectively, which verifies that the model has excellent performance in datasets in different fields.

In this paper, robust control strategies are explored to solve the problem of rendezvous maneuver for rigid spacecraft exposed to the external disturbance, actuator faults and unknown inertial parameters. To pursue the control objective, two adaptive controllers are constructed via the sliding mode control (SMC) technology. Firstly, a basic control scheme is designed in the event of unknown inertial parameters and external disturbance, where the Minimum-learning-parameter (MLP) algorithm is adopted for approximating the unknown system dynamics. Though effective, the basic controller is not applicable in the actuator fault scenarios. Considering this drawback, adaptive laws are designed in the second controller to tackling the actuator faults. It is illustrated that the proposed controllers will endow tracking errors with asymptotic stability and strong robustness to actuator faults. Finally, the effectiveness of the control strategies is verified by numerical simulations.

Traditional visual simultaneous localization and mapping (SLAM) systems mostly based on small-area static environments. In recent years, some studies focused on combining semantic information with visual SLAM. However, most of them are hard to obtain better performance in the large-scale dynamic environment. And the accuracy, rapidity of the system still needs to strengthen. In this paper, we develop a more efficient semantic SLAM system in the two-wheeled mobile robot by using semantic segmentation to recognize people, chairs, and other objects in every keyframe. With a preliminary understanding of the environment, fusing the RGB-D camera and encoders information, to localization and creating a dense colored octree map without dynamic objects. Besides, for the incomplete identification of movable objects, we used image processing algorithms to enhance the semantic segmentation effect. In the proposed method, enhanced semantic segmentation in keyframes dramatically increases the efficiency of the system. Moreover, fusing the different sensors can highly raise localization accuracy. We conducted experiments on various datasets and in some real environments and compared them with DRE-SLAM, DS-SLAM, to evaluate the performance of the proposed approach. The results suggest we significantly improve the processing efficiency, robustness, and quality of the map.

Attribute-based encryption (ABE) mechanism on lattices can against quantum attack and guarantees fine-grained access control of encrypted data, and it has been widely used in cloud computing and privacy protection. However, the high complexity of the trapdoor generation and delegation and preimage sampling are the main barriers for the development of ABE schemes in a post-quantum world. Thus, we proposed a security and efficient multi-authority ABE scheme from lattices in this paper. The scheme is based on the theory of MP12 and is proved to be secure under the standard model. In this scheme, we construct an optimized sampling algorithm to generate a $\mathbb {Z}_{q}$ -invertible matrix with a lower runtime. Based on it, combining the trapdoor generation and delegation algorithm in MP12, we designed multiple attribute authorities which can manage different attribute sets and generate private keys for the user independently. In addition, the Shamir’s secret sharing technique is introduced to support policies expressed in any monotone access structures. Compared with the existing related schemes, the proposed scheme can improve the functional agility and the flexibility of the access policy prominently, and it also can achieve a better performance with less lattice dimension, trapdoor storage occupation, and ciphertext expansion rate. The analysis shows that our scheme is feasible and superior in the large-scale distributed environment.

With the rapid growth of online medical treatments, social media has become a prevalent platform to spread health-related information. For public health campaigns, how to diffuse the message broadly and efficiently in the social network is a classical problem, which is named Influence Maximization (IM). IM can be considered as an algorithmic problem of finding a small set of network users as seed nodes that maximizes the spread of influence for a piece of information under a certain influence cascade model. With a lot of researches focusing on this field, most existing solutions are proposed to extract seed nodes only considering pure topological structures. In practice, nodes in health-related networks are often abundantly accompanied with other types of meaningful information such as node attributes (identity), second-order proximity, and edge attributes (user relationship), while conventional IM models overlook these information. Therefore, a general framework for incorporating the heterogeneous information into an IM model could be potentially helpful to find underlying seed nodes in health-related networks. Moreover, the stability and the computational cost are also the main challenges that current IM models face. To bridge these gaps, in this paper, we propose NE-IM (Network Embedding for Influence Maximization), a method that aspires to address both problems using representation learning. NE-IM composes of two components: Structure-based embedding and feature-based embedding. They are the projections of network structures and heterogeneous information respectively in a low dimensional space, so that each node in health-related network can be represented as a fix dimensional vector. Experimental results show that our methods significantly outperform baseline approaches.

This paper addresses the target localization problem in complex multipath propagation environment for three-dimensional (3-D) radar systems. Firstly, an approach based on the singular value decomposition (SVD) technique is developed to reduce the data dimension and formulate the joint multiple snapshot sparse representation problem in the signal subspace domain. Subsequently, a novel sparse representation based DOA estimation algorithm, combined with alternatingly iterative and dictionary refinement techniques, is proposed. The Cramér-Rao bounds (CRB) for the target DOA and attenuation coefficient estimations of multipath model are derived in closed forms. Experimental results based on both simulated data and measured data indicate that the target localization accuracy can be effectively enhanced by utilizing the proposed algorithm in complex terrain and/or limited snapshot scenarios.

Using clustering algorithms to automatically analyze EEGs of patients and to identify the characteristic waves of epilepsy is of high clinical value. Traditional clustering algorithms mostly use a calculated virtual single representative medoid point to describe the cluster structure, but this single representative medoid point has insufficient information. To accurately capture more accurate intracluster structural information, a representative multi-medoid points strategy is adopted, which describes the cluster structure by assigning representative weights to each sample in the cluster. Considering that the multi-view learning mechanism combines information from each view to improve the algorithm's clustering performance, a multi-view fuzzy clustering algorithm with multi-medoid (MvFMMdd) is proposed. This algorithm discards the approach of the traditional fuzzy clustering algorithm, which uses a single virtual representative point to characterize the cluster structure, and uses several real representative points to describe the cluster structure. Experiments verify the medical significance of the proposed algorithm.

Energy storage equipment is an important part of integrated energy systems, but the construction and operational costs of it are great. Therefore, it's difficult to apply energy storage equipment on a large scale. As an alternative measure, adjusting controllable loads including electric vehicles (EVs) and air conditioning loads jointly can transfer loads across time periods and reduce variation difference between heat and electric loads. These controllable loads can be regarded as “virtual energy storage system”. Managing the charging of EVs and heat storage of buildings, a joint virtual energy storage system including electric energy storage and thermal energy storage is proposed in this paper. Then, these models are integrated into a scheduling model for regional integrated energy system (RIES). The charging/discharging power management of joint virtual energy storage systems can be realized by arranging the charging of EVs based on vehicle-driven rules and by adjusting building indoor temperatures within the temperature comfort range. Finally, simulations are carried out to demonstrate the effectiveness of the scheduling method. Simulation results show that the RIES optimal scheduling model considering joint virtual energy storage can reduce operational costs. The stability of RIES is improved with the combination of virtual electrical and thermal energy storage.

Localization has always been an essential application of underwater acoustic sensor networks (UASNs), which plays an important role in routing strategies design, node recycling, and so on. The complex ocean condition, prior infrastructure deployment, and time synchronization among beacons challenge the application of the UASN's localization. To solve these problems, we propose a novel underwater acoustic sensor networks localization algorithm based on the virtual node assistance. The algorithm is classified into two parts based on the current marine environment, including Virtual node Assisted Static (VAS) localization algorithm and Virtual node Assisted Dynamic (VAD) localization algorithm. An auxiliary node, which does not directly participate in the localization, is deployed for virtual node setup, error measurement, and RSSI ranging. The GPS-equipped ship utilizes virtual node and geometry to realize the UASN's localization without complicated deployment procedures and time synchronization. The simulation results show that our proposed algorithm can achieve the properties, including high localization coverage and small localization error as well as low communication overhead in the UASNs.

In this paper, we propose a novel deep convolutional neural network (DCNN) for removing snowflakes from light field (LF) images. We observe that snowflakes in LF images always interrupt slopes in background scenes in epipolar plane images (EPIs), which means that snowflakes may be easily detected in EPIs. Our method takes 3D EPI volumes (i.e., stacked subaperture views along the same row or column of an LF image) as input. In this way, our snowflake detector based on a 3D residual network with a convolutional long short-term memory (ResNet-ConvLSTM) can utilize both contextual information and 3D scene structural information to effectively detect snowflakes of different sizes in LF images. Then, an encoder-decoder-based LF image restoration network is proposed to restore the background image. Finally, extensive experiments for comparison with the state-of-the-art methods demonstrate the effectiveness of our method for challenging scenes.

The Davey–MacKay (DM) concatenated code employing the symbol-level watermark decoding algorithm is able to correct a large number of binary insertions, deletions, and substitutions, while it has the high computational complexity for recovering the synchronization. In the DM concatenated scheme, at the large insertion/deletion probability, in order to achieve the reliable output, the watermark decoder needs to perform the forward and backward passes on a very large trellis. In this paper, a threshold is selected to prevent the paths having very low forward and backward quantities from participating in the calculation of log-likelihood ratios from the watermark decoder. Simulation results show that great reduction in the complexity of the decoding algorithm is achieved at a very slight expense of accuracy.

Locating the source of information in online social networks facilitates knowing the origins of events, verifying the authenticity of information and finding the initial spreaders of rumors. However, locating the source of information in online social networks is a challenge task. This is because the information diffusion process is dynamic and complex, and the observation of the process is restricted to a limited number of nodes. Many previous works have studied the source locating problem under synchronous diffusion models, but the information diffusion process in social networks is naturally asynchronous and stochastic. The source locating method designed for the synchronous diffusion process cannot be directly applied to the asynchronous diffusion process. Aiming at solving the source locating problem of the asynchronous diffusion process in online social networks, we propose a source locating method that consists of an estimator based on the correlation coefficient and a matrix to represent the diffusion time delay between nodes approximately. Five sampling strategies for choosing observable nodes are presented to cooperate with the source locating method. Numerical simulations show the proposed method is superior to the state-of-the-art method on the asynchronous diffusion process in three types of networks.

Reversible bimolecular chemical reactions are elementary chemical reactions. This paper investigated this reaction type based on DNA strand displacement, and proposed degradation, catalysis, and annihilation reactions along with the synchronization reaction modules. DNA reaction modules were compared with ideal reaction modules, and relative reaction rates analyzed using the Lotka-Volterra oscillator as an example to demonstrate the validity of these reaction modules. An arbitrary chemical reaction network was employed to numerically simulate and predict synchronization between two Lotka-Volterra oscillators.

In the era of the “Internet of everything,”people and devices are interconnected and keep exchanging private information, including confidential data about personal identity, locations, payments, transactions, and so on. The privacy leakage problem also becomes an essential security issue that challenges the management and authentication of these private information, which even restricts the communication efficiency in different “things.”With the occurrence of serious information security incidents, such as the Facebook data breach event in March 2018, more and more attention has been taken into protecting private information from leakage or misuse. The existing private information protection mechanisms are mostly built on the basis of data encryption, access control, or user authentication, bearing two shortcomings: (1) strictly protecting data but sacrificing the data usage efficiency; and (2) rare consideration of the related application scenario information. In this paper, a privacy information protection scheme is proposed, where private data is protected differently based on the data attributes and application scenarios, ensuring that the private data is “properly protected”. We first investigate the specific attributes of private data based on the search engine (Google) in different application scenarios, such as the data importance and dependence. Based on these attributes, private data are divided into four security domains in a given application scenario, which is further protected by applying different protection policies. By implementing the proposed scheme, it has been demonstrated that our protection scheme can balance well between the security and usage convenience of private information.

The increasing demand of mobile devices (MDs) for data services brings tremendous pressure to cellular networks. It has become a great challenge for traditional offloading techniques to balance the energy efficiency and quality of service. The concept of device-to-device (D2D) communication shows a huge potential in cellular offloading. In this paper, we investigate the scenario where MDs have the same demand for a common content and they cooperate to download the content by multihop relaying. We aim to minimize the total power consumption by grouping MDs in multihop D2D networks, while satisfying the minimum rate required by each MD. As the problem is NP-complete and the optimal solution cannot be found in polynomial time, we propose three greedy algorithms with different grouping strategies to trade off the performance and complexity. Simulation results demonstrate that the total power consumption can be saved significantly in the content delivery situation using cooperative D2D communication, and the proposed algorithms are suitable for static and dynamic networks with different advantages.

Shortest path queries have been widely used in location-based services (LBSs). To calculate the shortest path from an origin to a destination, an LBS provider usually needs to know the map data of the underlying road network, which can be rather costly especially if such data need to be kept continuously and up to date. A cost-effective way is that LBS providers outsource the shortest path query processing to cloud-based mapping services such as Google Maps, by retrieving the detailed path information from them through external requests. Due to the high cost of accessing data through external requests and the usage limits of mapping services, we propose two optimization techniques in this paper, namely, path sharing and path caching, to reduce the number of external requests and the user query response time. Unlike previous work where the underlying road network is given, this paper optimizes path query processing only based on query origins and destinations. The basic idea of path sharing optimization is that the path information of a query can be shared with another query q if q values origin and destination both lie on the path. To achieve this, we propose an effective method to compute whether or not a query origin/destination lies on a path only based on the Euclidean distance between them. Path caching, an extension of path sharing, lets an LBS provider answer path queries directly based on cached paths. To accomplish this, we first formulate the problem of constructing path cache as a knapsack problem and design a greedy algorithm to solve it; then, we devise an effective cache structure to support efficient cache lookup. Extensive experiments on Bing Maps and real data sets are conducted, and the results show the efficiency, scalability, and applicability of our proposed approaches.

With the rapid development of online social networks (OSN), the influence of rumor propagation on social life raises great concern. Traditional rumor-propagation models, which do not fully consider the features of OSN, are not suitable for use in OSN. In this paper, we focus on discovering a pattern of rumor-propagation phenomena in OSN, and propose a novel rumor-propagation model, inspired by a ball elastic-collision model, called the elastic collision-based rumor-propagation model (ECRModel). We investigate the dynamics of ball elastic collisions, which is similar to the dynamics of rumor propagation between nodes in OSN. We adopt the parameter relationships of the elastic collision model and apply them to rumor propagation in social networks. In the ECRModel, we do not directly adopt the node classification categories of “Ignorants, Spreaders, and Stiflers”, but divide the user nodes into three types: 1) inactive and never spread rumors; 2) active and spread rumors forward; and 3) inactive but have previously spread rumors. We mathematically model node interaction attributes, and analyze the spreading probabilities and the steady state, considering both individual perspectives with detailed attributes and integral perspectives with node-state densities. At last, we conduct a series of simulations, and the results verify the correctness of the analytical results. We further investigate the effects of detailed properties on rumor propagation, such as average out-degree of OSN, rumor confusingness degree, and each node's comprehensive influence.