A Novel Multi-Hop Algorithm for Wireless Network with Unevenly Distributed Nodes

Node location estimation is not only the promise of the wireless network for target recognition, monitoring, tracking and many other applications, but also one of the hot topics in wireless network research. In this paper, the localization algorithm for wireless network with unevenly distributed nodes is discussed, and a novel multi-hop localization algorithm based on Elastic Net is proposed. The proposed approach is formulated as a regression problem, which is solved by Elastic Net. Unlike other previous localization approaches, the proposed approach overcomes the shortcomings of traditional approaches assume that nodes are distributed in regular areas without holes or obstacles, therefore has a strong adaptability to the complex deployment environment. The proposed approach consists of three steps: the data collection step, mapping model building step, and location estimation step. In the data collection step, training information among anchor nodes of the given network is collected. In mapping model building step, the mapping model among the hop-counts and the Euclidean distances between anchor nodes is constructed using Elastic Net. In location estimation step, each normal node finds its exact location in a distributed manner. Realistic scenario experiments and simulation experiments do exhibit the excellent and robust location estimation performance.     

An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

Influence maximization of temporal social networks (IMT) is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread. To solve the IMT problem, we propose an influence maximization algorithm based on an improved K-shell method, namely improved K-shell in temporal social networks (KT). The algorithm takes into account the global and local structures of temporal social networks. First, to obtain the kernel value Ks of each node, in the global scope, it layers the network according to the temporal characteristic of nodes by improving the K-shell method. Then, in the local scope, the calculation method of comprehensive degree is proposed to weigh the influence of nodes. Finally, the node with the highest comprehensive degree in each core layer is selected as the seed. However, the seed selection strategy of KT can easily lose some influential nodes. Thus, by optimizing the seed selection strategy, this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization (KTIM). According to the hierarchical distribution of cores, the algorithm adds nodes near the central core to the candidate seed set. It then searches for seeds in the candidate seed set according to the comprehensive degree. Experiments show that KTIM is close to the best performing improved method for influence maximization of temporal graph (IMIT) algorithm in terms of effectiveness, but runs at least an order of magnitude faster than it. Therefore, considering the effectiveness and efficiency simultaneously in temporal social networks, the KTIM algorithm works better than other baseline algorithms.     

Optimization of a regularized distortion measure to generate curved high‐order unstructured tetrahedral meshes

We present a robust method for generating high‐order nodal tetrahedral curved meshes. The approach consists of modifying an initial linear mesh by first, introducing high‐order nodes, second, displacing the boundary nodes to ensure that they are on the computer‐aided design surface, and third, smoothing and untangling the mesh obtained after the displacement of the boundary nodes to produce a valid curved high‐order mesh. The smoothing algorithm is based on the optimization of a regularized measure of the mesh distortion relative to the original linear mesh. This means that whenever possible, the resulting mesh preserves the geometrical features of the initial linear mesh such as shape, stretching, and size. We present several examples to illustrate the performance of the proposed algorithm. Furthermore, the examples show that the implementation of the optimization problem is robust and capable of handling situations in which the mesh before optimization contains a large number of invalid elements. We consider cases with polynomial approximations up to degree ten, large deformations of the curved boundaries, concave boundaries, and highly stretched boundary layer elements. The meshes obtained are suitable for high‐order finite element analyses. Copyright © 2015 John Wiley & Sons, Ltd.     

Improvement of face recognition using light compensation technique on real-time imaging

Camera imaging systems are used widely. However, the resulting images may show unequal light distributions due to backlight. In this paper, an adaptive backlight compensation algorithm is presented for fixing the brightness and contrast in regions of interest, particularly for human faces. The framework is implemented in two stages. The first stage is the light compensation algorithm, which depends on face detection and focuses on the intensities of pixels in ‘face’ regions only. The second stage uses a distance weighting approach to address artificial light effects created by the first stage. This algorithm can adjust the imaging light distribution adaptively in order to solve the problem of backlight and achieve natural-looking pictures. For face recognition systems, this approach can improve the success rate for face recognition by 35% on average when images are backlit.     

E2-SCAN: an extended credit strategy-based energy-efficient security scheme for wireless ad hoc networks

Utilising the battery life and the limited bandwidth available in mobile ad hoc networks (MANETs) in the most efficient manner is an important issue, along with providing security at the network layer. The authors propose, design and describe E2-SCAN, an energy-efficient network layered security solution for MANETs, which protects both routing and packet forwarding functionalities in the context of the on demand distance vector protocol. E2-SCAN is an advanced approach that builds on and improves upon some of the state-of-the-art results available in the literature. The proposed E2-SCAN algorithm protects the routing and data forwarding operations through the same reactive approach, as is provided by the SCAN algorithm. It also enhances the security of the network by detecting and reacting to the malicious nodes. In E2-SCAN, the immediate one-hop neighbour nodes collaboratively monitor. E2-SCAN adopts a modified novel credit strategy to decrease its overhead as the time evolves. Through both analysis and simulation results, the authors demonstrate the effectiveness of E2-SCAN over SCAN in a hostile environment.     

Reliability assessment of MR fluid dampers in passive and semi-active seismic control of structures

In this paper, the reliability assessment of magnetorheological (MR) dampers in reducing structures’ seismic responses is studied. Two passive and semi-active control scenarios are considered and discussed. For semi-active control scenario, the Lyapunov control algorithm is used. In order to minimize the structure’s responses under a given earthquake excitation, an unconstrained optimization problem is first defined and solved via genetic algorithm to find an optimal diagonal matrix in Lyapunov’s control algorithm. The procedure is explained for a three-storey benchmark shear structure equipped with MR damper in all storeys. The structure’s responses reliability for various uncertainties are derived and interpreted based on standard Monte Carlo simulation approach. In the reliability problem, for the limit state function two cases based on human comfort and structural safety criteria are defined and investigated. Furthermore, the most influential uncertain variables and control scenarios on structures’ reliability are identified. Finally, a sensitivity analysis on the coefficient of variations of random variables on the structure’s responses reliability is accomplished. The results show that in the joint event, the MR damper is more reliable for acceleration control rather than drift control in passive control scenario. The aforementioned trend is reversed for semi-active control scenario. Additionally, passive control scenario is more reliable than the semi-active control scenario in reducing maximum acceleration of structure. However, in reducing maximum drift of structure, the values of the reliability in semi-active control scenario are almost similar to passive control scenario.     

Reliability-based failure cause assessment of collapsed bridge during construction

Until now, in many forensic reports, the failure cause assessments are usually carried out by a deterministic approach so far. However, it may be possible for the forensic investigation to lead to unreasonable results far from the real collapse scenario, because the deterministic approach does not systematically take into account any information on the uncertainties involved in the failures of structures.Reliability-based failure cause assessment (reliability-based forensic engineering) methodology is developed which can incorporate the uncertainties involved in structural failures and structures, and to apply them to the collapsed bridge in order to identify the most critical failure scenario and find the cause that triggered the bridge collapse. Moreover, to save the time and cost of evaluation, an algorithm of automated event tree analysis (ETA) is proposed and possible to automatically calculate the failure probabilities of the failure events and the occurrence probabilities of failure scenarios. Also, for reliability analysis, uncertainties are estimated more reasonably by using the Bayesian approach based on the experimental laboratory testing data in the forensic report. For the applicability, the proposed approach is applied to the Hang-ju Grand Bridge, which collapsed during construction, and compared with deterministic approach.     

基于Gabor脸和隐马尔可夫模型的人像识别算法

提出了基于Gabor小波变换和隐马尔可夫模型的人像识别算法。该算法先对人脸图像进行多分辨率的Gabor小波变换,采用主元分析法对每个结点进行降维,最后形成Gabor脸。把Gabor脸的每个特征结作为观测向量,对隐马尔可夫模型进行了训练,并把优化的模型参数用于人脸识别。实验结果表明,本文方法识别率高,复杂度较低,对部分遮挡的图像具有较大的容忍度。     

All-optical router with pulse-position modulation header processing in high-speed photonic packet switching networks

In future high-speed photonic packet switching networks, it is highly desirable to carry out robust alloptical header recognition to provide high-throughput routing. The authors present a pulse-position modulation header processing (PPM-HP) scheme, offering significantly reduced routing table size and employing a single bitwise AND operation to carry out correlation of the packet header with the entire routing table entries. The downsized routing table also offers multiple transmission modes (unicast, multicast and broadcast) in the optical layer and improves core network scalability where the number of core/edge nodes could be altered without the need for changing the number of routing table entries. The authors present modelling and simulation of the packet switching router based on PPM-HP. Noise propagation and crosstalk incurred in a multiple-hop routing scenario are investigated. The simulation results are presented and compared with the theoretical calculations.     

An Efficient Hybrid Model for Arabic Text Recognition

In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a Connectionist Temporal Classification layer (CTC). Moreover, during the training phase of the model, we introduce an algorithm of data augmentation to increase the quality of data. Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters, thus overcoming several problems related to this point. To train and test (evaluate) our approach, we used two Arabic handwritten text recognition databases, which are IFN/ENIT and KHATT. The Experimental results show that our new approach, compared to other methods in the literature, gives better results.     

一种黄金分割优化的极限学习机算法

针对极限学习机(ELM)存在大量隐层神经元个数和随机给定权值导致算法性能不稳定等问题,将黄金分割法(Golden Section)与ELM相结合提出了基于黄金分割优化的极限学习机算法(GS-ELM).首先通过黄金分割法对ELM隐含层节点数进行优化,接着再用该方法对ELM输入层权值和隐含层偏差进行优化.实验结果表明,相比较传统的BP神经网络,支持向量机和极限学习机,GS-ELM算法能获得较高的分类精度.     

Occlusion invariant face recognition using mean based weight matrix and support vector machine

In this paper, a novel occlusion invariant face recognition algorithm based on Mean based weight matrix (MBWM) technique is proposed. The proposed algorithm is composed of two phases—the occlusion detection phase and the MBWM based face recognition phase. A feature based approach is used to effectively detect partial occlusions for a given input face image. The input face image is first divided into a finite number of disjointed local patches, and features are extracted for each patch, and the occlusion present is detected. Features obtained from the corresponding occlusion-free patches of training images are used for face image recognition. The SVM classifier is used for occlusion detection for each patch. In the recognition phase, the MBWM bases of occlusion-free image patches are used for face recognition. Euclidean nearest neighbour rule is applied for the matching. GTAV face database that includes many occluded face images by sunglasses and hand are used for the experiment. The experimental results demonstrate that the proposed local patch-based occlusion detection technique works well and the MBWM based method shows superior performance to other conventional approaches.     

Recognition of control chart concurrent patterns using a neural network approach

Pattern recognition is an important issue in Statistical Process Control, as unnatural patterns exhibited by control charts can be associated with specific assignable causes adversely affecting the process. Neural network approaches to recognition of control chart patterns have been developed by several researchers in recent years, but to date these have been focused on recognition and analysis of single patterns such as sudden shifts, linear trends or cyclic patterns. This paper investigates the detection of concurrent patterns where more than one pattern exists simultaneously. The topology and training of a Back-Propagation Network (BPN) system is described. Extensive performance evaluation has been carried out using simulated data to develop a range of average run length-related performance indices, including new performance indices that are proposed to describe concurrent patterns recognition performance. Two evaluation scenarios were evaluated: in the first, unnatural patterns are already present; while in the second, patterns may appear progressively at any time. Numerical results are provided that indicate that the pattern recognizer can perform very well in the first scenario, while it performs effectively but not without deficiencies for some specific pattern combinations in the second evaluation approach. Limitations and potential improvements in the concurrent pattern recognition scheme are also discussed.     

An indirect approach for automatic generation of quadrilateral meshes with arbitrary line constraints

A new approach to the automatic generation of a quadrilateral mesh with arbitrary line constraints is proposed in this paper. It is an indirect all‐quad mesh generation and presented in the following steps: (1) discretizing the constrained lines within the domain; (2) converting the above domain to a triangular mesh together with the line constraints; (3) transforming the generated triangular mesh with line constraints to an all‐quad mesh through performing an advancing front algorithm from the line constraints, which enables the construction of quadrilaterals layer by layer, and roughly keeps the feature of the initial triangular mesh; (4) optimizing the topology of the quadrilateral mesh to reduce the number of irregular nodes; (5) smoothing the generated mesh toward high‐quality all‐quad mesh generation. Finally, a few application examples are given to demonstrate the reliability and usefulness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

基于双层相空间相似度的滚动轴承故障模式与故障程度的综合辨识

提出了一种基于双层相空间相似度分析算法结构,应用于滚动轴承故障类型和故障程度的综合辨识。该算法第一层结构中,对测试数据和样本数据进行相空间重构(PSR),得到在拓扑意义下等价的相空间,然后使用滑动窗截取数据段,采用归一化互相关函数(NCC)进行相空间相似度分析,实现轴承故障类型的分类;在第二层结构中,以已知不同故障程度数据之间的相空间相似度(PSS)为特征训练SVR结构,实现对故障程度的跟踪。实验信号分析结果表明,该方法能有效对轴承故障类型和故障程度进行综合辨识。与传统方法的对比表明该方法在准确性上有了一定的提高。     

LC-Grid: a linear global contact search algorithm for finite element analysis

The contact searching is computationally intensive and its memory requirement is highly demanding; therefore, it is significant to develop an efficient contact search algorithm with less memory required. In this paper, we propose an efficient global contact search algorithm with linear complexity in terms of computational cost and memory requirement for the finite element analysis of contact problems. This algorithm is named LC-Grid (Lei devised the algorithm and Chen implemented it). The contact space is decomposed; thereafter, all contact nodes and segments are firstly mapped onto layers, then onto rows and lastly onto cells. In each mapping level, the linked-list technique is used for the efficient storing and retrieval of contact nodes and segments. The contact detection is performed in each non-empty cell along non-empty rows in each non-empty layer, and moves to the next non-empty layer once a layer is completed. The use of migration strategy makes the algorithm insensitive to mesh size. The properties of this algorithm are investigated and numerically verified to be linearly proportional to the number of contact segments. Besides, the ideal ranges of two significant scale factors of cell size and buffer zone which strongly affect computational efficiency are determined via an illustrative example.     

Performance-driven object-oriented program re-modularisation

The aim is to achieve the highest possible speedup when distributing a program across a cluster of computational nodes. The speedup may be achieved by concurrent execution of the distributed modules. In order to maximise the concurrency, a two-stage approach is proposed. In the first stage, a novel statement-reordering algorithm reorders the statements within a given program, to maximise the distance between each call instruction and the instructions, which are data-dependent on any value affected by the call. In the second stage, a clustering algorithm is applied to look for a modular structure of the program which results in the highest concurrency in its execution. The clustering algorithm applies a novel performance evaluation function which is derived from the program code, automatically.     

3-Dimensional Bag of Visual Words Framework on Action Recognition

Human motion recognition plays a crucial role in the video analysis framework. However, a given video may contain a variety of noises, such as an unstable background and redundant actions, that are completely different from the key actions. These noises pose a great challenge to human motion recognition. To solve this problem, we propose a new method based on the 3-Dimensional (3D) Bag of Visual Words (BoVW) framework. Our method includes two parts: The first part is the video action feature extractor, which can identify key actions by analyzing action features. In the video action encoder, by analyzing the action characteristics of a given video, we use the deep 3D CNN pre-trained model to obtain expressive coding information. A classifier with subnetwork nodes is used for the final classification. The extensive experiments demonstrate that our method leads to an impressive effect on complex video analysis. Our approach achieves state-of-the-art performance on the datasets of UCF101 (85.3%) and HMDB51 (54.5%).     

Feature classification techniques in model-based object recognition

We describe a system that performs model-based recognition of the projections of generalized cylinders, and present new results on the final classification of the feature data. Two classification methods are proposed and compared. The first is a Bayesian technique that ranks the object space according to estimated conditional probability distributions. The second technique is a new feed-forward “neural” implementation that utilizes the back-propagation learning algorithm. The neural approach yields a 31.8% reduction in classification error for a database of twenty models relative to the Bayesian approach, although it does not provide an ordered ranking of the object space. The accuracy results of the neural approach represent a significant performance advance in feature-based recognition by perceptual organization without the use of depth information. Examples are provided using the results of a simple segmentation system applied to real image data.     

A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0

Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analysis of the temporal decomposition and computational complexity is performed. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed.