Image inpainting refers to filling in unknown regions with known knowledge, which is in full flourish accompanied by the popularity and prosperity of deep convolutional networks. Current inpainting methods have excelled in completing small-sized corruption or specifically masked images. However, for large-proportion corrupted images, most attention-based and structure-based approaches, though reported with state-of-the-art performance, fail to reconstruct high-quality results due to the short consideration of semantic relevance. To relieve the above problem, in this paper, we propose a novel image inpainting approach, namely cascading blend network (CBNet), to strengthen the capacity of feature representation. As a whole, we introduce an adjacent transfer attention (ATA) module in the decoder, which preserves contour structure reasonably from the deep layer and blends structure-texture information from the shadow layer. In a coarse to delicate manner, a multi-scale contextual blend (MCB) block is further designed to felicitously assemble the multi-stage feature information. In addition, to ensure a high qualified hybrid of the feature information, extra deep supervision is applied to the intermediate features through a cascaded loss. Qualitative and quantitative experiments on the Paris StreetView, CelebA, and Places2 datasets demonstrate the superior performance of our approach compared with most state-of-the-art algorithms.

High-resolution (HR) magnetic resonance (MR) images provide more detailed information for reliable diagnoses and quantitative medical image analyses. Deep convolutional neural networks (CNNs) have demonstrated their ability to effectively retrieve HR MR images from low-resolution (LR) MR images. However, most CNN-based super-resolution (SR) algorithms treat content and background information equally, ignoring the unique properties of MR images, such as low contrast, intricate tissue textures, and sparse backgrounds. We present a Wavelet Dual- Stream Network (WDN) for accurate MR SR that addresses the issues raised above. First, a wavelet transform is leveraged at the network's beginning to decouple the inputs, which are divided into high-frequency and low-frequency sub-bands. The high-frequency sub-bands relate to content information with larger frequency changes, and the low-frequency sub-bands correspond to background information. In addition, we devise a two-branch structure to reconstruct the high-frequency and low-frequency features separately. On the one hand, we design the U-Net Attention (U-A) mechanism for focusing the network's attention on regions with critical information. On the other hand, due to the correlation between high-frequency and low- frequency branches, We establish the Cross Attention Block (CAB) to accomplish the interaction between two branches. CAB takes advantage of the redundancy of information between different branches to distill the information of the current branch. Finally, the inverse wavelet transform is utilized to couple the modified high-frequency and low-frequency sub-bands as SR. Extensive experiments confirm the effectiveness of the WDN, which provides a clear improvement over the state-of-the-art method in both subjective and objective evaluations.

Abstract Single Image Super‐Resolution algorithms have made enormous progress in recent years. However, many previous Convolution Neural Network (CNN) based Super‐Resolution algorithms only stack uniform convolution layers of fixed kernel size, and frequently ignore inherent multi‐scale properties of the images, resulting in unsatisfactory reconstruction results. Here, a multi‐feature fusion attention network (MFFAN) is proposed for capturing information at diverse scales. MFFAN is composed of multiple efficient sparse residual group (ESRG) modules. Several multi‐scale feature fusion blocks (MSFFB) are constructed using a cascade manner in each ESRG module and it is capable of exploiting various cross scales information. Subsequently, a local‐global spatial attention block (LGSAB) is inserted at the tail of the ESRG module for further improving the interaction of inter‐pixel, which strengths essential features and suppresses irrelevant information. Additionally, owing to the fact that only feeding final output into the reconstruction layer has exacerbated the long‐range dependency problems, an enhanced hierarchy feature fusion block (EHFFB) is designed to fuse low‐level information and high‐level semantic information. Experiment results indicate that the proposed MFFAN is competitive in comparison to several state‐of‐the‐art algorithms.

Most machine intelligence or cloud computing can be formulated as multi-objective optimization problems (MOPs) with constraints, while evolutionary multi-objective optimization (EMO) is a powerful means to deal with them. However, its adaptation for dealing with complex constrained MOPs (CMOPs) keeps being under the scope of recent investigations. The main challenges are as follows. 1) The existing algorithms can not make full use of infeasible solution information in the evolution process. 2) There is no effective infeasible solution in the initial population, which causes the algorithms fall into local optimal feasible regions. In light of these two issues, this paper proposes an adaptive epsilon-constraint-handling technique with a detect-and-escape strategy to make full use of infeasible solutions in the whole evolution process. Then, the feasible solutions are saved to the external archive and take part in the population evolution by non-dominated sorting. Finally, the proposed method is embedded into the decomposition based multi-objective evolutionary framework (MOEA/D). Experiments on benchmark problems show that the proposed algorithm is highly competitive compared with state-of-the-art constrained evolutionary algorithms.

Recently, with the rise and progress of convolutional neural networks (CNNs), CNN-based single image super-resolution (SISR) methods have gained considerable advancement and showed great power for image reconstruction tasks. Never-theless, existing methods cannot dynamically adjust the network according with the input, which greatly impairs the practical performance of the network. To address this issue, a novel residual adaptive dense weight attention network (RADWAN) is proposed consisted of several adaptive residual groups (ARGs) to enhance the generalization performance of the network. Specifically, each ARG contains several adaptive dense weight attention blocks (ADWAB). It generates dense connection coefficients dynamically using an adaptive dense weight block (ADWB) for more accurate feature extraction. Besides, an avg-std channel attention block is further presented to maximize the potential of RADWAN to make the model focus on critical information. To adjustably utilize additional features from shallow and intermediate layers, we introduce adaptive short connections (ASC) and adaptive long connections (ALC) to effectively integrate abundant hier-archical features. Extensive experiments on several datasets have demonstrated the superiority of RADWAN over the state-of-the-art methods in aspects of both quantitative metrics and visual quality.

Nanonetworks are composed of interacting nano-nodes, whose size ranges from several hundred cubic nanometers to several cubic micrometers. The extremely constrained computational resources of nano-nodes, the fluctuations in their energy caused by energy harvesting processes, and their very limited transmission range at Terahertz (THz)-band frequencies (0.1-10 THz), make the design of routing protocols in nanonetworks very challenging. A multi-hop deflection routing algorithm based on reinforcement learning (MDR-RL) is proposed in this paper to dynamically and efficiently explore the routing paths during packet transmissions. First, new routing and deflection tables are implemented in nano-nodes, so that nano-nodes can deflect packets to other neighbors when route entries in the routing table are invalid. Second, one forward updating scheme and two feedback updating schemes based on reinforcement learning are designed to update the tables, namely, on-policy and off-policy updating schemes. Finally, extensive simulations in networks simulator-3 are conducted to analyze the performance of MDR-RL using different updating policies, as well as to compare the performance with other machine learning routing algorithms based on Neural Networks and Decision Tree. The results show that the MDR-RL can increase the packet delivery ratio and number of delivered packets, and can decrease the packet average hop count.

Recent studies in multiobjective particle swarm optimization (PSO) have the tendency to employ Pareto-based technique, which has a certain effect. However, they will encounter difficulties in their scalability upon many-objective optimization problems (MaOPs) due to the poor discriminability of Pareto optimality, which will affect the selection of leaders, thereby deteriorating the effectiveness of the algorithm. This paper presents a new scheme of discriminating the solutions in objective space. Based on the properties of Pareto optimality, we propose the dominant difference of a solution, which can demonstrate its dominance in every dimension. By investigating the norm of dominant difference among the entire population, the discriminability between the candidates that are difficult to obtain in the objective space is obtained indirectly. By integrating it into PSO, we gained a novel algorithm named many-objective PSO based on the norm of dominant difference (MOPSO/DD) for dealing with MaOPs. Moreover, we design a Lp-norm-based density estimator which makes MOPSO/DD not only have good convergence and diversity but also have lower complexity. Experiments on benchmark problems demonstrate that our proposal is competitive with respect to the state-of-the-art MOPSOs and multiobjective evolutionary algorithms.

BACKGROUND: Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data. RESULTS: In response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median. CONCLUSIONS: Our experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.

Abstract A general framework to investigate the interference and coverage probability is proposed in this paper for indoor terahertz (THz) communications with beamforming antennas. Due to the multipath effects of THz band (0.1–10 THz), the line of sight and non-line of sight interference from users and access points (APs) (both equipped with beamforming antennas) are separately analyzed based on distance-dependent probability functions. Moreover, to evaluate the effects of obstacles in real applications, a Poisson distribution blockage model is implemented. Moreover, the coverage probability is derived by means of signal to interference plus noise ratio (SINR). Numerical results are conducted to present the interference and coverage probability with different parameters, including the indoor area size, SINR threshold, numbers of interfering users and APs and half-power bandwidth of beamforming antenna.

The matrix completion problem has found many applications in image and graphics fields. Developing fast and exact algorithms still remains challenging. The truncated nuclear norm (TNN), taking advantage of priori target rank information, is known as a better surrogate function to the rank constraint than the traditional nuclear norm. However, the TNN penalized algorithms always converge slowly due to the two-step scheme for avoiding directly updating the noncovex functions. In this paper, we propose a computationally efficient algorithm, Momentum Adaptive and Rank Revealing (MARR), for the TNN regularized matrix completion problem. The distinct advantages are to (1) reduce iterations by introducing non-monotonic constraints, and (2) decrease computational burden by controlling the size of matrix. Moreover, the descent property and convergence of the search are proven. Experiments on image inpainting, including images and range data, show that the proposed algorithm achieves very competitive results visually and numerically compared to several state-of-the-art approaches, while providing substantial reduction of iterations and runtime, thereby more applicable in real-world problems.

Nanonetworks composed by communicating nano-devices enable new applications in the consumer, biomedical, and environmental fields. Three main characteristics introduce strict requirements for routing protocols design for nanonetworks, namely, short transmission range at Terahertz (THz) frequency (0.1-10 THz), fluctuations in the energy of nano-nodes due to the energy harvesting processes and very limited memory/buffer size of nano-nodes. In this paper, a multi-hop deflection routing algorithm based on Q-learning for energy-harvesting nanonetworks (MDRQEN) is proposed to guarantee the network energy efficiency, while ensuring a low packet loss probability. First, a deflection table is introduced to deflect the packets when the next hop nano-nodes are unavailable due to energy or memory/buffer constraints. Then, a Q-learning scheme is proposed to update the routing table and deflection table by utilizing the reward information contained in the forwarded packet from the previous nano-node. In the Q-learning update scheme, packet deflection ratio, packet loss ratio, packet hop count and node energy status of nano-nodes are taken into consideration. As numerically shown through extensive simulations in Network Simulator 3 (NS-3), the proposed MDRQEN algorithm can achieve a better packet delivery ratio and energy efficiency than random routing algorithm, flooding routing algorithm and the MDRQEN algorithm without the Q-learning update scheme.

Interference and coverage is a critical factor affecting the performance of nanonetworks in the terahertz (THz) band. In this paper, on the basis of THz channel model, the interferences from surrounding omnidirectional nanosensors (NSs) and beamforming Base Stations (BSs) are derived in closed forms by using stochastic geometry methods respectively. Furthermore, the corresponding Signal-to-Interference-plus-Noise-Ratio (SINR) and the coverage probabilities are investigated based on the proposed interference model. Simulation results, observed from the spectral windows at 1.0 THz, 4.5 THz and 9.1 THz, demonstrate that high density of BSs, beamforming antenna with small beam-width and low density of NSs are recommended to mitigate the interference and improve the coverage performance. Moreover, low frequency in THz band with low absorption coefficient is advocated to guarantee the correct reception with enough high received signal strength.

The optimization of the network throughput and transmission range is one of the most important issues in cognitive relay networks (CRNs). Existing research has focused on the dual-hop network, which cannot be extended to a triple-hop network due to its shortcomings, including the limited transmission range and one-way communication. In this paper, a novel, triple-hop relay scheme is proposed to implement time-division duplex (TDD) transmission among secondary users (SUs) in a three-phase transmission. Moreover, a superposition coding (SC) method is adopted for handling two-receiver cases in triple-hop networks with a cognitive relay. We studied a joint optimization of time and power allocation in all three phases, which is formulated as a nonlinear and concave problem. Both analytical and numerical results show that the proposed scheme is able to improve the throughput of SUs, and enlarge the transmission range of primary users (PUs) without increasing the number of hops.

In this paper, we consider the robust face recognition problem via iterative re-constrained group sparse classifier (IRGSC) with adaptive weights learning. Specifically, we propose a group sparse representation classification (GSRC) approach in which weighted features and groups are collaboratively adopted to encode more structure information and discriminative information than other regression based methods. In addition, we derive an efficient algorithm to optimize the proposed objective function, and theoretically prove the convergence. There are several appealing aspects associated with IRGSC. First, adaptively learned weights can be seamlessly incorporated into the GSRC framework. This integrates the locality structure of the data and validity information of the features into l2,p-norm regularization to form a unified formulation. Second, IRGSC is very flexible to different size of training set as well as feature dimension thanks to the l2,p-norm regularization. Third, the derived solution is proved to be a stationary point (globally optimal if p ≥ 1). Comprehensive experiments on representative data sets demonstrate that IRGSC is a robust discriminative classifier which significantly improves the performance and efficiency compared with the state-of-the-art methods in dealing with face occlusion, corruption, and illumination changes, and so on.

The massive point cloud data obtained through the computer vision is uneven in density together with a lot of noise and outliers, which will greatly reduce the point cloud search efficiency and affect the surface reconstruction. Based on that, this paper presents a filtering algorithm based on Voxel Grid Statistical Outlier (VGSO): Firstly, 3D voxel grid is created for the massive point cloud data approximating other points inside the voxel with the centroid of all points; then, the neighborhood of discrete points is analyzed statistically, calculating average distance of every point to its neighboring points and filtering the outliers outside the reference ranges of average distance from the data set; finally, the segmentation rules are improved according to the characteristics of KD-Tree. A large amount of experimental results show that this stable and reliable algorithm can compress and filter the point cloud data quickly and effectively. At the same time, it greatly accelerates the search speed.

To compensate for the shortcomings of existing methods used in TSP (Traveling Salesman Problem), such as the accuracy of solutions and the scale of problems, this paper proposed an improved particle swarm optimization by using a self-organizing construction mechanism and dynamic programming algorithm. Particles are connected in way of scale-free fully informed network topology map. Then dynamic programming algorithm is applied to realize the evolution and information exchange of particles. Simulation results show that the proposed method with good stability can effectively reduce the error rate and improve the solution precision while maintaining a low computational complexity.

The irrational use of limited network resources in conjunction with the unpredictable nature of traffic load injection in wireless multimedia sensor networks (WMSN) may lead to congestion. Traditional transmission schemes were not designed for supporting prioritized QoS, especially not for guaranteeing strict QoS required by real-time services such as voice and video. To overcome these deficiencies, an optimized rate control approach is proposed for multi-priority data transmission based on the extended Lotka-Volterra competitive model. The key idea is, when some new traffic flows are initialized and injected into the WMSN due to unexpected events, a novel bio-inspired rate control (Bio-RC) approach is designed to consider their effects on the system stability according to the limited network resources and competitions with others traffic flows, ensuring that the system will rapidly converge to a global and stable equilibrium point (EP) and all traffic flows are of peaceful coexistence and differentiated with QoS and priorities. At the same time, the network resources can be utilized adequately and congestion can be brought down or avoided effectively. Extensive simulations reveal that the proposed approach achieves adaptability and scalability to dynamic network traffic load, and coexistence with service differentiation for data flows.

A new method for performing a nonlinear form of manifold-oriented stochastic neighbor projection method is proposed. By the use of kernel functions, one can operate in the feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. The proposed method is termed as kernel-based manifoldoriented stochastic neighbor projection(KMSNP). By two different strategies, KMSNP is divided into two methods: KMSNP1 and KMSNP2. Experimental results on several databases show that, compared with the relevant methods, the proposed methods obtain higher classification performance and recognition rate. INTRODUCTION Kernel-based methods(kernel methods for short) have become a new hot topic in machine learning fields in recent years, their theoretical basis is statistical learning theory. Kernel methods are a class of algorithms for pattern analysis, whose best known element is the support vector machine(SVM) (Dardas and Georganas 2011).The methods skillfully introduce kernel function which not only reduces the curse of dimensionality (Cherchi and Guevara 2012, Xue et al. 2012), but also effectively solves the local minimum and incomplete statistical analysis in traditional pattern recognition methods on the premise of no additional computational capacity. As an availability way to resolve the problem of nonlinear pattern recognition, kernel methods approach the problem by mapping the data into a highdimensional feature space, where each coordinate corresponds to one feature of the data items, transforming the data into a set of points in a Euclidean space (Chen and Li 2011, Zhang et al. 2008). The theory of kernel methods can be traced back to 1909, Mercer proposed Mercer's theorem (Mercer 1909) which indicates that any ‘reasonable’ kernel function corresponds to some feature space. 1964, the use of Mercer's theorem for interpreting kernels as inner products in a feature space was introduced into machine learning by Aizerman et al. (AizermanI et al. 1964), but no sufficient importance has been attached to it. Until 1992, Vapnik et al. (Boser et al. 1992) successfully extended the SVM to the non-linear SVM by using kernel functions, it began to show its potential and advantages. Subsequently, more and more kernelbased methods were presented, such as: kernel principal component analysis(KPCA) (Xiao et al. 2012), kernel fisher discriminator(KFD) (Yang et al. 2005), kernel independent component analysis (KICA) (Zhang et al. 2013), kernel partial least squares(KPLS) (Helander et al. 2012) and so on. In this paper, we propose to use the kernel idea and present a method called kernel-based manifoldoriented stochastic neighbor projection(KMSNP) method through improving the manifold-oriented stochastic neighbor projection(MSNP) (Wu et al. 2011) technique. MSNP is based on stochastic neighbor embedding(SNE) (Hinton and Roweis 2002) and t-SNE (Maaten and Hinton 2008). The basic principle of SNE is to convert pairwise Euclidean distances into probabilities of selecting neighbors to model pairwise similarities while t-SNE uses student t-distribution to model pairwise dissimilarities in low-dimensional space. Different from SNE and t-SNE, MSNP converts pairwise dissimilarities of inputs to probability distribution related to geodesic distance in highdimensional space and uses Cauchy distribution to model stochastic distribution of features. Furthermore, it recovers the manifold structure through a linear projection by requiring the two distributions to be similar. Experiments demonstrate MSNP has unique advantages in terms of visualization and recognition task, but there are still two drawbacks in it: firstly, MSNP is an unsupervised method and lack of the idea of class label, so it is not suitable for pattern identification; secondly, since MSNP is a linear feature dimensionality reduction algorithm, it cannot effectively settle the nonlinear feature extraction problem. To overcome the disadvantages of MSNP, we have done some preliminary work. On the first, we introduced the idea of class label and presented a method called discriminative stochastic neighbor embedding analysis(DSNE) (Zheng et al. 2012, Chen Proceedings 27th European Conference on Modelling and Simulation ©ECMS Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang (Editors) ISBN: 978-0-9564944-6-7 / ISBN: 978-0-9564944-7-4 (CD) and Wang 2012). On the second, we think KMSNP can overcome the disadvantage mentioned above well. The rest of this paper is organized as follows: in Section 2, we provide a brief review of MSNP. Section 3 describes the detailed algorithm derivation of KMSNP. Furthermore, experiments on various databases are presented in Section 4. Finally, we provide some concluding remarks and describe several issues for future works in Section 5. MSNP Considering the problem of representing d-dimensional data vectors x1, x2, . . . , xN, by r-dimensional (r << d) vectors y1, y2, . . ., yN such that yi represents xi. The basic principle of MSNP is to convert pairwise dissimilarity of inputs to probability distribution related to geodesic distance in high-dimensional space, and then using Cauchy distribution to model stochastic distribution of features, finally, MSNP recovers the manifold structure through a linear projection by requiring the two distributions to be similar. Mathematically, the similarity of datapoint xi to datapoint xj is depicted as the following joint probability pij which means xi how possible to pick xj as its neighbor: exp( / 2) exp( / 2) geo ij ij geo ik k i D p D      (1) where Dij is the geodesic distance for xi and xj. In practice, MSNP calculates geodesic distance by using a two-phase method (Wu et al. 2011). Firstly, an adjacency graph G is constructed by K-nearest neighbor strategy. Secondly, the desired geodesic distance is approximated by the shortest path of graph G. This procedure is proposed in Isomap to estimate geodesic distance and the detail calculation steps can be found in (Tenenbaum et al. 2000). For low-dimensional representations, MSNP employs Cauchy distribution with  degree of freedom to construct joint probability qij. The probability qij indicates how possible point i and point j can be stochastic neighbors is defined as:

Recent developments in the field of home automation have shifted the technology away from the realms of research and into the homes of consumers. Together with the rapid adoption of the Internet, the ‘anywhere and anytime’ accessible home environment has been brought closer to a reality. Exciting as this prospect might be, significant security challenges arise from making the home environment accessible to anyone with Internet access. Hence, providing sufficient security to offer a reasonable level of protection for homeowner's privacy and safety is crucial for successful adoption of this technology. This study examines the security issues raised by the ‘anywhere and anytime’ accessible home environment. The existing approaches for addressing these security challenges and their weaknesses are reviewed. This study concludes with the proposal, implementation and evaluation of an improved approach for providing remote access security for the home environment.

Transmitting delay-sensitive video streaming over IEEE 802.11e wireless local area networks (WLANs) is becoming increasingly popular. However, the transmission of real-time video streaming is very challenging because of the time-varying wireless channels and video content characteristics. The authors propose an accurate model to assess the perceived quality of video streaming over WLANs with enhanced distributed coordination function (EDCF) mechanism. The analytical model considers not only the packet loss caused by wireless interference and channel fading, but also the effects of loss from channel access competition. Based on the Markov chain, the authors then present the discrete probability distribution of medium access control (MAC) layer packet service time by using the signal transfer function of the generalised state transition diagram. Moreover, the coding relation of lost video frames is also explored in the performance analysis of the proposed model. Simulations based on Network Simulator 2 (NS-2) are conducted to verify the performance of the analytical model. The results show that the proposed model provides superior accuracy for the perceived quality of MPEG-4 video streaming over IEEE 802.11e EDCF-based WLANs.

A multi-stage, multi-product, multi-constraint, and mixed continuous/batch plants planning problem is abstracted from production of diaphragm caustic soda, and the problem is formulated as a mathematic optimization model based on the objective to maximize the total profit with constraints involved resources, work manufacture processes and production capacity etc. An approach based on particle swarm optimization algorithm (PSO) with proportion-coding and dynamic-bounds coding scheme (PDCS) is proposed for the model. The validity and flexibility of the model and algorithm are verified by calculating the data from a real-world application.

With the internet technology developing and web information exploding, users urgently need a stronger web information retrieval function. For the better and stronger function, the sequence of search result queue and the correlation of query expression are the key problems that could be computed by similarity algorithm. Now we recommend a new method which is ontology-based weighted semantic tree similarity algorithm (OWSTS). In this paper, we introduce the construction method of OWSTS and the implementation of similarity algorithm. Then we use a deep groove ball bearing as an example to show how to implement intelligent retrieval system for machinery production. Finally the relative accuracy of retrieval results by OWSTS and generally used cosine vector space based similarity algorithm (TF*IDF) are compared. Experiment result shows that OWSTS algorithm is feasible for information retrieval and the similarity algorithm makes the sequence of search result queue fit for users semantic requirement.

A vision sensor is the robot's eye to perceive its environment, but the perception performance can be significantly affected by illumination conditions. This paper presents strategies of adaptive illumination control for robot vision to achieve the best scene interpretation. It investigates how to obtain the most comfortable illumination conditions for a vision sensor. In a "comfort" condition the image reflects the natural properties of the concerned object. "Discomfort" may occur if some scene information is lost. Strategies are proposed to optimize the pose and optical parameters of the luminaire and the sensor, with emphasis on controlling the intensity and avoiding glare.

This paper presents a novel field robot JL-I based on a reconfigurable concept for urban search and rescue applications. The robot consists of three identical modules; each module is an entire robotic system that can perform distributed activities. It features three-degrees-of-freedom (DOF) active joints actuated by serial and parallel mechanisms for changing shape and flexible docking mechanism. The docking mechanism enables adjacent modules to connect or disconnect flexibly and automatically. DOF analysis, working space analysis and the kinematics of the 3D active joint between connected modules are studied thoroughly. In the end a series of successful tests confirm the principles and the robot's capabilities.