The artificial endocrine system (AES) is a new branch of natural computing which uses ideas and takes inspiration from the information processing mechanisms contained in the mammalian endocrine system. It is a fast growing research field in which a variety of new theoretical models and technical methods have been studied for dealing with complex and significant problems. An overview of some recent advances in AES modeling and its applications is provided in this paper, based on the major and latest works. This review covers theoretical modeling, combinations of algorithms, and typical application fields. A number of challenges that can be undertaken to help move the field forward are discussed according to the current state of the AES approach.

We propose a novel curvature-aware simplification technique for point-sampled geometry based on the locally optimal projection (LOP) operator. Our algorithm includes two new developments. First, a weight term related to surface variation at each point is introduced to the classic LOP operator. It produces output points with a spatially adaptive distribution. Second, for speeding up the convergence of our method, an initialization process is proposed based on geometry-aware stochastic sampling. Owing to the initialization, the relaxation process achieves a faster convergence rate than those initialized by uniform sampling. Our simplification method possesses a number of distinguishing features. In particular, it provides resilience to noise and outliers, and an intuitively controllable distribution of simplification. Finally, we show the results of our approach with publicly available point cloud data, and compare the results with those obtained using previous methods. Our method outperforms these methods on raw scanned data.

This paper presents a new approach for modeling a virtual garment intuitively and simply by sketching garment style lines. The user sketches directly onto the surface of 3D virtual human from arbitrary viewing directions, and the 3D garment suited to the virtual human can be created automatically. First, a distance field based allocation algorithm is proposed to find the 3D point which has the shortest given distance to the virtual human along the view direction. Then, the 3D style lines are generated by transforming from the 2D strokes on the human model and all the garment pieces are recognized from the 3D style lines. Finally, the 3D garment model is constructed by using the angle and offset based interpolation and Delaunay triangulation. In addition, we propose a body feature based template reusing method to fit the 3D garment to different virtual human models. The method can be adapted to designer habits and improve the usefulness of garment design. Examples show that the method is useful and efficient.

We describe a novel approach to Bayes risk (BR) decoding for speech recognition, in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error (MWE) metric. To achieve this, we propose improved forward and backward algorithms on the lattices and the whole procedure is optimized recursively. The remarkable characteristics of the proposed approach are that the optimization procedure is expectation-maximization (EM) like and the formation of the updated result is similar to that obtained with the confusion network (CN) decoding method. Experimental results indicated that the proposed method leads to an error reduction for both lattice rescoring and lattice-based system combinations, compared with CN decoding, confusion network combination (CNC), and ROVER methods.

In a mobile/pervasive computing environment, one of the most important goals of monitoring continuous spatial queries is to reduce communication cost for location-updates. Existing work uses many cellular wireless connections, which would easily become the performance bottleneck of the overall system. This paper introduces a novel continuous kNN query monitoring method to reduce communication cost in the hybrid wireless network, where the moving objects in the wireless broadcasting system construct the ad-hoc network. Simulation results prove the efficiency of the proposed method, which leverages the wireless broadcasting channel as well as the WiFi link to alleviate the burden on the cellular uplink communication cost.

QoS-aware service composition is aimed to maximize the global QoS of a composite service when selecting candidate services. In a context sensitive service execution environment in pervasive computing, the context information for service composition is not static: device, policy, and user constraints, and QoS requirements may change, new services may be deployed, old ones withdrawn, or existing ones change their QoS parameters. This results in the current service composition plan failing or its QoS degrading from the optimum. In this paper, a runtime self-optimizing service composition framework is proposed. An implementation of a prototype for this framework is presented, addressing the issues of reducing extra delay while increasing global QoS in service composition in a dynamic context environment. Three service re-plan algorithms are compared that can be used in dynamic context environment, i.e., minimal-conflict hill-climbing repair genetic algorithm (MCHC-repair GA), an improved penalty-based GA, and our multi-population conflicts sorted repair genetic algorithm (MP-CS-repair GA), as well as three kinds of service composition mechanisms—with backup, without backup, and our context-aware service re-selection mechanisms. The results show that our MP-CS-repair GA and context-aware service re-selection method can reduce more extra delay while acquiring a higher global QoS for the composite service in a context sensitive environment. This context-aware service re-selection mechanism also shows some adaptability to different context change frequencies and user requirements for reducing computation cost in the self-optimizing process.

Central catadioptric cameras have been extensively adopted in robotics and surveillance due to their extensive field of view. To attain precise 3D information in these applications, it is important to calibrate the catadioptric cameras accurately. The existing calibration techniques either require prior knowledge of the mirror types, or highly depend on a conic estimation procedure, which might be ruined if there are only small portions of the conic visible on calibration images. In this paper, we design a novel planar pattern with concurrent lines as a calibration rig, which is more robust in conic estimation since the relationship among lines is taken into account. Based on the line properties, we propose a rough-to-fine approach suitable for the new planar pattern to calibrate central catadioptric cameras. This method divides the nonlinear optimization calibration problem into several linear sub-problems that are much more robust against noise. Our calibration method can estimate intrinsic parameters and the mirror parameter simultaneously and accurately, without a priori knowledge of the mirror type. The performance is demonstrated by both simulation and a real hyperbolic catadioptric imaging system.

This paper deals with a unified and novel approach for analyzing the frequency and time domain performance of grounding systems. The proposed procedure is based on solving the full set of Maxwell’s equations in the frequency domain, and enables the exact computation of very near fields at the surface of the grounding grid, as well as far fields, by simple and accurate closed-form expressions for solving Sommerfeld integrals. In addition, the soil ionization is easily considered in the proposed method. The frequency domain responses are converted to the time domain by fast inverse Laplace transform. The results are validated and have shown acceptable accuracy.