Volume 6, Number 4, 2009
Special Issue on 3D Technology and Virtual Environments: Techniques, Systems, and Applications (pp.319-363)
This paper presents a survey on virtual reality systems and provides an in-depth understanding toward the notion of immersion, according to the semantic meanings of the terms virtual and reality. The paper analyses the structure and functions of a virtual reality system and takes the three dimensional display as the immersive medium to identify the key issues for construction of virtual environments. The paper also reviews the development of virtual reality technology and introduces new image processing techniques into the design of virtual reality systems and virtual environments.
The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and waveletbased image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more effciently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.
Although computer capabilities have been improved significantly, a large-scale virtual reality (VR) system demands much more in terms of memory and computation than the current computer systems can offer. This paper discusses two important issues related to VR performance and applications in building navigation. These are dynamic loading of models based on cell segmentation for the optimal VR operation, and the route optimization based on path planning for easy navigation. The VR model of engineering and information technology complex (EITC) building at the University of Manitoba is built as an example to show the feasibility of the proposed methods. The reality, enhanced by three-dimensional (3D) real-time interactivity and visualization, leads navigators into a state of the virtual building immersion.
This paper presents a skin deformation algorithm for creating 3D characters or virtual human models. The algorithm can be applied to rigid deformation, joint dependent localized deformation, skeleton driven deformation, cross contour deformation, and free-form deformation (FFD). These deformations are computed and demonstrated with examples and the algorithm is applied to overcome the difficulties in mechanically simulating the motion of the human body by club-shape models. The techniques described in this article enables the reconstruction of dynamic human models that can be used in defining and representing the geometrical and kinematical characteristics of human motion.
This paper presents a model for simulating crowd evacuation and investigates three widely recognized problems. For the space continuity problem, this paper presents two computation algorithms: one uses grid space to evaluate the coordinates of the obstacle s bounding box and the other employs the geometry rule to establish individual evacuation routes. For the problem of collision, avoidance, and excess among the individuals, this paper computes the generalized force and friction force and then modifies the direction of march to obtain a speed model based on the crowd density and real time speed. For the exit selection problem, this paper establishes a method of selecting the exits by combining the exit s crowd state with the individuals. Finally, a particle system is used to simulate the behavior of crowd evacuation and produces useful test results.
This paper studies the model of the digital preservation of paper-cut after analyzing the state-of-the-art of the preservation of intangible cultural heritage at home and abroad, focusing on paper-cutting in Hebei, China in a comprehensive approach of sociology, anthropology, folk art, folklore, communication, computer, and information science. Models, methods, and solutions for the preservation and retention of national and folk cultural heritage are proposed. A virtual multimedia interactive system framework of the digital preservation of national and folk cultural heritage is constructed. The standards of paper-cut digitization are studied. The main content of the paper involves: regulated data collection and recording of the scattered Chinese folk paper-cut works in Hebei; digitization, optimization, compression, classification, icon and pattern extraction, vectorization, and work analysis of the first-hand material. This paper also investigates the demonstration, dissemination, database construction, and retrieval of the classified material, icons, and patterns; the demonstration of the reconstruction and application of icon and pattern database; the design and development of an immersive virtual gaming platform of the multi-media scenes and production process of folk paper-cut.
We present an overview of some recent developments in the area of mathematical modeling of maintenance decisions for multi-unit systems. The emphasis is on three main groups of multicomponent maintenance optimization models:the block replacement models, group maintenance models, and opportunistic maintenance models. Moreover, an example of a two-unit system maintenance process is provided in order to compare various maintenance policies.
The composition of the distillation column is a very important quality value in refineries, unfortunately, few hardware sensors are available on-line to measure the distillation compositions. In this paper, a novel method using sensitivity matrix analysis and kernel ridge regression (KRR) to implement on-line soft sensing of distillation compositions is proposed. In this approach, the sensitivity matrix analysis is presented to select the most suitable secondary variables to be used as the soft sensor s input. The KRR is used to build the composition soft sensor. Application to a simulated distillation column demonstrates the effectiveness of the method.
In this paper, a robust adaptive fuzzy dynamic surface control for a class of uncertain nonlinear systems is proposed. A novel adaptive fuzzy dynamic surface model is built to approximate the uncertain nonlinear functions by only one fuzzy logic system. The approximation capability of this model is proved and the model is implemented to solve the problem that too many approximators are used in the controller design of uncertain nonlinear systems. The shortage of explosion of complexity in backstepping design procedure is overcome by using the proposed dynamic surface control method. It is proved by constructing appropriate Lyapunov candidates that all signals of closed-loop systems are semi-globally uniformly ultimate bounded. Also, this novel controller stabilizes the states of uncertain nonlinear systems faster than the adaptive sliding mode controller (SMC). Two simulation examples are provided to illustrate the effectiveness of the control approach proposed in this paper.
This paper considers a concrete stochastic nonlinear system with stochastic unmeasurable inverse dynamics. Motivated by the concept of integral input-to-state stability (iISS) in deterministic systems and stochastic input-to-state stability (SISS) in stochastic systems, a concept of stochastic integral input-to-state stability (SiISS) using Lyapunov functions is first introduced. A constructive strategy is proposed to design a dynamic output feedback control law, which drives the state to the origin almost surely while keeping all other closed-loop signals almost surely bounded. At last, a simulation is given to verify the effectiveness of the control law.
We consider the Kalman filtering problem in a networked environment where there are partial or entire packet losses described by a two state Markovian process. Based on random packet arrivals of the sensor measurements and the Kalman filter updates with partial packet, the statistical properties of estimator error covariance matrix iteration and stability of the estimator are studied. It is shown that to guarantee the stability of the Kalman filter, the communication network is required to provide for each of the sensor measurements an associated throughput, which captures all the rates of the successive sensor measurements losses. We first investigate a general discrete-time linear system with the observation partitioned into two parts and give suffcient conditions of the stable estimator. Furthermore, we extend the results to a more general case where the observation is partitioned into n parts. The results are illustrated with some simple numerical examples.
In this paper, we present a modified projection method for the linear feasibility problems (LFP). Compared with the existing methods, the new method adopts a surrogate technique to obtain new iteration instead of the line search procedure with fixed stepsize. For the new method, we first show its global convergence under the condition that the solution set is nonempty, and then establish its linear convergence rate. Preliminary numerical experiments show that this method has good performance.
Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by authorized users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system (IDS) is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference (KDD 2009) intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS (NGIDS) involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.
This paper deals with the global asymptotic stability problem for Hopfield neural networks with time-varying delays. By resorting to the integral inequality and constructing a Lyapunov-Krasovskii functional, a novel delay-dependent condition is established to guarantee the existence and global asymptotic stability of the unique equilibrium point for a given delayed Hopfield neural network. This criterion is expressed in terms of linear matrix inequalities (LMIs), which can be easily checked by utilizing the recently developed algorithms for solving LMIs. Examples are provided to demonstrate the effectiveness and reduced conservatism of the proposed condition.
In this paper, we address the problem of structure identification of Volterra models. It consists in estimating the model order and the memory length of each kernel. Two methods based on input-output crosscumulants are developed. The first one uses zero mean independent and identically distributed Gaussian input, and the second one concerns a symmetric input sequence. Simulations are performed on six models having different orders and kernel memory lengths to demonstrate the advantages of the proposed methods.
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