The virtual network (VN) embedding/mapping problem is recognized as an essential question of network virtualization. The VN embedding problem is a major challenge in this field. Its target is to efficiently map the virtual nodes and virtual links onto the substrate network resources. Previous research focused on designing heuristic-based algorithms or attempting two-stage solutions by solving node mapping in the first stage and link mapping in the second stage. In this study, we propose a new VN embedding algorithm based on integer programming. We build a model of an augmented substrate graph, and formulate the VN embedding problem as an integer program with an objective function and some constraints. A factor of topology-awareness is added to the objective function. The VN embedding problem is solved in one stage. Simulation results show that our algorithm greatly enhances the acceptance ratio, and increases the revenue/cost (R/C) ratio and the revenue while decreasing the cost of the VN embedding problem.

Numerous smart card based authentication protocols have been proposed to provide strong system security and robust individual privacy for communication between parties these days. Nevertheless, most of them do not provide formal analysis proof, and the security robustness is doubtful. Chang and Cheng (2011) proposed an efficient remote authentication protocol with smart cards and claimed that their proposed protocol could support secure communication in a multi-server environment. Unfortunately, there are opportunities for security enhancement in current schemes. In this paper, we identify the major weakness, i.e., session key disclosure, of a recently published protocol. We consequently propose a novel authentication scheme for a multi-server environment and give formal analysis proofs for security guarantees.

Digital image halftoning is a widely used technique. However, achieving high fidelity tone reproduction and structural preservation with low computational time cost remains a challenging problem. This paper presents a highly parallel algorithm to boost real-time application of serial structure-preserving error diffusion. The contrast-aware halftoning approach is one such technique with superior structure preservation, but it offers only a limited opportunity for graphics processing unit (GPU) acceleration. Our method integrates contrast-aware halftoning into a new parallelizable error-diffusion halftoning framework. To eliminate visually disturbing artifacts resulting from parallelization, we propose a novel multiple quantization model and space-filling curve to maintain tone consistency, blue-noise property, and structure consistency. Our GPU implementation on a commodity personal computer achieves a real-time performance for a moderately sized image. We demonstrate the high quality and performance of the proposed approach with a variety of examples, and provide comparisons with state-of-the-art methods.

The focal problems of projection include out-of-focus projection images from the projector caused by incomplete mechanical focus and screen-door effects produced by projection pixilation. To eliminate these defects and enhance the imaging quality and clarity of projectors, a novel adaptive projection defocus algorithm is proposed based on multi-scale convolution kernel templates. This algorithm applies the improved Sobel-Tenengrad focus evaluation function to calculate the sharpness degree of intensity equalization and then constructs multi-scale defocus convolution kernels to remap and render the defocus projection image. The resulting projection defocus corrected images can eliminate out-of-focus effects and improve the sharpness of uncorrected images. Experiments show that the algorithm works quickly and robustly and that it not only effectively eliminates visual artifacts and can run on a self-designed smart projection system in real time but also significantly improves the resolution and clarity of the observer’s visual perception.

The faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.

We investigate two different kinds of resonant current regulators for a doubly fed induction generator (DFIG) under distorted grid voltage conditions: proportional integral resonant (PIR) regulator with traditional resonant part and vector proportional integral (VPI) regulator with VPI resonant part. Based on the mathematical model of DFIG under distorted grid voltage, the transfer function and frequency response characteristics of the two current regulators are analyzed and compared. The superiority of the VPI current regulator over the PIR regulator is pointed out, and the influence of discretization methods on the performance of the resonant current regulator is studied. All the results are validated by MATLAB simulation and experiments.

Demand response is becoming a promising field of study in operation and planning of restructured power systems. More attention has recently been paid to demand response programs. Customers can contribute to the operation of power systems by deployment demand response. The growth of customers’ participation in such programs may affect the planning of power systems. Therefore, it seems necessary to consider the effects of demand response in planning approaches. In this paper, the impact of demand responsiveness on decision making in generation expansion planning is modeled. Avoidance or deferment in installation of new generating units is comprehensively investigated and evaluated by introducing a new simple index. The effects of demand responsiveness are studied from the points of view of both customers and generation companies. The proposed model has been applied to a modified IEEE 30-bus system and the results of the study are discussed. Simulation results show that reducing just 3% of the customers’ demand (due to price elasticity) may result in a benefit of about 10% for customers in the long term.