In this paper, multiresolution critical-point filters (CPFs) are employed to image matching for frame rate up-conversion (FRUC). By CPF matching, the dense motion field can be obtained for representing object motions accurately. However, the elastic motion model does not hold in the areas of occlusion, thus resulting in blur artifacts in the interpolated frame. To tackle this problem, we propose a new FRUC scheme using an occlusion refined CPF matching interpolation (ORCMI). In the proposed approach, the occlusion refinement is based on a bidirectional CPF mapping. And the intermediate frames are generated by the bidirectional interpolation for non-occlusion pixels combined with unidirectional projection for the occlusion pixels. Experimental results show that ORCMI improves the visual quality of the interpolated frames, especially at the occlusion regions. Compared to the block matching based FRUC algorithm, ORCMI can achieve 1~2 dB PSNR gain for standard video sequences.

A novel color compensation method for multi-view video coding (MVC) is proposed, which efficiently exploits the inter-view dependencies between views with the existence of color mismatch caused by the diversity of cameras. A color compensation model is developed in RGB channels and then extended to YCbCr channels for practical use. A modified inter-view reference picture is constructed based on the color compensation model, which is more similar to the coding picture than the original inter-view reference picture. Moreover, the color compensation factors can be derived in both encoder and decoder, therefore no additional data need to be transmitted to the decoder. The experimental results show that the proposed method improves the coding efficiency of MVC and maintains good subjective quality.

In this paper, we propose an effective VLSI architecture of sub-pixel interpolation for motion compensation in the AVS HDTV decoder. To utilize the similar arithmetical operations of 15 luma sub-pixel positions, three types of interpolation filters are proposed. A simplified multiplier is presented due to the limited range of input in the chroma interpolation process. To improve the processing throughput, a parallel and pipelined computing architecture is adopted. The simulation results show that the proposed hardware implementation can satisfy the real-time constraint for the AVS HDTV (1 920×1 088) 30 fps decoder by operating at 108 MHz with 38.18k logic gates. Meanwhile, it costs only 216 cycles to accomplish one macroblock, which means the B frame sub-pixel interpolation can be realized by using only one set of the proposed architecture under real-time constraints.

To efficiently exploit the performance of single instruction multiple data (SIMD) architectures for video coding, a parallel memory architecture with power-of-two memory modules is proposed. It employs two novel skewing schemes to provide conflict-free access to adjacent elements (8-bit and 16-bit data types) or with power-of-two intervals in both horizontal and vertical directions, which were not possible in previous parallel memory architectures. Area consumptions and delay estimations are given respectively with 4, 8 and 16 memory modules. Under a 0.18-μm CMOS technology, the synthesis results show that the proposed system can achieve 230 MHz clock frequency with 16 memory modules at the cost of 19k gates when read and write latencies are 3 and 2 clock cycles, respectively. We implement the proposed parallel memory architecture on a video signal processor (VSP). The results show that VSP enhanced with the proposed architecture achieves 1.28× speedups for H.264 real-time decoding.

This paper first introduces a way to improve interactivity with high polygon count virtual objects through the “mixed” use of image-based representation within one object. That is, both 3D polygonal and image-based representations are maintained for an object, and switched for rendering depending on the functional requirement of the object. Furthermore, in order to reduce the popping effect and provide smooth and gradual transition during the object representation switch, the object is subdivided with the subdivided parts possibly represented differently, i.e., using 3D models or images. As for the image-based representation, the relief texture (RT) method is used. In particular, through the use of the mixed representation, a new way called TangibleScreen is proposed to provide object tangibility by associating the image-based representation with a physical prop (projecting the RTs) in a selective and flexible way. Overall, the proposed method provides a way to maintain an interactive frame rate with selective perceptual details in a large-scale virtual environment, while allowing the user to interact with virtual objects in a tangible way.

We propose a novel approach called adaptive fuzzy ant-based routing (AFAR), where a group of intelligent agents (or ants) builds paths between a pair of nodes, exploring the network concurrently and exchanging obtained information to update the routing tables. Routing decisions can be made by the fuzzy logic technique based on local information about the current network state and the knowledge constructed by a previous set of behaviors of other agents. The fuzzy logic technique allows multiple constraints such as path delay and path utilization to be considered in a simple and intuitive way. Simulation tests show that AFAR outperforms OSPF, AntNet and ASR, three of the currently most important state-of-the-art algorithms, in terms of end-to-end delay, packet delivery, and packet drop ratio. AFAR is a promising alternative for routing of data in next generation networks.

As one of the most important components of the wideband wireless access technique, orthogonal frequency division multiplexing (OFDM) has a high usage rate of spectrum and combats inter-symbol interference (ISI) in multi-path fading channel. However, when there are frequency offsets during the signal transmission, the inter-carrier interference (ICI) is introduced, which significantly degrades the performance. The existing ICI self-cancellation schemes such as PCC-OFDM are not optimum to minimize the interference considering both noise and ICI. In this paper, a new metric named SINR (signal-to-interference-and-noise ratio) is proposed. We discuss the optimization issue when a constant frequency offset exists and in time-varying channels. The optimum weighting-coefficient-pair (OWCP) is obtained, which maximizes SINR theoretically through the alternant iteration algorithm. Simulations show that the performance of OWCP-OFDM is better than that of PCC-OFDM, especially when the frequency offset is large. Although the ICI self-cancellation scheme suffers bandwidth inefficiency, from the simulation results we can also see that the performance of OWCP-OFDM is much better than that of the standard OFDM systems with the same bandwidth efficiency when a frequency offset exists. Moreover, since the redundant modulation provides the capability to suppress ICI as well as a receiving SNR gain, it can be considered as exchanging the bandwidth for SNR.

We introduce a new algebraic approach dealing with the problem of computing the topology of an arrangement of a finite set of real algebraic plane curves presented implicitly. The main achievement of the presented method is a complete avoidance of irrational numbers that appear when using the sweeping method in the classical way for solving the problem at hand. Therefore, it is worth mentioning that the efficiency of the proposed method is only assured for low-degree curves.

In this paper, we propose a new relational schema (R-schema) to XML schema translation algorithm, VQT, which analyzes the value cardinality and user query patterns and extracts the implicit referential integrities by using the cardinality property of foreign key constraints between columns and the equi-join characteristic in user queries. The VQT algorithm can apply the extracted implied referential integrity relation information to the R-schema and create an XML schema as the final result. Therefore, the VQT algorithm prevents the R-schema from being incorrectly converted into the XML schema, and it richly and powerfully represents all the information in the R-schema by creating an XML schema as the translation result on behalf of the XML DTD.

In this paper, we propose a technique for lowering the latency of the communication in a NoC (network on chip). The technique, which can support two qualities of service (QoS), i.e., the guaranteed throughput (GT) and best effort (BE), is based on splitting a wider link into narrower links to increase throughput and decrease latency in the NoC. In addition, to ease the synchronization and reduce the crosstalk, we use the 1-of-4 encoding for the smaller buses. The use of the encoding in the proposed NoC architecture considerably lowers the latency for both BE and GT packets. In addition, the bandwidth is increased while the power consumption of the links is reduced.

Heterogeneous computing (HC) environment utilizes diverse resources with different computational capabilities to solve computing-intensive applications having diverse computational requirements and constraints. The task assignment problem in HC environment can be formally defined as for a given set of tasks and machines, assigning tasks to machines to achieve the minimum makespan. In this paper we propose a new task scheduling heuristic, high standard deviation first (HSTDF), which considers the standard deviation of the expected execution time of a task as a selection criterion. Standard deviation of the expected execution time of a task represents the amount of variation in task execution time on different machines. Our conclusion is that tasks having high standard deviation must be assigned first for scheduling. A large number of experiments were carried out to check the effectiveness of the proposed heuristic in different scenarios, and the comparison with the existing heuristics (Max-min, Sufferage, Segmented Min-average, Segmented Min-min, and Segmented Max-min) clearly reveals that the proposed heuristic outperforms all existing heuristics in terms of average makespan.

Designers are required to plan for future expansion and also to estimate the grid’s future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid’s utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal behavior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

This paper proposes an analysis and a direct power control (DPC) design of a wind turbine driven doubly-fed induction generator (DFIG) under unbalanced network voltage conditions. A DFIG model described in the positive and negative synchronous reference frames is presented. Variations of the stator output active and reactive powers are fully deduced in the presence of negative sequence supply voltage and rotor flux. An enhanced DPC scheme is proposed to eliminate stator active power oscillation during network unbalance. The proposed control scheme removes rotor current regulators and the decomposition processing of positive and negative sequence rotor currents. Simulation results using PSCAD/EMTDC are presented on a 2-MW DFIG wind power generation system to validate the feasibility of the proposed control scheme under balanced and unbalanced network conditions.

This paper presents a powerful application of genetic algorithm (GA) for the minimization of the total harmonic current distortion (THCD) in high-power induction motors fed by voltage source inverters, based on an approximate harmonic model. That is, having defined a desired fundamental output voltage, optimal pulse patterns (switching angles) are determined to produce the fundamental output voltage while minimizing the THCD. The complete results for the two cases of three and five switching instants in the first quarter period of pulse width modulation (PWM) waveform are presented. Presence of harmonics in the stator excitation leads to a pulsing-torque component. Considering the fact that if the pulsing-torques are at low frequencies, they can cause troublesome speed fluctuations, shaft fatigue, and unsatisfactory performance in the feedback control system, the 5th, 7th, 11th, and 13th current harmonics (in the case of five switching angles) are constrained at some pre-specified values, to mitigate the detrimental effects of low-frequency harmonics. At the same time, the THCD is optimized while the required fundamental output voltage is maintained.

We present a new approach based on honey-bee mating optimization to estimate the state variables in distribution networks including distributed generators. The proposed method considers practical models of electrical equipments such as static var compensators, voltage regulators, and under-load tap changer transformers, which have usually nonlinear and discrete characteristics. The feasibility of the proposed approach is demonstrated by comparison with the methods based on neural networks, ant colony optimization, and genetic algorithms for two test systems, a network with 34-bus radial test feeders and a realistic 80-bus 20 kV network.