Long duration visual tracking of targets is quite challenging for computer vision, because the environments may be cluttered and distracting. Illumination variations and partial occlusions are two main difficulties in real world visual tracking. Existing methods based on hostile appearance information cannot solve these problems effectively. This paper proposes a feature-based dynamic tracking approach that can track objects with partial occlusions and varying illumination. The method represents the tracked object by an invariant feature model. During the tracking, a new pyramid matching algorithm was used to match the object template with the observations to determine the observation likelihood. This matching is quite efficient in calculation and the spatial constraints among these features are also embedded. Instead of complicated optimization methods, the whole model is incorporated into a Bayesian filtering framework. The experiments on real world sequences demonstrate that the method can track objects accurately and robustly even with illumination variations and partial occlusions.

Two characteristics of Chinese mixed traffic invalidate the conventional queuing delay estimates for western countries. First, the driving characteristics of Chinese drivers lead to different delays even though the other conditions are the same. Second, urban traffic flow in China is often hindered by pedestrians at intersections, such that imported intelligent traffic control systems do not work appropriately. Typical delay estimates for Chinese conditions were obtained from data for over 500 vehicle queues in Beijing collected using charge coupled device (CCD) cameras. The results show that the delays mainly depend on the proportion and positions of heavy vehicles in the queue, as well as the start-up situations (with or without interference). A simplified delay estimation model considers vehicle types and positions that compares well with the observed traffic delays.

Hierarchical art was used to solve the mixed mode placement for three dimensional (3-D) integrated circuit design. The 3-D placement flow stream includes hierarchical clustering, hierarchical 3-D floorplanning, vertical via mapping, and recursive two dimensional (2-D) global/detailed placement phases. With state-of-the-art clustering and de-clustering phases, the design complexity was reduced to enhance the placement algorithm efficiency and capacity. The 3-D floorplanning phase solved the layer assignment problem and controlled the number of vertical vias. The vertical via mapping transformed the 3-D placement problem to a set of 2-D placement sub-problems, which not only simplifies the original 3-D placement problem, but also generates the vertical via assignment solution for the routing phase. The design optimizes both the wire length and the thermal load in the floorplan and placement phases to improve the performance and reliability of 3-D integrate circuits. Experiments on IBM benchmarks show that the total wire length is reduced from 15% to 35% relative to 2-D placement with two to four stacked layers, with the number of vertical vias minimized to satisfy a pre-defined upper bound constraint. The maximum temperature is reduced by 16% with two-stage optimization on four stacked layers.

In traditional Chinese medicine, the coating on the tongue is considered to be a reflection of various pathologic factors. However, the conventional method to examine the tongue lacks an acceptable standard and does not provide the means for sharing information. This paper describes a segmentation method to extract tongue coatings. First, the tongue body was extracted from the original image using the watershed transform. Then, a threshold method was applied to the image to eliminate the light from the camera flash. Finally, a threshold method using the Otsu model in combination with a splitting-merging method was used in the red, green, and blue (RGB) space to extract the thin coating. The combination of the above two methods is applied in the hue, saturation, and value (HSV) space to extract the thick coating. The feasibility of this method is tested by experiments, and the accuracy of segmentation is 95.9%.

A networks-on-chip (NoC) cost-effective design method was given based on the globally-asynchronous locally-synchronous (GALS) interconnect structure. In this method, the synchronous mode was used to transmit data among routers, network interface (NI), and intellectual property (IP) via a synchronous circuit. Compared with traditional methods of implementing GALS, this method greatly reduces the transmission latency and is compatible with existing very large scale integration (VLSI) design tools. The platform designed based on the method can support two kinds of packetizing mechanisms, any topology, several kinds of traffic, and many configurable parameters such as the number of virtual channels, thus the platform is universal. An NoC evaluation methodology is given with a case study showing that the platform and evaluation methodology work well.

An efficient adaptive group of pictures (GOP)-level bit allocation algorithm was developed based on reverse dynamic programming (RDP). The algorithm gives the initial delay and sequence distortion curve with just one iteration of the algorithm. A simple GOP-level rate and distortion model was then developed for two-level constant quality rate control. The initial delay values and the corresponding optimal GOP-level bit allocation scheme can be obtained for video streaming along with the proper initial delay for various distortion tolerance levels. Simulations show that the algorithm provides an efficient solution for delay and buffer constrained GOP-level rate control for video streaming.

In a jamming environment with multiple wideband and narrowband jammers, global positioning system (GPS) receivers can use space-time processing to efficiently suppress the jamming. However, the computational complexity of space-time algorithms restricts their application in practical GPS receivers. This paper describes a reduced-rank multi-stage nested Wiener filter (MSNWF) based on subspace decomposition and Wiener filter (WF) to eliminate the effect of jamming in anti-jamming GPS receivers. A general sidelobe canceller (GSC) structure that is equivalent to the MSNWF is used to facilitate calculation of the optimal weights for the space-time processing. Simulation results demonstrate the satisfactory performance of the MSNWF to cancel jamming and the significant reduction in computational complexity by the reduced-rank processing. The technique offers a feasible space-time processing solution for anti-jamming GPS receivers.

Semantic extraction is essential for semantic interoperability in multi-enterprise business collaboration environments. Although many studies on semantic extraction have been carried out, few have focused on how to precisely and effectively extract semantics from multiple heterogeneous data schemas. This paper presents a semi-automatic semantic extraction method based on a neutral representation format (NRF) for acquiring semantics from heterogeneous data schemas. As a unified syntax-independent model, NRF removes all the contingencies of heterogeneous data schemas from the original data environment. Conceptual extraction and keyword extraction are used to acquire the semantics from the NRF. Conceptual extraction entails constructing a conceptual model, while keyword extraction seeks to obtain the metadata. An industrial case is given to validate the approach. This method has good extensibility and flexibility. The results show that the method provides simple, accurate, and effective semantic interoperability in multi-enterprise business collaboration environments.

In orthogonal frequency-division multiplexing (OFDM) systems, the capability to support high mobility is greatly limited by the intercarrier interference (ICI) caused by time-frequency selective fading channels. This paper presents a precoded OFDM system for ICI mitigation. A precoder is introduced to relieve the ICI by transmitting N-point composite information symbols at twice the subcarrier interval. A Hadamard-matrix-like pilot pattern is used to recover the composite information symbols in a postprocessor at the receiver. Simulations show that, compared to the conventional self-cancellation scheme, this scheme gives much better signal-to-interference-noise ratio performance with much less overhead. Furthermore, the scheme can support twice the vehicle speed in time-frequency selective fading channels than the standard OFDM systems without ICI mitigation.

ARIA is a new block cipher designed as the block cipher standard of South Korea. The current version is 1.0, which is an improvement of version 0.8 with the security using four kinds of S-boxes instead of two and an additional two rounds of encryptions. These improvements are designed to prevent the dedicated linear attack on ARIA version 0.8 by the four different kinds of S-boxes. This paper presents 12 linear approximations of a single round function that succeeds in attacking ARIA version 1.0 on 7, 9, or 9 rounds for key sizes of 128, 192, or 256 bits using any of these approximations. The corresponding data complexities are 2⁸⁷, 2¹¹⁹, and 2¹¹⁹, the counting complexities are 1.5×2⁸⁸, 2¹¹⁹, and 2¹¹⁹, the memory required for each attack on all three key versions is 2⁶⁴ bits and there are 12 weak key classes. These results are similar to the dedicated linear attack on ARIA version 0.8 and show that the improved version can also not effectively resist this type of attack.

Due to the uncertainty of the market demand in the supply chain, this paper characterized market demand as a fuzzy variable and proposed single-period and long-term contracts to coordinate the two members (supplier and buyer) in the supply chain. Comparison of the effectiveness of the two contracts indicates that a long-term contract is more effective than a single-period contract in improving the profit potential of both the total supply chain and each member in the supply chain. This conclusion is useful to the decision-maker in supply chains with fuzzy market demand.

An on-line method was developed to improve diagnostic accuracy and speed for analyzing running motors on site. On-line pre-measured data was used as the basis for constructing the membership functions used in a fuzzy neural network (FNN) as well as for network training to reduce the effects of various static factors, such as unbalanced input power and asymmetrical motor alignment, to increase accuracy. The preprocessed data and fuzzy logic were used to find the nonlinear mapping relationships between the data and the conclusions. The FNN was then constructed to carry motor fault diagnostics, which gives fast accurate diagnostics. The on-line fast motor fault diagnostics clearly indicate the fault type, location, and severity in running motors. This approach can also be extended to other applications.

Recent development of the peer-to-peer (P2P) live streaming technique has brought unprecedented new momentum to the Internet with the characters of effective, scalable, and low cost. However, before these applications can be successfully deployed as commercial applications, efficient access control mechanisms are needed. This work based on earlier research of the secure streaming architecture in Trust-Stream, analyzes how to ensure that only authorized users can access the original media in the P2P live streaming system by adopting a user authentication and key management scheme. The major features of this system include (1) the management server issues each authorized user a unique public key certificate, (2) the one-way hash chain extends the certificate's lifetime, (3) the original media is encrypted by the session key and delivered to the communication group, and (4) the session key is periodically updated and distributed with the media. Finally, analyses and test results show that scheme provides a secure, scalable, reliable, and efficient access control solution for P2P live streaming systems.

Support vector machines (SVMs) aim to find an optimal separating hyper-plane that maximizes separation between two classes of training examples (more precisely, maximizes the margin between the two classes of examples). The choice of the cost parameter for training the SVM model is always a critical issue. This analysis studies how the cost parameter determines the hyper-plane; especially for classifications using only positive data and unlabeled data. An algorithm is given for the entire solution path by choosing the ‘best’ cost parameter while training the SVM model. The performance of the algorithm is compared with conventional implementations that use default values as the cost parameter on two synthetic data sets and two real-world data sets. The results show that the algorithm achieves better results when dealing with positive data and unlabeled classification.

The modeling of headway/spacing between two consecutive vehicles in a queue has many applications in traffic flow theory and transport practice. Most known approaches have only studied vehicles on freeways. This paper presents a model for the spacing distribution of queuing vehicles at a signalized junction based on random-matrix theory. The spacing distribution of a Gaussian symplectic ensemble (GSE) fits well with recently measured spacing distribution data. These results are also compared with measured spacing distribution observed for the car parking problem. Vehicle stationary queuing and vehicle parking have different spacing distributions due to different driving patterns.

This paper shows that the protocol presented by Goyal et al. can be further simplified for a one-way function, with the simplified protocol being more practical for the decisional Diffie-Hellman assumption. Goyal et al. provided a general transformation from any honest verifier statistical zero-knowledge argument to a concurrent statistical zero-knowledge argument. Their transformation relies only on the existence of one-way functions. For the simplified transformation, the witness indistinguishable proof of knowledge protocols in “parallel” not only plays the role of preamble but also removes some computational zero-knowledge proofs, which Goyal et al. used to prove the existence of the valid openings to the commitments. Therefore, although some computational zero-knowledge proofs are replaced with a weaker notion, the witness indistinguishable protocol, the proof of soundness can still go through.

The availability of a good viewpoint space partition is crucial in three dimensional (3-D) object recognition on the approach of aspect graph. There are two important events depicted by the aspect graph approach, edge-edge-edge (EEE) events and edge-vertex (EV) events. This paper presents an algorithm to compute EEE events by characteristic analysis based on conicoid theory, in contrast to current algorithms that focus too much on EV events and often overlook the importance of EEE events. Also, the paper provides a standard flowchart for the viewpoint space partitioning based on aspect graph theory that makes it suitable for perspective models. The partitioning result best demonstrates the algorithm's efficiency with more valuable viewpoints found with the help of EEE events, which can definitely help to achieve high recognition rate for 3-D object recognition.

This paper investigates the controllability of two time-scale systems using both the time-scale separation model and the slow-fast order reduction model. This work considers the effect of a singular perturbation parameter on the model transformations to improve the criterion precision. The Maclaurin expansion method and homotopy arithmetic are introduced to obtain ?-dependent controllability criteria. Examples indicate that the ?-dependent controllability criteria are more accurate and that the controllability of two time-scale systems does not change during model transformations with these more accurate forms.