The mechanisms of TCP’s retransmission and reset will result in redundant packets. These redundant packets are often sent unnecessarily to the user over a slow last-hop link delaying useful traffic. This is a problem for wide-area wireless links, such as General Packet Radio Service (GPRS), because unnecessary transmissions waste already limited radio bandwidth, battery power at the mobile terminal and incurs monetary cost due to charging by data volume. The paper first describes a GPRS model, then discusses how to eliminate the redundant packets in GPRS network and presents the simulation results in Network Simulation 2 (NS 2). The more traffic is, the more the network can benefit. In heavy traffic, it can even get more than 30% improvement in throughput. Average delay and loss percent are also lowered.

Quality of Service (QoS) is a key factor in Web service advertising, choosing and runtime monitoring. Web service QoS is multi-faceted, fuzzy and dynamic. Current researches focus on implementation level performance assurance, ignoring domain specific or application level metrics which are also very important to service users. Industry Web service standards lack QoS expression. The support for QoS based service choice-making is very limited. We proposed an extended Web service QoS model based on configurable fuzzy synthetic evaluation system. Web service QoS is evaluated dynamically according to the service context. A QoS requirement description model is also given for service QoS requirement definition. An interactive Web service choice-making process is described, which takes QoS as a key factor when choosing from functionally equivalent services.

Security issues are always difficult to deal with in mobile ad hoc networks. People seldom studied the costs of those security schemes respectively and for some security methods designed and adopted beforehand, their effects are often investigated one by one. In fact, when facing certain attacks, different methods would respond individually and result in waste of resources. Making use of the cost management idea, we analyze the costs of security measures in mobile ad hoc networks and introduce a security framework based on security mechanisms cost management. Under the framework, the network system’s own tasks can be finished in time and the whole network’s security costs can be decreased. We discuss the process of security costs computation at each mobile node and in certain nodes groups. To show how to use the proposed security framework in certain applications, we give examples of DoS attacks and costs computation of defense methods. The results showed that more secure environment can be achieved based on the security framework in mobile ad hoc networks.

Wireless Ad Hoc Sensor Networks (WSNs) have received considerable academia research attention at present. The energy-constraint sensor nodes in WSNs operate on limited batteries, so it is a very important issue to use energy efficiently and reduce power consumption. To maximize the network lifetime, it is essential to prolong each individual node’s lifetime through minimizing the transmission energy consumption, so that many minimum energy routing schemes for traditional mobile ad hoc network have been developed for this reason. This paper presents a novel minimum energy routing algorithm named Load-Balanced Minimum Energy Routing (LBMER) for WSNs considering both sensor nodes’ energy consumption status and the sensor nodes’ hierarchical congestion levels, which uses mixture of energy balance and traffic balance to solve the problem of “hot spots” of WSNs and avoid the situation of “hot spots” sensor nodes using their energy at much higher rate and die much faster than the other nodes. The path router established by LBMER will not be very congested and the traffic will be distributed evenly in the WSNs. Simulation results verified that the LBMER performance is better than that of Min-Hop routing and the existing minimum energy routing scheme MTPR (Total Transmission Power Routing).

A new path planning method for mobile robots in globally unknown environment with moving obstacles is presented. With an autoregressive (AR) model to predict the future positions of moving obstacles, and the predicted position taken as the next position of moving obstacles, a motion path in dynamic uncertain environment is planned by means of an on-line real-time path planning technique based on polar coordinates in which the desirable direction angle is taken into consideration as an optimization index. The effectiveness, feasibility, high stability, perfect performance of obstacle avoidance, real-time and optimization capability are demonstrated by simulation examples.

A neural-network-based robust control design is suggested for control of a class of nonlinear systems. The design approach employs a neural network, whose activation functions satisfy the sector conditions, to approximate the nonlinear system. To improve the approximation performance and to account for the parameter perturbations during operation, a novel neural network model termed standard neural network model (SNNM) is proposed. If the uncertainty is bounded, the SNNM is called an interval SNNM (ISNNM). A state-feedback control law is designed for the nonlinear system modelled by an ISNNM such that the closed-loop system is globally, robustly, and asymptotically stable. The control design equations are shown to be a set of linear matrix inequalities (LMIs) that can be easily solved by available convex optimization algorithms. An example is given to illustrate the control design procedure, and the performance of the proposed approach is compared with that of a related method reported in literature.

In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables optimization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.

Transmission of variable bit rate (VBR) video, because of the burstiness of VBR video traffic, has high fluctuation in bandwidth requirement. Traffic smoothing algorithm is very efficient in reducing burstiness of the VBR video stream by transmitting data in a series of fixed rates. We propose in this paper a novel segment-based bandwidth allocation algorithm which dynamically adjusts the segmentation boundary and changes the transmission rate at the latest possible point so that the video segment will be extended as long as possible and the number of rate changes can be as small as possible while keeping the peak rate low. Simulation results showed that our approach has small bandwidth requirement, high bandwidth utilization and low computation cost.

Multi-sensor image registration has been widely used in remote sensing and medical image field, but registration performance is degenerated when heterogeneous images are involved. An image registration method based on multi-resolution shape analysis is proposed in this paper, to deal with the problem that the shape of similar objects is always invariant. The contours of shapes are first detected as visual features using an extended contour search algorithm in order to reduce effects of noise, and the multi-resolution shape descriptor is constructed through Fourier curvature representation of the contour’s chain code. Then a minimum distance function is used to judge the similarity between two contours. To avoid the effect of different resolution and intensity distribution, suitable resolution of each image is selected by maximizing the consistency of its pyramid shapes. Finally, the transformation parameters are estimated based on the matched control-point pairs which are the centers of gravity of the closed contours. Multi-sensor Landsat TM imagery and infrared imagery have been used as experimental data for comparison with the classical contour-based registration. Our results have been shown to be superior to the classical ones.

Driving facial animation based on tens of tracked markers is a challenging task due to the complex topology and to the non-rigid nature of human faces. We propose a solution named manifold Bayesian regression. First a novel distance metric, the geodesic manifold distance, is introduced to replace the Euclidean distance. The problem of facial animation can be formulated as a sparse warping kernels regression problem, in which the geodesic manifold distance is used for modelling the topology and discontinuities of the face models. The geodesic manifold distance can be adopted in traditional regression methods, e.g. radial basis functions without much tuning. We put facial animation into the framework of Bayesian regression. Bayesian approaches provide an elegant way of dealing with noise and uncertainty. After the covariance matrix is properly modulated, Hybrid Monte Carlo is used to approximate the integration of probabilities and get deformation results. The experimental results showed that our algorithm can robustly produce facial animation with large motions and complex face models.

This work re-examined the simulation result of game analysis (Joshi et al., 2000) based on an agent-based model, Santa Fe Institute Artificial Stock Market. Allowing for recent research work on this artificial model, this paper’s modified game simulations found that the dividend amplitude parameter is a crucial factor and that the original conclusion still holds in a not long period, but only when the dividend amplitude is large enough. Our explanation of this result is that the dividend amplitude parameter is a measurement of market uncertainty. The greater the uncertainty, the greater the price volatility, and so is the risk of investing in the stock market. The greater the risk, the greater the advantage of including technical rules.

Researching on the impact different steganographic software tools have audio statistical features, revealed the phenomenon that when messages are embedded in a WAV file by using a certain tool, the variation of statistical features in the WAV file which already contains messages embedded by the same tool is abruptly smaller than those in which messages have not been embedded. We call it “negative resonance phenomenon” temporarily. With the phenomenon above and Support Vector Machines (SVMs), we can detect the existence of hidden messages, and also identify the tools used to hide them. As shown by the experimental results, the proposed method can be very effectively used to detect hidden messages embedded by Hide4PGP, Stegowav and S-Tools4.

With the development of Ethernet systems and the growing capacity of modern silicon technology, embedded communication networks are playing an increasingly important role in embedded and safety critical systems. Hardware/software co-design is a methodology for solving design problems in processor based embedded systems. In this work, we implemented a new 1-cycle pipeline microprocessor and a fast Ethernet transceiver and established a low cost, high performance embedded network controller, and designed a TCP/IP stack to access the Internet. We discussed the hardware/software architecture in the forepart, and then the whole system-on-a-chip on Altera Stratix EP1S25F780C6 device. Using the FPGA environment and SmartBit tester, we tested the system’s throughput. Our simulation results showed that the maximum throughput of Ethernet packets is up to 7 Mbps, that of UDP packets is up to 5.8 Mbps, and that of TCP packets is up to 3.4 Mbps, which showed that this embedded system can easily transmit basic voice and video signals through Ethernet, and that using only one chip can realize that many electronic devices access to the Internet directly and get high performance.

In this paper we consider a single-machine scheduling model with deteriorating jobs and simultaneous learning, and we introduce polynomial solutions for single machine makespan minimization, total flow times minimization and maximum lateness minimization corresponding to the first and second special cases of our model under some agreeable conditions. However, corresponding to the third special case of our model, we show that the optimal schedules may be different from those of the classical version for the above objective functions.

Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough set theory has some particular characteristics. Consistency must be satisfied and cuts for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. In this paper, an immune algorithm for the problem is proposed. The correctness and effectiveness were shown in experiments. The discretization method presented in this paper can also be used as a data pretreating step for other symbolic knowledge discovery or machine learning methods other than rough set theory.

Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid approximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimization (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.

This paper presents a pooled-neighbor swarm intelligence approach (PNSIA) to optimal reactive power dispatch and voltage control of power systems. The proposed approach uses more particles’ information to control the mutation operation. The proposed PNSIA algorithm is also extended to handle mixed variables, such as transformer taps and reactive power source installation, using a simple scheme. PNSIA applied for optimal power system reactive power dispatch is evaluated on an IEEE 30-bus power system and a practical 118-bus power system in which the control of bus voltages, tap position of transformers and reactive power sources are involved to minimize the transmission loss of the power system. Simulation results showed that the proposed approach is superior to current methods for finding the optimal solution, in terms of both solution quality and algorithm robustness.

This paper deals with the synthesis of Petri net supervisor enforcing the more expressive constraints including marking terms, firing vector terms and Parikh vector terms. The method is developed to handle uncontrollable and unobservable transitions existing in the constraints. The “greater-than or equal” general constraints can also be transformed into “less-than or equal” Parikh constraints. An example is analyzed to show how the problem is solved. General constraint is first transformed into Parikh vector constraints, and Matrix-Transformation is proposed to obtain the admissible constraints without uncontrollable and unobservable transitions. Then the supervisor can be constructed based on constraints only consisting of Parikh vector terms. The method is proved to be more concise and effective than the method presented by Iordache and Moody especially when applied to large scale systems.

The central extension of the (1+1)-dimensional Poincaré algebra by including fermionic charges which obey not supersymmetric algebra, but a special graded algebra containing in the right hand side a central element only is obtained. The corresponding theory being the fermionic extension of the lineal gravity is proposed. We considered the algebra of generators, the field transformations and found Lagrangian and equation of motion, then we derived the Casimir operator and obtained the constant black hole mass.

Ningbo and its surrounding area is the forefront in the rapid economic development in the Yangtse delta, and the main production area for food supplies, cotton, edible oil and hemp; and at the same time, is the main area for wetland protection in Zhejiang Province. Our objectives were to quantify land cover change in Ningbo and its surrounding area from 1987 to 2000 and to analyze the causative factors of the change. Using 30-m resolution Landsat TM/ETM+ data and maximum likelihood classification method, we classified the study area into six land cover types: forest, agriculture, urban, freshwater, seawater and bottomland. The research results showed that significant changes in land cover occurred in the study area, and that agriculture and urban land cover change dominated most of the land cover change and were main causes for the changes of other types with human activities, such as urbanization, industrialization, etc. being the main factor while it was not very obvious whether climatic conditions have any role in the land cover changes. Agriculture, bottomland and other nature dominated land cover types are undergoing significant changes due to industrialization and urbanization, which threaten the stabilization of the environment. The study conclusion called for finding reasonable ways to solve the problems between land cover change and land use.

This paper presents a Zhejiang Province southeastern China seasonal temperature model based on GIS techniques. Terrain variables derived from the 1 km resolution DEM are used as predictors of seasonal temperature, using a regression-based approach. Variables used for modelling include: longitude, latitude, elevation, distance from the nearest coast, direction to the nearest coast, slope, aspect, and the ratio of land to sea within given radii. Seasonal temperature data, for the observation period 1971 to 2000, were obtained from 59 meteorological stations. Temperature data from 52 meteorological stations were used to construct the regression model. Data from the other 7 stations were retained for model validation. Seasonal temperature surfaces were constructed using the regression equations, and refined by kriging the residuals from the regression model and subtracting the result from the predicted surface. Latitude, elevation and distance from the sea are found to be the most important predictors of local seasonal temperature. Validation determined that regression plus kriging predicts seasonal temperature with a coefficient of determination (R²), between the estimated and observed values, of 0.757 (autumn) and 0.935 (winter). A simple regression model without kriging yields less accurate results in all seasons except for the autumn temperature.

Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.

Wavelet network, a class of neural network consisting of wavelets, is proposed to solve the inverse kinematics problem in robotic manipulator. A wavelet network suitable for dealing with multi-input and multi-output system is constructed. The network is optimized by reducing the number of wavelets handling large dimension problem according to the sample data. The algorithms for sparseness analysis of input data and fitting wavelets to the output data with orthogonal method are introduced. Then Levenberg-Marquardt algorithm is used to train the network. Simulation results showed that this method is capable of solving the inverse kinematics problem for PUMA560.

The nesting problem involves arranging pieces on a plate to maximize use of material. A new scheme for 2D irregular-shaped nesting problem is proposed. The new scheme is based on the NFP (No Fit Polygon) algorithm and a new placement principle for pieces. The novel placement principle is to place a piece to the position with lowest gravity center based on NFP. In addition, genetic algorithm (GA) is adopted to find an efficient nesting sequence. The proposed scheme can deal with pieces with arbitrary rotation and containing region with holes, and achieves competitive results in experiment on benchmark datasets.

Exotic options, or “path-dependent” options are options whose payoff depends on the behavior of the price of the underlying between 0 and the maturity, rather than merely on the final price of the underlying, such as compound options, reset options and so on. In this paper, a generalization of the Geske formula for compound call options is obtained in the case of time-dependent volatility and time-dependent interest rate by applying martingale methods and the change of numeraire or the change of probability measure. An analytic formula for the reset call options with predetermined dates is also derived in the case by using the same approach. In contrast to partial differential equation (PDE) approach, our approach is simpler.

To extract quantitative biophysical parameters such as leaf biomass and leaf chlorophyll concentration from the remotely sensed imagery, the effect of atmospheric attenuation must be removed. The refined empirical line (REL) method was used to calibrate the IKONOS multispectral imagery. The IKONOS digital numbers (DN) were converted to the at-satellite reflectance, then the linear relation between at-satellite reflectance and surface spectral reflectance (ρ_λ) was derived from six bright targets of known reflectance in the image, and modelled estimates of the image reflectance at ρ_λ=0. Validation targets were used to test the feasibility of REL method. The mean relative errors between ρ_λ retrieved from IKONOS image using REL method and ground-measured ρ_λ were 11%, 13%, 3% and 5% in the IKONOS blue, green, red and near-infrared (NIR) respectively. When dark targets are unavailable or measurement of dark target is inconvenient, the REL method was most crucial for retrieving surface spectral reflectance. The REL offers a simple approach for quantitative retrieval of biophysical parameters from IKONOS imagery.