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With the continuous progress of science and technology and economic level, manufacturing enterprises' direct labor costs that account for the proportion of production costs continue to decline, the proportion of manufacturing costs rise sharply, The traditional cost accounting method is hard to reflect the true cost information. For “activity” as the center, based on the “cost drivers” imputation cost attract more attention. Introducing ideas on costing to ERP management platform, improving traditional cost accounting method, has become a hot research by many scholars. This paper studies the important role of the operating cost method for enterprises to improve cost management, the competitiveness aspects of the use of ERP environment, and on the basis of analysis of today's mainstream ERP systems costing application, noting ERP integration with costing and the importance of the theoretical basis, combined with cases to specifically illustrate how to apply costing.

A general method is proposed to extract frequency dependent RLGC model from simulated S-parameters for multi-conductor transmission line structures with periodic three-dimensional geometries. It is demonstrated to be robust for both short and long structures and various geometries, and highly efficient for structures with a large number of lines. The method has been tested for transmission line systems with up to 100 lines, which has not been seen in literature. A mode tracking technique is employed to obtain the unique solution for the propagation constant matrix. Equivalent lumped circuit networks are used to ensure extraction accuracy at low frequencies down to DC and provide seamless transition to the general formulation as frequency increases. The Kramers-Kronig relation is applied for causality enforcement and extraction of inductance and capacitance values at infinite frequency.

Mobility, resource constraints and unreliable wireless links of mobile P2P networks will cause high data access latency and the communication overhead. Cooperative caching is widely seen as an effective solution to improve the overall system performance in mobile P2P networks. In this paper we present a novel cooperative caching scheme for mobile P2P networks. In our scheme the caching space of each node is divided into three parts: locale caching, cooperative caching and path caching, which respectively store the requested data objects of the nodes, the hot data objects in the networks and the data objects path. We also put forward the cache replacement strategy according to our scheme. Proposed cache replacement strategy not only takes into account the need of the nodes, but also pays attention to collaborative work between nodes. We evaluate the performance of our scheme by using NS-2. The experimental results show that the cache hit ratio is effectively increased and the average hops count is reduced.

Single shared tree and source rooted trees are two extreme approaches for the many-to-many multicast applications. These two approaches complement each other in terms of performance, protocol overhead and robustness. The problem we faced is to construct multiple shared trees for many-to-many multicast applications and reduce the management costs by minimizing the number of shared trees, which can be transformed into the set covering problem which is proved NP-complete. Then an Ant Colony Optimization(ACO) algorithm for multiple shared trees multicast based on set covering (MSTSC) is presented in this paper. Simulation experiments indicate ACO algorithm for MSTSC is efficiency and feasible.

Mobile Ad hoc network (MANET) is a multi-hops wireless network without fixed infrastructure. Now how to evaluate network survivability performance is the key work of the research of MNAET. According to the requirement of the survivability of MANET, a more comprehensive hierarchical model for quantitative evaluation of survivability is proposed. The model was constructed based on continuous time Markov chain and M/M/1/K queue theory, which could analysis the steady-state availability and transient packet loss rate, delay of the network in the case of fault. Analysis and simulation results show that this quantitative modeling and analysis are rational and useful evaluation approach to MANET survivability performance and the model could be applied in quantitative analysis of MANET survivability.

A >;30V high speed, low resolution and low cost arbitrary waveform generator has been prototyped. It uses FPGA transceivers and resistive weighting networks and broadband power amplification. The sampling rate of this arbitrary waveform generator is 6.25 Gsps, allowing rise times down to 130 ps. As 4 transceiver channels are used a 4 bit resolution is achieved. The generator is combined with a wide band (20MHz - 6 GHz) power amplifier allowing >; 20V at 50-Ohm load. The intended application is IC immunity testing. Here different waveforms and exact timing is needed. Using different FPGAs the concept can be extended to higher resolution and faster sampling rates. The project was completed in a 400 level RF design class during only one semester.

Near-field measurements are widely used to indicate the radiation from integrated circuits (IC). To minimize the number of field components measured in a near-field scan, it is necessary to calculate the electric field from magnetic field or vice versa. In this paper, a method based on plane wave spectrum theory is proposed to perform the H to E transformation, and a filtering technique is proposed to address the noise problem in the calculation. A simple active circuit in a 3-D full-wave simulation tool is used to validate this method. This method is useful to reduce the number of field components to be measured, resulting in faster near-field scanning.

The effects of geometric asymmetry on the electrical performance of multi-channel differential signaling in printed circuit boards are investigated in this paper. The asymmetry, which is the origin of noise coupled through mode conversion, is difficult to control in real channel structures, where a large number of vias and traces are populated. Supported by an efficient parametric simulation tool, this paper presents the effects of the asymmetry on mode conversion and differential mode crosstalk in real multi-channel structures. This extensive parametric study was used to define design guidelines to suppress this noise coupling by adding ground vias in strategic locations.

Accurate modeling of chip and chip-package is critical for EMI (Electromagnetic Interference) and RFI (RF Interference) analysis and prediction. In this paper, a model based on an array of dipoles from near-field measurement is proposed. A simple active circuit is simulated in a 3-D full-wave simulation tool, and the dipole model is calculated from the near-field data in the simulation using inverse method with regularization technique. This model has clear physical meaning, and it is validated using field at other place.

The power distribution network in a printed circuit board (PCB) is an effective path for high-speed digital noise. The noise can be coupled by overlapping power/ground areas or by vias passing through them. This paper details a modeling approach and applies it to a multilayer PCB with six power/ground planes. The multilayer power/ground plane structure is modeled using the cavity model combined with segmentation method, and the vias are modeled using a via-plate capacitance model. This model requires considerably less time, and the results closely match those of full-wave simulations.

This paper describes the application of the FSV to measured propagation data. A mobile-to-mobile measurements program is currently being conducted by our colleagues at the Institute for Telecommunication Sciences under the sponsorship of the Office of Spectrum Measurement. Two different measurement systems are being used to collect mobile-to-mobile data in a number of selected outdoor environments. One system uses a wideband time-domain channel sounder that uses pseudo-random noise sequences to generate channel impulse responses. The other system transmits a narrowband CW signal, and it is received by a spectrum analyzer. This paper explores the possibility of using FSV to quantify how well data obtained from these measurement systems compare.

A considerable amount of research has been carried out on studying the phase transition phenomenon in the complex network, however, the effective quantitative characterization has yet to be developed. The paper provides some analytical results for description on the behaviors of traffic flow in the mode of complex. Results indicate that the traffic exhibits nontrivial the phase transition behaviors that the total number, the average delay of packets and throughput of system distinctly fluctuates when the flow load is closed to the critical point. Analytical results accord well with the previous conclusions by means of simulation.

This research focuses on nonlinear behaviors of Internet traffic the on the model of scale-free networks. Attractiveness parameter, which denotes the index with great influence on the behaviors and performance of system, is considered. It is shown that traffic exhibits the phase transition, scaling and 1/f-type in the power spectrum when the load of network increases, further, the nonlinear behaviors become more apparent with the attractiveness parameter growing . By using the maximal Lyapunov exponent analysis, we find that the traffic behaviors exhibits nontrivial chaos property when traffic load is approach to the critical point, which indicates more efficiency promoted, there are more unpredictability behaviors of network tend to appear on.

The power distribution network in a printed circuit board (PCB) inside a compact-size enclosure is an effective path for high-speed digital noise to be coupled to the RF receivers inside the same enclosure, causing RF interference (RFI) issues. This noise coupling mechanism from PCB to shielding enclosure is investigated in this paper using the cavity model and the segmentation technique. In this approach, the structure of an enclosure with a PCB inside is divided into cavities with both horizontal and vertical connections. Modeling result agrees well with full wave simulations, and the simulation time is considerably reduced. Furthermore, the relationship among the noise coupling, the PCB-related resonances, and the enclosure-related resonances is studied as well.

A hybrid method is proposed for radio frequency interference (RFI) prediction of a metal enclosure with an aperture on the top wall. The structure is divided into several segments. While the fields in rectangular segments are described by cavity model, the segments with apertures are modeled by the commercial finite element solver (HFSS). Tangential field continuities along the common boundaries of different segments are enforced by the voltages and currents of boundary ports. Good agreement has been achieved between the hybrid method and full wave simulation.

Some security protocols or mechanisms have been designed for wireless sensor networks (WSNs). However, an intrusion detection system (IDS) should always be deployed on security critical applications to defense in depth. Due to the resource constraints, the intrusion detection system for traditional network cannot be used directly in WSNs. Several schemes have been proposed to detect intrusions in wireless sensor networks. But most of them aim on some specific attacks (e.g. selective forwarding) or attacks on particular layers, such as media access layer or routing layer. In this paper, we present a framework of machine learning based intrusion detection system for wireless sensor networks. Our system will not be limited on particular attacks, while machine learning algorithm helps to build detection model from training data automatically, which will save human labor from writing signature of attacks or specifying the normal behavior of a sensor node.

Real-world surface texture generally comprises rough geometry and various reflectance properties, which can dramatically change its appearance under different illumination directions, namely 3D surface texture. Thus, traditional methods for super resolution of 2D texture images cannot be directly used to generate 3D surface texture with higher resolution. This paper presents an efficient method for super resolutionon 3D surface texture. It focuses on synthesizing real-world surface textures with higher resolution and under varied illumination directions. To this extent, it first employs an Eigen-based method to represent 3D surface texture images under multiple illumination directions. Then it modifies the examplebased super resolution method proposed by Freemanet al. to generate new 3D surface texture. Experimental results based on a set of real-world surface textures are presented and show the effectiveness of the new algorithm.

This paper presents a novel 3D surface texture classification method based on self-similarity maps which are calculated directly from raw captured texture images. 3D surface textures have special properties for they are sensitive to illumination and view conditions. Some previous classification methods which are illumination invariant or rotation invariant have shown to be effective to this particularity, but most of them are model-based. We introduce a new approach to classify 3D surface texture image sets by automatically extracted self-similarity maps. Feature vectors are then generated from responses to a filter bank of the self-similarity maps. Classification is achieved by comparing the L2 distance between training and testing feature vectors. The experiment results show our approach achieves an accuracy of 95%.

Network architecture is a set of high-level principles that guides the technical design of the network. Current reality and changing requirements are eating away at the viability of the original Internet architecture. Much of the coherence of the original architecture is being lost in a patchwork of technical embellishments, each intended to satisfy a particular new requirements. In this paper, we introduce the architecture of active overlay networks (AON) by introducing active idea into overlay network to align better with current and future requirements. In addition, some new kinds of applications need several categories of network services simultaneously. So, the multiple factors service placement problem (MFPP) has been investigated carefully. Corresponding programming models and a heuristic algorithm for the MFPP have been proposed. The complexity analysis of the algorithm and the numerical experiments are also presented.

A hierarchical semantic perceptron grid architecture based neural network has been proposed in this paper, the semantic spotting is a key issue of this architecture, for finding solution to this problem, firstly we has formulated it, then a semantic neural network classifier frame has been proposed. And experiment results of this scenario were presented, to evaluate the effectiveness of this scenario, we compare this scenario with the SVM, NNet, KNN and NB, the average experimental results of the scenario are obviously superior to other conventional approaches.

Task scheduling is one key issue in computational overlay grid. For finding solution to this problem, we propose a Chaotic Neural Network Algorithm for Task Scheduling, the goal of this algorithm is to minimize the total execution time of task by appropriately mapping the task to processor in the parallel system. We formulate the task scheduling problem that considers heterogeneousness. To evaluate the effectiveness of this algorithm, we compare this algorithm with the simulated annealing, and the genetic algorithm etc. The average experimental result of the algorithm is obviously superior to other conventional algorithms.

Varied data mining techniques have been developed for intrusion detection. However, it is unclear which data mining technique is most effective. In this paper, we present our research work in developing a Multi-Class SLIPPER (MC-SLIPPER) system for intrusion detection to learn whether we can get benefit from boosting based learning algorithm. The key idea is to use the available binary SLIPPER as a basic module, which is a rule learner based on confidence-rated boosting. Multiple arbitral strategies based on prediction confidence are proposed to arbitrate results from all binary SLIPPER modules. Our system is evaluated on the KDDCUP'99 intrusion detection dataset. The experimental results show that we get best performance using a 5-7-5 BP neural network; and the performance using other arbitral strategies are better than the winner of the contest does in term of misclassification cost (MC)

Shared memory based switch is an important model of ATM switches. It can provide optimal switching performance for different traffic patterns. In this paper, a new model of shared memory based switch-associated memory based switch (AMS)-has been presented. AMS has more characteristics, such as simple switching principle, simple system architecture, suitability for different traffic patterns and different service classes, optimal switching performance and memory space utilization. It can also provide multicasting and broadcasting capabilities.

In this paper, an improved skew angle estimation technique is presented. Skew angle detection is an important part of any optical character recognition (OCR) and document analysis system. The method considers the lowermost pixels of some selected characters of the text, which may be subject to the Hough transform for skew angle detection. A fast approach is also proposed which works almost as accurately as the Hough transform for skew angle detection. Experimental results are presented and compared with results on several other skew detection methods.