An efficient reliable architecture for application layer anycast service

Anycast is a new service in IPv6, and there are some open issues about the anycast service. In this paper, we focus on efficient and reliable aspects of application layer anycast. We apply the requirement based probing routing algorithm to replace the previous period based probing routingalgorithm for anycast resolvers. We employ the twin server model among the anycast servers, therefore, try to present a reliable service in the Internet environment. Our theoretical analysis shows that the proposed architecture works well, and it offers a more efficient routing performance and fault tolerance capability.

A general rooted tree drawing algorithm

A general rooted tree drawing algorithm is designed in this paper. It satisfies the basic aesthetic criteria and can be well applied to binary trees. Given an area, any complex tree can be drawn within the area in users' favorite styles. The algorithm is efficient with O(LxNxlogN) time complexity and self-adaptive as well.

Medical image classification using an efficient data mining technique

Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.

Using autonomous mobile agents for efficient data collection in sensor networks

Sensor networks are emerging as the new frontier in sensing technology, however there are still issues that need to be addressed. Two such issues are data collection and energy conservation. We consider a mobile robot, or a mobile agent, traveling the network collecting information from the sensors themselves before their onboard memory storage buffers are full. A novel algorithm is presented that is an adaptation of a local search algorithm for a special case of the Asymmetric Traveling Salesman Problem with Time-windows (ATSPTW) for solving the dynamic scheduling problem of what nodes are to be visited so that the information collected is not lost. Our algorithms are given and compared to other work.

A resource-search and routing algorithm within PCDN autonomy area

This paper studied a new type of network model; it is formed by the dynamic autonomy area, the structured source servers and the proxy servers. The new network model satisfies the dynamics within the autonomy area, where each node undertakes different tasks according to their different abilities, to ensure that each node has the load ability fit its own; it does not need to exchange information via the central servers, so it can carry out the efficient data transmission and routing search. According to the highly dynamics of the autonomy area, we established dynamic tree structure-proliferation system routing and resource-search algorithms and simulated these algorithms. Test results show the performance of the proposed network model and the algorithms are very stable.

A winner determination algorithm for multi-unit combinatorial auctions with reserve prices

Combinatorial auction mechanisms have been used in many applications such as resource and task allocation, planning and time scheduling in multi-agent systems, in which the items to be allocated are complementary or substitutable. The winner determination in combinatorial auction itself is a NP-complete problem, and has attracted many attentions of researchers world wide. Some outstanding achievements have been made including CPLEX and CABOB algorithms on this topic. To our knowledge, the research into multi-unit combinatorial auctions with reserve prices considered is more or less ignored. To this end, we present a new algorithm for multi-unit combinatorial auctions with reserve prices, which is based on Sandholm's work. An efficient heuristic function is developed for the new algorithm. Experiments have been conducted. The experimental results show that auctioneer agent can find the optimal solution efficiently for a reasonable problem scale with our algorithm.

An adaptive policy-based vertical handoff algorithm for heterogeneous wireless networks

The next generation of wireless networks is envisioned as convergence of heterogeneous radio access networks. Since technologies are becoming more collaborative, a possible integration between IEEE 802.16 based network and previous generation of telecommunication systems (2G, ..., 3G) must be considered. A novel quality function based vertical handoff (VHO) algorithm, based on proposed velocity and average receive power estimation algorithms is discussed in this paper. The short-time Fourier analysis of received signal strength (RSS) is employed to obtain mobile speed and average received power estimates. Performance of quality function based VHO algorithm is evaluated by means of measure of quality of service (QoS). Simulation results show that proposed quality function, brings significant gains in QoS and more efficient use of resources can be achieved.

A heuristic algorithm for carton to pallet loading problem

This paper presents an algorithm used to solve a carton to pallet packing problem in a drink manufacturing firm. The aim was to determine the cartons loading sequence and the number pallets required, prior to dispatch by truck. The algorithm consists of a series of nine parts to solve this industrial application problem. The pallet loading solution relatively computationally efficient and reduces the number pallets required, compared to other 'trail and error' or manual spreadsheet calculation methods.

Channel estimation scheme for 3.9G wireless communication systems using RLS algorithm

Main challenges for a terminal implementation are efficient realization of the receiver, especially for channel estimation (CE) and equalization. In this paper, training based recursive least square (RLS) channel estimator technique is presented for a long term evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) wireless communication system. This CE scheme uses adaptive RLS estimator which is able to update parameters of the estimator continuously, so that knowledge of channel and noise statistics are not required. Simulation results show that the RLS CE scheme with 500 Hz Doppler frequency has 3 dB better performances compared with 1.5 kHz Doppler frequency.

Efficient algorithms for subwindow search in object detection and localization

Recently, a simple yet powerful branch-and-bound method called Efficient Subwindow Search (ESS) was developed to speed up sliding window search in object detection. A major drawback of ESS is that its computational complexity varies widely from O(n²) to O(n⁴) for n × n matrices. Our experimental experience shows that the ESS's performance is highly related to the optimal confidence levels which indicate the probability of the object's presence. In particular, when the object is not in the image, the optimal subwindow scores low and ESS may take a large amount of iterations to converge to the optimal solution and so perform very slow. Addressing this problem, we present two significantly faster methods based on the linear-time Kadane's Algorithm for 1D maximum subarray search. The first algorithm is a novel, computationally superior branchand- bound method where the worst case complexity is reduced to O(n³). Experiments on the PASCAL VOC 2006 data set demonstrate that this method is significantly and consistently faster (approximately 30 times faster on average) than the original ESS. Our second algorithm is an approximate algorithm based on alternating search, whose computational complexity is typically O(n²). Experiments shows that (on average) it is 30 times faster again than our first algorithm, or 900 times faster than ESS. It is thus wellsuited for real time object detection.

Efficient tensor based face recognition

This paper addresses the limitation of current multilinear PCA based techniques, in terms of pro- hibitive computational cost of testing and poor gen- eralisation in some scenarios, when applied to large training databases. We define person-specific eigen-modes to obtain a set of projection bases, wherein a particular basis captures variation across light- ings and viewpoints for a particular person. A new recognition approach is developed utilizing these bases. The proposed approach performs on a par with the existing multilinear approaches, whilst sig- nificantly reducing the complexity order of the testing algorithm.

Efficient cross-validation of the complete two stages in KFD classifier formulation

This paper presents an efficient evaluation algorithm for cross-validating the two-stage approach of KFD classifiers. The proposed algorithm is of the same complexity level as the existing indirect efficient cross-validation methods but it is more reliable since it is direct and constitutes exact cross-validation for the KFD classifier formulation. Simulations demonstrate that the proposed algorithm is almost as fast as the existing fast indirect evaluation algorithm and the twostage cross-validation selects better models on most of the thirteen benchmark data sets.

Solving the median shortest path problem in the planning and design of urban transportation networks using a vector labeling algorithm

This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.

An interactive evolutionary algorithm for MMIC low noise amplifier design

A layer-encoded interactive evolutionary algorithm (IEA) for optimization of design parameters of a monolithic microwave integrated circuit (MMIC) low noise amplifier is presented. The IEA comprises a combination of the genetic algorithm (GA) and the particle swarm optimization (PSO) technique. The layer-encoding structure allows human intervention in order to accelerate the process of evolution, whereas the GA and PSO technique are incorporated to enhance both global and local searches. With this combination of features, the proposed IEA has shown to be efficient in meeting all requirements and constraints of the MMIC. In addition, the IEA is able to optimize noise figure, current, and power gain of the MMIC amplifier design.

EFP-M2 : efficient model for mining frequent patterns in transactional database

Discovering frequent patterns plays an essential role in many data mining applications. The aim of frequent patterns is to obtain the information about the most common patterns that appeared together. However, designing an efficient model to mine these patterns is still demanding due to the capacity of current database size. Therefore, we propose an Efficient Frequent Pattern Mining Model (EFP-M2) to mine the frequent patterns in timely manner. The result shows that the algorithm in EFP-M2l is outperformed at least at 2 orders of magnitudes against the benchmarked FP-Growth.