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The evaluation and management of sensor uncertainty is particularly necessary in a noisy multi-sensor context. In this paper, focusing on the potential of distributed coordination among sensor nodes based on the built-in association between wireless sensor networks and multi-agent systems, meanwhile in a rough set technique senseof uncertainty, we show how an adaptive distributed coordination framework for a hierarchy of sensor data uncertainty performs local data fusion to increase the certainty of real-time sensor readings coherently, makes global rational decisions under imprecision and partial truth, and reconciles the conflicts somewhat, thus evolving adaptive and robust sensor uncertainty handling systems. Implementation results for an example sensor field demonstrate the application of our proposed approach. [ABSTRACT FROM AUTHOR]

The aim of the Political Districting Problem is to partition a zone into electoral districts with constraints such as contiguity, population equality, etc. By using statistical physics methods, the problem can be mapped onto a q-state Potts model system, and the political constraints are written as an energy function with interactions between sites or external fields acting on the system. This problem is then transformed into an optimization problem. In this paper, we apply the genetic algorithm to Political Districting Problem. We will illustrate the evolutionary strategy for GA and compare with results from other optimization algorithms. [ABSTRACT FROM AUTHOR]

A special nonlinear bilevel programming problem (BLPP), whose follower-level problem is a convex programming with a linear objective function in y, is transformed into an equivalent single-level programming by using Karush-Kuhn-Tucker (K-K-T) conditions. To solve the equivalent problem effectively, a new genetic algorithm is proposed. First, a linear programming (LP) is constructed to decrease the dimensions of the transformed problem. Then based on a constraint-handling scheme, a second-phase evolving process is designed for some offspring of crossover and mutation, in which the linear property of follower's function is used to generate high quality potential offspring. [ABSTRACT FROM AUTHOR]

In this paper, the particle swarm optimization (PSO) algorithm with constriction factor approach (CFA) is proposed to optimal hydro generators governor Proportional-Integral-Derivative (PID) gains for small hydraulic transients. And four different integral performance criteria of turbine speed deviation such as integrated absolute error (IAE), integral of time weighted absolute value of error (ITAE), integral of squared error (ISE) and integral of time weighted squared error (ITSE) have been taken as fitness function respectively, and the differences have been investigated and compared. A step speed disturbance test on no-load operation has been performed. The digital simulation results show that the proposed PSO method has stable convergence characteristic and good computational ability, and it can effectively optimal hydro generators governor PID gains. And the dynamic performance of hydro generators governor system for small hydraulic transients is much better if ITAE criterion is applied as fitness function. [ABSTRACT FROM AUTHOR]

Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality of the solutions found by artificial ants. The strategy is combined with the setting of other parameters to form a new ACO algorithm. Finally, the experimental results of computing traveling salesman problems indicate that the proposed algorithm is more effective than other ant methods. [ABSTRACT FROM AUTHOR]

Recently atomic cluster structures have been intensively studied because of their importance in physics, chemistry and material science. However, finding the lowest energy structure, which is the most stable configuration, is NP-hard. Differential Evolution (DE) algorithm is a new heuristic approach which mainly has three advantages: finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. In this paper we describe a new search method that uses differential evolution (DE) algorithm to optimize the geometry of small argon atom clusters. Experimental results show that the exact global optimal configuration of argon clusters with atom number N≤ 16 can be found in a reasonable computing time, and approximate optimization can also be obtained for clusters with N=30. From their 3-D geometry structures, we can see that their optimal energy structures are highly symmetrical. [ABSTRACT FROM AUTHOR]

It is recognized that evolutionary algorithms are inspired from evolutionary biology. In this paper, we set up a thermodynamic model of evolutionary algorithm. This model is intuitive and has a solid foundation in thermodynamics. It is our first step towards a unified theory of evolutionary algorithms. [ABSTRACT FROM AUTHOR]

A heuristic particle swarm optimization (HPSO) is proposed as a solution to one-dimensional cutting stock problem (1D-CSP), which incorporate genetic operators into particle swarm optimization (PSO). In this paper, a heuristic strategy that is based on the results of analysis of the optimal cutting pattern of particles with successful search processes is described, which process a global optimization problem of the cutting-stock as a sequential optimization problem by multiple stages. During every sequential stage, the best cutting pattern for the current situation is researched and processed. This strategy is repeated until all the required stocks have been generated. The simulation results prove the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

The dynamic vehicle routing problem is one of the most challenging combinatorial optimization tasks. The interest in this problem is motivated by its practical relevance as well as by its considerable difficulty. We present an approach to search for best routes in dynamic network. We propose a dynamic route evaluation model for modeling the responses of vehicles to changing traffic information, a modified Dijkstra's double bucket algorithm for finding the real-time shortest paths, and an improved evolutionary algorithm for searching the best vehicle routes in dynamic network. The proposed approach has been evaluated by simulation experiment using DVRPSIM. It has been found that the proposed approach quite efficient in finding real-time best vehicle routes where the customer nodes and network information changes dynamically. [ABSTRACT FROM AUTHOR]

In large-scale, complicated Wireless Sensor Networks, the cooperation among sensor nodes is a key topic, and has been receiving more and more attention. The dynamic coalition mechanism in Multi-Agent System is an important method for this topic, and then an energy-efficient coalition formation algorithm is needed since the energy resource of WSN is restricted. This paper proposes a WSN coalition formation algorithm based on Ant Colony System with dual-negative feedback characteristic. Coalition sponsor inclines to wake those sensor nodes which seldom or never joined coalitions before to form coalition, which can balance the energy consumption among sensor nodes, so as to extend the network lifetime for more tasks. The results of simulation experiment show the validity of this algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose to integrate particle swarm optimization algorithm into cultural algorithms frame to develop a more efficient cultural particle swarm algorithms (CPSA) for constrained multi-objective optimization problem. In our CPSA, the population space of cultural algorithms consists of n+1 subswarms which are used to search for the n single-objective optimums and an additional multiobjective optimum. The belief space accepts 20% elite particles form each subswarm and further takes crossover to create Pareto optimums. Niche Pareto tournament selection is further executed to ensure Pareto set to distribute uniformly along Pareto frontier. Additional memory of Pareto optimums spool is allocated and updated in each iteration to keep resultant Pareto solutions. Besides, a direct comparison method is employed to handle constraints without needing penalty functions. Two examples are presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The technology of electronic design automation (EDA) has improved the efficiency of design process, however, designer is still required much special knowledge of circuit. During the past decade, using genetic algorithm (GA) to design circuit had attracted many experts and scholars. However, too much more attention was focus on a circuit's function and many other factors had been neglected which caused the circuit had little applicability. This paper proposes an automated design approach for analog circuit based on a multi-objective adaptive GA. The multi-objective fitness evaluation method, which can dynamic adjust parameter, is selected. And a parallel evolution strategy which separates evolution of circuit structure and element value is adopted but also organically combined them by weight vectors. The experimental results indicate that this approach obviously be able to improve the evolution efficiency and could generate numbers of suitable circuits. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel fast evolutionary algorithm — cycle-wise genetic algorithm (CWGA) based on the theoretical analyses of a drug scheduling mathematical model for cancer chemotherapy. CWGA is more efficient than other existing algorithms to solve the drug scheduling optimization problem. Moreover, its simulation results match well with the clinical treatment experience, and can provide much more drug scheduling policies for a doctor to choose depending on the particular conditions of the patients. CWGA also can be widely used to solve other kinds of the real dynamic systems. [ABSTRACT FROM AUTHOR]

Gabor Wavelet Networks (GWN) is a method for face recognition. Evolutionary Algorithms are proved to be efficient to deal with GWN optimization problems. Inver-over Evolutionary Algorithm (IOEA) mixed with GWN performs well in face recognition because of its considerable effects on complex functions optimizations, which is called IOEA-GWN put forward by us, and shows higher recognition rate than Simple Genetic Algorithm mixed with GWN (called SGA-GWN) in the same experiment conditions according to our practices. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, each sensor node has severe resource constraints in terms of energy, computing device, and memory space. Especially, the memory space of the platform hardware is much smaller than that of the other computing systems. In this paper, we propose a OTL, which is an on-demand thread stack allocation scheme for MMU-less real-time sensor operating systems. The OTL enables to adaptively adjust the stack size by allocating stack frame based on the amount of each function's stack usage. The amount of the function's stack usage is checked at compile-time, and the adaptive adjustment of the stack occurs at run-time. Our experimental results show that the OTL significantly minimizes the spatial overhead of the threads' stacks with tolerable time overhead compared with fixed stack allocation mechanism of the existing sensor operating systems. [ABSTRACT FROM AUTHOR]

In this paper, we make an investigation into the discontinuous parameter identification in the case of elliptic problem. The discontinuous parameter is identified by evolutionary algorithm for the first time. For this kind of problem, we present a two-level evolutionary algorithm. The first level is the evolution for discontinuous point and the second level is the evolution for parameter. The numerical experiments suggest that the algorithm carries such features as good stability and adaptability and is not very sensitive to the noise in observation data as well. [ABSTRACT FROM AUTHOR]

In this paper, a protocol, called recursive priority inheritance (RPI) protocol, is proposed to solve complicated priority inversion problems as well as basic one. Our proposed protocol is implemented and tested in the Linux kernel. Additionally, the performance of our proposed protocol is evaluated and compared with previous protocols in the aspect of time and space complexity analyses. [ABSTRACT FROM AUTHOR]

This paper describes metaheuristic algorithm based on simulated annealing method,which is a nature-inspired method, to analyze and improve the efficiency of the design of Global Positioning System (GPS) surveying networks. Within the context of satellite surveying, a positioning network can be defined as a set of points which are coordinated by placing receivers on these point to determine sessions between them. The problem is to search for the best order in which these sessions can be observed to give the best possible schedule. The same problem arise in Mobile Phone surveying networks. Several case studies have been used to experimentally asses the performance of the proposed approach in terms of solution quality and computational effort. [ABSTRACT FROM AUTHOR]

We introduce an optimization of information granules (IG)-based fuzzy model with the aid of genetic algorithms (GAs) to describe projects in terms of complexity and development time in experimental software datasets. The proposed fuzzy model implements system structure and parameter identification with the aid of IG and GAs. To identify the structure and the parameters of fuzzy model we use genetic algorithms. The concept of information granulation was coped with to enhance the abilities of structural optimization of fuzzy model. Granulation of information realized with Hard C-Means clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the GAs and the least square method. An aggregate objective function is constructed in order to strike a sound balance between the approximation and generalization capabilities of the fuzzy model. The experimental results include well-known software data such as medical imaging system (MIS). [ABSTRACT FROM AUTHOR]

In high bit rate optical fiber communication systems, Polarization mode dispersion (PMD) is one of the main factors to signal distortion and needs to be compensated. PMD monitoring system is the key integral part of an adaptive PMD compensator. The degree of polarization (DOP) ellipsoid obtained by using a polarization scrambler can be used as a feedback signal for automatic PMD compensation. Generally, more than several thousands of sampling data of states of polarization (SOP) must be collected to insure getting a correct DOP ellipsoid. This would result in an unacceptable time consuming for adaptive PMD compensation. In this paper, we introduce the particle swarm optimization (PSO) algorithm in determining the real-time DOP ellipsoid with high precision, requiring only 100 sampling data of SOPs. Experimental results confirm that the PSO algorithm is effective for ellipsoid data fitting with high precision within 250ms using our hardware environment. [ABSTRACT FROM AUTHOR]

A novel quantum evolutionary algorithm based on immune operator (MQEA) is proposed. The algorithm can find out optimal solution by the mechanism in which antibody can be clone selected, immune cell can accomplish cross-mutation and Self-adaptive mutation, memory cells can be produced and similar antibodies can be suppressed. It not only can maintain quite nicely the population diversity than the classical evolutionary algorithm, but also can help to accelerate the convergence speed. The technique for improving the performance of MQEA has been described and its superiority is shown by some simulation experiments in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we introduce fuzzy-neural networks- Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPN) whose fuzzy rules include the information granules (about the real system) obtained through Information Granulation. We have developed a design methodology (genetic optimization using Genetic Algorithms) to find the optimal structure for fuzzy-neural networks that expanded from Group Method of Data Handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FPNN fixed by aid of genetic optimization that has search capability to find the optimal solution on the solution space. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules. The performance of genetically optimized FPNN (gFPNN) with fuzzy set-based polynomial neurons (FSPN) composed of fuzzy set-based rules is quantified through experimentation where we use a number of modeling benchmarks data which are already experimented with in fuzzy or neurofuzzy modeling. [ABSTRACT FROM AUTHOR]

In this paper, we developed an integrated multi-objective real-time controlled scheduling methodology for Dual Resource Constrained systems. The proposed methodology integrates simulation, neural network and fuzzy inference system approaches to obtain a schedule considering both state of the system and objectives. By means of a case study, we have demonstrated that the proposed methodology can be an effective tool for dynamic scheduling of DRC systems. [ABSTRACT FROM AUTHOR]

Traditional model of interrupt managemnent has been used for several decades. But it is often incapacity to incorporate reliability and temporal predictability demanded on real-time systems. Many solutions have been proposed to improve efficiency of interrupt handling such as In-line Interrupt Handling and Predictable Interrupt Management In this paper we propose a model that schedules interrupts in terms of their deadlines. Hard priorities of IRQs are still left to hardware. We only manager interrupts that can enter the kernel space so that hard real-time can be assured. Each interrupt will be scheduled only before its first execution according to their arrival time and deadlines so that the scheme is very simple and easy to be implemented. The scheme tries to make as many as possible ISRs finish their work within the time limit. Finally we do some experiments, which prove there is a big decrease of nested overtime interrupts, by means of task simulation on VxWorks. [ABSTRACT FROM AUTHOR]

When searching for good scheme, a good solution can be destroyed by an inappropriate choice of crossover points. Furthermore, because of the randomicity of crossover, mutation and selection, a better solution can hardly reach in last stage in EA, and the solution always traps in local optimal. Faced to "exploding" solution space, it is tough to find high quality solution just by increasing the population size, diversity of searching, and the number of iteration. In this paper, we design the immunity operator to improve the crossover result by utilizing the immunity theory. As the "guided mutation operator", the immunity operator substituted the "blind mutation operator" in normal EA, to restrain the degenerate phenomenon during the evolutionary process. We examine the algorithm with examples of TSP and gain promising result. [ABSTRACT FROM AUTHOR]

The vaccination which is a most effective protection against the virus is also a helpful part of the artificial immune systems (AIS). A vaccination in AIS means modifying some genes of an antibody in accordance with priori knowledge so as to gain higher affinity with greater probability. Since vaccination is problem specific, what can we do if we have no idea about the problem? To address this, we propose a pheromone based dynamic vaccination for real coded immune algorithms in this paper. The pheromone which is a term of the ant colony system is used as a carrier of knowledge learned from population's evolution and acts as a producer of dynamic vaccines. Experiments on numerical optimization problems indicate that the pheromone based vaccination operator has the ability of acquiring useful information about the objective functions and developing effective vaccines dynamically. [ABSTRACT FROM AUTHOR]

The integrated network (mixed with fixed network, cellular wireless network and ad hoc network etc.) would have different dynamic properties to the single-technique-based network, such as the movement of nodes and the change of delay of links. In this paper, we propose a new dynamic multicast routing model with a mechanism called local rearrangement to handle the changes in integrated network. Furthermore, D-IOA, an immune algorithm based on clone process, which employs gene library to improve the effectiveness of D-IOA to meet the real-time requirement in online multicast routing fields, is given to optimize the multicast sub-tree within the range of local rearrangement. The simulation results indicate that our algorithm balances three metrics better compared with other two famous dynamic multicast routing algorithms. [ABSTRACT FROM AUTHOR]

Multi-Objective Evolutionary Algorithms (MOEAs) have been proved efficient to deal with Multi-objective Optimization Problems (MOPs). Until now tens of MOEAs have been proposed. The unified mode would provide a more systematic approach to build new MOEAs. Here a new model is proposed which includes two sub-models based on two classes of different schemas of MOEAs. According to the new model, some representatives algorithms are decomposed and some interesting issues are discussed. [ABSTRACT FROM AUTHOR]

Evolutionary Algorithms are recognized to be efficient to deal with Multi-objective Optimization Problems(MOPs) which are difficult to be solved with traditional methods. Here a new Multi-objective Optimization Evolutionary Algorithm named DGPS which is compound with Geometrical Pareto Selection Method (GPS), Weighted Sum Method (WSM) and Dynamical Evolutionary Algorithm (DEA) is proposed. Some famous benchmark functions are carried out to test this algorithm's performance and the numerical experiments show that this algorithm runs much faster than SPEA2, NSGAII, HPMOEA and can obtain finer approximate Pareto fronts which include thousands of well-distributed points. [ABSTRACT FROM AUTHOR]

Evolutionary Computation made great success from the theory of natural selection devised by Charles Darwin. It was a process of randomly searching but not emphasizing each individuals respective functions. This paper proposed a hybrid optimization algorithm framework trying to incorporate natural selection and survival of the fittest and birds of a feather flock together. Aiming at balancing search results and search speed, we adopted the search strategy to classify the individuals by their fitness. Individuals classification differentiated respective function in search process, thats the excellent individuals mine the local optimal solution and others explore the search domain to find new local optimal solution. Experimental findings support the theoretical basis of the proposed framework. [ABSTRACT FROM AUTHOR]

There are multiple solution concepts in multi-objective optimization among which a decision maker has to select some good solutions usually which satisfy some trade-off criteria's. The need for potentially good solutions has always been one of the primary aims in multi-objective optimization. A complete representation of all these solutions is only possible with population based approaches like multi-objective evolutionary algorithms since then trade-off's can be calculated at each generation from the population members. Thus this paper proposes the use of multi-objective evolutionary algorithms for obtaining a complete representation of these good solutions. Theoretical results show how one can integrate search procedure for obtaining these solutions in population based evolutionary algorithms and some convergence results. Finally simulation results are presented on a number of test problems. [ABSTRACT FROM AUTHOR]

In integrated WLAN and cellular networks, we propose a power saving scheme that completely turns off WLAN interface in the idle state and wakes it up when there is incoming data from long-lived traffic. We also develop a Mobile IPv6 (MIPv6)-based signaling to turn on the WLAN interface only for long-lived traffic. It is shown via simulations that the proposed power saving scheme improves power efficiency over typical WLAN system. [ABSTRACT FROM AUTHOR]

The genetic algorithm (GA) often suffers from the premature convergence because of the loss of population diversity at an early stage of searching. This paper proposes a steep thermodynamical evolutionary algorithm (STEA), which utilizes a steep thermodynamical selection (STS) rule. STEA simulates the competitive mechanism between energy and entropy in annealing to systematically resolve the conflicts between selective pressure and population diversity in GA. This paper also proves that the rule STS has the approximate steepest descent ability of the free energy. Experimental results show that STEA is both far more efficient and much stabler than the thermodynamical genetic algorithm (TDGA). [ABSTRACT FROM AUTHOR]

This paper proposes a real-observation quantum-inspired evolutionary algorithm (RQEA) to solve a class of globally numerical optimization problems with continuous variables. By introducing a real observation and an evolutionary strategy, suitable for real optimization problems, based on the concept of Q-bit phase, RQEA uses a Q-gate to drive the individuals toward better solutions and eventually toward a single state corresponding to a real number varying between 0 and 1. Experimental results show that RQEA is able to find optimal or close-to-optimal solutions, and is more powerful than conventional real-coded genetic algorithm in terms of fitness, convergence and robustness. [ABSTRACT FROM AUTHOR]

Linear programming (LP) is an important field of optimization. Even though, interior point methods are polynomial algorithms, many LP practical problems are solved more efficiently by the primal and dual revised simplex methods (RSM); however, RSM has a poor performance in hard LP problems (HLPP) as in the Klee-Minty Cubes problem. Among LP methods, the hybrid method known as Simplex-Genetic (SG) is very robust to solve HLPP. The objective of SG is to obtain the optimal solution of a HLPP, taking advantages from each one of the combined methods -a genetic algorithm (GA) and the classical primal RSM-. In this paper a new SG method named Improved Simplex Genetic Method (ISG) is presented. ISG combines a GA (with special genetic operators) with both primal and dual RSM. Numerical experimentation using some instances of the Klee-Minty cubes problem shows that ISG has a better performance than both RSM and SG. [ABSTRACT FROM AUTHOR]

New communication network taking Internet as representative is being used extensively, but traditional communication theory cannot give good explanations and instructions on these new communication networks. Taking anthropotomy as a reference model, we analyze the systematic integration and functional requirements of new communication network with Internet as its representative in this paper. Further, we put forward the STL model of new communication network. Through the illustration for STL model, we have new knowledge on the Internet-representative communication network, which has certain use for the development of related theory and technologies. [ABSTRACT FROM AUTHOR]

In real-time systems, demand paging has not been used since worst case performance analysis technique for demand paging is not available. In this paper, we propose a technique to obtain the worst case bound on the demand paging costs and devise a worst case response time analysis framework considering the demand paging costs. We divide the technique into two different methods, DP-Pessimistic and DP-Accurate, depending on the accuracy and the complexity of analysis. DP-Accurate considers the page reuses among task instances and thus gives more accurate analysis results. We evaluate the accuracy of the proposed techniques comparing the analysis results from DP-Accurate and DP-Pessimistic. [ABSTRACT FROM AUTHOR]

Based on maximal slack first, this paper proposes a novel energy-efficient scheduling algorithm for periodic real time tasks. The scheduling solution combines static priority and dynamic speed adjustment mechanism to save energy. Simulation results show that the proposed algorithm outperforms other major scheduling schemes. [ABSTRACT FROM AUTHOR]

Embedded fault diagnosis technology requires high pertinency and small occupation space. Traditional fault diagnosis system can not satisfy the demand above. Aiming at this problem, a kind of embedded fault diagnosis expert system (E-FDES) based on CLIPS and ANN was brought forward. FDES and its relative technology were discussed, developing environment and design tool chain for E-FDES was established in Linux environment. Using modularization theory principle, system re-configuration and expansion ability were guaranteed, and data process components and graphic user interface components could be reduced to meet the embedded system running requirement. By application program interface of CLIPS and Protégé plug-in, knowledge base and rule base were realized, that decreased the developing time of system inference engine and reduced source occupation space. Industry experiments prove that this system runs stably in ARM-Linux embedded environment, switch smoothly and can recognize some common faults for the devices monitored. [ABSTRACT FROM AUTHOR]

In software fault-tolerant module, the key issue that affects the performance of fault-tolerant scheduling algorithm is how to predict precisely whether a primary is executable. In order to improve the prediction precision, a new algorithm named DPA, Deep-Prediction based Algorithm, is put forward. DPA uses prediction-table to predict whether a primary can be completed before it is scheduled. Simulation results show that DPA provides more execution time for primaries and wastes less processor time than the well-known similar algorithms. [ABSTRACT FROM AUTHOR]

Virtual development environment increases efficiency of embedded system development because it enables developers to develop, execute, and verify an embedded system without real hardware. We implemented a virtual development environment that is based on SystemC, a system modeling language. This environment was implemented by linking the AxD debugger with a SystemC-based hardware simulation environment through the RDI interface. We minimized modification of SystemC simulation engine so that the environment can be easily changed or extended with various SystemC models. Also, by using RDI, any debugging controller that support RDI can be used to develop an embedded software on the simulation environment. We executed example applications on the developed environment to verify operations of our implemented models and debugging functions. Our environment targets in ARM cores that are widely used in commercial business. [ABSTRACT FROM AUTHOR]

In this paper, a fast real time link adaptation scheme for wireless communication systems is proposed. The proposed scheme is employing multi-stage adaptation controls based on channel state information (CSI). The optimal link adaptation scheme is known to have high complexity to implement, and it shoud be run iteratively to deal with time varying wireless channels in a short time. The proposed method determines the speed of channel variation using CSI's, then it applies the most appropriate link adaptation methodology or algorithm to the current time varying rate of channels. For examples, a simple up/down transmit power adjustment is used for the very fast time varying channel condition. On the other hand, the optimal link adaptation scheme is directly applied in the very slow varying channel state. The proposed multi-stage link adaptation scheme can be easily implemented in real time wirelss communication systems because of its capability of selecting an appropriate real time link adaptation scheme adaptively to each channel variation rate. The design example on the selected platform shows that the proposed scheme is very efficient in real time applications of the link adaptation in wireless communications, while its performance is maintained close to the optimal. [ABSTRACT FROM AUTHOR]

File systems, which are embedded in low-power computer systems, have continuously improved energy saving features. However, the inevitable aging of the file system gives disks less chances to stay idle, thus increasing the energy consumption along with the decrease in performance. As a solution to the problem, we propose the energy-aware block reallocation framework (EAR), which is a software framework with a reallocation algorithm applicable to file systems. EAR dynamically reallocates fragmented data with the same relationship to disk locations where less disk accesses and seeks are achieved. The optimized file data layout improves the energy saving on aged disks. [ABSTRACT FROM AUTHOR]

This paper proposes and analyzes bandwidth reclaim scheme for IEEE 802.11 WLAN, which may suffer from severe bandwidth waste resulting from not only the variation of transmission rate and message length but also the overallocation to the real-time traffic in compensating for the delay due to the intervention of non-real-time messages. Built on top of the weighted round robin scheduling policy, we address that the polling order rearrangement according to the degree of overallocation can enhance reclaimability of unused network time and that the rearranegable slot has its message pending at the rearranging time. The simulation results show that the proposed scheme is able to reclaim up to 52.3 % of bandwidth waste when the number of streams is 2 and that it also provides stable throughput for utilization of 0.5 through 0.8. [ABSTRACT FROM AUTHOR]

We first demonstrate software prefetching provides an average 66.28% performance enhancement with much higher average power on six memory-intensive benchmarks. Then we propose a power-directed software prefetching algorithm with dynamic voltage scaling (PDP-DVS) that monitors a system's power and adapts the voltage level accordingly to guarantee no power increase while maintaining good performance boost. Our PDP-DVS algorithm achieves a 35.75% performance gain with only 1.19% power increase. [ABSTRACT FROM AUTHOR]

Media streaming delivery in wireless ad hoc networks is challenging due to the stringent resource restrictions, potential high loss rate and the decentralized architecture. To support long and high-quality streams, one viable approach is that a media stream is partitioned into segments, and then the segments are replicated in a network and served in a peer-to-peer fashion, however, the searching strategy for segments is one key problem with the approach. This paper proposes a hybrid ant-inspired search algorithm (HASA) for P2P media streaming distribution in Ad Hoc networks. It takes the advantages of random walkers and ant-inspired algorithms for search in unstructured P2P networks, such as low transmitting latency and less redundant query messages. We quantify the performance of our scheme in terms of response time and network query messages for media streaming distribution. Simulation results show that it can effectively improve the Search Efficiency for P2P media streaming distribution in Ad Hoc networks. [ABSTRACT FROM AUTHOR]

Flash memory is becoming increasingly important for embedded systems because it has many attractive features such as small size, fast access speeds, shock resistance, and light weight. Although flash memory has attractive features, it should perform garbage collection, which includes erase operations. The erase operations are very slow, and usually decrease the performance of the system. Besides, the number of the erase operations allowed to each block is also limited. To minimize the garbage collection time and evenly wear out, our proposed garbage collection policy focuses on minimizing the garbage collection time and wear-leveling. Trace-driven simulations show that the proposed policy performs better than existing garbage collection policies in terms of the garbage collection time and the endurance of flash memory. Specifically, we have shown that the performance improvement of our proposed policy against the greedy policy in terms of the endurance of flash memory is as much as 90.6%. [ABSTRACT FROM AUTHOR]

Data-driven Overlay Network (DONet) is especially suitable for live stream in P2P environment. However, it demands the cooperation of individual nodes to exchange their buffer maps. Since these nodes are selfish and have their own interests, they might cheat about their buffer maps to reduce the forwarding burden. We analyze the impact of this kind of cheating behavior on the streaming quality of DONet by experiment in this paper. The experimental results show that buffer map cheating has considerable negative impact on the streaming quality in DONet. [ABSTRACT FROM AUTHOR]

Recently issued standard IEEE 802.15.4 is a novel standard to achieve some critical features of wireless sensor networks. In this paper, we propose the algorithm to allocate GTSs according to the packet arrival rate from end devices and the number of devices in beacon enabled mode of IEEE 802.15.4 star topology networks. From simulation results, we show that our proposed algorithm enhances throughput comparing with the original IEEE 802.15.4 that uses only CAP duration for transmission. [ABSTRACT FROM AUTHOR]

This paper presents a new type of digital payment method and algorithm for embedded systems including mobile phone and other handheld devices. In the conventional mobile payment system, a payment signal transmitting device and a wireless internet connection device are separately provided to mobile systems. Therefore, the production cost of the mobile devices increases, and the complexity and the size of the mobile devices grow. In order to solve the problems, a new digital payment method using light signal containing payment-related information is suggested for mobile phone and embedded devices. The new method provides an economic and efficient solution to mobile payment system by updating embedded software without need of additional hardware modules to mobile devices. [ABSTRACT FROM AUTHOR]