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In this paper, we present deterministic and probabilistic methods for simulating PRAM com- putations on linear arrays with reconfigurable pipelined bus systems (LARPBS). The following results are established in this paper. (1) Eac...
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In this paper, we present deterministic and probabilistic methods for simulating PRAM com- putations on linear arrays with reconfigurable pipelined bus systems (LARPBS). The following results are established in this paper. (1) Each step of a p-processor PRAM with m=0(p) shared memory cells can be simulated by a p-processors LARPBS in O(log p) time, where the constant in the big-O notation is small. (2) Each step of a p-processor PRAM with m=Ω(p) shared memory cells can be simulated by a p-processors LARPBS in O(log m) time. (3) Each step of a p-processor PRAM can be simulated by a p-processor LARPBS in O(log p) time with probability larger than 1-1/p~c for all c>0.
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Most activities on the Internet can be recorded as log files of websites and website administrators can inspect log files to locate problems after any network intrusion occurs. However, since log files usually contain a huge quant...
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Most activities on the Internet can be recorded as log files of websites and website administrators can inspect log files to locate problems after any network intrusion occurs. However, since log files usually contain a huge quantity of data, without effective methods, it is generally not feasible for administrators to determine the concealed meanings within log files. One method for dealing with this issue is to use neural networks; this is an effective means to distinguish and classify abnormal data in log files, thus alleviating the administrator's burden. This paper presents the results of a study on intrusion detection on IIS (Internet information services) utilizing a hybrid intrusion detection system (IDS). The feasibility of the hybrid IDS is validated based on the Internet scanner system (ISS). In the intrusion detection system proposed, we used four different training data sets: 200, 800, 1400, and 2000. The system is trained either by Taguchi's experimental design or full factorial experimental design under different training data sets; the former can save much more time than the latter. Under Taguchi's experimental design, the best results are obtained when the training data set is of size 1400; overall accuracy in this case is 97.5%. On the contrary, for the full factorial experimental design, the best results are reached when the training data set is of size 2000; overall accuracy is 97.6%. Our study indicates that when to retrain the detector and how much time to allow for this training fully depend on the downgrade percentage of the detection rate, which determines the size of the retraining data set. To reduce the void time for updating the detector, the downgrade percentage should be restricted.
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We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to support a large volume of parallel communication of various patterns in ...
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We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to support a large volume of parallel communication of various patterns in constant time. An LARPBS can also be reconfigured into many independent subsystems and, thus, is able to support parallel implementations of divide-and-conquer computations like Strassen's algorithm. The main contributions of the paper are as follows. We develop five matrix multiplication algorithms with varying degrees of parallelism on the LARPBS computing model; namely, MM/sub 1/, MM/sub 2/, MM/sub 3/, and compound algorithms C/sub 1/(/spl epsiv/)and C/sub 2/(/spl delta/). Algorithm C/sub 1/(/spl epsiv/) has adjustable time complexity in sublinear level. Algorithm C/sub 2/(/spl delta/) implies that it is feasible to achieve sublogarithmic time using /spl sigma/(N/sup 3/) processors for matrix multiplication on a realistic system. Algorithms MM/sub 3/, C/sub 1/(/spl epsiv/), and C/sub 2/(/spl delta/) all have o(/spl Nscr//sup 3/) cost and, hence, are very processor efficient. Algorithms MM/sub 1/, MM/sub 3/, and C/sub 1/(/spl epsiv/) are general-purpose matrix multiplication algorithms, where the array elements are in any ring. Algorithms MM/sub 2/ and C/sub 2/(/spl delta/) are applicable to array elements that are integers of bounded magnitude, or floating-point values of bounded precision and magnitude, or Boolean values. Extension of algorithms MM/sub 2/ and C/sub 2/(/spl delta/) to unbounded integers and reals are also discussed.
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Failure detection (FD) is an important issue for supporting dependability in distributed healthcare systems to guarantee continuous, safe, secure, and dependable operation, and often is an important performance bottleneck in the e...
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Failure detection (FD) is an important issue for supporting dependability in distributed healthcare systems to guarantee continuous, safe, secure, and dependable operation, and often is an important performance bottleneck in the event of node failure. FD can be used to manage the health status of communication for delivering telemedicine services, and then to help distributed healthcare system reduce fatal accident rate and increase the reliability and safety of systems. Ensuring acceptable quality of service (QoS) is made difficult by the relative unpredictability of the network environment. In this paper, first, we compare QoS metrics of several adaptive FDs, discuss their properties and their relation, and then propose one optimization over the existing methods, called tuning adaptive margin failure detector (TAM FD), which significantly improves QoS, especially in the aggressive range and when the network is unstable. Second, we address the problem of most adaptive schemes, namely their need for a large window of samples. So we also analyze the impact of memory size on the performance of FDs, and then prove that the presented scheme is designed to use a fixed and very limited amount of memory for the distributed system. Our experimental results over several kinds of networks (Cluster, WiFi, LAN, Intercontinental WAN) show that the properties of the existing adaptive failure detectors, and demonstrate that the optimization is reasonable and acceptable. Furthermore, the extensive experimental results show what is the effect of memory size on the overall QoS of each adaptive failure detector. For our TAM FD, the effect of window size on their QoS is very small and can be negligible.
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Multiselection is the problem of selecting multiple elements at specified ranks from a set of arbitrary elements. In this paper, we first present an efficient algorithm for single-element selection that runs in O(p~(1/2) + (n/p) l...
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Multiselection is the problem of selecting multiple elements at specified ranks from a set of arbitrary elements. In this paper, we first present an efficient algorithm for single-element selection that runs in O(p~(1/2) + (n/p) log p log(κp/n)) time for selecting the κh smallest element from n elements on a p~(1/2) x p~(1/2) mesh-connected computer of p≤n processors, where the first component is for communication and second is for computation (data comparisons). Our algorithm is more computationally efficient than the existing result when p≥n~(1/2+ε) for any 0 <ε< 1/2. Combining our result for p = Ω(n~(1/2)) with the existing result for p = O(n~(1/2)) yields an improved computation time complexity for the selection problem on mesh t_(comp)~(sel) = O(min{(n/p)log p log(κp/n), (n/p+p)log(n/p)}). Using this algorithm as a building block, we then present two efficient parallel algorithms for multiselection on the mesh-connected computers. For selecting r elements from a set of n elements on a p~(1/2) x p~(1/2) mesh, p, r≤n, our first algorithm runs in time O(p~(1/2) + t_(comp)~(sel) min{r log r, logp}) with processors operating in the SIMD mode, which is time-optimal when p≤r. Allowing processors to operate in the MIMD mode, our second algorithm runs in O(p~(1/2) + t_(comp)~(sel) log r) time and is time-optimal for any r and p.
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We solve a number of important and interesting problems from graph theory on a linear array with a reconfigurable pipelined optical bus system. Our algorithms are based on fast matrix multiplication and extreme value finding algor...
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We solve a number of important and interesting problems from graph theory on a linear array with a reconfigurable pipelined optical bus system. Our algorithms are based on fast matrix multiplication and extreme value finding algorithms, and are currently the fastest al- gorithms. We also distinguish the two cases where weights have bounded/unbounded mag- nitude and precision.
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It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, ...
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It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.
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Many forecasting models based on the concept of fuzzy time series have been proposed in the past decades. Two main factors, which are the lengths of intervals and the content of forecast rules, impact the forecasted accuracy of th...
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Many forecasting models based on the concept of fuzzy time series have been proposed in the past decades. Two main factors, which are the lengths of intervals and the content of forecast rules, impact the forecasted accuracy of the models. How to find the proper content of the main factors to improve the forecasted accuracy has become an interesting research topic. Some forecasting models, which combined heuristic methods or evolutionary algorithms (such as genetic algorithms and simulated annealing) with the fuzzy time series, have been proposed but their results are not satisfied. In this paper, we use the particle swarm optimization to find the proper content of the main factors. A new hybrid forecasting model which combined particle swarm optimization with fuzzy time series is proposed to improve the forecasted accuracy. The experimental results of forecasting enrollments of students of the University of Alabama show that the new model is better than any existing models, and it can get better quality solutions based on the first-order and the high-order fuzzy time series, respectively.
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Fixed-power wireless sensor networks are prevalent and cost-effective. However, they face mote failures, RF interference from environmental noise and energy constraints. Routing protocols for such networks must overcome these prob...
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Fixed-power wireless sensor networks are prevalent and cost-effective. However, they face mote failures, RF interference from environmental noise and energy constraints. Routing protocols for such networks must overcome these problems to achieve reliability, energy efficiency and scalability in message delivery. Achievement of these requirements, however, poses conflicting demands. In this paper, we propose an efficient and reliable routing protocol (EAR) that achieves reliable and scalable performance with minimal compromise of energy efficiency. The routing design of EAR is based on four parameters - expected path length and a weighted combination of distance traversed, energy levels and link transmission success history, to dynamically determine and maintain the best routes. Simulation experiments of EAR with four existing protocols demonstrate that a design based on a combination of routing parameters exhibits collectively better performance than protocols based on just hop-count and energy or those using flooding.
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In this paper, a new hybrid particle swarm optimization model named HPSO that combines random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) is presented and used to solve the f...
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In this paper, a new hybrid particle swarm optimization model named HPSO that combines random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) is presented and used to solve the flow-shop scheduling problem (FSSP). The objective of FSSP is to find an appropriate sequence of jobs in order to minimize makespan. Makespan means the maximum completion time of a sequence of jobs running on the same machines in flow-shops. By the RK encoding scheme, we can exploit the global search ability of PSO thoroughly. By the IE scheme, we can enhance the local search ability of particles. The experimental results show that the solution quality of FSSP based on the proposed HPSO is far better than those based on GA [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Sol-itons and Fractals, 35, 851-861.] and NPSO [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Solitons and Fractals, 35, 851-861.], respectively.
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