Multi-sector multi-range control for self-organizing wireless networks

In this paper, we propose a distributed multi-sector multi-range (MSMR) control algorithm for supporting self-organizing wireless networks. The algorithm enables us to reduce the unnecessary coverage with fine-tuned range control and also to increase the network-wide capacity with enhanced spatial reusability. The proposed algorithm discovers neighboring nodes within the maximum transmission range at every node, divides its transmission area into multiple non-overlapping angular sectors of a given degree, chooses the home sector for each neighboring node according to its relative position, and constructs a spanning subgraph per sector by determining appropriate transmission range to maintain connectivity. Since the range control influences on network connectivity directly, we prove in the first place that the proposed algorithm preserves both network-wide and local connectivity as far as both connectivity exist in the network that uses the maximum transmission range. In order to investigate the performance of the proposed algorithm, we implemented it in the ns-2 simulator, and performed an extensive set of simulation study in comparison with other transmission range control schemes. The simulation results indicate that the proposed scheme is superior to other schemes with respect to the network-wide throughput and its normalized value per energy in various simulation configurations. In specific, the algorithm achieves minimally one order and maximally two orders of magnitude improvement in those performance evaluations. The improvement becomes more salient as the number of nodes increases and is immune to traffic type, network size, node distribution, or node density.     

Sequence classification via large margin hidden Markov models

We address the sequence classification problem using a probabilistic model based on hidden Markov models (HMMs). In contrast to commonly-used likelihood-based learning methods such as the joint/conditional maximum likelihood estimator, we introduce a discriminative learning algorithm that focuses on class margin maximization. Our approach has two main advantages: (i) As an extension of support vector machines (SVMs) to sequential, non-Euclidean data, the approach inherits benefits of margin-based classifiers, such as the provable generalization error bounds. (ii) Unlike many algorithms based on non-parametric estimation of similarity measures that enforce weak constraints on the data domain, our approach utilizes the HMM’s latent Markov structure to regularize the model in the high-dimensional sequence space. We demonstrate significant improvements in classification performance of the proposed method in an extensive set of evaluations on time-series sequence data that frequently appear in data mining and computer vision domains.     

Higher-order schemes for the Laplace transformation method for parabolic problems

In this paper we solve linear parabolic problems using the three stage noble algorithms. First, the time discretization is approximated using the Laplace transformation method, which is both parallel in time (and can be in space, too) and extremely high order convergent. Second, higher-order compact schemes of order four and six are used for the the spatial discretization. Finally, the discretized linear algebraic systems are solved using multigrid to show the actual convergence rate for numerical examples, which are compared to other numerical solution methods.     

Performance assessment in an interactive call center workforce simulation

In this paper, a new performance assessment methodology for human-in-the-loop call center systems at the level of customer-agent interactions (CAI) is proposed. We develop a team-in-the-loop simulation test bed, to analyze CAI-level performance of a service system using a temporal performance measure with time windows. The proposed framework should allow researchers to collect and analyze individual as well as team performance at a finer granularity than current call center efforts which focus on queue-centered analysis. The software framework is object-oriented and has been designed to be configurable. A sample simulation study in different scenarios is illustrated to provide the usages and advantages of the proposed method with index of Interactive Service Performance.     

Improving responsiveness of soft aperiodic tasks using proportional slack time

In a real-time system with both hard real-time periodic jobs and soft real-time aperiodic jobs, it is important to guarantee that the deadline of each periodic job is met, as well as to provide a fast response time for each aperiodic job. We propose an algorithm, called Proportional Slack Reserve (PSR), that produces an efficient schedule for such an environment. For every execution unit of a periodic job, the PSR algorithm reserves time which can be used for execution of aperiodic jobs. If reserved time is not available, the algorithm assigns a deadline to an aperiodic job for achieving better responsiveness of aperiodic jobs. The proposed algorithm can fully utilize processing power while meeting all deadlines of periodic jobs. It can also easily reclaim the time unused by the periodic job. We analytically show that for each aperiodic job, the response time in a PSR schedule is no longer than that in a TBS schedule, which is known to be efficient for servicing aperiodic jobs. We also present simulation results in which the response time of PSR is significantly improved over that of TBS, and moreover the performance of PSR compares favorably with TB(N) considering scheduling overhead.     

Multiple change-point detection of multivariate mean vectors with the Bayesian approach

Bayesian multiple change-point models are proposed for multivariate means. The models require that the data be from a multivariate normal distribution with a truncated Poisson prior for the number of change-points and conjugate priors for the distributional parameters. We apply the stochastic approximation Monte Carlo (SAMC) algorithm to the multiple change-point detection problems. Numerical results show that SAMC makes a significant improvement over RJMCMC for complex Bayesian model selection problems in change-point estimation.     

Hybrid Simulation of Miscible Mixing with Viscous Fingering

By modeling mass transfer phenomena, we simulate solids and liquids dissolving or changing to other substances. We also deal with the very small‐scale phenomena that occur when a fluid spreads out at the interface of another fluid. We model the pressure at the interfaces between fluids with Darcy's Law and represent the viscous fingering phenomenon in which a fluid interface spreads out with a fractal‐like shape. We use hybrid grid‐based simulation and smoothed particle hydrodynamics (SPH) to simulate intermolecular diffusion and attraction using particles at a computable scale. We have produced animations showing fluids mixing and objects dissolving.     

Effectiveness of MMORPG‐based instruction in elementary English education in Korea

This study investigated the effectiveness of massive multiplayer online role‐playing game (MMORPG)‐based (massive multiplayer online role‐playing game) instruction in elementary English education. The effectiveness of the MMORPG program was compared with face‐to‐face instruction and the independent variables (gender, prior knowledge, motivation for learning, self‐directed learning skills, computer skills, game skills, computer capacity, network speed, and computer accessibility) were examined to see how accurately achievement was predicted in MMORPG instruction. The results indicated that students studying English utilizing online role‐playing games showed higher scores in areas of listening, reading, and writing than those who attended face‐to‐face instruction classes. It was also found that prior knowledge, motivation for learning, and network speed were factors affecting achievement in English learning. These findings suggest that MMORPGs can play an important role in improving English communicative skills.