Unsupervised Segmentation of Stereoscopic Video Objects: Constrained Segmentation Fusion Versus Greedy Active Contours

In this paper, we present a novel memory access reduction scheme (MARS) for two-dimension fast cosine transform (2-D FCT). It targets programmable DSPs with high memory-access latency. It reduces the number of memory accesses by: 1) reducing the number of weighting factors and 2) combining butterflies in vector-radix 2-D FCT pruning diagram from two stages to one stage with an efficient structure. Hardware platform based on general purpose processor is used to verify the effectiveness of the proposed method for vector-radix 2-D FCT pruning implementation. Experimental results validate the benefits of the proposed method with reduced memory access, less clock cycle and fewer memory space compared with the conventional implementation.     

Application of explicit schemes for the simulation of the two-phase filtration process

In the paper, the new model based on the kinetic approach is proposed to describe the process of two-phase slightly compressible fluid filtration. The capillary and gravity forces are taken into account. The obtained hyperbolic continuity equations for phase liquids are approximated by the explicit three-level schemes with a sufficiently mild stability condition. Due to its logical simplicity, the computational algorithm can be easily adapted to the hybrid architecture of modern supercomputers. The results of computations on a graphics accelerator are presented for the problem on contaminant infiltration into water-saturated soil and the high parallelization efficiency is demonstrated.     

A dynamic-programming-based exact algorithm for general single-machine scheduling with machine idle time

This paper proposes an efficient exact algorithm for the general single-machine scheduling problem where machine idle time is permitted. The algorithm is an extension of the authors’ previous algorithm for the problem without machine idle time, which is based on the SSDP (Successive Sublimation Dynamic Programming) method. We first extend our previous algorithm to the problem with machine idle time and next propose several improvements. Then, the proposed algorithm is applied to four types of single-machine scheduling problems: the total weighted earliness-tardiness problem with equal (zero) release dates, that with distinct release dates, the total weighted completion time problem with distinct release dates, and the total weighted tardiness problem with distinct release dates. Computational experiments demonstrate that our algorithm outperforms existing exact algorithms and can solve instances of the first three problems with up to 200 jobs and those of the last problem with up to 80 jobs.     

An infrastructure for smart hospitals

In healthcare facilities most of the daily activities require strict coordination between clinicians, who often operate under heavy workloads and minimal workforce conditions in environments filled with increasingly complex technology. Ubiquitous Computing applications constitute a suitable solution for both reducing medical costs and improving patient safety by better supporting clinical processes. In this study we introduce an intelligent infrastructure for smart hospitals which implements basic services to optimize medical staff/patient interactions and grants ubiquitous and transparent access to clinical data stored in standard clinical databases. This infrastructure relies on the integration of Radio Frequency IDentification (RFID) and photosensor technologies to identify, locate and track clinicians and patients equipped with mobile devices and wearable RFID tags.     

How landscape ruggedness influences the performance of real-coded algorithms: a comparative study

Ruggedness has a strong influence on the performance of algorithms, but it has been barely studied in real-coded optimization, mainly because of the difficulty of isolating it from a number of involved topological properties. In this paper, we propose a framework consisting of increasing ruggedness function sets built by a mechanism which generates multiple funnels. This mechanism introduces different levels of sinusoidal distortion which can be controlled to isolate the singular influence of some related features. Some commonly used measures of ruggedness have been applied to analyze these sets of functions, and a numerical study to compare the performance of some representative algorithms has been carried out. The results confirm that ruggedness has an influence on the performance of the algorithm, proving that it depends on the multi-funnel structure and peak features, such as height and relative size of the global peak, and not on the number of peaks.     

How to Prove Impossibility Under Global Fairness: On Space Complexity of Self-Stabilizing Leader Election on a Population Protocol Model

A population protocol is one of distributed computing models for passively-mobile systems, where a number of agents change their states by pairwise interactions between two agents. In this paper, we investigate the solvability of the self-stabilizing leader election in population protocols without any kind of oracles. We identify the necessary and sufficient conditions to solve the self-stabilizing leader election in population protocols from the aspects of local memory complexity and fairness assumptions. This paper shows that under the assumption of global fairness, no protocol using only n−1 states can solve the self-stabilizing leader election in complete interaction graphs, where n is the number of agents in the system. To prove this impossibility, we introduce a novel proof technique, called closed-set argument. In addition, we propose a self-stabilizing leader election protocol using n states that works even under the unfairness assumption. This protocol requires the exact knowledge about the number of agents in the system. We also show that such knowledge is necessary to construct any self-stabilizing leader election protocol.     

Faster Approximate String Matching for Short Patterns

We study the classical approximate string matching problem, that is, given strings P and Q and an error threshold k, find all ending positions of substrings of Q whose edit distance to P is at most k. Let P and Q have lengths m and n, respectively. On a standard unit-cost word RAM with word size w≥log n we present an algorithm using time

Entanglement mean field theory: Lipkin–Meshkov–Glick Model

Entanglement mean field theory is an approximate method for dealing with many-body systems, especially for the prediction of the onset of phase transitions. While previous studies have concentrated mainly on applications of the theory on short-range interaction models, we show here that it can be efficiently applied also to systems with long-range interaction Hamiltonians. We consider the (quantum) Lipkin–Meshkov–Glick spin model, and derive the entanglement mean field theory reduced Hamiltonian. A similar recipe can be applied to obtain entanglement mean field theory reduced Hamiltonians corresponding to other long-range interaction systems. We show, in particular, that the zero temperature quantum phase transition present in the Lipkin–Meshkov–Glick model can be accurately predicted by the theory.     

Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units

Data corruption in SCADA systems refers to errors that occur during acquisition, processing, or transmission, introducing unintended changes to the original data. In SCADA-based power systems, the data gathered by remote terminal units (RTUs) is subject to data corruption due to noise interference or lack of calibration. In this study, an effective approach based on the fusion of the general regression neural network (GRNN) and the particle swarm optimization (PSO) technique is employed to deal with errors in RTU data. The proposed hybrid model, denoted as GRNN-PSO, is able to handle noisy data in a fast speed, which makes it feasible for practical applications. Experimental results show the GRNN-PSO model has better performance in removing the unintended changes to the original data compared with existing methods.