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We present an approach for blind separation of acoustic sources produced from multiple speakers mixed in realistic room environments. We first transform recorded signals into the time‐frequency domain to make mixing become instantaneous. We then separate the sources in each frequency bin based on an independent component analysis (ICA) algorithm. For the present paper, we choose the complex version of fixedpoint iteration (CFPI), i.e. the complex version of FastICA, as the algorithm. From the separated signals in the time‐frequency domain, we reconstruct output‐separated signals in the time domain. To solve the so‐called permutation problem due to the indeterminacy of permutation in the standard ICA, we propose a method that applies a special property of the CFPI cost function. Generally, the cost function has several optimal points that correspond to the different permutations of the outputs. These optimal points are isolated by some non‐optimal regions of the cost function. In different but neighboring bins, optimal points with the same permutation are at almost the same position in the space of separation parameters. Based on this property, if an initial separation matrix for a learning process in a frequency bin is chosen equal to the final separation matrix of the learning process in the neighboring frequency bin, the learning process automatically leads us to separated signals with the same permutation as that of the neighbor frequency bin. In each bin, but except the starting one, by chosen the initial separation matrix in such a way, the permutation problem in the time domain reconstruction can be avoided. We present the results of some simulations and experiments on both artificially synthesized speech data and real‐world speech data, which show the effectiveness of our approach.

The purpose of this paper is to develop an effective and efficient approach to exploit meta‐heuristic in particle swarm optimization (PSO) for the job shop scheduling problem (JSP), a class of NP‐hard optimization problems. The approach is to be built on a PSO with multiple independent swarms. PSO was inspired by bird flocking and animal social behaviors. The particles operate collectively like a swarm that flies through the hyperdimensional space to search for possible optimal solutions. The behavior of the particles is influenced by their tendency to learn from their personal past experience and from the success of their peers to adjust their flying speed and direction. Research in fusing the multiple‐swarm concept into PSO is well‐established in solving single objective optimization problems and multimodal problems.

This study examines the optimization of the JSP via a search space division scheme and use of the meta‐heuristic method of PSO by assigning each machine in a JSP an independent swarm of particles. The use of multiple swarms in PSO is motivated by the idea of “divide and conquer” to reduce the computational complexity incurred through solving a NP‐hard combinatorial optimization problem. The resulted design, JSP/PSO algorithm, fully exploits the computing power presented by the multiple‐swarm PSO.

Simulation experiments show that the proposed JSP/PSO algorithm can effectively solve the JSP problems from small to median size. If certain mechanism of information sharing between swarms can be incorporated, it is believed that the new design could offer even more computing power to tackle the large‐sized problems.

The proposed JSP/PSO algorithm is effective in solving JSPs. The proposed algorithm shows considerable promise when searching the space of non‐delay schedules. It demands relatively lower number of function evaluations compared to other state‐of‐the‐art. The drawback to the JSP/PSO is that the GT scheduling adopted is too computationally expensive. Future works will address this concern.

Embedded systems, Internet of Things (IoT) and mobile computing devices are used in various domains which include public-private infrastructure, industrial installation and critical environment. Generally, information handled by these devices is private and critical. Therefore, it must be appropriately secured from different attacks and hackers. Lightweight cryptography is an aspiring field which investigates the implementation of cryptographic primitives and algorithms for resource constrained devices. In this paper, a new compact hybrid lightweight encryption technique has been proposed. Proposed technique uses the fastest bit permutation instruction PERMS with S-box of PRESENT block cipher for non-linearity. An arbitrary n-bit permutation is performed using PERMS instruction in less than log (n) number of instructions. This new hybrid system has been analyzed for software performance on Advanced RISC Machine (ARM) and Intel processor whereas Cadens tool is used to analyze the hardware performance. The result of the proposed technique is improved by the factor of eight as compared to the PRESENT-GRP hybrid block cipher. Moreover, PERMS instruction bit permutation properties result a very good avalanche effect and compact implementation in the both hardware and software environment.

The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I.

Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed.

A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality.

The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.

This paper introduces an audio encryption algorithm based on permutation of audio samples using discrete modified Henon map followed by substitution operation with keystream generated from the modified Lorenz-Hyperchaotic system. In this work, the audio file is initially compressed by Fast Walsh Hadamard Transform (FWHT) for removing the residual intelligibility in the transform domain. The resulting file is then encrypted in two phases. In the first phase permutation operation is carried out using modified discrete Henon map to weaken the correlation between adjacent samples. In the second phase it utilizes modified-Lorenz hyperchaotic system for substitution operation to fill the silent periods within the speech conversation. Dynamic keystream generation mechanism is also introduced to enhance the correlation between plaintext and encrypted text. Various quality metrics analysis such as correlation, signal to noise ratio (SNR), differential attacks, spectral entropy, histogram analysis, keyspace and key sensitivity are carried out to evaluate the quality of the proposed algorithm. The simulation results and numerical analyses demonstrate that the proposed algorithm has excellent security performance and robust against various cryptographic attacks.

Quality function deployment (QFD) is a planning methodology to improve products, services and their associated processes by ensuring that the voice of the customer has been effectively deployed through specified and prioritised technical attributes (TAs). The purpose of this paper is two ways: to enhance the prioritisation of TAs: computer simulation significance test; and computer simulation confidence interval. Both are based on permutation sampling, bootstrap sampling and parametric bootstrap sampling of given empirical data.

The authors present a theoretical case for the use permutation sampling, bootstrap sampling and parametric bootstrap sampling. Using a published case study the authors demonstrate how these can be applied on given empirical data to generate a theoretical population. From this the authors describe a procedure to decide upon which TAs have significantly different priority, and also estimate confidence intervals from the theoretical simulated populations.

First, the authors demonstrate not only parametric bootstrap is useful to simulate theoretical populations. The authors can also employ permutation sampling and bootstrap sampling to generate theoretical populations. Then the authors obtain the results from these three approaches. qThe authors describe why there is a difference in results of permutation sampling, bootstrap and parametric bootstrap sampling. Practitioners can employ any approach, it depends how much variation in FWs is required by quality assurance division.

Using these methods provides QFD practitioners with a robust and reliable method for determining which TAs should be selected for attention in product and service design. The explicit selection of TAs will help to achieve maximum customer satisfaction, and save time and money, which are the ultimate objectives of QFD.

The aim of this paper is to find and prioritise multiple critical success factors (CSFs) for the implementation of LSS in the oil and gas industry.

Based on a preselected list of possible CFSs, experts are involved in screening them with the Delphi method. As a result, 22 customised CSFs are selected. To prioritise these CSFs, the step-wise weight assessment ratio analysis (SWARA) method is applied to find weights corresponding to the decision-making preferences. Since the regular permutation-based weight assessment can be classified as NP-hard, the problem is solved by a metaheuristic method. For this purpose, a genetic algorithm (GA) is used.

The resulting prioritisation of CSFs helps companies find out which factors have a high priority in order to focus on them. The less important factors can be neglected and thus do not require limited resources.

Only a specific set of methods have been considered.

The resulting prioritisation of CSFs helps companies find out which factors have a high priority in order to focus on them.

The methodology supports respective evaluations in general.

The paper contributes to the very limited research on the implementation of LSS in the oil and gas industry, and, in addition, it suggests the usage of SWARA, a permutation method and a GA, which have not yet been researched, for the prioritisation of CSFs of LSS.

People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches.

The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches.

Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach.

Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations.

This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

The purpose of this paper is to develop a feasible sequence-oriented new discrete particle swarm optimization (NDPSO) algorithm with novel particles’ updating mechanism for solving simple assembly line balancing problems (SALBPs).

In the NDPSO, a task-oriented representation is adopted to solve type I and type II SALBPs, and a particle directly represents a feasible task sequence (FTS) as a permutation. Then, the particle (permutation) is updated as a whole using the geometric crossover based on the edit distance with swaps for two permutations. Furthermore, the fragment mutation with adaptive mutation probability is incorporated into the NDPSO to improve exploration ability.

Case study illustrates the effectiveness of the NDPSO. Comparative results between the NDPSO and existing real-encoded PSO (CPSO) and direct discrete PSO (DDPSO) against benchmark instances of type I SALBP and type II SALBP show promising higher performance of the proposed NDPSO.

A novel particles’ updating mechanism for FTS-encoded particle is proposed to solve the SALBPs. The comparative results indicate that updating of FTS as a whole seems superior to existing updating of FTS by fragment with respect to exploration ability for solving SALBPs. The novel particles’ updating mechanism is also applicable to generalized assembly line balancing problems.

This research introduces a hypothesis for establishing typologies and patterns for architectural plans based on their climate, culture and orientation. The repetition and reproduction of spaces in architectural plans are rooted in mathematical equations. Factorial and permutation formulae are the type of equations used as scientific tools to define typology. In addition, a new perspective on culture and privacy in line with the Arabic house is included to illustrate the practicality of restricted plans according to cultural needs.

To make the approach accessible, the theory is integrated into a software using C++ as the programming tool. Accordingly, all patterns and typologies are reproduced by inserting digits or numbers to simulate the process of using permutations and factorials for the creation of diagrammatic patterns and, subsequently, architectural plans.

The authors recommend that this method be integrated in future housing studies at earlier stages to obtain a high number of alternatives for architectural plans. The results of mathematical permutation of this study will help architects and designers to evolve their methods and processes through creating alternative patterns and culture (and climate)-specific typologies to provide more design possibilities.

This study is set to improve the adjacency diagram theory into the adjacency diagram with orientation theory, which accounts for the geographical orientation to obtain more comprehensive and climate-responsive patterns.