Search results for: MUSIC algorithm

Authors: Said Baadel, Fadi Thabtah, Joan Lu

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Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard-partitioning techniques where each object is assigned to one cluster. In this paper, we propose an overlapping algorithm MCOKE which allows objects to belong to one or more clusters. The algorithm is different from fuzzy clustering techniques because objects that overlap are assigned a membership value of 1 (one) as opposed to a fuzzy membership degree. The algorithm is also different from other overlapping algorithms that require a similarity threshold to be defined as a priority which can be difficult to determine by novice users.

Keywords: data mining, k-means, MCOKE, overlapping

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Authors: Younis R. Elhaddad, Mohamed A. Alshaari

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Since 2700 B.C the problem of constructing magic squares attracts many researchers. Magic squares one of most difficult challenges for mathematicians. In this work, we describe how to construct and enumerate Pan- magic squares using genetic algorithm, using new chromosome encoding technique. The results were promising within reasonable time.

Keywords: genetic algorithm, magic square, pan-magic square, computational intelligence

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Authors: Patricia P. M. C. Lourenco, Sang Yong Kim, Anaisa D. A. De Sena

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K-Pop fandom is a paradoxical dichotomy of two conceptual contexts: the Korean single fandom and the international fandom; both strongly influence K-Pop brand attitude. Collectivist, South Korea’s fans showcase their undivided support to one artist comeback towards earning a triple-crown in domestic music charts. In contrast, individualist international fans collectively ship a plethora of artists and collaborate amongst themselves to the continuous expansion of K-Pop into a mainstream cultural glocalization in international music charts. The distinct idiosyncrasies between the two groups creates a heterogeneous K-Pop brand attitude that is challenging to tackle marketing wise for lack of homogeneity in the sub-cultural K-Pop fandom.

Keywords: K-Pop fandom, single-fandom, multi-fandom, individualism, collectivism, brand attitude, sub-culture

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Authors: Yuqing Chen, Ying Xu, Renfa Li

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The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new ALM algorithm to recover the weather monitoring data. A lot of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN.

Keywords: wireless sensor network, matrix completion, singular value thresholding, augmented Lagrange multiplier

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Authors: Sahin Emrah Amrahov, Fatih Aybar, Serhat Dogan

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In this paper we consider the rule reduct generation problem. Rule Reduct Generation (RG) and Modified Rule Generation (MRG) algorithms, that are used to solve this problem, are well-known. Alternative to these algorithms, we develop Pruning Rule Generation (PRG) algorithm. We compare the PRG algorithm with RG and MRG.

Keywords: rough sets, decision rules, rule induction, classification

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Authors: Sender Dovchin

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The recent bi/multilingual scholarship shows that the knowledge of ‘translingual English’ is understood in terms of transcultural flows of linguistic, semiotic and cultural resources, where these resources re-transform and are recontextualised to form new specific languages and perform new identities in diverse societal contexts. Drawing on linguistic ethnographic data from contemporary popular music artist in Mongolia, this paper addresses two main critical questions: (1) how new forms of specific languages are created when English becomes translingual English in local contexts; and (2) how new varieties of local identities are constructed and performed when English transforms into translingual English. The paper argues that popular music artists in post-socialist Mongolia should better be understood as active cultural producers, contrary to those dominant discourses which position artists in the periphery as passive recipients of popular culture. Positioned within the creative nature of the global digital resources and the increasing transcultural spread of linguistic and cultural modes and features, these young Mongolian popular music artists produce not only new forms of linguistic practices in the local contexts but also create varied new forms of identities of what it means to be a young Mongolian person in the modern society.

Keywords: multilingualism, translingualism, mongolia, english

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Authors: Nirmal Yadav, Tanuja Srivastava

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Shannon theory is an exact method to recover a band limited signals from its sampled values in discrete implementation, using sinc interpolators. But sinc based results are not much satisfactory for band-limited calculations so that convolution with window function, having compact support, has been introduced. Convolution Backprojection algorithm with window function is an approximation algorithm. In this paper, the error has been calculated, arises due to this approximation nature of reconstruction algorithm. This result will be defined for fan beam projection data which is more faster than parallel beam projection.

Keywords: computed tomography, convolution backprojection, radon transform, fan beam

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Authors: Shahram Jamali, Samira Hamed

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One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count.

Keywords: active queue management, RED, Markov model, random early detection algorithm

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Authors: Ahmed Saleh Summan

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This article presents the results of a technical report on the evaluation of occupational noise exposures among a musical conductor in a musical rehearsal hall (party–center). A calibrated noise dosimeter was used to measure the personal exposure of a music teacher/conductor for 8 hours in two days of rehearsal involving 90 players. Results showed that noise exposure levels were much higher than the permissible levels regulated 85dBA/8hr by NIOSH. In fact, the first day of measurements recorded the highest exposure levels (91 dBA). A number of factors contributed to these results, such as players number, types of instruments used, and activities. Noise control measures were recommended to solve this situation.

Keywords: noise exposure, music conductors, occupational noise, noise in rooms

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Authors: Juan Manuel Sanchez-Cartas, Gonzalo Leon

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A price competition algorithm for ABMs based on game theory principles is proposed to deal with the simulation of theoretical market models. The algorithm is applied to the classical Hotelling’s model and to a two-sided market model to show it leads to the optimal behavior predicted by theoretical models. However, when theoretical models fail to predict the equilibrium, the algorithm is capable of reaching a feasible outcome. Results highlight that the algorithm can be implemented in other simulation models to guarantee rational users and endogenous optimal behaviors. Also, it can be applied as a tool of verification given that is theoretically based.

Keywords: agent-based models, algorithmic game theory, multi-sided markets, price optimization

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Authors: Smail Tigani, Mohamed Ouzzif

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This work presents a discrete quantitative state prediction algorithm with intelligent behavior making it able to self-improve some performance aspects. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before the final decision. The auto-rectification mechanism is based on two parallel mathematical models. In one hand, the algorithm predicts the next state based on event transition matrix updated after each observation. In the other hand, the algorithm extracts its residues trend with a linear regression representing historical residues data-points in order to rectify the first decision if needs. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. Designed key performance indicator, computed during a Monte Carlo simulation, shows the advantages of the proposed approach compared with traditional one.

Keywords: discrete state, Markov Chains, linear regression, auto-adaptive systems, decision making, Monte Carlo Simulation

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Authors: B. Selma, S. Chouraqui

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The pancreas is an elongated organ that extends across the abdomen, below the stomach. In addition, it secretes certain enzymes that aid in food digestion. The pancreas also manufactures hormones responsible for regulating blood glucose levels. In the present paper, we propose a mathematical model to study the homeostasis of glucose and insulin in healthy human, and a simulation of this model, which depicts the physiological events after a meal, will be represented in ordinary humans. The aim of this paper is to design an algorithm which regulates the level of glucose in the blood. The algorithm applied the concept of expert system for performing an algorithm control in the form of an "active" used to prescribe the rate of insulin infusion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. The results showed a performance of the control system.

Keywords: modeling, algorithm, regulation, glucose-insulin, blood, control system

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Authors: Bircan Demiral

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Cognitive radio (CR) is the promising technology that addresses the spectrum scarcity problem for future wireless communications. Orthogonal Frequency Division Multiplexing (OFDM) technology provides more power band ratios for cognitive radio networks (CRNs). While CR is a solution to the spectrum scarcity, it also brings up the capacity problem. In this paper, a novel power allocation algorithm that aims at maximizing the sum capacity in the OFDM based cognitive radio networks is proposed. Proposed allocation algorithm is based on the previously developed water-filling algorithm. To reduce the computational complexity calculating in water filling algorithm, proposed algorithm allocates the total power according to each subcarrier. The power allocated to the subcarriers increases sum capacity. To see this increase, Matlab program was used, and the proposed power allocation was compared with average power allocation, water filling and general power allocation algorithms. The water filling algorithm performed worse than the proposed algorithm while it performed better than the other two algorithms. The proposed algorithm is better than other algorithms in terms of capacity increase. In addition the effect of the change in the number of subcarriers on capacity was discussed. Simulation results show that the increase in the number of subcarrier increases the capacity.

Keywords: cognitive radio network, OFDM, power allocation, water filling

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Authors: Majid Khalili

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This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new multi-objective variable neighborhood algorithm (MOVNS). A set of experimental instances are carried out to evaluate the algorithm by advanced multi-objective performance measures. The algorithm is carefully evaluated for its performance against available algorithm by means of multi-objective performance measures and statistical tools. The related results show that a variant of our proposed MOVNS provides sound performance comparing with other algorithms.

Keywords: no-wait hybrid flowshop scheduling; multi-objective variable neighborhood algorithm; makespan; total weighted tardiness

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Authors: Danielle Kim

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This paper analyzes how conceptual metaphors in the lyrics of popular songs can be potentially detrimental by influencing the public’s perception of women. Conceptual metaphors in songs often compare women to objects (objects that are fragile and breakable or primarily of monetary value) and animals. Many common conceptual metaphors in music refer to women as less than sovereign, rational humans, implying that women should be owned, controlled, and used. These comparisons are harmful because music is so influential and has the ability to create and perpetuate stereotypes. By examining the lyrics of the popular songs: Bob Dylan’s “Just like a woman,” Robin Thicke’s “Blurred Lines” (written by Marvin Gaye), and Chris Brown’s “Fine China,” we can discern subtle ways in which misogynistic language has become so imbedded into popular culture.

Keywords: conceptual metaphors, women studies, feminism, lyrics

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Authors: Archit Yajnik

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Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed.

Keywords: hidden markov model, natural language processing, POS tagging, viterbi algorithm

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Authors: Ajmal Hussain

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In Indian music parlance, Rasa denotes a distinct aesthetic experience that builds up in the mind of the listeners while listening to a piece of Indian classical music. The distinction of the experience is rooted in the concept that it gives rise to an enhanced awareness about the Self or God and creates a mental state detached from mundane issues of everyday life. The theory of Rasa was initially proposed in the context of theatre but became a part of Indian musicological discourse roughly two thousand years ago, however, to this day, it remains shrouded in mystery due to its religious associations and connotations. This paper attempts to demystify the theory of Rasa in the light of available scientific knowledge fund particularly in Brain and Mind sciences. The paper initially describes the religious context of the theory of Rasa and then discusses its classical formulations by Bharata and Abhinavagupta including the steps and stages laid down by the latter to explain the creation of musical experience. The classical formulations are then interpreted with reference to the scientific knowledge fund about the human mind and mechanics of perception. The study uses the model of human mind as proposed by Portuguese-American neuroscientist Antonio Damasio in his theory ‘A Nesting Principle’. On the basis of the findings by Damasio, the paper interprets the experience of Rasa from a scientific perspective and clarifies the sequence of steps and stages involved in the making of musical experience. The study concludes that although the classical formulations of Rasa identify key aspects of musical experience, the association of Rasa with religion is misleading. The association with religion does not depend upon musical stimulus but the intellectual orientation of the listener. It further establishes that the function of Rasa is more profound as, from an evolutionary perspective, it can be seen as a catalyst for higher consciousness.

Keywords: aesthetic, consciousness, music, Rasa

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Authors: Hussain K. Chaiel

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The development in the field of the digital signal processing (DSP) and the microelectronics technology reduces the complexity of the iterative algorithms that need large number of arithmetic operations. Virtex-Field Programmable Gate Arrays (FPGAs) are programmable silicon foundations which offer an important solution for addressing the needs of high performance DSP designer. In this work, Virtex-7 FPGA technology is used to implement an iterative algorithm to estimate the earthquake location. Simulation results show that an implementation based on block RAMB36E1 and DSP48E1 slices of Virtex-7 type reduces the number of cycles of the clock frequency. This enables the algorithm to be used for earthquake prediction.

Keywords: DSP, earthquake, FPGA, iterative algorithm

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Authors: A. Andreasyan, C. Connors

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The Elliptic Curve Digital Signature algorithm-based X509v3 certificates are becoming more popular due to their short public and private key sizes. Moreover, these certificates can be stored in Internet of Things (IoT) devices, with limited resources, using less memory and transmitted in network security protocols, such as Internet Key Exchange (IKE), Transport Layer Security (TLS) and Secure Shell (SSH) with less bandwidth. The proposed method gives another advantage, in that it increases the performance of the above-mentioned protocols in terms of key exchange by saving one scalar multiplication operation.

Keywords: cryptography, elliptic curve digital signature algorithm, key exchange, network security protocol

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Authors: Meng Wu

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Motion planning is a common task required to be fulfilled by robots. A strategy combining Ant Colony Optimization (ACO) and gravity gradient inversion algorithm is proposed for motion planning of mobile robots. In this paper, in order to realize optimal motion planning strategy, the cost function in ACO is designed based on gravity gradient inversion algorithm. The obstacles around mobile robot can cause gravity gradient anomalies; the gradiometer is installed on the mobile robot to detect the gravity gradient anomalies. After obtaining the anomalies, gravity gradient inversion algorithm is employed to calculate relative distance and orientation between mobile robot and obstacles. The relative distance and orientation deduced from gravity gradient inversion algorithm is employed as cost function in ACO algorithm to realize motion planning. The proposed strategy is validated by the simulation and experiment results.

Keywords: motion planning, gravity gradient inversion algorithm, ant colony optimization

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Authors: Sofiane Bououden, Ilyes Boulkaibet

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In terms of security of the information signal, the meta-heuristic Penguins Search Optimization Algorithm (PeSOA) is applied to synchronize chaotic encryption communications in the case of sensitive dependence on initial conditions in chaotic generator oscillator. The objective of this paper is the use of the PeSOA algorithm to exploring search space with random and iterative processes for synchronization of symmetric keys in both transmission and reception. Simulation results show the effectiveness of the PeSOA algorithm in generating symmetric keys of the encryption process and synchronizing.

Keywords: meta-heuristic, PeSOA, chaotic systems, encryption, synchronization optimization

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Authors: Watchara Songserm, Teeradej Wuttipornpun

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This paper presents a genetic algorithm based permutation and non-permutation scheduling heuristics (GAPNP) to solve a multi-stage finite capacity material requirement planning (FCMRP) problem in automotive assembly flow shop with unrelated parallel machines. In the algorithm, the sequences of orders are iteratively improved by the GA characteristics, whereas the required operations are scheduled based on the presented permutation and non-permutation heuristics. Finally, a linear programming is applied to minimize the total cost. The presented GAPNP algorithm is evaluated by using real datasets from automotive companies. The required parameters for GAPNP are intently tuned to obtain a common parameter setting for all case studies. The results show that GAPNP significantly outperforms the benchmark algorithm about 30% on average.

Keywords: capacitated MRP, genetic algorithm, linear programming, automotive industries, flow shop, application in industry

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Authors: Maria Armalita Tumimbang

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Jathilan or Kuda Lumping is a dance-drama with warfare as the main theme and the dancers mimicking mighty horsemen armed with sword in the middle of the battle field. However, to most people this dance-drama is more identical with magical nuanced dance and trance, beside the attractive and even dangerous acts of the dancers, such as eating shard or broken glass in a state of trance. Several music players play the accompaniment made up of incomplete gamelan set that include saron, kendang, gong, and kempul. In general, it remains unchanged with regards to the seemingly monotonous beat and occasional “bumps” that may lead the dancers into a trance state. The dances performed also tend to be of repetitive patterns. The development of Jathilan and other traditional art performance in this globalization and industrialization era can be divided into two: firstly, they are subjected to the power of industrialization, which means their performances are to be recorded for commercial purpose, and secondly, they are to be presented in live performances. To some people, live performances are preferable, and for some reasons, they represent a form of cultural résistance to globalization and industrialization. The present study is qualitative in nature. It aims to describe the music and performance of Jathilan in the era of globalization in Indonesia. The subject of this study is a traditional art group, Jathilan Kuda Pranesa of Godean, Yogyakarta. Data collection was conducted by interviews with the leader of the group, the dancers and music players, as well as the audience. The wave of globalization has brought strong capitalistic industrialization that render traditional arts simply into industrial commodities tailored to the need of the era. This very fact has made the repositioning of traditional art performance of Jathilan a necessity. And by repositioning we mean that Jathilans should be put back to their traditional forms and functions as they used to be.

Keywords: Jathilan, globalization, industrialization, music, performance

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Authors: Shreya Pare, Parvin Akhter

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Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures.

Keywords: classification, hyperspectral images, large distribution margin, modified fuzzy entropy function, multilevel thresholding, tree search algorithm, hyperspectral image classification using tree search algorithm

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Authors: Murilo G. Mendes

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This text presents part of the results presented in the Ph.D. thesis that has used John Baily's theory and method as well as its ethnographic application in the context of the fife flutes of the Banda Cabaçal dos Irmãos Aniceto in the state of Ceará, northeast of Brazil. John Baily is a British ethnomusicologist dedicated to studying the relationships between music, musical gesture, and embodied cognition. His methodology became a useful tool to highlight historical-social aspects present in the group's instrumental music. Remaining indigenous and illiterate, these musicians played and transmitted their music from generation to generation, for almost two hundred years, without any nomenclature or systematization of the fingering performed on the flute. In other words, his music, free from any theorization, is learned, felt, perceived, and processed directly through hearing and through the relationship between the instrument's motor skills and its sound result. For this reason, Baily's assumptions became fundamental in the analysis processes. As the author's methodology recommends, classes were held with the natives and provided technical musical learning and some important concepts. Then, transcriptions and analyses of musical aspects were made from patterns of movement on the instrument incorporated by repetitions and/or by the intrinsic facility of the instrument. As a result, it was discovered how the group reconciled its indigenous origins with the demand requested by the public power and the interests of the local financial elite from the mid-twentieth century. The article is structured from the cultural context of the group, where local historical and social aspects influence the social and musical practices of the group. Then, will be present the methodological conceptions of John Baily and, finally, their application in the music of the Irmãos Aniceto. The conclusion points to the good results of identifying, through this methodology and analysis, approximations between discourse, historical-social factors, and musical text. Still, questions are raised about its application in other contexts.

Keywords: Banda Cabaçal dos Irmãos Aniceto, John Baily, pífano, spatial-motor thinking

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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With the continuous increment of smart meter installations across the globe, the need for processing of the load data is evident. Clustering-based load profiling is built upon the utilization of unsupervised machine learning tools for the purpose of formulating the typical load curves or load profiles. The most commonly used algorithm in the load profiling literature is the K-means. While the algorithm has been successfully tested in a variety of applications, its drawback is the strong dependence in the initialization phase. This paper proposes a novel modified form of the K-means that addresses the aforementioned problem. Simulation results indicate the superiority of the proposed algorithm compared to the K-means.

Keywords: clustering, load profiling, load modeling, machine learning, energy efficiency and quality

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Authors: Nevena Jakovčević Stor, Ivan Slapničar

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Any polynomial can be expressed as a characteristic polynomial of a complex symmetric arrowhead matrix. This expression is not unique. If the polynomial is real with only real distinct roots, the matrix can be chosen as real. By using accurate forward stable algorithm for computing eigen values of real symmetric arrowhead matrices we derive a forward stable algorithm for computation of roots of such polynomials in O(n^2 ) operations. The algorithm computes each root to almost full accuracy. In some cases, the algorithm invokes extended precision routines, but only in the non-iterative part. Our examples include numerically difficult problems, like the well-known Wilkinson’s polynomials. Our algorithm compares favorably to other method for polynomial root-finding, like MPSolve or Newton’s method.

Keywords: roots of polynomials, eigenvalue decomposition, arrowhead matrix, high relative accuracy

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a new hybrid particle swarm optimization algorithm is proposed for the multi-objective flexible job shop scheduling problem that is very important and hard combinatorial problem. The Pareto approach is used for solving the multi-objective problem. Several new local search heuristics are integrated into an algorithm based on the critical block concept to enhance the performance of the algorithm. The algorithm is compared with the recently published multi-objective algorithms based on benchmarks selected from the literature. Several metrics are used for quantifying performance and comparison of the achieved solutions. The algorithms are also compared based on the Weighting summation of objectives approach. The proposed algorithm can find the Pareto solutions more efficiently than the compared algorithms in less computational time.

Keywords: swarm-based optimization, local search, Pareto optimality, flexible job shop scheduling, multi-objective optimization

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Authors: Meiling Yang, Shuguo Xie, Yilong Zhu

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Nowadays, due to diverse electric products and complex electromagnetic environment, the localization and troubleshooting of the electromagnetic radiation source is urgent and necessary especially on the condition of far field. However, based on the existing DOA positioning method, the system or devices are complex, bulky and expensive. To address this issue, this paper proposes a single antenna radiation source localization method. A single antenna moves to form a virtual antenna array combined with DOA and MUSIC algorithm to position accurately, meanwhile reducing the cost and simplify the equipment. As shown in the results of simulations and experiments, the virtual antenna array DOA estimation modeling is correct and its positioning is credible.

Keywords: virtual antenna array, DOA, localization, far field

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Authors: Y. Wang

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The Traveling salesman problem (TSP) is NP-hard in combinatorial optimization. The research shows the algorithms for TSP on the sparse graphs have the shorter computation time than those for TSP according to the complete graphs. We present an improved iterative algorithm to compute the sparse graphs for TSP by frequency graphs computed with frequency quadrilaterals. The iterative algorithm is enhanced by adjusting two parameters of the algorithm. The computation time of the algorithm is O(CN_maxn²) where C is the iterations, N_max is the maximum number of frequency quadrilaterals containing each edge and n is the scale of TSP. The experimental results showed the computed sparse graphs generally have less than 5n edges for most of these Euclidean instances. Moreover, the maximum degree and minimum degree of the vertices in the sparse graphs do not have much difference. Thus, the computation time of the methods to resolve the TSP on these sparse graphs will be greatly reduced.

Keywords: frequency quadrilateral, iterative algorithm, sparse graph, traveling salesman problem

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