Search results for: Distributed genetic algorithms

Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

Abstract:

Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: Landcover classification, artificial neural network, remote sensing, SPOT-5.

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Authors: Sunusi N., Kresna A. J., Islamiyati A., Raupong

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Hazard rate estimation is one of the important topics in forecasting earthquake occurrence. Forecasting earthquake occurrence is a part of the statistical seismology where the main subject is the point process. Generally, earthquake hazard rate is estimated based on the point process likelihood equation called the Hazard Rate Likelihood of Point Process (HRLPP). In this research, we have developed estimation method, that is hazard rate single decrement HRSD. This method was adapted from estimation method in actuarial studies. Here, one individual associated with an earthquake with inter event time is exponentially distributed. The information of epicenter and time of earthquake occurrence are used to estimate hazard rate. At the end, a case study of earthquake hazard rate will be given. Furthermore, we compare the hazard rate between HRLPP and HRSD method.

Keywords: Earthquake forecast, Hazard Rate, Likelihood point process, Point process.

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Authors: Ahsan Bin Tufail, Ali Abidi, Adil Masood Siddiqui, Muhammad Shahzad Younis

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An early and accurate detection of Alzheimer's disease (AD) is an important stage in the treatment of individuals suffering from AD. We present an approach based on the use of structural magnetic resonance imaging (sMRI) phase images to distinguish between normal controls (NC), mild cognitive impairment (MCI) and AD patients with clinical dementia rating (CDR) of 1. Independent component analysis (ICA) technique is used for extracting useful features which form the inputs to the support vector machines (SVM), K nearest neighbour (kNN) and multilayer artificial neural network (ANN) classifiers to discriminate between the three classes. The obtained results are encouraging in terms of classification accuracy and effectively ascertain the usefulness of phase images for the classification of different stages of Alzheimer-s disease.

Keywords: Biomedical image processing, classification algorithms, feature extraction, statistical learning.

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Authors: Rahib Abiyev, Vasif H. Abiyev, C. Ardil

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In this paper the development of neural network based fuzzy inference system for electricity consumption prediction is considered. The electricity consumption depends on number of factors, such as number of customers, seasons, type-s of customers, number of plants, etc. It is nonlinear process and can be described by chaotic time-series. The structure and algorithms of neuro-fuzzy system for predicting future values of electricity consumption is described. To determine the unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus. The simulation of neuro-fuzzy system has been performed.

Keywords: Fuzzy logic, neural network, neuro-fuzzy system, neuro-fuzzy prediction.

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Authors: Asim Kumar Pal, Debabrata Nath, Sumit Chakraborty

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This paper presents an economic game for sybil detection in a distributed computing environment. Cost parameters reflecting impacts of different sybil attacks are introduced in the sybil detection game. The optimal strategies for this game in which both sybil and non-sybil identities are expected to participate are devised. A cost sharing economic mechanism called Discriminatory Rewarding Mechanism for Sybil Detection is proposed based on this game. A detective accepts a security deposit from each active agent, negotiates with the agents and offers rewards to the sybils if the latter disclose their identity. The basic objective of the detective is to determine the optimum reward amount for each sybil which will encourage the maximum possible number of sybils to reveal themselves. Maintaining privacy is an important issue for the mechanism since the participants involved in the negotiation are generally reluctant to share their private information. The mechanism has been applied to Tor by introducing a reputation scoring function.

Keywords: Game theory, Incentive mechanism, Reputation, Sybil Attack

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Authors: Mustafa Abdalla, Khaled A. Madi, Rajab Farhat

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An array antenna system with innovative signal processing can improve the resolution of a source direction of arrival (DoA) estimation. High resolution techniques take the advantage of array antenna structures to better process the incoming waves. They also have the capability to identify the direction of multiple targets. This paper investigates performance of the DOA estimation algorithm namely; Capon and MUSIC on the uniform linear array (ULA). The simulation results show that in Capon and MUSIC algorithm the resolution of the DOA techniques improves as number of snapshots, number of array elements, signal-to-noise ratio and separation angle between the two sources θ increases.

Keywords: Antenna array, Capon, MUSIC, Direction-of-arrival estimation, signal processing, uniform linear arrays.

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Authors: Gkolfo I. Smani, Vasileios Megalooikonomou

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Influence and influence diffusion have been studied extensively in social networks. However, most existing literature on this task are limited on static networks, ignoring the fact that the interactions between users change over time. In this paper, the problem of maximizing influence diffusion in dynamic social networks, i.e., the case of networks that change over time is studied. The DM algorithm is an extension of Matrix Influence (MATI) algorithm and solves the Influence Maximization (IM) problem in dynamic networks and is proposed under the Linear Threshold (LT) and Independent Cascade (IC) models. Experimental results show that our proposed algorithm achieves a diffusion performance better by 1.5 times than several state-of-the-art algorithms and comparable results in diffusion scale with the Greedy algorithm. Also, the proposed algorithm is 2.4 times faster than previous methods.

Keywords: Influence maximization, dynamic social networks, diffusion, social influence.

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Authors: Aziz Sezgin

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We consider the problem of stabilization of an unstable heat equation in a 2-D, 3-D and generally n-D domain by deriving a generalized backstepping boundary control design methodology. To stabilize the systems, we design boundary backstepping controllers inspired by the 1-D unstable heat equation stabilization procedure. We assume that one side of the boundary is hinged and the other side is controlled for each direction of the domain. Thus, controllers act on two boundaries for 2-D domain, three boundaries for 3-D domain and ”n” boundaries for n-D domain. The main idea of the design is to derive ”n” controllers for each of the dimensions by using ”n” kernel functions. Thus, we obtain ”n” controllers for the ”n” dimensional case. We use a transformation to change the system into an exponentially stable ”n” dimensional heat equation. The transformation used in this paper is a generalized Volterra/Fredholm type with ”n” kernel functions for n-D domain instead of the one kernel function of 1-D design.

Keywords: Backstepping, boundary control, 2-D, 3-D, n-D heat equation, distributed parameter systems.

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Authors: A. Taheri

Abstract:

Inspired by topology of humpback whale flippers, a meta-model is designed for wing planform design. The net is trained based on experimental data using cascade-forward artificial neural network (ANN) to investigate effects of the amplitude and wavelength of sinusoidal leading edge configurations on the wing performance. Afterwards, the trained ANN is coupled with a genetic algorithm method towards an optimum design strategy. Finally, flow physics of the problem for an optimized rectangular planform and also a real flipper geometry planform is simulated using Lam-Bremhorst low Reynolds number turbulence model with damping wall-functions resolving to the wall. Lift and drag coefficients and also details of flow are presented along with comparisons to available experimental data. Results show that the proposed strategy can be adopted with success as a fast-estimation tool for performance prediction of wing planforms with wavy leading edge at preliminary design phase.  

Keywords: Humpback whale flipper, cascade-forward ANN, GA, CFD, Bionics.

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Authors: Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C. Ardil

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Minimization methods for training feed-forward networks with Backpropagation are compared. Feedforward network training is a special case of functional minimization, where no explicit model of the data is assumed. Therefore due to the high dimensionality of the data, linearization of the training problem through use of orthogonal basis functions is not desirable. The focus is functional minimization on any basis. A number of methods based on local gradient and Hessian matrices are discussed. Modifications of many methods of first and second order training methods are considered. Using share rates data, experimentally it is proved that Conjugate gradient and Quasi Newton?s methods outperformed the Gradient Descent methods. In case of the Levenberg-Marquardt algorithm is of special interest in financial forecasting.

Keywords: Backpropagation algorithm, conjugacy condition, line search, matrix perturbation

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Authors: M.Puviyarasan, C.Praveen

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In this study, Friction Stir Processing (FSP) a recent grain refinement technique was employed to disperse micron-sized (2 *m) SiCp particles into aluminum alloy AA6063. The feasibility to fabricate bulk composites through FSP was analyzed and experiments were conducted at different traverse speeds and wider volumes of the specimens. Micro structural observation were carried out by employing optical microscopy test of the cross sections in both parallel and perpendicular to the tool traverse direction. Mechanical property including micro hardness was evaluated in detail at various regions on the specimen. The composites had an excellent bonding with aluminum alloy substrate and a significant increase of 30% in the micro hardness value of metal matrix composite (MMC) as to that of the base metal has observed. The observations clearly indicate that SiC particles were uniformly distributed within the aluminum matrix.

Keywords: Friction Stir Processing, Metal matrix composite, Bulk composite.

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Authors: S.F. Faisal, A.H.M.A. Rahim, J.M. Bakhashwain

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Design of a fixed parameter robust STATCOM controller for a multi-machine power system through an H-? based loop-shaping procedure is presented. The trial and error part of the graphical loop-shaping procedure has been eliminated by embedding a particle swarm optimization (PSO) technique in the design loop. Robust controllers were designed considering the detailed dynamics of the multi-machine system and results were compared with reduced order models. The robust strategy employing loop-shaping and PSO algorithms was observed to provide very good damping profile for a wide range of operation and for various disturbance conditions. 

Keywords: STATCOM, Robust control, Power system damping, Particle Swarm Optimization, Loop-shaping.

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Authors: Wahiba Mohguen, Raïs El’hadi Bekka

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This paper presents a denoising method called EMD-Custom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE).

Keywords: Customized thresholding, ECG signal, EMD, hard thresholding, Soft-thresholding.

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Authors: Nader Gharibnavaz, Mostafa Mosadeghi, Naser Gharibnavaz

Abstract:

In today's world where everything is rapidly changing and information technology is high in development, many features of culture, society, politic and economy has changed. The advent of information technology and electronic data transmission lead to easy communication and fields like e-learning and e-commerce, are accessible for everyone easily. One of these technologies is virtual training. The "quality" of such kind of education systems is critical. 131 questionnaires were prepared and distributed among university student in Toba University. So the research has followed factors that affect the quality of learning from the perspective of staff, students, professors and this type of university. It is concluded that the important factors in virtual training are the quality of professors, the quality of staff, and the quality of the university. These mentioned factors were the most prior factors in this education system and necessary for improving virtual training.

Keywords: Training , Virtual Training, Strategic Positioning, Positioning Mapping, Unique Selling Proposition, Strong Brands, Indoors industry

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Authors: Erik Lilienblum, Robert Niese, Bernd Michaelis

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This paper proposes a novel stereo vision technique for top view book scanners which provide us with dense 3d point clouds of page surfaces. This is a precondition to dewarp bound volumes independent of 2d information on the page. Our method is based on algorithms, which normally require the projection of pattern sequences with structured light. We use image sequences of the moving stripe lighting of the top view scanner instead of an additional light projection. Thus the stereo vision setup is simplified without losing measurement accuracy. Furthermore we improve a surface model dewarping method through introducing a difference vector based on real measurements. Although our proposed method is hardly expensive neither in calculation time nor in hardware requirements we present good dewarping results even for difficult examples.

Keywords: stereo vision, 3d surface reconstruction, dewarpingdocuments, book scanner

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Authors: M. Vatani, A. R. Rahimi, F. Brazandeh, A. Sanati nezhad

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Although the STL (stereo lithography) file format is widely used as a de facto industry standard in the rapid prototyping industry due to its simplicity and ability to tessellation of almost all surfaces, but there are always some defects and shortcoming in their usage, which many of them are difficult to correct manually. In processing the complex models, size of the file and its defects grow extremely, therefore, correcting STL files become difficult. In this paper through optimizing the exiting algorithms, size of the files and memory usage of computers to process them will be reduced. In spite of type and extent of the errors in STL files, the tail-to-head searching method and analysis of the nearest distance between tails and heads techniques were used. As a result STL models sliced rapidly, and fully closed contours produced effectively and errorless.

Keywords: Layer manufacturing, STL files, slicing algorithm, nearest distance analysis.

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Authors: S Hariharan, K Chaitanya, P Muthuchidambaranathan

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The IEEE 802.22 working group aims to drive the Digital Video Broadcasting-Terrestrial (DVB-T) bands for data communication to the rural area without interfering the TV broadcast. In this paper, we arrive at a closed-form expression for average detection probability of Fusion center (FC) with multiple antenna over the κ − μ fading channel model. We consider a centralized cooperative multiple antenna network for reporting. The DVB-T samples forwarded by the secondary user (SU) were combined using Maximum ratio combiner at FC, an energy detection is performed to make the decision. The fading effects of the channel degrades the detection probability of the FC, a generalized independent and identically distributed (IID) κ − μ and an additive white Gaussian noise (AWGN) channel is considered for reporting and sensing respectively. The proposed system performance is verified through simulation results.

Keywords: IEEE 802.22, Cooperative spectrum sensing, Multiple antenna, κ − μ .

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Authors: Emanuele Stomeo, Tatiana Kalganova, Cyrille Lambert

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Evolvable hardware (EHW) refers to a selfreconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). A lot of research has been done in this area several different EA have been introduced. Every time a specific EA is chosen for solving a particular problem, all its components, such as population size, initialization, selection mechanism, mutation rate, and genetic operators, should be selected in order to achieve the best results. In the last three decade a lot of research has been carried out in order to identify the best parameters for the EA-s components for different “test-problems". However different researchers propose different solutions. In this paper the behaviour of mutation rate on (1+λ) evolution strategy (ES) for designing logic circuits, which has not been done before, has been deeply analyzed. The mutation rate for an EHW system modifies values of the logic cell inputs, the cell type (for example from AND to NOR) and the circuit output. The behaviour of the mutation has been analyzed based on the number of generations, genotype redundancy and number of logic gates used for the evolved circuits. The experimental results found provide the behaviour of the mutation rate to be used during evolution for the design and optimization of logic circuits. The researches on the best mutation rate during the last 40 years are also summarized.

Keywords: Evolvable hardware, mutation rate, evolutionarycomputation, design of logic circuit.

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Authors: L. Ashok Kumar, C. Nagarajan

Abstract:

The ultrasound imaging is very popular to diagnosis the disease because of its non-invasive nature. The ultrasound imaging slowly produces low quality images due to the presence of spackle noise and wave interferences. There are several algorithms to be proposed for the segmentation of ultrasound carotid artery images but it requires a certain limit of user interaction. The pixel in an image is highly correlated so the spatial information of surrounding pixels may be considered in the process of image segmentation which improves the results further. When data is highly correlated, one pixel may belong to more than one cluster with different degree of membership. There is an important step to computerize the evaluation of arterial disease severity using segmentation of carotid artery lumen in 2D and 3D ultrasonography and in finding vulnerable atherosclerotic plaques susceptible to rupture which can cause stroke.

Keywords: IMT measurement, Image Segmentation, common carotid artery, internal and external carotid arteries, ultrasound imaging.

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Authors: Omaima Bamasak

Abstract:

This paper presents a novel method that allows an agent host to delegate its signing power to an anonymous mobile agent in such away that the mobile agent does not reveal any information about its host-s identity and, at the same time, can be authenticated by the service host, hence, ensuring fairness of service provision. The solution introduces a verification server to verify the signature generated by the mobile agent in such a way that even if colluding with the service host, both parties will not get more information than what they already have. The solution incorporates three methods: Agent Signature Key Generation method, Agent Signature Generation method, Agent Signature Verification method. The most notable feature of the solution is that, in addition to allowing secure and anonymous signature delegation, it enables tracking of malicious mobile agents when a service host is attacked. The security properties of the proposed solution are analyzed, and the solution is compared with the most related work.

Keywords: Anonymous signature delegation, collusion resistance, e-commerce fairness, mobile agent security.

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Authors: Yanhui Zhang, Wenyu Yang

Abstract:

One of the difficulties of the vibration-based damage identification methods is the nonuniqueness of the results of damage identification. The different damage locations and severity may cause the identical response signal, which is even more severe for detection of the multiple damage. This paper proposes a new strategy for damage detection to avoid this nonuniqueness. This strategy firstly determines the approximates damage area based on the statistical pattern recognition method using the dynamic strain signal measured by the distributed fiber Bragg grating, and then accurately evaluates the damage information based on the Bayesian model updating method using the experimental modal data. The stochastic simulation method is then used to compute the high-dimensional integral in the Bayesian problem. Finally, an experiment of the plate structure, simulating one part of mechanical structure, is used to verify the effectiveness of this approach.

Keywords: Bayesian method, damage detection, fiber Bragg grating, structural health monitoring.

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Authors: T. T. Lu, H.Y. Liao, M .F. Chen

Abstract:

Recently, malware attacks have become more serious over the Internet by e-mail, denial of service (DoS) or distributed denial of service (DDoS). The Botnets have become a significant part of the Internet malware attacks. The traditional botnets include three parts – botmaster, command and control (C&C) servers and bots. The C&C servers receive commands from botmaster and control the distributions of computers remotely. Bots use DNS to find the positions of C&C server. In this paper, we propose an advanced hybrid peer-to-peer (P2P) botnet 2.0 (AHP2P botnet 2.0) using web 2.0 technology to hide the instructions from botmaster into social sites, which are regarded as C&C servers. Servent bots are regarded as sub-C&C servers to get the instructions from social sites. The AHP2P botnet 2.0 can evaluate the performance of servent bots, reduce DNS traffics from bots to C&C servers, and achieve harder detection bots actions than IRC-based botnets over the Internet.

Keywords: Peer-to-peer, Botnets, Botnet 2.0, Hybridpeer-to-peer

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Authors: M. V. Sudhamani, C. R. Venugopal

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This paper deals with the application for contentbased image retrieval to extract color feature from natural images stored in the image database by segmenting the image through clustering. We employ a class of nonparametric techniques in which the data points are regarded as samples from an unknown probability density. Explicit computation of the density is avoided by using the mean shift procedure, a robust clustering technique, which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. A non-parametric technique for the recovery of significant image features is presented and segmentation module is developed using the mean shift algorithm to segment each image. In these algorithms, the only user set parameter is the resolution of the analysis and either gray level or color images are accepted as inputs. Extensive experimental results illustrate excellent performance.

Keywords: Segmentation, Clustering, Image Retrieval, Features.

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Authors: Achuthsankar S. Nair, J. S. Jayasudha

Abstract:

The ever increasing use of World Wide Web in the existing network, results in poor performance. Several techniques have been developed for reducing web traffic by compressing the size of the file, saving the web pages at the client side, changing the burst nature of traffic into constant rate etc. No single method was adequate enough to access the document instantly through the Internet. In this paper, adaptive hybrid algorithms are developed for reducing web traffic. Intelligent agents are used for monitoring the web traffic. Depending upon the bandwidth usage, user-s preferences, server and browser capabilities, intelligent agents use the best techniques to achieve maximum traffic reduction. Web caching, compression, filtering, optimization of HTML tags, and traffic dispersion are incorporated into this adaptive selection. Using this new hybrid technique, latency is reduced to 20 – 60 % and cache hit ratio is increased 40 – 82 %.

Keywords: Bandwidth, Congestion, Intelligent Agents, Prefetching, Web Caching.

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Authors: V. Sudha, D. Sriram Kumar

Abstract:

In this paper, we present a low complexity hybrid scheme using conventional selective mapping (C-SLM) and clipping algorithms to reduce the high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signal. In the proposed scheme, the input data sequence (X) is divided into two sub-blocks, then clipping algorithm is applied to the first sub-block, whereas C-SLM algorithm is applied to the second sub-block in order to reduce both computational complexity and PAPR. The resultant time domain OFDM signal is obtained by combining the output of two sub-blocks. The simulation results show that the proposed hybrid scheme provides 0.45 dB PAPR reduction gain at CCDF value of 10-2 and 52% of computational complexity reduction when compared to C-SLM scheme at the expense of slight degradation in bit error rate (BER) performance.

Keywords: CCDF, Clipping, OFDM, PAPR, SLM.

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Authors: Nhon Do, Tam Pham

Abstract:

Knowledge bases are basic components of expert systems or intelligent computational programs. Knowledge bases provide knowledge, events that serve deduction activity, computation and control. Therefore, researching and developing of models for knowledge representation play an important role in computer science, especially in Artificial Intelligence Science and intelligent educational software. In this paper, the extensive deduction computational model is proposed to design knowledge bases whose attributes are able to be real values or functional values. The system can also solve problems based on knowledge bases. Moreover, the models and algorithms are applied to produce the educational software for solving alternating current problems or solving set of equations automatically.

Keywords: Educational software, artificial intelligence, knowledge base systems, knowledge representation.

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Authors: R. Prabha, N. Ramaraj

Abstract:

Routing in adhoc networks is a challenge as nodes are mobile, and links are constantly created and broken. Present ondemand adhoc routing algorithms initiate route discovery after a path breaks, incurring significant cost to detect disconnection and establish a new route. Specifically, when a path is about to be broken, the source is warned of the likelihood of a disconnection. The source then initiates path discovery early, avoiding disconnection totally. A path is considered about to break when link availability decreases. This study modifies Adhoc On-demand Multipath Distance Vector routing (AOMDV) so that route handoff occurs through link availability estimation.

Keywords: Mobile Adhoc Network (MANET), Routing, Adhoc On-demand Multipath Distance Vector routing (AOMDV), Link Availability.

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Authors: Houda Abadlia, Nadia Smairi, Khaled Ghedira

Abstract:

Multiobjective Particle Swarm Optimization (MOPSO) has shown an effective performance for solving test functions and real-world optimization problems. However, this method has a premature convergence problem, which may lead to lack of diversity. In order to improve its performance, this paper presents a hybrid approach which embedded the MOPSO into the island model and integrated a local search technique, Variable Neighborhood Search, to enhance the diversity into the swarm. Experiments on two series of test functions have shown the effectiveness of the proposed approach. A comparison with other evolutionary algorithms shows that the proposed approach presented a good performance in solving multiobjective optimization problems.

Keywords: Particle swarm optimization, migration, variable neighborhood search, multiobjective optimization.

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Authors: Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

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With the increase in popularity of mobile devices, new and varied forms of malware have emerged. Consequently, the organizations for cyberdefense have echoed the need to deploy more effective defensive schemes adapted to the challenges posed by these recent monitoring environments. In order to contribute to their development, this paper presents a malware detection strategy for mobile devices based on sequence alignment algorithms. Unlike the previous proposals, only the system calls performed during the startup of applications are studied. In this way, it is possible to efficiently study in depth, the sequences of system calls executed by the applications just downloaded from app stores, and initialize them in a secure and isolated environment. As demonstrated in the performed experimentation, most of the analyzed malicious activities were successfully identified in their boot processes.

Keywords: Android, information security, intrusion detection systems, malware, mobile devices.

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Authors: Paulo Gomes, Adelaide Figueiredo

Abstract:

We consider n individuals described by p standardized variables, represented by points of the surface of the unit hypersphere S_n-1. For a previous choice of n individuals we suppose that the set of observables variables comes from a mixture of bipolar Watson distribution defined on the hypersphere. EM and Dynamic Clusters algorithms are used for identification of such mixture. We obtain estimates of parameters for each Watson component and then a partition of the set of variables into homogeneous groups of variables. Additionally we will present a factor analysis model where unobservable factors are just the maximum likelihood estimators of Watson directional parameters, exactly the first principal component of data matrix associated to each group previously identified. Such alternative model it will yield us to directly interpretable solutions (simple structure), avoiding factors rotations.

Keywords: Dynamic Clusters algorithm, EM algorithm, Factor analysis model, Hierarchical Clustering, Watson distribution.

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