Search results for: Outlier removal algorithms

Authors: P.Saravanan, Narayanan .C.K., P.V.S.S Prakash, Prabhakara Rao .G.V

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Video Mosaicing is the stitching of selected frames of a video by estimating the camera motion between the frames and thereby registering successive frames of the video to arrive at the mosaic. Different techniques have been proposed in the literature for video mosaicing. Despite of the large number of papers dealing with techniques to generate mosaic, only a few authors have investigated conditions under which these techniques generate good estimate of motion parameters. In this paper, these techniques are studied under different videos, and the reasons for failures are found. We propose algorithms with incorporation of outlier removal algorithms for better estimation of motion parameters.

Keywords: Motion parameters, Outlier removal algorithms, Registering , and Video Mosaicing.

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Authors: A. M. Fahim, G. Saake, A. M. Salem, F. A. Torkey, M. A. Ramadan

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Data clustering is an important data exploration technique with many applications in data mining. We present an enhanced version of the well known single link clustering algorithm. We will refer to this algorithm as DCBOR. The proposed algorithm alleviates the chain effect by removing the outliers from the given dataset. So this algorithm provides outlier detection and data clustering simultaneously. This algorithm does not need to update the distance matrix, since the algorithm depends on merging the most k-nearest objects in one step and the cluster continues grow as long as possible under specified condition. So the algorithm consists of two phases; at the first phase, it removes the outliers from the input dataset. At the second phase, it performs the clustering process. This algorithm discovers clusters of different shapes, sizes, densities and requires only one input parameter; this parameter represents a threshold for outlier points. The value of the input parameter is ranging from 0 to 1. The algorithm supports the user in determining an appropriate value for it. We have tested this algorithm on different datasets contain outlier and connecting clusters by chain of density points, and the algorithm discovers the correct clusters. The results of our experiments demonstrate the effectiveness and the efficiency of DCBOR.

Keywords: Data Clustering, Clustering Algorithms, Handling Noise, Arbitrary Shape of Clusters.

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Authors: P. Ashok, G. M. Kadhar Nawaz

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Rough set theory is used to handle uncertainty and incomplete information by applying two accurate sets, Lower approximation and Upper approximation. In this paper, the rough clustering algorithms are improved by adopting the Similarity, Dissimilarity–Similarity and Entropy based initial centroids selection method on three different clustering algorithms namely Entropy based Rough K-Means (ERKM), Similarity based Rough K-Means (SRKM) and Dissimilarity-Similarity based Rough K-Means (DSRKM) were developed and executed by yeast dataset. The rough clustering algorithms are validated by cluster validity indexes namely Rand and Adjusted Rand indexes. An experimental result shows that the ERKM clustering algorithm perform effectively and delivers better results than other clustering methods. Outlier detection is an important task in data mining and very much different from the rest of the objects in the clusters. Entropy based Rough Outlier Factor (EROF) method is seemly to detect outlier effectively for yeast dataset. In rough K-Means method, by tuning the epsilon (ᶓ) value from 0.8 to 1.08 can detect outliers on boundary region and the RKM algorithm delivers better results, when choosing the value of epsilon (ᶓ) in the specified range. An experimental result shows that the EROF method on clustering algorithm performed very well and suitable for detecting outlier effectively for all datasets. Further, experimental readings show that the ERKM clustering method outperformed the other methods.

Keywords: Clustering, Entropy, Outlier, Rough K-Means, validity index.

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Authors: Zalizah Awang Long, Abdul Razak Hamdan, Azuraliza Abu Bakar

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Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.

Keywords: Outlier detection, frequent-outlier, outbreak, anomaly, surveillance, public health

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: Outlier detection, generative adversary networks, semi-supervised learning.

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Authors: Sun H. Hwang, Young E. Lee, Yunhan Ga, Gilwon Yoon

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EEG is a very complex signal with noises and other bio-potential interferences. EOG is the most distinct interfering signal when EEG signals are measured and analyzed. It is very important how to process raw EEG signals in order to obtain useful information. In this study, the EEG signal processing techniques such as EOG filtering and outlier removal were examined to minimize unwanted EOG signals and other noises. The two different mental states of resting and focusing were examined through EEG analysis. A focused state was induced by letting subjects to watch a red dot on the white screen. EEG data for 32 healthy subjects were measured. EEG data after 60-Hz notch filtering were processed by a commercially available EOG filtering and our presented algorithm based on the removal of outliers. The ratio of beta wave to theta wave was used as a parameter for determining the degree of focusing. The results show that our algorithm was more appropriate than the existing EOG filtering.

Keywords: EEG, focus, mental state, outlier, signal processing.

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Authors: Yogita, Durga Toshniwal

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Outlier detection in streaming data is very challenging because streaming data cannot be scanned multiple times and also new concepts may keep evolving. Irrelevant attributes can be termed as noisy attributes and such attributes further magnify the challenge of working with data streams. In this paper, we propose an unsupervised outlier detection scheme for streaming data. This scheme is based on clustering as clustering is an unsupervised data mining task and it does not require labeled data, both density based and partitioning clustering are combined for outlier detection. In this scheme partitioning clustering is also used to assign weights to attributes depending upon their respective relevance and weights are adaptive. Weighted attributes are helpful to reduce or remove the effect of noisy attributes. Keeping in view the challenges of streaming data, the proposed scheme is incremental and adaptive to concept evolution. Experimental results on synthetic and real world data sets show that our proposed approach outperforms other existing approach (CORM) in terms of outlier detection rate, false alarm rate, and increasing percentages of outliers.

Keywords: Concept Evolution, Irrelevant Attributes, Streaming Data, Unsupervised Outlier Detection.

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Authors: Nabil M. Hewahi, Motaz K. Saad

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In large datasets, identifying exceptional or rare cases with respect to a group of similar cases is considered very significant problem. The traditional problem (Outlier Mining) is to find exception or rare cases in a dataset irrespective of the class label of these cases, they are considered rare events with respect to the whole dataset. In this research, we pose the problem that is Class Outliers Mining and a method to find out those outliers. The general definition of this problem is “given a set of observations with class labels, find those that arouse suspicions, taking into account the class labels". We introduce a novel definition of Outlier that is Class Outlier, and propose the Class Outlier Factor (COF) which measures the degree of being a Class Outlier for a data object. Our work includes a proposal of a new algorithm towards mining of the Class Outliers, presenting experimental results applied on various domains of real world datasets and finally a comparison study with other related methods is performed.

Keywords: Class Outliers, Distance-Based Approach, Outliers Mining.

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Authors: Kalum Priyanath Udagepola, Zuo Decheng, Wu Zhibo, Yang Xiaozong

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Modern spatial database management systems require a unique Spatial Access Method (SAM) in order solve complex spatial quires efficiently. In this case the spatial data structure takes a prominent place in the SAM. Inadequate data structure leads forming poor algorithmic choices and forging deficient understandings of algorithm behavior on the spatial database. A key step in developing a better semantic spatial object data structure is to quantify the performance effects of semantic and outlier detections that are not reflected in the previous tree structures (R-Tree and its variants). This paper explores a novel SSRO-Tree on SAM to the Topo-Semantic approach. The paper shows how to identify and handle the semantic spatial objects with outlier objects during page overflow/underflow, using gain/loss metrics. We introduce a new SSRO-Tree algorithm which facilitates the achievement of better performance in practice over algorithms that are superior in the R*-Tree and RO-Tree by considering selection queries.

Keywords: Outlier, semantic spatial object, spatial objects, SSRO-Tree, topo-semantic.

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Authors: Hossein Riazoshams, Midi Habshah, Jr., Mohamad Bakri Adam

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The detection of outliers is very essential because of their responsibility for producing huge interpretative problem in linear as well as in nonlinear regression analysis. Much work has been accomplished on the identification of outlier in linear regression, but not in nonlinear regression. In this article we propose several outlier detection techniques for nonlinear regression. The main idea is to use the linear approximation of a nonlinear model and consider the gradient as the design matrix. Subsequently, the detection techniques are formulated. Six detection measures are developed that combined with three estimation techniques such as the Least-Squares, M and MM-estimators. The study shows that among the six measures, only the studentized residual and Cook Distance which combined with the MM estimator, consistently capable of identifying the correct outliers.

Keywords: Nonlinear Regression, outliers, Gradient, LeastSquare, M-estimate, MM-estimate.

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Authors: B. Silva, H. Figueiredo, I. C. Neves, T. Tavares

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Arthrobacter viscosus biomass was used for Cr(VI) biosorption. The effect of pH on Cr(VI) reduction and removal from aqueous solution was studied in the range of 1-4. The Cr(VI) removal involves both redox reaction and adsorption of metal ions on biomass surface. The removal rate of Cr(VI) was enhanced by very acid conditions, while higher solution pH values favored the removal of total chromium. The best removal efficiency and uptake were reached at pH 4, 72.5 % and 12.6 mgCr/gbiomass, respectively.

Keywords: Biosorption, chromium, pH, reduction.

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Authors: Bhaskar Thakker, Anoop Lal Vyas

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Wrist pulse analysis for identification of health status is found in Ancient Indian as well as Chinese literature. The preprocessing of wrist pulse is necessary to remove outlier pulses and fluctuations prior to the analysis of pulse pressure signal. This paper discusses the identification of irregular pulses present in the pulse series and intricacies associated with the extraction of time domain pulse features. An approach of Dynamic Time Warping (DTW) has been utilized for the identification of outlier pulses in the wrist pulse series. The ambiguity present in the identification of pulse features is resolved with the help of first derivative of Ensemble Average of wrist pulse series. An algorithm for detecting tidal and dicrotic notch in individual wrist pulse segment is proposed.

Keywords: Wrist Pulse Segment, Ensemble Average, Dynamic Time Warping (DTW), Pulse Similarity Vector.

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Authors: Rainer Schneider, Stephan Lau, Levin Kuhlmann, Simon Vogrin, Maciej Gratkowski, Mark Cook, Jens Haueisen

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Cardiac pulse-related artifacts in the EEG recorded simultaneously with fMRI are complex and highly variable. Their effective removal is an unsolved problem. Our aim is to develop an adaptive removal algorithm based on the matching pursuit (MP) technique and to compare it to established methods using a visual evoked potential (VEP). We recorded the VEP inside the static magnetic field of an MR scanner (with artifacts) as well as in an electrically shielded room (artifact free). The MP-based artifact removal outperformed average artifact subtraction (AAS) and optimal basis set removal (OBS) in terms of restoring the EEG field map topography of the VEP. Subsequently, a dipole model was fitted to the VEP under each condition using a realistic boundary element head model. The source location of the VEP recorded inside the MR scanner was closest to that of the artifact free VEP after cleaning with the MP-based algorithm as well as with AAS. While none of the tested algorithms offered complete removal, MP showed promising results due to its ability to adapt to variations of latency, frequency and amplitude of individual artifact occurrences while still utilizing a common template.

Keywords: matching pursuit, ballistocardiogram, artifactremoval, EEG/fMRI.

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Authors: Bashar Al-Shboul, Sung-Hyon Myaeng

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K-Means (KM) is considered one of the major algorithms widely used in clustering. However, it still has some problems, and one of them is in its initialization step where it is normally done randomly. Another problem for KM is that it converges to local minima. Genetic algorithms are one of the evolutionary algorithms inspired from nature and utilized in the field of clustering. In this paper, we propose two algorithms to solve the initialization problem, Genetic Algorithm Initializes KM (GAIK) and KM Initializes Genetic Algorithm (KIGA). To show the effectiveness and efficiency of our algorithms, a comparative study was done among GAIK, KIGA, Genetic-based Clustering Algorithm (GCA), and FCM [19].

Keywords: Clustering, Genetic Algorithms, K-means.

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Authors: Hazem M. Al-Mofleh, John E. Daniels, Joseph W. McKean

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In this paper numerous robust fitting procedures are considered in estimating spatial variograms. In spatial statistics, the conventional variogram fitting procedure (non-linear weighted least squares) suffers from the same outlier problem that has plagued this method from its inception. Even a 3-parameter model, like the variogram, can be adversely affected by a single outlier. This paper uses the Hogg-Type adaptive procedures to select an optimal score function for a rank-based estimator for these non-linear models. Numeric examples and simulation studies will demonstrate the robustness, utility, efficiency, and validity of these estimates.

Keywords: Asymptotic relative efficiency, non-linear rank-based, robust, rank estimates, variogram.

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Authors: Nermen N. Maximous, George F. Nakhla, W. K. Wan

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This research aimed at investigating the Cr (III), Cd (II) and Pb (II) removal efficiencies by using the newly synthesized metal oxides/ polyethersulfone (PES), Al2O3/PES and ZrO2/PES, membranes from synthetic wastewater and exploring fouling mechanisms. A Comparative study between the removal efficiencies of Cr (III), Cd (II) and Pb (II) from synthetic and natural wastewater by using adsorption onto agricultural by products and the newly synthesized Al2O3/PES and ZrO2/PES membranes was conducted to assess the advantages and limitations of using the metal oxides/PES membranes for heavy metals removal. The results showed that about 99 % and 88 % removal efficiencies were achieved by the tested membranes for Pb (II) and Cr (III), respectively.

Keywords: Adsorption, metals removal, ultrafiltrationmembranes, wastewater

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Authors: Jerina Majeed, Jayshree Ramkumar, S. Chandramouleeswaran, A. K. Tyagi

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Removal of various toxic species from aqueous streams is of great importance. Sorption is one of the important remediation procedures as it involves the use of cheap and easily available materials. Also the advantage of regeneration of the sorbent involves the possibility of using novel sorbents. Nanosorbents are very important as the removal is based on the surface phenomena and this is greatly affected by surface charge and area. Functionalization has been very important to bring about the removal of metal ions with greater selectivity.

Keywords: Mercury, lead, thiol functionalization, ZnO NPs.

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Authors: Mohammad A. Talaat, Magdi A. Koutb, Hoda S. Sorour

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In this paper we investigated a number of the Internet congestion control algorithms that has been developed in the last few years. It was obviously found that many of these algorithms were designed to deal with the Internet traffic merely as a train of consequent packets. Other few algorithms were specifically tailored to handle the Internet congestion caused by running media traffic that represents audiovisual content. This later set of algorithms is considered to be aware of the nature of this media content. In this context we briefly explained a number of congestion control algorithms and hence categorized them into the two following categories: i) Media congestion control algorithms. ii) Common congestion control algorithms. We hereby recommend the usage of the media congestion control algorithms for the reason of being media content-aware rather than the other common type of algorithms that blindly manipulates such traffic. We showed that the spread of such media content-aware algorithms over Internet will lead to better congestion control status in the coming years. This is due to the observed emergence of the era of digital convergence where the media traffic type will form the majority of the Internet traffic.

Keywords: Congestion Control, Media Traffic.

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Authors: Junior B. N. Adohinzin, Ling Xu

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Nitrogen is among the main nutrients encouraging the growth of organic matter and algae which cause eutrophication in water bodies. Therefore, its removal from wastewater has become a worldwide emerging concern. In this research, an innovative Membrane Bioreactor (MBR) system named “moving bed membrane bioreactor (MBMBR)” was developed and investigated under intermittently-aerated mode for simultaneous removal of organic carbon and nitrogen.

Results indicated that the variation of the intermittently aerated duration did not have an apparent impact on COD and NH₄⁺–N removal rate, yielding the effluent with average COD and NH₄⁺–N removal efficiency of more than 92 and 91% respectively. However, in the intermittently aerated cycle of (continuously aeration/0s mix), (aeration 90s/mix 90s) and (aeration 90s/mix 180s); the average TN removal efficiency was 67.6%, 69.5% and 87.8% respectively. At the same time, their nitrite accumulation rate was 4.5%, 49.1% and 79.4% respectively. These results indicate that the intermittently aerated mode is an efficient way to controlling the nitrification to stop at nitrition; and also the length of anoxic duration is a key factor in improving TN removal.

Keywords: Membrane bioreactor (MBR), Moving bed biofilm reactor (MBBR), Nutrients removal, Simultaneous nitrification and denitrification.

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Authors: V. Tanboonchuy, J.C. Hsu, N. Grisdanurak, C.H. Liao

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This study focused on arsenate removal by nano zero-valent iron (NZVI) in the gas-bubbled aqueous solution. It appears that solution acidified by H2SO4 is far more favorable than by CO2-bubbled acidification. In addition, as dissolved oxygen was stripped out of solution by N2 gas bubbling, the arsenate removal dropped significantly. To take advantages of common practice of carbonation and oxic condition, pretreatment of CO2 and air bubbling in sequence are recommended for a better removal of arsenate.

Keywords: Arsenic, arsenate, zero-valent iron.

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Authors: H. Jusoh, N. Sapari, R.Z. Raja Azie

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Iron in groundwater is one of the problems that render the water unsuitable for drinking. The concentration above 0.3 mg/L is common in groundwater. The conventional method of removal is by precipitation under oxic condition. In this study, iron removal under anaerobic conditions was examined by batch experiment as a main purpose. The process involved by purging of groundwater samples with H2S to form iron sulfide. Removal up to 83% for 1 mg/L iron solution was achieved. The removal efficiency dropped to 82% and 75% for the higher initial iron concentrations 3.55 and 5.01 mg/L, respectively. The average residual sulfide concentration in water after the process was 25*g/L. The Eh level during the process was -272 mV. The removal process was found to follow the first order reaction with average rate constant of 4.52 x 10-3. The half-life for the concentrations to reduce from initial values was 157 minutes.

Keywords: Anaerobic, chemical kinetics, hydrogen sulfide, iron, rate constant

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Authors: S. H. Mirhossaini, H. Godini, A. Jafari

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Biological Ammonia removal (nitrification), the oxidation of ammonia to nitrate catalyzed by bacteria, is a key part of global nitrogen cycling. In the first step of nitrification, chemolithoautotrophic ammonia oxidizer transform ammonia to nitrite, this subsequently oxidized to nitrate by nitrite oxidizing bacteria. This process can be affected by several factors. In this study the effect of influent COD on biological ammonia removal in a bench-scale biological reactor was investigated. Experiments were carried out using synthetic wastewater. The initial ammonium concentration was 25mgNH4 +-N L-1. The effect of COD between 247.55±1.8 and 601.08±3.24mgL-1 on biological ammonia removal was investigated by varying the COD loading supplied to reactor. From the results obtained in this study it could be concluded in the range of 247.55±1.8 to 351.35±2.05mgL-1, there is a direct relationship between amount of COD and ammonia removal. However more than 351.35±2.05 up to 601.08±3.24mgL-1 were found an indirect relationship between them.

Keywords: Ammonia biological removal, Nitrification, InfluentCOD.

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Authors: Kartikaningsih Danis, Yao-Hui Huang

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Various techniques including conventional and advanced have been employed for the boron treatment from water and wastewater. The electrocoagulation involves an electrolytic reactor for coagulation/flotation with aluminium as anode and cathode. There is aluminium as coagulant to be used for removal which may induce secondary pollution in chemical coagulation. The purpose of this study is to investigate and compare the performance between electrocoagulation and chemical coagulation on boron removal from synthetic wastewater. The effect of different parameters, such as pH reaction, coagulant dosage, and initial boron concentration were examined. The results show that the boron removal using chemical coagulation was lower. At the optimum condition (e.g. pH 8 and 0.8 mol coagulant dosage), boron removal efficiencies for chemical coagulation and electrocoagulation were 61% and 91%, respectively. In addition, the electrocoagulation needs no chemical reagents and makes the boron treatment easy for application.

Keywords: Electrocoagulation, chemical coagulation, aluminum electrode, boron removal.

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Authors: S. K. Satpathy, S. Panda, K. K. Nagwanshi, S. K. Nayak, C. Ardil

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Removing noise from the any processed images is very important. Noise should be removed in such a way that important information of image should be preserved. A decisionbased nonlinear algorithm for elimination of band lines, drop lines, mark, band lost and impulses in images is presented in this paper. The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and evaluation of new pixels for replacing the corrupted pixels. Removal of these artifacts is achieved without damaging edges and details. However, the restricted window size renders median operation less effective whenever noise is excessive in that case the proposed algorithm automatically switches to mean filtering. The performance of the algorithm is analyzed in terms of Mean Square Error [MSE], Peak-Signal-to-Noise Ratio [PSNR], Signal-to-Noise Ratio Improved [SNRI], Percentage Of Noise Attenuated [PONA], and Percentage Of Spoiled Pixels [POSP]. This is compared with standard algorithms already in use and improved performance of the proposed algorithm is presented. The advantage of the proposed algorithm is that a single algorithm can replace several independent algorithms which are required for removal of different artifacts.

Keywords: Filtering, Decision Based Algorithm, noise, imagerestoration.

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Authors: S. Potivichayanon, R. Kitleartpornpairoat

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The objectives were to identify cyanide-degrading bacteria and study cyanide removal efficiency. Agrobacterium tumefaciens SUTS 1 was isolated. This is a new strain of microorganisms for cyanide degradation. The maximum growth rate of SUTS 1 obtained 4.7 × 108 CFU/ml within 4 days. The cyanide removal efficiency was studied at 25, 50, and 150 mg/L cyanide. The residual cyanide, ammonia, nitrate, nitrite, pH, and cell counts were analyzed. At 25 and 50 mg/L cyanide, SUTS 1 obtained similar removal efficiency approximately 87.50%. At 150 mg/L cyanide, SUTS 1 enhanced the cyanide removal efficiency up to 97.90%. Cell counts of SUTS 1 increased when the cyanide concentration was set at lower. The ammonia increased when the removal efficiency increased. The nitrate increased when the ammonia decreased but the nitrite did not detect in all experiments. pH values also increased when the cyanide concentrations were set at higher.

Keywords: Biodegradation, Cyanide-degrading bacteria, Removal efficiency, Residual cyanide

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Authors: M. Donmez, F. Akbal

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In this study arsenate [As(V)] removal from drinking water by coagulation process was investigated. Ferric chloride (FeCl3.6H2O) and ferrous sulfate (FeSO4.7H2O) were used as coagulant. The effects of major operating variables such as coagulant dose (1–30 mg/L) and pH (5.5–9.5) were investigated. Ferric chloride and ferrous sulfate were found as effective and reliable coagulant due to required dose, residual arsenate and coagulant concentration. Optimum pH values for maximum arsenate removal for ferrous sulfate and ferric chloride were found as 8 and 7.5. The arsenate removal efficiency decreased at neutral and acidic pH values for Fe(II) and at the high acidic and high alkaline pH for Fe(III). It was found that the increase of coagulant dose caused a substantial increase in the arsenate removal. But above a certain ferric chloride and ferrous sulfate dosage, the increase in arsenate removal was not significant. Ferric chloride and ferrous sulfate dose above 8 mg/L slightly increased arsenate removal.

Keywords: Arsenic removal, coagulation, ıron salts, drinking water.

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Authors: K. Sontaya, B. Pitiyont, V. Punsuvon

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The objective of this study is to investigate the performance of the electrocoagulation process for color and COD removal in palm oil wastewater using a 10 L batch reactor. Iron was used as electrodes and the distance between electrodes was 2 cm. The effects of operating parameters: current voltage (6, 12 and 18 volt), reaction time (5, 15, 30, 45 and 60 min) and initial pH (4 and 9) of treatment efficiency were examine. The result showed that decolorization and COD removal efficiency increased with the increase in current voltage and reaction time. The proper condition for decolorization achieved at initial pH 4 and 9 were current voltage of 12 volt, reaction time 30 min. The decolorization efficiency reached 90.4% and 88.9%, respectively. COD removal was achiveved at current voltage 12 volt, reaction time 15 min. COD removal efficiency was 89.2 % and 83.0%, respectively. From the results, to show electrocoagulation process can treat palm oil mill wastewater in both acidic and basic condition at high efficiency for color and COD removal. Consequently, electrocoagulation process can be used or applied as a post-treatment step to improve the quality of the final discharge in term of color and residual COD removal.

Keywords: COD removal, decolorizaton, electrocoagulation, iron electrode, palm oil mill wastewater.

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Authors: Lai Lai Win Kyi, Nay Min Tun

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Sorting appears the most attention among all computational tasks over the past years because sorted data is at the heart of many computations. Sorting is of additional importance to parallel computing because of its close relation to the task of routing data among processes, which is an essential part of many parallel algorithms. Many parallel sorting algorithms have been investigated for a variety of parallel computer architectures. In this paper, three parallel sorting algorithms have been implemented and compared in terms of their overall execution time. The algorithms implemented are the odd-even transposition sort, parallel merge sort and parallel rank sort. Cluster of Workstations or Windows Compute Cluster has been used to compare the algorithms implemented. The C# programming language is used to develop the sorting algorithms. The MPI (Message Passing Interface) library has been selected to establish the communication and synchronization between processors. The time complexity for each parallel sorting algorithm will also be mentioned and analyzed.

Keywords: Cluster of Workstations, Parallel sorting algorithms, performance analysis, parallel computing and MPI.

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Authors: Walid Aydi, Nouri Masmoudi, Lotfi Kamoun

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Images of human iris contain specular highlights due to the reflective properties of the cornea. This corneal reflection causes many errors not only in iris and pupil center estimation but also to locate iris and pupil boundaries especially for methods that use active contour. Each iris recognition system has four steps: Segmentation, Normalization, Encoding and Matching. In order to address the corneal reflection, a novel reflection removal method is proposed in this paper. Comparative experiments of two existing methods for reflection removal method are evaluated on CASIA iris image databases V3. The experimental results reveal that the proposed algorithm provides higher performance in reflection removal.

Keywords: iris, pupil, specular highlights, reflection removal

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Authors: Z. Abdullah, A. R. Hamdan

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Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the area in data mining and it can be classified into partition, hierarchical, density based and grid based. Therefore, in this paper we do survey and review four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems as well as deriving more robust and scalable algorithms for clustering.

Keywords: Clustering, method, algorithm, hierarchical, survey.

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