Search results for: random forest classifier

Authors: Wafa' S.Al-Sharafat, Reyadh Naoum

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Society has grown to rely on Internet services, and the number of Internet users increases every day. As more and more users become connected to the network, the window of opportunity for malicious users to do their damage becomes very great and lucrative. The objective of this paper is to incorporate different techniques into classier system to detect and classify intrusion from normal network packet. Among several techniques, Steady State Genetic-based Machine Leaning Algorithm (SSGBML) will be used to detect intrusions. Where Steady State Genetic Algorithm (SSGA), Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and Zeroth Level Classifier system are investigated in this research. SSGA is used as a discovery mechanism instead of SGA. SGA replaces all old rules with new produced rule preventing old good rules from participating in the next rule generation. Zeroth Level Classifier System is used to play the role of detector by matching incoming environment message with classifiers to determine whether the current message is normal or intrusion and receiving feedback from environment. Finally, in order to attain the best results, Modified SSGA will enhance our discovery engine by using Fuzzy Logic to optimize crossover and mutation probability. The experiments and evaluations of the proposed method were performed with the KDD 99 intrusion detection dataset.

Keywords: MSSGBML, Network Intrusion Detection, SGA, SSGA.

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Authors: Yasmin Yahya, Roslan Ismail, Samreth Vanna, Khorn Saret

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The study on the tree growth for four species groups of commercial timber in Koh Kong province, Cambodia-s tropical rainforest is described. The simulation for these four groups had been successfully developed in the 5-year interval through year-60. Data were obtained from twenty permanent sample plots in the duration of thirteen years. The aim for this study was to develop stand table simulation system of tree growth by the species group. There were five steps involved in the development of the tree growth simulation: aggregate the tree species into meaningful groups by using cluster analysis; allocate the trees in the diameter classes by the species group; observe the diameter movement of the species group. The diameter growth rate, mortality rate and recruitment rate were calculated by using some mathematical formula. Simulation equation had been created by combining those parameters. Result showed the dissimilarity of the diameter growth among species groups.

Keywords: cluster analysis, diameter growth, simulation

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: Crime prediction, machine learning, public safety, smart city.

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Authors: Mohammad Tahaye Abadi

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The present paper concerns with the influence of fiber packing on the transverse plastic properties of metal matrix composites. A micromechanical modeling procedure is used to predict the effective mechanical properties of composite materials at large tensile and compressive deformations. Microstructure is represented by a repeating unit cell (RUC). Two fiber arrays are considered including ideal square fiber packing and random fiber packing defined by random sequential algorithm. The micromechanical modeling procedure is implemented for graphite/aluminum metal matrix composite in which the reinforcement behaves as elastic, isotropic solids and the matrix is modeled as an isotropic elastic-plastic solid following the von Mises criterion with isotropic hardening and the Ramberg-Osgood relationship between equivalent true stress and logarithmic strain. The deformation is increased to a considerable value to evaluate both elastic and plastic behaviors of metal matrix composites. The yields strength and true elastic-plastic stress are determined for graphite/aluminum composites.

Keywords: Fiber packing, metal matrix composites, micromechanics, plastic deformation, random

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Authors: B. Chemali, B. Tiliouine

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This article presents the main results of a numerical investigation on the uncertainty of dynamic response of structures with statistically correlated random damping Gamma distributed. A computational method based on a Linear Statistical Model (LSM) is implemented to predict second order statistics for the response of a typical industrial building structure. The significance of random damping with correlated parameters and its implications on the sensitivity of structural peak response in the neighborhood of a resonant frequency are discussed in light of considerable ranges of damping uncertainties and correlation coefficients. The results are compared to those generated using Monte Carlo simulation techniques. The numerical results obtained show the importance of damping uncertainty and statistical correlation of damping coefficients when obtaining accurate probabilistic estimates of dynamic response of structures. Furthermore, the effectiveness of the LSM model to efficiently predict uncertainty propagation for structural dynamic problems with correlated damping parameters is demonstrated.

Keywords: Correlated random damping, linear statistical model, Monte Carlo simulation, uncertainty of dynamic response.

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Authors: Alessandro Barbiero

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This paper proposes, implements and evaluates an original discretization method for continuous random variables, in order to estimate the reliability of systems for which stress and strength are defined as complex functions, and whose reliability is not derivable through analytic techniques. This method is compared to other two discretizing approaches appeared in literature, also through a comparative study involving four engineering applications. The results show that the proposal is very efficient in terms of closeness of the estimates to the true (simulated) reliability. In the study we analyzed both a normal and a non-normal distribution for the random variables: this method is theoretically suitable for each parametric family.

Keywords: Approximation, asymmetry, experimental design, interference theory, Monte Carlo simulations.

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Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev

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In this paper, approach to incoherent signal detection in multi-element antenna array are researched and modeled. Two types of useful signals with unknown wavefront were considered: first one, deterministic (Barker code), and second one, random (Gaussian distribution). The derivation of the sufficient statistics took into account the linearity of the antenna array. The performance characteristics and detecting curves are modeled and compared for different useful signals parameters and for different number of elements of the antenna array. Results of researches in case of some additional conditions can be applied to a digital communications systems.

Keywords: Antenna array, detection curves, performance characteristics, quadrature processing, signal detection.

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Authors: Long Jye Sheu, Horng-Shing Chiou, Wei Ching Chen

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We propose a new perspective on speech communication using blind source separation. The original speech is mixed with key signals which consist of the mixing matrix, chaotic signals and a random noise. However, parts of the keys (the mixing matrix and the random noise) are not necessary in decryption. In practice implement, one can encrypt the speech by changing the noise signal every time. Hence, the present scheme obtains the advantages of a One Time Pad encryption while avoiding its drawbacks in key exchange. It is demonstrated that the proposed scheme is immune against traditional attacks.

Keywords: one time pad, blind source separation, independentcomponent analysis, speech encryption.

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Authors: M. Victor Jose, V. Seenivasagam

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This paper proposes a cost-effective private grid using Object-based Grid Architecture (OGA). In OGA, the data process privacy and inter communication are increased through an object- oriented concept. The limitation of the existing grid is that the user can enter or leave the grid at any time without schedule and dedicated resource. To overcome these limitations, cost-effective private grid and appropriate algorithms are proposed. In this, each system contains two platforms such as grid and local platforms. The grid manager service running in local personal computer can act as grid resource. When the system is on, it is intimated to the Monitoring and Information System (MIS) and details are maintained in Resource Object Table (ROT). The MIS is responsible to select the resource where the file or the replica should be stored. The resource storage is done within virtual single private grid nodes using random object addressing to prevent stolen attack. If any grid resource goes down, then the resource ID will be removed from the ROT, and resource recovery is efficiently managed by the replicas. This random addressing technique makes the grid storage a single storage and the user views the entire grid network as a single system.

Keywords: Object Grid Architecture, Grid Manager Service, Resource Object table, Random object addressing, Object storage, Dynamic Object Update.

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Authors: Naser Ahmadi Sani, Karim Solaimani, Lida Razaghnia, Jalal Zandi

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In recent decades, rapid and incorrect changes in land-use have been associated with consequences such as natural resources degradation and environmental pollution. Detecting changes in land-use is one of the tools for natural resource management and assessment of changes in ecosystems. The target of this research is studying the land-use changes in Haraz basin with an area of 677000 hectares in a 15 years period (1996 to 2011) using LANDSAT data. Therefore, the quality of the images was first evaluated. Various enhancement methods for creating synthetic bonds were used in the analysis. Separate training sites were selected for each image. Then the images of each period were classified in 9 classes using supervised classification method and the maximum likelihood algorithm. Finally, the changes were extracted in GIS environment. The results showed that these changes are an alarm for the HARAZ basin status in future. The reason is that 27% of the area has been changed, which is related to changing the range lands to bare land and dry farming and also changing the dense forest to sparse forest, horticulture, farming land and residential area.

Keywords: HARAZ Basin, Change Detection, Land-use, Satellite Data.

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Authors: Phusit Phukamchanoad, Thananya Santithammakul, Suwaree Yordchim, Pennapa Palapin

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This research gathered local wisdom towards career building of people in Kamchanoad Community, Baan Muang sub-district, Baan Dung district, Udon Thani province. Data was collected through in-depth interviews with village headmen, community board, teachers, monks, Kamchanoad forest managers and revered elderly aged over 60 years old. All of these 30 interviewees have resided in Kamchanoad Community for more than 40. Descriptive data analysis result revealed that the most prominent local wisdom of Kamchanoad community is their beliefs and religion. Most people in the community have strongly maintained local tradition, the festival of appeasing Chao Pu Sri Suttho on the middle of the 6th month of Thai lunar calendar which falls on the same day with Vesak Day. 100 percent of the people in this community are Buddhist. They believe that Naga, an entity or being, taking the form of a serpent, named “Sri Suttho” lives in Kamchanoad forest. The local people worship the serpent and ask for blessings. Another local wisdom of this community is Sinh fabric weaving.

Keywords: Local Wisdom, Careers, Kamchanoad Community.

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Authors: Hong Son Hoang, Remy Baraille

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This paper addresses the problem of how one can improve the performance of a non-optimal filter. First the theoretical question on dynamical representation for a given time correlated random process is studied. It will be demonstrated that for a wide class of random processes, having a canonical form, there exists a dynamical system equivalent in the sense that its output has the same covariance function. It is shown that the dynamical approach is more effective for simulating and estimating a Markov and non- Markovian random processes, computationally is less demanding, especially with increasing of the dimension of simulated processes. Numerical examples and estimation problems in low dimensional systems are given to illustrate the advantages of the approach. A very useful application of the proposed approach is shown for the problem of state estimation in very high dimensional systems. Here a modified filter for data assimilation in an oceanic numerical model is presented which is proved to be very efficient due to introducing a simple Markovian structure for the output prediction error process and adaptive tuning some parameters of the Markov equation.

Keywords: Statistical simulation, canonical form, dynamical system, Markov and non-Markovian processes, data assimilation.

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Authors: Saeid Nahi, Seyed Mohammad Hossein Nabavi

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Nowadays, power systems, energy generation by wind has been very important. Noting that the production of electrical energy by wind turbines on site to several factors (such as wind speed and profile site for the turbines, especially off the wind input speed, wind rated speed and wind output speed disconnect) is dependent. On the other hand, several different types of turbines in the market there. Therefore, selecting a turbine that its capacity could also answer the need for electric consumers the efficiency is high something is important and necessary. In this context, calculating the amount of wind power to help optimize overall network, system operation, in determining the parameters of wind power is very important. In this article, to help calculate the amount of wind power plant, connected to the national network in the region Manjil wind, selecting the best type of turbine and power delivery profile appropriate to the network using Monte Carlo method has been. In this paper, wind speed data from the wind site in Manjil, as minute and during the year has been. Necessary simulations based on Random Numbers Simulation method and repeat, using the software MATLAB and Excel has been done.

Keywords: wind turbine, efficiency, wind turbine work points, Random Numbers, reliability

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: Student performance, supervised machine learning, prediction, classification, cross-validation.

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Authors: Ibrahim M. Buamod, Muattaz Elaneizi

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Mobile Ad hoc Network (MANET) is a wireless ad hoc self-configuring network of mobile routers (and associated hosts) connected by wireless links, the union of which forms an arbitrary topology, cause of the random mobility of the nodes. In this paper, an attempt has been made to compare these three protocols DSDV, AODV and DSR on the performance basis under different traffic protocols namely CBR and TCP in a large network. The simulation tool is NS2, the scenarios are made to see the effect of pause times. The results presented in this paper clearly indicate that the different protocols behave differently under different pause times. Also, the results show the main characteristics of different traffic protocols operating on MANETs and thus select the best protocol on each scenario.

Keywords: Awk, CBR, Random waypoint model, TCP.

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Authors: Shuimiao Wan, Chao Yuan, Baoguang Tian

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In this paper, a linear mixed model which has two random effects is broken up into two models. This thesis gets the parameter estimation of the original model and an estimation’s statistical qualities based on these two models. Then many important properties are given by comparing this estimation with other general estimations. At the same time, this paper proves the analysis of variance estimate (ANOVAE) about σ2 of the original model is equal to the least-squares estimation (LSE) about σ2 of these two models. Finally, it also proves that this estimation is better than ANOVAE under Stein function and special condition in some degree.

Keywords: Linear mixed model, Random effects, Parameter estimation, Stein function.

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Authors: Abhijit Mitra

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Maximal length sequences (m-sequences) are also known as pseudo random sequences or pseudo noise sequences for closely following Golomb-s popular randomness properties: (P1) balance, (P2) run, and (P3) ideal autocorrelation. Apart from these, there also exist certain other less known properties of such sequences all of which are discussed in this tutorial paper. Comprehensive proofs to each of these properties are provided towards better understanding of such sequences. A simple test is also proposed at the end of the paper in order to distinguish pseudo noise sequences from truly random sequences such as Bernoulli sequences.

Keywords: Maximal length sequence, pseudo noise sequence, punctured de Bruijn sequence, auto-correlation, Bernoulli sequence, randomness tests.

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Authors: Joy Jo-Yi Chang, Justie Su-Tzu Juan

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Visual secret sharing (VSS) was proposed by Naor and Shamir in 1995. Visual secret sharing schemes encode a secret image into two or more share images, and single share image can’t obtain any information about the secret image. When superimposes the shares, it can restore the secret by human vision. Due to the traditional VSS have some problems like pixel expansion and the cost of sophisticated. And this method only can encode one secret image. The schemes of encrypting more secret images by random grids into two shares were proposed by Chen et al. in 2008. But when those restored secret images have much distortion, those schemes are almost limited in decoding. In the other words, if there is too much distortion, we can’t encrypt too much information. So, if we can adjust distortion to very small, we can encrypt more secret images. In this paper, four new algorithms which based on Chang et al.’s scheme be held in 2010 are proposed. First algorithm can adjust distortion to very small. Second algorithm distributes the distortion into two restored secret images. Third algorithm achieves no distortion for special secret images. Fourth algorithm encrypts three secret images, which not only retain the advantage of VSS but also improve on the problems of decoding.

Keywords: Visual cryptography, visual secret sharing, random grids, multiple, secret image sharing

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Authors: Jana Doležalová, Josef Holík, Zdeněk Wimmer, Sándor T. Forczek

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Chlorine is one of the most abundant elements in nature, which undergoes a complex biogeochemical cycle. Chlorine bound in some substances is partly responsible for atmospheric ozone depletion and contamination of some ecosystems. As due to international regulations anthropogenic burden of volatile organochlorines (VOCls) in atmosphere decreases, natural sources (plants, soil, abiotic formation) are expected to dominate VOCl production in the near future. Examples of plant VOCl production are methyl chloride, and bromide emission from (sub)tropical ferns, chloroform, 1,1,1-trichloroethane and tetrachloromethane emission from temperate forest fern and moss. Temperate forests are found to emit in addition to the previous compounds tetrachloroethene, and brominated volatile compounds. VOCls can be taken up and further metabolized in plants. The aim of this work is to identify and quantitatively analyze the formed VOCls in temperate forest ecosystems by a cryofocusing/GC-ECD detection method, hence filling a gap of knowledge in the biogeochemical cycle of chlorine.

Keywords: chloroform, cryofocusing-GC-ECD, ozonedepletion, volatile organochlorines

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision making has not been far-fetched. Proper classification of these textual information in a given context has also been very difficult. As a result, a systematic review was conducted from previous literature on sentiment classification and AI-based techniques. The study was done in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that could correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy using the knowledge gain from the evaluation of different artificial intelligence techniques reviewed. The study evaluated over 250 articles from digital sources like ACM digital library, Google Scholar, and IEEE Xplore; and whittled down the number of research to 52 articles. Findings revealed that deep learning approaches such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Encoder Representations from Transformer (BERT), and Long Short-Term Memory (LSTM) outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also required to develop a robust sentiment classifier. Results also revealed that data can be obtained from places like Twitter, movie reviews, Kaggle, Stanford Sentiment Treebank (SST), and SemEval Task4 based on the required domain. The hybrid deep learning techniques like CNN+LSTM, CNN+ Gated Recurrent Unit (GRU), CNN+BERT outperformed single deep learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of development simplicity and AI-based library functionalities. Finally, the study recommended the findings obtained for building robust sentiment classifier in the future.

Keywords: Artificial Intelligence, Natural Language Processing, Sentiment Analysis, Social Network, Text.

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Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta

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Cosmic showers, during the transit through space, produce sub - products as a result of interactions with the intergalactic or interstellar medium which after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of High Energy Particle Showers involve a plethora of theoretical and experimental works with a host of constraints resulting in inaccuracies in measurements. Therefore, there exist a necessity to develop a readily available system based on soft-computational approaches which can be used for EAS analysis. This is due to the fact that soft computational tools such as Artificial Neural Network (ANN)s can be trained as classifiers to adapt and learn the surrounding variations. But single classifiers fail to reach optimality of decision making in many situations for which Multiple Classifier System (MCS) are preferred to enhance the ability of the system to make decisions adjusting to finer variations. This work describes the formation of an MCS using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN) with data inputs from correlation mapping Self Organizing Map (SOM) blocks and the output optimized by another SOM. The results show that the setup can be adopted for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

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Authors: Nawang Chhunid, Gagnesh Kumar

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On-chip memories consume a significant portion of the overall die space and power in modern microprocessors. On-chip caches depend on Static Random-Access Memory (SRAM) cells and scaling of technology occurring as per Moore’s law. Unfortunately, the scaling is affecting stability, performance, and leakage power which will become major problems for future SRAMs in aggressive nanoscale technologies due to increasing device mismatch and variations. 3T1D Dynamic Random-Access Memory (DRAM) cell is a non-destructive read DRAM cell with three transistors and a gated diode. In 3T1D DRAM cell gated diode (D1) acts as a storage device and also as an amplifier, which leads to fast read access. Due to its high tolerance to process variation, high density, and low cost of memory as compared to 6T SRAM cell, it is universally used by the advanced microprocessor for on chip data and program memory. In the present paper, it has been shown that 3T1D DRAM cell can perform better in terms of fast read access as compared to 6T, 4T, 3T SRAM cells, respectively.

Keywords: DRAM cell, read access time, tanner EDA tool write access time and retention time, average power dissipation.

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Authors: Lae-Jeong Park

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The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.

Keywords: Pedestrian detection, color segmentation, false positives, feature extraction.

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Authors: Ran-Zan Wang, Yao-Ting Lee

Abstract:

This paper proposes a visual cryptography by random grids scheme with identifiable shares. The method encodes an image O in two shares that exhibits the following features: (1) each generated share has the same scale as O, (2) any share singly has noise-like appearance that reveals no secret information on O, (3) the secrets can be revealed by superimposing the two shares, (4) folding a share up can disclose some identification patterns, and (5) both of the secret information and the designated identification patterns are recognized by naked eye without any computation. The property to show up identification patterns on folded shares establishes a simple and friendly interface for users to manage the numerous shares created by VC schemes.

Keywords: Image Encryption, Image Sharing, Secret Sharing, Visual Cryptography.

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Authors: Rohit Gupta, Ravinder Agarwal

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Surface electromyographic (sEMG) signal has the potential to identify the human activities and intention. This potential is further exploited to control the artificial limbs using the sEMG signal from residual limbs of amputees. The paper deals with the development of multichannel cost efficient sEMG signal interface for research application, along with evaluation of proposed class dependent statistical approach of the feature selection method. The sEMG signal acquisition interface was developed using ADS1298 of Texas Instruments, which is a front-end interface integrated circuit for ECG application. Further, the sEMG signal is recorded from two lower limb muscles for three locomotions namely: Plane Walk (PW), Stair Ascending (SA), Stair Descending (SD). A class dependent statistical approach is proposed for feature selection and also its performance is compared with 12 preexisting feature vectors. To make the study more extensive, performance of five different types of classifiers are compared. The outcome of the current piece of work proves the suitability of the proposed feature selection algorithm for locomotion recognition, as compared to other existing feature vectors. The SVM Classifier is found as the outperformed classifier among compared classifiers with an average recognition accuracy of 97.40%. Feature vector selection emerges as the most dominant factor affecting the classification performance as it holds 51.51% of the total variance in classification accuracy. The results demonstrate the potentials of the developed sEMG signal acquisition interface along with the proposed feature selection algorithm.

Keywords: Classifiers, feature selection, locomotion, sEMG.

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Authors: Vasundara Patel K. S., Harsha N. Bhushan, Kiran G. Gadag, Nischal Prasad B. N., Mohmmed Haroon

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The most widely used semiconductor memory types are the Dynamic Random Access Memory (DRAM) and Static Random Access memory (SRAM). Competition among memory manufacturers drives the need to decrease power consumption and reduce the probability of read failure. A technology that is relatively new and has not been explored is the FinFET technology. In this paper, a single cell Schmitt Trigger Based Static RAM using FinFET technology is proposed and analyzed. The accuracy of the result is validated by means of HSPICE simulations with 32nm FinFET technology and the results are then compared with 6T SRAM using the same technology.

Keywords: Schmitt trigger based SRAM, FinFET, and Static Noise Margin.

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Authors: Yao-Tang Chang, Chuen-Ching Wang, Shu-Han Hu

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In order to implement flexibility as well as survivable capacities over passive optical network (PON), a new automatic random fault-recovery mechanism with array-waveguide-grating based (AWG-based) optical switch (OSW) is presented. Firstly, wavelength-division-multiplexing and optical code-division multiple-access (WDM/OCDMA) scheme are configured to meet the various geographical locations requirement between optical network unit (ONU) and optical line terminal (OLT). The AWG-base optical switch is designed and viewed as central star-mesh topology to prohibit/decrease the duplicated redundant elements such as fiber and transceiver as well. Hence, by simple monitoring and routing switch algorithm, random fault-recovery capacity is achieved over bi-directional (up/downstream) WDM/OCDMA scheme. When error of distribution fiber (DF) takes place or bit-error-rate (BER) is higher than 10-9 requirement, the primary/slave AWG-based OSW are adjusted and controlled dynamically to restore the affected ONU groups via the other working DFs immediately.

Keywords: Random fault recovery mechanism, Array-waveguide-grating based optical switch (AWG- based OSW), wavelength-division-multiplexing and optical code-divisionmultiple-access (WDM/ OCDMA)

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Authors: T. Mladenov, F. Mujahid, E. Jung, D. Har

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The application of the synchronous dynamic random access memory (SDRAM) has gone beyond the scope of personal computers for quite a long time. It comes into hand whenever a big amount of low price and still high speed memory is needed. Most of the newly developed stand alone embedded devices in the field of image, video and sound processing take more and more use of it. The big amount of low price memory has its trade off – the speed. In order to take use of the full potential of the memory, an efficient controller is needed. Efficient stands for maximum random accesses to the memory both for reading and writing and less area after implementation. This paper proposes a target device independent DDR SDRAM pipelined controller and provides performance comparison with available solutions.

Keywords: DDR SDRAM, controller, effective implementation

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Authors: Ririn Dewi Cahyani, Muh. Hisjam, Wahyudi Sutopo, Kuncoro Harto Widodo

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Availability of raw materials is important for Indonesia as a furniture exporting country. Teak log as raw materials is supplied to the furniture industry by Perum Perhutani (PP). PP needs to involve carbon trading for nature conservation. PP also has an obligation in the Corporate Social Responsibility program. PP and furniture industry also must prosecute the regulations related to ecological issues and labor rights. This study has the objective to create the relationship model between supplier and manufacturer to fulfill teak log demand that involving teak forest carbon sequestration. A model is formulated as Goal Programming to get the favorable solution for teak log procurement and support carbon sequestration that considering economical, ecological, and social aspects of both supplier and manufacturer. The results show that the proposed model can be used to determine the teak log quantity involving carbon trading to achieve the seven goals to be satisfied the sustainability considerations.

Keywords: Availability of teak log, support carbon sequestration, goal programming, sustainability consideration.

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Authors: Said Ali Al-Hadhrami

Abstract:

Ranked set sampling (RSS) was first suggested to increase the efficiency of the population mean. It has been shown that this method is highly beneficial to the estimation based on simple random sampling (SRS). There has been considerable development and many modifications were done on this method. When a concomitant variable is available, ratio estimation based on ranked set sampling was proposed. This ratio estimator is more efficient than that based on SRS. In this paper some ratio type estimators of the population mean based on RSS are suggested. These estimators are found to be more efficient than the estimators of similar form using simple random sample.

Keywords: Bias, Efficiency, Ranked Set Sampling, Ratio Type Estimator

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