Current-Mode Electronically Tunable Universal Filter Using Only Plus-Type Current Controlled Conveyors and Grounded Capacitors

Shahram Minaei,Sait Türköz 한국전자통신연구원 2004 ETRI Journal Vol.26 No.4

In this paper we present a new current-mode electronically tunable universal filter using only plus-type current controlled conveyors (CCCII+s) and grounded capacitors. The proposed circuit can simultaneously realize lowpass, bandpass, and highpass filter functions— all at high impedance outputs. The realization of a notch response does not require additional active elements. The circuit enjoys an independent current control of parameters ωo and ωo / Q. No element matching conditions are imposed. Both its active and passive sensitivities are low.

Investigating priorities of engineering disciplines for addressing the technical needs of Iranian society using the Analytic Hierarchy Process

Behrooz Minaei Bidgoli,Somayeh Farhang Adib,Samira Yousefi Naghani 한국교육개발원 2014 KEDI Journal of Educational Policy Vol.11 No.2

Iranian society needs a trained expert workforce in light of the rapid advances in science and technology. This is as important as natural needs for community survival, and having an expert workforce leads to independence and power for the society. To this end, governments can facilitate social progress through appropriate and comprehensive planning, training young talent, and optimal use of the available tools and capacities. Considering the importance of expertise in engineering fields for industrialization, this paper investigates the importance and priority of the common engineering disciplines in Iran. To achieve this goal, several criteria must be considered simultaneously, which we did using the Analytic Hierarchy Process. Our findings include a prioritization of the disciplines in this branch of science.

A Probabilistic Algorithm for MANET Clustering

Fahimeh Dabaghi-Zarandi,Behrouz Minaei-Bidgoli,Zohreh Davarzani 보안공학연구지원센터 2014 International Journal of Future Generation Communi Vol.7 No.6

Mobile ad hoc network (MANET) is a type of ad hoc network that MANET nodes can change their locations and configure by themselves on the fly. Because of mobility the MANET nodes, the management of a large MANET is difficult, therefore, clustering in a MANET is an important technique. A large network is divided into several sub networks applying clustering method. When the topology of the network is dynamic and ad hoc, the process of clustering is very complicated. In this paper, we propose a Probabilistic Algorithm for MANET Clustering (PAMC) to improve the performance of this wireless technology. We simulate our algorithm and evaluate it based on two criteria: the average number of clusters and the average re-affiliation.

A Novel Grounded Inductor Realization Using a Minimum Number of Active and Passive Components

Erkan Yuce,Shahram Minaei,Oguzhan Cicekoglu 한국전자통신연구원 2005 ETRI Journal Vol.27 No.4

In this study, we present a new topology for realizing a grounded inductor employing only a single current conveyor, called a negative-type modified inverting second-generation current conveyor (MICCII-), and a minimum number of passive components, two resistors, and one capacitor. The non-ideality effects of the MICCII- on a simulated inductor are investigated. To demonstrate the performance of the presented inductance simulator, we use it to construct a third order Butterworth high-pass filter and a parallel resonant circuit. Simulation results are given to confirm the theoretical analysis.

A Novel Feature Selection Method in the Categorization of Imbalanced Textual Data

( Jafar Pouramini ),( Behrouze Minaei-bidgoli ),( Mahdi Esmaeili ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.8

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.

Anticancer Properties of Teucrium persicum in PC-3 Prostate Cancer Cells

Tafrihi, Majid,Toosi, Samane,Minaei, Tayebeh,Gohari, Ahmad Reza,Niknam, Vahid,Arab Najafi, Seyed Mahmoud Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.2

Crude extracts or phytochemicals obtained from some plants have potential anti-cancer properties. Teucrium persicum is an Iranian endemic plant belonging to the Lamiaceae family which has traditionally been used to relieve abdominal pains. However, the anti-cancer properties of this species of the Teucrium genus have not been investigated previously. In this study, we have used a highly invasive prostate cancer cell line, PC-3, which is an appropriate cell system to study anti-tumor properties of plants. A methanolic extract obtained from T persicum potently inhibited viability of PC-3 cells. The viability of SW480 colon and T47D breast cancer cells was also significantly decreased in the presence of the T persicum extract. Flow cytometry suggested that the reduction of cell viability was due to induction of apoptosis. In addition, the results of wound healing and gelatin zymography experiments supported anti-cell invasion activity of T persicum. Interestingly, sublethal concentrations of T persicum extract induced an epithelial-like morphology in a subpopulation of cells with an increase in E-Cadherin and ${\beta}$-Catenin protein levels at the cell membrane. These results strongly suggest that T persicum is a plant with very potent anti-tumor activity.

Analytical solution for dynamic behavior of multiwall carbon nanotubes subjected to mechanical shock loading

M. Tahani,M. H. Abolbashari,Gh. Mohajeri Minaei,S. T. Talebian 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.9

Dynamic behavior and frequency analysis of multiwall carbon nanotubes (MWCNTs) with finite length are investigated using an analyticalmethod. By applying multiple elastic shells and a linearized model of van der Waals forces, a comprehensive continuum dynamicmodel of MWCNTs is developed. Also, by utilizing Laplace transform, time histories of MWCNTs subjected to shock lading are obtained. Then by using the fast Fourier transform (FFT), the time responses are transferred to the frequency domain and natural frequenciesare found. The accuracy of results is verified by comparing the results of sudden loading with those obtained by numerical methods. Furthermore, an abrupt unloading after ramp loading is simulated, and the dependency of diameter and length on the axial and radialnatural frequencies of MWCNTs is examined. Also, by characterizing the longitudinal displacement, the wave propagation velocities areobtained and an explicit expression is found for the axial natural frequencies of MWCNTs subjected to shock loading.

Prediction of Stock Market Using an Ensemble Learning-based Intelligent Model

Mohammad-Taghi Faghihi-Nezhad,Behrouz Minaei-Bidgoli 대한산업공학회 2018 Industrial Engineeering & Management Systems Vol.17 No.3

AI-based models have shown that stock market is predictable despite its uncertainty and fluctuating nature. Research in this field has further dealt with predicting the next step price amount and less attention has been paid to the prediction of the next movement of price. However, in practice, the necessary requisite for decision-making and use of the results of prediction lies in considering the predictable trend of stock movement along with predicting stock price. Considering the widespread search in the literature on the matter, this paper takes into account, for the first time, two criteria of direction and price simultaneously for the prediction of the stock price. The proposed model has two stages and is developed based on ensemble learning and meta-heuristic optimization algorithms. The first stage predicts the direction of the next price movement. At the second stage, such prediction and other input variables create a new training dataset and the stock price is predicted. At each stage, in order to optimize the results, genetic algorithm (GA) optimization and particle swarm optimization (PSO) are applied. Evaluation of the results, on the real data of stock price, indicates that the proposed model has higher accuracy than other models used in the literature.

A Novel Similarity Measure for Sequence Data

Pandi, Mohammad. H.,Kashefi, Omid,Minaei, Behrouz Korea Information Processing Society 2011 Journal of information processing systems Vol.7 No.3

A variety of different metrics has been introduced to measure the similarity of two given sequences. These widely used metrics are ranging from spell correctors and categorizers to new sequence mining applications. Different metrics consider different aspects of sequences, but the essence of any sequence is extracted from the ordering of its elements. In this paper, we propose a novel sequence similarity measure that is based on all ordered pairs of one sequence and where a Hasse diagram is built in the other sequence. In contrast with existing approaches, the idea behind the proposed sequence similarity metric is to extract all ordering features to capture sequence properties. We designed a clustering problem to evaluate our sequence similarity metric. Experimental results showed the superiority of our proposed sequence similarity metric in maximizing the purity of clustering compared to metrics such as d2, Smith-Waterman, Levenshtein, and Needleman-Wunsch. The limitation of those methods originates from some neglected sequence features, which are considered in our proposed sequence similarity metric.