Original Research Paper
Optoelectronics and Photonics
F. Parandin; M. Malmir
Abstract
Background and Objectives: Recently, photonic crystals have been considered as the basic structures for the realization of various optical devices for high speed optical communication.Methods: In this research, two dimensional photonic crystals are used for designing all optical logic gates. A photonic ...
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Background and Objectives: Recently, photonic crystals have been considered as the basic structures for the realization of various optical devices for high speed optical communication.Methods: In this research, two dimensional photonic crystals are used for designing all optical logic gates. A photonic crystal structure with a triangular lattice is proposed for making NAND, XNOR, and OR optical logic gates. Using the structure as the intended logic gate is possible without the need to change the structure through the use of the phase difference at the inputs. Line and point defects have been used to propagate light from inputs to output. The logical values "0" and "1", are defined based on the amount of transferred optical power to the output.Results: The simple structure and the use of line and point defects, instead of ring resonators, reduce the complexity of the design and its use in optical logic integrated circuits. Another advantage of proposed structure, in comparison to the previous structures is the reduction in delay time that increases its speed. The maximum delay time of the proposed optical NAND, XNOR, and OR gates is about 0.1ps.Conclusion: In this study, one structure is suggested for realizing NAND, XNOR, and OR logic gates. This structure has a small size and low delay time, and is suitable for use in optical integrated circuits.
Original Research Paper
Electronics
S. Rahmati; E. Farshidi; J. Ganji
Abstract
Background and Objectives: In recent decades, due to the effect of the short channel, the use of CMOS transistors in the nanoscale has become a major concern. One option to deal with this issue is the use of nano-transistors.Methods: Using nano-transistors and multi-valued logic (MVL) can reduce the ...
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Background and Objectives: In recent decades, due to the effect of the short channel, the use of CMOS transistors in the nanoscale has become a major concern. One option to deal with this issue is the use of nano-transistors.Methods: Using nano-transistors and multi-valued logic (MVL) can reduce the level of chips and connections and have a direct impact on power consumption. The present study reports the design of a new method of Multiplexers (MUXs) based on quaternary logic and transistors of carbon nanotubes (CNTFET) and having a new look at the layout and use of MUXs.Results:The use of special rotary functions and unary operators in Quaternary logic in the design of MUXs reduced the number of CNTFETs from 27% to 54%. Also, the use of MUXs in the Adder structure resulted in a 54% reduction in Power Delay Product (PDP) and a 17.5% to 85.6% reduction in CNTFET counts.Conclusion: The simulated results display a significant improvement in the fabrication of Adders, average power consumption, speed, and PDP compared to the current best-performing techniques in the literature. The proposed operators and circuits were evaluated under various operating conditions. The results show the stability of the proposed circuits.
Original Research Paper
Power Electronics
P. Hamedani; A. Shoulaei
Abstract
Background and Objectives: Despite superior privileges that multiphase motors offer in comparison with their three-phase counterparts, in the field of multiphase linear induction motors (LIMs) few studies have been reported until now. To combine the advantages of both multiphase motors and linear induction ...
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Background and Objectives: Despite superior privileges that multiphase motors offer in comparison with their three-phase counterparts, in the field of multiphase linear induction motors (LIMs) few studies have been reported until now. To combine the advantages of both multiphase motors and linear induction motors, this paper concentrates on multiphase LIM drives considering the end effects.Methods: The main contributions of this paper can be divided into two major categories. First, a comparative study has been conducted about the dynamic performance of Fuzzy Logic Controller (FLC) and Genetic-PI controller for a seven-phase LIM drive; and second, because of the superior performance of the FLC method revealed from the results, the harmonic pollution of the FLC based LIM drive has been studied in the case of supplying through a five-level Cascaded H-bridge (CHB) VSI and then compared with the traditional two-level VSI fed one.Results: The five-level CHB-VSI has utilized a multiband hysteresis modulation scheme and the two-level VSI has used the traditional three-level hysteresis modulation strategy. Note that for harmonic distortion assessment both harmonic and interharmonic components are considered in THD calculations.Conclusion: The results validate the effectiveness of the proposed FLC for seven-phase LIM drive supplied with five-level CHB-VSI and guarantee for perfect control characteristics, lower maximum starting current, and significant harmonic and interharmonic reduction.
Original Research Paper
Artificial Intelligence
I. Behravan; S. M. Razavi
Abstract
Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, ...
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Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem.Methods: In this paper, a novel machine learning approach, which works in two phases, is introduced to predict the price of a stock in the next day based on the information extracted from the past 26 days. In the first phase of the method, an automatic clustering algorithm clusters the data points into different clusters, and in the second phase a hybrid regression model, which is a combination of particle swarm optimization and support vector regression, is trained for each cluster. In this hybrid method, particle swarm optimization algorithm is used for parameter tuning and feature selection. Results: The accuracy of the proposed method has been measured by 5 companies’ datasets, which are active in the Tehran Stock Exchange market, through 5 different metrics. On average, the proposed method has shown 82.6% accuracy in predicting stock price in 1-day ahead.Conclusion: The achieved results demonstrate the capability of the method in detecting the sudden jumps in the price of a stock.
Original Research Paper
Data Mining
R. Asgarnezhad; A. Monadjemi; M. SoltanAghaei
Abstract
Background and Objectives: Twitter Sentiment Classification is one of the most popular fields in information retrieval and text mining. Millions of people of the world intensity use social networks like Twitter. It supports users to publish tweets to tell what they are thinking about topics. There are ...
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Background and Objectives: Twitter Sentiment Classification is one of the most popular fields in information retrieval and text mining. Millions of people of the world intensity use social networks like Twitter. It supports users to publish tweets to tell what they are thinking about topics. There are numerous web sites built on the Internet presenting Twitter. The user can enter a sentiment target and seek for tweets containing positive, negative, or neutral opinions. This is remarkable for consumers to investigate the products before purchase automatically.Methods: This paper suggests a model for sentiment classification. The goal of this model is to investigate what is the role of n-grams and sampling techniques in Sentiment Classification application using an ensemble method on Twitter datasets. Also, it examines both binary and multiple classifications, which are classified datasets into positive, negative, or neutral classes.Results: Twitter Classification is an outstanding problem, which has very few free resources and not available due to modified authorization status. However, all Twitter datasets are not labeled and free, except for our applied dataset. We reveal that the combination of ensemble methods, sampling techniques, and n-grams can improve the accuracy of Twitter Sentiment Classification.Conclusion: The results confirmed the superiority of the proposed model over state-of-the-art systems. The highest results obtained in terms of accuracy, precision, recall, and f-measure.
Original Research Paper
Power
F. Amiri; M. Moradi
Abstract
Background and Objectives: Virtual inertia control, as a component of a virtual synchronous generator, is used for the implementation of synchronous generator behaviour in microgrids. In microgrids that include high-capacity distributed generation resources, in addition to virtual inertia, virtual damping ...
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Background and Objectives: Virtual inertia control, as a component of a virtual synchronous generator, is used for the implementation of synchronous generator behaviour in microgrids. In microgrids that include high-capacity distributed generation resources, in addition to virtual inertia, virtual damping can also lead to improvement of frequency stability of the microgrid. The purpose of the control method for the islanded microgrid is to be: 1) robust to the uncertainty of the microgrid parameters. 2) Weaken the disturbances on the islanded microgrid (wind turbine, solar cell, Loads). 3) Improved response speed related to microgrid frequency deviation (reduced settling time).Methods In this paper, designing a new robust control method for controlling virtual inertia in microgrids, with regard to virtual damping, has been attempted. The proposed method has a higher degree of freedom compared to the conventional robust controllers, which provides better control of the system.Results: Results of the proposed method for virtual inertia control with regard to virtual damping has been compared in several scenarios –with virtual inertia control based on optimized PI controllers with regard to virtual damping, virtual inertia control based on model predictive control (controller) with regard to virtual damping,Self-adaptive virtual inertia control using fuzzy logic, virtual inertia control with regard to virtual damping, and virtual inertia control without virtual damping (conventional methods). Compared to other control methods, the proposed controller has improved the settling time due to the frequency deviations of the islanded microgrid by 27%. According to the results of the scenarios, the proposed controller has been able to reduce the frequency error due to load and distributed generation resource disturbances and compared to other controllers, and this frequency deviation has been reduced by 68%.Conclusion: According to the simulation results, the proposed controller has a better performance than other controllers in improving the frequency stability of the islanded microgrid.
Original Research Paper
Digital Design
B. Soltani Farani; H. Dorosti; M. E. Salehi; Si M. Fakhraie
Abstract
Background and Objectives: Digital signal processors are widely used in energy constrained applications in which battery lifetime is a critical concern. Accordingly, designing ultra-low-energy processors is a major concern. In this work and in the first step, we propose a sub-threshold DSP processor.Methods: ...
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Background and Objectives: Digital signal processors are widely used in energy constrained applications in which battery lifetime is a critical concern. Accordingly, designing ultra-low-energy processors is a major concern. In this work and in the first step, we propose a sub-threshold DSP processor.Methods: As our baseline architecture, we use a modified version of an existing ultra-low-power general purpose processor. Afterwards, we make some modifications to add new instructions to the processor instruction set for better adapting to signal processing applications. In the second step, employing sub-threshold cores in many-core architectures, we use the proposed processor as simple basic cores in a many-core architecture.Results: In comparison with the baseline architecture, these modifications reduce the program memory size about 42% in average. In addition, data memory accesses are reduced about 60% in average, and more than 90% speed-up is achieved. According to the improvements in total execution time (93%) and power consumption (27%), the total consumed energy is reduced about 95% in average with at most 2.6% area overhead and without increasing the process variation effects on processor specifications.Conclusion: The results show that for parallel applications, such as FFT in LTE standard, exploiting sub-threshold processors in a many-core architecture not only can satisfy the required performance, but also reduce the power consumption about 50% or even more.
Original Research Paper
Power Divider
M. Mirzajani Darestani; M. Tavakoli; P. Amiri
Abstract
Background and Objectives: In this paper, a new design strategy was proposed in order to enhance bandwidth and efficiency of power amplifier.Methods: To realize the introduced design strategy, a power amplifier was designed using TSMC CMOS 0.18um technology for operating in the Ka band, i.e. the frequency ...
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Background and Objectives: In this paper, a new design strategy was proposed in order to enhance bandwidth and efficiency of power amplifier.Methods: To realize the introduced design strategy, a power amplifier was designed using TSMC CMOS 0.18um technology for operating in the Ka band, i.e. the frequency range of 26.5-40GHz. To design the power amplifier, first a power divider (PD) with a very wide bandwidth, i.e. 1-40GHz, was designed to cover the whole Ka band. The designed Doherty power amplifier consisted of two different amplification paths called main and auxiliary. To amplify the signal in each of the two pathways, a cascade distributed power amplifier was used. The main reason for combining the distributed structure and cascade structure was to increase the gain and linearity of the power amplifier.Results: Measurements results for designed power divider are in good agreement with simulations results. The simulation results for the introduced structure of power amplifier indicated that the gain of proposed power amplifier at the frequency of 26-35GHz was more than 30dB. The diagram of return loss at the input and output of power amplifier in the whole Ka band was less than -8dB. The maximum Power Added Efficiency (PAE) of the designed power amplifier was 80%. The output p 1dB of the introduced structure was 36dB, and the output power of power amplifier was 36dBm. Finally, the IP3 value of power amplifier was about 17dB.Conclusion: The strategy presented in this paper is based on usage of Doherty and distributed structures and a new wideband power divider to benefit from their advantages simultaneously.
Original Research Paper
Artificial Intelligence
S. F. Mirmousavi; S. Kianian
Abstract
Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are ...
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Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link prediction methods identify all path structures in a network and can determine the similarity degree between graph-extracted entities with high accuracy but are time-consuming instead. Most existing algorithms are only using one type of feature (global or local) to represent data, which not well described due to the large scale and heterogeneity of complex networks.Methods: In this paper, a new method presented for Link Prediction using node embedding due to the high dimensions of real-world networks. The proposed method extracts a smaller model of the input network by getting help from the deep neural network and combining global and local nodes in a way to preserve the network's information and features to the desired extent. First, the feature vector is being extracted by an encoder-decoder for each node, which is a suitable tool for modeling complex nonlinear phenomena. Secondly, both global and local information concurrently used to improve the loss function. More obvious, the clustering similarity threshold considered as the local criterion and the transitive node similarity measure used to exploit the global features. To the end, the accuracy of the link prediction algorithm increased by designing the optimization operation accurately.Results: The proposed method applied to 4 datasets named Cora, Wikipedia, Blog catalog, Drug-drug-interaction, and the results are compared with laplacian, Node2vec, and GAE methods. Experimental results show an average accuracy achievement of 0.620, 0.723, 0.875, and 0.845 on the mentioned datasets, and confirm that the link prediction can effectively improve the prediction performance using network embedding based on global similarity.
Original Research Paper
Hybrid EV chargers
H. Soltani Gohari; K. Abbaszadeh
Abstract
Background and Objectives: Power electronics infrastructures play an important role in charging different types of electric vehicles (EVs) especially Plug-in Hybrid EVs (PHEVs). Designing appropriate power converters is the topic of various studies.Method: In this paper, a novel bidirectional buck-boost ...
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Background and Objectives: Power electronics infrastructures play an important role in charging different types of electric vehicles (EVs) especially Plug-in Hybrid EVs (PHEVs). Designing appropriate power converters is the topic of various studies.Method: In this paper, a novel bidirectional buck-boost multifunctional integrated converter is presented which is capable of handling battery and fuel cell stack in plug-in hybrid electric vehicles. The proposed converter has the ability to work in five different operating modes (Charging/Propulsion (only battery)/ Propulsion (battery and FC)/ Regenerative braking/ V2G). The introduced multifunctional two-stage converter has the ability to work in all the above-mentioned modes in buck- boost condition, the feature that does not exist in the previous works. It is possible to control active and reactive power by using the effective dual-loop PI control method which is introduced in this paper. Working as an on-board charger and DC-DC converter (which interfaced between power sources and motor drive system) causes a decrease in the counts of the total components and an increase in system efficiency.Results: Operation principle and steady-state analysis of each stage of the proposed converter in all operating modes are provided in detail and in order to design an appropriate applicable converter, the design considerations and procedure are also explained for capacitive and inductive components. The proposed converter is simulated in MATLAB/SIMULAIN environment and results are provided. Voltage and current waveforms in all operating conditions are provided with their transient. FFT analysis of the input current (in the operating modes in which the converter absorb or deliver power from/to the grid) is also mentioned. A reduced-scale setup of the presented converter is built and tested and experimental results confirm simulation ones.Conclusion: A bidirectional buck-boost integrated converter in PHEVs applications is introduced in this paper. The design procedure of the presented converter is provided and also an effective control method to control active and reactive power during charging and V2G modes is introduced. A comparison study of the proposed converter with other similar converters introduced in recent years in terms of the number of high-frequency switches in each mode is also done. Simulation and experimental results are also provided.
Original Research Paper
Computational Intelligence
Zeinab Khatoun Pourtaheri
Abstract
Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance.Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify ...
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Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance.Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify not only important parameters, but also optimal levels for them. Minitab and Design Expert software programs are utilized to achieve the stability goals of this research.Results: The proposed approach is useful as a preprocessing method before employing heuristic ensemble classifiers; i.e., first discover optimal levels of important parameters and then apply these parameters to heuristic ensemble classifiers to attain the best results. Another significant difference between this research and previous works related to stability analysis is the definition of the response variable; an average of three criteria of the Pareto front is used as response variable.Finally, to clarify the performance of this method, obtained optimal levels are applied to a typical multi-objective heuristic ensemble classifier, and its results are compared with the results of using empirical values; obtained results indicate improvements in the proposed method.Conclusion: This approach can analyze more parameters with less computational costs in comparison with previous works. This capability is one of the advantages of the proposed method.
Original Research Paper
Computer Vision
M. Fakhredanesh; S. Roostaie
Abstract
Background and Objectives: Action recognition, as the processes of labeling an unknown action of a query video, is a challenging problem, due to the event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. A number of solutions proposed to solve action recognition ...
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Background and Objectives: Action recognition, as the processes of labeling an unknown action of a query video, is a challenging problem, due to the event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. A number of solutions proposed to solve action recognition problem. Many of these frameworks suppose that each video sequence includes only one action class. Therefore, we need to break down a video sequence into sub-sequences, each containing only a single action class.Methods: In this paper, we develop an unsupervised action change detection method to detect the time of actions change, without classifying the actions. In this method, a silhouette-based framework will be used for action representation. This representation uses xt patterns. The xt pattern is a selected frame of xty volume. This volume is achieved by rotating the traditional space-time volume and displacing its axes. In xty volume, each frame consists of two axes (x) and time (t), and y value specifies the frame number.Results: To test the performance of the proposed method, we created 105 artificial videos using the Weizmann dataset, as well as time-continuous camera-captured video. The experiments have been conducted on this dataset. The precision of the proposed method was 98.13% and the recall was 100%.Conclusion: The proposed unsupervised approach can detect action changes with a high precision. Therefore, it can be useful in combination with an action recognition method for designing an integrated action recognition system.
