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Open Access Article
1 - A dynamic scalable fast blockchain-based Framework for Smart Cities: The case study of Intelligent Transportation System
Mohammad Bagher Moradi Siamak Najjar Karimi amir hossein jalaliIssue 44 , Vol. 11 , Winter 2023With the emergence of smart cities vision, its large distributed applications such as intelligent transportation systems demand scalable low-latency trusted data exchange architecture with high storage and computational resources for storing the high-volume of IoT data MoreWith the emergence of smart cities vision, its large distributed applications such as intelligent transportation systems demand scalable low-latency trusted data exchange architecture with high storage and computational resources for storing the high-volume of IoT data and providing real-time services. In recent years, blockchain technology has gained extensive attention to fulfil the requirements of such highly distributed large systems. However, there are a number of technical challenges in the integration of blockchain and IoT applications. Firstly, Bitcoin blockchain with low scalability and throughput is not able to provide fast services. Secondly, there are limitations like constrained spaces for establishing big blockchain nodes storing a massive volume of data generated by numerous smart IoT devices or sensors inside the streets of cities. This paper argues that solving both issues in one large blockchain network is infeasible. Therefore, we prioritize this two weakness of blockchain in relation to such systems and propose two separate level of blockchain networks cooperating with each other asynchronously to address them. One network called Fast BlockChain (FBC) composed of multiple scalable sub-blockchain networks responsible for fast services. Another network, CityBC, supports the networks of FBC through the long-term storing of their data and providing their smart manager with knowledge for dynamic autonomous partitioning of them in order to decrease network-to-network communications and avoid wasting storage resources and network bandwidth. Furthermore, this paper evaluates the ideal size of sub-blockchain and then proposes a novel idea for an initial partitioning technique before using collected data by blockchain nodes for dynamic partition of network. Manuscript profile -
Open Access Article
2 - Noise elimination in automatic detection of epileptic seizures by wavelet transform using feature selection algorithm
akram asghari govarIssue 44 , Vol. 11 , Winter 2023One of the most important symptoms of epilepsy is convulsions, whose detailed analysis is performed by electroencephalography (EEG) signal. Electroencephalogram, as a clinical tool to illustrate the electrical activities of the brain accurately, provides an appropriate MoreOne of the most important symptoms of epilepsy is convulsions, whose detailed analysis is performed by electroencephalography (EEG) signal. Electroencephalogram, as a clinical tool to illustrate the electrical activities of the brain accurately, provides an appropriate method for diagnosing epilepsy disorders, which plays an important role in identifying this disease, especially seizures. Seizures resulting from epilepsy may have negative physical, psychological, and social consequences such as loss of consciousness and sudden death. With timely and correct identification of epilepsy, its effect can be treated with medicine or surgery. In this thesis, a brief review of the methods of identifying epilepsy using EEG signal analysis along with the separation of epileptic signals from healthy and normal signals has been done. Methods based on EEG analysis, from non-linear methods of signal processing, provide much better results due to the properties of signal dynamics Manuscript profile -
Open Access Article
3 - Diagnosis of Covid-19 using optimized convolutional neural network
mohammad fatehi mehdi taghizadeh mohammad moradi gholamhosein shojaatIssue 44 , Vol. 11 , Winter 2023According to the report of the World Health Organization, corona disease is the most dangerous and contagious disease in the world. Currently, the most common method used to diagnose corona disease is the polymer chain reaction laboratory technique of reverse transcript MoreAccording to the report of the World Health Organization, corona disease is the most dangerous and contagious disease in the world. Currently, the most common method used to diagnose corona disease is the polymer chain reaction laboratory technique of reverse transcription, but since this method requires time to confirm the presence of the virus in the laboratory and also due to the unavailability of diagnostic kits and its high costs, Suspected corona virus patients cannot be identified and treated in time; This, in turn, can increase the likelihood of spreading the disease.Another diagnostic method is the use of X-ray chest imaging technique as well as chest computed tomography scan. Also, the use of deep learning methods can be very important for faster and more accurate diagnosis of the lung problems of the corona virus.In this study, using optimized deep convolutional networks based on X-ray images, patients with corona virus were diagnosed.In this article, using the optimized convolutional neural network of healthy people and those with corona, with 10-Fold cross-validation, average accuracy of 98.9% and average sensitivity of 96.5% were obtained.According to the obtained results, it can be said that the proposed method has the ability to separate healthy and unhealthy signals with acceptable accuracy. Manuscript profile -
Open Access Article
4 - Calculation of Reflection Loss in Fundamental TE Mode Versus Angle of Tilted End Facet of Superluminescent Light Emitting Diodes
Mohammad Hosein Salman Yengejeh nasser moslehi milaniIssue 44 , Vol. 11 , Winter 2023In this paper we study the acquisition of fundamental TE mode reflection in rectangular cavities from a tilted face (end mirror) of superluminescent light emitting diodes (SLD). When its width is 3 micrometers and the inclination of the mirror is 3 ̊, the result will be MoreIn this paper we study the acquisition of fundamental TE mode reflection in rectangular cavities from a tilted face (end mirror) of superluminescent light emitting diodes (SLD). When its width is 3 micrometers and the inclination of the mirror is 3 ̊, the result will be a reduction of nearly 20 dB of reflection. While if the width is 6 micrometers, the inclination of the mirror 2 ̊ will cause an excess reflection loss of 20 dB. The results obtained in our paper for small tilt angles are the Gaussian approximation of the guided state. While our results differ greatly from the Gaussian approximation for larger angles and larger reflection losses. Manuscript profile -
Open Access Article
5 - Vector control of induction motor using moving sliding mode fuzzy controller
saman ebrahimi boukaniIssue 44 , Vol. 11 , Winter 2023A sliding motion can be divided into two phases: reaching phase and sliding phase. One of the features of sliding mode control is that it is robust to parameter uncertainties and external disturbances in the sliding phase. But in the reaching phase, SMC may be sensitive MoreA sliding motion can be divided into two phases: reaching phase and sliding phase. One of the features of sliding mode control is that it is robust to parameter uncertainties and external disturbances in the sliding phase. But in the reaching phase, SMC may be sensitive to parameter uncertainty and external disturbance. The moving sliding surface proposed by Choi et al can minimize or eliminate the reaching phase. In this article, the sliding mode fuzzy controller design method with a moving sliding surface is presented. The simulation results show the superiority of SMFC over classical SMC and PID controller in the presence of external disturbances. Manuscript profile -
Open Access Article
6 - Fatigue prediction of hybrid joints and perforated plates using neural network
Ali Yousefnezhad Oskooi vahid Pourmohammad Karim Samadzamini Firooz Esmaeili GoldaragIssue 44 , Vol. 11 , Winter 2023Hybrid connections (bolts, glue) and perforated plates are one of the most important topics in various industries, including aerospace. This type of process occurs due to the growth of small cracks in the metal structure as a result of cyclic or intermittent loading. Si MoreHybrid connections (bolts, glue) and perforated plates are one of the most important topics in various industries, including aerospace. This type of process occurs due to the growth of small cracks in the metal structure as a result of cyclic or intermittent loading. Since failures occur suddenly, terrible accidents such as plane crashes, shipwrecks, bridge collapses, and toxic radioactive fallout can occur. To prevent these incidents, fatigue tests are performed on a sample of parts that is similar to the real part, so that the fatigue life can be obtained through this method. However, because fatigue tests are time-consuming and expensive, artificial intelligence methods have been used in this research to estimate the fatigue life of hybrid joints and perforated plates. In the experimental part of this research, plates made of aluminum alloy 2024-T3, which is one of the widely used materials in aerospace, the used materials are screws made of Hex head M5 and a special adhesive made of Loctite 3421 (Henkel ltd). Fatigue tests are extracted as input and output data from the related article. Out of a total of 71 fatigue tests, 35 tests were performed for perforated plates, 18 tests for hybrid joints, and 18 tests for bolted joints. Also, according to the number of data, the best result was when 80% of the data was considered for training the network and 20% was used as test data to evaluate the performance of the network. Finally, the predicted output was compared with the actual output and it was seen that the best performance of the neural network was after normalizing the data, that the error value was close to zero. Manuscript profile
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Open Access Article
1 - Design of a Model Reference Adaptive Controller Using Modified MIT Rule for a Second Order System
Saeed Barghandan Aref DaeiFarshchiIssue 25 , Vol. 7 , Summer 2018Sometimes conventional feedback controllers may not perform well online because of the variation in process dynamics due to nonlinear actuators, changes in environmental conditions and variation in the character of the disturbances. To overcome the above problem, this p MoreSometimes conventional feedback controllers may not perform well online because of the variation in process dynamics due to nonlinear actuators, changes in environmental conditions and variation in the character of the disturbances. To overcome the above problem, this paper deals with the designing of a controller for a second order system with Model Reference Adaptive Control (MRAC) scheme using the MIT rule for adaptive mechanism. In this rule, a cost function is defined as a function of error between the outputs of the plant and the reference model, and controller parameters are adjusted in such a way so that this cost function is minimized. The designed controller gives satisfactory results, but is very sensitive to the changes in the amplitude of reference signal. It follows from the simulation work carried out in this paper that adaptive system becomes unstable if the value of adaptation gain or the amplitude of reference signal is sufficiently large. This paper also deals with the use of MIT rule along with the normalized algorithm to handle the variations in the reference signal, and this adaptation law is referred as modified MIT rule. The performances of the proposed control algorithms are evaluated and shown by means of simulation on MATLAB and Simulink Manuscript profile -
Open Access Article
2 - Design and Implementation of Compressor Controller using Optimized VSD algorithm
Amin Hadidi Payam Fathollahi RadIssue 20 , Vol. 5 , Spring 2017Considering the high consumption of the air compressors, a control system of screw compressor is designed and implemented to deal with energy saving and localization of mentioned compressor. In this paper the variable speed drive (VSD) control algorithm based on proport MoreConsidering the high consumption of the air compressors, a control system of screw compressor is designed and implemented to deal with energy saving and localization of mentioned compressor. In this paper the variable speed drive (VSD) control algorithm based on proportional-integral-derivative (PID) controller is optimized to decrease power consumption and more stable motor speed and outlet pressure. Automatic PI factors adjustment according to system behavior is goal of this control system. To show the validity of the proposed algorithm, we simulated P and I changes. The results of simulation and practical test on a GA111 Atlas Copco compressor were established which demonstrate that the proposed algorithm provides system stability improvement and as a consequence depreciation reduction and energy saving were achieved. Manuscript profile -
Open Access Article
3 - Calculation of Reflection Loss in Fundamental TE Mode Versus Angle of Tilted End Facet of Superluminescent Light Emitting Diodes
Mohammad Hosein Salman Yengejeh nasser moslehi milaniIssue 44 , Vol. 11 , Winter 2023In this paper we study the acquisition of fundamental TE mode reflection in rectangular cavities from a tilted face (end mirror) of superluminescent light emitting diodes (SLD). When its width is 3 micrometers and the inclination of the mirror is 3 ̊, the result will be MoreIn this paper we study the acquisition of fundamental TE mode reflection in rectangular cavities from a tilted face (end mirror) of superluminescent light emitting diodes (SLD). When its width is 3 micrometers and the inclination of the mirror is 3 ̊, the result will be a reduction of nearly 20 dB of reflection. While if the width is 6 micrometers, the inclination of the mirror 2 ̊ will cause an excess reflection loss of 20 dB. The results obtained in our paper for small tilt angles are the Gaussian approximation of the guided state. While our results differ greatly from the Gaussian approximation for larger angles and larger reflection losses. Manuscript profile -
Open Access Article
4 - Fatigue prediction of hybrid joints and perforated plates using neural network
Ali Yousefnezhad Oskooi vahid Pourmohammad Karim Samadzamini Firooz Esmaeili GoldaragIssue 44 , Vol. 11 , Winter 2023Hybrid connections (bolts, glue) and perforated plates are one of the most important topics in various industries, including aerospace. This type of process occurs due to the growth of small cracks in the metal structure as a result of cyclic or intermittent loading. Si MoreHybrid connections (bolts, glue) and perforated plates are one of the most important topics in various industries, including aerospace. This type of process occurs due to the growth of small cracks in the metal structure as a result of cyclic or intermittent loading. Since failures occur suddenly, terrible accidents such as plane crashes, shipwrecks, bridge collapses, and toxic radioactive fallout can occur. To prevent these incidents, fatigue tests are performed on a sample of parts that is similar to the real part, so that the fatigue life can be obtained through this method. However, because fatigue tests are time-consuming and expensive, artificial intelligence methods have been used in this research to estimate the fatigue life of hybrid joints and perforated plates. In the experimental part of this research, plates made of aluminum alloy 2024-T3, which is one of the widely used materials in aerospace, the used materials are screws made of Hex head M5 and a special adhesive made of Loctite 3421 (Henkel ltd). Fatigue tests are extracted as input and output data from the related article. Out of a total of 71 fatigue tests, 35 tests were performed for perforated plates, 18 tests for hybrid joints, and 18 tests for bolted joints. Also, according to the number of data, the best result was when 80% of the data was considered for training the network and 20% was used as test data to evaluate the performance of the network. Finally, the predicted output was compared with the actual output and it was seen that the best performance of the neural network was after normalizing the data, that the error value was close to zero. Manuscript profile -
Open Access Article
5 - Detection of Cardiac Hypertrophy by RVM and SVM Algorithms
fereshte morsaliIssue 18 , Vol. 5 , Autumn 2016The meaning of the hypertropy word is the increasing size.Heart hypertropy is symptoms of increase the thickness of the heart muscle that the left ventricular hypertrophy of them is the most common.The causes of hypertrophy heart disease are high blood pressure , aortic MoreThe meaning of the hypertropy word is the increasing size.Heart hypertropy is symptoms of increase the thickness of the heart muscle that the left ventricular hypertrophy of them is the most common.The causes of hypertrophy heart disease are high blood pressure , aortic valve stenosis and sport activities respectively. Assessment of that by using ECG signal analysis is essential Because the risk of heart disease, ventricular hypertrophy increase the timely diagnosis The ECG signal demonstrate heart electric activities and include some characteristic points such as P wave , QRS complex and the T wave is formed. In this thesis , an algorithm has been presented for assessment of diagnosis of ventricular hypertrophy . In The presented algorithm first picks of ECG signal has been assessed then a high degree of statistical information such as skewness،kurtosis،R peak height and cumulants also have been used . Manuscript profile -
Open Access Article
6 - Mathematical Modeling of Cancer Cells and Chemotherapy Protocol Dealing Optimization Using Fuzzy Differential Equations And Lypunov Stability Criterion
Hadi AbbasnejadIssue 14 , Vol. 4 , Autumn 2015Mathematical models can simulate the growth and proliferation of cells in the interaction with healthy cells, the immune system and measure the toxicity of drug and its effects on healthy tissue pay. One of the main goals of modeling the structure and growth of cancer c MoreMathematical models can simulate the growth and proliferation of cells in the interaction with healthy cells, the immune system and measure the toxicity of drug and its effects on healthy tissue pay. One of the main goals of modeling the structure and growth of cancer cells is to find a control model suitable for administration among patients. In this study, a new mathematical model is designed to describe the changes in different phases of the cycle T cell proliferation, the population of immune cells, the proposed concentration of drug toxicity and treatment using differential equation and fuzzy Lyapunov stability, an optimal treatment protocol. One feature to consider is the rate of clearance of the drug in the body. Manuscript profile -
Open Access Article
7 - Comparative study of computer simulation softwares
fatemeh fakharIssue 29 , Vol. 8 , Summer 2019One of the methods for analyzing systems is simulation. Network simulation is a technique that models the behavior of the network by performing transaction calculations between different network entities and using mathematical formulas and taking observations from netwo MoreOne of the methods for analyzing systems is simulation. Network simulation is a technique that models the behavior of the network by performing transaction calculations between different network entities and using mathematical formulas and taking observations from network products. A network simulator is a piece of software or hardware that predicts the behavior of a computer network without a real network. Users can customize the simulator to fulfill their analytical needs of their own systems. In the realm of research, the creation of a network, especially large networks, is difficult in a real-time scenario, and its implementation in the real world is not easily feasible and very costly. So simulators help network developers to control whether the network is capable of working in real time or not, or whether it has enough performance. This reduces the time and cost required to test the functionality of the network. In this paper, we have investigated and compared the simulation software of the network. For this purpose, 23 major network simulators are considered and the results of these comparisons are expressed in several different views in multi tables. Manuscript profile -
Open Access Article
8 - Image classification optimization models using the convolutional neural network (CNN) approach and embedded deep learning system
AKBAR PAYANDAN Seyed Hossein Hosseini NazhadIssue 30 , Vol. 8 , Autumn 2019Deep learning has progressed rapidly in recent years and has been applied in many fields, which are the main fields of artificial intelligence. Traditional methods of machine learning most use shallow structures to deal with a limited number of samples and computational MoreDeep learning has progressed rapidly in recent years and has been applied in many fields, which are the main fields of artificial intelligence. Traditional methods of machine learning most use shallow structures to deal with a limited number of samples and computational units. When the target objects have rich meanings, the performance and ability to generalize complex classification problems will be quite inadequate. The convolutional neural network (CNN), which has been developed in recent years, widely used in image processing; because it has high skills in dealing with image classification and image recognition issues and it has led to great care in many machine learning tasks and it has become a powerful and universal model of deep learning. The combination of deep learning and embedded systems has created good technical dimensions. In this paper, several useful models in the field of image classification optimization, based on convolutional neural network and embedded systems, are discussed. Since this paper focuses on usable models on the FPGA board, models known for embedded systems such as MobileNet, ResNet, ResNeXt and ShuffNet have been studied. Manuscript profile -
Open Access Article
9 - Landslide susceptibility modelling using integrated application of computational intelligence in Ahar County, Iran
Solmaz Abdollahizad Mohammad Ali Balafar Bakhtiar Feizizadeh Amin Babazadeh Sangar Karim SamadzaminiIssue 34 , Vol. 9 , Autumn 2020Landslide susceptibility analysis is beneficial information for a wide range of applications. We aimed to explore and compare three machine learning (ML) techniques, namely the random forests (RF), support vector machine (SVM) and multiple layer neural networks (MLP) fo MoreLandslide susceptibility analysis is beneficial information for a wide range of applications. We aimed to explore and compare three machine learning (ML) techniques, namely the random forests (RF), support vector machine (SVM) and multiple layer neural networks (MLP) for landslide susceptibility assessment in the Ahar county of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 266 locations with landslide potentiality was recognized in the context of the study, and the Pearson correlation technique utilized in order to select the influencing factors in landslide models. The association between landslides and conditioning factors was also evaluated using a probability certainty factor (PCF) model. Three landslide models (SVM, RF, and MLP) were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were employed to validate and contrast the predictive capability of the obtained three models. The findings of the study in terms of the Pearson correlation technique method for the importance ranking of conditioning factors in the context area uncovered that slope, aspect, normalized difference vegetation index (NDVI), and elevation have the highest impact on the occurrence of the landslide. All in all, the MLP model had the utmost rate of prediction capability (85.22 %), after which, the SVM model (78.26 %) and the RF model (75.22 %) demonstrated the second and third rates. Besides, the study revealed that benefiting the optimal machine with the proper selection of the techniques could facilitate landslide susceptibility modeling. Manuscript profile -
Open Access Article
10 - A dynamic scalable fast blockchain-based Framework for Smart Cities: The case study of Intelligent Transportation System
Mohammad Bagher Moradi Siamak Najjar Karimi amir hossein jalaliIssue 44 , Vol. 11 , Winter 2023With the emergence of smart cities vision, its large distributed applications such as intelligent transportation systems demand scalable low-latency trusted data exchange architecture with high storage and computational resources for storing the high-volume of IoT data MoreWith the emergence of smart cities vision, its large distributed applications such as intelligent transportation systems demand scalable low-latency trusted data exchange architecture with high storage and computational resources for storing the high-volume of IoT data and providing real-time services. In recent years, blockchain technology has gained extensive attention to fulfil the requirements of such highly distributed large systems. However, there are a number of technical challenges in the integration of blockchain and IoT applications. Firstly, Bitcoin blockchain with low scalability and throughput is not able to provide fast services. Secondly, there are limitations like constrained spaces for establishing big blockchain nodes storing a massive volume of data generated by numerous smart IoT devices or sensors inside the streets of cities. This paper argues that solving both issues in one large blockchain network is infeasible. Therefore, we prioritize this two weakness of blockchain in relation to such systems and propose two separate level of blockchain networks cooperating with each other asynchronously to address them. One network called Fast BlockChain (FBC) composed of multiple scalable sub-blockchain networks responsible for fast services. Another network, CityBC, supports the networks of FBC through the long-term storing of their data and providing their smart manager with knowledge for dynamic autonomous partitioning of them in order to decrease network-to-network communications and avoid wasting storage resources and network bandwidth. Furthermore, this paper evaluates the ideal size of sub-blockchain and then proposes a novel idea for an initial partitioning technique before using collected data by blockchain nodes for dynamic partition of network. Manuscript profile
