Mohammad Jalali; Reihaneh Kardehi Moghaddam; Naser Pariz
Abstract
Nowadays, sea wave energy is widely regarded as an energy source that is clean, renewable and highly available for power extraction. The subject of extracting the maximum power from sea waves in Iran is of great importance due to access to the Caspian Sea and the Persian Gulf. Moreover, there is ...
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Nowadays, sea wave energy is widely regarded as an energy source that is clean, renewable and highly available for power extraction. The subject of extracting the maximum power from sea waves in Iran is of great importance due to access to the Caspian Sea and the Persian Gulf. Moreover, there is a need for resources with no air pollution besides providing a part of the country's power demand without costly infrastructure, so this research field is highly interesting although has rarely been addressed so far. The main purpose of this paper is to use an appropriate control strategy to improve the performance of point absorbers. In this scheme, to consider high uncertainty in the parameters of the power take-off system in different atmospheric conditions and improve controller performance, a new improved black hole algorithm is introduced to tune fuzzy controller parameters. The proposed method is then implemented for tuning the fuzzy controller parameters in order to obtain the maximum power capture of wave energy converters. Compared to particle swarm optimization and conventional black hole algorithm, the results of the proposed method indicate enhancements in reference velocity tracking and absorbed power. Finally, some simulations are performed and the proposed controller is implemented for the wave spectrum of the Persian Gulf waters, so the performance of the proposed controller is evaluated.
Khosro Rezaee; Mohammad Khalil Nakhl Ahmadi; Maryam Saberi Anari
Abstract
Segmentation is a fundamental element in medical image processing (MIP) and has been extensively researched and developed to aid in clinical interpretation and utilization. This article discusses a method for segmenting abnormal masses or tumors in medical images that is both robust and effective. We ...
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Segmentation is a fundamental element in medical image processing (MIP) and has been extensively researched and developed to aid in clinical interpretation and utilization. This article discusses a method for segmenting abnormal masses or tumors in medical images that is both robust and effective. We suggested a method based on Active Contour (AC) and modified Level-set techniques to detect malignancies in magnetic resonance imaging (MRI), mammography, and computed tomography (CT). To segment malignant masses, the active contour approach, the energy function, the level-set method, and the proposed F function are employed. The system was evaluated using 160 medical images from two databases, including 80 mammograms and 80 MRI brain scans. The algorithm for segmenting suspicious segments has an accuracy, recall, and precision of 96.25%, 95.60%, and 95.71%, respectively. By adding this technique into tissue imaging devices, the accuracy of diagnosing images with a relatively large volume that are evaluated fast is increased. Cost savings, time savings, and high precision are all advantages of the approach that set it apart from similar systems.
Omnia Mezghani; Mahmoud Mezghani
Abstract
Mobile Wireless Sensors Networks (MWSNs) are used in several applications presenting difficult/dangerous environment and/or requiring the movement of sensors after initial deployment. Optimizing the use of the limited energy resource in a MWSN is a key challenge for researchers to maintain longer network ...
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Mobile Wireless Sensors Networks (MWSNs) are used in several applications presenting difficult/dangerous environment and/or requiring the movement of sensors after initial deployment. Optimizing the use of the limited energy resource in a MWSN is a key challenge for researchers to maintain longer network survival. This paper attempts to provide an energy-efficient data routing solution for large MWSNs. The aim of this work is to propose a cluster-based scheduling protocol for MWSN. The network is firstly divided into an optimal number of clusters according to sensors connectivity. Secondly, a sleep scheduling algorithm is proposed to save the energy consumption by turning off the overlapped nodes in the sensing field. This method is distributed among sensor nodes in each cluster. It is based on the perimeter coverage level of mobile sensor nodes to schedule their activities according to their weights. The weight is used to balance the energy consumption for all sensor nodes in a cluster. The proposed approach ranges from sensors deployment, their organization to their operational mode. Experimental results demonstrate that the proposed cluster-based scheduling algorithm, based on the perimeter coverage of sensors, provides higher energy efficiency and longer lifetime coverage for MWSNs as compared to other protocols.
Hoda Ghabeli; Amir Sabbagh Molahosseini; Azadeh Alsadat Emrani Zarandi
Abstract
In this paper, variable latency speculative Multiply-Accumulator (MAC) architectures are introduced. The proposed architectures use the idea of integrating the results vectors of multiplier in parallel with the accumulator to create asynchronous data paths design. The proposed variable latency speculative ...
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In this paper, variable latency speculative Multiply-Accumulator (MAC) architectures are introduced. The proposed architectures use the idea of integrating the results vectors of multiplier in parallel with the accumulator to create asynchronous data paths design. The proposed variable latency speculative MACs consist of two short and long data paths and a circuit is used to select a suitable path with minimum overhead. In order to investigate variable latency speculative MACs performances, proposed architectures have been synthesized using the Faraday’s 90 nm technology library, for operand lengths 8, 16 and 32 bits. Obtained results show that the proposed MAC architectures provide a variety of trade-offs in the power-delay-area space that outperform the existing designs that use only the integration technique.
Marziyeh Kashani; Atefeh Amindoust; Mahdi Karbasian; Abbas Sheikh Aboumasoudi
Abstract
In today's industrialized world, to survive in competitive markets, businesses are required to identify the expectations of their customers, whether explicitly or implicitly, and focus on these needs from the planning to the operational level. To produce customer-oriented products, it is important ...
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In today's industrialized world, to survive in competitive markets, businesses are required to identify the expectations of their customers, whether explicitly or implicitly, and focus on these needs from the planning to the operational level. To produce customer-oriented products, it is important to extract design requirements that meet the identified needs. The purpose of the present study, which has been done on Photovoltaic systems (PV), is to develop a model for the selection of the optimal components required to design a new product. In this regard, Customer Needs (CNs) which have been extracted from the first stage of the systems engineering process have been interpreted to Functional Requirements (FRs) using the first matrix of QFD. They have examined and prioritized by use of Analytical Network Process (ANP). Then FRs have entered the second matrix of QFD and examined along with leveled components based on the alternatives available for each component. Also, the Design Structure Matrix (DSM) has been used to evaluate the effect of elements upon each other in each phase. Finally, the optimal components are selected by the presented multi-objective mathematical model. Accurate assessment of customer needs using a systems engineering framework in addition to extracting important functional requirements to meet the needs as well as selecting the optimal components for new product design, by integrating the three methods QFD, ANP and DSM, using multi-objective mathematical modeling has not been done by other researchers so far.
Yaser Mehregan; Keyvan Mohebbi
Abstract
The successful operation of a wireless sensor network depends on the proper coverage of the environment, which in turn is affected by the number and location of sensors. In most cases, the sensors are placed randomly in the deployment region, so by default, most coverage is not achieved in their initial ...
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The successful operation of a wireless sensor network depends on the proper coverage of the environment, which in turn is affected by the number and location of sensors. In most cases, the sensors are placed randomly in the deployment region, so by default, most coverage is not achieved in their initial deployment. One of the major challenges for network design is to determine the location strategy of the sensors so that the deployed nodes can cover as many regions as possible. The objective of this study is to solve this problem in such a way that the energy consumption of the nodes is minimal. Because the power supply of the sensor nodes is a non-rechargeable battery. The proposed approach uses division and detection of uncovered regions. Then a greedy method based on the topology and properties of the nodes and the network deployment region is presented to select the optimal nodes and cover the region. The proposed approach is simulated and the evaluation results show a decrease in the displacement of the sensors for more coverage and a reduction in energy consumption compared to similar works.
Navid Habibi; MohammadReza Salehnamadi; Ahmad Khademzadeh
Abstract
Applying semiconductor technology, network-on-chips (NoCs) are designed on silicon chips to expand on-chip communications. Three-dimensional (3D) mesh-based architecture is also known as a basic NoC architecture characterized by better energy consumption and latency compared with two-dimensional (2D) ...
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Applying semiconductor technology, network-on-chips (NoCs) are designed on silicon chips to expand on-chip communications. Three-dimensional (3D) mesh-based architecture is also known as a basic NoC architecture characterized by better energy consumption and latency compared with two-dimensional (2D) ones. Recently developed architectures are based on regular mesh. However, there are serious drawbacks in NoC architectures including high power consumption, energy consumption, and latency. Therefore, making an improvement in topology diameter would overcome these shortcomings. Accordingly, a new 3D mesh-based NoC architecture is proposed in the present study utilizing the star node, consisting of a new 3D topology with small diameter and new deadlock-free routing. The diameter of this architecture is then compared with its counterparts. Afterwards, the scalable universal matrix multiplication algorithm (SUMMA) is implemented in the proposed architecture. The results indicate a smaller network diameter, lower energy consumption (32%), less network latency (8.6%), as well as enhancement in throughput average (13.6%). The proposed matrix multiplication algorithm also implies improvement in the cost of the proposed architecture in comparison with its counterparts.
Mohammadbagher Shahgholian; Davood Gharavian
Abstract
In this paper, we use a nonlinear hierarchical model predictive control (MPC) to stabilize the Segway robot. We also use hardware in the loop (HIL) simulation in order to model the delay response of the wheels' motor and verify the control algorithm. In Two-Wheeled Personal Transportation Robots (TWPTR), ...
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In this paper, we use a nonlinear hierarchical model predictive control (MPC) to stabilize the Segway robot. We also use hardware in the loop (HIL) simulation in order to model the delay response of the wheels' motor and verify the control algorithm. In Two-Wheeled Personal Transportation Robots (TWPTR), changing the center of mass location and value, the nonlinearity of the equations, and the dynamics of the system are topics complicating the control problem. A nonlinear MPC predicts the dynamics of the system and solves the control problem efficiently, but requires the exact information of the system models. Since model uncertainties are unavoidable, the time-delay control (TDC) method is used to cancel the unknown dynamics and unexpected disturbances. When TDC method is applied, the results show that the maximum required torque for engines is reduced by 7%. And the maximum displacement of the robot has dropped by 44% around the balance axis. In other words, robot stability has increased by 78%. Due to the cost of implementing control in practice, this research runs the HIL simulation for the first time. The use of this simulation helps in implementing the control algorithms without approximation, and also the system response can be discussed in a more realistic way.
Fatemeh Heydari Pirbasti; Mahmoud Modiri; Kiamars Fathi-Hafshejani; Alireza Rashidi-Komijan
Abstract
With the expansion of human activities, the volume of waste and hazardous waste produced has increased dramatically. Increasing the volume of waste has created challenges such as transportation hazards, cleanup, disposal, energy consumption, and most important environmental problems. The difficulty of ...
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With the expansion of human activities, the volume of waste and hazardous waste produced has increased dramatically. Increasing the volume of waste has created challenges such as transportation hazards, cleanup, disposal, energy consumption, and most important environmental problems. The difficulty of unsafe waste control is one of the critical studies topics. Finding the optimal location of hazardous waste disposal is one of the issues that, if done properly, can significantly reduce the aforementioned challenges. The increasing volume of information, the complexity of multivariate decision criteria, have led to the lack of conventional methods for finding the optimal location. Machine learning methods have proven to be effective and superior in many areas. In this paper, a new method based on machine learning for finding the optimal location of hazardous waste disposal is presented. In the proposed method, after applying clustering in the separation of the desired areas, the gray wolf algorithm optimization is used to find the optimal location of waste disposal. In order to apply the gray wolf optimization algorithm, a multivariate target function is defined. Cluster centers as were chosen as location of waste disposal. Proposed method is performed on collected data from the study area in Iran, Tehran province. Proposed clustering method is evaluated and compared with some metaheuristics algorithm. The simulation results of the proposed method show cost reduction in finding the desired locations compared to similar researches. Also, Xi and Separation index was used for evaluation of the proposed clustering method to select the best location. The number of best locations using Xi and Separation index claim the superiority of the proposed method.
