Communications
H. Alizadeh Ghazijahani; M. Atashbar
Abstract
Background and Objectives: The combination of multiple-input-multiple-output (MIMO) with a Visible light communication (VLC) system leads to a higher speed of data transmission named the MIMO-VLC system. In multi-user (MU) MIMO-VLC, an LED array transmits signals to users. These signals are categorized ...
Read More
Background and Objectives: The combination of multiple-input-multiple-output (MIMO) with a Visible light communication (VLC) system leads to a higher speed of data transmission named the MIMO-VLC system. In multi-user (MU) MIMO-VLC, an LED array transmits signals to users. These signals are categorized as signals of private information for each user and signals of public information for all users. Methods: In this research, we design an omnidirectional precoding to transmit the signals of public information in the MU-MIMO-VLC network. We aim to maximize the achievable rate which leads to maximizing the received mean power at the possible location of the users. Besides maximizing the achievable rate, we consider equal mean transmission power constraints in all LEDs to achieve higher power efficiency of the power amplifiers used in the LED array. Based on this, we formulate an optimization problem in which the constraint is in the form of a manifold, and utilize a gradient method projected on the manifold to solve the problem. Results: Simulation results indicate that the proposed omnidirectional precoding can achieve superior received mean power besides more than 10x bit error rate reduction compared to the classical form without precoding utilization. Conclusion: In this research, we proposed an omnidirectional precoding for transmitting the public signals in the MU-MIMO-VLC system. The proposed optimization problem maximizes the received mean power constrained with equal transmission mean power of LEDs in the array.
Computational Intelligence
Z. K. Pourtaheri
Abstract
Background and Objectives: According to this fact that a typical autonomous underwater vehicle consumes energy for rotating, smoothing the path in the process of path planning will be especially important. Moreover, given the inherent randomness of heuristic algorithms, stability analysis of heuristic ...
Read More
Background and Objectives: According to this fact that a typical autonomous underwater vehicle consumes energy for rotating, smoothing the path in the process of path planning will be especially important. Moreover, given the inherent randomness of heuristic algorithms, stability analysis of heuristic path planners assumes paramount importance.Methods: The novelty of this paper is to provide an optimal and smooth path for autonomous underwater vehicles in two steps by using two heuristic optimization algorithms called Inclined Planes system Optimization algorithm and genetic algorithm; after finding the optimal path by Inclined Planes system Optimization algorithm in the first step, the genetic algorithm is employed to smooth the path in the second step. Another novelty of this paper is the stability analysis of the proposed heuristic path planner according to the stochastic nature of these algorithms. In this way, a two-level factorial design is employed to attain the stability goals of this research.Results: Utilizing a Genetic algorithm in the second step of path planning offers two advantages; it smooths the initially discovered path, which not only reduces the energy consumption of the autonomous underwater vehicle but also shortens the path length compared to the one obtained by the Inclined Planes system optimization algorithm. Moreover, stability analysis helps identify important factors and their interactions within the defined objective function.Conclusion: This proposed hybrid method has implemented for three different maps; 36.77%, 48.77%, and 50.17% improvements in the length of the path are observed in the three supposed maps while smoothing the path helps robots to save energy. These results confirm the advantage of the proposed process for finding optimal and smooth paths for autonomous underwater vehicles. Due to the stability results, one can discover the magnitude and direction of important factors and the regression model.
Artificial Intelligence
H. Karim Tabbahfar; F. Tabib Mahmoudi
Abstract
Background and Objectives: Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish ...
Read More
Background and Objectives: Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish surface water bodies from other land covers. This research has a special consideration to the effect of different types of land covers around water bodies. For this reason, two different water bodies, lake and wetland, have been used to evaluate the implementation results.Methods: The main objective of this research is to evaluate the capabilities of the genetic algorithm in optimum selection of the spectral indices extracted from Sentinel-2 satellite image due to distinguish surface water bodies in two case studies: 1) the pure water behind the Karkheh dam and 2) the Shadegan wetland having water mixed with vegetation. In this regard, the set of optimal indices is obtained with the genetic algorithm followed by the support vector machine (SVM) classifier. Results: The evaluation of the classification results based on the optimum selected spectral indices showed that the overall accuracy and Kappa coefficient of the recognized surface water bodies are 98.18 and 0.9827 in the Karkheh dam and 98.04 and 0.93 in Shadegan wetland, respectively. Evaluation of each of the spectral indices measured in both study areas was carried out using quantitative decision tree (DT) classifier. The best obtained DT classification results show the improvements in overall accuracy by 1.42% in the Karkheh Dam area and 1.56% in the Shadegan Wetland area based on the optimum selected indices by genetic algorithm followed by SVM classifier. Moreover, the obtained classification results are superior compared with Random Forest classifier using the optimized set of spectral features.Conclusion: Applying the genetic algorithm on the spectral indices was able to obtain two optimal sets of effective indices that have the highest amount of accuracy in classifying water bodies from other land cover objects in the study areas. Considering the collective performance, genetic algorithm selects an optimal set of indices that can detect water bodies more accurately than any single index.
M. Hasanluo; F. Soleimanian Gharehchopogh
Abstract
A successful software should be finalized with determined and predetermined cost and time. Software is a production which its approximate cost is expert workforce and professionals. The most important and approximate software cost estimation (SCE) is related to the trained workforce. Creative nature ...
Read More
A successful software should be finalized with determined and predetermined cost and time. Software is a production which its approximate cost is expert workforce and professionals. The most important and approximate software cost estimation (SCE) is related to the trained workforce. Creative nature of software projects and its abstract nature make extremely cost and time of projects difficult to estimate. Various methods have been presented in the software project cost estimation for performing a software project in the area of software engineering. COCOMO II model is one of the most documented models among template-based methods that has been proposed by Bohm. Common methods for estimating the time and cost are essentially abstract, accordingly, providing new methods for SCE is required and necessary. In this paper, a new method is presented to solve the problem of SCE by using hybrid particle swarm optimization (PSO) algorithm and K-nearest neighbor (KNN) algorithm. The method was evaluated on 6 multiple datasets with 8 different evaluation criteria. Obtained results show the more accurate performance of the proposed method.
