JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT)
Year:2021 | Volume:10 | Issue:1|Papers Count:7

In this paper, a simple structured light system is designed to produce the three-dimensional points cloud from the un-textured surfaces. The system consists of two cameras and a planer laser, in which the 3D contents are produced through the stereo images taken from the light reflected by the intersection of a planer laser and the 3D surface of an object. There was no control over how the laser swept through the surface and the instantaneous parameters of the laser plane were not known in advance. Considering the knowledge of the internal camera calibration parameters and the relative orientation of the stereo-pairs, the video captured by the cameras are normalized during the epipolar re-sampling process. Next, in each pair of simultaneous frames, the matched points located at the 3D section of the laser’s plane are then identified. During the simultaneous space intersection of the matched points, a constraint is applied to enforce the singularity of the covariance matrix of 3D points lie in the intersection of the laser's plane and the 3D surface of an object to ensure their co-planarity. By applying this statistical constraint, the precision of the surface 3D reconstruction was improved up to 41% in this structured light system.

The aim of this study is to investigate writings to find out the mood of people in typing texts. In this study, 14640 tweets related to airlines were used to analyze emotions in three categories: positive, negative and neutral. The novel proposed approach has three main steps. In the first step, we perform a pre-processing operation to purify the dataset. In the second step, using the Imperialist Competitive Algorithm (ICA), the main keywords from all the existing texts are extracted. Keywords are the words that have the most impact on categorization. Then, a convolution neural network (CNN) is exploited to extract more features. In the last step, classification, using a multilayer perceptron neural network (MLP), is applied. In the proposed new method, unlike the conventional methods in which words go to the next stage after preprocessing, we use the Imperialist Competitive Algorithm to extract the main words from all these words, which in turn causes There is a significant reduction in the volume of input words. using this new proposed approach, we achieved precision, accuracy and recall of 0. 990, 0. 983 and 0. 875, respectively. The experimental results indicated the superiority of the proposed method the comparison with other well-known approaches.

Electronic Warfare Support Systems (ESM/ELINT) receive, process, analyze and direction finding signals sent by radars. Direction Finding and determining the Direction of Arrival (DOA) is one of the most important radar parameters that plays an important role in the operations of processing, de-interleaving, Clustering, classification and location of radars. Various techniques are used to direction finding (DF) and estimate the angle of Arrival. Among the methods, amplitude comparison direction finding (ADF) is one of the most common techniques due to its high speed and low computational complexity. Multipath fading degrades the performance of Direction Finding systems. In hilly and suburban environments, the direction finding accur

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by behaviors in nature. In this paper, new nature-inspired algorithm based on behavior of water streams of rain, for solving of real-valued continues optimization problems is introduced. The proposed algorithm does not require the information of the first or second order Derivatives of the object function. Hence, it is a direct method. We investigate the properties of this algorithm. Besides, we apply the proposed algorithm to solve a non-linear optimization problem, where the object function is highly irregular (neither convex nor concave). In addition, the global optimal solution can be found. In the proposed algorithm, the searcher agents are a collection of water currents, which moved based on gravity. The proposed algorithm has been developed from a motivation to find a simpler and more effective search algorithm to optimize multi-dimensional numerical test functions. It is effective in searching and finding an optimum solution from a large search domain within an acceptable CPU time. Statistical analysis compared the solution quality with well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.

The k-nearest neighbor's algorithm (KNN) is one of the most widely used and useful nonparametric classification algorithms. The classification mechanism of this algorithm involves computing the distance between new instances and the instances whole classes are known. When the dataset contains non-numerical (ordinal and nominal) attributes, the performance of the algorithm can be significantly affected by how this distance is measured. In this paper, we attempt to improve the performance of the KNN algorithm by presenting a new solution for computing the distance of non-numerical traits. For this purpose, the Particle Swarm Optimization (PSO) algorithm is used. The task of this algorithm is to determine the best value of the distance between two states in a non-integer trait so that the accuracy of the KNN algorithm is increased. UCI University Learning Repository Data is used to test this idea. The results obtained from the proposed algorithm are compared with several other improved algorithms and show the useful improvement of this mechanism.

Massive Multiple-Input Multiple-Output (mMIMO) is a promising approach for the next generation wireless telecommunication systems. In these systems, having a suitable approach for channel estimation is mandatory in order to increase the data rate and spectral efficiency. Distributed Compressed Sensing (DCS) is prominent in extracting joint sparse channel state information (CSI). Here, we have utilized Alternating Direction Method of Multipliers (ADMM) approach to generate quasi-orthogonal pilot sequences, in order to improve the channel estimation approach based on DCS approach. In simulation results, it is represented that ADMM-based pilot sequences are very powerful in extracting CSI of the joint sparse channel ensembles.

This paper mainly aims to determine the optimal drug dosage for the purpose of reducing the population of cancer cells in melanoma patients. To do so, Reinforcement Learning method and the eligibility traces algorithm are employed, giving us the advantage of creating a compromise between the two algorithms of the reinforcement learning, being Monte-Carlo and Temporal Difference. Furthermore, it can be said that using this approach, there was no need to employ a mathematical model in the whole process. However, as its implementation on the real system was not possible, a delayed nonlinear mathematical model is used to investigate the performance of the proposed controller and simulate the behavior of the environment. It should be noted this mathematical model made use of no control method. This is the first time that population control of cancer cells is applied and tested on this model. To know of the optimal dosage of the drug, it should be mentioned that the drug is required to prevent the side effects on healthy/normal cells as much as possible. According to the obtained results, the eligibility traces algorithm is able to control and reduce the population of cancer cells through injecting the sub-optimal drug dose. This will increase the level of immunity in our body. Finally, to demonstrate the advantage of a selective method of increasing the rate of cancer cell death, this method is compared with the Q-learning algorithm and optimal control. By applying the fault to the sensor, the performance of the proposed controller to reduce cancer cells was investigated. The adaptability of the proposed method with the environment changes is checked afterwards. To this end, uncertainty in the system parameters and initial conditions are applied and the population of cancer cells are controlled in five melanoma patients. Moreover, having added noise to the system, it was shown that the eligibility traces algorithm is able to control the population of cancer cells and make it reach zero. Additionally, the convergence speed of both eligibility traces algorithm and Q learning algorithm in reducing the number of cancer cells for different learning rates was investigated.