A mobile robot mapping model inspired from the place cells functionality of hippocampus based on dimension reduction technique

ABSTRACT

In this paper, a new mobile robot mapping algorithm inspired from the functionality of hippocampus cells is presented. Place cells in hippocampus can store a map of the environment. This model fuses odometry and vision data based on dimensionality reduction technique, hierarchically. These two types of data are first fused and then considered as inputs to the place cell model. Place cells do the clustering of places. The proposed Place cell model has two types of inputs: Grid cells input and input from the lateral entorhinal cortex (LEC). The LEC is modelled based on the dimension reduction technique. Therefore, the data that causes locations different to be inserted into the place cell from this layer. Another contribution is proposing a new unsupervised dimension reduction method based on k-means. The method can find perpendicular independent dimensions. Also, the distance of cluster centres found in these dimensions is maximised. The method was compared with LDA and PCA in standard functions. Although LDA is a supervised method, the result showed that the proposed unsupervised method outperformed. To evaluate the place cells model, sequences of images collected by a mobile robot was used and similar results to real place cells achieved.     

A heuristic supervised Euclidean data difference dimension reduction for KNN classifier and its application to visual place classification

In this paper, we propose a novel supervised dimension reduction algorithm based on K-nearest neighbor (KNN) classifier. The proposed algorithm reduces the dimension of data in order to improve the accuracy of the KNN classification. This heuristic algorithm proposes independent dimensions which decrease Euclidean distance of a sample data and its K-nearest within-class neighbors and increase Euclidean distance of that sample and its M-nearest between-class neighbors. This algorithm is a linear dimension reduction algorithm which produces a mapping matrix for projecting data into low dimension. The dimension reduction step is followed by a KNN classifier. Therefore, it is applicable for high-dimensional multiclass classification. Experiments with artificial data such as Helix and Twin-peaks show ability of the algorithm for data visualization. This algorithm is compared with state-of-the-art algorithms in classification of eight different multiclass data sets from UCI collection. Simulation results have shown that the proposed algorithm outperforms the existing algorithms. Visual place classification is an important problem for intelligent mobile robots which not only deals with high-dimensional data but also has to solve a multiclass classification problem. A proper dimension reduction method is usually needed to decrease computation and memory complexity of algorithms in large environments. Therefore, our method is very well suited for this problem. We extract color histogram of omnidirectional camera images as primary features, reduce the features into a low-dimensional space and apply a KNN classifier. Results of experiments on five real data sets showed superiority of the proposed algorithm against others.     

Electrophoretic deposition of ZnO–CeO2 mixed oxide nanoparticles

ZnO–CeO₂ nanoparticles were synthesized by microwave combustion and deposited by electrophoretic deposition (EPD). The effect of using two different alcohols, ethanol and methanol, was investigated on EPD behavior and morphology of deposited film. Moreover, the effect of concentration of nanoparticles and applied voltage on the mass of deposit and the variation in the current density were investigated. With a change in the alcohol type, the surface morphology of deposition changed and some voids were observed on the deposition surface in ethanol. In all cases, with increasing concentration of nanoparticles in suspension, the number of developed cracks increased. Besides, a rise in voltage led to an increase in the number of cracks. The EPD processes in ethanol and methanol suspension were simulated over time using different zero boundary conditions. Hemi‐spherical morphology was seen for the nanoparticles deposited in ethanol. This kind of growth was simulated based on the changes in electrical field.     

Microencapsulation of disperse dye particles with nano film coating through layer by layer technique

Throughout the past ten years, comprehensive understanding of fundamental and applied research has focused on functional coating and specifically on microencapsulaion. In this study, weak polycation poly(allylamine hydrochloride) and strong polyanion poly(sodium styrene sulfonate) were used for fabrication of nano film through layer by layer technique on the surface of disperse dye particles. Then micron‐sized particles were surrounded by poly(urea formaldehyde) using in‐situ polymerization. Chemical structure, surface morphology, and size distribution of these novel microcapsules were characterized by Fourier transform infrared spectrometry, differential scanning calorimetry, optical microscopy, and scanning electronic microscopy. Size and surface morphology of the microcapsules can be optimized by selecting proper weight ratio of urea to formaldehyde and core to shell material type, and amount of surfactant and agitation rate. This technology demonstrated good capability in several applications in textile industry, such as dying fabrics because of saving huge amount of water and showing slow‐release property of dye without using dye assistant agents. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Modeling of Ground Water Salinity on the Caspian Southern Coasts

Water salinity is one of the main restrictive factors in water exploiting. Also, unsuitable management and exploitation of water resources has led to an increase of surface and groundwater salinity. Thus, in view of human needs to water resources, it is necessary to study and define water salinity factors in order to weaken these factors. This research has been conducted to investigate the factors of groundwater salinity and also, to provide a model for estimating groundwater salinity on the Caspian southern coasts. Data included in the model are: water qualitative examinations in the area, annual precipitation and evaporation, water table depth, surface water salinity, aquifer formation (Transmissivity) and distance from Caspian Sea. Surface and groundwater salinity was estimated by sampling in different sites on the Caspian southern coasts. Then, Multivariate Regression method was used by using SPSS software. In this stage, groundwater EC has been used as a variable for water salinity or dependent variable and groundwater salinity factors have been used as independent variables. A linear model and a non-linear model were presented. The models efficiency was evaluated by applying them in the sites that their data were not used for presenting the models. Finally, groundwater EC average map was provided by using the presented non-linear model and Geographic Information System in the Eastern part of Mazandaran province. In view of salinity hazard increases in the coastal terrains and agricultural areas, the places with high hazard salinity must be defined and managed to decrease water resources salinity.     

An Improved Method for Edge Detection and Image Segmentation Using Fuzzy Cellular Automata

Image segmentation is one of the most important and challenging problems in image processing. The main purpose of image segmentation is to partition an image into a set of disjoint regions with uniform attributes. In this study, we propose an improved method for edge detection and image segmentation using fuzzy cellular automata. In the first stage, we introduce a new edge detection method based on fuzzy cellular automata, called the texture histogram, and empirically demonstrate the efficiency of the proposed method and its robustness in denoising images. In the second stage, we propose an edge detection algorithm by considering the mean values of the edges matrix. In this algorithm, we use four fuzzy rules instead of 32 fuzzy rules reported earlier in the literature. In the third and final stage, we use the local edge in the edge detection stage to more accurately accomplish image segmentation. We demonstrate that the proposed method produces better output images in comparison with the separate segmentation and edge detection methods studied in the literature. In addition, we show that the method proposed in this study is more flexible and efficient when noise is added to an image.     

Random forest and rotation forest ensemble methods for classification of epileptic EEG signals based on improved 1D‐LBP feature extraction

In this study, an efficient method for extracting and selecting features of unrefined Electroencephalogram (EEG) signals according to the one‐dimensional local binary pattern (1D‐LBP) is presented. Considering that taking a correct decision on various issues particularly in the field of diagnosing diseases, such as epilepsy, is of paramount importance, a functional approach is designed to extract the optimal features of EEG signals. The proposed method is comprised of two main steps: First, extraction and selection of features is performed based on a novel improved 1D‐LBP model followed by data normalization through principal component analysis (PCA); as combining 1D‐LBP neighboring models and PCA (1D‐LBP^c2p) method. The second step includes classification using two of the best ensemble classification algorithms, that is, random forest and rotation forest. A comparative evaluation is performed between the proposed methods and 13 distinct reported approaches including uniform and non‐uniform 1D‐LBP. The results are demonstrating that the combining method presented in our approaches has superiority along with efficiency by providing higher accuracy compared to the other models and classifiers. The proposed method in this paper can be considered as a new method for feature extraction and selection of other kinds of EEG signals and data sets.     

The effect of resin formulation on the cellular morphology and mechanical properties of phenolic foams

In the present study, the effects of blowing agent concentration, surfactant, and resin viscosity on the cellular structure, density, and compressive strength of phenolic foams were investigated. The mechanism of foaming was studied by thermal analyses, as well. The scanning electron microscopy was performed to investigate the morphology of foams. The presence of surfactant was essential to obtain a foam structure. By increasing the amount of blowing agent in the formulation, the bubbles became larger. The variation of the resin viscosity had the sharp effect on the cell size and its distribution so that the cell size dropped from 108 to 77 μm in the sample with the highest viscosity. The mechanical properties were significantly affected by foam structure as well as the cell uniformity. By decreasing the average cell sizes, the compression strength and modulus were improved up to more than 60%. Finally, the optimum values for viscosity of resin and, blowing agent, and surfactant concentrations were obtained. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48331.     

Task scheduling using NSGA II with fuzzy adaptive operators for computational grids

Scheduling algorithms have an essential role in computational grids for managing jobs, and assigning them to appropriate resources. An efficient task scheduling algorithm can achieve minimum execution time and maximum resource utilization by providing the load balance between resources in the grid. The superiority of genetic algorithm in the scheduling of tasks has been proven in the literature. In this paper, we improve the famous multi-objective genetic algorithm known as NSGA-II using fuzzy operators to improve quality and performance of task scheduling in the market-based grid environment. Load balancing, Makespan and Price are three important objectives for multi-objective optimization in the task scheduling problem in the grid. Grid users do not attend load balancing in making decision, so it is desirable that all solutions have good load balancing. Thus to decrease computation and ease decision making through the users, we should consider and improve the load balancing problem in the task scheduling indirectly using the fuzzy system without implementing the third objective function. We have used fuzzy operators for this purpose and more quality and variety in Pareto-optimal solutions. Three functions are defined to generate inputs for fuzzy systems. Variance of costs, variance of frequency of involved resources in scheduling and variance of genes values are used to determine probabilities of crossover and mutation intelligently. Variance of frequency of involved resources with cooperation of Makespan objective satisfies load balancing objective indirectly. Variance of genes values and variance of costs are used in the mutation fuzzy system to improve diversity and quality of Pareto optimal front. Our method conducts the algorithm towards best and most appropriate solutions with load balancing in less iteration. The obtained results have proved that our innovative algorithm converges to Pareto-optimal solutions faster and with more quality.