A Novel Method for L Band SAR Image Segmentation Based on Pulse Coupled Neural Network

Pulse Coupled Neural Network (PCNN) is claimed as a third generation neural network. PCNN has wide purpose in image processing  such as segmentation, feature extraction, sharpening etc.  Not like another neural network architecture, PCNN do not need training. The only weaknes point  of PCNN is parameter tune due to  seven parameters in its five equations. In this research we proposed a novel method for segmentation based on modified PCNN.  In order to evaluate the proposed method, we processed L Band Multipolarisation  Synthetic Apperture Radar Image. The Results showed all area extracted both by using PCNN and ICM-PCNN from the SAR image are match to the groundtruth. There fore the proposed method is work properly.Copyright © 2017  International Journal of  Artificial Intelegence Research.All rights reserved

Manual and automatic image analysis segmentation methods for blood flow studies in microchannels

In blood flow studies, image analysis plays an extremely important role to examine raw data obtained by high-speed video microscopy systems. This work shows different ways to process the images which contain various blood phenomena happening in microfluidic devices and in microcirculation. For this purpose, the current methods used for tracking red blood cells (RBCs) flowing through a glass capillary and techniques to measure the cell-free layer thickness in different kinds of microchannels will be presented. Most of the past blood flow experimental data have been collected and analyzed by means of manual methods, that can be extremely reliable, but they are highly time-consuming, user-intensive, repetitive, and the results can be subjective to user-induced errors. For this reason, it is crucial to develop image analysis methods able to obtain the data automatically. Concerning automatic image analysis methods for individual RBCs tracking and to measure the well known microfluidic phenomena cell-free layer, two developed methods are presented and discussed in order to demonstrate their feasibility to obtain accurate data acquisition in such studies. Additionally, a comparison analysis between manual and automatic methods was performed.This project has been funded by Portuguese national funds of FCT/MCTES (PIDDAC) through the base funding from the following research units: UIDB/00532/2020 (Transport Phenomena Research Center—CEFT), UIDB/04077/2020 (Mechanical Engineering and Resource Sustainability Center—MEtRICs), UIDB/00690/2020 (CIMO). The authors are also grateful for the partial funding of FCT through the projects, NORTE-01-0145-FEDER-029394 (PTDC/EMD-EMD/29394/2017) and NORTE-01-0145-FEDER-030171 (PTDC/EMD-EMD/30171/2017) funded by COMPETE2020, NORTE2020, PORTUGAL2020 and FEDER. D. Bento acknowledges the PhD scholarship SFRH/BD/ 91192/2012 granted by FCT

Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study

2015 Annual Research Symposium Abstract Book

2015 annual volume of abstracts for science research projects conducted by students at Trinity Colleg

Emergent properties of microbial activity in heterogeneous soil microenvironments:Different research approaches are slowly converging, yet major challenges remain

Over the last 60 years, soil microbiologists have accumulated a wealth of experimental data showing that the usual bulk, macroscopic parameters used to characterize soils (e.g., granulometry, pH, soil organic matter and biomass contents) provide insufficient information to describe quantitatively the activity of soil microorganisms and some of its outcomes, like the emission of greenhouse gases. Clearly, new, more appropriate macroscopic parameters are needed, which reflect better the spatial heterogeneity of soils at the microscale (i.e., the pore scale). For a long time, spectroscopic and microscopic tools were lacking to quantify processes at that scale, but major technological advances over the last 15 years have made suitable equipment available to researchers. In this context, the objective of the present article is to review progress achieved to date in the significant research program that has ensued. This program can be rationalized as a sequence of steps, namely the quantification and modeling of the physical-, (bio)chemical-, and microbiological properties of soils, the integration of these different perspectives into a unified theory, its upscaling to the macroscopic scale, and, eventually, the development of new approaches to measure macroscopic soil characteristics. At this stage, significant progress has been achieved on the physical front, and to a lesser extent on the (bio)chemical one as well, both in terms of experiments and modeling. In terms of microbial aspects, whereas a lot of work has been devoted to the modeling of bacterial and fungal activity in soils at the pore scale, the appropriateness of model assumptions cannot be readily assessed because relevant experimental data are extremely scarce. For the overall research to move forward, it will be crucial to make sure that research on the microbial components of soil systems does not keep lagging behind the work on the physical and (bio)chemical characteristics. Concerning the subsequent steps in the program, very little integration of the various disciplinary perspectives has occurred so far, and, as a result, researchers have not yet been able to tackle the scaling up to the macroscopic level. Many challenges, some of them daunting, remain on the path ahead

Brainless but smart: Investigating cognitive-like behaviors in the acellular slime mold physarum polycephalum

Evolutionary pressures to improve fitness, have enabled living systems to make adaptive decisions when faced with heterogeneous and changing environmental and physiological conditions. This dissertation investigated the mechanisms of how environmental and physiological factors affect the behaviors of non-neuronal organisms. The acellular slime mold Physarum polycephalum was used as the model organism, which is a macroscopic, unicellular organism, that self-organizes into a network of intersecting tubules. Without using neurons, P. polycephalum can solve labyrinth mazes, build efficient tubule networks, and make adaptive decisions when faced with complicated trade-offs, such as between food quality and risk, speed and accuracy, and exploration and exploitation. However, the understanding of the mechanisms used by P. polycephalum in exhibiting such behaviors is very limited. Therefore, the objective of this dissertation is to understand the mechanisms adopted by non-neuronal organisms to explore and exploit resources in the physical environment, using environmental and physiological information. To this end, the dissertation characterizes the direction and amount of influence between different regions of tubule-shaped P. polycephalum cells in binary food choice experiments. The results show that when the two food sources are identical in quality, the regions near the food source act as the drivers of P. polycephalum tubule behavior. Conversely, when one of the food sources is more enriched with nutrients, the regions near the rejected food source were found to drive the tubule behavior. Secondly, a generalized choice-making criterion was formulated to determine the choice-making behaviors of P. polycephalum, examine whether sufficient experimental time was given to make a choice, and determine the time point at which a choice was made. The criterion was tested on binary food choice experiments using P. polycephalum tubules. The results show that P. polycephalum made a choice for the option for the better food option, except when the differences in food quality were low. Moreover, the criterion was found to not determine the choice-making behaviors when the food sources presented were identical in quality. Thirdly, the dissertation investigated whether P. polycephalum cells modify their future exploratory behavior using their past foraging experience. The results did not find a strong influence of the past foraging experience on the exploratory networks formed by P. polycephalum cells. Finally, P. polycephalum exploratory behaviors were examined and compared when the cells were in high-energy versus low-energy physiological conditions. Interestingly, the study found the P. polycephalum cells in low-energy conditions show an increased tendency to split themselves into multiple autonomous cells. Additionally, the behavior is shown to increase the fitness of the cell by increasing its foraging efficiency