Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Categorical organization and machine perception of oscillatory motion patterns

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2000.Includes bibliographical references (p. 126-132).Many animal behaviors consist of using special patterns of motion for communication, with certain types of movements appearing widely across animal species. Oscillatory motions in particular are quite prevalent, where many of these repetitive movements can be characterized by a simple sinusoidal model with very specific and limited parameter values. We develop a computational model of categorical perception of these motion patterns based on their inherent structural regularity. The model proposes the initial construction of a hierarchical ordering of the model parameters to partition them into sub-categorical specializations. This organization is then used to specify the types and layout of localized computations required for the corresponding visual recognition system. The goal here is to do away with ad hoc motion recognition methods of computer vision, and instead exploit the underlying structural description for a motion category as a motivating mechanism for recognition. We implement this framework and present an analysis of the approach with synthetic and real oscillatory motions, and demonstrate its applicability within an interactive artificial life environment. With this categorical foundation for the description and recognition of related motions, we gain insight into the basis and development of a machine vision system designed to recognize these patterns.by James W. Davis.Ph.D

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Structure and functional characterization of the pheromone binding protein 2 from Ostrinia furnacalis

Animal olfaction has an immense impact on their survival. The insect olfactory system is the most exquisitely sensitive in the animal kingdom. Moth antennae contain hair-like structures called sensilla, which are involved in detecting chemical signals. A male moth can detect pheromone released by the female from a far distance. The hydrophobic pheromone molecules pass through the pores of the sensillum cuticle and enter into the sensillum lymph. Pheromone-bonding protein (PBP) present in the lymph of the sensilla of the male moth antennae binds and transports the pheromone molecules through the aqueous layer to the receptors that initiate signaling, which leads to mating. PBPs bind to pheromone with high affinity at neutral pH in the open conformation and undergo a conformational switch, and release the ligand at acidic pH. Ligand release and binding occur through the concerted pH-dependent mechanism where two molecular gates (the histidine gate, His70-His95, and the C-terminal tail) play a critical role. Ostrinia furnacalis is an agricultural insect pest. The Ostrinia furnacalis pheromone binding protein 2 (OfurPBP2) has more than 50%, similarly with the well-studied PBPs including Antheraea Polyphemus pheromone binding protein1 (ApolPBP1) and Bombyx mori pheromone binding protein (BmorPBP). However, there are remarkable differences in both biological gates; a) one of the histidine-gate residues, His70, is substituted by arginine, b) the C-terminal tail has seven charged residues as compared to three. The molecular impact of these substitutions on structure and mechanism of action is unknown. Furthermore, structure and mechanistic studies of several of these proteins are needed to gain the knowledge to design inhibitors through pheromone mimetics, which constitutes a novel mechanism to control these pests.Our work has dissected the structural details to understand the structural mechanism of pheromone binding and release in this pest. NMR investigations have shown that OfurPBP2 undergoes conformational heterogeneity at acidic pH of 4.5. We have used small-angle X-ray scattering (SAXS) to show the protein is homogeneous, well-folded, and has a compact globular shape. OfurPBP2 consists of seven helices with residues 2-14 (α1a), 16-22(α1b), 27–37 (α2), 46–60 (α3), 70–80 (α4), 84–100 (α5), 107–124(α6), and 131-143 (α7) which are arranged in a globular fold, and contains the three disulfide bridges 19-54, 50-108, and 97-117 enclosing a large hydrophobic binding pocket inside. The structure of the OfurPBP2 contains a C-terminal helix (α7) residues 131-143 extended outside the hydrophobic pocket, which is in contrast with previously studied PBPs, where they have a random coil at pH 6.5. OfurPBP2 binds the pheromones at high pH. The MD simulations were carried to identify the flexible region in the protein structure

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available