Nabendu Chaki

This book provides an insight on different types of hardware and software-based caries detection methods and their limitations from treatment perspective. The different chapters are motivated to address some of the important gaps in the prevailing approaches and suggest suitable solution to them. Overall, it helps the reader to understand the caries detection methods to identify interproximal and occlusal caries. This also assists the practitioners. The book identifies multiple research issues on dental caries detection that open the practitioners’ horizons to an array of domains from which they can conclude practical insights about their area of interest.

The authors go on to describe potential challenges due to HSI being acquired in hundreds of narrow and contiguous bands, represented as a 3-dimensional image cube, often causing the bands to contain information redundancy.

This book provides information about different types of dimensionality reduction (DR) methods and their effectiveness in hyperspectral data processing. The authors first explain how hyperspectral imagery (HSI) plays an important role in remote sensing due to its high spectral resolution that enables better identification of different materials on the earth’s surface. The authors go on to describe potential challenges due to HSI being acquired in hundreds of narrow and contiguous bands, represented as a 3-dimensional image cube, often causing the bands to contain information redundancy. They then show how processing a large number of bands adds challenges in terms of computation complexity that reduces efficiency. The authors then present how DR is an essential step in hyperspectral data analysis to solve these issues. Overall, the book helps readers understand the DR processes and its impact in effective HSI analysis.

This book discusses enterprise hierarchies, which view a target system with varying degrees of abstraction. These requirement refinement hierarchies can be represented by goal models. It is important to verify that such hierarchies capture the same set of rationales and intentions and are in mutual agreement with the requirements of the system being designed. The book also explores how hierarchies manifest themselves in the real world by undertaking a data mining exercise and observing the interactions within an enterprise. The inherent sequence-agnostic property of goal models prevents requirement analysts from performing compliance checks in this phase as compliance rules are generally embedded with temporal information. The studies discussed here seek to extract finite state models corresponding to goal models with the help of model transformation. The i*ToNuSMV tool implements one such algorithm to perform model checking on i* models. In turn, the AFSR framework provides a new goal model nomenclature that associates semantics with individual goals. It also provides a reconciliation machinery that detects entailment or consistency conflicts within goal models and suggests corrective measures to resolve such conflicts. The authors also discuss how the goal maintenance problem can be mapped to the state-space search problem, and how A* search can be used to identify an optimal goal model configuration that is free from all conflicts. In conclusion, the authors discuss how the proposed research frameworks can be extended and applied in new research directions. The GRL2APK framework presents an initiative to develop mobile applications from goal models using reusable code component repositories.

This book discusses enterprise hierarchies, which view a target system with varying degrees of abstraction. These requirement refinement hierarchies can be represented by goal models. It is important to verify that such hierarchies capture the same set of rationales and intentions and are in mutual agreement with the requirements of the system being designed. The book also explores how hierarchies manifest themselves in the real world by undertaking a data mining exercise and observing the interactions within an enterprise. The inherent sequence-agnostic property of goal models prevents requirement analysts from performing compliance checks in this phase as compliance rules are generally embedded with temporal information. The studies discussed here seek to extract finite state models corresponding to goal models with the help of model transformation. The i*ToNuSMV tool implements one such algorithm to perform model checking on i* models. In turn, the AFSR framework provides a new goal model nomenclature that associates semantics with individual goals. It also provides a reconciliation machinery that detects entailment or consistency conflicts within goal models and suggests corrective measures to resolve such conflicts. The authors also discuss how the goal maintenance problem can be mapped to the state-space search problem, and how A* search can be used to identify an optimal goal model configuration that is free from all conflicts. In conclusion, the authors discuss how the proposed research frameworks can be extended and applied in new research directions. The GRL2APK framework presents an initiative to develop mobile applications from goal models using reusable code component repositories.

The book presents various state-of-the-art approaches for process synchronization in a distributed environment. The range of algorithms discussed in the book starts from token based mutual exclusion algorithms that work on tree based topology. Then there are interesting solutions for more flexible logical topology like a directed graph, with or without cycle. In a completely different approach, one of the chapters presents two recent voting-based DME algorithms. All DME algorithms presented in the book aim to ensure fairness in terms of first come first serve (FCFS) order among equal priority processes. At the same time, the solutions consider the priority of the requesting processes and allocate resource for the earliest request when no such request from a higher priority process is pending.

The book presents various state-of-the-art approaches for process synchronization in a distributed environment. The range of algorithms discussed in the book starts from token based mutual exclusion algorithms that work on tree based topology. Then there are interesting solutions for more flexible logical topology like a directed graph, with or without cycle. In a completely different approach, one of the chapters presents two recent voting-based DME algorithms. All DME algorithms presented in the book aim to ensure fairness in terms of first come first serve (FCFS) order among equal priority processes. At the same time, the solutions consider the priority of the requesting processes and allocate resource for the earliest request when no such request from a higher priority process is pending.

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical. Exploring Neural Networks with C# presents the important properties of neural networks while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand. Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks. Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programyandlang=en

The book focuses on an image processing technique known as binarization. It provides a comprehensive survey over existing binarization techniques for both document and graphic images. A number of evaluation techniques have been presented for quantitative comparison of different binarization methods. The book provides results obtained comparing a number of standard and widely used binarization algorithms using some standard evaluation metrics. The comparative results presented in tables and charts facilitates understanding the process. In addition to this, the book presents techniques for preparing a reference image which is very much important for quantitative evaluation of the binarization techniques. The results are produced taking image samples from standard image databases.

This book presents Hyper-lattice, a new algebraic model for partially ordered sets, and an alternative to lattice. The authors analyze some of the shortcomings of conventional lattice structure and propose a novel algebraic structure in the form of Hyper-lattice to overcome problems with lattice. They establish how Hyper-lattice supports dynamic insertion of elements in a partial order set with a partial hierarchy between the set members. The authors present the characteristics and the different properties, showing how propositions and lemmas formalize Hyper-lattice as a new algebraic structure.

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations—making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical. Exploring Neural Networks with C# presents the important properties of neural networks—while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand. Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks. Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programy&lang=en

This Springer Brief presents a comprehensive survey of the existing methodologies of background subtraction methods. It presents a framework for quantitative performance evaluation of different approaches and summarizes the public databases available for research purposes. This well-known methodology has applications in moving object detection from video captured with a stationery camera, separating foreground and background objects and object classification and recognition. The authors identify common challenges faced by researchers including gradual or sudden illumination change, dynamic backgrounds and shadow and ghost regions. This brief concludes with predictions on the future scope of the methods. Clear and concise, this brief equips readers to determine the most effective background subtraction method for a particular project. It is a useful resource for professionals and researchers working in this field.

The book focuses on an image processing technique known as binarization. It provides a comprehensive survey over existing binarization techniques for both document and graphic images. A number of evaluation techniques have been presented for quantitative comparison of different binarization methods. The book provides results obtained comparing a number of standard and widely used binarization algorithms using some standard evaluation metrics. The comparative results presented in tables and charts facilitates understanding the process. In addition to this, the book presents techniques for preparing a reference image which is very much important for quantitative evaluation of the binarization techniques. The results are produced taking image samples from standard image databases.