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The immune system is a distributed decentralized system that functions without any centralized control. The immune system has millions of cells that function somewhat independently and can detect and respond to pathogens with considerable speed and efficiency. Lymph nodes are physical anatomical structures that allow the immune system to rapidly detect pathogens and mobilize cells to respond to it. Lymph nodes function as: 1) information processing centres, and 2) a distributed detection and response network. We introduce biologically inspired computing that uses lymph nodes as inspiration. We outline applications to diverse domains like mobile robots, distributed computing clusters, peer-to-peer networks and online social networks. We argue that lymph node inspired computing systems provide powerful metaphors for distributed computing and complement existing artificial immune systems. We view our work as a first step towards holistic simulations of the immune system that would capture all the complexities and the power of a complex adaptive system like the immune system. Ultimately this would lead to immune system inspired computing that captures all the complexities and power of the immune system in human-engineered complex systems.

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades.Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase.

Neural machine translation (NMT) has achieved remarkable progress by drawing inspiration from the information-processing principles of biological neural systems. In this work, we develop a convolutional-enhanced NMT model that replaces recurrent encoders with multi-layer one-dimensional CNNs, thereby more effectively capturing long-distance dependencies and hierarchical feature abstractions–akin to how the visual cortex hierarchically processes spatial patterns. We systematically explore two convolutional kernel shapes (2 1 and 3 1) across 3, 6, 10, and 12 layers, and evaluate on the high-quality LDC Chinese–English corpus (1.25 M training sentences) with the NIST02–08 test sets. Our best configuration–a 6-layer network with 3 1 kernels–yields an average BLEU score of 35.558, representing a +0.99 improvement over the recurrent-baseline. Analysis reveals that 6–10 layers strike the optimal balance between expressive power and over-parameterization, while kernel shape influences the model’s sensitivity to local versus broader context.

In this work, we compare the basketball scoring performance of two imaginary (simulated) mechanical robots in conditions of erroneous information-processing circuits: Machine, whose moves are controlled by a conventional digital computer and Man, controlled by a random pulse computer composed of biologically-inspired circuits which execute basic arithmetic operations. This is the first comparative study of robustness of the digital and the random pulse computing paradigms, with respect to the error rate of the information-processing circuits (perr), for a mechanical robot. In spite of the fact that Man’s computer consists of only about 100 logic gates while Machine’s requires about 3500 gates, Man achieves a significantly higher scoring probability for perr in the range from 0.01% all the way to 10%, while at lower perr, both converge to the perfect score. Furthermore, Man’s hits make up a smooth Gaussian distribution with a vanishing probability of making large misses even at the highest perr, while Machine is prone to spectacular misses already at perr as low as 1 part-per-million. These findings indicate that the biologically inspired computation requires less hardware for the same task, and ensures higher robustness and better behaving operation than digital computation, which are characteristics of importance for the survivability of living beings.

Self-Organization in Sensor and Actor Networks explores self-organization mechanisms and methodologies concerning the efficient coordination between intercommunicating autonomous systems.Self-organization is often referred to as the multitude of algorithms and methods that organise the global behaviour of a system based on inter-system communication. Studies of self-organization in natural systems first took off in the 1960s. In technology, such approaches have become a hot research topic over the last 4-5 years with emphasis upon management and control in communication networks, and especially in resource-constrained sensor and actor networks. In the area of ad hoc networks new solutions have been discovered that imitate the properties of self-organization. Some algorithms for on-demand communication and coordination, including data-centric networking, are well-known examples. Key features include: Detailed treatment of self-organization, mobile sensor and actor networks, coordination between autonomous systems, and bio-inspired networking. Overview of the basic methodologies for self-organization, a comparison to central and hierarchical control, and classification of algorithms and techniques in sensor and actor networks. Explanation of medium access control, ad hoc routing, data-centric networking, synchronization, and task allocation issues. Introduction to swarm intelligence, artificial immune system, molecular information exchange. Numerous examples and application scenarios to illustrate the theory. Self-Organization in Sensor and Actor Networkswill prove essential reading for students of computer science and related fields; researchers working in the area of massively distributed systems, sensor networks, self-organization, and bio-inspired networking will also find this reference useful.

While current Semantic Web technologies are well-suited for data publication and integration, the design and deployment of dynamic, autonomous and long-lived multi-agent systems (MAS) on the Web is still in its infancy. Following the vision of hypermedia MAS and Linked Systems, we propose to use a value-passing fragment of Milner’s Calculus to formally specify the generic hypermedia-driven behaviour of Linked Data agents and the Web as their embedding environment. We are specifically interested in agent coordination mechanisms based on stigmergic principles. When considering transient marker-based stigmergy, we identify the necessity of generating server-side effects during the handling of safe and idempotent agent-initiated resource requests. This design choice is oftentimes contested with an imprecise interpretation of HTTP semantics, or with rejecting environments as first-class abstractions in MAS. Based on our observations, we present a domain model and a SPARQL function library facilitating the design and implementation of stigmergic coordination between Linked Data agents on the Web. We demonstrate the efficacy our of modelling approach in a Make-to-Order fulfilment scenario involving transient stigmergy and negative feedback as well as by solving a problem instance from the (time constrained) Trucks World domain as presented in the fifth International Planning Competition.

As the state-of-the-art imaging technologies became more and more advanced, yielding scientific data at unprecedented detail and volume, the need to process and interpret all the data has made image processing and computer vision increasingly important.

A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: * Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear—but theoretically rigorous—understanding of evolutionary algorithms, with an emphasis on implementation * Gives a careful treatment of recently developed EAs—including opposition-based learning, artificial fish swarms, bacterial foraging, and many others— and discusses their similarities and differences from more well-established EAs * Includes chapter-end problems plus a solutions manual available online for instructors * Offers simple examples that provide the reader with an intuitive understanding of the theory * Features source code for the examples available on the author's website * Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

Provides a comprehensive overview of wireless computing in medicine, with technological, medical, and legal advances This book brings together the latest work of leading scientists in the disciplines of Computing, Medicine, and Law, in the field of Wireless Health. The book is organized into three main sections. The first section discusses the use of distributed computing in medicine. It concentrates on methods for treating chronic diseases and cognitive disabilities like Alzheimer's, Autism, etc.  It also discusses how to improve portability and accuracy of monitoring instruments and reduce the redundancy of data. It emphasizes the privacy and security of using such devices. The role of mobile sensing, wireless power and Markov decision process in distributed computing is also examined. The second section covers nanomedicine and discusses how the drug delivery strategies for chronic diseases can be efficiently improved by Nanotechnology enabled materials and devices such as MENs and Nanorobots. The authors will also explain how to use DNA computation in medicine, model brain disorders and detect bio-markers using nanotechnology. The third section will focus on the legal and privacy issues, and how to implement these technologies in a way that is a safe and ethical. * Defines the technologies of distributed wireless health, from software that runs cloud computing data centers, to the technologies that allow new sensors to work * Explains the applications of nanotechnologies to prevent, diagnose and cure disease * Includes case studies on how the technologies covered in the book are being implemented in the medical field, through both the creation of new medical applications and their integration into current systems * Discusses pervasive computing's organizational benefits to hospitals and health care organizations, and their ethical and legal challenges Wireless Computing in Medicine: From Nano to Cloud with Its Ethical and Legal Implications is written as a reference for computer engineers working in wireless computing, as well as medical and legal professionals. The book will also serve students in the fields of advanced computing, nanomedicine, health informatics, and technology law.

Nature inspired computation is an old idea, first proposed in the early fifties by Alan Turing, one of the founders of computer science. Turing suggested computational models of pattern formation in living systems based on systems of coupled reaction-diffusion equations giving rise to spatial patterns due to self-organization of substances in chemical concentrations. Since the pioneering work by Turing, many optimization algorithms stimulated by real-world features have gained great popularity and impact, thanks to their efficiency in solving nonlinear design problems. Nature-inspired computation has permeated into almost all areas of sciences, engineering and industries, from data mining to optimization, from computational intelligence to signal processing, from image analysis and vision systems to industrial applications. The book provides an introductory tour of the most popular nature inspired computational strategies. The book is subdivided in two parts, briefly describing the inspiration and motivation of natural processes and phenomena, main players, design principles, the scope of each branch, current trends and open problems. In the first section, attention is focused on Artificial and Spiking Neural Networks (Chapter 2), Evolutionary and Genetic Algorithms (Chapter 3), and Swarm Intelligence algorithms (Chapter 4). In the second section, we present the emergent knowledge and technologies in Multiscale Nature processes (Chapter 5), Quantum Computing and Quantum Cryptography (Chapter 6), Encryption and Secure Communication system (Chapter 7), Image processing and Vision systems (Chapter 8), and finally on Nanophotonics Information (Chapter 9).