Nadia Nedjah

Bio: Nadia Nedjah is an academic researcher from Rio de Janeiro State University. The author has contributed to research in topics: Modular exponentiation & Hardware architecture. The author has an hindex of 24, co-authored 319 publications receiving 2489 citations. Previous affiliations of Nadia Nedjah include Monash University, Clayton campus & University of Manchester.

Swarm Intelligent Systems

Abstract: Methodologies Based on Particle Swarm Intelligence.- Swarm Intelligence: Foundations, Perspectives and Applications.- Waves of Swarm Particles (WoSP).- Grammatical Swarm: A Variable-Length Particle Swarm Algorithm.- SWARMs of Self-Organizing Polymorphic Agents.- Experiences Using Particle Swarm Intelligence.- Swarm Intelligence - Searchers, Cleaners and Hunters.- Ant Colony Optimisation for Fast Modular Exponentiation using the Sliding Window Method.- Particle Swarm for Fuzzy Models Identification.- A Matlab Implementation of Swarm Intelligence based Methodology for Identification of Optimized Fuzzy Models.

Review of methodologies and tasks in swarm robotics towards standardization

Abstract: Swarm Robotics (SR) is an extension of the study of Multi-Robot Systems that exploits concepts of communication, coordination and collaboration among a large number of robots. The massive parallelization yielded by the robots working together can make a task faster than in the case of the usage of a single complex robot. One of the main aspects in robotic swarms is that the control is decentralized by definition and distributed among the robots of the swarm, improving the system robustness and fault-tolerance. Furthermore, this characteristic often allows the emergence of collective behaviors from the robot's interaction with each other and with the environment through their embodied sensors and actuators. In most cases, the number of inputs from sensor readings turns analytical solutions hard or even impossible. Thus, many ad-hoc approaches are contributed to deal with the situation at hand. The main goal of this review is to find out, through the study of existing research works of the field, the reason behind the lack of exploitation of swarm robotic systems in real-world applications. For this purpose, we first review the different possibilities of study in SR: physical and simulated robotic platforms, development methodologies and the variety of basic tasks and collective behaviors. We then briefly describe some fields related do SR that have a big impact on the development of SR. After that, based on existing taxonomies found in literature, we categorize existing research works regarding SR in two large main groups: those that deal with SR design and those that deal with tasks as required in SR. The review of both existing robots and techniques in the literature show a diversity of approaches to discuss SR issues. Nonetheless, it is easily noticeable from these works that there is a clamant absence of solid real-world applications of SR. An analysis of the interests and bottlenecks of this field indicates that the number of research works is smaller than those in other related areas. This suggests that, even though with many research studies, the field of SR is not yet mature enough, mainly due the absence of a universal methodology and generic robots that can be used in any, or at least in many, applications. Thus, we emphasize, discuss and analyze the urgent need for standardization of many aspects in SR, including hardware and software, as to allow a possible flourishing of SR applicability to real-world applications. This standardization could accelerate a great deal the field of SR, thus facilitating the development of SR solutions for applications that impact our daily life.

Soft computing in big data intelligent transportation systems

Abstract: We presentNeverStop, which utilizes genetic algorithms and fuzzy control methods in big data intelligent transportation systems.We integrate the fuzzy control module into the NeverStop system design. The fuzzy control architecture completes the integration and modeling of the traffic control systems.We present a genetic algorithm illustrating how the average waiting time is derived. The involvement amplifies the NeverStop system and facilitates the fuzzy control module.NeverStop utilizes fuzzy control method and genetic algorithm to adjust the waiting time for the traffic lights, consequently the average waiting time can be significantly reduced. The academic and industry have entered big data era in many computer software and embedded system related fields. Intelligent transportation system problem is one of the important areas in the real big data application scenarios. However, it is posing significant challenge to manage the traffic lights efficiently due to the accumulated dynamic car flow data scale. In this paper, we present NeverStop, which utilizes genetic algorithms and fuzzy control methods in big data intelligent transportation systems. NeverStop is constructed with sensors to control the traffic lights at intersection automatically. It utilizes fuzzy control method and genetic algorithm to adjust the waiting time for the traffic lights, consequently the average waiting time can be significantly reduced. A prototype system has been implemented at an EBox-II terminal device, running the fuzzy control and genetic algorithms. Experimental results on the prototype system demonstrate NeverStop can efficiently facilitate researchers to reduce the average waiting time for vehicles.

Evolutionary Computation: from Genetic Algorithms to Genetic Programming

Abstract: This chapter presented the biological motivation and fundamental aspects of evolutionary algorithms and its constituents, namely genetic algorithm, evolution strategies, evolutionary programming and genetic programming. Most popular variants of genetic programming are introduced. Important advantages of evolutionary computation while compared to classical optimization techniques are also discussed.

The C programming language

Abstract: This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library. As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

Computational Intelligence: An Introduction

Abstract: The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.