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 programmers 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."

The C programming language

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.

Computational Intelligence: An Introduction

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf/

Artificial neural networks in hardware: A survey of two decades of progress

Cours d'economie politique

We propose an efficient localization method for swarm robotic systems.The method, based on Min-Max and PSO algorithms, presents low positioning error.We demonstrate that the method overcomes the drawback of the Min-Max method.It is shown that the processing time grows with the swarm connectivity. In a wireless sensors network in general, and a swarm of robots in particular, solving the localization problem consists of discovering the sensor's or robot's positions without the use of external references, such as the Global Positioning System - GPS. In this problem, the solution is performed based on distance measurements to existing reference nodes also known as anchors. These nodes have knowledge about their respective positions in the environment. Aiming at efficient yet accurate method to approach the localization problem, some bio-inspired algorithms have been explored. In this sense, targeting the accuracy of the final result rather than the efficiency of the computational process, we propose a new localization method based on Min-Max and Particle Swarm Optimization. Generally, the performance results prove the effectiveness of the proposed method for any swarm configuration. Furthermore, its efficiency is demonstrated for high connectivity swarms. Specifically, the proposed method was able to reduce the localization average error by 84%, in the worst case, considering a configuration of 10 anchors and 100 unknown nodes and by almost 100%, in the best case, considering 30 anchors and 200 unknown nodes. This proves that for high connectivity networks or swarms, the proposed method provides almost exact solution to the localization problem, which is a big shift forward in the state-of-the-art methods.

Distributed efficient localization in swarm robotics using Min-Max and Particle Swarm Optimization

Synchronous finite state machines are very important for digital sequential designs. Among other important aspects, they represent a powerful way for synchro- nizing hardware components so that these components may cooperate adequately in the fulfillment of the main objective of the hardware design. In this paper, we propose an evolutionary methodology to solve one of the problems related to the design of finite state machines. We optimally solve the state assignment NP-complete problem using a quantum inspired evolutionary algorithm. This is motivated by the fact that with an optimal state assignment one can physically implement the state machine using a minimal hardware area and response time.

/pdf/quantum-inspired-evolutionary-state-assignment-for-2jiqirk6ry.pdf

Quantum-Inspired Evolutionary State Assignment for Synchronous Finite State Machines

Network-on-chip (NoC) are considered the next generation of communication infrastructure, which will be omnipresent in most of industry, office and personal electronic systems. In platform-based methodology, an application is implemented by a set of collaborating intellectual properties (IPs) blocks. In this paper, we use two multi-objective evolutionary algorithms to address the problem of selecting the most adequate set of IPs (from an available library) that best implements the application. The IP selection optimization is driven by the minimization of hardware area, total execution time and power consumption.

Optimal IP Assignment for Efficient NoC-based System Implementation using NSGA-II and MicroGA

In this paper, we propose an overall architecture for hardware implementation of genetic algorithms The proposed architecture is independent of such specifics It implements the fitness computation using a neural networks

Hardware architecture for genetic algorithms

Networks-on-Chips (NoCs) have been used as an interesting option in design of communication infrastructures for embedded systems, providing a scalable structure and balancing the communication between cores. Because several data packets can be transmitted simultaneously through the network, an efficient routing strategy must be used in order to avoid congestion delays. In this paper, ant colony algorithms were used to find and optimize routes in a mesh-based NoC, where several randomly generated applications have been mapped. The routing optimization is driven by the minimization of total latency in packets transmission between tasks. The simulation results show the effectiveness of the ant colony inspired routing by comparing it with general purpose algorithms for deadlock free routing.

Nadia Nedjah

Papers

Distributed efficient localization in swarm robotics using Min-Max and Particle Swarm Optimization

Quantum-Inspired Evolutionary State Assignment for Synchronous Finite State Machines

Optimal IP Assignment for Efficient NoC-based System Implementation using NSGA-II and MicroGA

Hardware architecture for genetic algorithms

Routing for applications in NoC using ACO-based algorithms