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Cloud Computing (CC) is a model that enables ubiquitous, convenient, and on-demand network access to a shared pool of configurable computing resources. In CC applications, it is possible to access both software and hardware architectures remotely and with little or no knowledge about their physical or logical locations. Due to its low deployment and management costs, the CC paradigm is being increasingly used in a wide variety of online services and applications, including remote computation, software-as-a-service, off-site storage, entertainment, and communication platforms. However, several aspects of CC applications, such as system design, optimization, and security issues, have become too complex to be efficiently treated using traditional algorithmic approaches under the increasingly high complexity and performance demands of current applications. Recently, advances in Computational Intelligence (CI) techniques have fostered the development of intelligent solutions for CC applications. CI methods such as artificial neural networks, deep learning, fuzzy logic, and evolutionary algorithms have enabled improving CC paradigms through their capabilities of extracting knowledge from high quantities of real-world data, thus further optimizing their design, performance, and security with respect to traditional techniques. This chapter introduces recent CI techniques, reviews the main applications of CI in CC, and presents challenges and research trends.

Biometric systems are the ensemble of devices, procedures, and algorithms for the automatic recognition of individuals by means of their physiological or behavioral characteristics. Although biometric systems are traditionally used in high-security applications, recent advancements are enabling the application of these systems in less-constrained conditions with non-ideal samples and with real-time performance. Consequently, biometric technologies are being increasingly used in a wide variety of emerging application scenarios, including public infrastructures, e-government, humanitarian services, and user-centric applications. This chapter introduces recent biometric technologies, reviews emerging scenarios for biometric recognition, and discusses research trends.

Computational Intelligence (CI) techniques are receiving increasing attention by the industrial and academic communities involved in the design of automatic systems for industrial and environmental monitoring and control applications. CI techniques are able to aggregate inputs from several heterogeneous sensors, adapt themselves to wide ranges of operational and environmental conditions, and cope with incomplete or noise-affected data. With current computing architectures evolving towards smaller size, higher computational power, and more affordable cost, a great number of devices can embed CI techniques to support different kinds of applications. In this paper, we present a survey of the recent CI methods designed for the main processing steps of industrial and environmental monitoring systems.

This chapter discusses the application of the innovative methods described in Chap. 5 and their experimental evaluation. The methods were tested to evaluate the different biometric aspects of the implemented techniques, including accuracy, robustness, speed, cost, interoperability, usability, acceptability, security, and privacy.

In this chapter, innovative methods for touchless, less-constrained palmprint recognition are described. In particular, these palmprint recognition systems do not require contact of the hand with any surface or the use of a fixed positioning system. The only constraint imposed on the user is that the hand must be placed open, facing the CCD cameras, and inside the field of view of the cameras. Three-dimensional models are used to compute a representation that is independent of the acquisition position.

Less-constrained biometric systems are of interest for the implementation of biometric systems that can overcome the limitations of usability, acceptance, and speed of current systems. More specifically, the goals of less-constrained systems are to perform biometric recognition with touchless acquisitions and less cooperation from the users and at greater distances.