Francisco Chávez

Hybrid laser pointer detection algorithm based on template matching and fuzzy rule-based systems for domotic control in real home environments

A fundamental problem for disabled or elderly people is to manage their homes while carrying out an almost normal life, which implies using and interacting with a number of home devices. Recent studies in smart homes have proposed methods to use a laser pointer for interacting with home devices, which represents a more user-friendly and less expensive home device control environment. However, detecting the laser spot on the original non-filtered images, using standard and non-expensive cameras, and considering real home environments with varying conditions, is currently an open problem.This paper proposes a hybrid technique, where a classic technique used in image detection processes, such as Template Matching, has been combined with a Fuzzy Rule Based System for detecting a laser spot in real home environments. The idea is to use this new approach to improve the success rate of the previous algorithms used for detecting the laser spot, decreasing as much as possible the false offs of the system, because, the detection of a false laser spot could lead to dangerous situations.Using this new hybrid technique a better success rate has been obtained, eliminating almost completely the possibility of dangerous situations that may occur due to incorrect detection of the laser spot in real home environments.

Ephemeral computing and bioinspired optimization: Challenges and opportunities

Computational devices with significant computing power are pervasive yet often under-exploited since they are frequently idle or performing non-demanding tasks. Exploiting this power can be a cost-effective solution for solving complex computational tasks. Device-wise, this computational power can some times comprise a stable, long-lasting availability windows but it will more frequently take the form of brief, ephemeral bursts, mainly in the presence of devices “lent” voluntarily by their users. A highly dynamic and volatile computational landscape emerges from the collective contribution of numerous such devices. Algorithms consciously running on these environments require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to their intrinsic features: decentralized functioning, intrinsic parallelism, resilience, and adaptiveness. The latter is essential to exert advanced self-control on the functioning and/or structure of the algorithm. Much has been done in providing self-adaptation capabilities to these techniques, yet the science of self-★ bionspired algorithms is still nascent, in particular regarding to higher-level self-adaptation, and self-management in the context of large scale optimization problems and distributed ephemeral computing technologies. Deploying bioinspired techniques on this scenario will also pave the way for the application of other techniques on this computational domain.

An independent and non-intrusive laser pointer environment control device system

In this paper we present a system developed to control a computer environment for the handicapped by means of a laser pointer. The system looks for a laser spot position which is projected on the environment by using a laser pointer. Handicapped people can thus select the device they want by using the laser pointer. Once the laser spot is found, the device is controlled by means of a domotic system, using KNX architecture. We have developed a system that is able to recognize and act on the device that the handicapped person wants to use.

Genetic tuning of a laser pointer environment control device system for handicapped people with fuzzy systems

In this paper we present a new approach for laser-based environment device control systems by laser pointer for handicapped people. The paper proposes the design of a Fuzzy Rule Base System for laser pointer detection. The idea is to improve the success rate of the previous approaches decreasing as much as possible the false offs, i.e., the detection of a false laser spot (since this could lead to dangerous situations). To this end, Genetic Fuzzy Systems have also been employed for improving the laser spot system detection thus reducing the system false offs, that is the main objective in this problem. The system presented in this paper, using a Fuzzy Rule Base System adjusted by a Genetic Algorithm, shows a better success rate, and the most important thing, the not desired false offs are completely avoided.

A Cross-Platform Assessment of Energy Consumption in Evolutionary Algorithms

Energy consumption is a matter of paramount importance in nowadays environmentally conscious society. It is also bound to be a crucial issue in light of the emergent computational environments arising from the pervasive use of networked handheld devices and wearables. Evolutionary algorithms (EAs) are ideally suited for this kind of environments due to their intrinsic flexibility and adaptiveness, provided they operate on viable energy terms. In this work we analyze the energy requirements of EAs, and particularly one of their main flavours, genetic programming (GP), on several computational platforms and study the impact that parametrisation has on these requirements, paving the way for a future generation of energy-aware EAs. As experimentally demonstrated, handheld devices and tiny computer models mainly used for educational purposes may be the most energy efficient ones when looking for solutions by means of EAs.

Musical genre classification by means of Fuzzy Rule-Based Systems: A preliminary approach

Musical Genre is part of the basic information required for classifying musical audio, and fundamental for music information retrieval systems. The problem of automatic musical genre detection has attracted large attention in the last decade, due to the emergence of digital music databases and Internet. Although a number of techniques has been applied to the problem, no general solution still exists, due to the imprecise features that properly define musical genre. This paper presents a preliminary attempt to apply Fuzzy Rule-Based System (FRBS) in cooperation with Evolutionary Algorithms to musical genre classification. The novelty of the approach — which allows us to use fuzzy information extracted from audio files — is aligned with the fuzzy nature of the problem at hand, where no clear-cut rules are available for the classification. Preliminary results presented allows to foresee the potential of the technique.

Evolutionary learning of a laser pointer detection fuzzy system for an environment control system

Recent studies in smart homes have proposed methods to use a laser pointer for interacting with home devices, which represents a more user-friendly and less expensive home device control environment. However, detecting the laser spot on the original non-filtered images, using standard and non expensive cameras, and considering real home environments with varying conditions, is currently an open problem. In this paper we propose a hybrid technique, combining a classic technique used in image detection processes, such as Template Matching, with an evolutionary learning of a Fuzzy Rule Based Systems for the laser spot detection system in real home environments. This proposal improves the success rate in images without laser spot of the previous classical and non-classical algorithms used for detecting the laser spot in previous works, decreasing the detection of the false offs which could lead to dangerous situations. Experimental results on a real home environment show the effectiveness of the proposed approach.

Self-adjusting focus of attention in combination with a genetic fuzzy system for improving a laser environment control device system

Graphical abstractDisplay Omitted HighlightsNew interaction system in real environments by means of a laser pointer.New approach using a self-adjusting focus of attention that increases the results.The paper can detect the laser spot in a real environment.The system uses: genetic fuzzy systems and genetic programming to self-adjust the FOA. This paper presents a new algorithm capable of improving the accuracy level of a laser pointer detector used within an interactive control device system. A genetic programming based approach has been employed to develop a focus of attention algorithm, which works cooperatively with a genetic fuzzy system. The idea is to improve the detection of laser-spots depicted on images captured by video cameras working on home environments. The new and more accurate detection system, in combination with an environment control system, allows to send correct orders to home devices. The algorithm is capable of eradicating false offs, thus preventing devices to autonomously activate/deactivate appliances when orders have not been really signalled by users. Moreover, by adding self-adjusting capabilities with a genetic fuzzy system the computer vision algorithm focuses its attention on a narrower area of the image. Extensive experimental results show that the combination of the focus of attention technique with dynamic thresholding and genetic fuzzy systems improves significantly the accuracy of the laser-spot detection system while maintaining extremely low false off rates in comparison with previous approaches.

Fuzzy rule based system ensemble for music genre classification

Automatic music retrieval processes rely on classification and tagging systems. Among the tags usually employed for classifying music, genre is a prominent one. This paper presents an ensemble of classifiers that uses a hybrid genetic fuzzy approach. By using a set of Fuzzy Rule Based Systems automatically tuned by means of a Genetic Algorithm, and structured in two layers, the system is capable of correctly classifying classical and jazz samples randomly chosen from a wide set of authors and styles. The ensemble is built on top of a previously developed method that profits from non-precise information by using Fuzzy Systems. The inherently ambiguous information frequently related to music genre is properly managed by a Fuzzy Rule Based System that focuses on random samples extracted from the audio to be analyzed. A set of these Fuzzy Rule Based Systems are then applied simultaneously to a number of samples, and the final system is in charge of processing the partial information obtained by each of the Fuzzy Rule Based System. The experimental setup and results take into account harmonic principles and their relationship with the specific genre considered. The system is capable of providing good classification accuracy by using an extremely narrow set of features.

Application Areas of Ephemeral Computing: A Survey

It is increasingly common that computational devices with significant computing power are underexploited. Some of the reasons for that are due to frequent idle-time or to the low computational demand of the tasks they perform, either sporadically or in their regular duty. The exploitation of this otherwise-wasted computational power is a cost-effective solution for solving complex computational tasks. Individually device-wise, this computational power can sometimes comprise a stable, long-lasting availability window but it will more frequently take the form of brief, ephemeral bursts. Then, in this context a highly dynamic and volatile computational landscape emerges from the collective contribution of such numerous devices. Algorithms consciously running on this kind of environment require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to some of the features they inherit from their biological sources of inspiration, namely decentralized functioning, intrinsic parallelism, resilience, and adaptiveness. Deploying bioinspired techniques on this scenario, and conducting analysis and modelling of the underlying Ephemeral Computing environment will also pave the way for the application of other non-bioinspired techniques on this computational domain. Computational creativity and content generation in video games are applications areas of the foremost economical interest and are well suited to Ephemeral Computing due to their intrinsic ephemeral nature and the widespread abundance of gaming applications in all kinds of devices. In this paper, we will explain why and how they can be adapted to this new environment.