GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance

The Use of Genetic Algorithms in UV Disinfection of Drinking Water

In order to have drinking water, some countries have to use chlorine. It is use cause is effective and it’s cheap. An alternative to this process is the UV disinfection of drinking water. Most of the devices in the market use UV bulbs or mercury lamps. The UV LED, which is cheaper and smaller, allows creating new smaller devices. The main contribution of this paper is the use of Genetic Algorithms to help design a drinking water device with UV LEDs

Editor's Note

The term 'Digital Economy' was coined for the first time by Don Tapscott in 1995 in his best-seller The Digital Economy: Promise and Peril in the Age of Networked Intelligence. When he wrote the book 20 years ago, he announced how he thought the Internet would fully transform the nature of business and government. We have now extended the concept, illustrating how digital technologies are rapidly transforming business practices, the economy and societies. Technology, and its impact on business strategy and society, continues to rise in importance. The Digital Economy, sometimes also called “Digital Business” has become a philosophy for many top executive teams as they seek competitive advantages in a world of fast moving technological change. When we talk about digital technologies, we are not only talking about the internet, nor only ICT (Information and Communications Technology), but other concepts such as mobile, telecommunications or content. The digital economy is by no means an exclusively economic concept. Therefore, it might be more appropriate to speak of digital society or digital technology. What matters is that digital is a transverse concept that affects individuals, businesses and public administrations

PI Stabilization for Congestion Control of AQM Routers with Tuning Parameter Optimization

In this paper, we consider the problem of stabilizing network using a new proportional- integral (PI) based congestion controller in active queue management (AQM) router; with appropriate model approximation in the first order delay systems, we seek a stability region of the controller by using the Hermite- Biehler theorem, which isapplicable to quasipolynomials. A Genetic Algorithm technique is employed to derive optimal or near optimal PI controller parameters

Comparison between Famous Game Engines and Eminent Games

Nowadays game engines are imperative for building 3D applications and games. This is for the reason that the engines appreciably reduce resources for employing obligatory but intricate utilities. This paper elucidates about a game engine, popular games developed by these engines and its foremost elements. It portrays a number of special kinds of contemporary game developed by engines in the way of their aspects, procedure and deliberates their stipulations with comparison

Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation

Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse

GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance

GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance