Outdoor images are used in a vast number of applications, such as surveillance, remote sensing, and autonomous navigation. The greatest issue with these types of images is the effect of environmental pollution: haze, smog, and fog originating from suspended particles in the air, such as dust, carbon and water drops, which cause degradation to the image. The elimination of this type of degradation is essential for the input of computer vision systems. Most of the state-of-the-art research in dehazing algorithms is focused on improving the estimation of transmission maps, which are also known as depth maps. The transmission maps are relevant because they have a direct relation to the quality of the image restoration. In this paper, a novel restoration algorithm is proposed using a single image to reduce the environmental pollution effects, and it is based on the dark channel prior and the use of morphological reconstruction for the fast computing of transmission maps. The obtained experimental results are evaluated and compared qualitatively and quantitatively with other dehazing algorithms using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index; based on these metrics, it is found that the proposed algorithm has improved performance compared to recently introduced approaches.

The use of fuzzy logic controllers in refrigeration and air conditioning systems, RACs, has as main objective to maintain certain thermal and comfort conditions. In this sense, fuzzy controllers have proven to be a viable option for use in RACs due to their ease of implementation and their ability to integrate with other control systems and control improvements, as well as their ability to achieve potential energy savings. In this document, we present a review of the application of fuzzy controls in RACs based on vapor compression technology. Application information is discussed for each type of controller, according to its application in chillers, air conditioning systems, refrigerators, and heat pumps. In addition, this review provides detailed information on controller design, focusing on the potential to achieve energy savings; this design discusses input and output variables, number and type of membership functions, and inference rules. The future perspectives on the use of fuzzy control systems applied to RACs are shown as well. In other words, the information in this document is intended to serve as a guide for the creation of controller designs to be applied to RACs.

Induction motor fault identification is essential to improve efficiency in industrial processes improving costs, production line and maintenance time. This paper presents a novel motor fault detection methodology based on Quaternion Signal Analysis (QSA). The proposed method establishes the quaternion coefficients as the value of motor current measurement and the variables x, y and z are the measurements from a triaxial-accelerometer mounted on the induction motor chassis. The method obtains the rotation of quaternions and applies quaternion rotation statistics such as mean, cluster shades and cluster prominence in order to get their features, and these are used to classify the motor state using the tree classification algorithm. This methodology is validated experimentally and compared to other methods to determine the efficiency of this method for feature detection and motor fault identification and classification.

The present paper focuses on self-similar network traffic generation. Network traffic modeling studies the generation of synthetic sequences. The generated sequences must have similar features to the measured traffic. Exact methods for generating self-similar sequences are not appropriate for long traces. Our main objective in the present paper is to improve the efficiency of Paxson's method for synthesizing self-similar network traffic. Paxson's method uses a fast, approximate synthesis for the power spectrum of the FGN and uses the inverse Fourier transform to obtain the time-domain sequences. We demonstrate that a linear approximation can be used to determine the power spectrum of the FGN. This linear approximation reduces the complexity of the computation without compromising the accuracy in synthesizing the power spectrum of the FGN. Our results show that long traces can be generated in much less time. To compare our method with existing ones, we will measure the running time in generating long and short sample paths from the FGN. We will also conduct experiments to show that our method can generate self-similar traffic for specified Hurst parameters with high accuracy.