The recognition systems of patterns in images are mechanisms that filter the information that provides an image to highlight the area of interest for the user. Usually, these mechanisms are based on mathematical transformations that allow the processor to perform interpretations based on the geometry or shape of the image. However, the strategies that implement mathematical transformations are limited, since the effectiveness of these techniques is reduced by changing the morphology or resolution of the image. This paper presents a partial solution to this limitation with a digital image processing technique based on a deep learning neural network (DNN). This technique incorporates a mechanism that allows the DNN to determine the facial expression of a person, based on the segmented information of the image of their face. By segmenting the image and processing its characteristics in parallel, the proposed technique increases the effectiveness of recognizing facial gestures in different images even when modifying their characteristics.

The constant change and transformation in organizations have made markets increasingly innovative and competitive, creating in this way the need to incorporate advanced technology to capture, process and analyze information from the current environment through the use of tools and / or instruments that facilitate decision making in an accurate way and even anticipate in their markets, making this a strategic increase for the organization. The objective of this paper is to carry out an analysis and contextualization in relation to Technological Surveillance Systems, with the purpose of exposing the characteristics and elements of management in the framework of technological surveillance. On the other hand, it will be introduced to the phases that make up a Technological Surveillance process, emphasizing the methodology for the selection of ICT / software tools. Finally, some of the existing tools in the market that allow successfully carry out each of the phases of this process are described.

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Some case studies treated by physiotherapists or orthopedists to measure the alignment of the lower extremities during a gait cycle are based on empirical methods of visual observation. This methodology does not guarantee total success, since it depends on the experience of the specialist, what can cause irreversible damage to patients, such as: hip displacement, wear and overload of the joints of a single lower limb. Although, this problem has been addressed in the investigation by means of devices implementation with sensors or methods of processing sequences of images and videos, this topic is still under investigation because the current methods depend on many external elements and data given by an expert in the area. Therefore, this paper proposes a partial solution to this problem by systematizing the experience of a specialist through a computational learning method.