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The satellite navigation systems (GNSS), had their beginnings in the seventies (70s), through the development of global positioning systems (GPS) by the United States and being functional at civilian level from 1993, when 24 satellites were achieved in orbit, which allowed to start the commercial race to implement services and technologies based on the position, among which may excel air navigation systems, vehicular routing systems, and geodetic and topographic surveys, In turn, other countries were developing similar systems, such as Russia of the former USSR with GLONASS, which developed systems parallel to U.S. technology, as well as Europe developed the GALILEO system and China Beidou, in order not to depend only on the authorizations and restrictions of U.S. systems, In this sense, this article presents a solution model to improve the accuracy of topographic surveys in the city of Bogota and surroundings with terrestrial support, which is based on the determination of the local geoidal model for Bogota version BGM2020v1. 0 and BGM2020v2.0, and the densification of the geodetic network of the city with the determination and retaking of more than 300 passive points, as well as the installation of 6 active reference stations, provided with the N-TRIP system and finally the generation of a high precision digital terrain model of 60000 ha of the urban area of the city from LIDAR.

Currently, mobile devices, tablets and smartphones, among others, integrate geolocation systems supported by GPS or A-GPS sensors that have digital mapping functionalities, with the possibility of storage, routing and location information systems, among those, called location-based services (LBS). This technology allows the user to integrate approximate apparent position in real time between 3 and 6 meters. This approximation to the real position is valid for the LBS in applications of routing and tourism among others, but not for cadastral type surveys, urban inventories, cartographic updating or topographic support points among others. The article presents the integration of the processes required to obtain sub metric precisions in geolocation with mobile devices, in order to make accessible to all the professional use and the integration of new services.

A color quantization technique that combines the operations of two existing methods is proposed. The first method considered is the Greedy orthogonal bi-partitioning method. This is a very popular technique in the color quantization field that can obtain a solution quickly. The second method, called Ant-tree for color quantization, was recently proposed and can obtain better images than some other color quantization techniques. The solution described in this article combines both methods to obtain images with good quality at a low computational cost. The resulting images are always better than those generated by each method applied separately. In addition, the results also improve those obtained by other well-known color quantization methods, such as Octree, Median-cut, Neuquant, Binary splitting or Variance-based methods. The features of the proposed method make it suitable for real-time image processing applications, which are related to many practical problems in diverse disciplines, such as medicine and engineering.

This article proposes a color quantization strategy that combines two color quantization methods: Binary Splitting and Ant tree for Color Quantization. This solution combines a splitting method, which is faster, and a clustering-based method, which generates better quantized images. Given that time is a fundamental factor when considering a method for real-time applications, the proposed strategy attempts to exploit both of these methods for obtaining good quantized images with a low computational cost. The result of this approach not only generates better images than when Binary Splitting and Ant tree for Color Quantization are applied separately, but also helps to improve other methods frequently used for color quantization such as Wu’s method, Octree, Variance-based method and Neuquant.

Nowadays, the information provided by digital photographs is very complete and very relevant in different professional fields, such as scientific or forensic photography. Taking this into account, it is possible to determine the date when they were taken, as well as the type of device that they were taken with, and thus be able to locate the photograph in a specific context. This is not the case with analog photographs, which lack any information regarding the date they were taken. Extracting this information is a complicated task, so classifying each photograph according to the date it was taken is a laborious task for a human expert. Artificial intelligence techniques make it possible to determine the characteristics and classify the images automatically. Within the field of artificial intelligence, convolutional neural networks are one of the most widely used methods today. This article describes the application of convolutional neural networks to automatically classify photographs according to the year they were taken. To do this, only the photograph is used, without any additional information. The proposed method divides each photograph into several segments that are presented to the network so that it can estimate a year for each segment. Once all the segments of a photograph have been processed, a general year for the photograph is calculated from the values generated by the network for each of its segments. In this study, images taken between 1960 and 1999 were analyzed and classified using different architectures of a convolutional neural network. The computational results obtained indicate that 44% of the images were classified with an error of less than 5 years, 20.25% with a marginal error between 5 and 10 years, and 35.75% with a higher marginal error of more than 10 years. Due to the complexity of the problem, the results obtained are considered good since 64.25% of the photographs were classified with an error of less than 10 years. Another important result of the study carried out is that it was found that the color is a very important characteristic when classifying photographs by date. The results obtained show that the approach given in this study is an important starting point for this type of task and that it allows placing a photograph in a specific temporal context, thus facilitating the work of experts dedicated to scientific and forensic photography.

The distribution of services on multi-agent systems allows it to reduce to the agents their computational load. The functionality of the system does not reside in the agents themselves, however it is ubiquitously distributed so that allows you to perform tasks in parallel avoiding an additional computational cost to the elements in the system. The distribution of services that offers SCODA (Distributed and Specialized Agent Communities) allows an intelligent management of these services provided by agents of the system and the parallel execution of threads that allow to respond to requests asynchronously, which implies an improvement in the performance of the system at both the computational level as the level of quality of service in the control of these services. The comparison carried out in the case of study that is presented in this paper demonstrates the existing improvement in the distribution of services on systems based on SCODA.