As telecommunication systems evolve towards new-generation architectures, likewise, new protocols are created in order to improve efficiency. One of these protocols is Transmission Control Protocol (TCP), which controls the transmission bit rate in function of network congestion. Nevertheless, in wireless communications, there appear problems such as noise and interference, for which TCP was not designed. Based on these problems, there exist some methods trying to mitigate congestion, such as explicit loss notifications and the use of end-to-end codification. The aim of this work was to propose a wireless TCP protocol improvement, considering a negative acknowledgment (NACK), which allows to differentiate between losses due to congestion and losses due to wireless channel issues. NACK employs a small protocol packet and produces improvement in the quality of service metrics. The experiments were carried out in in-door and out-door environments, over an online video game scenario, and over a long-distance wireless link between two islands. The average results show a 25-percent delay improvement and a 5-percent jitter improvement when compared to the original TCP Reno protocol, while for throughput a 90-percent improvement was achieved for distances between 100 and 414 m.

This article presents a study based on evaluating different techniques to automatically recognize the basic emotions of people with Down syndrome, such as anger, happiness, sadness, surprise, and neutrality, as well as the statistical analysis of the Facial Action Coding System, determine the symmetry of the Action Units present in each emotion, identify the facial features that represent this group of people. First, a dataset of images of faces of people with Down syndrome classified according to their emotions is built. Then, the characteristics of facial micro-expressions (Action Units) present in the feelings of the target group through statistical analysis are evaluated. This analysis uses the intensity values of the most representative exclusive action units to classify people’s emotions. Subsequently, the collected dataset was evaluated using machine learning and deep learning techniques to recognize emotions. In the beginning, different supervised learning techniques were used, with the Support Vector Machine technique obtaining the best precision with a value of 66.20%. In the case of deep learning methods, the mini-Xception convolutional neural network was used to recognize people’s emotions with typical development, obtaining an accuracy of 74.8%.

Since the year 2007, Escuela Politécnica del Ejército has been carrying out lab and field tests to evaluate the performance of current digital television systems. ISDB-Tb is the Japanese with Brazilian modifications digital terrestrial television standard adopted by Ecuadorian Government, which is able to transmit multiple television programs with interactive applications and high definition contents. This paper details tests about generation, transmission and reception of data flow TS containing multimedia, audio, video and interactive applications, using free distribution tools. This paper tries to be a reference document for those who want to start experimenting quickly, easily and cheaply, with interactive multimedia TS generation.