Identifying the polarity of a text given the emotion of its author

The rise of digital communication systems provides an almost infinite source of information that can be useful to feed classification algorithms, so it makes use of an already categorized collection of opinions of the social network Twitter for the formation and generation of a model of classification of short texts; which aims to categorize the emotional tone found in an author’s Spanish-language digital text. In addition, linguistic, lexicographic and opinion mining computational tools are used to implement a series of methods that allow to automatically finding coincidences or orientations that allow determining the polarity of sentences and categorize them as positive, negative or neutral considering their lemmas. The results obtained from the analysis of emotions and polarity of this project, on the test phrases allow to observe a direct relationship between the categorized emotional tone and it is positive, negative or neutral classification, which allows to provide additional information to know the intention that the author had when he created the sentence. Determining these characteristics can be useful as a consistent information objective that can be leveraged by sectors where the prevalence of a product or service depends on user opinion, product rating or turns with satisfaction metrics.

Introducing perforation when applying turbo codes

The problem of adapting the degree of redundancy introduced in the process of error-correcting coding to the changing characteristics of the data transmission channel is urgent. Turbo codes, used in a variety of digital communication systems, are capable of correcting multiple errors occurring in the data transmission channel. The article compares the decoding efficiency for various options for introducing perforation into the code sequence generated by the turbo code encoder. Based on the comparison results, recommendations were made on the most appropriate option for the introduction of perforation.

Effects of Deep Learning Technologies on Employment in the Field of Digital Communication Systems

There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance. This paper provides an overview of the current state-of-the-art developments associated with deep learning and artificial intelligence and the ongoing revolutions that this technology is having not only on the field of digital communication systems but also related technology fields. This paper will also explore issues and concerns related to past technological unemployment challenges, as well as opportunities that may be present as a result of these ongoing technological upheavals.

Avoiding normalization uncertainties in deep learning architectures for end-to-end communication

Recently, deep learning is considered to optimize the end-to-end performance of digital communication systems. The promise of learning a digital communication scheme from data is attractive, since this makes the scheme adaptable and precisely tunable to many scenarios and channel models. In this paper, we analyse a widely used neural network architecture and show that the training of the end-to-end architecture suffers from normalization errors introduced by an average power constraint. To solve this issue, we propose a modified architecture: shifting the batch slicing after the normalization layer. This approach meets the normalization constraints better, especially in the case of small batch sizes. Finally, we experimentally demonstrate that our modified architecture leads to significantly improved performance of trained models, even for large batch sizes where normalization constraints are more easily met.<br>

Digital Bandpass Modulation Methods

This chapter presents mathematical models of baseband and bandpass digital communication systems based on binary and quaternary phase-shift keying, frequency-shift keying, and quadrature amplitude modulation. The systems are deduced as special cases from the general generic system structure and the related theory of orthonormal basis functions. The systems are uniquely presented using mathematical operators and detailed derivatives for signals in time and frequency domains at the system’s vital points, that is, the transmitter, the receiver, and the noise generator, using the concepts of both stochastic (continuous and discrete) and deterministic (continuous and discrete) signal processing. The vital characteristics of the system and its blocks are expressed in terms of amplitude spectral density, autocorrelation functions, power and energy spectral densities, and bit error probability.