Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine.

Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.

Detection of changes in literary writing style using N-grams as style markers and supervised machine learning.

The analysis of an author's writing style implies the characterization and identification of the style in terms of a set of features commonly called linguistic features. The analysis can be extrinsic, where the style of an author can be compared with other authors, or intrinsic, where the style of an author is identified through different stages of his life. Intrinsic analysis has been used, for example, to detect mental illness and the effects of aging. A key element of the analysis is the style markers used to model the author's writing patterns. The style markers should handle diachronic changes and be thematic independent. One of the most commonly used style marker in extrinsic style analysis is n-gram. In this paper, we present the evaluation of traditional n-grams (words and characters) and dependency tree syntactic n-grams to solve the task of detecting changes in writing style over time. Our corpus consisted of novels by eleven English-speaking authors. The novels of each author were organized chronologically from the oldest to the most recent work according to the date of publication. Subsequently, two stages were defined: initial and final. In each stage three novels were assigned, novels of the initial stage corresponded to the oldest and those at the final stage to the most recent novels. To analyze changes in the writing style, novels were characterized by using four types of n-grams: characters, words, Part-Of-Speech (POS) tags and syntactic relations n-grams. Experiments were performed with a Logistic Regression classifier. Dimension reduction techniques such as Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) algorithms were evaluated. The results obtained with the different n-grams indicated that all authors presented significant changes in writing style over time. In addition, representations using n-grams of syntactic relations have achieved competitive results among different authors.

Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing.

There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological care has been performed by neurorehabilitation therapies, to improve the patients' life quality and facilitating their performance in society. One way to know how the neurorehabilitation therapies contribute to help patients is by measuring changes in their brain activity by means of electroencephalograms (EEG). EEG data-processing applications have been used in neuroscience research to be highly computing- and data-intensive. Our proposal is an integrated system of Electroencephalographic, Electrocardiographic, Bioacoustic, and Digital Image Acquisition Analysis to provide neuroscience experts with tools to estimate the efficiency of a great variety of therapies. The three main axes of this proposal are: parallel or distributed capture, filtering and adaptation of biomedical signals, and synchronization in real epochs of sampling. Thus, the present proposal underlies a general system, whose main objective is to be a wireless benchmark in the field. In this way, this proposal could acquire and give some analysis tools for biomedical signals used for measuring brain interactions when it is stimulated by an external system during therapies, for example. Therefore, this system supports extreme environmental conditions, when necessary, which broadens the spectrum of its applications. In addition, in this proposal sensors could be added or eliminated depending on the needs of the research, generating a wide range of configuration limited by the number of CPU cores, i.e., the more biosensors, the more CPU cores will be required. To validate the proposed integrated system, it is used in a Dolphin-Assisted Therapy in patients with Infantile Cerebral Palsy and Obsessive-Compulsive Disorder, as well as with a neurotypical one. Event synchronization of sample periods helped isolate the same therapy stimulus and allowed it to be analyzed by tools such as the Power Spectrum or the Fractal Geometry.

Energy-Efficient Industrial Internet of Things Software-Defined Network by Means of the Peano Fractal.

The Industrial Internet of Things (IIoT) network generates great economic benefits in processes, system installation, maintenance, reliability, scalability, and interoperability. Wireless sensor networks (WSNs) allow the IIoT network to collect, process, and share data of different parameters among Industrial IoT sense Node (IISN). ESP8266 are IISNs connected to the Internet by means of a hub to share their information. In this article, a light-diffusion algorithm in WSN to connect all the IISNs is designed, based on the Peano fractal and swarm intelligence, i.e., without using a hub, simply sharing parameters with two adjacent IINSs, assuming that any IISN knows the parameters of the rest of these devices, even if they are not adjacent. We simulated the performance of our algorithm and compared it with other state-of-the-art protocols, finding that our proposal generates a longer lifetime of the IIoT network when few IISNs were connected. Thus, there is a saving-energy of approximately 5% but with 64 nodes there is a saving of more than 20%, because the IIoT network can grow in a 3 n way and the proposed topology does not impact in a linear way but log 3 , which balances energy consumption throughout the IIoT network.