GPS Based Design of the Local Clock Control System based on the Optimally Unbiased Moving Average Filter

In this paper we made the simulation steering of the local clock t'ime errors with simple moving average (MA), optimally unbiased moving average (OMA), the two and three-state Kalman filters. The references signal (precise time) was suministred by GPS. In this task we have two important activities, estimating and the error control, so the,principal parameter in this study is the root mean square error (RMSE) of steering. When steering the GPS-based time error in the local clock with four filters, we found out that, of the filter with the same time constant, the optimally unbiased MA filter desmostred the steering error between the two and three state Kalman filter.Universidad de Guanajuat

Análisis del modelo de sombreado normal-logarítmico aplicado a un enlace LoRa punto a punto

LoRa is a low-power wireless technology and long range used in wide area network. In addition to LoRa, there are other wireless technologies for data transmission with several qualities. However, LoRa technology can be exploited in different applications such as Wireless Sensor Network (WSN), tracking and location. This is due to its great advantage to support a high number of communication devices. The LoRa technology is based on the Chirp Spread Spectrum (CSS) modulation, this provides robustness to noise and other phenomena of degradation channel. In this work, it is presented the LoRa Technology performance in indoor and quasi-indoor environments with a point-to-point connection to estimate the distance based on the Received Signal Strength Indication (RSSI). The RSSI measurements, the estimating logarithmic function to compute distances and errors are shown in this manuscript. Finally, it can be concluded that the results are satisfactory, with an acceptable margin of error for indoor and quasi-indoor environments.LoRa es una tecnología inalámbrica de bajo consumo de potencia y largo alcance empleada en redes de área amplia. Además de LoRa, en el mercado existen otras tecnologías inalámbricas para transmisión de datos con diversas cualidades. Sin embargo, la tecnología LoRa se puede explotar en diferentes aplicaciones tales como redes de sensores inalámbricas (WSN, por sus siglas en inglés), rastreo y localización. Esto debido a su gran ventaja de soportar un gran número de dispositivos de comunicación. La tecnología LoRa está basada en la técnica de modulación Chirp Spread Spectrum (CSS, por sus siglas en inglés), esto le proporciona robustez ante el ruido y otros fenómenos de degradación de canal. En el presente trabajo se presenta el desempeño de la tecnología LoRa en ambientes interiores y semi-interiores con una conexión punto a punto para estimar la distancia basado en el indicador de la intensidad de la señal recibida (RSSI, por sus siglas en inglés). Las mediciones de RSSI realizadas, la estimación de las funciones logarítmicas para calcular las distancias y los errores, son presentados en este documento. Finalmente, se puede concluir que los resultados obtenidos son satisfactorios, con un margen de error aceptable para ambientes interiores y semi-interiores

Aprendizaje basado en coeficientes de fourier para la identificación de daño en plantas de cultivos

Spectral signature analysis is one of the most widely used diagnostic methods to identify plant diseases. To this end, different information acquisition techniques must be considered to detect the different levels of a particular disease or pest, as in the case of fungal diseases. In this study, cucurbit plants were considered in three stages of levels of a fungal disease were identified which are leaves in the fungal germination stage, leaves with first symptoms, and diseased leaves. A database with spectral signatures of zucchini leaves was used. Then, frequency analysis of spectral features is proposed using Fourier transform to extract features from the obtained coefficients and from classification blocks with support vector machines for damage level estimation. Classification accuracies of 98.3% were demonstrated. Therefore, this method can be used to diagnose the damage levels in different crops.El análisis de firmas espectrales es uno de los métodos de diagnóstico más utilizados para identificar enfermedades en las plantas. Con este fin, se deben considerar diferentes técnicas de adquisición de información para detectar los diferentes niveles de una enfermedades o plaga en particular, como en el caso de las enfermedades fúngicas. En este estudio, se consideraron plantas cucúrbitas en las cuales se identificaron tres etapas de niveles de una enfermedad fúngica que son las hojas en la etapa de germinación del hongo, hojas con primeros síntomas y hojas enfermas. Se utilizó una base de datos con firmas espectrales de hojas de calabacita. A continuación, se propone el análisis de frecuencia de las características espectrales utilizando la transformada de Fourier para extraer características de los coeficientes obtenidos y partir de bloques de clasificación con máquinas de vectores de soporte para la estimación del nivel de daño. Se demostraron precisiones de clasificación del 98.3%. Por lo tanto, este método se puede utilizar para diagnosticar el grado de daño en diferentes cultivos

Incorporating Breast Asymmetry Studies into CADx Systems

Breast cancer is one of the global leading causes of death among women, and an early detection is of uttermost importance to reduce mortality rates. Screening mammograms, in which radiologists rely only on their eyesight, are one of the most used early detection methods. However, characteristics, such as the asymmetry between breasts, a feature that could be very difficult to visually quantize, is key to breast cancer detection. Due to the highly heterogeneous and deformable structure of the breast itself, incorporating asymmetry measurements into an automated detection system is still a challenge. In this study, we proposed the use of a bilateral registration algorithm as an effective way to automatically measure mirror asymmetry. Furthermore, this information was fed to a machine learning algorithm to improve the accuracy of the model. In this study, 449 subjects (197 with calcifications, 207 with masses, and 45 healthy subjects) from a public database were used to train and evaluate the proposed methodology. Using this procedure, we were able to independently identify subjects with calcifications (accuracy = 0.825, AUC = 0.882) and masses (accuracy = 0.698, AUC = 0.807) from healthy subjects