Semantic Segmentation of Fish and Underwater Environments Using Deep Convolutional Neural Networks and Learned Active Contours

The conservation of marine resources requires constant monitoring of the underwater environment by researchers. For this purpose, visual automated monitoring systems are of great interest, especially those that can describe the environment using semantic segmentation based on deep learning. Although they have been successfully used in several applications, such as biomedical ones, obtaining optimal results in underwater environments is still a challenge due to the heterogeneity of water and lighting conditions, and the scarcity of labeled datasets. Even more, the existing deep learning techniques oriented to semantic segmentation only provide low resolution results, lacking the enough spatial details for a high performance monitoring. To address these challenges, a combined loss function based on the active contour theory and level set methods is proposed to refine the spatial segmentation resolution and quality. To evaluate the method, a new underwater dataset with pixel annotations for three classes (fish, seafloor, and water) was created using images from publicly accessible datasets like SUIM, RockFish, and DeepFish. The performance of architectures of convolutional neural networks (CNNs), such as UNet and DeepLabV3+, trained with different loss functions (cross entropy, dice, and active contours) was compared, finding that the proposed combined loss function improved the segmentation results by around 3%, both in the metric Intercept Over Union (IoU) as in Hausdorff Distance (HD).2022-2

QuinuaSmartApp: A Real-Time Agriculture Precision IoT Cloud Platform to Crops Monitoring

IoT networks, cloud-based applications and the use of artificial intelligence models in precision agriculture present an important opportunity to increase production and optimize the use of water resources, which will allow the development of sustainable and responsible agriculture in the face of global food security. In order to provide real-time remote monitoring of quinoa crops, this article proposes and implements an integrated architecture based on sensor networks, drones with multispectral and Lidar cameras and cloud computing-based applications. The system has hardware and software applications that enable Quinoa crop monitoring during the different stages of its growth. Additionally, it comprises weather stations providing real-time data which permits actualising the predictive models that can be used for local climate change projections. The monitoring of the level of humidity in the crop field through soil stations feeds the training database based on machine learning that allows generating the projection of water demand, which allows more efficient and better-planned use of crop water. Additionally, it implements a service of warning messages, attended by experts who are connected to the system in order to provide technical recommendations to help deal with this issue in order to lessen the impact of pests and diseases in the field.2023-2

Instalación y configuración de Linux Zentyal server para la administración de servicios de infraestructura IT

El presente artículo desarrolla la instalación, configuración y puesta en marcha de un servidor Zentyal en su versión 7.0, emulando una red empresarial, donde se consideran las zonas roja, naranja y verde que son conocidas como la zona de internet, zona desmilitarizada y zona local respectivamente. Se instalarán y se pondrán en marcha módulos que provee Zentyal para el uso de cortafuegos, DHCP, Proxys, entre otros. El artículo se divide en las temáticas, en donde cada una abordará la configuración y puesta en marcha de cada servicio.This article develops the installation, configuration and start-up of a Zentyal server in version 7.0, emulating a business network, where the red, orange and green zones are considered, which are known as the internet zone, demilitarized zone and local zone respectively. Modules provided by Zentyal for the use of firewalls, DHCP, Proxys, among others, will be installed and put into operation. The article is divided into the themes, where each one will address the configuration and start-up of each service

Semantic Segmentation of Fish and Underwater Environments Using Deep Convolutional Neural Networks and Learned Active Contours

The conservation of marine resources requires constant monitoring of the underwater environment by researchers. For this purpose, visual automated monitoring systems are of great interest, especially those that can describe the environment using semantic segmentation based on deep learning. Although they have been successfully used in several applications, such as biomedical ones, obtaining optimal results in underwater environments is still a challenge due to the heterogeneity of water and lighting conditions, and the scarcity of labeled datasets. Even more, the existing deep learning techniques oriented to semantic segmentation only provide low resolution results, lacking the enough spatial details for a high performance monitoring. To address these challenges, a combined loss function based on the active contour theory and level set methods is proposed to refine the spatial segmentation resolution and quality. To evaluate the method, a new underwater dataset with pixel annotations for three classes (fish, seafloor, and water) was created using images from publicly accessible datasets like SUIM, RockFish, and DeepFish. The performance of architectures of convolutional neural networks (CNNs), such as UNet and DeepLabV3+, trained with different loss functions (cross entropy, dice, and active contours) was compared, finding that the proposed combined loss function improved the segmentation results by around 3%, both in the metric Intercept Over Union (IoU) as in Hausdorff Distance (HD)

Practical and Potential Applications of an Unmanned Airship based on Automatic Control - Embedded Computer System Design

Unmanned Airship is useful and applicable for a variety of fields especially in resource exploration and environmental monitoring. This measure enhances the flexibility, on-demand supply and reasonable cost. This is a fruitful approach to meet actual needs. In this paper, we propose an embedded automatic computer system architecture design for an Unmanned Airship in terms of hardware system, software system and automatic control algorithm perspectives. At last, we discuss the practical results in resource exploration and environmental monitoring that we have achieved as well as other potential applications of the Unmanned Airship