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Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.


Author(s):  
Stefan Ionita ◽  
Stefan Velicu

The main objective of the research paper is the theoretical and experimental analysis of the method proposed for sealing (clogging) cracks in asphalt, by means of a cylindrical bitumen bar, enriched with plastic and rubber granules (obtained from the use of waste), which melts and infuses into the cracked zone by rotation and friction against it. After analyzing the technical characteristics of the sealed area and the time required to apply the bitumen layer, this method can be chosen in the future to the detriment of the expensive operations of partial milling of the cracked wear layer, making possible the repair of cracks by sealing(clogging), using the friction procedure. The research results highlighted the diminution of road maintenance costs using the method of friction, the decrease of cracks repair time, maintaining the initial characteristics of the repaired area, incorporating a waterproofing material (plastic and rubbber granules from recycled waste), keeping the wear layer in good conditions, possibility of embedding an intelligent system of traffic monitoring at low costs etc.


Author(s):  
Brahim Jabir ◽  
Noureddine Falih

<span>In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected.</span>


Author(s):  
Sorin Andrei Negru ◽  
Marilena Manea ◽  
Gabriel Jiga

The main objective of the research paper is the theoretical and experimental analysis of the method proposed for sealing (clogging) cracks in asphalt, by means of a cylindrical bitumen bar, enriched with plastic and rubber granules (obtained from the use of waste), which melts and infuses into the cracked zone by rotation and friction against it. After analyzing the technical characteristics of the sealed area and the time required to apply the bitumen layer, this method can be chosen in the future to the detriment of the expensive operations of partial milling of the cracked wear layer, making possible the repair of cracks by sealing(clogging), using the friction procedure. The research results highlighted the diminution of road maintenance costs using the method of friction, the decrease of cracks repair time, maintaining the initial characteristics of the repaired area, incorporating a waterproofing material (plastic and rubbber granules from recycled waste), keeping the wear layer in good conditions, possibility of embedding an intelligent system of traffic monitoring at low costs etc.


Author(s):  
Robert Cerna Duran ◽  
◽  
Brian Meneses Claudio ◽  
Alexi Delgado

The increase in garbage production today is due to the exponential growth of the population worldwide, due to the fact that thousands of tons of garbage are generated daily around the world, but the mismanagement that gives them has become an environmental problem since 33% of all the garbage generated is not recycled, for that reason it is estimated that within the next three decades the amount of waste worldwide will increase to 70%. That is why in the present research work it is proposed to make an intelligent system based on the Internet of Things (IoT) that allows monitoring the garbage containers in real time representing with percentages the state of these containers and these can be collected in time by garbage trucks, and thus avoid the increase of garbage in the streets and the various types of problems that these would cause. As a result, it was obtained that the System does comply with the established conditions because it allows to monitor in real time representing by percentages the state of the garbage container, which indicates 40% as almost full and 80% indicates that it is already available for collection. Finally, it is concluded that using the Garbage Container Monitoring System will allow to better optimize the collection process and, in addition, the problems that are usually perceived today due to the amount of garbage that are registered in the streets will decrease. Keywords-- Internet of Things; Intelligent system; Real time; Environmental Problem; Monitoror; Percentage.


2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Aliaa M. Alabdali

With the growing need of technology into varied fields, dependency is getting directly proportional to ease of user-friendly smart systems. The advent of artificial intelligence in these smart systems has made our lives easier. Several Internet of Things- (IoT-) based smart refrigerator systems are emerging which support self-monitoring of contents, but the systems lack to achieve the optimized run time and data security. Therefore, in this research, a novel design is implemented with the hardware level of integration of equipment with a more sophisticated software design. It was attempted to design a new smart refrigerator system, which has the capability of automatic self-checking and self-purchasing, by integrating smart mobile device applications and IoT technology with minimal human intervention carried through Blynk application on a mobile phone. The proposed system automatically makes periodic checks and then waits for the owner’s decision to either allow the system to repurchase these products via Ethernet or reject the purchase option. The paper also discussed the machine level integration with artificial intelligence by considering several features and implemented state-of-the-art machine learning classifiers to give automatic decisions. The blockchain technology is cohesively combined to store and propagate data for the sake of data security and privacy concerns. In combination with IoT devices, machine learning, and blockchain technology, the proposed model of the paper can provide a more comprehensive and valuable feedback-driven system. The experiments have been performed and evaluated using several information retrieval metrics using visualization tools. Therefore, our proposed intelligent system will save effort, time, and money which helps us to have an easier, faster, and healthier lifestyle.


2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Tanqiu Wang

For the purpose of improving the efficiency of garment design, the computer-aided garment design virtual reality (VR) model for surplus fabric removal and reuse without segmentation of cutting pieces is analyzed in this paper to provide the architecture of the computer-aided garment design CAD system. The form of dividing the garment into multiple types of nonsegmented pieces is adopted so that each nonsegmented piece stands for a complete design element unit. Based on this structure, the computer analysis of garment design based on CAD can be connected at a deeper level, which will not only improve the design efficiency of new garments but also reduce the design time at the client terminal and enhance the quality of the design. Through the experimental operation of prototypes, it is verified that the intelligent system proposed in this paper can implement the design of prototypes quickly and effectively.


Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 148
Author(s):  
Nikita Andriyanov ◽  
Ilshat Khasanshin ◽  
Daniil Utkin ◽  
Timur Gataullin ◽  
Stefan Ignar ◽  
...  

Despite the great possibilities of modern neural network architectures concerning the problems of object detection and recognition, the output of such models is the local (pixel) coordinates of objects bounding boxes in the image and their predicted classes. However, in several practical tasks, it is necessary to obtain more complete information about the object from the image. In particular, for robotic apple picking, it is necessary to clearly understand where and how much to move the grabber. To determine the real position of the apple relative to the source of image registration, it is proposed to use the Intel Real Sense depth camera and aggregate information from its depth and brightness channels. The apples detection is carried out using the YOLOv3 architecture; then, based on the distance to the object and its localization in the image, the relative distances are calculated for all coordinates. In this case, to determine the coordinates of apples, a transition to a symmetric coordinate system takes place by means of simple linear transformations. Estimating the position in a symmetric coordinate system allows estimating not only the magnitude of the shift but also the location of the object relative to the camera. The proposed approach makes it possible to obtain position estimates with high accuracy. The approximate root mean square error is 7–12 mm, depending on the range and axis. As for precision and recall metrics, the first is 100% and the second is 90%.


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