Autonomous configuration of communication systems for IoT smart nodes supported by machine learning

Machine Learning brings intelligence services to IoT systems, with Edge Computing contributing for edge nodes to be part of these services, allowing data to be processed directly in the nodes in real time. This paper introduces a new way of creating a self-configurable IoT node, in terms of communications, supported by machine learning and edge computing, in order to achieve a better efficiency in terms of power consumption, as well as a comparison between regression models and between deploying them in edge or cloud fashions, with a real case implementation. The correct choice of protocol and configuration parameters can make the difference between a device battery lasting 100 times more. The proposed method predicts the energy consumption and quality of signal using regressions based on node location, distance and obstacles and the transmission power used. With an accuracy of 99.88% and a margin of error of 1.504 mA for energy consumption and 98.68% and a margin of error of 1.9558 dBm for link quality, allowing the node to use the best transmission power values for reliability and energy efficiency. With this it is possible to achieve a network that can reduce up to 68% the energy consumption of nodes while only compromising in 7% the quality of the network. Besides that, edge computing proves to be a better solution when energy efficient nodes are needed, as less messages are exchanged, and the reduced latency allows nodes to be configured in less time.info:eu-repo/semantics/publishedVersio

Use of compost in the fertilization of golf greens

Portugal has very good climatic and landscape conditions for the golf practice. Nevertheless the maintenance of the greens required high inputs of fertilizers. The aim of the present work was to evaluate the replacement of the conventional fertilizers by an organic compost in the fertilization of golf greens

Mobile communication systems to control UAVs: Measurements of QoS parameters

This paper proposes to identify a propagation model that considers the unmanned aerial vehicles (UAVs) unique characteristics, contemplating two actual wireless technologies, UMTS and LTE, which are theoretically capable of supporting a real-time video service admitting more than one quality index according to the RF conditions. Several measurements were made in a specific outdoor rural scenario in order to understand if the current network infrastructure is prepared to support this type of service using these vehicles, by simulating a real case scenario and considering critical locations where the loss of Quality of Service (QoS) can be significant due to the hole phenomenon that occurs over the antennas/base stations, raising the probability to occur handover.info:eu-repo/semantics/acceptedVersio

Portfolio choice with high frequency data : CRRA preferences and the liquidity effect

This paper suggests a new approach for portfolio choice. In this frame- work, the investor, with CRRA preferences, has two objectives: the maximization of the expected utility and the minimization of the portfolio expected illiquidity. The CRRA utility is measured using the portfolio realized volatility, realized skewness and realized kurtosis, while the portfolio illiquidity is measured using the well-known Amihud illiquidity ratio. Therefore, the investor is able to make her choices directly in the expected utility/liquidity (EU/L) bi-dimensional space. We conduct an empiri- cal analysis in a set of fourteen stocks of the CAC 40 stock market index, using high frequency data for the time span from January 1999 to December 2005 (seven years). The robustness of the proposed model is checked according to the out-of-sample per- formance of different EU/L portfolios relative to the minimum variance and equally weighted portfolios. For different risk aversion levels, the EU/L portfolios are quite competitive and in several cases consistently outperform those benchmarks, in terms of utility, liquidity and certainty equivalent.info:eu-repo/semantics/publishedVersio

Improve irrigation timing decision for agriculture using real time data and machine learning

With the constant evolution of technology and the constant appearance of new solutions that, when combined, manage to achieve sustainability, the exploration of these systems is increasingly a path to take. This paper presents a study of machine learning algorithms with the objective of predicting the most suitable time of day for water administration to an agricultural field. With the use of a high amount of data previously collected through a Wireless Sensors Network (WSN) spread in an agricultural field it becomes possible to explore technologies that allow to predict the best time for water management in order to eliminate the scheduled irrigation that often leads to the waste of water being the main objective of the system to save this same natural resource.info:eu-repo/semantics/acceptedVersio

Sustainable irrigation system for farming supported by machine learning and real-time sensor data

Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.info:eu-repo/semantics/publishedVersio

Remission of extensive lentigo maligna after treatment with imiquimod

Lentigo maligno é um melanoma in situ que mais comumente surge em áreas expostas à radiação ultravioleta, nos pacientes idosos. O tratamento é realizado, principalmente, para minimizar o risco de progressão para lentigo maligno melanoma. O presente relato se refere a uma paciente idosa com lesões recorrentes de lentigo maligno na face, tratada com sucesso com imiquimod tópico, mostrando que este pode ser um tratamento útil, em determinados casos da doença.Lentigno maligna is a melanoma in situ that most commonly appears on areas exposed to ultraviolet radiation, in elderly patients. Treatment is required mainly to minimize the risk of progression to lentigo maligna melanoma. The present report refers to an elderly patient with recurrent lesions of lentigo maligna in her face, who was successfully treated with topical imiquimod, which showed to be a useful therapy for some cases of the disease

YOLOX-Ray: An efficient attention-based single-staged object detector tailored for industrial inspections

Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray’s performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving mAP50 values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, mAP50:95, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray’s ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections.info:eu-repo/semantics/publishedVersio