Aquaponic health is a very important in the food industry field, as currently there is a huge amount of fishing farms, and the demands are growing in the whole world. This work examines the process of developing an innovative aquaponics health monitoring system that incorporates high-tech back-end innovation sensors to examine fish and crop health and a data analytics framework with a low-tech front-end approach to feedback actions to farmers. The developed system improves the state-of-the-art in terms of aquaponics life cycle monitoring metrics and communication technologies, and the energy consumption has been reduced to make a sustainable system.
The current understanding of CO2 emission concentrations in hybrid vehicles (HVs) is limited, due to the complexity of the constant changes in their power-train sources. This study aims to address this problem by examining the accuracy, speed and size of traditional and advanced machine learning (ML) models for predicting CO2 emissions in HVs. A new long short-term memory (LSTM)-based model called UWS-LSTM has been developed to overcome the deficiencies of existing models. The dataset collected includes more than 20 parameters, and an extensive input feature optimization has been conducted to determine the most effective parameters. The results indicate that the UWS-LSTM model outperforms traditional ML and artificial neural network (ANN)-based models by achieving 97.5% accuracy. Furthermore, to demonstrate the efficiency of the proposed model, the CO2-concentration predictor has been implemented in a low-powered IoT device embedded in a commercial HV, resulting in rapid predictions with an average latency of 21.64 ms per prediction. The proposed algorithm is fast, accurate and computationally efficient, and it is anticipated that it will make a significant contribution to the field of smart vehicle applications.
Recent advances in state-of-the-art camera-based AI mechanisms in the automated driving field have leveraged great progress in the installation and widespread use of this technology along the recent years. However, vehicles with automated driving capabilities are usually equipped with a wide range of sensors that complement the perception capacity of camera-based AI algorithms. For this reason, this paper tries to reveal the degree of readiness of one of the most used open-source AI models for Level 2 automated driving. To this end, a set of simulated common driving scenarios were used to evaluate the predictions. The results obtained clearly indicate that the current capacity of this camera-based DNN model is not sufficient to be the only source of information in the process of environment perception of a Level 2 automated vehicle, and therefore, further progress in the context awareness needs to be achieved to consider its sole use in the perception stage.
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