Cognitive radio is a paradigm that proposes managing the radio electric spectrum dynamically by integrating the spectrum sensing, decision-making, sharing, and mobility stages. In the decision-making stage, the best available channel is selected for transmitting secondary user data in an opportunistic fashion, and the success of that stage depends on the efficiency of the primary user characterization model. Use of the long short-term memory technique based on the deep learning concept is proposed in order to reduce the forecasting error present in the future estimation of primary users in the GSM and WiFi frequency bands. The results show that long short-term memory has the capacity needed to improve channel use forecasting significantly more than other methods such as multilayer perceptron neural networks, Bayesian networks, and adaptive neuro-fuzzy inference systems (ANFIS-Grid). It is concluded that although long shortterm memory exhibits better performance generating forecasts for time series, computing complexity is higher due to the existence of input, forget, and output gates within the neural structure; therefore, implementation is feasible in cognitive radio networks based on centralized network topologies.
The objective of the present work, which was sponsored by the French Ministèrs de l'Industrie, is to save “vulnerable” metals, i.e., those for which supply is uncertain and/or those that have to be imported. The production of alloys at the lowest price from a number of stocks of scrap alloys of various composition and from unalloyed metals has been achieved through the use of a new algorithm built into a computer program. The method differs from the usual linear programming and avoids the shortcomings of the well-known algorithms. Results are given showing the profits foundries can achieve when using this algorithm. Finally, some practical aspects of the introduction of this software in a foundry are presented, with unexpected consequences on the replacement of used-up stocks.
Abstract. The 2021 Scientific Initiatives in ISPRS funded this project called ISRS-SHY from “SHare mY ground truth”. It was intended as a collector of geographic data to support image analysis by sharing the necessary ground truth data needed for rigorous analysis. Regression and classification tasks that use remote sensing imagery necessarily require some control on the ground. The rationale behind this project is that often data on the ground is collected during projects, but is not valued by sharing across projects and teams globally. Internet has improved the way that data are shared, but there are still limitations related to discoverability of the data and its integrity. In other words, data are usually kept in local storage or, if in an accessible server, they are not documented and therefore they will not be picked up during search. In this initiative we created a portal using the Geonode environment to provide a hub for sharing data between research groups and openly to the community. The portal was then tested within the framework of three projects, with several participants each. The data that was uploaded and shared covered all types of geographic data formats and sizes. Further sharing was done in the context of teaching activities in higher education.The results show the importance of creating easy means to find data and share it across stakeholders. Qualitative results are discussed, and future steps will focus on quantitative assessment of the portal’s usage, e.g. number of registered users in time, number of visits, and other key performance indicators. The results of this project are to be considered also in light of the effort in the scientific community to make research data available, i.e. FAIR - Findability, Accessibility, Interoperability, and Reuse of digital assets.
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