Development of an artificial neural network based methodology, in order to estimate radio propagation path loss in built-up outdoor environment

The use of Artificial Neural Networks, so as to predict the propagation path loss in built-up outdoor areas, has been investigated. The novelty of the work at hand emanates from the determination of the kind of information which must be gathered from the built-up environment, according to its local architectural characteristics. That information is later fed to, and utilized from, appropriate Artificial Neural Networks. The effects of parameters such as the operating frequency, the transmitter’s height and its distance from the receiver have been investigated. Two methods of Artificial Neural Network parameter optimization, based on differential evolution, have been proposed in order to make sure that the benefits from the input information are reaped to the biggest extend possible: the former enables the determination of their optimal inner architecture, while the latter deals with optimizing the weights between the synapses, given a predetermined inner structure. Moreover, and in order to compare results with the Artificial Neural Networks, the machine learning method of Random Forests has been implemented in order to obtain path loss predictions. Apart from comparing the results between them, the combination of the two methods in a meta-model in order to obtain even better results, has been proposed.Μελετήθηκε η χρήση Τεχνητών Νευρωνικών Δικτύων για την εκτίμηση της απώλειας διαδρομής ραδιοκυμάτων σε δομημένα περιβάλλοντα ανοιχτού χώρου. Η πρωτοτυπία της διατριβής αφορά στην εξειδίκευση του είδους της πληροφορίας που πρέπει να αντληθεί για το περιβάλλον διάδοσης βάσει των τοπικών αρχιτεκτονικών χαρακτηριστικών που το καθορίζουν. Η αξιοποίηση αυτής της πληροφορίας γίνεται από κατάλληλα Τεχνητά Νευρωνικά Δίκτυα. Η μελέτη πραγματοποιήθηκε λαμβάνοντας ως επιπλέον παραμέτρους την συχνότητα, το ύψος του πομπού και την απόστασή του από τον δέκτη. Προκειμένου να διασφαλιστεί πως τα εσωτερικά χαρακτηριστικά των χρησιμοποιούμενων Τεχνητών Νευρωνικών Δικτύων είναι τέτοια που να επιτρέπουν την καλύτερη δυνατή αξιοποίηση της πληροφορίας στην είσοδό τους, προτάθηκαν δύο μέθοδοι για την βελτιστοποίησή τους, βασιζόμενες στην Διαφορική Εξέλιξη: η πρώτη αφορά τον καθορισμό της βέλτιστης αρχιτεκτονικής τους, ενώ η δεύτερη επιτρέπει την βελτιστοποίηση των βαρών τους, για δεδομένη εσωτερική αρχιτεκτονική. Συμπληρωματικά με τα Τεχνητά Νευρωνικά Δίκτυα, και προς σύγκριση με αυτά, χρησιμοποιήθηκε η μέθοδος μηχανικής μάθησης των Τυχαίων Δασών για τον υπολογισμό της απώλειας διαδρομής. Πέρα από την σύγκριση των αποτελεσμάτων των δύο μεθόδων προτάθηκε επιπλέον μία μέθοδος για τον συνδυασμό τους με στόχο την περαιτέρω βελτίωση της ακρίβειας πρόβλεψης

Visible light positioning : a machine learning approach

Visible light positioning (VLP) systems have experienced substantial revolutionary progress over the past year because they can offer great positioning accuracy without needing any additional infrastructure, as conventional radio-frequency (RF)-based systems. Received signal strength (RSS)-based VLP systems are a promising approach to many indoor positioning estimation problems, but still suffer from difficulty in providing high accuracy and reliability. A potential solution to these challenges is to combine VLP systems, and machine learning (ML) approaches to enhance the position prediction accuracy in two-dimensional (2-D) spaces, or more complex problems. In this paper, we propose a ML approach to accurately predict the 2-D indoor position of a mobile receiver (eg. an automated guided vehicles-AGV), based on the measured RSS values of 4 photodiodes (PDs) forming a star architecture. We examine and evaluate the performance of different ML learners applied to the above-described problem. The proposed ML and Neural Network (NN) methods exhibit great accuracy results in predicting the 2-D coordinates of a PD-based receiver

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments of Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research

Artificial intelligence in visible light positioning for indoor IoT : a methodological review

Indoor communication and positioning are significant fields of applications for indoor Internet of Things (IoT) given the rapid growth of IoT in smart cities, smart grids, and smart industries. Visible light positioning (VLP) has become more and more attractive for researchers to provide indoor location-aware IoT services. Additionally, artificial intelligence (AI) has attracted considerable research effort to address the challenges in visible-light communication (VLC) systems. This is an emerging technology in next-generation wireless networks. However, despite the rapid progress, the use of AI in localization, navigation, and position estimation is still underexplored in VLC systems, and various research challenges are still open. This methodological review summarizes the research efforts regarding the use of AI in the field of VLP, to improve the position estimation accuracy in both two-dimensional (2D) and three-dimensional (3D) indoor IoT applications. This treatise also presents open issues and potential future directions for motivating further research in the field. Various databases were utilized in this paper: Scopus, Google Scholar, and IEEE Xplore; obtained 88 papers from 2017 to early 2023. Most (68%) of the AI articles in VLP systems are machine learning (ML) methods applied for localization and position estimation in VLC systems, while the other 32% of the research articles focussed on evolutionary algorithms. ML and evolutionary models may present limitations in terms of complexity and time-consuming nature but offer highly accurate, robust, reliable, and cost-effective results in terms of position estimation over conventional approaches

Quality Assessment of Ground Coffee Samples from Greek Market Using Various Instrumental Analytical Methods, In Silico Studies and Chemometrics

Coffee is one of the most widely consumed beverages worldwide due to its sensory and potential health-related properties. In the present comparative study, a preparation known as Greek or Turkish coffee, made with different types/varieties of coffee, has been investigated for its physicochemical attributes (i.e., color), antioxidant/antiradical properties, phytochemical profile, and potential biological activities by combining high-throughput analytical techniques, such as infrared spectroscopy (ATR-FTIR), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and in silico methodologies. The results of the current study revealed that roasting degree emerged as the most critical factor affecting these parameters. In particular, the L* color parameter and total phenolic content were higher in light-roasted coffees, while decaffeinated coffees contained more phenolics. The ATR-FTIR pinpointed caffeine, chlorogenic acid, diterpenes, and quinic esters as characteristic compounds in the studied coffees, while the LC-MS/MS analysis elucidated various tentative phytochemicals (i.e., phenolic acids, diterpenes, hydroxycinnamate, and fatty acids derivatives). Among them, chlorogenic and coumaric acids showed promising activity against human acetylcholinesterase and alpha-glucosidase enzymes based on molecular docking studies. Therefore, the outcomes of the current study provide a comprehensive overview of this kind of coffee preparation in terms of color parameters, antioxidant, antiradical and phytochemical profiling, as well as its putative bioactivity