Analysis and evaluation of Wi-Fi indoor positioning systems using smartphones

This paper attempts to analyze the main algorithms used in Machine Learning applied to the indoor location. New technologies are facing new challenges. Satellite positioning has become a typical application of mobile phones, but stops working satisfactorily in enclosed spaces. Currently there is a problem in positioning which is unresolved. This circumstance motivates the research of new methods. After the introduction, the first chapter presents current methods of positioning and the problem of positioning indoors. This part of the work shows globally the current state of the art. It mentions a taxonomy that helps classify the different types of indoor positioning and a selection of current commercial solutions. The second chapter is more focused on the algorithms that will be analyzed. It explains how the most widely used of Machine Learning algorithms work. The aim of this section is to present mathematical algorithms theoretically. These algorithms were not designed for indoor location but can be used for countless solutions. In the third chapter, we learn gives tools work: Weka and Python. the results obtained after thousands of executions with different algorithms and parameters showing main problems of Machine Learning shown. In the fourth chapter the results are collected and the conclusions drawn are shown

Contextually and identity aware 5G services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe fifth generation (5G) mobile networks aim to be ten times faster than the existing 4G connection, whilst providing low latency, and flexibility. Hence, various alterations are planned to the existing network infrastructure to be able to reach the 5G expected performance levels. The main technologies that were used, to ensure high performance, flexible network, and efficient resource allocation, are Software Defined Network and Network Function Virtualization. As these technologies are replacing the device-based architecture with, a service-based architecture. This thesis provides a design of location database interactive web interface and interactive mobile application. The implementation of real time video streaming location server, the streaming system's performance parameters demonstrated a high level of QoS (0.07ms jitter and 9.53ms delay). In regard to experimental examination, it measured the localisation coverage, accuracy measurements and a highly scalable security solution. The localisation coverage and accuracy measurements were achieved through the mmWave and VLC link transmitters. The proposed simulated annealing algorithm aimed at data optimisation for location measurements accuracy showed results of the average location error of x and y which showed significant improvement from x= 22.5 and y=21.6 to x=11.09 and y= 11.63. The proposed indoor location security solution showed significant results, as it provides a high scalability solution using the VNF. The solution showed that it was not 100% effective, as some of the fake discover packets still reached the DHCP server. This was due to the high load of traffic passing through the network. Nonetheless, 90% of the fake DHCP discover packets never reached the DHCP server because the scripts began blocking all fake discover packets after realising it was an attack. This conveys that the proposed system was able to run successfully without crashing or overloading the controller. Overall, the main challenges facing 5G have been addressed with their proposed solutions, which showed promising results. Conclusively showing that there is a lot more space for technological advancements to support the future of mobile networks.European Union’s Horizon 2020 research program - the Internet of Radio-Light (IoRL) project H2020-ICT 761992

Artificial Intelligence for detection and prevention of mold contamination in tomato processing

openIl presente elaborato si propone di analizzare l'uso dell'intelligenza artificiale attraverso il riconoscimento di immagini per rilevare la presenza di muffa nei pomodori durante il processo di essiccazione. La muffa nei pomodori rappresenta un rischio sia per la salute umana sia per l'industria alimentare, comportando, anche, una serie di problemi che vanno oltre l'aspetto estetico. Essa è causata principalmente da funghi che si diffondono rapidamente sulla superficie dei pomodori. Tale processo compromette così la qualità con la conseguente produzione di tossine che possono influire sulla salute umana. L'obiettivo sperimentale di questo lavoro è il problema dello spreco e della perdita di prodotto nell'industria alimentare. Quando i pomodori sono colpiti da muffe, infatti, diventano inadatti al consumo, con conseguente perdita di cibo. Lo spreco di pomodori a causa delle muffe rappresenta anche la perdita di preziose risorse, utili alla produzione, come terra, acqua, energia e tempo. Il proposito è testare, anche nella fase iniziale, la capacità di un algoritmo di rilevamento degli oggetti per identificare la muffa, e adottare misure preventive. L'analisi sperimentale ha previsto l'addestramento dell'algoritmo con un'ampia serie di foto, tra cui pomodori sani e rovinati di diversi tipi, forme e consistenze. Per etichettare le immagini e creare le epoche di addestramento è stato quindi utilizzato YOLOv7, l'algoritmo di rilevamento degli oggetti scelto, basato su reti neurali. Per valutare le prestazioni sono state utilizzate metriche di valutazione, tra cui “Precision” e “Recall”. L'ipotesi di applicazione dell'intelligenza artificiale in futuro sarà un grande potenziale per migliorare i processi di produzione alimentare, facilitando, così, l'identificazione delle muffe. Il rilevamento rapido delle muffe faciliterebbe la separazione tempestiva dei prodotti contaminati, riducendo così il rischio di diffusione delle tossine e preservando la qualità degli alimenti non contaminati. Questo approccio contribuirebbe a ridurre al minimo gli sprechi alimentari e le inefficienze delle risorse associate allo scarto di grandi quantità di prodotto. Inoltre, l'integrazione della computer vision nel contesto dell'HACCP (Hazard Analysis Critical Control Points) potrebbe migliorare i protocolli di sicurezza alimentare grazie a un rilevamento accurato e tempestivo. Questa tecnologia potrà offrire, dando priorità alla prevenzione, una promettente opportunità per migliorare la qualità, l'efficienza e la sostenibilità dei futuri processi di produzione alimentare.This study investigates the use of computer vision couples with artificial intelligence to detect mold in tomatoes during the drying process. Mold presence in tomatoes poses threats to human health and the food industry as it leads to several issues beyond appearance. It is primarily caused by fungi that spread rapidly over the tomato surface, compromising their quality, and potentially producing toxins that can harm human health. The experimental aim of this work focused on the issue of wastage and loss within the food industry. When tomatoes succumb to mold, they become unsuitable for consumption, resulting in a loss of food and resources. Considering that tomato production requires resources such as land, water, energy, and time, wasting tomatoes due to mold also represents a waste of these valuable resources. The goal was to evaluate the mold detection capabilities of an object detection algorithm, particularly in its early stages, to facilitate preventative measures. This experimental analysis entailed training the algorithm with an extensive array of images, encompassing a variety of healthy and spoiled tomatoes of different shapes, types, textures and drying stages. The chosen object detection algorithm, YOLOv7, is convolutional neural network-based and was utilized for image labeling and training epochs. Evaluation metrics, including precision and recall, were utilized to assess the algorithm's performance. The implementation of artificial intelligence in the future has significant potential for enhancing food production processes by streamlining mold identification. Prompt mold detection would expedite segregation of contaminated products, thus reducing the risk of toxin dissemination and preserving the quality of uncontaminated food. This approach could minimize food waste and resource inefficiencies linked to discarding significant product amounts. Furthermore, integrating computer vision in the HACCP (Hazard Analysis Critical Control Points) context could enhance food safety protocols via accurate and prompt detection. By prioritizing prevention, this technology offers a promising chance to optimize quality, efficiency, and sustainability of future food production processes

Perception, extension, and enclosure of space

Man is enabled to explore his immediate spaces due to the combined functioning of sensory perception and neuromuscular co-ordination. Relying upon learned redundancies in the world about him and being aware of his own physical capabilities, he is able to avoid chaos as he utilizes his space. The senses of vision, audition, and touch successfully co-operate to allow man an orderly manner of movement and to awaken him to the world outside himself. Over the millenia man has established territories to assure himself and his family a place to rest, mate, and rear offspring. Man has erected extensions, or invisible bubbles, of varying dimensions about himself in his dealings with his own as well as with other species. Man is not solely dependent on instinctive processes as are lower orders of animal life; instead he may think abstractly. Due to this capability, man has been able to convert many of his extensions to physically enclosed spaces which he is able to control and organize about his life

Nasa university program review conference. summary report, mar. 1 - 3, 1965

The purpose of the NASA University Program Review Conference was to describe the nature of the Program, the manner in which it is being conducted, the results that it is producing, and the impact it may be having. The presentations, except for some expository papers by NASA offi- cials, were made by members of the university and nonprofit community. ference message as it has come to me, a university professor spending a year in making a study of NASA-University relations under a NASA contract with my institution. In preparing the report, my guiding principle has been to try to maximize its usefulness by making it accurate, brief, and prompt. These qualities are largely incompatible, and I am sure that the result of my search for an optimum compromise will please no one. Open editorializing is mainly confined to a brief section constituting my Evaluation of Program. The complete transcript will shortly be available, to stand as the authoritative source for statements that anyone may wish to attribute to the speakers

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research