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Relevant bibliographies by topics / Edge machine learning / Dissertations / Theses

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Dissertations / Theses on the topic 'Edge machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Tydén, Amanda, and Sara Olsson. "Edge Machine Learning for Animal Detection, Classification, and Tracking." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166572.

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A research field currently advancing is the use of machine learning on camera trap data, yet few explore deep learning for camera traps to be run in real-time. A camera trap has the purpose to capture images of bypassing animals and is traditionally based only on motion detection. This work integrates machine learning on the edge device to also perform object detection. Related research is brought up and model tests are performed with a focus on the trade-off regarding inference speed and model accuracy. Transfer learning is used to utilize pre-trained models and thus reduce training time and

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2

Drolia, Utsav. "Adaptive Distributed Caching for Scalable Machine Learning Services." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1004.

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Applications for Internet-enabled devices use machine learning to process captured data to make intelligent decisions or provide information to users. Typically, the computation to process the data is executed in cloud-based backends. The devices are used for sensing data, offloading it to the cloud, receiving responses and acting upon them. However, this approach leads to high end-to-end latency due to communication over the Internet. This dissertation proposes reducing this response time by minimizing offloading, and pushing computation close to the source of the data, i.e. to edge servers a

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Tania, Zannatun Nayem. "Machine Learning with Reconfigurable Privacy on Resource-Limited Edge Computing Devices." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292105.

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Distributed computing allows effective data storage, processing and retrieval but it poses security and privacy issues. Sensors are the cornerstone of the IoT-based pipelines, since they constantly capture data until it can be analyzed at the central cloud resources. However, these sensor nodes are often constrained by limited resources. Ideally, it is desired to make all the collected data features private but due to resource limitations, it may not always be possible. Making all the features private may cause overutilization of resources, which would in turn affect the performance of the who

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Martin, Sébastien Ph D. Massachusetts Institute of Technology. "The edge of large-scale optimization in transportation and machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122388.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 273-284).<br>This thesis focuses on impactful applications of large-scale optimization in transportation and machine learning. Using both theory and computational experiments, we introduce novel optimization algorithms to overcome

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PULIGHEDDU, CORRADO. "Machine Learning-Powered Management Architectures for Edge Services in 5G Networks." Doctoral thesis, Politecnico di Torino, 2022. https://hdl.handle.net/11583/2973797.

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Liu, Sige. "Bandit Learning Enabled Task Offloading and Resource Allocation in Mobile Edge Computing." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29719.

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The Internet-of-Things (IoT) is envisioned as a promising paradigm for carrying the interconnections of massive devices through various communications protocols. With the rapid development of fifth-generation (5G), IoT has incentivized a large number of new computation-intensive applications and bridges diverse technologies to provide ubiquitous services with intelligence. However, with billions of devices anticipated to be connected in IoT systems in the coming years, IoT devices face a series of challenges from their inherent features. For instance, the IoT devices are usually densely depl

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Arnesson, Pontus, and Johan Forslund. "Edge Machine Learning for Wildlife Conservation : Detection of Poachers Using Camera Traps." Thesis, Linköpings universitet, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177483.

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This thesis presents how deep learning can be utilized for detecting humans ina wildlife setting using image classification. Two different solutions have beenimplemented where both of them use a camera-equipped microprocessor to cap-ture the images. In one of the solutions, the deep learning model is run on themicroprocessor itself, which requires the size of the model to be as small as pos-sible. The other solution sends images from the microprocessor to a more pow-erful computer where a larger object detection model is run. Both solutions areevaluated using standard image classification metr

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Erlandsson, Niklas. "Utilizing machine learning in wildlife camera traps for automatic classification of animal species : An application of machine learning on edge devices." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104952.

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A rapid global decline in biodiversity has been observed in the past few decades, especially in large vertebrates and the habitats supporting these animal populations. This widely accepted fact has made it very important to understand how animals respond to modern ecological threats and to understand the ecosystems functions. The motion activated camera (also known as a camera trap) is a common tool for research in this field, being well-suited for non-invasive observation of wildlife. The images captured by camera traps in biological studies need to be classified to extract information, a tra

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Hasanaj, Enis, Albert Aveler, and William Söder. "Cooperative edge deepfake detection." Thesis, Jönköping University, JTH, Avdelningen för datateknik och informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53790.

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Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models can be a very time-consuming process. This research proposes a solution to training deepfake detection models cooperatively on the edge. This is done in order to evaluate if the training process, among other things, can be made more efficient wit

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MAZZIA, VITTORIO. "Machine Learning Algorithms and their Embedded Implementation for Service Robotics Applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2968456.

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11

Jidling, Carl. "Strain Field Modelling using Gaussian Processes." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-315254.

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This report deals with reconstruction of strain fields within deformed materials. The method relies upon data generated from Bragg edge measurements, in which information is gained from neutron beams that are sent through the sample. The reconstruction has been made by modelling the strain field as a Gaussian process, assigned a covariance structure customized by incorporation of the so-called equilibrium constraints. By making use of an approximation scheme well suited for the problem, the complexity of the computations has been significantly reduced. The results from numerical simulations in

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Galanis, Ioannis. "RESOURCE MANAGEMENT IN EDGE COMPUTING FOR INTERNET OF THINGS APPLICATIONS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1871.

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The Internet of Things (IoT) computing paradigm has connected smart objects “things” and has brought new services at the proximity of the user. Edge Computing, a natural evolution of the traditional IoT, has been proposed to deal with the ever-increasing (i) number of IoT devices and (ii) the amount of data traffic that is produced by the IoT endpoints. EC promises to significantly reduce the unwanted latency that is imposed by the multi-hop communication delays and suggests that instead of uploading all the data to the remote cloud for further processing, it is beneficial to perform computati

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13

Bartoli, Giacomo. "Edge AI: Deep Learning techniques for Computer Vision applied to embedded systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16820/.

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In the last decade, Machine Learning techniques have been used in different fields, ranging from finance to healthcare and even marketing. Amongst all these techniques, the ones adopting a Deep Learning approach were revealed to outperform humans in tasks such as object detection, image classification and speech recognition. This thesis introduces the concept of Edge AI: that is the possibility to build learning models capable of making inference locally, without any dependence on expensive servers or cloud services. A first case study we consider is based on the Google AIY Vision Kit, an inte

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14

Della, Penna Roberto. "Edge Cloud Computing Middleware per Predictive Maintenance in Industria 4.0." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20065/.

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Il paradigma Edge Cloud Computing ha assunto, negli ultimi anni, un ruolo sempre più importante, soprattutto nel contesto di Industria 4.0, in cui la nascita di nuovi modelli di business basati sull’acquisizione e l’analisi di dati si sono scontrati con le limitazioni imposte dal modello architetturale classico introdotto dal paradigma Cloud Computing. In particolare, la latenza, il consumo di banda, e la dipendenza da un’infrastruttura centralizzata sono stati i fattori abilitanti per l’evoluzione del modello a due livelli, composto da sorgenti dati e infrastruttura cloud, verso l’introduzion

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Di, Donato Davide. "Sviluppo, Deployment e Validazione Sperimentale di Architetture Distribuite di Machine Learning su Piattaforma fog05." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19021/.

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Ultimamente sta crescendo sempre di più l'interesse riguardo al fog computing e alle possibilità che offre, tra cui la capacità di poter fruire di una capacità computazionale considerevole anche nei nodi più vicini all’utente finale: questo permetterebbe di migliorare diversi parametri di qualità di un servizio come la latenza nella sua fornitura e il costo richiesto per le comunicazioni. In questa tesi, sfruttando le considerazioni sopra, abbiamo creato e testato due architetture di machine learning distribuito e poi le abbiamo utilizzate per fornire un servizio di predizione (legato al cond

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16

Dantas, Cruz Breno. "Promoting Systematic Practices for Designing and Developing Edge Computing Applications via Middleware Abstractions and Performance Estimation." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103010.

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Mobile, IoT, and wearable devices have been transitioning from passive consumers to active generators of massive amounts of user-generated data. Edge-based processing eliminates network bottlenecks and improves data privacy. However, developing edge applications remains hard, with developers often have to employ ad-hoc software development practices to meet their requirements. By doing so, developers introduce low-level and hard-to-maintain code to the codebase, which is error-prone, expensive to maintain, and vulnerable in terms of security. The thesis of this research is that modular middle

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Rinaldi, Riccardo. "Deployment e Gestione di Applicazioni di Federated Learning in Edge Cloud Computing basate sul Framework Fog05." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Il Federated Learning è la nuova branca del Machine Learning nata per sopperire al bisogno di nuovi sistemi architetturali che siano in grado di gestire i Big Data e allo stesso tempo garantire la privacy di eventuali dati sensibili. Per poter operare a queste due condizioni si è pensato di raccogliere i dati in un database centralizzato in modo che questi non lascino mai i margini della rete. Ecco perché è subentrato il mondo dell’edge computing in cui dispositivi intelligenti, posti tra il cloud e le Things, hanno il compito di pre-processare i dati raccolti per poi aggregarli su un unico se

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18

Eliasson, Anton. "Anomaly Detection in Industrial Networks using a Resource-Constrained Edge Device." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74530.

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The detection of false data-injection attacks in industrial networks is a growing challenge in the industry because it requires knowledge of application and protocol specific behaviors. Profinet is a common communication standard currently used in the industry, which has the potential to encounter this type of attack. This motivates an examination on whether a solution based on machine learning with a focus on anomaly detection can be implemented and used to detect abnormal data in Profinet packets. Previous work has investigated this topic; however, a solution is not available in the market y

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Feraudo, Angelo. "Distributed Federated Learning in Manufacturer Usage Description (MUD) Deployment Environments." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Il costante avanzamento dei dispositivi Internet of Things (IoT) in diversi ambienti, ha provocato la necessità di nuovi meccanismi di sicurezza e monitoraggio in una rete. Tali dispositvi sono spesso considerati fonti di vulnerabilità sfruttabili da malintenzionati per accedere alla rete o condurre altri attacchi. Questo è dovuto alla natura stessa dei dispositivi, ovvero offrire servizi aventi a che fare con dati sensibili (p.es. videocamere) seppur con risorse molto limitate. Una soluzione in questa direzione, è l'impiego della specifica Manufacturer Usage Description (MUD), che impone al m

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20

Cleve, Oscar, and Sara Gustafsson. "Automatic Feature Extraction for Human Activity Recognitionon the Edge." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260247.

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This thesis evaluates two methods for automatic feature extraction to classify the accelerometer data of periodic and sporadic human activities. The first method selects features using individual hypothesis tests and the second one is using a random forest classifier as an embedded feature selector. The hypothesis test was combined with a correlation filter in this study. Both methods used the same initial pool of automatically generated time series features. A decision tree classifier was used to perform the human activity recognition task for both methods.The possibility of running the devel

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21

KOBEISSI, AHMAD. "VERSO IL CONCETTO DI SMART CITY: SOLUZIONI IOT EDGE-CLOUD." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/996248.

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Since the term was coined by Kevin Ashton in 1999, the Internet of Things (IoT) did not gain considerable popularity until 2010 where it became a strategic priority for governments, companies, and research centers. Despite this large-scale interest, IoT only reached mass markets in 2014 in the form of wearable devices and fitness trackers, home automation, industrial asset monitoring, and smart energy meters. The ‘things’ refer to sensors and other smart devices with the ability to monitor an object’s state, or even control it using actuators. Ashton envisaged that when such sensors and smart

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SPALLANZANI, MATTEO. "Un framework per l’analisi dei sistemi di apprendimento automatico." Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2020. http://hdl.handle.net/11380/1200571.

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Il fare predizioni si fonda sulla comprensione degli schemi del nostro ambiente. Possiamo aver accesso alla realtà fisica grazie a strumenti di misura che ci forniscono dati nei quali speriamo di trovare schemi utili. Lo sviluppo delle macchine calcolatrici ha permesso di stoccare grandi insiemi di dati e di processarli ad alta velocità. L’apprendimento automatico studia sistemi capaci di automatizzare l’identificazione di schemi in grandi insiemi di dati utilizzando i computer. L’apprendimento automatico è al cuore della scienza dei dati e dell’intelligenza artificiale, due campi di ricerca c

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23

Strallhofer, Daniel, and Jonatan Ahlqvist. "Classifying Urgency : A Study in Machine Learning for Classifying the Level of Medical Emergency of an Animal’s Situation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231476.

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This paper explores the use of Naive Bayes as well a Linear Support Vector Machines in order to classify a text based on the level of medical emergency. The primary source of testing will be an online veterinarian service’s customer data. The aspects explored are whether a single text gives enough information for a medical decision to be made and if there are alternative data gathering processes that would be preferred. Past research has proven that text classiﬁers based on Naive Bayes and SVMs can often give good results. We show how to optimize the results so that important decisions can be

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Harmassi, Mariem. "Thing-to-thing context-awareness at the edge." Thesis, La Rochelle, 2019. http://www.theses.fr/2019LAROS037.

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L'Internet des objets (IdO) comprend aujourd'hui une riche offre d'objets connectés, qui permettent de collecter et de partager en continu des données hétérogènes se rapportant à leurs environnements. Ceci a permis l'émergence d'un nouveau type d'applications, qui sont basées sur ces données et permettent de faciliter la vie des citoyens. Ces applications de l'Internet des objets sont dites « sensibles au contexte ». Grâce aux données collectées sur le contexte de l'utilisateur, elles sont en mesure d'adapter leur comportement de manière autonome, sans intervention humaine. Dans cette thèse, n

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SACCO, ALESSIO. "Towards Autonomous Computer Networks in Support of Critical Systems." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2968454.

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Da, Silva Silvestre Guthemberg. "Designing Adaptive Replication Schemes for Efficient Content Delivery in Edge Networks." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2013. http://tel.archives-ouvertes.fr/tel-00931562.

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La disponibilité des contenus partagés en ligne devient un élément essentiel pour toute la chaîne de distribution de vidéos. Pour fournir des contenus aux utilisateurs avec une excellente disponibilité et répondre à leurs exigences toujours croissantes, les opérateurs de content delivery networks (CDNs) doivent assurer une haute qualité de services, définie par des métriques comme le taux de transfert ou la latence inclus dans les contrats de Service Level Agreement (SLA). La réplication adaptative se présente comme un mécanisme de stockage très prometteur pour at- teindre cet

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27

Blount, Christopher. "Near infrared reflectance in Anura." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/near-infrared-reflectance-in-anura(f730de01-8d4a-43de-b2dd-2ef3027bfc2f).html.

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Increased near infrared (NIR) reflection, closely resembling the red edge found in leaves, has been known in frogs for many years. Whereas previously thought of as an isolated rarity, we have shown that it is likely far more prevalent than previously believed, occurring in multiple distinct family groups and world regions. To date, there are now 26 anuran species known to demonstrate increased NIR reflectance, from 12 different genera, 4 families, and 3 ecozones. The visible/NIR reflection spectra of each individual measured was found to be characteristic of its species; whether it was wild or

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Sadat, Mohammad Nazmus. "QoE-Aware Video Communication in Emerging Network Architectures." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin162766498933367.

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Busacca, Fabio Antonino. "AI for Resource Allocation and Resource Allocation for AI: a two-fold paradigm at the network edge." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/573371.

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5G-and-beyond and Internet of Things (IoT) technologies are pushing a shift from the classic cloud-centric view of the network to a new edge-centric vision. In such a perspective, the computation, communication and storage resources are moved closer to the user, to the benefit of network responsiveness/latency, and of an improved context-awareness, that is, the ability to tailor the network services to the live user's experience. However, these improvements do not come for free: edge networks are highly constrained, and do not match the resource abundance of their cloud counterparts. In such a

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FERDINANDI, Marco. "A Learning Sensors Platform for Health and Safety Applications." Doctoral thesis, Università degli studi di Cassino, 2020. http://hdl.handle.net/11580/74754.

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Nowadays, people and environment health and safety are increasingly threatened by human activities. Industrial processes, or more in general, air and water contamination are worsening the planet life conditions. Increasing people awareness and improving the regulation system play a key role to face and tackle this global phenomenon. To this aim, reliable and low-cost technologies for a pervasive and ubiquitous environmental monitoring are really needed, especially for developing and poorer countries. This Ph.D. thesis has been focused on the development of the SENSIPLUS Embedded System for hea

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Kheffache, Mansour. "Energy-Efficient Detection of Atrial Fibrillation in the Context of Resource-Restrained Devices." Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76394.

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eHealth is a recently emerging practice at the intersection between the ICT and healthcare fields where computing and communication technology is used to improve the traditional healthcare processes or create new opportunities to provide better health services, and eHealth can be considered under the umbrella of the Internet of Things. A common practice in eHealth is the use of machine learning for a computer-aided diagnosis, where an algorithm would be fed some biomedical signal to provide a diagnosis, in the same way a trained radiologist would do. This work considers the task of Atrial Fibr

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Lanzarone, Lorenzo Biagio. "Manutenzione predittiva di macchinari industriali tramite tecniche di intelligenza artificiale: una valutazione sperimentale." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22853/.

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Nella società è in corso un processo di evoluzione tecnologica, il quale sviluppa una connessione tra l’ambiente fisico e l’ambiente digitale, per scambiare dati e informazioni. Nella presente tesi si approfondisce, nel contesto dell’Industria 4.0, la tematica della manutenzione predittiva di macchinari industriali tramite tecniche di intelligenza artificiale, per prevedere in anticipo il verificarsi di un imminente guasto, identificandolo prima ancora che si possa verificare. La presente tesi è divisa in due parti complementari, nella prima parte si approfondiscono gli aspetti teorici relativ

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Longo, Eugenio. "AI e IoT: contesto e stato dell’arte." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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L’intelligenza artificiale costituisce un ramo dell’informatica che permette di programmare e progettare sistemi in grado di dotare le macchine di caratteristiche considerate tipicamente umane. L’internet delle cose rappresenta una rete di oggetti fisici in grado di connettersi e scambiare dati con altri dispositivi tramite internet. Nonostante L’internet delle cose e l’intelligenza artificiale rappresentino due concetti diversi, riescono ad integrarsi per creare nuove soluzioni con un elevato potenziale. L’utilizzo combinato di queste due tecnologie permette di aumentare il valore di entrambe

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Hameed, Khurram. "Computer vision based classification of fruits and vegetables for self-checkout at supermarkets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2519.

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The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide r

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Cherdo, Yann. "Détection d'anomalie non supervisée sur les séries temporelle à faible coût énergétique utilisant les SNNs." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4018.

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Dans le cadre de la maintenance prédictive du constructeur automobile Renault, cette thèse vise à fournir des solutions à faible coût énergétique pour la détection non supervisée d'anomalies sur des séries temporelles. Avec l'évolution récente de l'automobile, de plus en plus de données sont produites et doivent être traitées par des algorithmes d'apprentissage automatique. Ce traitement peut être effectué dans le cloud ou directement à bord de la voiture. Dans un tel cas, la bande passante du réseau, les coûts des services cloud, la gestion de la confidentialité des données et la perte de don

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Bessinger, Zachary. "An Automatic Framework for Embryonic Localization Using Edges in a Scale Space." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1262.

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Localization of Drosophila embryos in images is a fundamental step in an automatic computational system for the exploration of gene-gene interaction on Drosophila. Contour extraction of embryonic images is challenging due to many variations in embryonic images. In the thesis work, we develop a localization framework based on the analysis of connected components of edge pixels in a scale space. We propose criteria to select optimal scales for embryonic localization. Furthermore, we propose a scale mapping strategy to compress the range of a scale space in order to improve the efficiency of the

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Axelsson, Nils. "Dynamic Programming Algorithms for Semantic Dependency Parsing." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138594.

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Dependency parsing can be a useful tool to allow computers to parse text. In 2015, Kuhlmann and Jonsson proposed a logical deduction system that parsed to non-crossing dependency graphs with an asymptotic time complexity of O(n3), where “n” is the length of the sentence to parse. This thesis extends the deduction system by Kuhlmann and Jonsson; the extended deduction system introduces certain crossing edges, while maintaining an asymptotic time complexity of O(n4). In order to extend the deduction system by Kuhlmann and Jonsson, fifteen logical item types are added to the five proposed by Kuhl

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Nair, Abhishek Ramdas. "Low Power Machine Learning Systems for Energy Efficient Edge Devices." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5621.

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Energy-efficient devices are essential in the world of edge computing and the tiny Machine Learning (tinyML) paradigm. Edge devices are often constrained by the available compu- tational power and hardware resource. To this end, there is a need for systems to implement low-power designs for machine learning at the edge. This research aims at designing edge device deployable low power frameworks honouring the hardware constraints. One such novel system is presented here as an in-filter computing framework that can be used for designing ultra-light classifiers for time-series data. Unlike

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Joshi, Sanket Ramesh. "HBONext: An Efficient Dnn for Light Edge Embedded Devices." Thesis, 2021. http://dx.doi.org/10.7912/C2/17.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Every year the most effective Deep learning models, CNN architectures are showcased based on their compatibility and performance on the embedded edge hardware, especially for applications like image classification. These deep learning models necessitate a significant amount of computation and memory, so they can only be used on high-performance computing systems like CPUs or GPUs. However, they often struggle to fulfill portable specifications due to resource, energy, and real-time constraints. Hardware accelerators have recently bee

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"Monocular Depth Estimation with Edge-Based Constraints and Active Learning." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.54881.

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abstract: The ubiquity of single camera systems in society has made improving monocular depth estimation a topic of increasing interest in the broader computer vision community. Inspired by recent work in sparse-to-dense depth estimation, this thesis focuses on sparse patterns generated from feature detection based algorithms as opposed to regular grid sparse patterns used by previous work. This work focuses on using these feature-based sparse patterns to generate additional depth information by interpolating regions between clusters of samples that are in close proximity to each other. These

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Gonçalves, Diogo Miguel Costa. "Health estimations using edge computing concepts in the context of the aeronautical industry." Master's thesis, 2019. http://hdl.handle.net/10316/87842.

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Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia<br>Na industria aeronautica, a manutenção de aeronaves é agendada utilizando uma estimativa pessimista do tempo de vidados componentes, tornando-a bastante ineficiente.Estas estimativas têm de representar o pior cenário possivel, demodo a asegurar a seguranca dos passageiros.Sistemas de Prognostics and health management (PHM) têmsido usados, com sucesso, em outras áreas, com a intenção demelhorar as estimativas do tempo de vida.Edge computing é um novo paradigma que pretende aumentara velocidade

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Khochare, Aakash. "Abstractions and Optimizations for Data-driven Applications Across Edge and Cloud." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6182.

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Modern data driven applications have a novel set of requirements. Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras and drone video feeds. These data driven applications, usually composed as workflows, tend to have high bandwidth and low latency requirements in order to extract timely results from large data sources. Other applications may necessitate the use of multiple geographically distributed resources. Such requirements may be driven by data privacy regulations such

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(10911822), Priyank Kalgaonkar. "AI on the Edge with CondenseNeXt: An Efficient Deep Neural Network for Devices with Constrained Computational Resources." Thesis, 2021.

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Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irreleva

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Hult, Jim, and Pontus Pihl. "Inspecting product quality with computer vision techniques : Comparing traditional image processingmethodswith deep learning methodson small datasets in finding surface defects." Thesis, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-54056.

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Quality control is an important part of any production line. It can be done manually but is most efficient if automated. Inspecting qualitycan include many different processes but this thesisisfocusedon the visual inspection for cracks and scratches. The best way of doingthis at the time of writing is with the help of Artificial Intelligence (AI), more specifically Deep Learning (DL).However, these need a training datasetbeforehand to train on and for some smaller companies, this mightnotbean option. This study triesto find an alternative visual inspection method,that does notrelyon atrained d

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Kalgaonkar, Priyank B. "AI on the Edge with CondenseNeXt: An Efficient Deep Neural Network for Devices with Constrained Computational Resources." Thesis, 2021. http://dx.doi.org/10.7912/C2/64.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove)

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(10157291), Yi-Yu Lai. "Relational Representation Learning Incorporating Textual Communication for Social Networks." Thesis, 2021.

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<div>Representation learning (RL) for social networks facilitates real-world tasks such as visualization, link prediction and friend recommendation. Many methods have been proposed in this area to learn continuous low-dimensional embedding of nodes, edges or relations in social and information networks. However, most previous network RL methods neglect social signals, such as textual communication between users (nodes). Unlike more typical binary features on edges, such as post likes and retweet actions, social signals are more varied and contain ambiguous information. This makes it more chall

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DE, VITA Fabrizio. "Deep learning techniques for Intelligent Cyber Physical Systems: towards a new generation of smart and autonomous things." Doctoral thesis, 2021. http://hdl.handle.net/11570/3183267.

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We are living an era constantly surrounded by objects blended with the environment. During the last decade, the advent of the Internet of Things totally revolutionized the way we interact with these objects, and acted as a catalyzer for the creation of a wide variety of Cyber Physical Systems exposing services to support the human being under different aspects of his life. Today, the maximum expression of Cyber Physical Systems are smart environments whose task is to simplify (or automate) certain types of operations to help their ``guests'' during the daily activities. Cloud and Edge computin

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(8775917), Mengmeng Zhu. "SEARCHING THE EDGES OF THE PROTEIN UNIVERSE USING DATA SCIENCE." Thesis, 2020.

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<p>Data science uses the latest techniques in statistics and machine learning to extract insights from data. With the increasing amount of protein data, a number of novel research approaches have become feasible.</p><p>Micropeptides are an emerging field in the protein universe. They are small proteins with <= 100 amino acid residues (aa) and are translated from small open reading frames (sORFs) of <= 303 base pairs (bp). Traditionally, their existence was ignored because of the technical difficulties in isolating them. With technological advances, a growing number of micropeptides have been c

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