474 research outputs found
Machine Learning Tips and Tricks for Power Line Communications
4openopenTonello A.M.; Letizia N.A.; Righini D.; Marcuzzi F.Tonello, A. M.; Letizia, N. A.; Righini, D.; Marcuzzi, F
Generative AI for Space-Air-Ground Integrated Networks (SAGIN)
Recently, generative AI technologies have emerged as a significant
advancement in artificial intelligence field, renowned for their language and
image generation capabilities. Meantime, space-air-ground integrated network
(SAGIN) is an integral part of future B5G/6G for achieving ubiquitous
connectivity. Inspired by this, this article explores an integration of
generative AI in SAGIN, focusing on potential applications and case study. We
first provide a comprehensive review of SAGIN and generative AI models,
highlighting their capabilities and opportunities of their integration.
Benefiting from generative AI's ability to generate useful data and facilitate
advanced decision-making processes, it can be applied to various scenarios of
SAGIN. Accordingly, we present a concise survey on their integration, including
channel modeling and channel state information (CSI) estimation, joint
air-space-ground resource allocation, intelligent network deployment, semantic
communications, image extraction and processing, security and privacy
enhancement. Next, we propose a framework that utilizes a Generative Diffusion
Model (GDM) to construct channel information map to enhance quality of service
for SAGIN. Simulation results demonstrate the effectiveness of the proposed
framework. Finally, we discuss potential research directions for generative
AI-enabled SAGIN.Comment: 9page, 5 figure
Improving the Security of Smartwatch Payment with Deep Learning
Making contactless payments using a smartwatch is increasingly popular, but
this payment medium lacks traditional biometric security measures such as
facial or fingerprint recognition. In 2022, Sturgess et al. proposed WatchAuth,
a system for authenticating smartwatch payments using the physical gesture of
reaching towards a payment terminal. While effective, the system requires the
user to undergo a burdensome enrolment period to achieve acceptable error
levels. In this dissertation, we explore whether applications of deep learning
can reduce the number of gestures a user must provide to enrol into an
authentication system for smartwatch payment. We firstly construct a
deep-learned authentication system that outperforms the current
state-of-the-art, including in a scenario where the target user has provided a
limited number of gestures. We then develop a regularised autoencoder model for
generating synthetic user-specific gestures. We show that using these gestures
in training improves classification ability for an authentication system.
Through this technique we can reduce the number of gestures required to enrol a
user into a WatchAuth-like system without negatively impacting its error rates.Comment: Master's thesis, 74 pages. 32 figure
Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework
The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
We present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the
complexity of vascular systems, which are highly variating in shape, size,
and structure. Existing model-based methods provide some degree of
control and variation in the structures produced, but fail to capture the
diversity of actual anatomical data. We developed VesselVAE, a recursive
variational Neural Network that fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold encoding branch
connectivity along with geometry features describing the target surface.
After training, the VesselVAE latent space can be sampled to generate
new vessel geometries. To the best of our knowledge, this work is the
first to utilize this technique for synthesizing blood vessels. We achieve
similarities of synthetic and real data for radius (.97), length (.95), and
tortuosity (.96). By leveraging the power of deep neural networks, we
generate 3D models of blood vessels that are both accurate and diverse,
which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes.
Keywords: Vascular 3D model
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
We present a data-driven generative framework for synthesizing blood vessel
3D geometry. This is a challenging task due to the complexity of vascular
systems, which are highly variating in shape, size, and structure. Existing
model-based methods provide some degree of control and variation in the
structures produced, but fail to capture the diversity of actual anatomical
data. We developed VesselVAE, a recursive variational Neural Network that fully
exploits the hierarchical organization of the vessel and learns a
low-dimensional manifold encoding branch connectivity along with geometry
features describing the target surface. After training, the VesselVAE latent
space can be sampled to generate new vessel geometries. To the best of our
knowledge, this work is the first to utilize this technique for synthesizing
blood vessels. We achieve similarities of synthetic and real data for radius
(.97), length (.95), and tortuosity (.96). By leveraging the power of deep
neural networks, we generate 3D models of blood vessels that are both accurate
and diverse, which is crucial for medical and surgical training, hemodynamic
simulations, and many other purposes.Comment: Accepted for MICCAI 202
Long-term future prediction under uncertainty and multi-modality
Humans have an innate ability to excel at activities that involve prediction of complex object dynamics such as predicting the possible trajectory of a billiard ball after it has been hit by the player or the prediction of motion of pedestrians while on the road. A key feature that enables humans to perform such tasks is anticipation. There has been continuous research in the area of Computer Vision and Artificial Intelligence to mimic this human ability for autonomous agents to succeed in the real world scenarios. Recent advances in the field of deep learning and the availability of large scale datasets has enabled the pursuit of fully autonomous agents with complex decision making abilities such as self-driving vehicles or robots. One of the main challenges encompassing the deployment of these agents in the real world is their ability to perform anticipation tasks with at least human level efficiency.
To advance the field of autonomous systems, particularly, self-driving agents, in this thesis, we focus on the task of future prediction in diverse real world settings, ranging from deterministic scenarios such as prediction of paths of balls on a billiard table to the predicting the future of non-deterministic street scenes. Specifically, we identify certain core challenges for long-term future prediction: long-term prediction, uncertainty, multi-modality, and exact inference. To address these challenges, this thesis makes the following core contributions. Firstly, for accurate long-term predictions, we develop approaches that effectively utilize available observed information in the form of image boundaries in videos or interactions in street scenes. Secondly, as uncertainty increases into the future in case of non-deterministic scenarios, we leverage Bayesian inference frameworks to capture calibrated distributions of likely future events. Finally, to further improve performance in highly-multimodal non-deterministic scenarios such as street scenes, we develop deep generative models based on conditional variational autoencoders as well as normalizing flow based exact inference methods. Furthermore, we introduce a novel dataset with dense pedestrian-vehicle interactions to further aid the development of anticipation methods for autonomous driving applications in urban environments.Menschen haben die angeborene Fähigkeit, Vorgänge mit komplexer Objektdynamik vorauszusehen, wie z. B. die Vorhersage der möglichen Flugbahn einer Billardkugel, nachdem sie vom Spieler gestoßen wurde, oder die Vorhersage der Bewegung von Fußgängern auf der Straße. Eine Schlüsseleigenschaft, die es dem Menschen ermöglicht, solche Aufgaben zu erfüllen, ist die Antizipation. Im Bereich der Computer Vision und der Künstlichen Intelligenz wurde kontinuierlich daran geforscht, diese menschliche Fähigkeit nachzuahmen, damit autonome Agenten in der realen Welt erfolgreich sein können. Jüngste Fortschritte auf dem Gebiet des Deep Learning und die Verfügbarkeit großer Datensätze haben die Entwicklung vollständig autonomer Agenten mit komplexen Entscheidungsfähigkeiten wie selbstfahrende Fahrzeugen oder Roboter ermöglicht. Eine der größten Herausforderungen beim Einsatz dieser Agenten in der realen Welt ist ihre Fähigkeit, Antizipationsaufgaben mit einer Effizienz durchzuführen, die mindestens der menschlichen entspricht. Um das Feld der autonomen Systeme, insbesondere der selbstfahrenden Agenten, voranzubringen, konzentrieren wir uns in dieser Arbeit auf die Aufgabe der Zukunftsvorhersage in verschiedenen realen Umgebungen, die von deterministischen Szenarien wie der Vorhersage der Bahnen von Kugeln auf einem Billardtisch bis zur Vorhersage der Zukunft von nicht-deterministischen Straßenszenen reichen. Insbesondere identifizieren wir bestimmte grundlegende Herausforderungen für langfristige Zukunftsvorhersagen: Langzeitvorhersage, Unsicherheit, Multimodalität und exakte Inferenz. Um diese Herausforderungen anzugehen, leistet diese Arbeit die folgenden grundlegenden Beiträge. Erstens: Für genaue Langzeitvorhersagen entwickeln wir Ansätze, die verfügbare Beobachtungsinformationen in Form von Bildgrenzen in Videos oder Interaktionen in Straßenszenen effektiv nutzen. Zweitens: Da die Unsicherheit in der Zukunft bei nicht-deterministischen Szenarien zunimmt, nutzen wir Bayes’sche Inferenzverfahren, um kalibrierte Verteilungen wahrscheinlicher zukünftiger Ereignisse zu erfassen. Drittens: Um die Leistung in hochmultimodalen, nichtdeterministischen Szenarien wie Straßenszenen weiter zu verbessern, entwickeln wir tiefe generative Modelle, die sowohl auf konditionalen Variations-Autoencodern als auch auf normalisierenden fließenden exakten Inferenzmethoden basieren. Darüber hinaus stellen wir einen neuartigen Datensatz mit dichten Fußgänger-Fahrzeug- Interaktionen vor, um Antizipationsmethoden für autonome Fahranwendungen in urbanen Umgebungen weiter zu entwickeln
Distributed deep learning inference in fog networks
Today's smart devices are equipped with powerful integrated chips and built-in heterogeneous sensors that can leverage their potential to execute heavy computation and produce a large amount of sensor data. For instance, modern smart cameras integrate artificial intelligence to capture images that detect any objects in the scene and change parameters, such as contrast and color based on environmental conditions. The accuracy of the object recognition and classification achieved by intelligent applications has improved due to recent advancements in artificial intelligence (AI) and machine learning (ML), particularly, deep neural networks (DNNs).
Despite the capability to carry out some AI/ML computation, smart devices have limited battery power and computing resources. Therefore, DNN computation is generally offloaded to powerful computing nodes such as cloud servers. However, it is challenging to satisfy latency, reliability, and bandwidth constraints in cloud-based AI. Thus, in recent years, AI services and tasks have been pushed closer to the end-users by taking advantage of the fog computing paradigm to meet these requirements. Generally, the trained DNN models are offloaded to the fog devices for DNN inference. This is accomplished by partitioning the DNN and distributing the computation in fog networks.
This thesis addresses offloading DNN inference by dividing and distributing a pre-trained network onto heterogeneous embedded devices. Specifically, it implements the adaptive partitioning and offloading algorithm based on matching theory proposed in an article, titled "Distributed inference acceleration with adaptive dnn partitioning and offloading". The implementation was evaluated in a fog testbed, including Nvidia Jetson nano devices. The obtained results show that the adaptive solution outperforms other schemes (Random and Greedy) with respect to computation time and communication latency
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