M.A. Zuñiga Zamalloa
36 records found
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The proliferation of video recording devices and facial recognition technology has led to significant privacy concerns, as surveillance systems can capture and identify individuals without their consent. Traditional facial obfuscation systems, which introduce pixel-level perturba
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This research investigates the impact of multipath signals in UWB communications and explores their potential to improve localization accuracy of tags using the additional information captured in the Channel Impulse Response (CIR). While traditional localization typically relies
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Indoor localisation is a well-researched topic and it is a challenge to improve the accuracy of existing techniques. In recent years, edge computing and federated learning have opened up new possibilities and challenges for indoor localisation. This thesis presents a federated im
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Exploring the Impact of Single-Character Attacks in Federated Learning Language Classification
Introducing the Novel Single-Character Strike
Federated learning (FL) is a privacy preserving machine learning approach which allows a machine learning model to be trained in a distributed fashion without ever sharing user data. Due to the large amount of valuable text and voice data stored on end-user devices, this approach
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Federated learning provides a lot of opportunities, especially with the built-in privacy considerations. There is however one attack that might compromise the utility of federated learning: backdoor attacks [14]. There are already some existing defenses, like flame [13] but they
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Abstract— Federated Learning (FL) makes it possible for a network of clients to jointly train a machine learning model, while also keeping the training data private. There are several approaches when designing a FL network and while most existing research is focused on a single-s
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This thesis presents Screen Antenna - A Visible Light Communication (VLC) system that integrates data transmission and reception, with the conventional pixel display capability of RGB LEDs. The system is constructed with off-the-shelf components and runs on the Arduino Due microc
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End-to-end Automatic Speech Recognition (ASR) systems improved drastically in recent years and they work extremely well on many large datasets. However, research shows that these models failed to capture the variability in speech production and have biases against the variant cau
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Due to recent developments in DNA sequencing technology, there is a growing abundance of available genomic data. To process this information for use in fields such as healthcare and forensics, raw sequencing data have to be processed using computationally intensive algorithms. Cu
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The considerable increase in the number of devices needing connectivity, such as mobile phones and Internet of Things (IoT) devices, has led to an exponential rise in data volumes during the last years, that will surely continue over the next decade. Therefore, it will be increas
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People Counting Using Low-cost FMCW MIMO Radar
Achieving Tracking for Counting and Classification of Groups of People using FMCW Radar
For the development of automatic People Counting systems, radar is increasingly becoming a popular technology because of the increasingly stringent privacy requirements for people demographic information and the requirement to operate in a challenging environment. Because of the
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In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particular, servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) com
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In recent years, the big data era has produced an increasing volume and complexity of data that requires processing. To analyze and process these large amounts of data, applications are being scaled on large clusters using distributed data processing frameworks. A more recent tre
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Convolutional neural networks (CNNs) are often pruned to achieve faster training and inference speed while also requiring less memory. Nevertheless, during computation, most modern GPUs cannot take advantage of the sparsity automatically, especially on networks with unstructured
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Smart contracts allow for the collaboration and transaction processes between multiple parties/organisations to be automated and conducted in a neutral environment. In many situations these agreements are confidential and running a smart contract that contains private/sensitive i
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Blockchain networks are increasingly recognized as a disruptive technology across sectors such as online services, finance, supply chain, administration etc. They are underpinned by smart contracts which provide programmatic instruction for the blockchain to operate. A major obst
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Smart contracts play an important role within the blockchain by ensuring that valid transactions are being recorded. However, there are critical concerns regarding the security and privacy of data within these blockchain applications. This research provides information on how the
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Block-chain technology is gaining momentum in both industry and academics. With
this momentum there are a lot of potential gains, but also potential risk involved. This papers proposes a solution for security risks, like a man-in-the-middle-attack, of the permissioned block-c ...
this momentum there are a lot of potential gains, but also potential risk involved. This papers proposes a solution for security risks, like a man-in-the-middle-attack, of the permissioned block-c ...
Both the assignment problem and the multi-agent pathfinding problem are common problems in the fields of robotics and transportation. The joint problem of multi-agent pathfinding extended with the assignment of goals to agents, matching, is something that has not been studied muc
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In the Multi-Agent Pathfinding with Matching (MAPFM) problem, agents from a team are matched with and routed towards one of their team's goals without colliding with other agents. The sum of path costs of all agents is minimized. In prior works, Conflict Based Min-Cost-Flow (CBM)
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