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In this paper we present a novel algorithm for onboard radial distortion correction for unmanned aerial vehicles (UAVs) equipped with an inertial measurement unit (IMU), that runs in real-time. This approach makes calibration procedures redundant, thus allowing for exchange of optics extemporaneously. By utilizing the IMU data, the cameras can be aligned with the gravity direction. This allows us
The inherent flexibility of 5G networks come with a high degree of configuration and management complexity. This makes the performance outcome for UEs, more than ever, dependent on intricate configurations and interplay between algorithms at various network components. In this paper, we take initial steps towards a performance exposure system at the base station using a data-driven approach for pr
Bayesian inference under a set of priors, called robust Bayesian analysis, allows for estimation of parameters within a model and quantification of epistemic uncertainty in quantities of interest by bounded (or imprecise) probability. Iterative importance sampling can be used to estimate bounds on the quantity of interest by optimizing over the set of priors. A method for iterative importance samp
Extracting speaker-dependent paralinguistic information out of a person's voice, provides an opportunity for adaptive behaviour related to speaker information in speech processing applications. For instance, in audio-based conversational applications, adapting responses to the attributes of the correspondent is an integral part in making the conversations effective. Two speaker attributes that hum
The European Copernicus programme plans to install a constellation of multiple polar orbiting satellites (Copernicus Anthropogenic CO2 Monitoring Mission, CO2M mission) for observing atmospheric CO2 content with the aim to estimate fossil fuel CO2 emissions. We explore the impact of potential CO2M observations of column-averaged CO2 (XCO2), nitrogen dioxide (NO2), and aerosols in a 200 × 200 km2 d
Pseudo-Random Injections (PRIs) have been used in several applications in symmetric-key cryptography, such as in the idealization of Authenticated Encryption with Associated Data (AEAD) schemes, building robust AEAD, and, recently, in converting a committing AEAD scheme into a succinctly committing AEAD scheme. In Crypto 2024, Bellare and Hoang showed that if an AEAD scheme is already committing,
Automated cryptanalysis has seen a lot of attraction and success in the past decade, leading to new distinguishers or key-recovery attacks against various ciphers. We argue that the improved efficiency and usability of these new tools have been undervalued, especially for design processes. In this article, we break for the first time the classical iterative design paradigm for symmetric-key primit
Cardinality and rank functions are ideal ways of regularizing under-determined linear systems, but optimization of the resulting formulations is made difficult since both these penalties are non-convex and discontinuous. The most common remedy is to instead use the ℓ1- and nuclear norms. While these are convex and can therefore be reliably optimized, they suffer from a shrinking bias that degrades
In post-quantum cryptography (PQC), Learning With Errors (LWE) is one of the dominant underlying mathematical problems. For example, in NIST's PQC standardization process, the Key Encapsulation Mechanism (KEM) protocol chosen for standardization was Kyber, an LWE-based scheme. The primal and the dual attacks are the two main strategies considered for solving the underlying LWE problem of multiple
The 5th generation of mobile networks introduces a new Network Function (NF) that was not present in previous generations, namely the Network Data Analytics Function (NWDAF). Its primary objective is to provide advanced analytics services to various entities within the network and also towards external application services in the 5G ecosystem. One of the key use cases of NWDAF is mobility trajecto
In this paper, we present two new provable nonce-misuseresistantAEAD modes based on tweakable block ciphers and universalhash functions. These new modes target equipping high-speed applicationswith nonce-misuse-resistant AEAD (MRAE). The first mode, LowLatency Synthetic IV (LLSIV), targets similar performance on single-coreplatforms to SCT-2, while eliminating the bottlenecks that make SCT-2not fu
We investigate the persistance of embedded eigenvalues under perturbations of a certain self-adjoint Schrödinger-type differential operator in L2(R; Rn), with an asymptotically periodic potential. The studied perturbations are small and belong to a certain Banach space with a specified decay rate, in particular, a weighted space of continuous matrix valued functions. Our main result is that the se
Network Data Analytics Function (NWDAF) is a key component in 5G networks, introduced by 3G Partnership Project (3GPP) standards, that leverages machine learning to optimize network performance. The 3GPP standards mandate that mobile network operators should retrain NWDAF models to maintain accuracy. However, the presence of adversarial user equipment (UE) can introduce corrupted data points durin
Ensuring availability is a critical requirement for the Internet of Things (IoT). CIDER, a recovery architecture, and its follow-up scheme, Lazarus, are solutions to address this issue. CIDER introduced a new hardware module, the Authenticated Watchdog Timer (AWDT), to keep IoT devices running in normal mode as long as trusted authenticated tickets are received from a hub. If valid tickets are not
Motivation: Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesia
This paper considers the problem of recovering either a low rank matrix or a sparse vector from observations of linear combinations of the vector or matrix elements. Recent methods replace the non-convex regularization with ℓ1 or nuclear norm relaxations. It is well known that this approach recovers near optimal solutions if a so called restricted isometry property (RIP) holds. On the other hand i
This paper presents a novel method for 3D human pose and shape estimation from images with sparse views, using joint points and silhouettes, based on a parametric model. Firstly, the parametric model is fitted to the joint points estimated by deep learning-based human pose estimation. Then, we extract the correspondence between the parametric model of pose fitting and silhouettes in 2D and 3D spac
