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For a generalized (or non-central) camera model, the minimal problem for two views of six points has efficient solvers. However, minimal problems of three views with four points and three views of six lines have not yet been explored and solved, despite the efforts from the computer vision community. This paper develops the formulations of these two minimal problems and shows how state-of-the-art
We have implemented and evaluated a secondary organic aerosol scheme within the chemistry transport model TM5-MP in this work. In earlier versions of TM5-MP the secondary organic aerosol (SOA) was emitted as Aitken-sized particle mass emulating the condensation. In the current scheme we simulate the formation of secondary organic aerosol from oxidation of isoprene and monoterpenes by ozone and hyd
Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study d
Side-channel resilience is a crucial feature when assessing whether a postquantum cryptographic proposal is sufficiently mature to be deployed. In this paper, we propose a generic and efficient adaptive approach to improve the sample complexity (i.e., the required number of traces) of plaintext-checking (PC) oracle-based sidechannel attacks (SCAs), a major class of key recovery chosen-ciphertext S
In the present study, we apply deterministic chaos theory to investigate nonlinear dynamics in monthly precipitation at Mokpo, Korea, after wavelet decomposition. The wavelet transform is used not only for removal of noise but also for extraction of low and high frequency components in the data, representing low-dimensional dynamics. In order to determine an appropriate decomposition level for the
The Internet of Things (IoT) is a large network of connected devices. In IoT, devices can communicate with each other or back-end systems to transfer data or perform assigned tasks. Communication protocols used in IoT depend on target applications but usually require low bandwidth. On the other hand, IoT devices are constrained, having limited resources, including memory, power, and computational
Data analytics is regarded as an important function of 5G networks. The Network Data Analytics Function (NWDAF) is standardized in 3GPP to enhance 5G network performance by analyzing data from network functions and user equipment. Abnormal behavior detection, which is part of the NWDAF framework, has the potential to be a powerful tool to improve 5G network security. Despite this, only limited res
This work proposes a novel design for secure cloud storage systems using a third party to meet three seemingly opposing demands: reduce storage requirements on the cloud, protect against erasures (data loss), and maintain confidentiality of the data. More specifically, we achieve storage cost reductions using data deduplication without requiring system users to trust that the cloud operates honest
Firekite is a synchronous stream cipher using a pseudo-random number generator (PRNG) whose security is conjectured to rely on the hardness of the Learning Parity with Noise (LPN) problem. It is one of a few LPN-based symmetric encryption schemes, and it can be very efficiently implemented on a low-end SoC FPGA. The designers, Bogos, Korolija, Locher and Vaudenay, demonstrated appealing properties
In this paper, we present a framework for doing localization from distance measurements, given an estimate of the local motion. We show how we can register the local motion of a receiver, to a global coordinate system, using trilateration of given distance measurements from the receivers to senders in known positions. We describe how many different motion models can be formulated within the same t
In this paper, we show that a software implementation of IND-CCA-secure Saber key encapsulation mechanism protected by first-order masking and shuffling can be broken by deep learning-based power analysis. Using an ensemble of deep neural networks trained at the profiling stage, we can recover the session key and the secret key from 257 × N and 24 × 257 × N traces, respectively, where N is the num
We consider a perturbation problem for embedded eigenvalues of a self-adjoint differential operator in L2(R;Rn). In particular, we study the set of all small perturbations in an appropriate Banach space for which the embedded eigenvalue remains embedded in the continuous spectrum. We show that this set of small perturbations forms a smooth manifold and we specify its co-dimension. Our methods invo
Connected and automated vehicles (CAVs) will be a key component of future cooperative intelligent transportation systems (C-ITS). Since the adoption of C-ITS is not foreseen to happen instantly, not all of its elements are going to be connected at the early deployment stages. We consider a scenario where vehicles approaching a traffic light are connected to each other, but the traffic light itself
It is difficult to perform 3D reconstruction from on-vehicle gathered video due to the large forward motion of the vehicle. Even object detection and human sensing models perform significantly worse on onboard videos when compared to standard benchmarks because objects often appear far away from the camera compared to the standard object detection benchmarks, image quality is often decreased by mo
Realistic modelling of pedestrians in Autonomous Vehicles (AV)s and AV testing is crucial to avoid lethal collisions in deployment. The majority of AV trajectory forecasting literature do not utilize the motion cues present in 3D human pose because it is hard to gather large datasets of articulated 3D pedestrian motion. In this thesis we discuss the difficulties in data gathering and propose a ped
Whereas theoretical attacks on standardized crypto primitives rarely lead to actual practical attacks, the situation is different for side-channel attacks. Improvements in the performance of side-channel attacks are of utmost importance.In this paper, we propose a framework to be used in key-recovery side-channel attacks on CCA-secure post-quantum encryption schemes. The basic idea is to construct
Hard learning problems are central topics in recent cryptographic research. Many cryptographic primitives relate their security to difficult problems in lattices, such as the shortest vector problem. Such schemes include the possibility of decryption errors with some very small probability. In this paper we propose and discuss a generic attack for secret key recovery based on generating decryption
Impulsive noise is one of the most challenging forms of interference in an underwater acoustic environment. In this paper, we present an underwater acoustic channel deconvolution method based on a sparse representation framework. The application of the method enables a channel impulse response reconstruction that is robust to impulsive noise. By exploiting the inherent structure in the channel res
