Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

Autonomous vehicles are becoming central for the future of mobility, supported by advances in deep learning techniques. The performance of aself-driving system is highly dependent on the quality of the perception task. Developments in sensor technologies have led to an increased availability of 3D scanners such as LiDAR, allowing for a more accurate representation of the vehicle's surroundings, leading to safer systems. The rapid development and consequent rise of research studies around self-driving systems since early 2010, resulted in a tremendous increase in the number and novelty of object detection methods. After the first wave of works that essentially tried to expand known techniques from object detection in images, more recently there has been a notable development in newer and more adapted to LiDAR data works. This paper addresses the existing literature on object detection using LiDAR data within the scope of self-driving and brings a systematic way for analysing it. Unlike general object detection surveys, we will focus on point-cloud data, which presents specific challenges, notably its high-dimensional and sparse nature. This work introduces a common object detection pipeline and taxonomy to facilitate a thorough comparison between different techniques and, departing from it, this work will critically examine the representation of data (critical for complexity reduction), feature extraction and finally the object detection models. A comparison between performance results of the different models is included, alongside with some future research challenges.This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n. 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

Amyloid formation as a protective mechanism and a new Alzheimer's disease model

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references.Numerous neurodegenerative diseases are pathologically characterized by idiosyncratic protein amyloid inclusions. Not surprisingly amyloid fibrils have long been proposed to be the toxic protein species in these neurodegenerative diseases. However, more recent work has begun to suggest that the formation of ordered inclusions serves a protective role and that soluble oligomers on pathway to amyloid formation cause neuronal death. In that regard, ordered protein inclusions, such as aggresomes, have also been shown to facilitate the asymmetric inheritance of protein damage during the mitoses of cells ranging from E. coli to human stem cells. Yeast prion proteins are another group of proteins capable of adapting an amyloid conformation. The self-templating amyloid fold allows yeast prions to act as non-Mendelian elements of inheritance. We have shown that yeast prion amyloid fibrils, especially upon prion protein overexpression, localize to the IPOD (insoluble protein deposit), an ordered inclusion proximal to the vacuole, and that the majority of the prion amyloid is asymmetrically inherited upon cell division. I used the yeast prion Rnq1 to investigate how amyloid formation contributes to proteotoxicity. Ectopic overexpression of Rnq1 was extremely toxic, but only if the endogenous Rnq1 protein had adopted its amyloid conformation. The Hsp40 co-chaperone Sis1 was able to counteract the Rnq1-induced toxicity when co-overexpressed. In collaboration with Doug Cyr's lab I showed that Sis1-mediated amyloid formation was cytoprotective and that disordered non-amyloid aggregates induced toxicity. These results provide evidence that the formation of ordered inclusions can be cytoprotective. I further characterized Rnq1 toxicity, conducted two genome-ide screens for modifiers and found that Rnq1 induced a G2/M cell cycle arrest. Rnq1 overexpression resulted in the mislocalization of the core spindle pole body component Spc42 to the IPOD and an unduplicated spindle pole body. In mammalian cells aggresomes localize to centrosomes, the mammalian equivalent of the yeast spindle pole body. The finding that a yeast prion can interact with a spindle pole body component represents a new connection between the IPOD and aggresomes. Lastly, I studied a yeast model of A[beta] 1-42 toxicity. Accumulation of the amyloid beta peptide is thought to be causal in both sporadic and familial Alzheimer's disease. In collaboration with Kent Matlack I developed a yeast model that expressed A[beta] 1-42 in a manner recapitulating mammalian A[beta] 1-42 generation and that was amenable to screens for genetic modifiers of A[beta] 1-42 toxicity. The screen identified the yeast homolog of PICALM, a known Alzheimer's disease risk factor. I showed that A[beta] 1-42 expression resulted in a defect in endocytosis that could be reverted by several of the genetic suppressors. In collaboration with the Caldwell lab, we showed that the genetic modifiers also modulated A[beta] 1-42 toxicity in a neuronal setting, C. elegans glutamatergic neurons. Finally, we showed that PICALM could protect primary rat cortical neuron cultures from A[beta] oligomer toxicity.by Sebastian Treusch.Ph.D

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS