1,210 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments
Modern robotic systems are required to operate in challenging environments,
which demand reliable localization under challenging conditions. LiDAR-based
localization methods, such as the Iterative Closest Point (ICP) algorithm, can
suffer in geometrically uninformative environments that are known to
deteriorate point cloud registration performance and push optimization toward
divergence along weakly constrained directions. To overcome this issue, this
work proposes i) a robust fine-grained localizability detection module, and ii)
a localizability-aware constrained ICP optimization module, which couples with
the localizability detection module in a unified manner. The proposed
localizability detection is achieved by utilizing the correspondences between
the scan and the map to analyze the alignment strength against the principal
directions of the optimization as part of its fine-grained LiDAR localizability
analysis. In the second part, this localizability analysis is then integrated
into the scan-to-map point cloud registration to generate drift-free pose
updates by enforcing controlled updates or leaving the degenerate directions of
the optimization unchanged. The proposed method is thoroughly evaluated and
compared to state-of-the-art methods in simulated and real-world experiments,
demonstrating the performance and reliability improvement in LiDAR-challenging
environments. In all experiments, the proposed framework demonstrates accurate
and generalizable localizability detection and robust pose estimation without
environment-specific parameter tuning.Comment: 20 Pages, 20 Figures Submitted to IEEE Transactions On Robotics.
Supplementary Video: https://youtu.be/SviLl7q69aA Project Website:
https://sites.google.com/leggedrobotics.com/x-ic
Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics
It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM âSchwingungen in rotierenden Maschinenâ. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name âEuropean Conference on Rotordynamicsâ. This new international profile has also been
emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations
Cerebrovascular dysfunction in cerebral small vessel disease
INTRODUCTION:
Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD.
METHODS:
Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medicationsâ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs.
RESULTS:
Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion.
DISCUSSION:
Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function
Cybersecurity: Past, Present and Future
The digital transformation has created a new digital space known as
cyberspace. This new cyberspace has improved the workings of businesses,
organizations, governments, society as a whole, and day to day life of an
individual. With these improvements come new challenges, and one of the main
challenges is security. The security of the new cyberspace is called
cybersecurity. Cyberspace has created new technologies and environments such as
cloud computing, smart devices, IoTs, and several others. To keep pace with
these advancements in cyber technologies there is a need to expand research and
develop new cybersecurity methods and tools to secure these domains and
environments. This book is an effort to introduce the reader to the field of
cybersecurity, highlight current issues and challenges, and provide future
directions to mitigate or resolve them. The main specializations of
cybersecurity covered in this book are software security, hardware security,
the evolution of malware, biometrics, cyber intelligence, and cyber forensics.
We must learn from the past, evolve our present and improve the future. Based
on this objective, the book covers the past, present, and future of these main
specializations of cybersecurity. The book also examines the upcoming areas of
research in cyber intelligence, such as hybrid augmented and explainable
artificial intelligence (AI). Human and AI collaboration can significantly
increase the performance of a cybersecurity system. Interpreting and explaining
machine learning models, i.e., explainable AI is an emerging field of study and
has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-
Towards Robot Autonomy in Medical Procedures Via Visual Localization and Motion Planning
Robots performing medical procedures with autonomous capabilities have the potential to positively effect patient care and healthcare system efficiency. These benefits can be realized by autonomous robots facilitating novel procedures, increasing operative efficiency, standardizing intra- and inter-physician performance, democratizing specialized care, and focusing the physicianâs time on subtasks that best leverage their expertise. However, enabling medical robots to act autonomously in a procedural environment is extremely challenging. The deforming and unstructured nature of the environment, the lack of features in the anatomy, and sensor size constraints coupled with the millimeter level accuracy required for safe medical procedures introduce a host of challenges not faced by robots operating in structured environments such as factories or warehouses. Robot motion planning and localization are two fundamental abilities for enabling robot autonomy. Motion planning methods compute a sequence of safe and feasible motions for a robot to accomplish a specified task, where safe and feasible are defined by constraints with respect to the robot and its environment. Localization methods estimate the position and orientation of a robot in its environment. Developing such methods for medical robots that overcome the unique challenges in procedural environments is critical for enabling medical robot autonomy. In this dissertation, I developed and evaluated motion planning and localization algorithms towards robot autonomy in medical procedures. A majority of my work was done in the context of an autonomous medical robot built for enhanced lung nodule biopsy. First, I developed a dataset of medical environments spanning various organs and procedures to foster future research into medical robots and automation. I used this data in my own work described throughout this dissertation. Next, I used motion planning to characterize the capabilities of the lung nodule biopsy robot compared to existing clinical tools and I highlighted trade-offs in robot design considerations. Then, I conducted a study to experimentally demonstrate the benefits of the autonomous lung robot in accessing otherwise hard-to-reach lung nodules. I showed that the robot enables access to lung regions beyond the reach of existing clinical tools with millimeter-level accuracy sufficient for accessing the smallest clinically operable nodules. Next, I developed a localization method to estimate the bronchoscopeâs position and orientation in the airways with respect to a preoperatively planned needle insertion pose. The method can be used by robotic bronchoscopy systems and by traditional manually navigated bronchoscopes. The method is designed to overcome challenges with tissue motion and visual homogeneity in the airways. I demonstrated the success of this method in simulated lungs undergoing respiratory motion and showed the methodâs ability to generalize across patients.Doctor of Philosoph
Mesoscopic Physics of Quantum Systems and Neural Networks
We study three different kinds of mesoscopic systems â in the intermediate region between macroscopic and microscopic scales consisting of many interacting constituents:
We consider particle entanglement in one-dimensional chains of interacting fermions. By employing a field theoretical bosonization calculation, we obtain the one-particle entanglement entropy in the ground state and its time evolution after an interaction quantum quench which causes relaxation towards non-equilibrium steady states. By pushing the boundaries of the numerical exact diagonalization and density matrix renormalization group computations, we are able to accurately scale to the thermodynamic limit where we make contact to the analytic field theory model. This allows to fix an interaction cutoff required in the continuum bosonization calculation to account for the short range interaction of the lattice model, such that the bosonization result provides accurate predictions for the one-body reduced density matrix in the Luttinger liquid phase.
Establishing a better understanding of how to control entanglement in mesoscopic systems is also crucial for building qubits for a quantum computer. We further study a popular scalable qubit architecture that is based on Majorana zero modes in topological superconductors. The two major challenges with realizing Majorana qubits currently lie in trivial pseudo-Majorana states that mimic signatures of the topological bound states and in strong disorder in the proposed topological hybrid systems that destroys the topological phase. We study coherent transport through interferometers with a Majorana wire embedded into one arm.
By combining analytical and numerical considerations, we explain the occurrence of an amplitude maximum as a function of the Zeeman field at the onset of the topological phase â a signature unique to MZMs â which has recently been measured experimentally [Whiticar et al., Nature Communications, 11(1):3212, 2020]. By placing an array of gates in proximity to the nanowire, we made a fruitful connection to the field of Machine Learning by using the CMA-ES algorithm to tune the gate voltages in order to maximize the amplitude of coherent transmission. We find that the algorithm is capable of learning disorder profiles and even to restore Majorana modes that were fully destroyed by strong disorder by optimizing a feasible number of gates.
Deep neural networks are another popular machine learning approach which not only has many direct applications to physical systems but which also behaves similarly to physical mesoscopic systems. In order to comprehend the effects of the complex dynamics from the training, we employ Random Matrix Theory (RMT) as a zero-information hypothesis: before training, the weights are randomly initialized and therefore are perfectly described by RMT. After training, we attribute deviations from these predictions to learned information in the weight matrices.
Conducting a careful numerical analysis, we verify that the spectra of weight matrices consists of a random bulk and a few important large singular values and corresponding vectors that carry almost all learned information. By further adding label noise to the training data, we find that more singular values in intermediate parts of the spectrum contribute by fitting the randomly labeled images. Based on these observations, we propose a noise filtering algorithm that both removes the singular values storing the noise and reverts the level repulsion of the large singular values due to the random bulk
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