7,510 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange
Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy.
In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur.
In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease
Design and Implementation of Indoor Disinfection Robot System
After the outbreak of COVID-19 virus, disinfection has become one of the important means of epidemic prevention. Traditional manual disinfection can easily cause cross infection problems. Using robots to complete disinfection work can reduce people's social contact and block the spread of viruses. This thesis implements an engineering prototype of a indoor disinfection robot from the perspective of product development, with the amin of using robots to replace manual disinfection operations.
The thesis uses disinfection module, control module and navigation module to compose the hardware of the robot. The disinfection module uses ultrasonic atomizers, UV-C ultraviolet disinfection lamps, and air purifiers to disinfect and disinfect the ground and air respectively. The control module is responsible for the movement and obstacle avoidance of the robot. The navigation module uses Raspberry Pi and LiDAR to achieve real-time robot positioning and two-dimensional plane mapping.
In terms of robot software,we have done the following work: (1) Based on the ROS framework, we have implemented functions such as SLAM mapping, location positioning, and odometer data calibration.(2) Customize communication protocols to manage peripheral devices such as UV-C lights, ultrasonic atomizers, air purifiers, and motors on the control board. (3) Develop an Android mobile app that utilizes ROSBridge's lightweight communication architecture to achieve cross platform data exchange between mobile devices and navigation boards, as well as network connectivity and interaction between mobile phones and robots
Finally, this thesis implements an engineering prototype of a household disinfection robot from the perspective of product development
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Genome editing tool for studying <i>Ciona robusta</i> nervous system differentiation
The development of the central nervous system (CNS) depends on complex gene regulatory networks (GRN) that orchestrate the specification, patterning and differentiation of neural cell types. Taking advantage of the unique characteristics of a simple chordate, the ascidian Ciona robusta, I identified a group of genes, expressed in the nervous system of Ciona, to investigate the network that controls specification inside the central and peripheral nervous system. The approach used to study functionally these genes has been the gene editing, performed by CRISPR/cas9 technique, indeed in this work I had the opportunity to evaluate the whole process of this emerging technique, preparing and testing several sgRNAs on the selected genes. Moreover, I focused my attention on a type of sensory neuron, belonging to the peripheral nervous system, Bipolar tail neurons (BTNs), through the investigation of two poorly studied genes, Rimbp and LZTS, both expressed in the BTNs neurons. This thesis amplified the knowledge on their involvement in the gene regulatory network of BTNs during Ciona nervous system development. Here I showed that CRISPR/Cas9-mediated knockout of LZTS in the epidermis results in extra BTNs, suggesting LZTS functions as a repressor during differentiation and specification of BTNs. All these data provide new insight into the development of the Ciona nervous system, encouraging further studies to clarify and confirm LZTS role in the Ciona nervous system development
Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment
Medical image segmentation has made significant progress when a large amount
of labeled data are available. However, annotating medical image segmentation
datasets is expensive due to the requirement of professional skills.
Additionally, classes are often unevenly distributed in medical images, which
severely affects the classification performance on minority classes. To address
these problems, this paper proposes Co-Distribution Alignment (Co-DA) for
semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal
predictions on unlabeled data to marginal predictions on labeled data in a
class-wise manner with two differently initialized models before using the
pseudo-labels generated by one model to supervise the other. Besides, we design
an over-expectation cross-entropy loss for filtering the unlabeled pixels to
reduce noise in their pseudo-labels. Quantitative and qualitative experiments
on three public datasets demonstrate that the proposed approach outperforms
existing state-of-the-art semi-supervised medical image segmentation methods on
both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an
mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824
and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.Comment: Paper appears in Bioengineering 2023, 10(7), 86
Multi-sensor Mapping in natural environment: Three-Dimensional Reconstruction and temporal alignment
The objective of this thesis is the adaptation and development of robotic techniques, suitable for geometric three dimensional reconstruction of natural environments, leading into the temporal alignment of natural outdoor surveys.
The objective has been achieved by adapting the state-of-the-art in field robotics and computer vision, such as sensor fusion and visual \acrfull{SLAM}. Throughout this thesis, we combine data generated by cameras, lasers and an inertial measurement unit, in order to geometrically reconstruct the surrounding scene as well as to estimate the trajectory.
By supporting cameras with laser depth information, we show that it is possible to stabilize the state-of-the-art in visual odometry, and recover scale for visual maps. We also show that factor graphs are powerful tools for sensor fusion, and can be used for a generalized approach involving multiple sensors.
Using semantic knowledge, we constrain the \acrfull{ICP} in order to build keyframes as well as to align them both spatially and temporally. Hierarchical clustering of ICP-generated transformations is then used to both eliminate outliers and find alignment consensus, followed by an optimization scheme based on a factor graph that includes loop closure. Data was captured using a portable robotic sensor suite consisting of three cameras, three dimensional lidar, and an inertial navigation system. Throughout this thesis, data was captured in the natural environment using a wearable sensor suite, conceived in the first months of this thesis. The data was acquired in monthly intervals over 12 months, by revisiting the same trajectory between August 2020 and July 2021.
Finally, it has been shown that it is possible to align monthly surveys, taken over a year using the conceived sensor suite, and to provide insightful metrics for change evaluation in natural environment.Ph.D
Formation control of autonomous vehicles with emotion assessment
Autonomous driving is a major state-of-the-art step that has the potential to transform the mobility of individuals and goods fundamentally. Most developed autonomous ground vehicles (AGVs) aim to sense the surroundings and control the vehicle autonomously with limited or no driver intervention. However, humans are a vital part of such vehicle operations. Therefore, an approach to understanding human emotions and creating trust between humans and machines is necessary. This thesis proposes a novel approach for multiple AGVs, consisting of a formation controller and human emotion assessment for autonomous driving and collaboration. As the interaction between multiple AGVs is essential, the performance of two multi-robot control algorithms is analysed, and a platoon formation controller is proposed. On the other hand, as the interaction between AGVs and humans is equally essential to create trust between humans and AGVs, the human emotion assessment method is proposed and used as feedback to make autonomous decisions for AGVs. A novel simulation platform is developed for navigating multiple AGVs and testing controllers to realise this concept. Further to this simulation tool, a method is proposed to assess human emotion using the affective dimension model and physiological signals such as an electrocardiogram (ECG) and photoplethysmography (PPG). The experiments are carried out to verify that humans' felt arousal and valence levels could be measured and translated to different emotions for autonomous driving operations. A per-subject-based classification accuracy is statistically significant and validates the proposed emotion assessment method. Also, a simulation is conducted to verify AGVs' velocity control effect of different emotions on driving tasks
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