8,041 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.
The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design
Photocatalysis in the Wastewater Treatment
The use of photocatalysis for wastewater treatment is an important area of research, which is not yet fully exploited at an industrial level and has significant potential in the disposal of many industrial effluents, particularly the effluents that are difficult to treat by conventional treatment processes. This reprint tries to know the latest advances in the field of wastewater treatment by photocatalysis. In this sense, it is worth mentioning the treatments based on photolysis, TiO2/solar light, oxidants/ultraviolet irradiation, oxidants/catalyst/ultraviolet irradiation, etc. In addition, the reprint describes catalyst manufacturing methods and reaction mechanisms
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
DATA AUGMENTATION FOR SYNTHETIC APERTURE RADAR USING ALPHA BLENDING AND DEEP LAYER TRAINING
Human-based object detection in synthetic aperture RADAR (SAR) imagery is complex and technical, laboriously slow but time critical—the perfect application for machine learning (ML). Training an ML network for object detection requires very large image datasets with imbedded objects that are accurately and precisely labeled. Unfortunately, no such SAR datasets exist. Therefore, this paper proposes a method to synthesize wide field of view (FOV) SAR images by combining two existing datasets: SAMPLE, which is composed of both real and synthetic single-object chips, and MSTAR Clutter, which is composed of real wide-FOV SAR images. Synthetic objects are extracted from SAMPLE using threshold-based segmentation before being alpha-blended onto patches from MSTAR Clutter. To validate the novel synthesis method, individual object chips are created and classified using a simple convolutional neural network (CNN); testing is performed against the measured SAMPLE subset. A novel technique is also developed to investigate training activity in deep layers. The proposed data augmentation technique produces a 17% increase in the accuracy of measured SAR image classification. This improvement shows that any residual artifacts from segmentation and blending do not negatively affect ML, which is promising for future use in wide-area SAR synthesis.Outstanding ThesisMajor, United States Air ForceApproved for public release. Distribution is unlimited
ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting
Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment.
The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented.
Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable
Structured Epipolar Matcher for Local Feature Matching
Local feature matching is challenging due to the textureless and repetitive
pattern. Existing methods foucs on using appearance features and global
interaction and matching, while the importance of geometry prior in local
feature matching has not been fully exploited. Different from these methods, in
this paper, we delve into the importance of geometry prior and propose
Structured Epipolar Matcher (SEM) for local feature matching, which can
leverage the geometric information in a iterative matching way. The proposed
model enjoys several merits. First, our proposed Structured Feature Extractor
can model the relative positional relationship between pixels and
high-confidence anchor points. Second, our proposed Epipolar Attention and
Matching can filter out irrelevant areas by utilizing the epipolar constraint.
Extensive experimental results on five standard benchmarks demonstrate the
superior performance of our SEM compared to state-of-the-art methods
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