SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity

We propose a simple yet effective metric that measures structural similarity between visual instances of architectural floor plans, without the need for learning. Qualitatively, our experiments show that the retrieval results are similar to deeply learned methods. Effectively comparing instances of floor plan data is paramount to the success of machine understanding of floor plan data, including the assessment of floor plan generative models and floor plan recommendation systems. Comparing visual floor plan images goes beyond a sole pixel-wise visual examination and is crucially about similarities and differences in the shapes and relations between subdivisions that compose the layout. Currently, deep metric learning approaches are used to learn a pair-wise vector representation space that closely mimics the structural similarity, in which the models are trained on similarity labels that are obtained by Intersection-over-Union (IoU). To compensate for the lack of structural awareness in IoU, graph-based approaches such as Graph Matching Networks (GMNs) are used, which require pairwise inference for comparing data instances, making GMNs less practical for retrieval applications. In this paper, an effective evaluation metric for judging the structural similarity of floor plans, coined SSIG (Structural Similarity by IoU and GED), is proposed based on both image and graph distances. In addition, an efficient algorithm is developed that uses SSIG to rank a large-scale floor plan database. Code will be openly available.Comment: To be published in ICCVW 2023, 10 page

Advancing Sustainable Approaches in Architecture by Means of Design-to-Robotic-Production

The construction sector accounts for about 40% of material-, energy- and process-related carbon dioxide (CO2) emissions , which can be reduced by introducing data-driven Circular Economy (CE) approaches . For instance, Design-to-Robotic-Production (D2RP) methods developed in the Robotic building lab, at Technical University (TU) Delft are embedding data-driven systems into building processes. Their potential to contribute to sustainability through increased material-, process-, and energy-efficiency has been explored in several case studies that are presented in this paper. The assumption is that by using these methods and reclaimed wood to minimize demand for new resources and reduce deforestation along the way, CO2 emissions can be considerably reduced

Potential enrichments in malaria diagnostics: hyperspectral imaging and group-equivariant neural networks

Proper diagnostics are essential in the combat against severe diseases which mainly have big impacts in remote areas in poor countries. A focus direction within the NC4I group at DCSC, Delft University of Technology, is the development of new imaging modalities and the design and implementation of smarter algorithms for improved detection of parasitic diseases. The first part of my research exploits hyperspectral imagery (HI) as new potential imaging modality of thin blood smears that could highly improve on preparation time, labor intensiveness and use of materials. HI retrieves both spatial and spectral information of the observed objects simultaneously, thus providing the ability to discriminate near similar constituents within the blood smear. In doing so, it enables the possibility of label-free detection. In this thesis, the development and building of such a system is addressed and carried out. In the context of malaria, it is shown that HI is promising and lays a profound foundation for further exploration. The design and evaluation of improved generalizing neural networks characterize the essence of the second and larger part of the research. Several group-equivariant networks are evaluated and compared with conventional convolutional networks which shows that efficient and redefined integration of weights can help build smarter and more robust classifiers for the detection of parasites. In group-equivariant networks, re-interpreting the way feature maps are connected to one another manifests in the development of convolutional stages that equivary under an increased amount of transformations besides merely translations. It is shown that enlarging the heuristic of that transformation group (the extra amount of transformations the operations are equivariant under) significantly contributes to better performance without necessarily increasing the size or changing the architecture of the networks. Compared to the aforementioned baseline (conventional convolutional stages), the best network (being equivariant under 16 equidistant rotations and mirror reflections) improves approximately 2-fold on all relevant performance metrics, among which are accuracy, sensitivity, specificity, precision, and the F1-score which are common measures in the classification of malaria. The networks were tested on the Rajaraman database. Furthermore, the pre-trained models are used as classifiers for a different database extracted from the microscope build by AiDx medical. At least for this specific database, it is shown that the more realistic transformations the pre-trained networks equivary under, the more robust they are.Mechanical Engineering | Systems and Contro

Advancing Sustainable Approaches in Architecture by Means of Design-to-Robotic-Production

The construction sector accounts for about 40% of material-, energy- and process-related carbon dioxide (CO2) emissions , which can be reduced by introducing data-driven Circular Economy (CE) approaches . For instance, Design-to-Robotic-Production (D2RP) methods developed in the Robotic building lab, at Technical University (TU) Delft are embedding data-driven systems into building processes. Their potential to contribute to sustainability through increased material-, process-, and energy-efficiency has been explored in several case studies that are presented in this paper. The assumption is that by using these methods and reclaimed wood to minimize demand for new resources and reduce deforestation along the way, CO2 emissions can be considerably reduced

AI-supported approach for human-building interaction implemented at furniture scale

Human-Building Interaction (HBI) relies on sensor-actuator networks that are increasingly supported by Artificial Intelligence (AI). This paper presents a novel AI-supported Design-to-Robotic-Production-Assembly and -Operation (D2RPA&O) approach for reconfigurable furniture. It involves a multidisciplinary approach that relies on the integration of various domains such as architecture, robotics, computer, and material science. It contributes to the advancement of HBI by employing spatial reconfiguration relying on AI and lightweight material design, which is of relevance, particularly when the furniture consists of non-identical but similar components that are re−/ configured in a variety of possible combinations

Computer Vision for Terrain Mapping and 3D Printing In-situ of Extra/-terrestrial Habitats

This paper addresses the complexities inherent in constructing sustainable extraterrestrial habitats within lava tubes that are envisioned as promising locations for human habitation and scientific inquiry. These environments are characterized by various challenges, which are addressed in this case by integrating computer vision (CV) techniques and 3D printing in-situ. The CV component generates a detailed depth map from synthetic imagery to combine this depth map with an adaptive 3D printing process, which is proposed to ensure level surfaces at various depths, facilitating precise foundation and habitat placement within the demanding context of lava tubes. Significantly, synthetic imagery is employed due to the absence of real lava tube photos at this early stage of the current exploration. The focal point lies in utilizing advanced deep learning (DL) algorithms and convolutional neural networks (CNN) to generate depth maps for extra/-terrestrial environments. This research represents a platform for further knowledge development in the fields of CV and its application to 3D printing in-situ, hence opening new avenues for sustainable extraterrestrial habitats.</p

Mechanical Trapping of DNA in a Double-Nanopore System

Nanopores have become ubiquitous components of systems for single-molecule manipulation and detection, in particular DNA sequencing where electric field driven translocation of DNA through a nanopore is used to read out the DNA molecule. Here, we present a double-pore system where two nanopores are drilled in parallel through the same solid-state membrane, which offers new opportunities for DNA manipulation. Our experiments and molecular dynamics simulations show that simultaneous electrophoretic capture of a DNA molecule by the two nanopores mechanically traps the molecule, increasing its residence time within the nanopores by orders of magnitude. Remarkably, by using two unequal-sized nanopores, the pore of DNA entry and exit can be discerned from the ionic current blockades, and the translocation direction can be precisely controlled by small differences in the effective force applied to DNA. The mechanical arrest of DNA translocation using a double-pore system can be straightforwardly integrated into any solid-state nanopore platform, including those using optical or transverse-current readouts