Lamination And Microstructuring Technology for a Bio-Cell Multiwell array

Microtechnology becomes a versatile tool for biological and biomedical applications. Microwells have been established long but remained non-intelligent up to now. Merging new fabrication techniques and handling concepts with microelectronics enables to realize intelligent microwells suitable for future improved cancer treatment. The described technology depicts the basis for the fabrication of a elecronically enhanced microwell. Thin aluminium sheets are structured by laser micro machining and laminated successively to obtain registration tolerances of the respective layers of 5..10\^A$\mu$m. The microwells lasermachined into the laminate are with 50..80\^A$\mu$m diameter, allowing to hold individual cells within the well. The individual process steps are described and results on the microstructuring are given.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing

Multi-functional metasurface architecture for amplitude, polarization and wavefront control

Metasurfaces (MSs) have been utilized to manipulate different properties of electromagnetic waves. By combining local control over the wave amplitude, phase, and polarization into a single tunable structure, a multi-functional and reconfigurable metasurface can be realized, capable of full control over incident radiation. Here, we experimentally validate a multi-functional metasurface architecture for the microwave regime, where in principle variable loads are connected behind the backplane to reconfigurably shape the complex surface impedance. As a proof-of-concept step, we fabricate several metasurface instances with static loads in different configurations (surface mount capacitors and resistors of different values in different connection topologies) to validate the approach and showcase the different achievable functionalities. Specifically, we show perfect absorption for oblique incidence (both polarizations), broadband linear polarization conversion, and beam splitting, demonstrating control over the amplitude, polarization state, and wavefront, respectively. Measurements are performed in the 4-18 GHz range inside an anechoic chamber and show good agreement with theoretically-anticipated results. Our results clearly demonstrate the practical potential of the proposed architecture for reconfigurable electromagnetic wave manipulation.Comment: 6 pages, 5 figure

XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges

We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES, which seeks to offer naturally low-latency operation and cost-effectiveness, overcoming the critical scalability issues faced by existing solutions. iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in wireless communications. Empowered by intelligent (meta)surfaces, PWEs transform the wave propagation phenomenon into a software-defined process. We leverage PWEs to i) create, and then ii) selectively copy the scattered RF wavefront of an object from one location in space to another, where a machine learning module, accelerated by FPGAs, translates it to visual input for an XR headset using PWEdriven, RF imaging principles (XR-RF). This makes for an XR system whose operation is bounded in the physical layer and, hence, has the prospects for minimal end-to-end latency. Over large distances, RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The paper provides a tutorial on the iCOPYWAVES system architecture and workflow. A proof-of-concept implementation via simulations is provided, demonstrating the reconstruction of challenging objects in iCOPYWAVES produced computer graphics

3D reconstruction from video using a mobile robot.

An autonomous robot able to navigate inside an unknown environment and reconstruct full 3D scene models using monocular video has been a long term goal in the field of Machine Vision. A key component of such a system is the reconstruction of surface models from estimated scene structure. Sparse 3D measurements of real scenes are readily estimated from N-view image sequences using structure-from-motion techniques. In this thesis we present a geometric theory for reconstruction of surface models from sparse 3D data captured from N camera views. Based on this theory we introduce a general N-view algorithm for reconstruction of 3D models of arbitrary scenes from sparse data. Using a hypothesise and verify strategy this algorithm reconstructs a surface model which interpolates the sparse data and is guaranteed to be consistent with the feature visibility in the N-views. To achieve efficient reconstruction independent of the number of views a simplified incremental algorithm is developed which integrates the feature visibility independently for each view. This approach is shown to converge to an approximation of the real scene structure and have a computational cost which is linear in the number of views. Surface hypothesis are generated based on a new incremental planar constrained Delaunay triangulation algorithm. We present a statistical geometric framework to explicitly consider noise inherent in estimates of 3D scene structure from any real vision system. This approach ensures that the reconstruction is reliable in the presence of noise and missing data. Results are presented for reconstruction of both real and synthetic scenes together with an evaluation of the reconstruction performance in the presence of noise

3D reconstruction from video using a mobile robot.

An autonomous robot able to navigate inside an unknown environment and reconstruct full 3D scene models using monocular video has been a long term goal in the field of Machine Vision. A key component of such a system is the reconstruction of surface models from estimated scene structure. Sparse 3D measurements of real scenes are readily estimated from N-view image sequences using structure-from-motion techniques. In this thesis we present a geometric theory for reconstruction of surface models from sparse 3D data captured from N camera views. Based on this theory we introduce a general N-view algorithm for reconstruction of 3D models of arbitrary scenes from sparse data. Using a hypothesise and verify strategy this algorithm reconstructs a surface model which interpolates the sparse data and is guaranteed to be consistent with the feature visibility in the N-views. To achieve efficient reconstruction independent of the number of views a simplified incremental algorithm is developed which integrates the feature visibility independently for each view. This approach is shown to converge to an approximation of the real scene structure and have a computational cost which is linear in the number of views. Surface hypothesis are generated based on a new incremental planar constrained Delaunay triangulation algorithm. We present a statistical geometric framework to explicitly consider noise inherent in estimates of 3D scene structure from any real vision system. This approach ensures that the reconstruction is reliable in the presence of noise and missing data. Results are presented for reconstruction of both real and synthetic scenes together with an evaluation of the reconstruction performance in the presence of noise