Microwave Imaging System Based on Signal Analysis in a Planar Environment for Detection of Abdominal Aortic Aneurysms.

A proof-of-concept of a microwave imaging system for the fast detection of abdominal aortic aneurysms is shown. This experimental technology seeks to overcome the factors hampering the fast screening for these aneurysms with the usual equipment, such as high cost, long-time operation or hazardous exposure to chemical substances. The hardware system is composed of 16 twin antennas mastered by a microcontroller through a switching network, which connects the antennas to the measurement instrument for sequential measurement. The software system is run by a computer, mastering the whole system, automatizing the measurement process and running the signal processing and medical image generation algorithms. Two image generation algorithms are tested: Delay-and-Sum (DAS) and Improved Delay-and-Sum (IDAS). Own-modified versions of these algorithms adapted to the requirements of our system are proposed. The system is carefully calibrated and fine-tuned with known objects placed at known distances. An experimental proof-of-concept is shown with a human torso phantom, including an aorta phantom and an aneurysm phantom placed in different positions. The results show good imaging capabilities with the potential for detecting and locating possible abdominal aortic aneurysms and reporting acceptable errors.

Non-Invasive Microwave-Based Imaging System for Early Detection of Breast Tumours.

This work introduces a microwave-based system able to detect tumours in breast phantoms in a non-invasive way. The data acquisition system is composed of a hardware system which involves high-frequency components (antennas, switches and cables), a microcontroller, a vector network analyser used as measurement instrument and a computer devoted to the control and automation of the operation of the system. Concerning the software system, the computer runs a Python script which is in charge of mastering and automatising all the required stages for the data acquisition, from initialisation of the hardware system to performing and saving the measurements. We also report on the design of the high-performance broadband antenna used to carry out the measurements, as well as on the algorithm employed to build the final medical images, based on an adapted version of the so-called Improved Delay-and-Sum (IDAS) algorithm improved by a Hamming window filter and averaging preprocessing. The calibration and start-up of the system are also described. The experimental validation includes the use of different tumour models with different dielectric properties inside the breast phantom. The results show promising tumour detection capabilities, even when there is low dielectric contrast between the tumoural and healthy tissues, as is the usual case for dense breasts in young women.

Implementation of Microwave Circuits Using Stereolithography.

In this work, the use of additive manufacturing techniques through stereolithography for the manufacture of high-frequency circuits and devices is presented. Both the resin and the 3D printer used in this research are general-purpose commercial materials, not specifically intended for the implementation of microwave networks. The manufacturing and metallization procedures used to produce substrates for the design of planar microwave circuits are described, introducing the characterization process carried out to determine the electrical properties of the resin used. The ultrasonic techniques that allow the structural analysis of the manufactured substrates are also described. The electrical characterization provides a relative dielectric permittivity of 3.25 and a loss tangent of 0.03 for the resin used. In addition, the structural analysis shows a homogeneity and a finish of the manufactured parts that is not achievable using fused deposition modeling techniques. Finally, as a proof of concept, the design and manufacture of a complex geometry stepped impedance filter on a multi-height substrate using stereolithography techniques is presented, which allows for reducing the size of the traditional implementation of the same filter while maintaining its high-frequency response performance.

Low-Cost Additive Manufacturing Techniques Applied to the Design of Planar Microwave Circuits by Fused Deposition Modeling.

This work presents a study on the implementation and manufacturing of low-cost microwave electronic circuits, made with additive manufacturing techniques using fused deposition modeling (FDM) technology. First, the manufacturing process of substrates with different filaments, using various options offered by additive techniques in the manufacture of 3D printing parts, is described. The implemented substrates are structurally analyzed by ultrasound techniques to verify the correct metallization and fabrication of the substrate, and the characterization of the electrical properties in the microwave frequency range of each filament is performed. Finally, standard and novel microwave filters in microstrip and stripline technology are implemented, making use of the possibilities offered by additive techniques in the manufacturing process. The designed devices were manufactured and measured with good results, which demonstrates the possibility of using low-cost 3D printers in the design process of planar microwave circuits.

Breaking free from chemical spreadsheets.

Drug discovery scientists often consider compounds and data in terms of groups, such as chemical series, and relationships, representing similarity or structural transformations, to aid compound optimisation. This is often supported by chemoinformatics algorithms, for example clustering and matched molecular pair analysis. However, chemistry software packages commonly present these data as spreadsheets or form views that make it hard to find relevant patterns or compare related compounds conveniently. Here, we review common data visualisation and analysis methods used to extract information from chemistry data. We introduce a new framework that enables scientists to work flexibly with drug discovery data to reflect their thought processes and interact with the output of algorithms to identify key structure-activity relationships and guide further optimisation intuitively.