A millimeter-wave (mmW) classifier system applied to images synthesized from a codedaperture based computational imaging (CI) radar is presented. A developed physical model of a CI system is used to generate the image dataset for the classification algorithm. A convolutional neural network (CNN) is integrated with the physical model and trained using the dataset comprising of synthesized mmW images obtained directly from the developed CI physical model. A k-fold cross validation technique is applied during the training process to validate the classification model. The coded-aperture CI concept enables image reconstruction from a significantly reduced number of back-scattered measurements by facilitating physical layer compression. This physical layer compression can substantially simplify the data acquisition layer of imaging radars, which is realized using only two channels in this article. The integration of the classification algorithm with the CI numerical model is particularly important in enabling the training step to be carried out using relevant system metrics and without the necessity for experimental data. Leveraging the CI numerical model generated data, training step for the classification algorithm is achieved in real-time while also confirming that the numerically trained CI classifier offers high accuracy with both simulated and experimental data. The classifier integrated physical model also enables performance analysis of the classification algorithm to be carried out as a function of key system metrics such as signal-to-noise (SNR) level, ensuring a complete understanding of the classification accuracy under different operating conditions. The trained CI system is tested with synthesized mmW images from the physical model and a classification accuracy of 89% is achieved. The proposed model is also verified using experimental data validating the fidelity of the developed CI integrated classifier system. A classification latency of 3.8 ms per frame is achieved, paving the way for real-time automated threat detection (ATD) for security-screening applications.INDEX TERMS Millimetre-wave, imaging radars, computational imaging, neural networks, image classification, coded-aperture.

We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed.

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We report on the development of a handheld three-dimensional (3D) terahertz scanning system with an aspherical telecentric 3D-printed f-θ lens using selective laser sintering. The lens covers a broader scan line of 50 mm with its larger aperture, compared to the 20 mm range in our initial work, which was presented at the European Microwave Week 2021. In order to evaluate the adaptability of the optomechanical components with different sensor units, two different integrated frequency-modulated continuous wave radar modules based on monolithic microwave integrated circuit technology, operating in W- and D-bands are tested within the measurement scheme. The optomechanical part consists of a galvanometer scanner mirror and the f-θ lens. The optical system enables B-scans perpendicular to the manual translational movement of the sensor unit by its user. An integrated guiding wheel system with rotary encoder makes it possible to correlate the measurement points to their respective locations, enabling complete 3D volumetric inspection of the corresponding structures, which is particularly useful for the inspection along cracks and welds.

Terahertz technologies for non-destructive testing (NDT) are continuing to find their way into the industrial sector in the context of very specific inspection tasks. Part of this development is the capability to adapt terahertz systems in such a way that they can meet the sometimes harsh challenges and requirements of real-world industrial scenarios. One such scenario is the inspection of components with limited available measurement space. In particular, we show here the terahertz NDT inspection of the mica insulation of generator bars of turbogenerators at power plants, where an early on-site detection of defects and cracks in the insulation can be crucial, but where only few centimeters of space between adjacent bars are available. To address this problem, we have developed a measurement system combining a 100 GHz all-electronic terahertz transceiver with a low-loss dielectric waveguide antenna with 90 degree tip. We achieve sub-wavelength image resolution by scanning the waveguide antenna's tip over the surface of the generator bars in a near-field measurement setup. Employing a frequency-modulated continuous wave technique, we obtain depth-resolved, volumetric terahertz images of the objects under test. We discuss here the implementation and performance of the implemented measurement system for terahertz NDT inspection. keywords: terahertz, non-destructive testing, dielectric waveguides, frequency-modulated continuous wave, millimeter waves, power generators

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