Monitoring crack growth is essential for maintaining steel structures subjected to cyclic loading, such as bridges, cranes, offshore platforms, and wind energy towers. A reliable crack detection method ensures the timely identification of crack initiation and propagation in critical structural components, allowing for necessary repairs or restorations before they cause service interruptions, accidents, or structural failures. This paper presents a real-time crack detection system based on inductive thermography to monitor crack growth in an SM490 steel welded specimen under cyclic loading. The system operates by generating eddycurrents, which inducelocalized heating at the crack tips. This temperature increase is captured using an infrared (IR) camera, and the analysis of IR images enables precise identification of crack location and growth in real time. Additionally, the system is highly efficient, with a low power consumption of only 200 W, making it a practical and effective solution for on-site crack monitoring. These characteristics emphasize its strong potential for non-destructive testing (NDT) applications.

The in-plane thermal diffusivity of soft materials is critical information for the development of functional thermal materials in nano/microscale integrated devices. The laser induced local periodic heating and the imaging of the spatially distributed periodic temperature response is important technique to determine in-plane thermophysical properties.^[1] However, the method is highly affected by the 3D geometry of the sample and the environment of the surface such as the vacuum condition. To avoid this multidimensional effect to the thermal analysis, the frequency range of the periodic heating must be as high as possible, which is usually limited by the frame rate of the imaging system. In this study, the principle of heterodyne signal analysis was applied to thermal imaging to analyse the periodic heating response on the sample surface, which has a frequency much higher than the frame rate of the imaging system. (Fig. 1) The in-plane thermal diffusivity of thin polymer films was determined by phase analysis of the in-plane periodic temperature response induced by photo-thermal effects generated by irradiating with a near-infrared laser (l = 830 nm) at a micro-focus using an optical system with a wide frequency that exceeds the frame rate of InSb IR camera (sensitivity range 3 mm – 5 mm).

In the field of Raman spectroscopy (RS), particularly when working with biologi-cal samples, identifying the chemical compounds most involved in specific pathologies is of critical importance for pathologists. The correlation between chemical substances present in biological tissue and pathology can contribute not only to a deeper understanding of the disease itself but also to the development of novel artificial intelligence-based diagnostic methodologies. Motivated by these clinical challenges, we propose a method to identify the most discriminative spectral bands by leveraging the synergy between Topological Machine Learning (TML) and Raman Spectroscopy. The intrinsic explainability of part of the TML pipeline can indeed play a key role in the detection of such spectral bands, e.g. the proteins most associated with the disease. In order to evaluate the performance of our method, we apply it to three case studies: the RS of biological tissue related to the chondrogenic bone tumors, the RS of cerebrospinal fluid associated with Alzheimer’s disease and the RS of pancreatic tissue. The results obtained with our method are promising in pinpointing which spectral bands are most relevant for diagnosis, but they also highlight the need for further investigation.

In the framework of the SURPRISE EU project, Compressive Sensing paradigm was applied for the development of a laboratory demonstrator with improved spatial sampling operating from visible up to Medium InfraRed (MIR). The demonstrator, which utilizes a commercial Digital Micromirror Device modified by replacing its front window with one transparent up to MIR, has 10 bands in the VIS-NIR range and 2 bands in the MIR range, showing a super resolution factor up to 32. Measurements performed in the MIR spectral range using hot sources as targets, show that CS is effective in reconstructing super-resolved hot targets.

The time change in data measured by infrared thermography is used to analyze thermoelastic stress. Displacement caused by the load on the measurement object is the cause of the apparent temperature change. To prevent this, it is effective to photograph the measurement object with a visible camera synchronized with the infrared thermography. The displacement of the measurement object calculated from the visible image is converted to the movement in the infrared thermography, and the displacement is corrected. In this study, a system was developed using thermoelastic stress analysis (TSA) to measure temperature changes due to the load when a car passes over a steel bridge.

Infrared cameras play an important role in various fields, such as research and development, inspection and surveillance, and the higher the performance of an infrared camera, the more important the specifications are. However, in the field of long-range surveillance, it is difficult to strictly grasp the performance of infrared cameras because those are affected by atmospheric conditions. The performance of DRI (Detection, Recognition, Identification), one of the specifications used in infrared cameras for surveillance, is merely a simulated value from each company. In this verification, we used a MWIR long focal infrared camera to verify whether there was any difference between the simulation values in a real environment and the actual usage conditions.

The analysis of tissue samples from 17 subjects clinically diagnosed with chronic pancreatitis, ductal adenocarcinoma, or classified as controls has been collected and ana- lyzed by Raman spectroscopy (RS). Such data are classified using a recent methodology which combines machine learning with advanced Topological Data Analysis (TDA) tech- niques, known as Topological Machine Learning (TML). A classification accuracy of 82% was achieved following a cross-validation scheme with patient stratification, suggesting that the combination of RS and topological data analysis holds significant potential for distinguishing between the three diagnostic categories. When restricted to binary classifica- tion (cancer vs. no cancer), performance increases to 88%. This approach offers a promising and fast method to support clinical diagnoses, potentially improving diagnostic accuracy and patient outcomes.

Cutaneous melanoma is an aggressive form of skin cancer and a leading cause of cancer-related mortality. In this sense, Raman Spectroscopy (RS) could represent a fast and effective method for melanoma-related diagnosis. We therefore introduced a newmethod based on RS to distinguish Compound Naevi (CN) from Primary Cutaneous Melanoma (PCM) from ex vivo solid biopsies. To this aim, integrating Confocal Raman Micro-Spectroscopy (CRM) with four Machine Learning (ML) algorithms: Linear Discrimi nant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), and Random Forest Classifier (RFC). We focused our attention on the comparison between traditional pre-processing operations with Continuous Wavelet Transform (CWT).
In particular, CWT led to the maximum classification accuracy, which was of ∼89.0%, which highlighted the method as promising in view of future implementations in devices for everyday use.