Narrow Results

Various optical techniques were used to investigate relevant parameters involved in photodynamic therapy (PDT) of human basal cell carcinomas (BCCs). The aim of the study was to compare the diagnostic and therapeutic outcome when using topically applied methyl-esterified δ-aminolevulinic acid (ALA-ME) and δ-aminolevulinic acid (ALA). A total of 35 pathologically verified BCCs in 14 patients were investigated. A diode laser, emitting continuous light at 633 nm, was used to induce PDT. The diagnostic measurements were performed before, during, and after PDT. Laser-induced fluorescence (LIF) was used to monitor the build-up of the ALA/ALA-ME-induced protoporphyrin IX (PpIX). The superficial tissue perfusion was measured with laser-Doppler perfusion imaging (LDPI) and the temperature of the lesion and the surrounding tissue was imaged with an IR-camera. A clear demarcation between the lesion and the normal skin was detected with LIF before the treatment for both PpIX precursors. The fluorescence measurements suggest that PpIX builds up to a higher degree and more selectively in the tumour following ALA-ME as compared to ALA. The LDPI measurements indicate a local transient restriction in blood perfusion immediately post-PDT. The measurement with the IR-camera revealed a temperature rise of about 1–2 °C during the treatment.

Coatings composed of mixed nanoparticles in Silica and Titania sol matrices were prepared by sol–gel spray coating method. The coatings were characterized using scanning electron microscopy (SEM), FT-IR spectroscopy and Infrared thermal imaging. The thermal Infrared Emissivity of the coatings was determined using IR-1 spectrometer. The coating solutions were applied onto sand blasted aluminum substrates. Results indicated that mixed nanoparticles can be well dispersed in sol to give coatings with good heat emission properties. High emissivity of 0.915–0.941 was achieved, and the coatings showed cooling efficiency of 7.3–14%. The coatings can be prepared in an easy and controlled way using sol–gel method and are a potential in radiative cooling applications.

People’s need for healthcare capacity has become increasingly critical as the elderly population continues to grow in most communities. Approximately 25–47% of seniors fall annually, and early detection of poor balance can significantly reduce their risk. Automated fall detection with big data analytics is key to maintaining the safety of the elderly in smart cities. Visible image systems (VIS) in smart buildings, on the other hand, visible image systems (VIS) in smart buildings may compromise the privacy of seniors by enabling technologies for intelligent big data analytics (IBDA). Thermal imaging (TI) is less obtrusive than visual imaging and can be used in combination with machine vision to perform a wide range of IBDAs. In this study, we present a novel two-step method for detecting falls in TI frames using deep learning (DL). As the first step, tracking tools are used to locate people’s locations. A novel modified deep transfer learning (TL) technique is used to classify the trajectory created by the tracking approach for people who are at risk of falling. Fall detection by the IBDA will be connected to the Internet of medical things (IoMT) and used as smart technology in the process of big data-assisted pervasive surveillance and health analytics. According to an analysis of the publicly available thermal fall dataset, our method outperforms traditional fall detection methods, with an average error of less than 3%. Additionally, IoMT platforms facilitate data processing, real-time monitoring and healthcare management. Our smart scheme for using big data analytics to enable intelligent decisions is compatible with the various spaces and provides a comfortable and safe environment for current and future elderly people.

Monitoring the fetal growth and diagnosing any possible abnormality plays a vital role in ensuring the healthy growth of a fetus. Certain health issues like Hyperthermia, Premature Rupture of Membranes (PROM) and Intrauterine Growth Restriction (IUGR) has to be diagnosed early. A pilot study comprising of 27 pregnant and 2 non-pregnant subjects was conducted to check the effectiveness of Thermal imaging in predicting the fetal growth. The heat dissipated by the fetus to the maternal abdominal wall is acquired as a surface thermal distribution. These images were processed qualitatively and quantitatively for better understanding. There was a consistent higher thermal pattern for pregnant women. A more pronounced temperature pattern is notable in the umbilical region that correlates with gestation age. However, as thermal pattern varies with age, gestation period and BMI, it is advisable to track the same person and compare the images for better assessment. This pilot study justifies the need for more elaborate study in building a database for classification and interpretation of thermogram to detect fetal abnormality with reduced human interpretation.

Infrared images have several applications such as security, health, passenger monitoring, and so on. The quality of infrared image gets affected by noise, blurring effect, and low illumination environment. Due to the low contrast, blurring, and hazy effects in infrared images, state-of-the-art techniques are frequently unable to achieve appropriate edge details. Thus, an edge detection algorithm is proposed using a modified Von Neumann neighborhood kernel and taxicab geometry-based shortest path method. It has been found to perform in a better manner compared to earlier studies in a similar field. The objective of the proposed method is to produce sharp, less noisy and robust edge lines. First, pre-processing of the image is done for edge-preserving smoothing of an infrared image using a smoothing parameter. Second, image segmentation is done based on a two-level threshold value computed by a modified Von Neumann-based kernel. Then, Fourier transform of the segmented image is done to remove spike noise followed by the inverse Fourier transform to produce the final edge lines. The simulation experiment results show that the proposed method is found to yield robust and sharp edge lines compared to other state-of-the-art methods both numerically and visually. Moreover, the whole process takes less computation time.

An innovative NDT (non-destructive testing) technique for interrogating materials for their defects has been developed successfully. The technique has a novel approach to data analysis by employing intensity, RGB signal re-mix and wavelength variation of a thermally generated IR-beam onto the specimen under test which can be sensed and displayed on a computer screen as an image. Specimen inspection and data analysis are carried out through pixel level re-ordering and shelving techniques within a transformed image file using a sequence grouping and regrouping software system, which is specifically developed for this work. The interaction between an impact damaged RIM composite structure and thermal energy is recorded, analyzed, and modeled using an equivalent Electronic circuit. Effect of impact damage on the integrity of the composite structure is also discussed.