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This paper presents a microneedle thermocouple probe designed for temperature measurements in biological samples, addressing a critical need in the field of biology. Fabricated on a Silicon-On-Insulator (SOI) wafer, the probe features a doped silicon (Si) /chrome (Cr) /gold (Au) junction, providing a high Seebeck coefficient, rapid response times, and excellent temperature resolution. The microfabrication process produces a microneedle with a triangular sensing junction. Finite Element Analysis (FEA) was employed to evaluate the thermal time constant and structural integrity in tissue, supporting the probe’s suitability for biological applications. Experimental validation included temperature measurements in ex-vivo tissue and live Xenopus laevis oocytes. Notably, intracellular thermogenesis was detected by increasing extracellular potassium concentration to depolarize the oocyte membrane, resulting in a measurable temperature rise. These findings highlight the probe's potential as a robust tool for monitoring temperature variations in biological systems. 

Image restoration and denoising has been a challenging problem in optics and computer vision. There has been active research in the optics and imaging communities to develop a robust, data-efficient system for image restoration tasks. Recently, physics-informed deep learning has received wide interest in scientific problems. In this paper, we introduce a three-dimensional integral imaging-based physics-informed unsupervised CycleGAN algorithm for underwater image descattering and recovery using physics-informed CycleGAN (Generative Adversarial Network). The system consists of a forward and backward pass. The base architecture consists of an encoder and a decoder. The encoder takes the clean image along with the depth map and the degradation parameters to produce the degraded image. The decoder takes the degraded image generated by the encoder along with the depth map and produces the clean image along with the degradation parameters. In order to provide physical significance for the input degradation parameter w.r.t a physical model for the degradation, we also incorporated the physical model into the loss function. The proposed model has been assessed under the dataset curated through underwater experiments at various levels of turbidity. In addition to recovering the original image from the degraded image, the proposed algorithm also helps to model the distribution under which the degraded images have been sampled. Furthermore, the proposed three-dimensional Integral Imaging approach is compared with the traditional deep learning-based approach and 2D imaging approach under turbid and partially occluded environments. The results suggest the proposed approach is promising, especially under the above experimental conditions.