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Lensless devices paired with deep learning models have recently shown great promise as a novel approach to biological screening. As a first step toward performing automated lensless cell identification non-invasively, we present a field-portable, compact lensless system that can detect and classify smeared whole blood samples through layers of scattering media. In this system, light from a partially coherent laser diode propagates through the sample, which is positioned between two layers of scattering media, and the resultant opto-biological signature is captured by an image sensor. The signature is transformed via local binary pattern (LBP) transformation, and the resultant LBP images are processed by a convolutional neural network (CNN) to identify the type of red blood cells in the sample. We validated our system in an experimental setup where whole blood samples are placed between two diffusive layers of increasing thickness, and the robustness of the system against variations in the layer thickness is investigated. Several CNN models were considered (i.e., AlexNet, VGG-16, and SqueezeNet), individually optimized, and compared against a traditional learning model that consists of principal component decomposition and support vector machine (PCA + SVM). We found that a two-stage SqueezeNet architecture and VGG-16 provide the highest classification accuracy and Matthew’s correlation coefficient (MCC) score when applied to images acquired by our lensless system, with SqueezeNet outperforming the other classifiers when the thickness of the scattering layer is the same in training and test data (accuracy: 97.2%; MCC: 0.96), and VGG-16 resulting the most robust option as the thickness of the scattering layers in test data increases up to three times the value used during training. Altogether, this work provides proof-of-concept for non-invasive blood sample identification through scattering media with lensless devices using deep learning. Our system has the potential to be a viable diagnosis device because of its low cost, field portability, and high identification accuracy. 

The study of high-speed phenomena in underwater environments is pivotal across diverse scientific and engineering domains. This paper introduces a high-speed (3D) integral imaging (InIm) based system to 1) visualize high-speed dynamic underwater events, and 2) detect modulated signals for potential optical communication applications. The proposed system is composed of a high-speed camera with a lenslet array-based integral imaging setup to capture and reconstruct 3D images of underwater scenes and detect temporally modulated optical signals. For 3D visualization, we present experiments to capture the elemental images of high-speed underwater events with passive integral imaging, which were then computationally reconstructed to visualize 3D dynamic underwater scenes. We present experiments for 3D imaging and reconstruct the depth map of high-speed underwater dynamic jets of air bubbles, offering depth information and visualizing the 3D movement of these jets. To detect temporally modulated optical signals, we present experiments to demonstrate the ability to capture and reconstruct high-speed underwater modulated optical signals in turbidity. To the best of our knowledge, this is the first report on high-speed underwater 3D integral imaging for 3D visualization and optical signal communication. The findings illustrate the potential of high-speed integral imaging in the visualization and detection of underwater dynamic events, which can be useful in underwater exploration and monitoring. 

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.