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We present design, characterization, and testing of an inexpensive, sheath-flow based microfluidic device for three-dimensional (3D) hydrodynamic focusing of cells in imaging flow cytometry. In contrast to other 3D sheathing devices, our device hydrodynamically focuses the cells in a single-file near the bottom wall of the microchannel that allows imaging cells with high magnification and low working distance objectives, without the need for small device dimensions. The relatively large dimensions of the microchannels enable easy fabrication using less-precise fabrication techniques, and the simplicity of the device design avoids the need for tedious alignment of various layers. We have characterized the performance of the device with 3D numerical simulations and validated these simulations with experiments of hydrodynamic focusing of a fluorescently dyed sample fluid. The simulations show that the width and the height of the 3D focused sample stream can be controlled independently by varying the heights of main and side channels of the device, and the flow rates of sample and sheath fluids. Based on simulations, we also provide useful guidelines for choosing the device dimensions and flow rates for focusing cells of a particular size. Thereafter, we demonstrate the applicability of our device for imaging a large number of RBCs using brightfield microscopy. We also discuss the choice of the region of interest and camera frame rate so as to image each cell individually in our device. The design of our microfluidic device makes it equally applicable for imaging cells of different sizes using various other imaging techniques such as phase-contrast and fluorescence microscopy.

Recent advancements in computer vision processing need potent tools to create realistic deepfakes. A generative adversarial network (GAN) can fake the captured media streams, such as images, audio, and video, and make them visually fit other environments. So, the dissemination of fake media streams creates havoc in social communities and can destroy the reputation of a person or a community. Moreover, it manipulates public sentiments and opinions toward the person or community. Recent studies have suggested using the convolutional neural network (CNN) as an effective tool to detect deepfakes in the network. But, most techniques cannot capture the inter-frame dissimilarities of the collected media streams. Motivated by this, this paper presents a novel and improved deep-CNN (D-CNN) architecture for deepfake detection with reasonable accuracy and high generalizability. Images from multiple sources are captured to train the model, improving overall generalizability capabilities. The images are re-scaled and fed to the D-CNN model. A binary-cross entropy and Adam optimizer are utilized to improve the learning rate of the D-CNN model. We have considered seven different datasets from the reconstruction challenge with 5000 deepfake images and 10000 real images. The proposed model yields an accuracy of 98.33% in AttGAN a , 99.33% in GDWCT b , 95.33% in StyleGAN, 94.67% in StyleGAN2, and 99.17% in StarGAN c real and deepfake images, that indicates its viability in experimental setups. a Facial Attribute Editing by Only Changing What You Want (AttGAN) b Group-wise deep whitening-and-coloring transformation (GDWCT) c A GAN capable of learning mappings among multiple domains (StarGAN)