Skin lesions are in a category of disease that is both common in humans and a major cause of death. The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases. Deep Convolutional Neural Networks (CNNs) are now the most prevalent computer algorithms for the purpose of disease classification. As with all algorithms, CNNs are sensitive to noise from imaging devices, which often contaminates the quality of the images that are fed into them. In this paper, a deep CNN (Inception-v3) is used to study the effect of image noise on the classification of skin lesions. Gaussian noise, impulse noise, and noise made up of a compound of the two are added to an image dataset, namely the Dermofit Image Library from the University of Edinburgh. Evaluations, based on t-distributed Stochastic Neighbor Embedding (t-SNE) visualization, Receiver Operating Characteristic (ROC) analysis, and saliency maps, demonstrate the reliability of the Inception-v3 deep CNN in classifying noisy skin lesion images.

In this paper, we present a polyhedron-shaped floating autostereoscopic display viewable from 360 degrees using integral photography (IP) and multiple semitransparent mirrors. IP combined with polyhedron-shaped multiple semitransparent mirrors is used to achieve a 360 degree viewable floating three-dimensional (3D) autostereoscopic display, having the advantage of being able to be viewed by several observers from various viewpoints simultaneously. IP is adopted to generate a 3D autostereoscopic image with full parallax property. Multiple semitransparent mirrors reflect corresponding IP images, and the reflected IP images are situated around the center of the polyhedron-shaped display device for producing the floating display. The spatial reflected IP images reconstruct a floating autostereoscopic image viewable from 360 degrees. We manufactured two prototypes for producing such displays and performed two sets of experiments to evaluate the feasibility of the method described above. The results of our experiments showed that our approach can achieve a floating autostereoscopic display viewable from surrounding area. Moreover, it is shown the proposed method is feasible to facilitate the continuous viewpoint of a whole 360 degree display without flipping.

No abstract

Surface tension forces, pressure forces, and drag forces arise once a micro-particle comes into contact with a gas bubble or a biological cell in diverse physical and biomedical applications such as targeted therapy, sorting, and characterization of cancer cells. We experimentally demonstrate that these forces can be estimated, scaled-up to the sensory range of a human operator, and sensed during a transparent bilateral tele-manipulation using an electromagnetic system and a haptic device. We find good agreement between the estimated interaction forces and the measured forces using a calibrated microforce sensing probe. The maximum interaction force between a trapped paramagnetic micro-particle and an oxygen bubble is estimated to be 4 μN. The estimated interaction force is scaled-up and used in the design of a tele-manipulation system (haptic device and an electromagnetic system) that enables motion control of the bubble in a two-dimensional space, while sensing the interaction forces with the bubble. We demonstrate experimentally that the operator senses maximum interaction force (surface tension, pressure, and drag forces) with the same order of magnitude as the calculated theoretical forces. The estimation of interaction forces at this scale provides broad possibilities in targeted therapy and characterization of cancer cells.

No abstract