Likelihood of survival of coronavirus in a respiratory droplet deposited on a solid surface

We predict and analyze the drying time of respiratory droplets from a COVID-19 infected subject, which is a crucial time to infect another subject. The drying of the droplet is predicted by diffusion-limited evaporation model for a sessile droplet placed on a partially-wetted surface with a pinned contact line. The variation of droplet volume, contact angle, ambient temperature, and humidity are considered. We analyze the chances of the survival of the viruses present in the droplet, based on the lifetime of the droplets in several conditions, and find that the chances of survival of the virus are strongly affected by each of these parameters. The magnitude of shear stress inside the droplet computed using the model is not large enough to obliterate the virus. We also explore the relationship between the drying time of a droplet and the growth rate of the spread of COVID-19 for five different cities, and find that they are weakly correlated

Adaptive Protocols for Interactive Communication

How much adversarial noise can protocols for interactive communication tolerate? This question was examined by Braverman and Rao (IEEE Trans. Inf. Theory, 2014) for the case of "robust" protocols, where each party sends messages only in fixed and predetermined rounds. We consider a new class of non-robust protocols for Interactive Communication, which we call adaptive protocols. Such protocols adapt structurally to the noise induced by the channel in the sense that both the order of speaking, and the length of the protocol may vary depending on observed noise. We define models that capture adaptive protocols and study upper and lower bounds on the permissible noise rate in these models. When the length of the protocol may adaptively change according to the noise, we demonstrate a protocol that tolerates noise rates up to $1/3$. When the order of speaking may adaptively change as well, we demonstrate a protocol that tolerates noise rates up to $2/3$. Hence, adaptivity circumvents an impossibility result of $1/4$ on the fraction of tolerable noise (Braverman and Rao, 2014).Comment: Content is similar to previous version yet with an improved presentatio

Survey on Classification of Brain Tumor using Wavelet Transform and PNN

This paper presents, a new method for Brain Tumor Classification using Probabilistic Neural Network with Discrete Wavelet Transformation is proposed. Human inspection was the conventional method available for computerized tomography, magnetic resonance brain images classification and tumor detection. The classification methods that are operator assisted are impractical incase of large amount of data that are also non reproducible. Operator performance leads to serious inaccuracies in classification by producing noise in Computerized Tomography and Magnetic Resonance images. Neural Network techniques has shown great potential in the field of medical diagnosis. Hence, in this paper the Probabilistic Neural Network with Discrete Wavelet Transform was applied for classification of brain tumors. Classification was performed in two steps, i) Dimensionality reduction and Feature extraction using the Discrete Wavelet Transform and ii) classification using Probabilistic Neural Network (PNN). Evaluation was performed on image data base of Brain Tumor images. The proposed method gives better accuracy when compared to previous methods of classification

Coded exposure photography: motion deblurring using fluttered shutter

In a conventional single-exposure photograph, moving objects or moving cameras cause motion blur. The exposure time defines a temporal box filter that smears the moving object across the image by convolution. This box filter destroys important high-frequency spatial details so that deblurring via deconvolution becomes an illposed problem. Rather than leaving the shutter open for the entire exposure duration, we ”flutter ” the camera’s shutter open and closed during the chosen exposure time with a binary pseudo-random sequence. The flutter changes the box filter to a broad-band filter that preserves high-frequency spatial details in the blurred image and the corresponding deconvolution becomes a well-posed problem. We demonstrate that manually-specified point spread functions are sufficient for several challenging cases of motionblur removal including extremely large motions, textured backgrounds and partial occluders. ACM Transactions o Graphics (TOG

Reinterpretable Imager: Towards Variable Post-Capture Space, Angle and Time Resolution in Photography

We describe a novel multiplexing approach to achieve tradeoffs in space, angle and time resolution in photography. We explore the problem of mapping useful subsets of time-varying 4D lightfields in a single snapshot. Our design is based on using a dynamic mask in the aperture and a static mask close to the sensor. The key idea is to exploit scene-specific redundancy along spatial, angular and temporal dimensions and to provide a programmable or variable resolution tradeoff among these dimensions. This allows a user to reinterpret the single captured photo as either a high spatial resolution image, a refocusable image stack or a video for different parts of the scene in post-processing. A lightfield camera or a video camera forces a-priori choice in space-angle-time resolution. We demonstrate a single prototype which provides flexible post-capture abilities not possible using either a single-shot lightfield camera or a multi-frame video camera. We show several novel results including digital refocusing on objects moving in depth and capturing multiple facial expressions in a single photo

A Practical Approach to 3D Scanning in the Presence of Interreflections, Subsurface Scattering and Defocus

Global or indirect illumination effects such as interreflections and subsurface scattering severely degrade the performance of structured light-based 3D scanning. In this paper, we analyze the errors in structured light, caused by both long-range (interreflections) and short-range (subsurface scattering) indirect illumination. The errors depend on the frequency of the projected patterns, and the nature of indirect illumination. In particular, we show that long-range effects cause decoding errors for low-frequency patterns, whereas short-range effects affect high-frequency patterns. Based on this analysis, we present a practical 3D scanning system which works in the presence of a broad range of indirect illumination. First, we design binary structured light patterns that are resilient to individual indirect illumination effects using simple logical operations and tools from combinatorial mathematics. Scenes exhibiting multiple phenomena are handled by combining results from a small ensemble of such patterns. This combination also allows detecting any residual errors that are corrected by acquiring a few additional images. Our methods can be readily incorporated into existing scanning systems without significant overhead in terms of capture time or hardware. We show results for several scenes with complex shape and material properties