No abstract

Over a period of decades there are lot of frequent disruptions due to natural, man-made and technological disasters which are seriously effecting the society, environment and economy. Hence it is vital that an effective integrated disaster management must be defined by integrating various types of disasters for being equipped in real time to face disasters in an extremely short span of time. In this paper a framework for disaster management is defined based on cloud and internet of things. A disaster management use case is developed based on the defined framework by integrating natural and manmade disasters. Natural disaster events are integrated to derive the probable insurance claims based on historical data and for the insurance agencies to be equipped in the event of disaster. Manmade disaster events will alert the end users when disaster events are about to occur. Here heterogeneous devices and data are firmly integrated to monitor various disaster events at one stop.

Image-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation & majordisadvantage. To tackle these limitations, this article has introduced a robust and reliable image stitching methodology (l,r-Stitch Unit), which considers multiple non-homogeneous image sequences as input to generate a reliable panoramically stitched wide view as the final output. The l,r-Stitch Unit further consists of a pre-processing, post-processing sub-modules & a l,r-PanoED-network, where each submodule is a robust ensemble of several deep-learning, computer-vision & image-handling techniques. This article has also introduced a novel convolutional-encoder-decoder deep-neural-network (l,r-PanoEDnetwork) with a unique split-encoding-network methodology, to stitch non-coherent input left, right stereo image pairs. The encoder-network of the proposed l,r-PanoED extracts semantically rich deep-featuremaps from the input to stitch/map them into a wide-panoramic domain, the feature-extraction & featuremapping operations are performed simultaneously in the l,r-PanoED's encoder-network based on the split-encoding-network methodology. The decoder-network of l,r-PanoED adaptively reconstructs the output panoramic-view from the encoder networks' bottle-neck feature-maps. The proposed l,r-Stitch Unit has been rigorously benchmarked with alternative image-stitching methodologies on our custom-built traffic dataset and several other public-datasets. Multiple evaluation metrics (SSIM, PSNR, MSE, L α,β,γ , FM-rate, Average-latency-time) & wild-Conditions (rotational/color/intensity variances, noise, etc) were considered during the benchmarking analysis, and based on the results, our proposed method has outperformed among other image-stitching methodologies and has proved to be effective even in wild non-homogeneous inputs.INDEX TERMS Deep feature extraction, encoder-decoder cnn, image mosaicing, multi-image registration, non-homogeneous image stitching.

The usage of transportation systems is inevitable; any assistance module which can catalyze the flow involved in transportation systems, parallelly improving the reliability of processes involved is a boon for day-today human lives. This paper introduces a novel, cost-effective, and highly responsive Post-active Driving Assistance System, which is "Adaptive-Mask-Modelling Driving Assistance System" with intuitive wide field-of-view modeling architecture. The proposed system is a vision-based approach, which processes a panoramic-front view (stitched from temporal synchronous left, right stereo camera feed) & simple monocular-rear view to generate robust & reliable proximity triggers along with co-relative navigation suggestions. The proposed system generates robust objects, adaptive field-of-view masks using FRCNN+Resnet-101_FPN, DSED neural-networks, and are later processed and mutually analyzed at respective stages to trigger proximity alerts and frame reliable navigation suggestions. The proposed DSED network is an Encoder-Decoder-Convolutional-Neural-Network to estimate lane-offset parameters which are responsible for adaptive modeling of field-of-view range(157 0-210 0) during live inference. Proposed stages, deep-neural-networks, and implemented algorithms, modules are state-of-the-art and achieved outstanding performance with minimal loss(L{p, t}, L δ , L T otal) values during benchmarking analysis on our custom-built, KITTI, MS-COCO, Pascal-VOC, Make-3D datasets. The proposed assistance-system is tested on our custom-built, multiple public datasets to generalize its reliability and robustness under multiple wild conditions, input traffic scenarios & locations. INDEX TERMS Adaptive field of view modeling, Automotive applications, Driving assistance systems, Lane detection and analysis, Object detection and tracking, spatial auto-correlation

This paper proposes two methods for the compression of biological sequences like DNA/RNA. Although many algorithms both lossy and lossless exist in the literature, they vary by the compression ratio. Moreover, existing algorithms show different compression ratios for different inputs. Our proposed methods exhibit nearly constant compression ratio which helps us to know the amount of storage needed in advance. For the first method, we call it CryptoCompress, we use a blend of Cryptographic hash function and partition theory to achieve this compression. The second method, we call it RefCompress, uses a reference DNA for compression. This paper showcases that the proposed methods have constant compression ratio compared to most of the existing methods.