A Video Upgradation of Low Vision AVI Video by Individual Pixel Channel Intensity Measurement and Its Enhancement

From the past few decades, the researchers and scholars have done the quality work in video and image processing and a wide range of outcomes has been discover and invented including the resolutions and sensitivity. Apart from these work there are many aspects are still hidden such as record a high dynamic range images and videos in low-light conditions especially when light is very low. When the intensity of noise is greater than the signal then the traditional denoising techniques cannot done their work properly. For this problem, many approaches being designed and developed to enhance the low-light video but Low contrast and noise remains a barrier to visually pleasing videos in low light conditions. To capture the videos in social gatherings, concerts, parties, musical events, dark forest and in security monitoring situations are still unsolved problem. In such conditions the video enhancement of low light video is really a tedious and tough job. This paper is proposing a new approach of video enhancement. The work is further going on to find a technique for better visibility of video

A Survey: Wireless Body Area Network for Health Monitoring

With an increasingly mobile society and the worldwide deployment of mobile and wireless networks, the wireless infrastructure can support many current and emerging health care applications. Citizens, being patients or non-patients, will not only be able to get medical advice from a distance but will also be able to send from any location full detailed and accurate vital signal measurements, as if they had been taken in medical centers. Towards this direction, the proposed system is highly customizable vital signal monitoring system based on Wireless Body Area Networks (WBAN). The proposed system allows the incorporation of diverse medical sensors via wireless connections and the live transmission of the measured vital signals over public wireless networks to healthcare providers. This paper discusses different scenarios where this wearable health monitoring system can be used and different types of sensors are used to measure the different parameters such as temperatures, glucose, heart beats, ECG, EEG, etc. Finally, through a case study, we demonstrate how the diabetic patient takes the advantage of this system

Global patterns and predictions of seafloor biomass using random forests

A comprehensive seafloor biomass and abundance database has been constructed from 24 oceanographic institutions worldwide within the Census of Marine Life (CoML) field projects. The machine-learning algorithm, Random Forests, was employed to model and predict seafloor standing stocks from surface primary production, water-column integrated and export particulate organic matter (POM), seafloor relief, and bottom water properties. The predictive models explain 63% to 88% of stock variance among the major size groups. Individual and composite maps of predicted global seafloor biomass and abundance are generated for bacteria, meiofauna, macrofauna, and megafauna (invertebrates and fishes). Patterns of benthic standing stocks were positive functions of surface primary production and delivery of the particulate organic carbon (POC) flux to the seafloor. At a regional scale, the census maps illustrate that integrated biomass is highest at the poles, on continental margins associated with coastal upwelling and with broad zones associated with equatorial divergence. Lowest values are consistently encountered on the central abyssal plains of major ocean basins The shift of biomass dominance groups with depth is shown to be affected by the decrease in average body size rather than abundance, presumably due to decrease in quantity and quality of food supply. This biomass census and associated maps are vital components of mechanistic deep-sea food web models and global carbon cycling, and as such provide fundamental information that can be incorporated into evidence-based management