The primary objective is to identify and segments the multiple, partly occluded objects in the image. The subsequent stage carry out our approach, primarily start with frame conversion. Next in the preprocessing stage, the Gaussian filter is employed for image smoothening. Then from the preprocessed image, Multi objects are segmented through modified ontology-based segmentation, and the edge is detected from the segmented images. After that, from the edge detected frames area is extracted, which results in object detected frames. In the feature extraction stage, attributes such as area, contrast, correlation, energy, homogeneity, color, perimeter, circularity are extorted from the detected objects. The objects are categorized as human or other objects (bat/ball) through the feed-forward back propagation neural network classifier (FFBNN) based upon the extracted attributes.
Summary
Deepwater utilization and underwater sound communication with inadequate bandwidth, elongated propagation interruption, signal failure issues, and sensor node malfunction due to ecological circumstances are the limitations of the underwater wireless sensor network. Further, the challenges in underground wireless sensor network (WSN) include superior bandwidth, high power expenses, information handling, and cross‐layer design. On the other hand, cooperative communications facilitate competent employment of communication resources, by permitting nodes in a network to work together in information communication. As wireless sensor networks adapt to multi‐hop transmission in high data traffic, there is a high demand for achieving energy efficiency. Moreover, in the course of proximity nodes to the sink, an energy hole is started for the extended network lifetime. This paper proposes the design of multi‐hoped cooperative communication‐based wireless underground sensor networks that guarantees energy efficiency in a hostile environment. In addition, network coverage and connectivity improvement are also improved by this proposed design.
In this article, we proposed a method to estimate pancreas shrinkage with pancreas β cell insulin secretion. The β cells in the pancreas secrete insulin and digestive enzymes after food consumption. Conventionally, the pancreas structure estimation is done with magnetic resonance imaging (MRI) and ultrasound imaging techniques. However, the structure of the pancreas changes due to islet cell death. The presence of islet cells is detected through near infrared (NIR) spectroscopy signal acquired from the epigastric region (pancreas) of the abdomen. Subsequently, the NIR spectroscopy signal from the pancreas is analyzed with multi synchrosqueezing transform (MSST); whereas, the β cell insulin secretion varies for diabetic and nondiabetic persons. The existence of β cell and insulin secretion correlates with Root Mean Square (RMS) and kurtosis via a multivariate regression model to evaluate pancreas shrinkage. In terms of numerical results, NIR spectroscopy signal from the pancreas was obtained for about 20 nondiabetic and 20 diabetic persons. The pancreas shrinkage was estimated with 88% accuracy. The results are validated with MRI pancreas images for earlier detection of the apoptotic pancreas. The pancreas shrinkage causes lower insulin emission and unpredictable blood glucose in diabetic patients. Analysis of NIR spectroscopy signals of the pancreas with MSST was done to obtain higher‐order and lower‐order frequency components.
The evolution in computing strategies has shown wonders in reducing the reachability issue among different end devices. After centralized approaches, decentralized approaches started to take action, but with the latency in data pre-processing, computing very simple requests was the same as for the larger computations. Now it's time to have a simple decentralized environment called edge that is created very near to the end device. This makes edge location friendly and time friendly to different kinds of devices like smart, sensor, grid, etc. In this chapter, some of the serious and non-discussed security issues and privacy issues available on edge are explained neatly, and for a few of the problems, some solutions are also recommended. At last, a separate case study of edge computing challenges in healthcare is also explored, and solutions to those issues concerning that domain are shown.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.