We analyze the autocorrelation structure for a class of scene-based MPEG video models at the groups-of-pictures (GOP) (course grain) and frame (fine grain) levels assuming an arbitrary scene-length distribution. At the GOP level, we establish the relationship between the scene-length statistics and the short-range/long-range dependence (SRD/LRD) of the underlying model. We formally show that when the intrascene dynamics exhibit SRD, the overall model exhibits LRD if and only if the second moment of the scene length is infinite. Our results provide the theoretical foundation for several empirically derived scene-based models. We then study the impact of traffic correlations on the packet loss performance at a video buffer. Two popular families of scene-length distributions are investigated: Pareto and Weibull. In the case of Pareto distributed scene lengths, it is observed that the performance is rather insensitive to changes in the buffer size even as the video model enters the SRD regime. For Weibull distributed scene lengths, we observe that for small buffers the loss performance under a frame-level model can be larger than its GOP-level counterpart by orders of magnitude. In this case, the reliance on GOP-level models will result in very optimistic results.

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Even with the most cutting-edge tools, treating and monitoring patients—including children, elders, and suspected COVID-19 patients—remains a challenging activity. This study aimed to track multiple COVID-19-related vital indicators using a wearable monitoring device with an Internet of Things (IOT) focus. Additionally, the technology automatically alerts the appropriate medical authorities about any breaches of confinement for potentially contagious patients by tracking patients’ real-time GPS data. The wearable sensor is connected to a network edge in the Internet of Things cloud, where data are processed and analyzed to ascertain the state of body function. The proposed system is built with three tiers of functionalities: a cloud layer using an Application Peripheral Interface (API) for mobile devices, a layer of wearable IOT sensors, and a layer of Android web for mobile devices. Each layer performs a certain purpose. Data from the IoT perception layer are initially collected in order to identify the ailments. The following layer is used to store the information in the cloud database for preventative actions, notifications, and quick reactions. The Android mobile application layer notifies and alerts the families of the potentially impacted patients. In order to recognize human activities, this work suggests a novel integrated deep neural network model called CNN-UUGRU which mixes convolutional and updated gated recurrent subunits. The efficiency of this model, which was successfully evaluated on the Kaggle dataset, is significantly higher than that of other cutting-edge deep neural models and it surpassed existing products in local and public datasets, achieving accuracy of 97.7%, precision of 96.8%, and an F-measure of 97.75%.

Modern agribusiness is becoming increasingly reliant on computer-based systems which was formerly performed by humans. One such technological innovation is the embedded system-based sensor array module such as flex sensor, temperature sensor, and pH sensor that have been used to monitor the turmeric finger growth characteristics. The experimental work has been tested with five different nodes and the average flex sensor resistance changes in five nodes are calculated. Among the five nodes, nodes II and V were diseased. Purposely node II was left as such and node V was treated with Pseudomonas and viride to restrict the Rhizome rot disease attack. As a result, after cultivation, it was found that the Rhizome rot disease attack on node V is comparatively lesser than node II. The greatest advantage of this method is that it helps the farmers to detect the Rhizome rot disease and also prevent it an early stage by monitoring the growth of the turmeric fingers when it is under the soil.