Energy consumption has become dominant issue for wireless internet of things (IoT) networks with battery-powered nodes. The prevailing mechanism allowing to reduce energy consumption is duty-cycling. In this technique the node sleeps most of the time and wakes up only at selected moments to extend the lifespan of nodes up to 5-10 years. Unfortunately, the scheduled duty-cycling technique is always a trade-off between energy consumption and delay in delivering data to the target node. The delay problem can be alleviated with an additional wake-up radio (WuR) channel. In the paper we present original power consumption models for various duty-cycling schemes. They are the basis for checking whether WuR approach is competitive with scheduled duty-cycling techniques. We determine the maximum energy level that an additional wake-up radio can consume to become a reasonable alternative of widely used duty-cycling techniques for typical IoT networks.
The problem of reliable detection of life-threatening situations in the neurosurgical patient undergoing treatment in the ICU is still far from reaching a satisfactory solution, although several methods of clinical and instrumental evaluation have recently been developed for the early detection of oncoming signs of danger. Continuous monitoring of intracranial pressure (ICP) provides neurosurgeons with valuable information about the current condition of the patient. However, it is increasingly felt that traditional methods of extracting information from the ICP signal have reached their natural limits, mostly because of difficulties in fitting the appropriate mathematical model to this non-linear and non-stationary process. Successful implementations of artificial neural networks in many medical tasks have encouraged the application of this method of ICP processing. Two problems are considered: the prediction of trends in ICP, and recognition of the configuration of unfavourable symptoms likely to signal danger for the neurosurgical patient. The construction of neural network predictors of ICP trends is based on wavelet pre-processing of the original signal. The approach to the second task involves pre-processing of the ICP with spectral and statistical methods and classification of the extracted features of the current signal on an arbitrarily selected scale of danger.
Introduction: Subcutaneous emphysema is defined as the abnormal presence of air or gas in the body tissues or tissue spaces. In dentistry, however, subcutaneous emphysema is most often a consequence of compressed air being forced into the subcutaneous tissues through the intra-oral barrier. Most often the air comes from the slow hand-piece, the turbine and the dental air syringe, although recent reports have surfaced of emphysema being caused by air coming from an air abrasion tool, CO 2 laser or a cryotherapy device. Case description: A case of air emphysema following by drying the cavity with dental syringe. Discussion of prophylactic and treatment of air emphysema. Conclusions: Subcutaneous emphysema is one of the less common complications that may occur during the treatment of carious lesions. It is usually limited to moderate swelling of soft tissues, which due to slow regression becomes an unpleasant complication for the patient. The air that has entered to the subcutaneous tissue carries the risk of causing an infection like cellulitis or necrosis of fascial and therefore requires the use of antibiotic therapy. Although emphysema is a medical complication, often harmless, it should be remembered that there is always a risk of serious consequences requiring specialized treatment. Prompt and appropriate diagnosis enables effective treatment of emphysema.
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