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In spite of spectrum sensing, aggregate interference from cognitive radios (CRs) remains as a deterring factor to the realization of spectrum sharing. We provide a systematic approach of evaluating the aggregate interference (I aggr ) experienced at a victim primary receiver. In our approach, we model the received power versus propagation distance relations between a primary transmitter, primary receiver, and CRs. Our analytical framework differs from the previous works in that we have formulated the relationship between I aggr and the sensing inaccuracy of CRs. Energy detector is assumed for the purpose of spectrum sensing. I aggr is expressed explicitly as a function of the number of energy samples collected (N) and the threshold signalto-noise ratio level used for comparison (SNR ε ). The theoretical analysis is then applied to a practical scenario of spectrum sharing between digital TV broadcast and the IEEE 802.22 wireless regional area network systems. The impact on digital TV reception is evaluated in terms of signal-to-interference ratio. The proposed method allows us to determine the appropriate wireless regional area network operating conditions that fulfill the signal-to-interference ratio requirement imposed by regulator.

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Personal safety is concerned to be as a crucial part for the industrial workers while working in an industrial environment. Industries provide personal protective equipment to their workers to ensure their safety, similarly the workers are also meant to wear and follow all the regulations regarding the personal protective equipment's (PPEs) provided to them. Our study provides the methodology to detect the industrial safety helmet using the surveillance cameras. In this study, we have trained two different single shot detector models i.e., Single Shot Detector (SSD) MobilenetV2 and Single Shot Detector (SSD) Resnet50 and used transfer learning methodology to detect the industrial safety helmet. We have utilized a publically available dataset from Kaggle website and utilized that dataset for the purpose of training the models. Furthermore, the models evaluation is done based on these parameters i.e., classification loss, localization loss, regularization loss and total loss. However, we concluded that the SSD Mobilenet V2 performs better than SSD Resnet50 model based on loss parameters. For SSD Mobilenet v2 we achieved a classification loss of 0.11, localization loss of 0.05, regularization loss of 0.15, and a total loss as 0.32 respectively. Moreover, the graphs for the loss of each model has also been studied.