Spectrum sensing is critical in allowing the cognitive radio network, which will be used in the next generation of wireless communication systems. Several approaches, including cyclostationary process, energy detectors, and matching filters, have been suggested over the course of several decades. These strategies, on the other hand, have a number of disadvantages. Energy detectors have poor performance when the signal-to-noise ratio (SNR) is changing, cyclostationary detectors are very complicated, and matching filters need previous knowledge of the main user (PU) signals. Additionally, these strategies rely on thresholds under particular signal-noise model assumptions in addition to the thresholds, and as a result, the detection effectiveness of these techniques is wholly dependent on the accuracy of the sensor. In this way, one of the most sought-after difficulties among wireless researchers continues to be the development of a reliable and intelligent spectrum sensing technology. In contrast, multilayer learning models are not ideal for dealing with time-series data because of the large computational cost and high rate of misclassification associated with them. For this reason, the authors propose a hybrid combination of long short-term memory (LSTM) and extreme learning machines (ELM) to learn temporal features from spectral data and to exploit other environmental activity statistics such as energy, distance, and duty cycle duration for the improvement of sensing performance. The suggested system has been tested on a Raspberry Pi Model B+ and the GNU-radio experimental testbed, among other platforms.
Dental X-ray segmentation uses different image processing (IP) methods helpful in diagnosing medical applications, clinical purposes & in real-time. These methods aim to define the segmentation of various tooth structures in dental X-rays which are utilized to identify caries, tooth fractures, treatment of root canals, periodontal diseases, etc. The manual segmentation of Dental X-ray images for medical diagnosis is very complex and time-consuming from broad clinical databases. Orchard & Bouman is a color quantization approach used to evaluate a successful cluster division using an eigenvector of a color covariance matrix. It is repeated until the number of target clusters is reached. It is optimal for large clusters with Gaussian distributions to integrate different types of information on probabilism and spatial constraint by iteratively upgrading the later probability of the proposed model. Results of segmentation are achieved when iteration converges. Testing the proposed model's effectiveness will involve texture, distance sensing, and nature images. Experimental results show that our model achieves a higher segmentation precision with approximately 78.98 PSNR than MRF models based on pixels or regions.
Security is a key factor in most of the systems used widely in day to day life. Most of the real time applications and systems are facing security problem very often, therefore we have attempted to build a general framework for all existing and future systems. Wireless or mobile networks emerged to replace the wired networks. MANET is an emerging research area with practical applications. This new generation of networks is different from wired one in many aspects like network infrastructure, re-sources and routing protocols, routing devices etc. However, MANET is particularly vulnerable due to its fundamental characteristics, such as open medium, dynamic topology, distributed cooperation, and constrained capability. This article provides an overview of past and current work in the area of security research of mobile ad hoc networks -as well as emerging work in different approaches to provide security features to routing in mobile ad hoc networks (MANET). Authentication, integrity and encryption are the key issues pertaining to network security. Traditional authentication schemes cannot be effectively used in such decentralized networks. Here, we present an end-to-end data authentication scheme that relies on mutual trust between nodes. In Mobile Adhoc Networks there must be two security systems: one to protect the data transmission and another one to make the routing secure. There are already well studied point to point security systems that can be used for protecting network transmissions. But there is not much work has been done about how make secure routing in MANET for volatile nodes.
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