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.

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Despite the present generation has 3-D, our nation utilizes 2-D media in studies. The mixture of AR tech and study material form a newly type of automation system and actions to improve efficiency and raise interest of giving lesson and gaining knowledge for trainer in real-life situations. Augmented Reality is a new, integrated approach features from computer everywhere, laptop, and the public computer. The center offers unique capabilities, including a virtual world, with and vague manual control of point of view and interaction. The research gives a launch to augmented reality (AR) technology, its educational opportunities. Important technology and ways of communicating in the context of studies.

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n this article, we investigate the generalized leaky integrate-and-fire (GLIF) neuron model with stochastic synaptic conductance. A neuron remains connected with other neuron via dendrites and axons at synapse, which can be treated as an electrical capacitor. Dendrites carry electro-chemical signals from input neuron to synapse whereas axons are responsible for their transmission form synapse to other neurons. Concentration of these electro-chemicals in synapse varies during entire time period. We investigate the effect of varying concentration of electro-chemicals at synapse in a single neuron model. Concentration variation of electro-chemicals at synapse is incorporated as noise in GLIF model. Excitatory and inhibitory synaptic conductance of neuron in GLIF is assumed as stochastic entities driven by Gaussian White noise. Stationary state membrane potential distribution for the proposed model is computed with reflecting boundary conditions, which is noticed as geometrically distributed. In order to investigate spiking activity and information encoding mechanism, an extensive simulation based study has been carried out. Temporal encoding technique is used to analyze the encoding mechanism. It is noticed that ISI distribution has higher variance with respect to excitatory input than inhibitory input. ISI distribution also exhibits the power-law behavior for electro-chemical balance situation.