Abstract: The main aim of a communication system is to
achieve maximum performance. In Cognitive Radio any user or
transceiver has ability to sense best suitable channel, while channel is
not in use. It means an unlicensed user can share the spectrum of a
licensed user without any interference. Though, the spectrum sensing
consumes a large amount of energy and it can reduce by applying
various artificial intelligent methods for determining proper spectrum
holes. It also increases the efficiency of Cognitive Radio Network
(CRN). In this survey paper we discuss the use of different learning
models and implementation of Artificial Neural Network (ANN) to
increase the learning and decision making capacity of CRN without
affecting bandwidth, cost and signal rate.
Abstract: Artificial Neural Network (ANN) can be trained using
back propagation (BP). It is the most widely used algorithm for
supervised learning with multi-layered feed-forward networks.
Efficient learning by the BP algorithm is required for many practical
applications. The BP algorithm calculates the weight changes of
artificial neural networks, and a common approach is to use a twoterm
algorithm consisting of a learning rate (LR) and a momentum
factor (MF). The major drawbacks of the two-term BP learning
algorithm are the problems of local minima and slow convergence
speeds, which limit the scope for real-time applications. Recently the
addition of an extra term, called a proportional factor (PF), to the
two-term BP algorithm was proposed. The third increases the speed
of the BP algorithm. However, the PF term also reduces the
convergence of the BP algorithm, and criteria for evaluating
convergence are required to facilitate the application of the three
terms BP algorithm. Although these two seem to be closely related,
as described later, we summarize various improvements to overcome
the drawbacks. Here we compare the different methods of
convergence of the new three-term BP algorithm.
Abstract: Wireless sensor network (WSN) is a network of many interconnected networked systems, they equipped with energy resources and they are used to detect other physical characteristics. On WSN, there are many researches are performed in past decades. WSN applicable in many security systems govern by military and in many civilian related applications. Thus, the security of WSN gets attention of researchers and gives an opportunity for many future aspects. Still, there are many other issues are related to deployment and overall coverage, scalability, size, energy efficiency, quality of service (QoS), computational power and many more. In this paper we discus about various applications and security related issue and requirements of WSN.
Abstract: In the cloud computing hierarchy IaaS is the lowest
layer, all other layers are built over it. Thus it is the most important
layer of cloud and requisite more importance. Along with advantages
IaaS faces some serious security related issue. Mainly Security
focuses on Integrity, confidentiality and availability. Cloud
computing facilitate to share the resources inside as well as outside of
the cloud. On the other hand, cloud still not in the state to provide
surety to 100% data security. Cloud provider must ensure that end
user/client get a Quality of Service. In this report we describe
possible aspects of cloud related security.
Abstract: During signal transmission, the combined effect of the
transmitter filter, the transmission medium, and additive white
Gaussian noise (AWGN) are included in the channel which distort
and add noise to the signal. This causes the well defined signal
constellation to spread causing errors in bit detection. A compact pi
neural network with minimum number of nodes is proposed. The
replacement of summation at each node by multiplication results in
more powerful mapping. The resultant pi network is tested on six
different channels.
Abstract: Memristor is also known as the fourth fundamental
passive circuit element. When current flows in one direction through
the device, the electrical resistance increases and when current flows
in the opposite direction, the resistance decreases. When the current
is stopped, the component retains the last resistance that it had, and
when the flow of charge starts again, the resistance of the circuit will
be what it was when it was last active. It behaves as a nonlinear
resistor with memory. Recently memristors have generated wide
research interest and have found many applications. In this paper we
survey the various applications of memristors which include non
volatile memory, nanoelectronic memories, computer logic,
neuromorphic computer architectures low power remote sensing
applications, crossbar latches as transistor replacements, analog
computations and switches.