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 Volume 1, Issue 1, Jan 2013
 Volume 1, Issue 1, Jan 2013
 Volume 1, Issue 1, Jan 2013
 Volume 1, Issue 1, Jan 2013
 Volume 1, Issue 1, Jan 2013
 Volume 1, Issue 1, Jan 2013
 Volume 1, Issue 1, Jan 2013

Volume 1, Issue 2, February 2013

International Journal of Advance Research in Applied Physics (IJOARAP)

Synthesis and application of magnetic nanomaterials for memory storage devices[ ]

Nanomaterials have been the topic of intense industrial research for the past several years. They have unique physical, chemical, optical and magnetic properties as compared to those of their bulky counterparts. Intense research has been done on reducing the size of memory devices in the electronics industry. This requirement has made it extremely urgent to explore the materials with larger scope of functionalities on their surfaces. In this respect the conventional silicon based materials have been engineered for better and better control and modification of their surfaces but unfortunately this conventional route does not provide us with both higher functionalities as well as high speeds. In this regard, magnetic nanomaterials have been researched for interesting and far sighted effects. These have been the hot destinations for their sensitive responses in the biomedical and diagnostic applications. Magnetism at nanoscale is also significantly different from the one observed at bulk scale. Magnetic nanoparticles are normally made up of materials which have sensitive magnetic properties arising from the unpaired electrons in their d-orbitals and their coupling effect with their nuclear spins. This review explores the synthesis methods of the magnetic nanomaterials and their possible implementation in making memory based electronic storage devices. This also highlights the significant benefits and aspects that compel the use for investigating memory based potential for magnetic nanomaterials.

Mind Control Using Psycotronics[ ]

This paper deals with the mind control using psychotronics as a directed energy weapons & its relevant uses in human life in present day. These device works on the principle of squad(ssss) or e.m wave. We are very thankful Prof. Sitanshu Ray for his tireless support and his lucid way of explaining us the salient points really helped us to write this paper.

Efficient Node Energy Based Encryption and Filtering of False Data Injection in WSN[ ]

Wireless Sensor Networks (WSN), an element of pervasive computing, are presently being used on a large scale to monitor real-time environmental status Designing cost-efficient, secure network protocols for Wireless Sensor Networks (WSNs) is a challenging problem because sensors are resource-limited wireless devices. In critical sensor deployments it is important to ensure the authenticity and integrity of sensed data. Error control is of significant importance for Wireless Sensor Networks (WSNs) because of their severe energy constraints and the low power communication requirements. Sensor devices are the power consumption devices to achieve low power consumption and high security we need to avoid the rekeying ,stale keys, reducing the false data from malicious node and dynamic energy based keying technology were used. Since the communication cost is the most dominant factor in a sensorfs energy consumption, we introduce an Efficient Node Energy Based Encoding (ENEBE) and Filtering of False data Injection. In addition to the goal of saving energy, minimal transmission is imperative for some military application and real time applications. ENEBE and FFDI is able to efficiently detect and filter false data injected into the network by malicious from outside. The Efficient Node Energy Based Encoding (ENEBE) and Filtering of False data Injection (FFDI) consists of two operational modes (OM-I and OM-II), each of which is optimal for different scenarios. In OM-I, each node monitors its one-hop neighbors where OM-II statistically monitors downstream nodes. Our designed framework performs better than other comparable schemes in the literature with an overall percent improvement in energy savings.

Artificial Neural Network for Energetic Dynamic Circuit Simulizations[ ]

This paper provides the An Artificial Neural Network (ANN) is mathematical model that is presented for dynamic circuit Simulizations model. The ANN is used to learn modelling for dynamic circuitfs simulation, in modelling for circuit simulation, there are major applications that need to be distinguished because of their different requirements. The designs of artificial neural network model are constructed with layers of units, and thus are termed multilayer ANNs. Dynamic circuits are used widely in custom ANN circuits to achieve high speed in smaller area, and effetely to lower power consumption due to glitch-free operation. The classical approach to obtain suitable compact physical model and table model for circuit simulation.


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