N. V. S. N. Sarma

Compact Circularly Polarized Asymmetrical Fractal Boundary Microstrip Antenna for Wireless Applications

Compact fractal boundary microstrip antenna is proposed for circular polarization (CP). By replacing the sides of a square patch with asymmetrical prefractal curves, two orthogonal modes are excited for CP operation. The structure is asymmetric along the principal axes (x, y). The indentation parameter of the fractal boundary curve is optimized to design compact CP antennas. Experimental results show that 10-dB return loss and 3-dB axial-ratio bandwidths of the proposed fractal boundary Ant 2 are 162 and 50 MHz, respectively, at operating frequency of around 2540 MHz. Results show that an excellent CP is achieved with a single probe feed, besides reduction in the antenna size by applying fractal boundary concept.

Triband Circularly Polarized Koch Fractal Boundary Microstrip Antenna

A novel single-layer single-probe-feed asymmetrical fractal boundary microstrip antenna is considered for triband circular polarization (CP) operation. Four different structures-without-slot (Ant1), rectangular (Ant2), fractal (Ant3), optimized-fractal-slot (Ant4)-are studied for multiband CP radiation. Perturbation in the structure for triband CP radiation is introduced by employing optimized asymmetrical Koch fractal curves as boundaries of a square patch and embedded rectangular slot. The generated 3-dB axial-ratio bandwidths of Ant 4 are 3.2%, 1.6%, and 3.0% at operating frequencies around 2.45, 3.4, and 5.8 GHz, respectively. Measured results that are in close agreement with the simulation results demonstrate that the proposed antenna is well suited for the WLAN/WiMAX wireless applications.

Fault Diagnosis of Planar Antenna Arrays Using Neural Networks

A systematic method for the diagnosis of planar antenna arrays from far fleld radiation pattern using neural networks is presented. Two types of neural networks, Radial basis function (RBF) and Probabilistic neural network (PNN) are considered for the performance comparison. Deviation pattern is used as input to the neural network to determine the location of the faulty element and error in excitation.

Energy Efficient ECC Encryption Using ECDH

Elliptic curve cryptography (ECC) provides a secure means of exchanging keys among communicating hosts using the Diffie–Hellman (DH) key exchange algorithm. This work presents an implementation of ECC encryption making use of the DH key exchange algorithm. Encryption and decryption of text messages have also been attempted. In ECC, we normally start with mapping a character of message to an affine point on the elliptic curve, which is called encoding. A comparison of the proposed algorithm and Koblitz’s method shows that the proposed algorithm is as secure as Koblitz’s encoding and has less computational complexity due to the elimination of encoding, thereby improving energy efficiency of the crypto-system to be used in resource constrained applications, such as wireless sensor networks (WSNs). It is almost infeasible to attempt a brute force attack. The security strength of the algorithm is proportional to key length. As the key length increases, the data that can be sent at a time also increase.

Energy Efficient Clustering using Jumper Firefly Algorithm in Wireless Sensor Networks

Wireless Sensor Network (WSN) is a major and very interesting technology, which consists of small battery powered sensor nodes with limited power resources. The sensor nodes are inaccessible to the user once they are deployed. Replacing the battery is not possible every time. Hence in order to improve the lifetime of the network, energy efficiency of the net- work needs to be maximized by decreasing the energy consumption of all the sensor nodes and balancing energy consumption of every node. Several protocols have been proposed earlier to improve the network lifetime using optimization algorithms. Firefly is a metaheuristic approach. In this paper, Energy efficient clustering for wireless sensor networks using Firefly and Jumper Firefly algorithms are simulated. A new cost function has been defined to minimize the intra- cluster distance to optimize the energy consumption of the network. The performance is compared with the existing protocol LEACH (Low Energy Adaptive Clustering Hierarchy).

Maximizing Network Lifetime through Varying Transmission Radii with Energy Efficient Cluster Routing Algorithm in Wireless Sensor Networks

One of the major issues in wireless sensor network is developing an energy-efficient routing protocol. LEACH is very effective in enhancing lifetime of the nodes from routing aspect. This paper proposes the scheduling of energy efficient cluster nodes close to the base station called EECL algorithm. Both LEACH and EECL algorithms have been varied with different transmission radii and compared for the improvement of network lifetime. Simulation results show that there has been significant improvement in energy conservation of wireless sensor networks with EECL. randomized rotation of local cluster base stations evenly distribute the energy load among the sensors in the network. It uses localized coordination to enable scalability and robustness for dynamic networks and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. In this paper, an improved LEACH protocol for data gathering and aggregation is proposed. Such innovation can extend the lifetime of the whole network due to much less energy dissipation for data transmission to base station (2).

Energy Efficient Routing Protocol with Improved Clustering Strategies for Homogeneous Wireless Sensor Networks

new energy aware routing protocol to optimize energy consumption and prolong network lifetime for Wireless Sensor Networks (WSNs) had been proposed in this paper. Cluster based routing techniques like the conventional Low Energy Adaptive Clustering Hierarchy (LEACH) are used to achieve scalable solutions and extend the network lifetime until the last node dies. Improved Energy Efficient LEACH (IMP-EEL) has been proposed in this work and compared with the other existing algorithms like LEACH, Residual Energy LEACH (RES-EL) and Distributed Residual Energy LEACH (DIS-RES-EL). The proposed algorithm (IMP-RES- EL) outperformed all the other algorithms in extending network lifetime, network stability, sending aggregated packets to Cluster heads (CHs) and to Base station(BS) and CH formation during their entire lifetime considered. With optimum routing established within the network as per the newly proposed clustering threshold, IMP-EEL has significantly reduced energy consumption and maintained 72% more energy efficiency than the LEACH homogeneous system.

Using Neural Networks for Fault Detection in Planar Antenna Arrays

A method to diagnose on-ofi faults in a planar antenna arrays using far fleld radiation pattern is presented. A systematic approach is suggested for detecting location of faulty elements using Artiflcial Neural Networks (ANN). Radial Basis Function neural network (RBF) and Probabilistic neural network (PNN) are considered for performance comparison.

Identification of LPI Radar signals by higher order spectra and neural network techniques

LPI Radars use continuous wave, wide bandwidth low power signals of the order of a few watts making its detection difficult. The important advantage of LPI radar is to go undetected, while maintaining a strong battlefield awareness. Common spectral analysis and conventional methods fail to detect emissions of LPI radars and even normal radars in noisy environments. This leads to use higher order spectral analysis (HOSA) techniques enabling us to extract much more information from the same intercept and hence facilitating detection. This paper reports the results of HOSA techniques (bi-spectrum, bi-coherence and tri-spectrum) and artificial neural networks (ANNs), applied to LPI radar signals. Bi-phase barker coded signals of different lengths, P1, P2, P3 and P4 Polyphase coded signals and Frank signal are analyzed using HOSA techniques to produce 2-D signatures of these signals. An artificial neural network (ANN) is trained on these signatures so that it will be able to detect and identify the LPI radar signal whose type is unknown when received. The results obtained clearly indicate the promising capability of these techniques to identify the type of LPI signal even with SNRs as low as -3 dB.

Hybrid evolutionary algorithm based beamforming for smart antenna system

In this paper, a Hybrid evolutionary algorithm is applied to control the smart antenna patterns satisfying certain constrains like steering the main beam towards a signal of interest, placement of deep nulls in the directions of undesired signals, etc. In adaptive beamforming arrays, using Least Mean Square (LMS) algorithm alone suffered the problem of getting stuck at local minima and converging after large number of iterations. A Genetic algorithm (GA) is a global optimisation technique. The combination of LMS and GA can explore the large optimisation function surface than the individual resulting in faster convergence to global minimum. Simulation results are presented to illustrate the performance of the Hybrid approach.