The increasing demand of routing in the field of communication is the most important subject in ad hoc networks now a days. Flying Ad Hoc Network (FANET) is one of the emerging areas that evolved from Mobile Ad Hoc Networks. Selecting the best optimal path in any network is a real challenge for a routing protocol. Because the network performance like throughput, Quality of Service (QoS), user experience, response time and other key parameters depend upon the efficiency of the algorithm running inside the routing protocol. The complexity and diversity of the problem is augmented due to dynamic spatial and temporal mobility of FANET nodes. Due to these challenges the performance and efficiency of the routing protocol becomes very critical. This paper presents a novel routing protocol for FANET using modified AntHocNet. Ant colony optimization technique or metaheuristics in general has shown better dependability and performance as compared to other legacy best path selection techniques. Energy stabilizing parameter introduced in this study improves energy efficiency and overall network performance. Simulation results show that the proposed protocol is better than generic Ant Colony Optimization (ACO) and other traditional routing protocols utilized in FANET. INDEX TERMS FANET, routing, nature inspired algorithms, ACO.

In rhis work a iiew technique is presented f a . blind chunnel rynaliza~ion. Must of the existing techniques perfor-m channel esrimatior? in firs1 phuse and eqzrdizotion in second phase. The algorithm pr.e.~enred hew provides not on1,v the direct blind equalizntion of fhe chcmnel oiitprrts but also provides the w h a l e d channe( in parallel. This technique utilizes three layered Artificial Neural Networks (ANN) model accompanied with learning algorithm for updufing of the weights. This learni*rg algorithm iriilizes the EiiclideaH distance error as well as the statistics to be moiiiraiiied ai three different lajrers of' ANN. The weights between first two layers provide an eqwlizatioiz matri.y and oufpiif of second layer gives estimrrte of s o w c e symbok. The weights between second and rhird layer provide channel esfimate.

An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel. Simulation results show that the proposed scheme produces significantly better results compared to state-of-art schemes.

Abstract-A technique for magnetic resonance brain image classification using perceptual texture features, fuzzy weighting and support vector machine is proposed. In contrast to existing literature which generally classifies the magnetic resonance brain images into normal and abnormal classes, classification with in the abnormal brain which is relatively hard and challenging problem is addressed here. Texture features along with invariant moments are extracted and the weights are assigned to each feature to increase classification accuracy. Multiclass support vector machine is used for classification purpose. Results demonstrate that the classification accuracy of the proposed scheme is better than the state of art existing techniques.

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel.