International Journal on Intelligent Electronic Systems 

About: International Journal on Intelligent Electronic Systems is an academic journal. The journal publishes majorly in the area(s): Ćuk converter & Flyback converter. It has an ISSN identifier of 0973-9238. It is also open access. Over the lifetime, 109 publications have been published receiving 1002 citations.

An adaptive energy efficient mac protocol for wireless sensor networks

Abstract: Sensor networks are deployed in remote locations with limited processor capabilities, memory capacities, and battery supplies. Wireless Sensor Networks (WSN) detects environmental information with sensors in remote settings. One problem facing WSNs is the inability to resupply power to these energy-constrained devices due to their remoteness. Therefore to extend a WSN's effectiveness, the lifetime of the network must be increased by making them as energy efficient as possible. An energy-efficient medium access control (MAC) can boost a WSN's lifetime. This paper focuses on a protocol stack solution that deals with MAC layer, that minimizes the energy consumption and delay required to transmit packets across the network. It is based on Sensor Medium Access Control (S-MAC) called Adaptive SMAC protocol designed for sensor networks. It enables low duty cycle operation in a multi-hop network and common sleep schedules to reduce control overhead and enable traffic adaptive wakeup. To reduce control overhead and latency, introduces coordinated sleeping among neighboring nodes. It is a contention based protocol based on CSMA/CA mechanism. This protocol is simulated in NS-2 and performance evaluated using various topologies under various traffic conditions. In addition with this we tried to improve the energy efficiency of Adaptive SMAC with the help of a new design called Adaptive Cross MAC protocol

Colour Based Image Segmentation Using Fuzzy C-Means Clustering

Abstract: Mostly due to the progresses in spatial resolution of satellite imagery, the methods of segment- based image analysis for generating and updating geographical information are becoming more and more important. This work presents a new image segmentation based on colour features with Fuzzy c-means clustering unsupervised algorithm. The entire work is divided into two stages. First enhancement of color separation of satellite image using decorrelation stretching is carried out and then the regions are grouped into a set of five classes using Fuzzy c-means clustering algorithm. Using this two step process, it is possible to reduce the computational cost avoiding feature calculation for every pixel in the image. Although the colour is not frequently used for image segmentation, it gives a high discriminative power of regions present in the image. In remote sensing, the process of image segmentation is defined as: "the search for homogenous regions in an image and later the classification of these regions". It also means the partitioning of an image into meaningful regions based on homogeneity or heterogeneity criteria. Image segmentation techniques can be differentiated into the following basic concepts: pixel oriented, Contour-oriented, region-oriented, model oriented, color oriented and hybrid. Color segmentation of image is a crucial operation in image analysis and in many computer vision, image interpretation, and pattern recognition system, with applications in scientific and industrial field(s) such as medicine, Remote Sensing, Microscopy, content based image and video retrieval, document analysis, industrial automation and quality control. The performance of color segmentation may significantly affect the quality of an image understanding system .The most common features used in image segmentation include texture, shape, grey level intensity, and color. The constitution of the right data space is a common problem in connection with segmentation/classification. In order to construct realistic classifiers, the features that are sufficiently representative of the physical process must be searched. In the literature, it is observed that different transforms are used to extract desired information from remote-sensing images or biomedical images. Segmentation evaluation techniques can be generally divided into two categories (supervised and unsupervised). The first category is not applicable to remote sensing because an optimum segmentation (ground truth segmentation) is difficult to obtain. Moreover, available segmentation evaluation techniques have not been thoroughly tested for remotely sensed data. Therefore, for comparison purposes, it is possible to proceed with the classification process and then indirectly assess the segmentation process through the produced classification accuracies. For image segment based classification, the images that need to be classified are segmented into many homogeneous areas with similar spectrum information firstly, and the image segments' features are extracted based on the specific requirements of ground features classification. The color

A novel approach for computation-efficient rekeying for multicast key distribution

Abstract: An important problem for secure group communication is key distribution. Most of the centralized group key management schemes employ high rekeying cost. Here we introduce a novel approach for computation efficient rekeying for multicast key distribution. This approach reduces the rekeying cost by employing a hybrid group key management scheme (involving both centralized and contributory key management schemes). The group controller uses the MDS Codes, a class of error control codes, to distribute the multicast key dynamically. In order to avoid frequent rekeying as and when the user leaves, a novel approach is introduced where clients recompute the new group key with minimal computation. This approach ensures forward secrecy as well as backward secrecy and significantly reduces the rekeying cost and communication cost. This scheme well suits wireless applications where portable devices require low computation.

Finger print recognition using discrete wavelet transform

Abstract: The most common approach for fingerprint analysis is using minutiae that identifies corresponding features and evaluates the resemblance between two fingerprint impressions. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem. Finger print recognition can be done effectively using texture classification approach. Important aspect here is appropriate selection of features that recognize the finger print. We propose an effective combination of features for multi-scale and multi-directional recognition of fingerprints. The features include standard deviation, kurtosis, and skewness . We apply the method by analyzing the finger prints with discrete wavelet transform (DWT) . We used Canberra distance metric for similarity comparison between the texture classes. We trained 30 images and obtained an overall performance up to 96%.