Volume-1, Issue-1, April 2015

Abstract

Current digital terrestrial television networks make use of either single frequency networks (SFN) or multi frequency networks (MFN). SFN is the network where several transmitters transmit the same signal over the same frequency channel. MFN is the frequency network where each transmitter transmits the signal over different frequency channels. Compared to MFN,SFN is the best approach because of the efficient utilization of the Radio spectrum, Low power operation and multi-channel capacity. On growing market demand, need to maintain the quality, flexibility, mobility and interactivity successfully without any overloading and overlapping issues. For this we introduce the following two techniques. One is Hierarchical Modulation (HLSI) and another one is Orthogonal Frequency Division Multiplexing (O-LSI).In order to improve the performance of the SFN operation, DVB-NGH standard incorporates multiple input multiple output (MIMO) antenna diversity mechanisms. In this paper, we describe the benefits in the utilization of MIMO and its application to DVB standards, i.e. DVB-NGH and DVB-T2 (Terrestrial 2nd generation). Following a description of the transmission techniques adopted for the DVB-NGH specification, we furthermore provide theoretical and physical layer simulation results to illustrate the performance gains of MIMO in various channel models.

Keywords: DVB-NGH, Hierarchical Modulation, orthogonal Local Service Insertion, SFN, MIMO Technique

References

Keywords: DVB-NGH, Hierarchical Modulation, orthogonal Local Service Insertion, SFN, MIMO Technique

Abstract

This paper presents the design and implementation of the tetrolets based system for automatic skeletal Bone Age Assessment (BAA). The system works according to the renowned Tanner and Whitehouse (TW2) method, based on the carpal and phalangeal Region of Interest (ROI). The system ensures accurate and robust BAA for the age range 0-10 years for both girls and boys. Given a left hand-wrist radiograph as input, the system estimates the bone age by deploying novel techniques for segmentation, feature extraction, feature selection and classification. Tetrolets are used in combination with Particle Swarm Optimization (PSO) for segmentation. From the segmented wrist bones, the carpal and phalangeal ROI are identified and are used in morphological feature extraction. PCA is employed as a feature selection tool to reduce the size of the feature vector. The selected features are fed in to an ID3 decision tree classifier, which outputs the class to which the radiograph is categorized, which is mapped onto the final bone age. The system was evaluated on a set of 100 radiographs (50 for girls and 50 for boys), and the results are discussed. The performance of system was evaluated with the help of radiologist expert diagnoses. The system is very reliable with minimum human intervention, yielding excellent results.

Keywords: Bone Age Assessment (BAA), TW2, radiograph, Particle Swarm Optimization (PSO), Tetrolets, ID3, Classification.

References

Keywords: Bone Age Assessment (BAA), TW2, radiograph, Particle Swarm Optimization (PSO), Tetrolets, ID3, Classification.