Implementation and Evaluation of an Improved Header Compression for 6LoWPAN

Extending IPv6 to IEEE 802.15.4-based Low power Wireless Personal Area Networks requires efficient header compression mechanisms to adapt to their limited bandwidth, memory and energy constraints. This paper presents an experimental evaluation of an improved header compression scheme which provides better compression of IPv6 multicast addresses and UDP port numbers compared to existing mechanisms. This scheme outperforms the existing compression mechanism in terms of data throughput of the network and energy consumption of nodes. It enhances throughput by up to 8% and reduces transmission energy of nodes by about 5%.

Classification of Mammograms and DW T based Detection of Microcalcification

Cancer treatment is most effective when it is detected early and the progress in treatment will be closely related to the ability to reduce the proportion of misses in the cancer detection task. The effectiveness of algorithms for detecting cancers can be greatly increased if these algorithms work synergistically with those for characterizing normal mammograms. This research work combines computerized image analysis techniques and neural networks to separate out some fraction of the normal mammograms with extremely high reliability, based on normal tissue identification and removal. The presence of clustered microcalcifications is one of the most important and sometimes the only sign of cancer on a mammogram. 60% to 70% of non-palpable breast carcinoma demonstrates microcalcifications on mammograms [44], [45], [46].WT based techniques are applied on the remaining mammograms, those are obviously abnormal, to detect possible microcalcifications. The goal of this work is to improve the detection performance and throughput of screening-mammography, thus providing a ‘second opinion ‘ to the radiologists. The state-of- the- art DWT computation algorithms are not suitable for practical applications with memory and delay constraints, as it is not a block transfonn. Hence in this work, the development of a Block DWT (BDWT) computational structure having low processing memory requirement has also been taken up.