Fibre optic sensor for the detection of adulterant traces in coconut oil

The design and development of a fibre optic evanescent wave refractometer for the detection of trace amounts of paraffin oil and palm oil in coconut oil is presented. This sensor is based on a side-polished plastic optical fibre. At the sensing region, the cladding and a small portion of the core are removed and the fibre nicely polished. The sensing region is fabricated in such a manner that it sits perfectly within a bent mould. This bending of the sensing region enhances its sensitivity. The oil mixture of different mix ratios is introduced into the sensing region and we observed a sharp decrease in the output intensity. The observed variation in the intensity is found to be linear and the detection limit is 2% (by volume) paraffin oil/palm oil in coconut oil. The resolution of this refractometric sensor is of the order of 10−3. Since coconut oil is consumed in large volumes as edible oil in south India, this fibre optic sensor finds great relevance for the detection of adulterants such as paraffin oil or palm oil which are readily miscible in coconut oil. The advantage of this type of sensor is that it is inexpensive and easy to set up. Another attraction of the side-polished fibre is that only a very small amount of analyte is needed and its response time is only 7 s.

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.