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A new approach incorporating adaptive lighting intensity for micro-crack inspection of solar wafers with variable thickness is proposed. Wafer thickness is measured with a pair of laser displacement sensors and the lighting intensity is adaptively adjusted to normalize near infrared (NIR) transmission based on measured thickness. This technique enables the image contrast be maintained at relatively uniform intensity in response to the variation of the solar wafer thickness. An improved version of Niblack segmentation algorithm is developed for this application. Experimental results demonstrate the competitiveness of the proposed system compared with established techniques, and achieves better performance both visually and quantitatively. Meanwhile, the runtime is consistently less than 1 s, corresponding to a throughput rate of approximately 3600 wafers/h. These results suggest that the methods and procedures are suitable for online processing of solar wafers.

This paper presents a review of the machine detection systems for micro-crack inspection of solar wafers and cells. To-date, there are various methods and procedures that have been developed at various laboratories around the world to inspect solar wafers and solar cells for manufacturing defects. This paper on micro-crack detection offers a comprehensive and an up-to-date review of different detection strategies, describing the main features of each technique together with its strengths as well as weaknesses. This paper will benefit researchers and practitioners, especially those who want to develop automatic inspection systems for inspecting solar wafers or solar cells for micro-cracks.

Eurycoma longifolia Jack (ELJ), better known as Malaysian Ginseng, is fast becoming popular due to its efficacy as a general health and testosterone booster, and for its known aphrodisiac properties. With the growing popularity of ELJ, together with the increasing consumer demand for healthy foods, it is expected that the number of healthy food suppliers boasting claims that their products are enriched with this medicinal herb will also increase. It is, therefore, important to device a strategy that can rapidly provide corroborating evidence in order to assess the authenticity of such a claim onsite. A portable electronic taste sensor employing digital signal processing (DSP) technology was developed to meet this objective. The sensor was constructed from eight kinds of lipid membranes forming the multichannel artificial taste-sensing equipment. The system was trained to detect ELJ into four different concentrations: 0.01%, 0.03%, 0.05% and 0.08%. Over 100 samples were tested, from which the taste system was able to correctly classify the test mixtures at a greater than 90% success rate. This classification success rate, however, dropped to well below 50% when data from multichannel were used independently, indicating the between-channel variations were relatively small. Data fusion via principal component analysis helped enlarge the variation with the first and second principal components, contributed nearly 92% of the total variance. Equipped with 32-bit state-of-the-art DSPs, the taste system allowed both training and testing schemes to be implemented directly onboard.

Solving an inverse problem constitutes one of the most challenging tasks in electrical impedance tomography because of the mathematical complexity of the subject. The most commonly used methods include the finite element and boundary element methods. Both are mathematically involved procedures which are rarely taught at undergraduate level. An alternative approach based on a network of resistors is also useful for this type of inversion problem. This paper describes the use of resistance network analogues for electrical imaging. The algorithm is based on the modified Newton-Raphson method.