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Phase‐locked loops (PLLs) are among the most important mixed‐signal building blocks of modern communication and control circuits, where they are used for frequency and phase synchronization, modulation, and demodulation as well as frequency synthesis. The growing popularity of PLLs has increased the need to test these devices during prototyping and production. The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. This is because most analog and mixed signal circuits are tested by their functionality, which is both time consuming and expensive. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques can be employed to automate fault classification. As a possible solution, we use the back propagation neural network (BPNN) to classify the faults in the designed charge‐pump PLL. In order to classify the faults, the BPNN was trained with various training algorithms and their performance for the test structure was analyzed. The proposed method of fault classification gave fault coverage of 99.58%.

The manual examination of histological images like computed tomography (CT) images by physicians is prone to subjectivity and limited intra and inter-surgeon reproducibility, due to its heavy reliance on human interpretation. As result of which, diagnosis of cancer especially in lungs becomes less accurate and unreliable. So, a computer-aided diagnosis (CAD) system, based on artificial intelligence that efficiently detects nodules of any shape and size, is used for diagnosis without human intervention. In this work, we have developed a two stage CAD system in which the first stage involves pre-processing applied for a better quality image to enable higher success rate on detection following which the cancerous nodule region is segmented. The second stage involves artificial neural network (ANN) architecture which is trained using a modified BFGS algorithm.The proposed system was trained, tested, and evaluated specifically on the problem of detecting lung cancer nodules found on CT images to give a positive detection. A significant comparative analysis was done between the proposed method and several existing CAD systems used for lung nodule diagnosis and the proposed method using training-based neural networks prove to provide accuracy of 96.7% and also better specificity; thus, the overall performance of the CAD scheme was improved substantially.

Background:Management guidelines about the thyroid disease in pregnancy are silent about the postpartum course of new onset subclinical hypothyroidism (SCH). Hence, we analyzed the 2 years outcome of SCH diagnosed during pregnancy.Materials and Methods:We conducted this retrospective study using the medical records of patients with new onset SCH during pregnancy between 2010 and 2013 (n = 718). Patients who stopped their levothyroxine after delivery with a 2-year follow-up record were included. We excluded patients with known thyroid disorders and continuous use of drugs that affect the thyroid results. The patients were divided into two groups (Group 1 – euthyroid and Group 2 – hypothyroid) based on the final outcome after 2 years. The data were analyzed using appropriate statistical methods and a P < 0.05 was considered statically significant.Results:A total of 559 (77.8%) women stopped levothyroxine after delivery, and the final follow-up data were available for 467 patients only. At the end of 2 years, 384 (82.2%) remained euthyroid, and the remaining 83 (17.8%) developed hypothyroidism. SCH and overt hypothyroidism were seen in 22 and 61 patients, respectively. Group 2 patients had higher mean age (25.5 vs. 23.6 years), goiter (51 vs. 2%), initial thyroid stimulating hormone (7.9 vs. 5.1 μIU/mL), and thyroid antibody positivity (76 vs. 13%) (P < 0.001).Conclusion:The majority of patients with SCH during pregnancy remain euthyroid after delivery. Advanced age, goiter, positive family history, and thyroid autoimmunity increase the future risk of hypothyroidism in patients with SCH diagnosed during pregnancy.

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