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This work proposes a new algorithm for global routing using monotone staircase channels obtained from VLSI floorplan topology. Unlike the existing global routers that follow block placement stage, it immediately follows the floorplanning stage of VLSI design. The monotone staircase channels are identified using the results of recent ( log ) top-down hierarchical monotone staircase bipartition. The worst case time complexity of the proposed global routing algorithm is ( 2 ), where , and denote the number of blocks, nets and the number of terminals in a given net respectively for a given floorplan. Experimental results on the MCNC/GSRC floorplanning benchmark circuits show that our method obtained 100% routability for each of the nets, without any over-congestion through the monotone staircase channels. The wire length for each of the -terminal ( ≥ 2) nets is comparable to the steiner length of that net in almost all cases.

Condition monitoring is one of the routine tasks in all major process industries. The mechanical parts such as a motor, gear, bearings are the major components of a process industry and any fault in them may cause a total shutdown of the whole process, which may result in serious losses. Therefore, it is very crucial to predict any approaching defects before its occurrence. Several methods exist for this purpose and many research are being carried out for better and efficient models. However, most of them are based on the processing of raw sensor signals, which is tedious and expensive. Recently, there has been an increase in the feature based condition monitoring, where only the useful features are extracted from the raw signals and interpreted for the prediction of the fault. Most of these are handcrafted features, where these are manually obtained based on the nature of the raw data. This of course requires the prior knowledge of the nature of data and related processes. This limits the feature extraction process. However, recent development in the autoencoder based feature extraction method provides an alternative to the traditional handcrafted approaches; however, they have mostly been confined in the area of image and audio processing. In this work, we have developed an automated feature extraction method for on-line condition monitoring based on the stack of the traditional autoencoder and an on-line sequential extreme learning machine(OSELM) network. The performance of this method is comparable to that of the traditional feature extraction approaches. The method can achieve 100% detection accuracy for determining the bearing health states of NASA bearing dataset. The simple design of this method is promising for the easy hardware implementation of Internet of Things(IoT) based prognostics solutions.

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