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In this paper, we present a mixed-height standard cell placement flow for digital circuit blocks. To our best knowledge, commercial tools currently do not support this type of flow in a fully automated manner. In our placement flow, we leverage a commercial placement tool and integrate it with several new point tools. Promising experimental results are reported to demonstrate the efficacy of our placement flow.

Building the behavioral model for each analog circuit is an efficient approach for mixed-signal system verification. If an automatic model generator is available, it is useful for designers to reduce the extra efforts. Instead of modeling the relationship between circuit inputs and outputs directly, a divide and conquer approach is proposed in [8] to divide the circuit into several small building blocks and model the behavior of each block easily. Although the regression efforts have been greatly alleviated in this structure-based approach, the preparation of the training patterns is still a big issue. In this work, a different approach is proposed to build the behavioral model of each internal block in structure-based approach without regression. Therefore, no training patterns are required in the calibration process. As shown in the experimental results, the model accuracy is still kept in the proposed approach while the efficiency of behavioral model generator is greatly improved.

Building the behavioral model for each circuit is an efficient approach for mixed-signal system verification. If an automatic model generator is available to generate the required behavioral model from the given circuit specifications or netlist, it is useful for designers to reduce the extra efforts. In this paper, two automation methodologies are proposed to build the behavioral models for speeding up mixed-signal system verification. For some popular circuit blocks, dedicated behavioral model generators are developed to obtain the corresponding models directly from the given specifications. For generic circuit blocks, a divide and conquer approach is proposed to extract the required behavioral models from the given circuit netlist. Instead of modeling the relationship between circuit inputs and outputs directly, dividing the circuit into several small building blocks can greatly reduce the model construction efforts without losing the generality for different circuits. As shown in the experimental results, the proposed model generation environment does generate the corresponding behavioral models automatically with good accuracy.

Building the behavioral model for each circuit block is an efficient approach for mixed-signal system verification. If an automatic model generator is available to extract the required behavioral model from the given circuit netlist, it is useful for designers to reduce the extra efforts. Instead of modeling the relationship between circuit inputs and outputs directly, this paper proposes a divide and conquer approach to alleviate the difficulty on building behavioral models. By dividing the circuit into several small building blocks, it is much easier to model the behavior of each block by the black-box approach Therefore, the model construction efforts can be greatly reduced without losing the generality for different circuits. As shown in the experimental results, the proposed approach does generate the corresponding behavioral models automatically with good accuracy.

This research will examine the perception of public art by public users, includingenvironmental, aesthetic, cultural, social and economic, and interpersonal impacts inthe City of Philadelphia, Pennsylvania. Through 1050 anonymous questionnairesbased on three surveys, this study will be an example to elaborate on and clarify thelink between the role of public art by the perceptions as well as by the preferences andits interaction with public users, including residents and visitors in the City ofPhiladelphia. The result of this research may provide some suggestions to the governments, community developers, city planners and suburban designers in thefuture.