Mining engineering metaknowledge from electronic documents is important in devising new design solutions for emerging applications as well as in identifying new business opportunities. Continuously and comprehensively reviewing and assessing the entire technical literature to find the most relevant ideas and solutions is cumbersome as engineering innovations are constantly being proposed. In particular, high performance analog circuit design has been at the forefront of technological innovations given their importance in applications, like telecommunications, mobile applications, and health care. This paper proposes a model and mining techniques for representing analog circuit design metaknowledge. The metaknowledge representation includes three components: an associative component presenting the conceptual hierarchy of the considered circuits, a component expressing the performance capabilities (e.g., tradeoffs and bottlenecks) of the circuits, and a causal modeling part describing the more likely starting ideas and design plans used to create a circuit. Knowledge structures can be used to tackle new applications, identify opportunities to improve existing circuits, and validate design correctness. A case study illustrates the proposed knowledge structure for a set of 30 modern, high-frequency analog circuits.

This paper presents a reasoning-based approach to analog circuit synthesis using ordered node clustering representations (ONCR) to describe alternative circuit features and symbolic circuit comparison to characterize performance tradeoffs of synthesized solutions. Case studies illustrate application of the proposed methods to topology selection and refinement.

This paper proposes an automated technique for systematically generating comparison data between two analog circuits. The comparison data presents the similar and distinguishing performance characteristics of the circuits with respect to DC-gain, bandwidth, common-mode rejection ratio, noise, and sensitivity. The comparison data is important for getting insight into the common and unique benefits of a circuit, selecting fitting circuit topologies for system design, and refining and optimizing circuit topologies. The technique utilizes matching of the topologies and symbolic expressions of the compared circuits to find the nodes with similar electric behavior. The impact on performance of the unmatched nodes is used to express the differentiating characteristics of the circuits. Experiments illustrate the technique for a pair of analog circuit designs.

This paper presents a technique for automated generation of hierarchical classification schemes to express the main similarities and differences between analog circuits. The produced classification schemes offer insight about the uniqueness and importance of specific design features in setting various performance attributes as well as the limiting factors of designs. Hence, the classification schemes serve as a systematic way of relating one circuit design to alternatives. The automatically produced classification schemes for a set of OpAmps are discussed.

Cyber Physical Systems are distributed systems-of-systems that integrate sensing, processing, networking and actuation. Aggregating physical data over space and in time emerges as an intrinsic part of data acquisition, and is critical for dependable decision making under performance and resource constraints. This paper presents a Linear Programming-based method for optimizing the aggregation of data sampled from geographically-distributed areas while satisfy timing, precision, and resource constraints. The paper presents experimental results for data aggregation, including a case study on gas detection using a network of sensors.