Regression is widely utilized in a variety of biological problems involving continuous outcomes. There are a number of methods for building regression models ranging from linear models to more complex nonlinear ones. While linear regression techniques can identify linear correlations between input and output, in many practical applications, the relations are nonlinear. These relations can be modeled by nonlinear regression techniques effectively. However, many models built with nonlinear techniques have limited interpretation, which is crucial in many biological problems. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features, and hence is able to provide a simple interpretation. We tested the approach on a seacoast chemical sensors dataset, a Stockori flowering time dataset, and three datasets from the UCI repository. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of conventional random forests regression. It demonstrates high potential in terms of prediction performance and interpretation ease on studying nonlinear relationships of the subjects.

Tristate buses, commonly used in high-performance designs, raise testability problems because one-hot conditions, required for functional operation, may not be maintained during scan testing. We present a novel method to automatically generate and insert a bus encoder that ensures one-hot bus operation. Each bus is analyzed individually; a customized encoder is then generated and optimized for best testability and minimal area and delay. We introduce two new bus encoder designs which form the basis of a hierarchically algorithm that scales up to any number of bus inputs.

This demonstration introduces AdapTex: a new text entry system that suggests words and phrases to the user based on the current working context on the device, the user's history on the device and an initial corpus analysis. User studies show that AdapTex speeds up text entry while reducing errors.

Recent test-cost reduction methods are based on controlling the initial state (seed) of a pseudo-random pattern generator (PRPG) so that deterministic values are loaded in selected scan cells. Combined with an unload-data compression technique, PRPG seeding reduces test data volume and application time. This paper presents a method of mapping each scan load to multiple PRPG seeds, computed so that test pattern count, data volume, and, therefore, test cost are minimized. This method also allows smaller and fewer PRPGs, reducing the area overhead of test-compression circuitry. The results on deep-submicron industrial designs, show significant test cost reduction when this method is applied with either X-tolerant or X-free unload-data compression

Scan-based tests created by automatic test pattern generators (ATPG) can be efficiently compressed and applied in a deterministic built-in self-test (DBIST) architecture. However, the BIST environment adds significant complexity to failure diagnosis. We present a simple scan-compatible diagnosis solution - streaming DBIST (SDBIST), which is based on a low-overhead hierarchical compactor SDBIST allows continuously monitoring streaming scanout data for reduced-volume expect-data diagnosis, on-line fail-data collection and selective scan cell masking.

X-tolerant deterministic BIST (XDBIST) was recently presented as a method to efficiently compress and apply scan patterns generated by automatic test pattern generation (ATPG) in a logic built-in self-test architecture. In this paper we introduce a novel selector architecture that allows arbitrary compression ratios, scales to any number of scan chains and minimizes area overhead. XDBIST test-coverage, full X-tolerance and scan-based diagnosis ability are preserved and are the same as deterministic scan-ATPG.

We present a novel method to efficiently generate, compress and apply test patterns in a logic BIST architecture. Patterns are generated by a modified automatic test pattern generator (ATPG) and are encoded as linear feedback shift register (LFSR) initial values (seeds); one or more patterns can be encoded into a single LFSR seed. During test application, seeds are loaded into the LFSR with no cycle overhead. The method presented achieves reductions of at least 100x in test data and 10x in tester cycles compared to deterministic ATPG while maintaining complete fault coverage, as confirmed by experimental results on industrial designs.

Logic built-in self test (BIST) is increasingly being adopted to improve test quality and reduce test costs for rapidly growing designs. Compared to deterministic automated test pattern generation (ATPG), BIST presents inherent fault diagnostic challenges. Previous diagnostic techniques have been limited in their diagnosis resolution and/or require significant hardware overhead. This paper proposes an interval-based scan-unload method that ensures diagnosis resolution down to gate-level faults with minimal hardware overhead. Tester fail-data collection is based on a novel construct incorporated into the design-extensions of the standard test-interface language (STIL). The implementation of the proposed method is presented and analyzed.