To enhance focused eating and dining socialization, previous Human-Food Interaction research has indicated that external devices can support these dining objectives and immersion. However, methods that focus on the food itself and the diners themselves have remained underdeveloped. In this study, we integrated biofeedback with food, utilizing diners' heart rates as a source of the food's appearance to promote focused eating and dining socialization. By employing LED lights, we dynamically displayed diners' real-time physiological signals through the transparency of the food. Results revealed significant effects on various aspects of dining immersion, such as awareness perceptions, attractiveness, attentiveness to each bite, and emotional bonds with the food. Furthermore, to promote dining socialization, we established a "Sharing Bio-Sync Food" dining system to strengthen emotional connections between diners. Based on these findings, we developed tableware that integrates biofeedback into the culinary experience.

To enhance the dining experience, prior studies in Human-Computer Interaction (HCI) and gastrophysics have demonstrated that modifying the static shape of solid foods can amplify taste perception. However, the exploration of dynamic shape-changing mechanisms in liquid foods remains largely untapped. In the present study, we employ cymatics, a scientific discipline focused on utilizing sound frequencies to generate patterns in liquids and particles—to augment the drinking experience. Utilizing speakers, we dynamically reshaped liquids exhibiting five distinct taste profiles and evaluated resultant changes in taste perception and drinking experience. Our research objectives extend beyond merely augmenting taste from visual to tactile sensations; we also prioritize the experiential aspects of drinking. Through a series of experiments and workshops, we revealed a significant impact on taste perception and overall drinking experience when mediated by cymatics effects. Building upon these findings, we designed and developed tableware to integrate cymatics principles into gastronomic experiences.

Convex-optimization techniques are very popular in the very large-scale-integration design society due to their guaranteed convergence to a global optimal point. The table data need to be fitted into convex forms to be used in the convex optimization problems. Fitting the tables into polynomials, which are analytically convex under logarithmic transformation, may suffer from the excessive fitting errors as the fitting problem is nonconvex. In this paper, we propose to directly adjust the lookup-table values into a numerically convex lookup table without any explicit analytical form. We show that numerically "convexifying" the lookup-table data with minimum perturbation can be formulated as a convex semidefinite optimization problem, and hence, optimality can be reached in polynomial time. We also propose three algorithms to make the table data smooth to enable faster convergence of the convex optimizer. Results from extensive experiments on industrial cell libraries demonstrate 9.6 improvement in fitting error over a well-developed polynomial-fitting procedure. We illustrate the effectiveness of this model in a convex optimization problem by providing results for using our model in the optimal gate sizing of standard cells. We observe a 5.07% improvement in the delay of International Symposium on Circuits and Systems (ISCAS) benchmark circuits over the polynomial-fitting procedure.

Statistical timing analysis has been widely applied to predict the timing yield of VLSI circuits when process variations become significant. Existing statistical latch timing methods are either having exponential complexity or unable to treat the random variable's self-dependence caused by the coexistence of level-sensitive latches and feedback loops. In this paper, an efficient iterative statistical timing algorithm with provable convergence is proposed for latch-based circuits with feedback loops. Based on a new notion of iteration mean, we prove that the algorithm converges unconditionally. Moreover, we show that the converged value of iteration mean ca be used to predict the circuit yield during design time. Tested by ISCAS'89 benchmark circuits, the proposed algorithm shows a error of 1.1% and speedup of 303 /spl times/ on average when compared with the Monte Carlo simulation.

An efficient and accurate statistical static timing analysis (SSTA) algorithm is reported in this paper, which features 1) a conditional linear approximation method of the MAX/MIN timing operator, 2) an extended canonical representation of correlated timing variables, and 3) a variation pruning method that facilitates intelligent tradeoff between simulation time and accuracy of simulation result. A special design focus of the proposed algorithm is on the propagation of the statistical correlation among timing variables through nonlinear circuit elements. The proposed algorithm distinguishes itself from existing block-based SSTA algorithms in that it not only deals with correlations due to dependence on global variation factors but also correlations due to signal propagation path reconvergence. Tested with the International Symposium on Circuits and Systems (ISCAS) benchmark suites, the proposed algorithm has demonstrated very satisfactory performance in terms of both accuracy and running time. Compared with Monte-Carlo-based statistical timing simulation, the output probability distribution got from the proposed algorithm is within 1.5% estimation error while a 350 times speed-up is achieved over a circuit with 5355 gates.

A recent study shows that the existing first-order canonical timing model is not sufficient to represent the dependency of the gate/wire delay on the processing and operational variations when these variations become more and more significant. Due to nonlinear mapping from variation sources to the gate/wire delay, the distribution of the delay will no longer be Gaussian even if variation sources are normally distributed. A novel “quadratic timing model” is proposed to capture the nonlinearity of the dependency of gate/wire delays and arrival times on the variation sources. Systematic methodology is also developed to evaluate the correlation and distribution of the quadratic timing model. Based on these, a statistical static timing analysis algorithm that retains the complete correlation information during timing analysis and has linear computation complexity with respect to both the circuit size and the number of variation sources is proposed. Tested on the ISCAS circuits, the proposed algorithm shows significant accuracy improvement over the existing first-order algorithm with a small amount of computational cost.

Recent study shows that the existing first order canonical timing model is not sufficient to represent the dependency of the gate delay on the variation sources when processing and operational variations become more and more significant. Due to the nonlinearity of the mapping from variation sources to the gate/wire delay, the distribution of the delay is no longer Gaussian even if the variation sources are normally distributed. Anovelquadratic timing model is proposed to capture the non-linearity of the dependency of gate/wire delays and arrival times on the variation sources. Systematic methodology is also developed to evaluate the correlation and distribution of the quadratic timing model. Based on these, a novel statistical timing analysis algorithm is propose which retains the complete correlation information during timing analysis and has the same computation complexity as the algorithm based on the canonical timing model. Tested on the ISCAS circuits, the proposed algorithm shows 10 × accuracy improvement over the existing first order algorithm while no significant extra runtime is needed.

State of the art statistical timing analysis (STA) tools often yield less accurate results when timing variables become correlated due to global source of variations and path reconvergence. To the best of our knowledge, no good solution is available for dealing both types of correlations simultaneously. In this paper, we present a novel extended pseudo-canonical timing model to retain and evaluate both types of correlation during statistical timing analysis with minimum computation cost. Also, an intelligent pruning method is introduced to enable trade-off runtime with accuracy. Tested with ISCAS benchmark suites, our method shows both high accuracy and high performance. For example, on the circuit c6288, our distribution estimation error shows 15/spl times/ accuracy improvement compared with previous approaches.

Convex optimization has gained popularity due to its capability to reach global optimum in a reasonable amount of time. Convexity is often ensured by fitting the table data into analytically convex forms such as posynomials. However, fitting the look-up tables into the posynomial forms with minimum error itself may not be a convex optimization problem and hence excessive fitting errors may be introduced. In this paper, we propose to directly adjust the look-up table values into a numerically convex look-up table without explicit analytical form. We show that numerically "convexifying" the table data with minimum perturbation can be formulated as a convex semidefinite optimization problem and hence optimality can be reached in polynomial time. Without an explicit form limitation, we find that the fitting error is significantly reduced while the convexity is still ensured. As a result, convex optimization algorithms can still be applied. Furthermore, we also develop a "smoothing" algorithm to make the table data smooth and convex to facilitate the optimization process. Results from extensive experiments on industrial cell libraries demonstrate that our method reduces 30/spl times/ fitting error over a well-developed posynomial fitting algorithm. Its application to circuit tuning is also presented.