There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

A diverse spectrum of technology drivers such as improved thermal barriers, higher efficiency thermoelectric energy conversion, phase-change memory, heat-assisted magnetic recording, thermal management of nanoscale electronics, and nanoparticles for thermal medical therapies are motivating studies of the applied physics of thermal transport at the nanoscale. This review emphasizes developments in experiment, theory, and computation in the past ten years and summarizes the present status of the field. Interfaces become increasingly important on small length scales. Research during the past decade has extended studies of interfaces between simple metals and inorganic crystals to interfaces with molecular materials and liquids with systematic control of interface chemistry and physics. At separations on the order of ∼1 nm, the science of radiative transport through nanoscale gaps overlaps with thermal conduction by the coupling of electronic and vibrational excitations across weakly bonded or rough interface...

/pdf/nanoscale-thermal-transport-ii-2003-2012-4upcs4bq86.pdf

Nanoscale thermal transport. II. 2003–2012

The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

Introduction to heat transfer

Conventional cell delay modeling approaches calculate the propagation delay and output transition time of a CMOS logic cell, which is subjected to a noisy input waveform, by approximating this noisy waveform with a saturated ramp signal and then utilizing cell library delay look-up tables to report the output timing information. Modeling the input waveform as a saturated ramp may however result in significant error in the timing parameters of interest because the actual output waveform can be very different from the one that is implied by a simple saturated ramp input. This paper therefore presents, gcdm, a gain-based cell delay modeling technique for accurate computation of the electrical output waveform of a CMOS logic cell under a noisy input waveform. The key contribution of gcdm is that it directly calculates the output waveform of the logic cell without the need to approximate the input waveform. In effect, gcdm requires a new pre-characterization process for each cell in the library, resulting in construction of a small-signal gain lookup table. This lookup table-based approach is compatible with the existing timing analysis tools. The high accuracy of our approach is confirmed by Spice simulations.

Gain-based Cell Delay Modeling

This paper presents STAX, a crosstalk target set compaction framework to reduce the complexity of the crosstalk ATPG process by pruning non-fault-producing targets. In general, existing pruning techniques do not employ their processes in a cost-effective manner. Neither do they handle process variations properly. To address the first weakness, this paper presents a framework to determine a sequence of available analysis and pruning tool invocations to prune as many of the crosstalk targets as fast as possible. As a result, an initially enormous collection of crosstalk targets is usually reduced to a very small set of targets via a vectorless process. A statistical static timing analyzer is developed and embedded to address the second shortcoming of existing approaches. Experimental results on ISCAS’85 benchmark demonstrate that STAX greatly improves the runtime compared to other crosstalk target pruning methodologies, including ATPG, with no prior target set compaction.

/pdf/stax-statistical-crosstalk-target-set-compaction-4g1oylhndl.pdf

STAX: Statistical Crosstalk Target Set Compaction

Thermoelectric generation (TEG) has increasingly drawn attention for being environmentally friendly. A few researches have focused on improving TEG efficiency at system level on vehicle radiators. The most recent reconfiguration algorithm shows improvement on performance but suffers from major drawback on computational time and energy overhead, and non-scalability in terms of array size and processing frequency. In this paper, we propose a novel TEG array reconfiguration algorithm that determines near-optimal configuration with an acceptable computational time. More precisely, with O(N) time complexity, our prediction-based fast TEG reconfiguration algorithm enables all modules to work at or near their maximum power points (MPP). Additionally, we incorporate prediction methods to further reduce the runtime and switching overhead during the reconfiguration process. Experimental results present 30% performance improvement, almost 100 χ reduction on switching overhead and 13 χ enhancement on computational speed compared to the baseline and prior work. The scalability of our algorithm makes it applicable to larger scale systems such as industrial boilers and heat exchangers.

Prediction-based fast thermoelectric generator reconfiguration for energy harvesting from vehicle radiators

Social media became popular and percolated almost all aspects of our daily lives. While online posting proves very convenient for individual users, it also fosters fast-spreading of various rumors. The rapid and wide percolation of rumors can cause persistent adverse or detrimental impacts. Therefore, researchers invest great efforts on reducing the negative impacts of rumors. Towards this end, the rumor classification system aims to detect, track, and verify rumors in social media. Such systems typically include four components: (i) a rumor detector, (ii) a rumor tracker, (iii) a stance classifier, and (iv) a veracity classifier. In order to improve the state-of-the-art in rumor detection, tracking, and verification, we propose VRoC, a tweet-level variational autoencoder-based rumor classification system. VRoC consists of a co-train engine that trains variational autoencoders (VAEs) and rumor classification components. The co-train engine helps the VAEs to tune their latent representations to be classifier-friendly. We also show that VRoC is able to classify unseen rumors with high levels of accuracy. For the PHEME dataset, VRoC consistently outperforms several state-of-the-art techniques, on both observed and unobserved rumors, by up to 26.9%, in terms of macro-F1 scores.

VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text

Leakage power consumption has recently become a great concern for modern embedded systems. FinFET technologies, power gating, and near- and super-threshold regimes can significantly reduce the power consumption. However, there lacks a comprehensive analysis of jointly applying the aforementioned power saving techniques. In this paper, we investigate the application of power gating to FinFET circuits operating in near- and super-threshold voltage regimes for embedded system applications. A joint optimization algorithm is proposed to determine the width/length, position and threshold type of the sleep transistor together with the operating voltage constrained to a certain deadline, and with the goal of minimizing energy per operation. Experimental results demonstrate that the proposed algorithm achieves up to 99.9% energy reductions when compared to the near-threshold approach without power gating and 95.3% when compared to deadline-free optimization.

Shahin Nazarian

Papers

Gain-based Cell Delay Modeling

STAX: Statistical Crosstalk Target Set Compaction

Prediction-based fast thermoelectric generator reconfiguration for energy harvesting from vehicle radiators

VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text

Deadline-Aware Joint Optimization of Sleep Transistor and Supply Voltage for FinFET Based Embedded Systems