Algorithmic techniques for nanometer VLSI design and manufacturing closure

As Very Large Scale Integration (VLSI) technology moves to the nanoscale regime, design and manufacturing closure becomes very difficult to achieve due to increasing chip and power density. Imperfections due to process, voltage and temperature variations aggravate the problem. Uncertainty in electrical characteristic of individual device and wire may cause significant performance deviations or even functional failures. These impose tremendous challenges to the continuation of Moore's law as well as the growth of semiconductor industry. Efforts are needed in both deterministic design stage and variation-aware design stage. This research proposes various innovative algorithms to address both stages for obtaining a design with high frequency, low power and high robustness. For deterministic optimizations, new buffer insertion and gate sizing techniques are proposed. For variation-aware optimizations, new lithography-driven and post-silicon tuning-driven design techniques are proposed. For buffer insertion, a new slew buffering formulation is presented and is proved to be NP-hard. Despite this, a highly efficient algorithm which runs > 90x faster than the best alternatives is proposed. The algorithm is also extended to handle continuous buffer locations and blockages. For gate sizing, a new algorithm is proposed to handle discrete gate library in contrast to unrealistic continuous gate library assumed by most existing algorithms. Our approach is a continuous solution guided dynamic programming approach, which integrates the high solution quality of dynamic programming with the short runtime of rounding continuous solution. For lithography-driven optimization, the problem of cell placement considering manufacturability is studied. Three algorithms are proposed to handle cell flipping and relocation. They are based on dynamic programming and graph theoretic approaches, and can provide different tradeoff between variation reduction and wire- length increase. For post-silicon tuning-driven optimization, the problem of unified adaptivity optimization on logical and clock signal tuning is studied, which enables us to significantly save resources. The new algorithm is based on a novel linear programming formulation which is solved by an advanced robust linear programming technique. The continuous solution is then discretized using binary search accelerated dynamic programming, batch based optimization, and Latin Hypercube sampling based fast simulation

A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky–Golay Filter for ECG Denoising

Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics

Blind Inpainting with Object-aware Discrimination for Artificial Marker Removal

Medical images often contain artificial markers added by doctors, which can negatively affect the accuracy of AI-based diagnosis. To address this issue and recover the missing visual contents, inpainting techniques are highly needed. However, existing inpainting methods require manual mask input, limiting their application scenarios. In this paper, we introduce a novel blind inpainting method that automatically completes visual contents without specifying masks for target areas in an image. Our proposed model includes a mask-free reconstruction network and an object-aware discriminator. The reconstruction network consists of two branches that predict the corrupted regions with artificial markers and simultaneously recover the missing visual contents. The object-aware discriminator relies on the powerful recognition capabilities of the dense object detector to ensure that the markers of reconstructed images cannot be detected in any local regions. As a result, the reconstructed image can be close to the clean one as much as possible. Our proposed method is evaluated on different medical image datasets, covering multiple imaging modalities such as ultrasound (US), magnetic resonance imaging (MRI), and electron microscopy (EM), demonstrating that our method is effective and robust against various unknown missing region patterns

Gain scheduled torque compensation of PMSG-based wind turbine for frequency regulation in an isolated grid

Frequency stability in an isolated grid can be easily impacted by sudden load or wind speed changes. Many frequency regulation techniques are utilized to solve this problem. However, there are only few studies designing torque compensation controllers based on power performances in different Speed Parts. It is a major challenge for a wind turbine generator (WTG) to achieve the satisfactory compensation performance in different Speed Parts. To tackle this challenge, this paper proposes a gain scheduled torque compensation strategy for permanent magnet synchronous generator (PMSG) based wind turbines. Our main idea is to improve the anti-disturbance ability for frequency regulation by compensating torque based on WTG speed Parts. To achieve higher power reserve in each Speed Part, an enhanced deloading method of WTG is proposed. We develop a new small-signal dynamic model through analyzing the steady-state performances of deloaded WTG in the whole range of wind speed. Subsequently, H∞ theory is leveraged in designing the gain scheduled torque compensation controller to effectively suppress frequency fluctuation. Moreover, since torque compensation brings about untimely power adjustment in over-rated wind speed condition, the conventional speed reference of pitch control system is improved. Our simulation and experimental results demonstrate that the proposed strategy can significantly improve frequency stability and smoothen power fluctuation resulting from wind speed variations. The minimum of frequency deviation with the proposed strategy is improved by up to 0.16 Hz at over-rated wind speed. Our technique can also improve anti-disturbance ability in frequency domain and achieve power balance

Anomalous Dome-like Superconductivity in RE2(Cu1-xNix)5As3O2(RE=La, Pr, Nd)

Significant manifestation of interplay of superconductivity and charge density wave, spin density wave or magnetism is dome-like variation in superconducting critical temperature (Tc) for cuprate, iron-based and heavy Fermion superconductors. Overall behavior is that the ordered temperature is gradually suppressed and the Tc is enhanced under external control parameters. Many phenomena like pesudogap, quantum critical point and strange metal emerge in the different doping range. Exploring dome-shaped Tc in new superconductors is of importance to detect emergent effects. Here, we report that the observation of superconductivity in new layered Cu-based compound RE2Cu5As3O2 (RE=La, Pr, Nd), in which the Tc exhibits dome-like variation with maximum Tc of 2.5 K, 1.2 K and 1.0 K as substituting Cu by large amount of Ni ions. The transitions of T* in former two compounds can be suppressed by either Ni doping or rare earth replacement. Simultaneously, the structural parameters like As-As bond length and c/a ratio exhibit unusual variations as Ni-doping level goes through the optimal value. The robustness of superconductivity, up to 60% of Ni doping, reveals the unexpected impurity effect on inducing and enhancing superconductivity in this novel layered materialsComment: 16 pages, 5 figures. Comments are welcom

Multiple adaptive model predictive controllers for frequency regulation in wind farms

Frequent and inadequate power regulation could significantly impact the main shaft mechanical load and the fatigue of wind turbines, which imposes a stringent requirement to perform frequency regulation. However, the existing work on frequency regulation mainly uses torque compensation to improve the frequency response, while few of them consider the mechanical fatigue of the main shaft caused by torque compensation of the frequency controller. In this paper, the mechanical fatigue of the main shaft can be mitigated in all of the speed sections thanks to the proposed frequency regulation controllers. Precisely, a multiple adaptive model predictive controller (MAMPC), which seamlessly integrates the multiple model predictive control (MMPC) and the real-time AutoRegressive with eXogenous inputs (ARX) model, is proposed. It nicely handles the rate of change in compensation torque to mitigate the mechanical load on the shaft in all of the speed sections. The effectiveness of our method is verified through extensive simulations. With the proposed method, the minimum frequency deviation can be reduced, and the number of fatigue cycles of the main shaft can be extended

IκBα polymorphism at promoter region (rs2233408) influences the susceptibility of gastric cancer in Chinese

<p>Abstract</p> <p>Background</p> <p>Nuclear factor of kappa B inhibitor alpha (IκBα) protein is implicated in regulating a variety of cellular process from inflammation to tumorigenesis. The objective of this study was to investigate the susceptibility of rs2233408 T/C genotype in the promoter region of <it>IκBα </it>to gastric cancer and the association of this polymorphism with clinicopathologic variables in gastric cancer patients.</p> <p>Methods</p> <p>A population-based case-control study was conducted between 1999 and 2006 in Guangdong Province, China. A total of 564 gastric cancer patients and 566 healthy controls were enrolled in this study. rs2233408 genotypes in <it>IκBα </it>were analyzed by TaqMan SNP genotyping assay.</p> <p>Results</p> <p>Both rs2233408 T homozygote (TT) and T heterozygotes (TC and TT) had significantly reduced gastric cancer risk (TT: OR = 0.250, 95% CI = 0.069-0.909, <it>P </it>= 0.035; TC and TT: OR = 0.721, 95% CI = 0.530-0.981, <it>P </it>= 0.037), compared with rs2233408 C homozygote (CC). rs2233408 T heterozygotes were significantly associated with reduced risk of intestinal-type gastric cancer with ORs of 0.648 (95% CI = 0.459-0.916, <it>P </it>= 0.014), but not with the diffuse or mix type of gastric cancer. The association between rs2233408 T heterozygotes and gastric cancer appeared more apparent in the older patients (age>40) (OR = 0.674, 95% CI = 0.484-0.939, <it>P </it>= 0.02). rs2233408 T heterozygotes was associated with non-cardiac gastric cancer (OR = 0.594, 95% CI = 0.411-0.859, <it>P </it>= 0.006), but not with cardiac gastric cancer. However, rs2233408 polymorphism was not associated with the prognosis of gastric cancer patients.</p> <p>Conclusions</p> <p><it>IκBα </it>rs2233408 T heterozygotes were associated with reduced risk of gastric cancer, especially for the development of certain subtypes of gastric cancer in Chinese population.</p

A secure partition-based document image watermarking scheme

In this paper, a new document image watermarking method based on a secure partitioning scheme is proposed and tested. In the method, a document image is securely divided into weight-invariant partitions, followed by selectively modifying characters to embed watermarks. The high security of a watermark results from applying a probabilistic metaheuristic algorithm, namely the Ant Colony System (ACS), to approximate the involved Bottleneck Hamiltonian Path Problem to generate key-dependent image partitions. For better efficiency, the farthest point heuristic and the multi-scale strategy are introduced into the ant colony system. Our experimental results demonstrate that the proposed watermarking scheme is secure, efficient, and robust to common attacks. The proposed secure partition scheme could serve as a general framework to introduce high security to prevailing watermarking techniques. Copyright © 2010 Inderscience Enterprises Ltd

A new asymmetric inclusion region for minimum weight triangulation

Minimum weight triangulation, Inclusion region, One-sided β-skeleton,