Boosting performance of transactional memory through O-GEHL predictors

Time-based Software Transactional Memory (STM) exploits a global clock to validate transactional data and guarantee consistency of transactions. While this method is simple to implement it results in contentions over the clock if transactions commit simultaneously. The alternative method is thread local clock (TLC) which exploits local variables to maintain consistency of transactions. However, TLC may increase false aborts and degrade performance of STMs. In this paper, we analyze global clock and TLC in the context of STM systems, highlighting both the implementation trade-offs and the performance implications of the two techniques. We demonstrate that neither global clock nor TLC is optimum across applications. To counter this challenge, we introduce two optimization techniques: The first optimization technique is Adaptive Clock (AC) which dynamically selects one of the two validation techniques based on probability of conflicts. AC is a speculative approach and relies on software O-GEHL predictors to speculate future conflicts. The second optimization technique is AC+ which reduces timing overhead of O-GEHL predictors by implementing the predictors in hardware. In addition, we exploit information theory to eliminate unnecessary computational resources and reduce storage requirements of the O-GEHL predictors. Our evaluation with TL2 and Stamp benchmark suite reveals that AC is effective and improves execution time of transactional applications up to 65%.     

极点配置自校正预估PID控制器

对于时滞过程,本文提出了一种新的自校正预估 PID 控制器,它实现了极点配置和静态抗干扰性能,克服了文献[2]提出的自校正预估 PI 控制器和文献[5]提出的自校正预估 PID 控制器不能在线校正控制器参数的缺点.仿真例子说明了本文结果的有效性.     

General Data Analysis Supported SPC

The issue of autocorrelation in statistical process control (SPC) data has been and continues to be a much discussed and researched topic. In nearly all published studies and in SPC training modules, the predominate models for autocorrelation come from a class of time-series models known as autoregressive-integrated moving average (ARIMA) models. However, little attention is given to a variety of systematic patterns pervasive in SPC applications that are not adequately modeled by the ARIMA framework. In this paper, we suggest the need for an even broader perspective than defined by ARIMA models. Furthermore, we argue that effective SPC training not only requires attention to data analysis tools for modeling systematic patterns but also to tools for diagnostically checking the distributional assumptions of variable and attribute charts.     

一种收敛于全局无偏最优解的非线性自回归（NAR）预测

本文研究了基于Ｖｏｌｔｅｒｒａ级数展开的非线性自回归预测模型的参数估计问题，从线性自适应ＩＩＲ波滤输出误差算法的观点，研究ＮＡＲ模型的参数估计问题，利用平均收敛条件，提出一可收敛于全局无偏最优解的新算法，解决了一般的预测误差算法的有偏解问题。     

Peer nominations on leadership as a predictor of the pass-fail criterion in naval air training.

After three months of pre-flight training, nine sections of cadets (N=268) were asked to nominate the three best-qualified and the three least qualified for military leadership in their sections. The leadership scores, ACE Test scores, Officer-Like-Qualities grade assigned by officers in charge, and final overall pre-flight average were correlated with a pass-fail criterion of flight training. The leadership score and the final pre-flight average were about equally predictive of the criterion (r of .27 and .28, respectively). The other two measures failed to correlate significantly with the criterion. A rationale for the predictive value of peer ratings for flight training success is suggested. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Estimating the number of different selection decisions resulting from the use of alternate predictor composites.

A method was described whereby d, the number of different selection decisions resulting from the use of alternate predictor composites may be rapidly estimated from the proportion of applicants accepted and the correlation between the composites. It was further demonstrated that the correlation between 2 optimally weighted composites based upon m and n variables (m n) is equal to the ratio of the 2 multiple correlations. 2 examples were used to illustrate the use of d in evaluating selection strategies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Supporting multi-user real-time VBR video service using dynamic bandwidth management based on prediction

Real-time variable bit rate (VBR) video is expected to take a significant portion of multimedia applications. However, plentiful challenges to VBR video service provision have been raised for its characteristic of high traffic abruptness. To support multi-user real-time VBR video transmission with high bandwidth utilization and satisfied quality of service (QoS), this article proposes a practical dynamic bandwidth management scheme. This scheme forecasts future media rate of VBR video by employing time-domain adaptive linear predictor and using media delivery index (MDI) as both QoS measurement and complementary management reference. In addition, to support multi-user application, an adjustment priorities classified strategy is also put forward. Finally, a test-bed based on this management scheme is established. The experimental results demonstrate that the scheme proposed in this article is efficient with bandwidth utilization increased by 20%-60% compared to a fixed service rate and QoS guaranteed.     

Novel SREKF-based recurrent neural predictor for narrowband/FM interference rejection in GPS

The GPS provides accurate positioning and timing information that is useful in various applications. A new adaptive neural predictor for GPS jamming suppression applications is proposed. The effective and computationally efficient square-root extended Kalman filter (SREKF) algorithm is adopted to adjust the synaptic weights in the nonlinear recurrent architecture and thereby estimate the stationary and non-stationary narrowband/FM waveforms. Cholesky factorization is employed in Riccati recursion to improve numerical stability because of the propagation of round-off errors in conventional KF equations. The main characteristics of the proposed SREKF-based canceller are their rapid convergence and favorable tracking performance. Simulation results reveal that its SNR improvement factor exceeds the factors of conventional LMS, RLS, ENA and TLFN filters in single-tone CWI, multi-tone CWI, pulse CWI and FM jamming environments, respectively.