In designing high-speed system, signal reflection and crosstalk often cause many signal integrity issues. We are using Mentor PADS2007/HyperLynx to design a high-speed system with TMS320DM6446, a high performance video processing chip. The pre-layout high-speed signal lines of this DSP image processing system are throughout simulated. These widespread issues of signal integrity, especially reflection and crosstalk in the high-speed printed circuit board are addressed, and the corresponding solutions are presented.
Front matter pages come before the papers or chapters in a published work. Front matter includes the title page, copyright and notices page, and table of contents. It can also include a foreword, preface, or introduction; series information; lists of contributors, sponsors or reviewers; lists of abbreviations and notations; and conversion tables.
This paper presents the results of an analysis aiming at identifying the main injury severity factors associated with road collisions that occur during snowstorms, including traffic conditions, road geometry and environment, pavement surface conditions as well as vehicle and driver characteristics. A multilevel multinomial logit model is introduced for capturing the hierarchical nature of the collision data between individual collisions and the vehicles and persons involved. Different from past studies, the modeling effort focuses on the collisions that occurred over snowstorms so that the effect of weather related factors are not masked due to the imbalance of data sample between collisions occurred under normal conditions and those under snowstorms. This approach is also necessary for ensuring that the incremental effect of different weather severity, as well as winter road maintenance operations, could be captured. Collisions that occurred on a number of highway routes from the province of Ontario, Canada, over six winter seasons (2000-2006), were selected for this analysis. It was found that factors related to drivers (age, sex, condition), road characteristics (number of lanes, speed limit, road surface conditions), vehicle type, position in vehicle, use of safety belt, and traffic volume have statistically significant effects on collision severity outcome. In general, the modeling results indicate that good road surface conditions, high traffic volume, young and male drivers and new vehicles are associated with reduced injury severity levels. The authors' analysis, however, did not confirm the main finding from literature, that is, severer weather, such as higher precipitation intensity and wind speed, is associated with lesser collision severity.
Accurate estimation of photometric redshifts (photo-$z$) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to estimate photo-$z$ and investigate the systematic uncertainties affecting the results. Using galaxy flux and color as input features, we construct a mapping between input features and redshift by using a training set of simulated data, generated from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and COSMOS catalogue, with the expected instrumental effects of the planned China Space Station Telescope (CSST). To improve the accuracy and confidence of predictions, we incorporate inverse variance weighting and perturb the catalog using input feature errors. Our results show that weighted RF can achieve a photo-$z$ accuracy of $\rm \sigma_{NMAD}=0.025$ and an outlier fraction of $\rm \eta=2.045\%$, significantly better than the values of $\rm \sigma_{NMAD}=0.043$ and $\rm \eta=6.45\%$ obtained by the widely used Easy and Accurate Zphot from Yale (EAZY) software which uses template-fitting method. Furthermore, we have calculated the importance of each input feature for different redshift ranges and found that the most important input features reflect the approximate position of the break features in galaxy spectra, demonstrating the algorithm's ability to extract physical information from data. Additionally, we have established confidence indices and error bars for each prediction value based on the shape of the redshift probability distribution function, suggesting that screening sources with high confidence can further reduce the outlier fraction.
Road safety performance function ( SPF ) analysis using data-driven and nonparametric methods, especially recent developed deep learning approaches, has gained increasing achievements. However, due to the learning mechanisms are hidden in a " black box " in deep learning, traffic features extraction and intelligent importance analysis are still unsolved and hard to generate. This paper focuses on this problem using a deciphered version of deep neural networks ( DNN ) , one of the most popular deep learning models. This approach builds on visualization, feature importance and sensitivity analysis, can evaluate the contributions of input variables on model ' s " black box " feature learning process and output decision. Firstly, a visual feature importance ( ViFI ) method that describes the importance of input features is proposed by adopting diagram and numerical-analysis. Secondly, by observing the change of weights using ViFI on unsupervised training and fine-tuning of DNN, the final contributions of input features are calculated according to importance equations for both steps that we proposed. Sequentially, a case study based on a road SPF analysis is demonstrated, using data collected from a major Canadian highway, Highway 401. The proposed method allows effective deciphering of the model ' s inner workings and allows the significant features to be identified and the bad features to be eliminated. Finally, the revised dataset is used in crash modeling and vehicle collision prediction, and the testing result verifies that the deciphered and revised model achieves state-of-the-art performance.
Extracting coupling matrix from given S-parameters can be viewed as an inverse problem for microwave filters, which is of importance for filter design and tuning. In this letter, a regularized deep belief network (R-DBN) is proposed to handle this inverse modeling problem. The training of an R-DBN consists of two steps. First, in unsupervised training, to accommodate the characteristics of input data, this model is constructed with a series of traditional restricted Boltzmann machines (RBMs), which are equipped with a continuous version of transfer function for continuous data processing. In addition, this training can provide suitable weights and bias for the following step. Second, in supervised training, Bayesian regularization is employed to increase modeling ability and prevent overfitting. Two experiments with different simulation environments are illustrated, and the calibration results show high accuracy and robustness in a more intelligent way using this method.
This paper presents an empirical study focusing on identifying the main factors that affect the capacity and free-flow speed (FFS) of urban freeways under inclement winter weather conditions. The weather and road surface condition factors examined include air temperature, wind speed, hourly snow intensity, visibility, snow on ground, and road surface condition describing the road slipperiness caused mainly by snow events. Data on traffic operations and the associated weather and road conditions observed at two freeway locations over the 2010–2012 winter seasons were used in an extensive statistical analysis. Linear regression models were calibrated for both capacity and FFS reductions as related to various weather and road condition variables. It was found that visibility and road surface conditions had a statistically significant effect on both capacity and FFS. Snow intensity was found to be significant only when the visibility factor was excluded; this finding suggests a refutation of these two factors on capacity and FFS. The modeling results were compared with those recommended by the Highway Capacity Manual 2010, showing that, in many cases, the manual could underestimate or overestimate the effects of winter weather conditions and that the proposed models provided a more reasonable estimate at a higher level of granularity.
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