IEEE Transactions on Pattern Analysis and Machine Intelligence

tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.

Probability and Random Processes

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

Preparing the books to read every day is enjoyable for many people. However, there are still many people who also don't like reading. This is a problem. But, when you can support others to start reading, it will be better. One of the books that can be recommended for new readers is computer aided analysis of electronic circuits algorithms and computational techniques. This book is not kind of difficult book to read. It can be read and understand by the new readers.

Computer-aided analysis of electronic circuits: Algorithms and computational techniques

The Art of Multiprocessor Programming

Erosion wear at the bend of pipe during tailings slurry transportation: Numerical study considering inlet velocity, particle size and bend angle

With the rapid increase in demand for the construction and development of island cities in the South China Sea, the urban heat island phenomenon in such cities should become a key factor to be considered in future urban planning. This paper took Sanya, China as a typical case, and long-term field experiments were conducted in the Mangrove Bay Area in summer and winter. An innovative urban green space cooling model was proposed, using the “green space cooling index” to quantitatively characterize the green space cooling effect, and aiming to minimize the intensity of urban heat islands. This paper studied the optimization method of green space planning and layout under the constraint of a centralized green space total area. Moreover, a genetic algorithm was adopted to optimize the calculation and the layout of the urban green space. The experimental results showed that the urban heat island intensity was more significant at night and was less effective in the daytime during summer. In winter, the urban heat island intensity had a greater effect in the daytime and was less influential at night. Finally, optimization results indicated that the average urban heat island intensity in summer was reduced by 8.8% under the optimal layout urban green space of 0.025 km2. The maximum reduction in heat island intensity occurred at 7:00 am (0.48 °C). When 0.0625 km2 urban green space was planned, the average urban heat island intensity index in summer was reduced to 0.27 °C, with a decrease of 20.5%.

/pdf/optimization-methods-of-urban-green-space-layout-on-tropical-1n131pqj.pdf

Optimization Methods of Urban Green Space Layout on Tropical Islands to Control Heat Island Effects

Supervised learning on Deep Neural Networks (DNNs) is data hungry. Optimizing performance of DNN in the presence of noisy labels has become of paramount importance since collecting a large dataset will usually bring in noisy labels. Inspired by the robustness of K-Nearest Neighbors (KNN) against data noise, in this work, we propose to apply deep KNN for label cleanup. Our approach leverages DNNs for feature extraction and KNN for ground-truth label inference. We iteratively train the neural network and update labels to simultaneously proceed towards higher label recovery rate and better classification performance. Experiment results show that under the same setting, our approach outperforms existing label correction methods and achieves better accuracy on multiple datasets, e.g.,76.78% on Clothing1M dataset.

KNN-enhanced Deep Learning Against Noisy Labels.

This article discusses the construction of a nodal bridge on Shanghai Rail Transit Line 10 above Line 6. Based on the analysis of the advantages and disadvantages of various construction methods, a new type of asymmetric loading cantilever assembly method with a bridge erecting machine is proposed. Firstly, the east half of the bridge is constructed to form a T-shaped structure, and then the bridge erecting machine uses the built T-shaped structure to provide a fulcrum to pass the holes and complete the construction of the west half of the bridge. The two parts are then closed. The method proposed in this paper can solve construction issues when working space under a bridge is limited, and the conventional balanced cantilever assembly method cannot be used for construction. 

Construction of the Continuous U-shaped Beam Bridge of Shanghai Rail Transit Line 10

With aggressive scaling down of feature sizes in VLSI fabrication, process variations, crosstalk and buffering have become critical issues to achieve timing closure in VLSI designs. Timing analysis and optimization techniques need to consider each of them and also their interactions. There are many statistical timing analysis researches to handle the problems introduced by process variations, but how to get the bounds of timing yield and how to use these techniques to verify the clock validity still need investigations. Timing budgeting is an essential step in optimization problems (e.g., gate sizing, dual-Vth assignment), and with process variations, how to reduce the design overhead in optimization becomes important. Dynamic programming is a useful technique to handle optimization problems, such as buffering, technology mapping and slicing floorplan problems, where many maxplus merge operations of solution lists are involved. So the speed-up of the maxplus merge operations will benefit many algorithms. In addition, prior researches were focusing on how to buffer a single net, but because of the huge number of buffers, the buffering of a whole circuit is required. With process variations on devices and interconnects, deterministic buffering techniques become pessimistic. How to efficiently get a good buffering solution needs investigations. The hierarchical design has become a useful technique for large circuit design, so IP reuse becomes a common practice. But since coupling capacitance becomes dominant, the delay from an input pin to an output pin is not constant any more. How to accurately specify the timing behavior of IPs with crosstalk is urgent. In this research, we investigate these analysis and optimization problems. 
In our first work, we propose two algorithms to compute the bounds of statistical delays. In our second work, a statistical checking of the structural conditions for correct clocking in latch-based designs is proposed, where the central problem is to compute the probability of having a positive cycle in a graph with random edge weights. In our third work, we consider the changes of both means and variances of delays in optimization, and formulate the timing budgeting under process variations as a linear programming problem using a robust optimization technique. In our fourth work, we propose a flexible data structure that can achieve universal speed-up under all cases for the merge operations. In our fifth work, the timing constrained minimal buffer insertion problem is formulated as a convex-cost flow dual problem, and we propose two algorithms based on convex-cost flow and min-cut techniques, respectively, to solve it in combinational circuits. In our sixth work, solutions from deterministic buffering are used to guide the statistical buffering in order to reduce the solution space in dynamic programming and achieve predictable results. Our last work presents two conservative macro-models that specify the timing behaviors of combinational hard IP blocks with crosstalk effects.

/pdf/timing-analysis-and-optimization-techniques-for-vlsi-3ufodkyuyn.pdf

Hai Zhou

Papers

Erosion wear at the bend of pipe during tailings slurry transportation: Numerical study considering inlet velocity, particle size and bend angle

Optimization Methods of Urban Green Space Layout on Tropical Islands to Control Heat Island Effects

KNN-enhanced Deep Learning Against Noisy Labels.

Construction of the Continuous U-shaped Beam Bridge of Shanghai Rail Transit Line 10

Timing analysis and optimization techniques for vlsi circuits