【問題描述】 在一個N*N的點陣中，如N=4,你現(xiàn)在站在（1，1），出口在（4，4）。你可以通過上、下、左、右四種移動方法，在迷宮內(nèi)行走，但是同一個位置不可以訪問兩次，亦不可以越界。表格最上面的一行加黑數(shù)字A[1..4]分別表示迷宮第I列中需要訪問并僅可以訪問的格子數(shù)。右邊一行加下劃線數(shù)字B[1..4]則表示迷宮第I行需要訪問并僅可以訪問的格子數(shù)。如圖中帶括號紅色數(shù)字就是一條符合條件的路線。 給定N，A[1..N] B[1..N]。輸出一條符合條件的路線，若無解，輸出NO ANSWER。（使用U，D，L，R分別表示上、下、左、右。） 2 2 1 2 （4，4） 1 （2，3） （3，3） （4，3） 3 （1，2） （2，2） 2 （1，1） 1 【輸入格式】 第一行是數(shù)m (n < 6 )。第二行有n個數(shù)，表示a[1]..a[n]。第三行有n個數(shù)，表示b[1]..b[n]。 【輸出格式】 僅有一行。若有解則輸出一條可行路線，否則輸出“NO ANSWER”。
標簽： 點陣
上傳時間： 2014-06-21
上傳用戶：llandlu
This book is intended as a thorough introduction to both PCI and PCI-X. Is as a “companion” to the specifications. If you’re designing boards or systems using offthe-shelf PCI interface silicon, this book together with the silicon vendor’s data sheets should be sufficient for your needs. On the other hand, if your goal is to design PCI silicon, motherboards or backplanes, you will undoubtedly need to reference the specifications for additional detail.
標簽： introduction companion intended thorough
上傳時間： 2014-08-18
上傳用戶：hoperingcong
data are often used interchangeably, they are actually very different. Data is a set of unrelated information, and as such is of no use until it is properly evaluated. Upon evaluation, once there is some significant relation between data, and they show some relevance, then they are converted into information. Now this same data can be used for different purposes. Thus, till the data convey some information, t
標簽： interchangeably are different unrelated
上傳時間： 2017-09-26
上傳用戶：cxl274287265
it is an example for stm32 usb. i had tried it. it deid not work on my board. buy it might be right for you.
上傳時間： 2015-03-26
上傳用戶：sjxelle
Computational models are commonly used in engineering design and scientific discovery activities for simulating complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who want to develop an understanding and/or predictive capability for complex behaviors typically observed in the corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system parameters, such as size or location dimensions and material properties, are adjusted to improve the performance of a system, as defined by one or more system performance objectives. Such optimization or tuning of the virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling methods are often used in uncertainty quantification to calculate a distribution on system performance measures, and to understand which uncertain inputs contribute most to the variance of the outputs. A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased models. These capabilities generally lead to improved designs and system performance in earlier design stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product development costs. In addition to providing this practical environment for answering system performance questions, the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized methods and meta-algorithms
標簽： Optimization and Uncertainty Quantification
上傳時間： 2016-04-08
上傳用戶：huhu123456
Providing QoS while optimizing the LTE network in a cost efficient manner is very challenging. Thus, radio scheduling is one of the most important functions in mobile broadband networks. The design of a mobile network radio scheduler holds several objectives that need to be satisfied, for example: the scheduler needs to maximize the radio performance by efficiently distributing the limited radio re- sources, since the operator’s revenue depends on it.
標簽： Communications Future Mobile LTE
上傳時間： 2020-05-27
上傳用戶：shancjb
The investigation of the propagation channel is becoming more and more important in mod- ern wireless communication. The demand for spectral efficiency motivates exploitation of all channels that can possibly be used for communications. Nowadays, a common trend for designing physical layer algorithms is to adapt the transceiving strategy, either by maximizing the diversity gains or by utilizing the coherence of the channels to improve the signal-to-noise power ratio.
標簽： Characterization Propagation Channel
上傳時間： 2020-05-31
上傳用戶：shancjb
Applications of microelectromechanical systems (MEMS) and microfabrica- tion have spread to different fields of engineering and science in recent years. Perhaps the most exciting development in the application of MEMS technol- ogy has occurred in the biological and biomedical areas. In addition to key fluidic components, such as microvalves, pumps, and all kinds of novel sensors that can be used for biological and biomedical analysis and mea- surements, many other types of so-called micro total analysis systems (TAS) have been developed.
標簽： Applications Technologies Bio-MEMS and
上傳時間： 2020-06-06
上傳用戶：shancjb
The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
標簽： Auto-Machine-Learning-Methods-Sys tems-Challenges
上傳時間： 2020-06-10
上傳用戶：shancjb
CPCI_E標準規(guī)范 CompactPCI? Express SpecificationThe documents in this section may be useful for reference when reading the specification. The revision listed for each document is the latest revision at the time this specification was published. Newer revisions of these documents may exist, so refer to the newest revision. Many of these documents are referenced throughout this specification. Refer to the newest revision of the document unless a specific revision is referenced. ? PCI Express Base Specification 3.0. PCI Special Interest Group (PCI-SIG). ? PCI Express Card Electromechanical (CEM) Specification 3.0. PCI Special Interest Group (PCI-SIG). ? PCI Express to PCI/PCI-X Bridge Specification, Rev. 1.0. PCI Special Interest Group (PCI-SIG). ? PCI Express Jitter White Paper. PCI Special Interest Group (PCI-SIG). ? PCIe Rj Dj BER White Paper. PCI Special Interest Group (PCI-SIG). ? PHY Electrical Test Specification for PCI Express Architecture. PCI Special Interest Group (PCI SIG). ? System Management Bus (SMBus) Specification, Version 2.0. Smart Battery System Implementer’
標簽： CPCIE
上傳時間： 2022-02-23
上傳用戶：
