Generate random bits, convert to bipolar, corrupt the message by passing through noise and decode using hard decision decoding and calculate BER
標簽： Generate bipolar convert corrupt
上傳時間： 2013-12-25
上傳用戶：1966640071
Specification File adjacencyListGragh class GeneralGraph: use adjacency list to implement the graph which data structure is vector Construct methods: * public GeneralGraph(): contain an empty vector store the vertex and a boolean determines whether graph is directed or not, defaulted is undirected
標簽： adjacencyListGragh Specification GeneralGraph adjacency
上傳時間： 2013-12-13
上傳用戶：lyy1234
As a programming language, C is rather like Pascal or Fortran.. Values are stored in variables. Programs are structured by defining and calling functions. Program flow is controlled using loops, if statements and function calls. Input and output can be directed to the terminal or to files. Related data can be stored together in arrays or structures.
標簽： programming variables language Fortran
上傳時間： 2017-05-18
上傳用戶：hongmo
I would like to thank my advisor, Dr. A. Lynn Abbott, for helping me throughout my research, Gary Fleming and the rest of the people at NASA Langley who provided all the flight information and image sequences, and my parents who supported me in my decision to enter graduate study. Also, thanks to Phichet Trisirisipal and Xiaojin Gong for helping when I had computer vision questions, and Nathan Herald for his help creating an illustration.
標簽： A. throughout research advisor
上傳時間： 2017-06-18
上傳用戶：maizezhen
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
Long-TermEvolution(LTE)isarguablyoneofthemostimportantstepsinthecurrentphaseof the development of modern mobile communications. It provides a suitable base for enhanced services due to increased data throughput and lower latency figures, and also gives extra impetus to the modernization of telecom architectures. The decision to leave the circuit- switched domainoutofthescope ofLTE/SAEsystem standardization might soundradical but itindicatesthatthetelecomworldisgoingstronglyfortheall-IPconcept----andthedeployment of LTE/SAE is concrete evidence of this global trend.
標簽： Deployment Handbook LTE-SAE The
上傳時間： 2020-06-01
上傳用戶：shancjb
This book is the result of works dedicated to specific applications of metaheuristics in smart electrical grids. From electric transmission, distribution networks to electric microgrids, the notion of intelligence refers to the ability to propose acceptable solutions in an increasingly more restrictive environment. Most often, it refers to decision-making assisting tools designed to support all human action.
標簽： Metaheuristics Intelligent Electrical Networks for
上傳時間： 2020-06-07
上傳用戶：shancjb
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
標簽： Convolutional Networks Neural Guide to
上傳時間： 2020-06-10
上傳用戶：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
標簽： interpretable-machine-learning
上傳時間： 2020-06-10
上傳用戶：shancjb
