卷积神经网络（CNNS）权值优化算法

《数据结构、算法与应用：C++语言描述》不仅仅介绍了理论知识，还提供了50多个应用实例及600多道练习题。 该资源中就是其习题答案与代码。

【实例简介】高斯函数，拉普拉斯函数源码，很好的！ Log，锐化，平滑 形态学的细化、开、腐蚀、骨架化、扩张等

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该压缩包中包含一个完整的语音识别程序，代码使用matlab实现，使用了经典的GMM，HMM模型。附件中还包含完整的说明文档，介绍了一些基本原理，和该源码的使用方法。是语音识别入门必读源码之一。该源码只需要很小的改动就可以用来做说话人识别。 ：}

5G无线通信系统关键技术（剑桥大学出版社） 2017年出版 对于5G所有最新技术进行了详细说明 很全的工具书Key Technologies for5G Wireless SystemsVINCENT W. S, WONGUniversity of British ColumbiaROBERT SCHOBERUniversity of Erlangen-NurembergDERRICK WING KWAN NGUniversity of New South WalesLI-CHUN WANGNational Chiao-Tung University即CAMBRIDGEUNIVERSITY PRESSCAMBRIDGEUNIVERSITY PRESSUniversity Printing House. Cambridge CB2 SBS. United KindomOne Liberty Plaza, 20h Floor New York, NY I(H0X, USA477 williamstown Road, port Melbourne, yic 3207 australia48424, 2nd Floor, Ansar Rod, Daryaganj. Delhi- I l4XH2, India79 Anson Road, #o6-(/ 00, Singapore 079%MCambridge University Press is part of the Lniversity of CambridgeIt furthers the University s mission by disseminating knowledge in the pursuit ofeducation, leaming and research at the highest international levels of excellence.www.cermbrid吧eInformtiononthistitlewww.cambridgeorg/978110713241810,1017③781316771655C Cambridge University Press 2017This puhlication is in copyright. Subjcct to sututonry exceptionand to the provisions of relewant collective licensing agreementsno reproduction of any part may take place without the writtenpermission of Cutmbridgre University Press.First published 2(117Printed in the United Kingdom by TJ International Ltd. Padstow, CornwallA catalogue recor for this pudlieafiove is aailable fromm the British LibraryLibrary of Congress Cataloging- in Pi hlicaiomz dataNames: Wong, Vincent W.S., editorTitle: Key technologies for 5G wireless systems/edited by Vincent W.S. Wong [and 3 otherOther titles key technologies for five g wireless svstemsDescription: Carmbrisige: New York, NY: Cambridge Lniversity Press, 2017.Identifiers: l CCN 2016045220)1 ISBN 9781 172418 (hardback)Subjects: LCSH: Wireless communication systems, I Machine-to-machinecommunications. Internet of things.Classitication: LCC TKs1032K49 2(17 DDC 621.38450-dc23LcrecordavailaBleathttps://lccnioc-gov/2016m5220)ISBN 978-1-107-17241- HardbackCambridge University Press has no responsibility for the persistence or accuracy ofURLs for extermal or third-party Internet websites referred to in this puhlication,and does not guarantee that any content on such websites is, or will remainaccurate of appropriateContentsList of Contributorspage xvIPrefaceKXIOverview of New Technolog ies for 5G SystemsVincent W S, Wong, Robert Schober, Derrick Wing Kwan Ng, and Li-Chun Wang1.1 Introduction1.2 Cloud Radio Access Networks1.3 Cloud Computing and Fog Computing1. 4 Non-orthogonal Multiple Access1. 5 Flexible Physical Layer Design334.4671. 6 Massive MIMo1. 7 Full-Duplex Communications1. 8 Millimeter wave1.9 Mobile Data Offloading, LTE-Unlicensed, and Smart Data Pricing131. 10 IoT M2M. and D2D1. I1 Radio Resource Management, Interference Mitigation, and Caching61. 12 Energy Harvesting Communications1. 13 Visible Light Communication19Acknowledgments20ReferencesPart I Communication Network Architectures for 5G Systems25Cloud Radio Access Networks for 5G Systems27Chih-Lin I, Jinn Huang, Xueyan Husang, Rongwved Ren, and Yami. Chen2.1 Rethinking the Fundamentals for 5G Systems272 User- Centric Networks2923 C-RAN Basics292.3.1 C-RAN Challenges Toward SGI302.4 Next Generation Fronthaul Interface (NGFI: The FH Solutionfor SGC-RAN312. 4.1 Proof-of-Concept Development of NGFI33Contents2.5 Proof-of-Concept Verification of Virtualized C-RAN2.5.1 Data packets3725.2 Test Procedure382.5.3 Test Results392. 6 Rethinking the Protocol Stack for C-RAN2.6.1 Motivation402.6.2 Multilevel Centralized and Distributed Protocol Stack402.7 Conclusion45AcknowledgmentsReferencesFronthaul-Aware Design for Cloud Radio Access Networks48Liang Liu, Wei Yu, and Osvaldo Simeone3. 1 Introduction483.2 Fronthaul-Aware Cooperative Transmission and Reception493. 2.1 Uplink513.2.2 Downlink573.3 Fronthaul-Aware Data Link and Physical layers61.3. I Uplink633.3.2 Downlink693.4 Conclusion73Acknowledgments74References74MobEdge computing76Ben Liang4.1 Introduction764.2 Mobile Edge Computing774.3 Reference architecture794.4 Benefits and Application Scenarios804 4.1 User-Oriented Use cases4. 4.2 Operator-Oriented Use Ca814 5 Research challenges824.5.1 Computation Offloading824.5.2 Communication Access to Computational Resources834.5.3 Multi-resource Schedulin844.5 4 Mobility Management854.5.5 Resource Allocation and Pricing4.5.6 Network functions virtualization864.5, 7 Security and Pri864.5.8 Integration with Emerging Technologies874.6 Conclusion88ReferencesContentsDecentralized Radio Resource Management for Dense HeterogeneousWireless networksAbolfazl Mehhodniya and Fumiyuki Adach5.1 Introduction925.2 System Model935.2.1 SINR Expression5.2.2 Load and Cost Function Expressions955.3 Joint BSCSA/UECSA ON/OFF Switching Scheme965.3.1 StrateTy Selection and Beacon Transmission53.2 UE AssocIation5.3.3 Proposed Channel Segregation Algorithms985.3.4 Mixed-Strategy Update3.4 Computer Simulation5.5 Conclusion104Acknowledgments04References105Part ll Physical Layer Communication Techniques107Non-Orthogonal Multiple Access(NOMA)for 5G Systems109Wei Llang, Zhiguo Ding, and H. Vincent Poor6.1 Introduction1106.2 NOMA in Single-Input Single-Output(SISO)Systems1126.2.1 The basics of nomaI126. 2. 2 Impact of User Pairing on NOMA136.2,3 Cognitive Radio Inspired NOMA6. 3 NOMA in MIMO Systems1206.3.1 System Model for MIMO-NOMA Schemes1216.3.2 Design of Precoding and Detection Matrices with Limited CSIT 1236.3.3 Design of Precoding and Detection Matrices with Perfect CSIT 1266.4 Summary and Future Directions128ReferencesFlexible Physical Layer Design133Maximilian Matthe, Martin Danneberg, Dan Zhang, and Gerhard Fettweis7.1 Introduction1337. 2 Generalized Frequency Division Multiplexing357.3 Software-Defined waveform1377. 3. 1 Time Domain Processing1387.3.2 Implementation Architecture1387.4 GFDM Receiver Design14174 Synchronization unit1427. 4.2 Channel Estimation Unit1474.3 MIMo-GFDM Detection Unit145Contents7.5 Summary and Outlook147Acknowledgments148References488Distributed Massive MIMO in Cellular Networks15IMichail Matthaiou and Shi Jin8. I Introduction15l8. 2 Massive MIMO: Basic Principles1528.2.1 Uplink Downlink Channel Models1538.2.2Favorable Propagation1548.3 Performance of Linear Receivers in a Massive MIMO Uplink1548.4 performance of linear precoders in a massive mimo downlink1578. s Channel estimation in massive mimo systems1588.5.1 Uplink Transmission1598.5.2 Downlink Transmission1608.6 Applications of Massive MIMO Technology1618.6.1 Full-Duplex Relaying with Massive Antenna Arrays1618.6.2 Joint Wireless Information Transfer and Energy Transfer forDistributed massive mimo1638.7 Open Future Research Directions1678. 8 Conclusionl68References169Full-Duplex Protocol Design for 5G Networks172Tanelf Ahonen and Risto wichman9.1 Introduction1729. 2 Basics of Full-Duplex Systems1739.2.1 In-Band Full-Duplex Operation Mode1739.2.2 Self-Interference and Co-channel Interference1749.2.3 Full-Duplex Transceivers in Communication Links1759. 2. 4 Other Applications of Full-Duplex Transceivers1789.3 Design of Full-Duplex Protocols1799.3, 1 Challenges and Opportunities in Full-Duplex Operation1799.3.2 Full-Duplex Communication Scenarios in 5G NetworksR9.4 Analysis of Full-Duplex Protocols1829.4.1 Operation Modes in Wideband Fading Channels1829. 4, 2 Full- Duplex Versus Half-Duplex in Wideband Transmission1849.5 Conclusion1849.5.1 Prospective Scientific Research DirectionsI849.5.2 Full-Duplex in Commercial 5G Networks185RLItrtncekl8610Millimeter Wave Communications for 5G Networks188Jiho Song, Miguel R Castellanos, and David J. LoweContentsⅸx10.1 Motivations and Opportunities18810.2 Millimeter Wave Radio Propagation18910. 2.1 Radio Attenuation1890. 2. 2. Free-Space Path LOSs19I10.2.3 Severe shadow19310.2 4 Millimeter Wave Channel model19310.2.5 Link Budget Analysis19410.3 Beamforming Architectures19510.3, Analog beamforming solutions19610.3.2 Hybrid Beamforming Solutions20010.3.3 Low-Resolution Receiver Architecture2010.4 Channel Acquisition Techniques20110.4.1 Subspace Sampling for Beam Alignment20210.4.2 Compressed Channel estimation Techniques20510.5 Deployment Challenges and Applications20710.5.1 EM Exposure at Millimeter Wave Frequencies20710.5.2 Heterogeneous and Small-Cell Networks208Acknowledgments209References209Interference Mitigation Techniques for Wireless Networks214Koralia N Pappi and George K, Karag annidis1 1.1 Introduction21411.2 The Interference Management Challenge in the 5G vision21411. 2. 1 The 5G Primary Goals and Their Impact on Interference2141 1.2.2 Enabling Technologies for Improving Network Efficiencyand Mitigating Interference21611.3 Improving the Cell-Edge User Experience: Coordinated Multipoint218I 1.3.1 Deployment Scenarios and Network Architecture2181 13. 2 CoMP Techniques for the Uplink22011.3.3 CoMP Techniques for the Downlink2211 1.4 Interference Alignment: Exploiting Signal Space Dimensions2231 1.4.1 The Concept of Linear Interference Alignment224L1. 4.2 The Example of the X-Channel225I 1. 4.3 The K-User Interference Channel and Cellular NetworksAsymptotic Interference Alignment22611.4.4 Cooperative Interferenee Networks22711.4.5 Insight from IA into the Capacity Limits of Wireless Networks 22711.5 Compute-and-Forward Protocol: Cooperation at the ReceiverSide for the Uplink22811.5.1 Encoding and Decoding of the CoF Protocol22811.5.2 Achievable-Rate Region and Integer Equation Selection23011.5.3 Advantages and Challenges of the CoF Protocol232IL6 Conclusion233References233

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毕业仓库管理系统源码以及需要的资源，有需要的可以直接下载

快速傅里叶变换是应用最广泛的一种谐波检测方法，但直接利用快速傅里叶变换进行谐波检测存在较大的误差，影响谐波分析结果的准确性。通过加汉宁窗及插值修正算法可以改善计算谐波频率、相位和幅值的准确度。简述了电力系统谐波检测非同步采样加汉宁窗插值算法的原理，并采用巴特沃斯低通滤波器滤除高频噪声。MATLAB仿真结果表明，加汉宁窗插值算法具有检测精度好，实现简单的优点。