Tabla de Contenidos: “…Getting started with Responsible AI in your organization -- Regulatory compliance in Azure Policy for Azure Machine Learning -- Azure Security Benchmark -- Federal Risk and Authorization Management Program -- New Zealand Information Security Manual (restricted) -- NIST SP 800-53 Rev. 5 -- Reserve Bank of India IT Framework for Banks v2016 -- Compliance auditing and reporting -- Azure portal -- Azure Resource Graph Explorer -- Compliance automation in Azure -- Azure Blueprints -- IaC -- Summary -- Part 2: Securing Your Data -- Chapter 4: Data Protection and Governance -- Working with data governance in Azure -- Identifying challenges -- Exploring benefits -- Getting started using cloud data best practices -- Exploring Azure tools and resources -- Storing and retrieving data in Azure Machine Learning -- Connecting datastores -- Adding data assets -- Encrypting and securing data -- Encryption at rest -- Encryption in transit -- Exploring backup and recovery -- Reviewing backup options for your datastores -- Recovering your workspace -- Summary -- Chapter 5: Data Privacy and Responsible AI Best Practices -- Technical requirements -- Working with Python -- Running a notebook in Azure Machine Learning -- Installing the SmartNoise SDK -- Installing Fairlearn -- Discovering and protecting sensitive data -- Identifying sensitive data -- Exploring data anonymization -- Introducing differential privacy -- Mitigating fairness -- Fairlearn -- Working with model interpretability -- Exploring the Responsible AI dashboard -- Exploring FL and secure multi-party computation -- FL with Azure Machine Learning -- Summary -- Further reading -- Part 3: Securing and Monitoring Your AI Environment -- Chapter 6: Managing and Securing Access -- Working with the PoLP -- Authenticating with Microsoft Entra ID -- Implementing RBAC -- Working with built-in roles…”

Tabla de Contenidos: “…ACPI Options -- acpi -- acpi_sleep -- acpi_sci -- acpi_irq_ balance -- acpi_irq_ nobalance -- acpi_irq_isa -- acpi_irq_pci -- acpi_os_name -- acpi_osi -- acpi_serialize -- acpi_skip_ timer_override -- acpi_dbg_layer -- acpi_fake_ecdt -- acpi_generic_ hotkey -- acpi_pm_good -- ec_intr -- memmap -- memmap -- pnpacpi -- processor.max_ cstate -- processor.nocst -- SCSI Options -- max_luns -- max_report_ luns -- scsi_dev_flags -- PCI Options -- PCI -- Plug and Play BIOS Options -- noisapnp -- pnpbios -- pnp_reserve_ irq -- pnp_reserve_ dma -- pnp_reserve_io -- pnp_reserve_ mem -- SELinux Options -- checkreqprot -- enforcing -- selinux -- selinux_ compat_net -- Network Options -- netdev -- rhash_entries -- shapers -- thash_entries -- Network File System Options -- lockd.nlm_ grace_period -- lockd.nlm_ tcpport -- lockd.nlm_ timeout -- lockd.nlm_ udpport -- nfsroot -- nfs.callback_ tcpport -- nfs.idmap_ cache_timeout -- Hardware-Specific Options -- nousb -- lp -- parport -- parport_init_ mode -- nr_uarts -- Timer-Specific Options -- enable_timer_ pin_1 -- disable_timer_ pin_1 -- enable_8254_ timer -- disable_8254_ timer -- hpet -- clocksource -- Miscellaneous Options -- dhash_entries -- elevator -- hashdist -- combined_ mode -- max_loop -- panic -- pause_on_oops -- profile -- Kernel Build Command-Line Reference -- Informational Targets -- Cleaning Targets -- Configuration Targets -- Build Targets -- Packaging Targets -- Documentation Targets -- Architecture-Specific Targets -- Analysis Targets -- Kernel Configuration Option Reference -- EXPERIMENTAL -- LOCALVERSION -- AUDIT -- IKCONFIG -- EMBEDDED -- MODULES -- IOSCHED_NOOP -- IOSCHED_AS -- IOSCHED_ DEADLINE -- IOSCHED_CFQ -- SMP -- M386 -- X86_GENERIC -- NR_CPUS -- SCHED_SMT -- PREEMPT_NONE -- PREEMPT_ VOLUNTARY -- PREEMPT -- PREEMPT_BKL -- NOHIGHMEM -- HIGHMEM4G -- HIGHMEM64G -- FLATMEM_ MANUAL…”

Tabla de Contenidos: “…Conclusion -- Exercises -- References -- Chapter 3: Feed-Forward Neural Networks -- A Short Review of Network's Architecture and Matrix Notation -- Output of Neurons -- A Short Summary of Matrix Dimensions -- Example: Equations for a Network with Three Layers -- Hyper-Parameters in Fully Connected Networks -- A Short Review of the Softmax Activation Function for Multiclass Classifications -- A Brief Digression: Overfitting -- A Practical Example of Overfitting -- Basic Error Analysis -- Implementing a Feed-Forward Neural Network in Keras -- Multiclass Classification with Feed-Forward Neural Networks -- The Zalando Dataset for the Real-World Example -- Modifying Labels for the Softmax Function: One-Hot Encoding -- The Feed-Forward Network Model -- Keras Implementation -- Gradient Descent Variations Performances -- Comparing the Variations -- Examples of Wrong Predictions -- Weight Initialization -- Adding Many Layers Efficiently -- Advantages of Additional Hidden Layers -- Comparing Different Networks -- Tips for Choosing the Right Network -- Estimating the Memory Requirements of Models -- General Formula for the Memory Footprint -- Exercises -- References -- Chapter 4: Regularization -- Complex Networks and Overfitting -- What Is Regularization -- About Network Complexity -- lp Norm -- l2 Regularization -- Theory of l2 Regularization -- Keras Implementation -- l1 Regularization -- Theory of l1 Regularization and Keras Implementation -- Are the Weights Really Going to Zero? …”

Tabla de Contenidos: “…6.4 Spherical Microphone Arrays 142 -- 6.5 Spherical Adaptive Algorithms 148 -- 6.6 Comparative Studies 149 -- 6.7 Comparison of Linear and Spherical Arrays for DSR 152 -- 6.8 Conclusions and Further Reading 154 -- References 155 -- Part Three FEATURE ENHANCEMENT -- 7 From Signals to Speech Features by Digital Signal Processing 161 / Matthias WŠ olfel -- 7.1 Introduction 161 -- 7.1.1 About this Chapter 162 -- 7.2 The Speech Signal 162 -- 7.3 Spectral Processing 163 -- 7.3.1 Windowing 163 -- 7.3.2 Power Spectrum 165 -- 7.3.3 Spectral Envelopes 166 -- 7.3.4 LP Envelope 166 -- 7.3.5 MVDR Envelope 169 -- 7.3.6 Warping the Frequency Axis 171 -- 7.3.7 Warped LP Envelope 175 -- 7.3.8 Warped MVDR Envelope 176 -- 7.3.9 Comparison of Spectral Estimates 177 -- 7.3.10 The Spectrogram 179 -- 7.4 Cepstral Processing 179 -- 7.4.1 Definition and Calculation of Cepstral Coefficients 180 -- 7.4.2 Characteristics of Cepstral Sequences 181 -- 7.5 Influence of Distortions on Different Speech Features 182 -- 7.5.1 Objective Functions 182 -- 7.5.2 Robustness against Noise 185 -- 7.5.3 Robustness against Echo and Reverberation 187 -- 7.5.4 Robustness against Changes in Fundamental Frequency 189 -- 7.6 Summary and Further Reading 191 -- References 191 -- 8 Features Based on Auditory Physiology and Perception 193 / Richard M. …”

Tabla de Contenidos: “…26 -- 2.2 Antenna Design Fundamentals 26 -- 2.2.1 Antenna Fundamental Parameters 26 -- 2.2.2 LP Antenna Design and Example 29 -- 2.3 CP Antenna Design 31 -- 2.3.1 CP Antenna Fundamentals and Types 31 -- 2.3.2 Simple CP Antenna Design Example 35 -- 2.3.3 Technical Challenges in Designing GNSS Antennas 36 -- References 40 -- 3 Satellite GNSS Antennas 41 -- 3.1 Navigation Antenna Requirements 41 -- 3.2 Types of Antenna Deployed 41 -- 3.3 Special Considerations for Spacecraft Antenna Design 50 -- 3.3.1 Passive Intermodulation Effects 50 -- 3.3.2 Multipactor Effects 52 -- References 52 -- 4 Terminal GNSS Antennas 55 -- 4.1 Microstrip Antenna for Terminal GNSS Application 55 -- 4.1.1 Single-Feed Microstrip GNSS Antennas 55 -- 4.1.2 Dual-Feed Microstrip GNSS Antennas 60 -- 4.1.3 Design with Ceramic Substrate 64 -- 4.2 Spiral and Helix GNSS Antennas 66 -- 4.2.1 Helix Antennas 66 -- 4.2.2 Spiral Antennas 71 -- 4.3 Design of a PIFA for a GNSS Terminal Antenna 73 -- References 79 -- 5 Multimode and Advanced Terminal Antennas 81 -- 5.1 Multiband Terminal Antennas 81 -- 5.1.1 Multiband Microstrip GNSS Antennas 82 -- 5.1.2 Multiband Helix Antennas for GNSS 88 -- 5.2 Wideband CP Terminal Antennas 95 -- 5.2.1 Wideband Microstrip Antenna Array 95 -- 5.2.2 High-Performance Universal GNSS Antenna Based on Spiral Mode Microstrip Antenna Technology 96 -- 5.2.3 Wideband CP Hybrid Dielectric Resonator Antenna 96 -- 5.2.4 Multi-Feed Microstrip Patch Antenna for Multimode GNSS 98 -- 5.3 High-Precision GNSS Terminal Antennas 102.…”

Tabla de Contenidos: “…Stochastic Pooling -- 2.4.1.4. Lp Pooling -- 2.4.1.5. Mixed Pooling -- 2.4.1.6. …”

Tabla de Contenidos: “…Gang -- A kind of visualization spatial clustering algorithm / G.Q. Fan and L.P. Ma -- A dynamic packet assignment algorithm based on ECC / Y.Q. …”

Tabla de Contenidos: “…About the Author xiii Preface xv 1 Forecasting a Monthly Time Series 1 1.1 Introduction 1 1.2 Forecasting Using LV(p) Models 1 1.2.1 Basic or Regular LV(p) Models 1 1.2.2 Special LV(p) Models 6 1.3 Forecasting Using the LVARMA(p,q,r) Model 8 1.3.1 Special Notes on the ARMA Model 9 1.3.2 Application of Special LVAR Models 10 1.4 Forecasting Using TGARCH(a,b,c) Models 12 1.4.1 Application of ARCH(a), GARCH(b), and TARCH(c) Models 14 1.4.2 Application of TGARCH(a,b,0) Models 14 1.4.3 Application of TGARCH(a,b,c) Models 20 1.4.4 Other Alternative Models 20 1.5 Instrumental Variables Models 20 1.5.1 Application of the GMM Estimation Method 21 1.5.2 Application of the TSLS Estimation Method 36 1.6 Special Notes and Comments on Residual Analysis 42 1.6.1 Specific Residual Analysis 43 1.6.2 Additional Special Notes and Comments 61 1.6.3 Serial Correlation Tests 65 1.7 Statistical Results Using Alternative Options 67 1.7.1 Application of an Alternative Coefficient Covariance Matrix 67 1.7.2 Application of Selected Combinations of Options 70 1.7.3 Final Notes and Conclusions 71 2 Forecasting with Time Predictors 73 2.1 Introduction 73 2.2 Application of LV(p) Models of HS on MONTH by YEAR 73 2.2.1 Special LV(12) Models of HS on MONTH by YEAR 73 2.2.2 Application of the Omitted Variables Test - Likelihood Ratio 75 2.2.3 Heterogeneous Model of HS on HS(−12) and Month by YEAR 79 2.3 Forecast Models of HS on MONTH by YEAR 79 2.3.1 Application of LV(1) Models of HS on MONTH by YEAR 79 2.3.2 Application of Basic LV(p) Models of HS on MONTH by YEAR 82 2.3.3 Application of AR(q) Models of HS on MONTH by YEAR 86 2.3.4 Application of ARMA(q,r) Models of HS on MONTH by YEAR 89 2.3.5 Application of LVAR(p,q) Models of HS on MONTH by YEAR 89 2.3.6 Application of LVAR(p,q) Models of HS on YEAR by MONTH 92 2.4 Heterogeneous Classical Growth Models 95 2.4.1 Forecasting Based on LV(p) Het_CGMs of HS 95 2.4.2 Forecasting Based on AR(q) Het_CGMs 99 2.4.3 Forecasting Based on LVAR(p,q) Het_CGMs 101 2.5 Forecast Models of G in Currency.wf1 103 2.5.1 LVAR(p,q) Additive Models of G by @Month with @Trend 104 2.5.2 LV(1) Heterogeneous Models of G by @Month 111 2.6 Forecast Models of G on G(−1) and Polynomial Time Variables 116 2.6.1 Heterogeneous Model of G on G(−1) and Polynomial T by @Month 116 2.6.2 Forecast Model of G on G(−1) with Heterogeneous Polynomial Trend 138 2.7 Forecast Models of CURR in Currency.wf1 140 2.7.1 Developing Scatter Graphs with Regressions 141 2.7.2 Additive Forecast Models of CURR with a Time Predictor 143 2.7.3 Interaction Forecast Models of CURR 159 2.7.4 Forecast Models Based on Subsamples 169 3 Continuous Forecast Models 185 3.1 Introduction 185 3.2 Forecasting of FSPCOM 185 3.2.1 Simple Continuous Models of FSPCOM 185 3.2.2 LVAR(P,Q) Models of Y = FSPCOM with Polynomial Trend 190 3.2.3 Translog Models with Time Predictor 195 3.3 Forecasting Based on Subsamples 207 3.3.1 Lag Variable Models With Lower and Upper Bounds 209 3.4 Special LV(12) Models of HS with Upper and Lower Bounds 222 3.4.1 Special LVARMA(12,q,r) Model of LNYul Without Time Predictor 223 3.4.2 Special LVARMA(12,q,r) of LNYul With Time Predictor 223 4 Forecasting Based on (Xt,Yt) 229 4.1 Introduction 229 4.2 Forecast Models Based on (Xt,Yt) 229 4.3 Data Analysis Based on a Monthly Time Series 230 4.4 Forecast Models without a Time Predictor 230 4.4.1 Two-Way Interaction Models 230 4.4.2 Cobb-Douglass Model and Alternatives 235 4.5 Translog Quadratic Model 236 4.5.1 Forecasting Using a Subsample 240 4.5.2 Forecast Model with Trend 243 4.6 Forecasting of FSXDP 247 4.6.1 Forecasting of Y2 Based on a Subsample 247 4.6.2 Extension of the Model (4.25) with Time Variables 252 4.7 Translog Linear Models 256 4.7.1 Basic Translog Linear Model 256 4.7.2 Tanslog Linear Model with Trend 256 4.7.3 Heterogeneous Tanslog Linear Model 260 4.8 Application of VAR Models 262 4.8.1 Unstructured VAR Models Based on (X1t,Y1t) 262 4.8.2 The Simplest VAR Models with Alternative Trends 264 4.8.3 Complete Heterogeneous VAR Models by @Month 270 4.8.4 Bayesian VAR Models 271 4.8.5 VEC Models 271 4.9 Forecast Models Based on (Y1t,Y2t) 275 4.9.1 Forecast Models Based on Figures 4.42a and b 275 4.9.2 Reciprocal Causal Effects Models 279 4.9.3 Models with the Time Independent Variables 280 4.10 Special Notes and Comments 287 5 Forecasting Based On (X1t,X2t,Yt) 289 5.1 Introduction 289 5.2 Translog Linear Models Based on (X1,X2,Y1) 289 5.2.1 Basic Translog Linear Model 289 5.2.2 Tanslog Linear Model with Trend 292 5.2.3 Tanslog Linear Model with Heterogeneous Trends 292 5.3 Translog Linear Models Based on (X1,X2,Y2) 293 5.3.1 Translog Linear Models Using the Subsample {@Year>1990} 296 5.3.2 Translog Linear Models Using the Subsample {@Year>1975} 298 5.3.3 Translog Linear Models Using the Whole Sample 298 5.4 Forecast Models Using Original (X1,X2,Y) 300 5.4.1 Model Based on Figure 5.6a 300 5.4.2 Model Based on Figure 5.6b 301 5.4.3 Model Based on Figure 5.6c 307 5.5 Alternative Forecast Models Using Original (X1,X2,Y) 310 5.5.1 Three-Way Interaction Based on Figure 5.14a 311 5.5.2 Three-Way Interaction Based on Figure 5.14b and c 311 5.6 Forecasting Models with Trends Using Original (X1,X2,Y) 311 5.7 Application of VAR Models Based on (X1t,X2t,Y1t) 316 5.7.1 Unrestricted VAR Models 316 5.7.2 The Simplest Two-Way Interaction VAR Model 317 5.7.3 The Simplest Three-Way Interaction VAR Model 318 5.8 Applications of the Object "System" 320 5.8.1 The MLV(1,1,1) Models of (Y1,Y2,Y3) on (Y1(−1),Y2(−1),Y3(−1)) 320 5.8.2 Circular Effects MLV(1,1,1) Models 328 5.9 Models Presenting Causal Relationships Y1,Y2, and Y3 335 5.9.1 Triangular Effects Models 335 5.9.2 Circular Effects Models 340 5.9.3 Reciprocal Effects Models 341 5.10 Extended Models 344 5.10.1 Extension to the Models with Additional Exogenous Variables 344 5.10.2 Extension to the Models with Alternative Trends 347 5.10.3 Extension to LVARMA(p,q,r) 352 5.10.4 Extension to Heterogeneous Regressions by Months 356 5.11 Special Notes and Comments 369 6 Forecasting Quarterly Time Series 371 6.1 Introduction 371 6.2 Alternative LVARMA(p,q,r) Of a Single Time Series 371 6.2.1 LV(P) Forecast Model of GCDANt 371 6.2.2 LVARMA(p,q.r) Forecast Models of GCDN 372 6.2.3 Forecast Models of GCDAN with Time Variables 374 6.2.4 Special Notes on Uncommon Models 381 6.3 Complete Heterogeneous LV(2) Models of GCDAN By @Quarter 383 6.3.1 Using the Simplest Equation Specification 383 6.3.2 Using a Complete Equation Specification 387 6.4 LV(2) Models of GCDAN with Exogenous Variables 387 6.4.1 LV(2) Models with an Exogenous Variable 387 6.4.2 LV(2) Models with Two Exogenous Variables 390 6.5 Alternative Forecast Models Based on (Y1,Y2) 393 6.5.1 LV(2) Basic Interaction Models 393 6.5.2 LV(2) Models of (Y1,Y2) with an Exogenous Variable and @Trend 394 6.5.3 LV(2) Models of (Y1,Y2) with two Exogenous Variables and Trend 400 6.5.4 LV(2) Models of (Y1,Y2) with Three Exogenous Variables and Trend 409 6.6 Triangular Effects Models Based on (X1,X2,Y1) 413 6.6.1 Partial Two-Way Interaction LV(p) TE_Models 413 6.6.2 A Complete Two-Way Interaction LV(p) TE_Models 414 6.6.3 Three-Way Interaction LV(p) TE_ Models 415 6.7 Bivariate Triangular Effects Models Based on (X1,X2,Y1,Y2) 417 6.7.1 Partial Two-Way Interaction Models 417 6.7.2 Three-Way Interaction TE_Models 418 6.8 Models with Exogenous Variables and Alternative Trends 422 6.8.1 Models Based on (X1,X2,Y1) 422 6.8.2 Models Based on (X1,X2,Y1,Y2) with Trend 424 6.9 Special LV(4) Models with Exogenous Variables 427 6.10 Models with Exogenous Variables by @Quarter 433 6.10.1 Alternative Models Based on the Whole Sample 433 6.10.2 Forecasting Based on each Quarter's Level 435 7 Forecasting Based on Time Series by States 447 7.1 Introduction 447 7.2 Models Based on a Bivariate (Y1_1,Y1_2) 447 7.2.1 Alternative LV(p) Models Based on Figure 7.1a 448 7.2.2 Alternative LV(p) Models Based on Figure 7.1b 451 7.2.3 Alternative LV(p) Models Based on Figure 7.1c 454 7.3 Advanced LP(p) Models of (Y1_1,Y1_2) 455 7.3.1 Two-Way Interaction LV(p) Models 455 7.3.2 Three-Way Interaction LV(p) Models 456 7.3.3 Alternative Additive Models 456 7.4 Advanced LP(p) Models of (Y1_1,Y1_2,Y1_3) 457 7.4.1 Triangular Effects Model of (Y1_1,Y1_2,Y1_3) 457 7.4.2 Full-Lag Variables Triangular Effects Model 462 7.4.3 Translog-Linear Triangular Effects Model 466 7.5 Full-Lag Variables Circular Effects Model 466 7.5.1 Two-Way Interaction…”

Tabla de Contenidos: “…Borchersen3, W. Dean1, P. Hyttel2, L.P. Sørensen3 and C. Palmer4 -- 51. Genome edited salmon: fish welfare as part of sustainability criteria -- T. …”

Tabla de Contenidos: “…Hashing with Linear Probing -- 13.1: Formulation and Preliminaries -- 13.2: Performance Measures for LP Hashing -- 13.3: All Cells Other than HTn-1 in the Hash-Table of n Cells are Occupied -- 13.4: m-Keys Hashed into a Hash Table of n Cells Leaving Cell HTn-1 Unoccupied -- 13.5: The Probability Distribution for the Length of a Search…”

Tabla de Contenidos: “…_Int_yrbDRWGx -- _Int_mvWxOBgk -- _Int_e1ynDwng -- _Int_E5WPbRvd -- _Int_ik07RnQT -- _Int_uixqLP4p -- _Int_MQCIDSbu -- _Int_vW101Fs3 -- _Int_MECdmoIg -- _Int_il1DBMWc -- _Int_im4ch9Qs -- _Int_KjAnUAkS -- _Int_RQnK0GqL -- _Int_MqK5ubgh -- _Int_pcKZkOxh -- _Int_nVxBbOCf -- _Int_OhycVcV5 -- _Int_MdzmHSAR -- _Int_WMRKuNSs -- _Int_CbYOG8Xf -- _Int_rMoP6Y4c -- _Int_ryKbdLPF -- _Int_q4kK1Ige -- _Int_uNRm8pdn -- _Int_qQYLWHFL -- _Int_g6u1uoJ1 -- OLE_LINK6 -- _Int_plBgvcrr -- _Int_EBC0KAuq -- _Int_UwUd5a5S -- _Int_OURbvCOX -- _Int_SGc11RZW -- _Int_0luoJlVa -- _Int_ytaf4YEa -- _Int_DyeYW99t -- _Int_X7oA6gKG -- _Int_91RIYQKN -- _Int_1sbY7MpC -- _Int_SUH43hNk -- _Int_dk4CsUJz -- _Int_zOmEaDJc -- OLE_LINK14 -- OLE_LINK16 -- OLE_LINK17 -- _Int_iOPc4I79 -- _Int_vObvpBwX -- _Int_Sh0PvUCQ -- _Int_FkbdxX9t -- _Int_xqObit9S -- OLE_LINK19 -- _Hlk149855058 -- _Int_W5E01Lay -- _Int_9YpkIXGW -- _Int_k3fsPxvI -- _Int_D56ZjsqT -- _Int_Q5LrUWR3 -- _Int_bkaDCfSC -- _Int_2yOM05By -- _Int_rNsi5wbp -- _Int_KyRSlOuT -- _Int_P00FIjSH -- _Int_CencMsfh -- _Int_3lslfkJe -- _Int_7oSSbBgT -- OLE_LINK22 -- _Int_0M403O60 -- _Int_GmK1TWt6 -- _Int_6DkODWnF -- _Int_1A9bfKrt -- _Int_SkAVqdcG -- OLE_LINK9 -- _Hlk151532699 -- OLE_LINK10 -- OLE_LINK5 -- OLE_LINK11 -- _Hlk151709071 -- OLE_LINK8 -- _Hlk154054230 -- OLE_LINK12 -- OLE_LINK13 -- OLE_LINK15 -- OLE_LINK17 -- __codelineno-0-1 -- OLE_LINK18 -- _Hlk154408967 -- OLE_LINK1 -- OLE_LINK2 -- OLE_LINK3 -- OLE_LINK4 -- OLE_LINK5 -- OLE_LINK7 -- _Hlk162119449 -- OLE_LINK10 -- OLE_LINK12 -- OLE_LINK11 -- OLE_LINK1 -- OLE_LINK4 -- OLE_LINK7 -- _Hlk162119449 -- OLE_LINK10 -- OLE_LINK12 -- OLE_LINK11 -- _Hlk162122907 -- OLE_LINK9 -- OLE_LINK1 -- OLE_LINK18 -- OLE_LINK19 -- OLE_LINK21 -- OLE_LINK22 -- OLE_LINK23 -- OLE_LINK24 -- OLE_LINK20 -- OLE_LINK29 -- _Hlk167132856 -- OLE_LINK25 -- _Hlk167133000 -- _Hlk167133017 -- OLE_LINK26 -- OLE_LINK27…”

Tabla de Contenidos: “…Kadi -- The Frankfurt Neutron Source – FRANZ by  O. Meusel, L.P. Chau, M. Heilmann, H. Klein, H. Podlech, U. …”

Tabla de Contenidos: “…6.1 Euler's Method -- 6.2 Heun's Method - Trapezoidal Method -- 6.3 Runge‐Kutta Method -- 6.4 Predictor‐Corrector Method -- 6.4.1 Adams‐Bashforth‐Moulton Method -- 6.4.2 Hamming Method -- 6.4.3 Comparison of Methods -- 6.5 Vector Differential Equations -- 6.5.1 State Equation -- 6.5.2 Discretization of LTI State Equation -- 6.5.3 High‐order Differential Equation to State Equation -- 6.5.4 Stiff Equation -- 6.6 Boundary Value Problem (BVP) -- 6.6.1 Shooting Method -- 6.6.2 Finite Difference Method -- Chapter 7 Optimization -- 7.1 Unconstrained Optimization -- 7.1.1 Golden Search Method -- 7.1.2 Quadratic Approximation Method -- 7.1.3 Nelder‐Mead Method -- 7.1.4 Steepest Descent Method -- 7.1.5 Newton Method -- 7.1.6 Conjugate Gradient Method -- 7.1.7 Simulated Annealing -- 7.1.8 Genetic Algorithm -- 7.2 Constrained Optimization -- 7.2.1 Lagrange Multiplier Method -- 7.2.2 Penalty Function Method -- 7.3 MATLAB Built‐In Functions for Optimization -- 7.3.1 Unconstrained Optimization -- 7.3.2 Constrained Optimization -- 7.3.3 Linear Programming (LP) -- 7.3.4 Mixed Integer Linear Programming (MILP) -- 7.4 Neural Network[K‐1] -- 7.5 Adaptive Filter[Y‐3] -- 7.6 Recursive Least Square Estimation (RLSE)[Y‐3] -- Chapter 8 Matrices and Eigenvalues -- 8.1 Eigenvalues and Eigenvectors -- 8.2 Similarity Transformation and Diagonalization -- 8.3 Power Method -- 8.3.1 Scaled Power Method -- 8.3.2 Inverse Power Method -- 8.3.3 Shifted Inverse Power Method -- 8.4 Jacobi Method -- 8.5 Gram‐Schmidt Orthonormalization and QR Decomposition -- 8.6 Physical Meaning of Eigenvalues/Eigenvectors -- 8.7 Differential Equations with Eigenvectors -- 8.8 DoA Estimation with Eigenvectors[Y-3] -- Chapter 9 Partial Differential Equations -- 9.1 Elliptic PDE -- 9.2 Parabolic PDE -- 9.2.1 The Explicit Forward Euler Method -- 9.2.2 The Implicit Backward Euler Method…”

Tabla de Contenidos: “…-- 1.1.2 A Brief History of Gs -- 1.2 mmWave Spectrum, Challenges, and Opportunities -- 1.3 Framework Level Requirements for mmWave Wireless Links -- 1.4 Circuit Aspects -- 1.5 Outline of the Book -- Acknowledgement -- References -- Chapter 2 5G Circuits from Requirements to System Models and Analysis -- 2.1 RF Requirements Governed by 5G System Targets -- 2.2 Radio Spectrum and Standardization -- 2.3 System Scalability -- 2.4 Communication System Model for RF System Analysis -- 2.5 System-Level RF Performance Model -- 2.5.1 Transmitter, Receiver, Antenna Array and Transceiver Architectures for RF and Hybrid Beamforming -- 2.6 Radio Propagation and Link Budget -- 2.6.1 Radio Propagation Model -- 2.6.2 Link Budgeting -- 2.7 Multiuser Multibeam Analysis -- 2.8 Conclusion -- Acknowledgement -- References -- Chapter 3 Millimetre-Wave Beam-Space MIMO System for 5G Applications -- 3.1 Introduction -- 3.2 Beam-Space Massive MIMO System -- 3.2.1 System Model -- 3.2.2 Saleh-Valenzuela Channel Model -- 3.3 Array Response Vector -- 3.3.1 mmWave Beam-Space Massive (mWBSM)-MIMO System -- 3.4 Discrete Lens Antenna Array -- 3.5 Beam Selection Algorithm -- 3.6 Mean Sum Assignment-Based Beam User Association -- 3.6.1 Performance Evaluation -- 3.7 Conclusion -- References -- Part II: Oscillator &amp -- Amplifier -- Chapter 4 Gain-Bandwidth Enhancement Techniques for mmWave Fully-Integrated Amplifiers -- 4.1 RLC Tank -- 4.1.1 RC Low-Pass (LP) Filter -- 4.1.2 RLC Band-Pass (BP) Filter -- 4.2 Coupled Resonators -- 4.2.1 Bode-Fano (B-F) Limit -- 4.2.2 Capacitively Coupled Resonators -- 4.2.3 Inductively Coupled Resonators…”

Tabla de Contenidos: “…Preface xix -- Acknowledgements xxv -- List of Abbreviations xxvii -- 1 Introduction to 𝚺𝚫 Modulators: Fundamentals, Basic Architecture and Performance Metrics 1 -- 1.1 Basics of Analog-to-Digital Conversion 2 -- 1.1.1 Sampling 3 -- 1.1.2 Quantization 4 -- 1.1.3 Quantization White Noise Model 5 -- 1.1.4 Noise Shaping 8 -- 1.2 Sigma-Delta Modulation 9 -- 1.2.1 From Noise-shaped Systems to ΣΔ Modulators 10 -- 1.2.2 Performance Metrics of ΣΔMs 11 -- 1.3 The First-order ΣΔ Modulator 13 -- 1.4 Performance Enhancement and Taxonomy of ΣΔMs 16 -- 1.4.1 ΣΔM System-level Design Parameters and Strategies 17 -- 1.4.2 Classification of ΣΔMs 18 -- 1.5 Putting All The Pieces Together: From ΣΔMs to ΣΔ ADCs 19 -- 1.5.1 Some Words about ΣΔ Decimators 20 -- 1.6 ΣΔ DACs 22 -- 1.6.1 System Design Trade-offs and Signal Processing in ΣΔ DACs 22 -- 1.6.2 Implementation of Digital ΣΔMs used in DACs 24 -- 1.7 Summary 25 -- References 26 -- 2 Taxonomy of 𝚺𝚫 Architectures 29 -- 2.1 Second-order ΣΔ Modulators 30 -- 2.1.1 Alternative Representations of Second-order ΣΔMs 31 -- 2.1.2 Second-Order ΣΔM with Unity STF 34 -- 2.2 High-order Single-loop ΣΔMs 35 -- 2.3 Cascade ΣΔ Modulators 39 -- 2.3.1 SMASH ΣΔM Architectures 46 -- 2.4 Multi-bit ΣΔ Modulators 49 -- 2.4.1 Influence of Multi-bit DAC Errors 49 -- 2.4.2 Dynamic Element Matching Techniques 50 -- 2.4.3 Dual Quantization 53 -- 2.4.3.1 Dual-quantization Single-loop ΣΔMs 53 -- 2.4.3.2 Dual-quantization Cascade ΣΔMs 54 -- 2.5 Band-pass ΣΔ Modulators 55 -- 2.5.1 Quadrature BP-ΣΔMs 56 -- 2.5.2 The z → −z2 LP-BP Transformation 58 -- 2.5.3 BP-ΣΔMs with Optimized NTF 58 -- 2.5.4 Time-interleaved and Polyphase BP-ΣΔMs 61.…”

“…—Bart Mosley, principal and chief investment officer, Alprion Capital Management LP…”

“…Once you gain an understanding of how to solve LP problems for communication networks using the GLPK descriptions in this book, you will also be able to easily apply your knowledge to other solvers…”

“…Packed with detailed, practical guidance on developing and managing a private equity compliance program, it offers up-to-date case studies and an analysis of critical regulatory enforcement actions on private equity funds in areas including conflict of interest, fees, expenses, LP fun raising disclosures, and valuations. • Provides real-world compliance guidance • Offers information that is tailored to the current compliance practices employed by GPs in the private equity industry. • Provides guidance on managing the compliance risks associated with cybersecurity and information technology risk • Serves as a PE-focused complement to the author's previous book, Hedge Fund Compliance If you’re a private equity investor or compliance officer looking for trusted guidance on analyzing conflicts, fees, and risks, this is one reference you can’t be without…”

“…Professor Rimas’ musical career has comprised hundreds of performances of classical music in all forms (orchestra, ensemble, and solo) as well as jazz improvisations and Lithuanian folk songs sung by the author, 16 LP and CD recordings, and over 150 recordings for Lithuanian State Radio. …”

“…Sessions focused on machine learning, including Alina Matyukhina's (Canadian Institute for Cybersecurity) reveal of the methods dishonest actors use ML to mimic the coding style of software developers in open source projects; Chakri Cherukuri's (Bloomberg LP) discussion of how to apply machine learning and deep learning techniques in quantitative finance; and Cibele Montez Halasz's (Twitter) description of time..…”