Andere Kunden interessierten sich auch für
![Medical Image Security]( "Medical Image Security")
Medical Image Security39,99 €![Improvement of Effective Architectures for Discrete Wavelet Transform]( "Improvement of Effective Architectures for Discrete Wavelet Transform")
Improvement of Effective Architectures for Discrete Wavelet Transform32,99 €![Optimization Technique - Job Scheduling]( "Optimization Technique - Job Scheduling")
Optimization Technique - Job Scheduling30,99 €![Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals]( "Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals")
Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals29,99 €![SECURED DATA DEDUPLICATION TECHNIQUE FOR EFFECTIVE STORAGE]( "SECURED DATA DEDUPLICATION TECHNIQUE FOR EFFECTIVE STORAGE")
SECURED DATA DEDUPLICATION TECHNIQUE FOR EFFECTIVE STORAGE44,99 €![Automatic Intrusion Prevention Technique to Improve Network Security]( "Automatic Intrusion Prevention Technique to Improve Network Security")
Automatic Intrusion Prevention Technique to Improve Network Security23,99 €![Introduction to Parallel Algorithm Technique]( "Introduction to Parallel Algorithm Technique")
Introduction to Parallel Algorithm Technique32,99 €
The applications of digitally modulated signals are still in progress and expansion. Automatic Modulation Identification (AMI) is important to classify the digitally modulated signals of arbitrary modulation schemes. AMI is crucial in military applications, like electronic surveillance, and interference recognition. In civil applications, AMI can be employed in Software Defined Radio (SDR), signal monitoring, intelligent modems, Cognitive Radio (CR), etc. This book therefore, provides a new metric of success for classification of the digitally modulated signals: Multi-user Chirp Spread Spectrum (CSS), 2FSK, 4FSK, 8FSK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM, 128QAM, and 256QAM signals, using MATLAB. The analysis should help shed some light on digitally modulated signals, adaptive neural fuzzy inference system (ANFIS), wavelet transform, high order statistics, signals classification approaches, and should be especially useful to professionals in communications and the newcomers to this field to determine the ability and limitation associated with the ANFIS based digitally modulated signal classification.