LEADER 09290nam 22005533 450 001 9911009387303321 005 20240407090435.0 010 $a9780750359313 010 $a0750359315 035 $a(MiAaPQ)EBC31253007 035 $a(Au-PeEL)EBL31253007 035 $a(CKB)31356152300041 035 $a(Exl-AI)31253007 035 $a(OCoLC)1427332197 035 $a(EXLCZ)9931356152300041 100 $a20240407d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSignal Processing with Python $eA Practical Approach 205 $a1st ed. 210 1$aBristol :$cInstitute of Physics Publishing,$d2024. 210 4$dİ2024. 215 $a1 online resource (297 pages) 225 1 $aIOP Ebooks Series 311 08$a9780750359306 311 08$a0750359307 327 $aIntro -- Acknowledgements -- Editor biographies -- Irshad Ahmad Ansari -- Varun Bajaj -- List of contributors -- Contributor biographies -- Outline placeholder -- Mosabber Uddin Ahmed -- Meena Anandan -- Zahra Ghanbari -- Aakash Kumar Jain -- K P Madhavan -- Manas Kumar Mishra -- Ishrat Jahan Mohima -- Mohammad Hassan Moradi -- Poorya Moradi -- Rohini Palanisamy -- Nakul Kishor Pathak -- K K Mujeeb Rahman -- Mozhdeh Saghalaini -- Deepika Sainani -- Urvashi Prakash Shukla -- Abhishek Kumar Singh -- Mahdi Taghaddossi -- Pandiyarasan Veluswamy -- Chapter Automatic feature extraction using deep learning for automatic modulation classification implemented with Python -- 1.1 Introduction -- 1.2 Proposed AMC based on DL framework -- 1.3 System model for dataset generation -- 1.4 Generalized feature extraction system using DL -- 1.4.1 Spatial feature extraction using CNNs -- 1.4.2 CLDNN-based approach -- 1.4.3 MCLDNN based approach -- 1.4.4 Complement of multichannel structure from bimodal information using BMCCLDNN models -- 1.5 Data preparation -- 1.5.1 Python code (libraries, file paths) -- 1.5.2 Python code (random shuffling of data) -- 1.5.3 Python code (data preparation for training) -- 1.5.4 Python code (data reshaping) -- 1.5.5 Python code (model training) -- 1.5.6 Python code (model evaluation) -- 1.5.7 Python code (model prediction) -- 1.6 Conclusion -- Acknowledgments -- Bibliography -- Chapter Applying B-value and empirical equivalence hypothesis testing to intellectual and developmental disabilities electroencephalogram data -- 2.1 Introduction -- 2.2 Materials -- 2.2.1 Dataset -- 2.2.2 Pre-processing -- 2.3 Feature extraction -- 2.3.1 DWT -- 2.3.2 PSD -- 2.4 Statistical method -- 2.4.1 The two-stage hypothesis testing based on EEB -- 2.4.2 Implementation (Python code) -- 2.5 Results. 327 $a2.5.1 Comparison of two groups of IDD and TDC under the same conditions -- 2.5.2 Comparison of rest-state and music stimuli for each group of IDD and TDC -- 2.6 Conclusion -- Bibliography -- Chapter Filter design and denoising technique for ECG signals -- 3.1 Introduction -- 3.2 Filter types -- 3.2.1 Time domain filters -- 3.2.2 Frequency selective filters -- 3.3 Python libraries for filter design -- 3.4 Filter specifications -- 3.5 Mapping of the digital frequency -- 3.6 Time domain filters -- 3.6.1 Moving average window -- 3.6.2 Derivative filter -- 3.7 Frequency selective filters -- 3.7.1 FIR filter -- 3.7.2 IIR filters -- 3.7.3 Adaptive filter -- 3.8 Conclusion -- Bibliography -- Chapter Electroencephalogram signal processing with Python -- 4.1 Introduction -- 4.2 Principal and primary actions in EEG signal processing -- 4.2.1 Importing EEG signals to Python environment -- 4.2.2 Saving the details of EEG data on other variables -- 4.2.3 Modifying the imported EEG data -- 4.2.4 Extracting data from a raw object -- 4.2.5 Saving objects -- 4.2.6 Working with events -- 4.3 Exposure signals in time and frequency -- 4.3.1 Displaying data information in time and frequency -- 4.3.2 Topomap displaying -- 4.3.3 Saving MNE-produced plots and images -- 4.4 EEG signals preprocessing -- 4.4.1 Setting an EEG reference -- 4.4.2 Removing bad channels and data spans -- 4.4.3 Filtering the data and resampling -- 4.4.4 Artifact removal by ICA -- 4.5 EEG signal processing -- 4.5.1 Evoke responses analysis -- 4.5.2 Time-frequency analysis -- 4.5.3 Source localization -- Bibliography -- Chapter AG-PSO: prediction of heart diseases for an unbalanced dataset using feature extraction -- 5.1 Introduction -- 5.2 Literature survey -- 5.3 Proposed methodology -- 5.3.1 Data augmentation and imbalance (SMOTE) -- 5.3.2 Feature subset selection (BPSO). 327 $a5.3.3 Prediction (various ML algorithms) -- 5.4 Parameter settings for the simulation study -- 5.4.1 Experimental dataset -- 5.4.2 Experimentation platform -- 5.4.3 Evaluation metric -- 5.5 Results and analysis -- 5.6 Conclusion -- Bibliography -- Chapter Python based bio-signal processing: mitigation of baseline wandering in pre-recorded electrooculogram -- 6.1 Introduction -- 6.1.1 Baseline wander noise -- 6.1.2 Muscle tremor noise -- 6.1.3 Powerline interference -- 6.1.4 EOGs and their clinical use -- 6.2 Introduction to Google Colab -- 6.2.1 How to use Google Colab -- 6.3 Algorithm for correction of baseline wandering in pre-recorded EOG signals -- 6.3.1 Steps 1-3 -- 6.3.2 Step 4 -- 6.3.3 Steps 5-6 -- 6.3.4 Step 7 -- 6.3.5 Step 8 -- 6.3.6 Step 9 -- 6.3.7 Step 10 -- 6.3.8 Steps 11 and 12 -- 6.4 Conclusion -- Bibliography -- Chapter Efficient nanoscale device modeling using artificial neural networks with TensorFlow and Keras libraries in Python -- 7.1 Introduction -- 7.1.1 Motivation and background -- 7.1.2 Problem statement and objectives -- 7.2 Brief literature survey -- 7.2.1 MuGFETs and their characteristics -- 7.2.2 ANNs and their application in compact modeling -- 7.2.3 Previous work using ANNs for modeling MuGFETs -- 7.3 Methodology -- 7.3.1 Overview of proposed approach -- 7.3.2 Implementation details -- 7.3.3 Hypertuning for optimizing performance -- 7.3.4 DNN modeling and training -- 7.4 Results and analysis -- 7.4.1 Model evaluation and comparison with TCAD simulations -- 7.4.2 Potential and limitations of the proposed approach -- 7.5 Conclusion -- Bibliography -- Chapter A Python-based comparative study of convolutional neural network-based approaches for the early detection of breast cancer -- 8.1 Introduction -- 8.2 Related works -- 8.3 Methodology -- 8.3.1 Dataset -- 8.3.2 Image preprocessing -- 8.3.3 Convolutional neural network. 327 $a8.3.4 Model overview -- 8.3.5 Evaluation metrics -- 8.4 Result analysis -- 8.5 Conclusion -- Acknowledgments -- Bibliography -- Chapter Maximum power point tracking for partially shaded photovoltaic system using advanced signal processing -- 9.1 Introduction -- 9.2 Modeling and characteristics of PV systems -- 9.2.1 Modeling of PV cells -- 9.2.2 Modeling of PV modules -- 9.2.3 Characteristics of PV modules -- 9.2.4 PV system schematics -- 9.3 MPPT: concept and traditional techniques -- 9.3.1 Need and concept of MPPT -- 9.3.2 Traditional MPPT concepts -- 9.4 Challenges of MPPT -- 9.4.1 Dynamic changes in temperature and irradiance -- 9.4.2 PSC and its challenges -- 9.4.3 MPPT requirements -- 9.5 Soft computing methods -- 9.5.1 Fuzzy logic -- 9.5.2 Artificial neural network -- 9.5.3 Adaptive neural fuzzy inference system -- 9.5.4 Extreme learning mechanism -- 9.5.5 Extremum seeking control -- 9.5.6 Reinforcement learning -- 9.5.7 Bayesian network -- 9.6 Meta-heuristic techniques -- 9.6.1 Swarm intelligence -- 9.6.2 Evolutionary algorithms -- 9.6.3 Mathematics- and physics-based methods -- 9.6.4 Sociology-based methods -- 9.7 Exact algorithms -- 9.7.1 Mathematics-based algorithms -- 9.7.2 Techniques based on P V array characteristics -- 9.8 Hardware configuration-based MPP methods -- 9.8.1 Array reconfiguration-based methods -- 9.8.2 Power electronics-based methods -- 9.9 Analysis and comparison of various techniques -- 9.10 Challenges and future scope -- 9.11 Conclusion -- Bibliography -- Chapter Automating Monte Carlo simulation data analysis using Python in Anaconda environment -- 10.1 Advanced nodes design -- 10.1.1 A simple memory design -- 10.1.2 Monte Carlo simulations -- 10.1.3 Challenges in Monte Carlo simulation -- 10.1.4 Fast Monte Carlo simulations -- 10.1.5 Validation of design for tool evaluation. 327 $a10.2 Simulation setup and tools overview -- 10.3 Analysis of simulation results with Python -- 10.4 Conclusion -- 10.5 Future scope -- Bibliography. 330 $aThis book explores the domain of signal processing using Python, with the help of working examples and accompanying code and introduces the concepts of Python programming via signal processing with numerous hands-on examples and code snippets. 410 0$aIOP Ebooks Series 606 $aSignal processing$7Generated by AI 606 $aPython (Computer program language)$7Generated by AI 615 0$aSignal processing 615 0$aPython (Computer program language) 700 $aAnsari$b Irshad Ahmad$01741364 701 $aBajaj$b Varun$01741363 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911009387303321 996 $aSignal Processing with Python$94395678 997 $aUNINA LEADER 01533oam 2200301z- 450 001 9910163368303321 005 20230913112557.0 010 $a958-8940-46-X 035 $a(CKB)3710000001047834 035 $a(BIP)030928195 035 $a(Exl-AI)993710000001047834 035 $a(EXLCZ)993710000001047834 100 $a20230306c2016uuuu -u- - 101 0 $aeng 200 10$aEl don de la vida 210 $cDEBOLS!LLO 215 $a1 online resource (168 p.) 311 08$a607-11-0439-4 330 8 $aEn un banco de un parque de Medelli?n un hombre viejo conversa con un compadre, mientras la ciudad se mueve y cambia, mientras la vida pasa; el viejo, sin embargo, quiere que todo siga igual. Para fijar el tiempo lleva una libreta en la que anota los nombres de los muertos que ha conocido en vida. Ya suman 657 y quiere llegar pronto a los 700. ENGLISH DESCRIPTION On a bench in a park in Medellin an old man and his close friend sit deep in conversation while the city shifts and changes, and time passes by. In an attempt to maintain the status quo, the old man carries a small notebook where he writes down the names of all his dead: they already add up to 657, and he soon wants to reach the 700 mark. 606 $aDeath in literature$7Generated by AI 606 $aColombian fiction$7Generated by AI 615 0$aDeath in literature 615 0$aColombian fiction 700 $aVallejo$b Fernando$0694498 906 $aBOOK 912 $a9910163368303321 996 $aEl don de la vida$93599030 997 $aUNINA