Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Advanced Security Solutions for Multimedia
| Advanced Security Solutions for Multimedia |
| Autore | Ansari Irshad Ahmad |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bristol : , : Institute of Physics Publishing, , 2021 |
| Descrizione fisica | 1 online resource (276 pages) |
| Altri autori (Persone) |
BajajVarun
SinhalRishi SharmaTarun Kumar NajafiEsmaeil ShahManan GohilJay PatelJay WuHanzhou AbazarMahdie |
| Collana | IOP Ebooks Series |
| Soggetto topico |
Data encryption (Computer science)
Digital watermarking |
| ISBN | 0-7503-4572-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Editor biographies -- Irshad Ahmad Ansari -- Varun Bajaj -- Contributor biographies -- Mahdie Abazar -- Parmeshwar Birajadar -- Seyed Mostafa FakhrAhmad -- Vikram M Gadre -- Ali Ghorbani -- Jay Gohil -- Abdelhamid Helali -- Sunil Kumar Jauhar -- Ameya Kshirsagar -- S Kuppa -- Hassen Maaref -- V M Manikandan -- Suja Cherukullapurath Mana -- Peyman Masjedi -- Ridha Mghaieth -- Amina Msolli -- Esmaeil Najafi -- Akash S Palde -- Jay Patel -- D S Raghukumar -- Vishal Rajput -- Antti Rissanen -- Marjo Rissanen -- T Saipraba -- Sagar G Sangodkar -- Manan Shah -- Tarun Kumar Sharma -- Rishi Sinhal -- M Suresha -- Niranjan Suthar -- Mohammad Taheri -- Hanzhou Wu -- Chapter 1 Blind image watermarking with efficient dual restoration feature -- 1.1 Introduction -- 1.2 Literature review -- 1.3 Proposed fragile watermarking scheme -- 1.3.1 Watermark pre-processing -- 1.3.2 Watermark embedding -- 1.3.3 Watermark extraction -- 1.3.4 Self-recovery process -- 1.4 Experimental results and discussion -- 1.4.1 Tamper detection anaylsis -- 1.4.2 Self-recovery of the tampered portion -- 1.5 Conclusion -- Acknowledgements -- References -- Chapter 2 Secure, robust and imperceptible image watermarking scheme based on sharp frequency localized contourlet transform -- 2.1 Introduction -- 2.2 The properties of SFLCT -- 2.3 The proposed SFLCT watermarking scheme -- 2.3.1 Computing strength factors -- 2.4 Implementations and results of the proposed SFLCT scheme -- 2.4.1 Robustness of the proposed SFLCT scheme -- 2.4.2 The security examination of the proposed scheme -- 2.5 Comparative analysis of the proposed scheme -- 2.6 Conclusion -- References -- Chapter 3 Content watermarking and data hiding in multimedia security -- 3.1 Introduction -- 3.2 Content watermarking in multimedia security -- 3.2.1 Introduction.
3.2.2 Content watermarking technique reviews -- 3.2.3 Table pertaining to research work on content watermarking in multimedia security -- 3.2.4 Inference -- 3.3 Data hiding in multimedia security -- 3.3.1 Background -- 3.3.2 Data hiding technique reviews -- 3.3.3 Table pertaining to research work on data hiding in multimedia security -- 3.3.4 Inference -- 3.4 Conclusion -- Acknowledgments -- References -- Chapter 4 Recent advances in reversible watermarking in an encrypted domain -- 4.1 Introduction -- 4.2 Preliminaries -- 4.2.1 Cover source and formats -- 4.2.2 Encryption methods -- 4.2.3 Evaluation metrics -- 4.2.4 Auxiliary data -- 4.3 State-of-the-art methods -- 4.3.1 General framework -- 4.3.2 Reserving room after encryption -- 4.3.3 Reserving room before encryption -- 4.3.4 Challenges and opportunities -- 4.4 Conclusion -- Acknowledgements -- References -- Chapter 5 An analysis of deep steganography and steganalysis -- 5.1 Introduction -- 5.2 Deep learning -- 5.2.1 Steganalysis -- 5.2.2 Steganography -- 5.3 Conclusion -- References -- Chapter 6 Recent trends in reversible data hiding techniques -- 6.1 Introduction -- 6.2 Types of RDH schemes -- 6.2.1 RDH in natural images -- 6.2.2 RDH in encrypted images -- 6.2.3 RDH through encryption (RDHTE) -- 6.3 Analysis of RDH schemes -- 6.4 Image dataset for experimental study -- 6.5 Future scope of the research in RDH -- 6.6 Conclusion -- References -- Chapter 7 Anatomized study of security solutions for multimedia: deep learning-enabled authentication, cryptography and information hiding -- 7.1 Introduction -- 7.2 Hurdles in conventional approaches for security -- 7.2.1 Vulnerability due to expansion -- 7.2.2 Authentication and computational latency -- 7.2.3 Discrepancy in authentication -- 7.3 Vulnerability to multimedia content -- 7.3.1 Data disclosure -- 7.3.2 Content manipulation. 7.3.3 Link sharing -- 7.3.4 Steganography -- 7.3.5 Common workspace -- 7.4 Analysis of security solutions for multimedia content -- 7.4.1 Cryptography -- 7.4.2 Data hiding -- 7.4.3 Deep learning enabled authentication -- 7.5 Future scope -- 7.6 Conclusion -- Acknowledgements -- References -- Chapter 8 New lightweight image encryption algorithm for the Internet of Things and wireless multimedia sensor networks -- 8.1 Introduction -- 8.2 Cryptographic primitives -- 8.2.1 Cryptanalysis -- 8.2.2 Cryptography system -- 8.3 Proposed lightweight algorithm -- 8.4 Safety assessment -- 8.4.1 Statistical analysis -- 8.4.2 Sensitivity test: robustness against differential attacks -- 8.4.3 Calculations speed analysis -- 8.5 Conclusion -- References -- Chapter 9 Applying the capabilities of machine learning for multimedia security: an analysis -- 9.1 Introduction -- 9.2 Overview of machine learning -- 9.2.1 Classification -- 9.2.2 Regression -- 9.2.3 Deep learning -- 9.3 Machine learning algorithms for multimedia security -- 9.4 Advantages of using ML based security mechanism for multimedia -- 9.5 Conclusion -- References -- Chapter 10 Assistive communication technology options for elderly care -- 10.1 Introduction -- 10.2 Cameras for patient monitoring in hospitals -- 10.2.1 Cameras for patient supervising in elderly care -- 10.2.2 Extending camera monitoring from the hospital to the home -- 10.2.3 Home-access video service as experienced by family members -- 10.2.4 Home-access video service as experienced by staff -- 10.2.5 New contexts and possibilities for camera surveillance in elderly care -- 10.3 Home-access monitoring and security -- 10.4 Benefits of the service -- 10.4.1 Benefit for the hospital patient -- 10.4.2 Benefit to the patient's relatives -- 10.4.3 Benefit to the organization -- 10.5 Requirements for the service model. 10.5.1 When is a home-access camera a facet of quality? -- 10.5.2 Conditions for practice -- 10.6 Security issues in networked health infrastructure -- 10.6.1 Information security at the strategic level -- 10.6.2 Different layers of security -- 10.6.3 Key elements of safe IT infrastructure in healthcare in the future -- 10.7 Deploying novel surveillance services in healthcare -- 10.7.1 Underlining the basics -- 10.7.2 Design cycles and relevant frames for design -- 10.7.3 Shared leadership -- 10.7.4 Challenges of innovation adaptation -- 10.7.5 New service models and translational design challenges -- 10.8 Conclusion -- References -- Chapter 11 Deep learning approach for scenario-based abnormality detection -- 11.1 Introduction -- 11.2 Literature study -- 11.3 Scenario understanding -- 11.3.1 Key frame extraction using instance segmentation -- 11.3.2 State full artifacts modelling -- 11.3.3 Action recognition and attention of key action -- 11.3.4 A hybrid model for spatio-temporal features -- 11.3.5 Classification and captioning -- 11.4 Abnormality detection -- 11.4.1 Natural abnormality translation -- 11.5 Datasets -- 11.6 Challenges -- 11.7 Trends and strengths -- 11.8 Conclusion -- References -- Chapter 12 Ear recognition for multimedia security -- 12.1 Introduction -- 12.1.1 Components of a biometric system -- 12.1.2 Modes of operation -- 12.1.3 Performance evaluation metrics -- 12.2 Ear recognition -- 12.3 Ear detection -- 12.4 Ear feature extraction -- 12.4.1 Multiresolution technique for feature extraction -- 12.4.2 Deep learning technique for feature extraction -- 12.4.3 Identification and verification experiments -- 12.5 Conclusion -- Acknowledgements -- References -- Chapter 13 Secure multimedia management: currents trends and future avenues -- 13.1 Introduction -- 13.2 Data collection and screening -- 13.3 Results. 13.3.1 General performance of selected publications -- 13.3.2 Performance of countries, institutions, and authors -- 13.3.3 Performance of journals, citations, and keywords -- 13.3.4 Factorial analysis -- 13.3.5 Co-citation network -- 13.3.6 Collaboration worldwide -- 13.4 Conclusion -- References. |
| Record Nr. | UNINA-9910915783003321 |
Ansari Irshad Ahmad
|
||
| Bristol : , : Institute of Physics Publishing, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Signal Processing with Python : A Practical Approach
| Signal Processing with Python : A Practical Approach |
| Autore | Ansari Irshad Ahmad |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bristol : , : Institute of Physics Publishing, , 2024 |
| Descrizione fisica | 1 online resource (297 pages) |
| Altri autori (Persone) | BajajVarun |
| Collana | IOP Ebooks Series |
| Soggetto topico |
Signal processing
Python (Computer program language) |
| ISBN |
9780750359313
0750359315 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- 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.
2.5.1 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). 5.3.3 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. 8.3.4 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. 10.2 Simulation setup and tools overview -- 10.3 Analysis of simulation results with Python -- 10.4 Conclusion -- 10.5 Future scope -- Bibliography. |
| Record Nr. | UNINA-9911009387303321 |
|
Ansari Irshad Ahmad
|
||
| Bristol : , : Institute of Physics Publishing, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||