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From traditional fault tolerance to blockchain / / Wenbing Zhao
| From traditional fault tolerance to blockchain / / Wenbing Zhao |
| Autore | Zhao Wenbing |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (480 pages) |
| Disciplina | 004.36 |
| Soggetto topico |
Electronic data processing - Distributed processing
Blockchains (Databases) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-5231-4344-4
1-119-68211-8 1-119-68212-6 1-119-68208-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555299403321 |
Zhao Wenbing
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| Hoboken, New Jersey : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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From traditional fault tolerance to blockchain / / Wenbing Zhao
| From traditional fault tolerance to blockchain / / Wenbing Zhao |
| Autore | Zhao Wenbing |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (480 pages) |
| Disciplina | 004.36 |
| Soggetto topico |
Electronic data processing - Distributed processing
Blockchains (Databases) |
| ISBN |
1-5231-4344-4
1-119-68211-8 1-119-68212-6 1-119-68208-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910831067603321 |
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Zhao Wenbing
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| Hoboken, New Jersey : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent Electronic Devices
| Intelligent Electronic Devices |
| Autore | Zhao Wenbing |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (220 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
180-degree conduction
6-DOF robot arm action quality assessment actuator adaptive network-based fuzzy inference system (ANFIS) Aedes aegypti Aedes albopictus as low as diagnostically acceptable (ALADA) broadcast circuit brushless DC motor built-in self-test communications and information processing computer science and engineering cone-beam computerized tomography (CBCT) current-voltage characteristics diagnostic ability drift region dual-input converter electrical circuits and devices electromagnetic interference electromagnetic lock electrostatic discharge (ESD) Eu3+ and In3+ ions galvanic isolation high voltage gain high-definition multimedia interface holding voltage (Vh) human activity analysis hydrothermal method laser pointer latchup (LU) lateral diffusion MOS (LDMOS) leakage energy recycling low cost low-dosed lower limb exoskeleton mosquitoes multi-robots multiple simple current mirror n-channel lateral diffused MOSFET (nLDMOS) negative differential resistance non-uniform conduction oscillator path programming PCB layout photoluminescence properties radiation resistance regions with convolutional neural network (R-CNN) secondary breakdown current (It2) secondary freeform lens device (SFLD) selective anatomy analytic iteration reconstruction (SA2IR) sensorless motor drive skeletal feature representation sound recognition module sparse projections speed control stearic tabu search test pattern generation threshold voltage transmission-line pulse system (TLP system) voltage clamping voltage-controlled oscillator wearable robot ZnO-based nanowires |
| ISBN | 3-03928-974-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404081403321 |
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Zhao Wenbing
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 2020 International Conference on Pattern Recognition and Intelligent Systems / / Wenbing Zhao
| Proceedings of the 2020 International Conference on Pattern Recognition and Intelligent Systems / / Wenbing Zhao |
| Autore | Zhao Wenbing |
| Pubbl/distr/stampa | New York, New York : , : Association for Computing Machinery, , 2020 |
| Descrizione fisica | 1 online resource (136 pages) : illustrations |
| Disciplina | 006.33 |
| Collana | ACM international conference proceedings series |
| Soggetto topico | Expert systems (Computer science) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910418310303321 |
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Zhao Wenbing
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| New York, New York : , : Association for Computing Machinery, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems / / Wenbing Zhao
| Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems / / Wenbing Zhao |
| Autore | Zhao Wenbing |
| Pubbl/distr/stampa | New York, New York : , : Association for Computing Machinery, , 2022 |
| Descrizione fisica | 1 online resource (102 pages) |
| Disciplina | 006.4 |
| Collana | ACM Other conferences |
| Soggetto topico |
Pattern recognition systems
Image processing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910588789803321 |
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Zhao Wenbing
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| New York, New York : , : Association for Computing Machinery, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems / / Wenbing Zhao, Xinguo Yu
| Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems / / Wenbing Zhao, Xinguo Yu |
| Autore | Zhao Wenbing |
| Pubbl/distr/stampa | New York : , : Association for Computing Machinery, , 2023 |
| Descrizione fisica | 1 online resource (123 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Machine learning Computer science |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910739500803321 |
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Zhao Wenbing
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| New York : , : Association for Computing Machinery, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Selected Papers from IEEE ICKII 2018 / Wenbing Zhao, Teen-Hang Meen
| Selected Papers from IEEE ICKII 2018 / Wenbing Zhao, Teen-Hang Meen |
| Autore | Zhao Wenbing |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (82 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
bandpass filter
total harmonic distortion (THD) long short term memory (LSTM) integrated passive device intertwined spiral inductor global navigation satellite system (GNSS) hardware in the loop (HIL) interdigital capacitor inertial navigation system (INS) finite-time convergence control (FTCC) digital speckle correlation measurement method discrete grey prediction model (DGPM) interior permanent magnet synchronous motor fuzzy logic full pixel search algorithm maximum torque per voltage (MTPV) spiral capacitor gated recurrent unit (GRU) chattering microelectronics system (MEMS) field weakening maximum torque per ampere (MTPA) hardware implementation AC power supply |
| ISBN |
9783039212743
3039212745 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367570003321 |
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Zhao Wenbing
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sensing and Signal Processing in Smart Healthcare
| Sensing and Signal Processing in Smart Healthcare |
| Autore | Zhao Wenbing |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (198 p.) |
| Soggetto topico |
English
For ELT / ESL learning, courses, examinations and certificates Language and Linguistics Language teaching and learning |
| Soggetto non controllato |
ambient assisted living
arrhythmia auditory perception autoencoder Bayesian inference behavioral analysis biometrics brain cancer detection Cascaded-Integrator-Comb (CIC) filter computer vision computer-assisted screening Cramér-Rao lower bound (CRLB) CUDA data adaptive demodulator deep learning denoising autoencoder Dynamic Time Warping eHealth endothelial dysfunction fixed point math FPGA gesture recognition Graphics Processing Units (GPUs) heartbeat classification Hidden Markov Model human monitoring human motion human-computer interaction hyperspectral imaging impaired sensor indoor localisation indoor monitoring Inertial Measurement Unit (IMU) inertial sensors intelligent luminaires Internet of Things (IoT) K-means Kalman filter keypoints feature matching machine learning motion estimation n/a near-infrared images OpenCL OpenMP photoplethysmography scale invariant feature transform simulation sleep pose recognition smart homes software engineering spline function Structural Health Monitoring subharmonics Time of Arrival (TOA) Time of Flight unsupervised clustering usability wearable sensors web control access web security Wi-Fi wireless sensor network |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557483503321 |
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Zhao Wenbing
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Technology-Enabled Motion Sensing and Activity Tracking for Rehabilitation
| Technology-Enabled Motion Sensing and Activity Tracking for Rehabilitation |
| Autore | Zhao Wenbing |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Institution of Engineering & Technology, 2022 |
| Descrizione fisica | 1 online resource (319 pages) |
| Disciplina | 617.03 |
| Collana | Healthcare Technologies Series |
| Soggetto topico |
Medical rehabilitation
Machine learning Motion detectors |
| ISBN |
9781523155460
1523155469 9781839534119 1839534117 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Title -- Copyright -- Contents -- About the author -- List of figures -- List of tables -- Introduction -- Part I Motion sensing technologies -- 1 Inertial measurement units -- 1.1 Accelerometer -- 1.2 Gyroscope -- 1.3 Magnetometer -- 1.4 Rehabilitation studies using IMUs -- 1.4.1 Studies using low-level IMUs -- 1.4.2 Studies using prepackaged professional sensors containing IMUs -- 1.4.3 Studies using consumer-grade devices containing IMUs -- 1.4.4 Studies using wearable trackers -- 2 Force and pressure sensing -- 2.1 Types of pressure sensors -- 2.1.1 Piezoelectric pressure sensors -- 2.1.2 Resistive pressure sensors -- 2.1.3 Capacitive pressure sensors -- 2.1.4 Optical pressure sensors -- 2.2 Applications in motion tracking for rehabilitation -- 2.2.1 Epionics SPINE system -- 2.2.2 Force plates -- 2.2.3 Smart insoles and smart shoes -- 2.3 Energy harvesting in smart shoes -- 3 E-Textile-based sensing -- 3.1 Conductive elastomer -- 3.1.1 Working principle -- 3.1.2 Attaching conductive elastomer to fabric -- 3.1.3 Motion tracking with conductive elastomer -- 3.1.4 New development -- 3.2 Commercial elastic sensors -- 3.3 Other approaches -- 4 Muscle activity sensing with myography -- 4.1 Electromyography -- 4.1.1 EMG in upper-extremity stroke therapy -- 4.1.2 EMG in recovery progress evaluation of anterior cruciate ligament reconstructed subjects -- 4.2 Machanomyography -- 4.3 Force myography -- 4.4 Optical myography -- 4.5 Summary -- 5 Vision-based motion sensing -- 5.1 Microsoft Kinect sensor -- 5.2 Feasibility studies of using Kinect in rehabilitation -- 5.3 Kinect-based systems in rehabilitation -- 5.3.1 Kinect-based system with visual feedback only -- 5.3.2 Kinect-based system with performance quality feedback -- 5.3.3 Integration of Kinect and other sensing modalities -- 5.4 Beyond Kinect -- 6 Instrumented gloves.
6.1 Gloves based on IMUs -- 6.1.1 Calibration -- 6.1.2 Signal processing -- 6.1.3 Reference systems for evaluation -- 6.1.4 Accuracy evaluation -- 6.1.5 Repeatability and reliability evaluation -- 6.1.6 Classification of activities -- 6.2 Gloves based on flex sensors -- 6.3 Gloves based on optical sensors -- 6.3.1 FBG-based approach -- 6.3.2 Light-attenuation-based approach -- 6.3.3 Optical linear encoder -- 6.4 Gloves based on Hall effect -- Part II Human motion recognition and exergames -- 7 Measurement of basic parameters -- 7.1 Mechanics of body movements -- 7.1.1 Anatomical planes -- 7.1.2 Joints and their movements -- 7.1.3 Range of motion -- 7.2 Joint angle measurement with various sensing modalities -- 7.2.1 Joint angle measurement with IMU -- 7.2.2 Joint angle measurement with Kinect -- 7.3 Measurement theories -- 7.4 Evaluating a new measurement instrument -- 7.4.1 Root mean square error -- 7.4.2 Student's t-test -- 7.4.3 Pearson's coefficient of correlation -- 7.4.4 Intraclass correlation coefficient -- 7.4.5 Bland-Altman limits of agreement -- 8 Machine-learning-based activity recognition -- 8.1 Data pre-processing -- 8.2 Data segmentation -- 8.3 Feature engineering -- 8.3.1 Feature extraction -- 8.3.2 Feature selection -- 8.4 Supervised machine learning -- 8.4.1 Mathematical model for supervised machine learning -- 8.4.2 Cross validation -- 8.4.3 Common supervised machine-learning models -- 8.4.4 Performance evaluation for classification -- 8.4.5 Performance evaluation for regression -- 8.5 Unsupervised machine learning -- 8.6 Deep learning -- 8.7 Assessment of rehabilitation exercises -- 8.7.1 Activity recognition -- 8.7.2 Performance quality assessment -- 8.7.3 Clinical assessment -- 9 Rule-based activity recognition -- 9.1 Ad hoc rule-based studies -- 9.2 General-purpose rule-based activity recognition -- 9.2.1 Rule encoding method. 9.2.2 Real-time motion tracking -- 9.2.3 Fuzzy interference extension -- 10 Exergames -- 10.1 Commercial game-console-based exergames -- 10.1.1 Wii -- 10.1.2 Xbox -- 10.1.3 PlayStation -- 10.2 Custom-developed exergames -- 10.2.1 IMU -- 10.2.2 Kinect -- 10.2.3 Wii balance board -- 10.2.4 Mobile apps -- Part III Technology-facilitated rehabilitation -- 11 Technology-facilitated physical rehabilitation -- 11.1 Framework for physical rehabilitation -- 11.2 Motor control and motor learning -- 11.3 Interventions for improve motor function -- 11.4 Technology in physical rehabilitation -- 11.4.1 Augmented reality in physical rehabilitation -- 11.4.2 Smartphone use in physical rehabilitation -- 12 Technology-facilitated occupational rehabilitation -- 12.1 Framework for occupational therapy -- 12.2 Occupational therapy for return to work -- 12.3 Technology in occupational therapy -- 12.3.1 Assistive technology -- 12.3.2 Telerehabilitation -- 12.3.3 Exergames -- 12.4 Tracking of activities of daily living -- 13 Technology-facilitated speech rehabilitation -- 13.1 Common speech-related disorders -- 13.1.1 Aphasia -- 13.1.2 Dysarthria -- 13.1.3 Apraxia of speech -- 13.1.4 Dyslalia -- 13.1.5 Hearing impairment -- 13.1.6 Resonance disorders -- 13.1.7 Cognitive communication disorders -- 13.1.8 Expressive disorders -- 13.1.9 Fluency disorders -- 13.1.10 Articulation disorders -- 13.2 Standard speech and language therapy -- 13.3 Lee Silverman Voice Treatment -- 13.4 Computer-based speech therapy -- 14 Technology-facilitated pulmonary rehabilitation -- 14.1 Clinical scales and tests in pulmonary rehabilitation -- 14.1.1 The Borg Rating of Perceived Exertion -- 14.1.2 Dyspnea ratings -- 14.1.3 The Wisconsin Upper Respiratory Symptom Survey -- 14.1.4 Numeric rating scale as a measure of dyspnea -- 14.1.5 Medical Research Council dyspnea scale. 14.1.6 Functional independence measure -- 14.1.7 Cumulative illness rating scale -- 14.1.8 St. Georg's respiratory questionnaire -- 14.1.9 Feeling thermometer -- 14.1.10 The six-minute walk test -- 14.1.11 Short physical performance battery -- 14.1.12 Functional ambulation category -- 14.2 Exercise training -- 14.2.1 Endurance training -- 14.2.2 Interval training -- 14.2.3 Resistance/strength training -- 14.2.4 Upper limb training -- 14.2.5 Flexibility training -- 14.2.6 Neuromuscular electrical stimulation -- 14.2.7 Inspiratory muscle training -- 14.3 Pulmonary rehabilitation for COPD -- 14.3.1 Functional testing and measurement of physiological parameters -- 14.3.2 Telehealth -- 14.3.3 Technology-facilitated exercise training -- 14.3.4 Technology-facilitated self-management -- 14.4 Pulmonary rehabilitation for COVID-19 -- 15 Technology-facilitated cognitive rehabilitation -- 15.1 The impact of physical activities on cognition for children and young adults -- 15.2 Technology-facilitated detection of mild cognition impairment and dementia -- 15.2.1 Video-based detection of MCI -- 15.2.2 MCI-detection via fully-instrumented smart home -- 15.2.3 MCI-detection via minimally instrumented smart home -- 15.2.4 MCI-detection via non-mobility IADL tracking -- 15.3 Cognitive rehabilitation for older adults -- 16 Technology-facilitated mental health rehabilitation -- 16.1 Regular physical exercises and mental health -- 16.1.1 Psychological mechanisms -- 16.1.2 Inflammatory mechanisms -- 16.1.3 Psychological mechanisms -- 16.2 Rehabilitation for patients with autism spectrum disorder -- 16.2.1 Clinical scales in ASD studies -- 16.2.2 Social attention -- 16.2.3 Imitation -- 16.2.4 Cognitive load -- 16.2.5 Facial expression and emotion recognition -- 16.2.6 Physical exercise-based intervention. 16.3 Exercise-based intervention for patients with major depressive disorder -- 16.3.1 Clinical assessments in MDD studies -- 16.3.2 Supporting studies -- 16.3.3 Nonsupporting studies -- 16.4 Exercise-based rehabilitation for patients with post-traumatic stress disorder -- Conclusion -- References -- Index. |
| Record Nr. | UNINA-9911006985203321 |
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Zhao Wenbing
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| Institution of Engineering & Technology, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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