| Nome |
# |
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Learning in Nonstationary Environments: A Survey, file e0c31c0e-ab5e-4599-e053-1705fe0aef77
|
846
|
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Privacy-Preserving Deep Learning With Homomorphic Encryption: An Introduction, file d74f85d1-f5f2-483c-ac36-4c0b252201b7
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652
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Learning Discrete-Time Markov Chains Under Concept Drift, file e0c31c0e-8359-4599-e053-1705fe0aef77
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580
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Hierarchical Change-Detection Tests, file e0c31c0f-9a23-4599-e053-1705fe0aef77
|
426
|
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TinyML for UWB-radar based presence detection, file f22ed75d-ffbf-450e-8c11-8d9f9dd4f394
|
285
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What Planner for Ambient Intelligence Applications?, file e0c31c0a-1f50-4599-e053-1705fe0aef77
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269
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Temporal/spatial model-based fault diagnosis vs. Hidden Markov models change detection method: Application to the Barcelona water network, file e0c31c08-86f8-4599-e053-1705fe0aef77
|
211
|
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Database challenges for exploratory computing, file e0c31c08-8280-4599-e053-1705fe0aef77
|
205
|
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A Self-Building and Cluster-Based Cognitive Fault Diagnosis System for Sensor Networks, file e0c31c0e-2534-4599-e053-1705fe0aef77
|
86
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Leak detection and localization in water distribution networks by combining expert knowledge and data-driven models, file e0c31c12-59f0-4599-e053-1705fe0aef77
|
84
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METHOD AND SYSTEM FOR OBJECT DETECTION AND CLASSIFICATION, file e0c31c0f-15ee-4599-e053-1705fe0aef77
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62
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Model- vs. data-based approaches applied to fault diagnosis in potable water supply networks, file e0c31c11-8b1f-4599-e053-1705fe0aef77
|
54
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A Cloud to the Ground: The New Frontier of Intelligent and Autonomous Networks of Things, file e0c31c10-f744-4599-e053-1705fe0aef77
|
44
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An energy harvesting solution for computation offloading in Fog Computing networks, file e0c31c11-ef3e-4599-e053-1705fe0aef77
|
43
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A Tiny Transformer-Based Anomaly Detection Framework for IoT Solutions, file 94a245f0-936e-48bb-bfc2-807c2c67b8a5
|
34
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T4C: A Framework for Time-Series Clustering-as-a-Service, file bb7b2b82-6c5e-4247-b4a0-36a48e5e0414
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27
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RTI Goes Wild: Radio Tomographic Imaging for Outdoor People Detection and Localization, file e0c31c10-f75b-4599-e053-1705fe0aef77
|
11
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A Cloud to the Ground: The New Frontier of Intelligent and Autonomous Networks of Things, file e0c31c0a-42b2-4599-e053-1705fe0aef77
|
6
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On-device Subject Recognition in UWB-radar Data with Tiny Machine Learning, file 9167ce8f-f78c-4e5f-aaf2-7f3f43e73869
|
5
|
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A Self-Building and Cluster-Based Cognitive Fault Diagnosis System for Sensor Networks, file e0c31c08-86f2-4599-e053-1705fe0aef77
|
4
|
|
RTI Goes Wild: Radio Tomographic Imaging for Outdoor People Detection and Localization, file e0c31c09-315e-4599-e053-1705fe0aef77
|
4
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Time-Variant Variational Transfer for Value Functions, file e0c31c12-8c09-4599-e053-1705fe0aef77
|
4
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Learning in nonstationary environments: A hybrid approach, file e0c31c0b-2508-4599-e053-1705fe0aef77
|
3
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Moving Convolutional Neural Networks to Embedded Systems: The AlexNet and VGG-16 Case, file e0c31c0c-b490-4599-e053-1705fe0aef77
|
3
|
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AI, Machine Learning e Data Mining, file e0c31c12-669b-4599-e053-1705fe0aef77
|
3
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A transfer-learning approach for corrosion prediction in pipeline infrastructures, file fc8684dd-e8c5-44f8-9b48-4b3d793457a2
|
3
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A Cognitive Fault Diagnosis System for Distributed Sensor Networks, file e0c31c08-8b95-4599-e053-1705fe0aef77
|
2
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GINGER: a minimizing-effects reprogramming paradigm for distributed sensor networks, file e0c31c08-8bbf-4599-e053-1705fe0aef77
|
2
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Learning in Nonstationary Environments: A Survey, file e0c31c09-3160-4599-e053-1705fe0aef77
|
2
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INDIANA: An interactive system for assisting database exploration, file e0c31c0e-68a0-4599-e053-1705fe0aef77
|
2
|
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Prototyping and Metrological Characterization of a Data Acquisition and Processing System Based on Edge Computing, file e0c31c0f-c975-4599-e053-1705fe0aef77
|
2
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Distributed Deep Convolutional Neural Networks for the Internet-of-Things, file e0c31c10-ed4c-4599-e053-1705fe0aef77
|
2
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A Computational Intelligence Characterization of Solar Magnetograms, file e0c31c11-36c8-4599-e053-1705fe0aef77
|
2
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Reducing the Computation Load of Convolutional Neural Networks through Gate Classification, file e0c31c11-41b9-4599-e053-1705fe0aef77
|
2
|
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CNAS: Constrained Neural Architecture Search, file 3b89d707-4ee7-4e1f-bcb4-2197e5f6590a
|
1
|
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Exploiting self-similarity for change detection, file e0c31c08-80e8-4599-e053-1705fe0aef77
|
1
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A robust, adaptive, solar powered WSN framework for aquatic environmental monitoring, file e0c31c08-8104-4599-e053-1705fe0aef77
|
1
|
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Ensembles of Change-Point Methods to Estimate the Change Point in Residual Sequences, file e0c31c08-86ef-4599-e053-1705fe0aef77
|
1
|
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An Ensemble of HMMs for Cognitive Fault Detection in Distributed Sensor Networks, file e0c31c08-86f0-4599-e053-1705fe0aef77
|
1
|
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A reconfigurable and element-wise ICI-based change-detection test for streaming data, file e0c31c08-86f4-4599-e053-1705fe0aef77
|
1
|
|
A learning-based algorithm for optimal mac parameters setting in IEEE 802.15.4 wireless sensor networks, file e0c31c08-86f6-4599-e053-1705fe0aef77
|
1
|
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A cognitive monitoring system for contaminant detection in intelligent buildings, file e0c31c08-8bc0-4599-e053-1705fe0aef77
|
1
|
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A Hierarchy of Change-Point Methods for Estimating the Time Instant of Leakages in Water Distribution NetworksArtificial Intelligence Applications and Innovations, file e0c31c08-8bc2-4599-e053-1705fe0aef77
|
1
|
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A high-frequency sampling monitoring system for environmental and structural applications, file e0c31c08-8c9b-4599-e053-1705fe0aef77
|
1
|
|
A Real-Time Monitoring Framework for Landslide and Rock-Collapse Forecasting, file e0c31c08-8ce4-4599-e053-1705fe0aef77
|
1
|
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Sensors and Wireless Sensor Networks as Data Sources: Models and Languages, file e0c31c09-19df-4599-e053-1705fe0aef77
|
1
|
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Online model-free sensor fault identification and dictionary learning in Cyber-Physical Systems, file e0c31c0a-1a29-4599-e053-1705fe0aef77
|
1
|
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Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss, file e0c31c0a-238e-4599-e053-1705fe0aef77
|
1
|
|
A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service, file e0c31c10-ef7c-4599-e053-1705fe0aef77
|
1
|
|
An energy harvesting solution for computation offloading in Fog Computing networks, file e0c31c10-ef7f-4599-e053-1705fe0aef77
|
1
|
|
Incremental On-Device Tiny Machine Learning, file e0c31c10-ef81-4599-e053-1705fe0aef77
|
1
|
|
Learning Convolutional Neural Networks in presence of Concept Drift, file e0c31c11-41b8-4599-e053-1705fe0aef77
|
1
|
| Totale |
3.988 |