Nome |
# |
Unequal loss protection and multiple description coding: a performance comparison, file e384c42e-0c2b-d4b2-e053-9f05fe0a1d67
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388
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Rilevazione automatica di microtubuli astrali in immagini di
microscopia a fluorescenza, file e384c430-1c6e-d4b2-e053-9f05fe0a1d67
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310
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A distributed video streaming platform supporting multiple description coding, file e384c42e-18db-d4b2-e053-9f05fe0a1d67
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230
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Decoder driven adaptive distributed arithmetic coding, file e384c433-13e1-d4b2-e053-9f05fe0a1d67
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82
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Detection of freezing of gait in people withParkinson’s disease using smartphones, file e384c432-82b5-d4b2-e053-9f05fe0a1d67
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74
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An algorithm for Parkinson's disease speech classification based on isolated words analysis, file e384c433-b46d-d4b2-e053-9f05fe0a1d67
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74
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Single-channel EEG classification of sleep stages based on REM microstructure, file e384c433-f2c0-d4b2-e053-9f05fe0a1d67
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57
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Smartphone-Based Evaluation of Postural Stability in Parkinson’s Disease Patients During Quiet Stance, file e384c432-00a5-d4b2-e053-9f05fe0a1d67
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47
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Prediction of Freezing of Gait in Parkinson’s Disease using Wearables and Machine Learning, file e384c432-a1cd-d4b2-e053-9f05fe0a1d67
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47
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A new index to assess turning quality and postural stability in patients with Parkinson's disease, file e384c432-4b84-d4b2-e053-9f05fe0a1d67
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45
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Deep learning for Parkinson's disease: a case study on Freezing of Gait, file e384c432-6a9e-d4b2-e053-9f05fe0a1d67
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37
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Speech impairment in Parkinson’s disease: acoustic analysis of unvoiced consonants in Italian native speakers, file e384c434-4036-d4b2-e053-9f05fe0a1d67
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33
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Smartphone-based estimation of item 3.8 of the MDS-UPDRS-III for assessing leg agility in people with Parkinson’s disease”, file e384c432-288b-d4b2-e053-9f05fe0a1d67
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31
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Assessing REM sleep behaviour disorder: from machine learning classification to the definition of a continuous dissociation index, file e384c434-975d-d4b2-e053-9f05fe0a1d67
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25
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A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease, file e384c434-9e23-d4b2-e053-9f05fe0a1d67
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18
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A method for astral microtubule tracking in fluorescence
images of cells doped with taxol and nocodazole, file e384c432-4990-d4b2-e053-9f05fe0a1d67
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17
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Sleep Quality through Vocal Analysis: a Telemedicine Application, file e384c434-ea57-d4b2-e053-9f05fe0a1d67
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16
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Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor, file e384c434-4e8a-d4b2-e053-9f05fe0a1d67
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15
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Transparent encryption techniques for H.264/AVC and H.264/SVC compressed video, file e384c42e-0c84-d4b2-e053-9f05fe0a1d67
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11
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Efficient slice-aware H.264/AVC video transmission over Time-Driven Priority networks, file e384c42e-31df-d4b2-e053-9f05fe0a1d67
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8
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Correlation between wearable inertial sensor data and standardised Parkinson's disease axial impairment measures using machine learning, file 2057fa34-8d83-464f-94c8-52af6fb03ef7
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7
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Multimedia signal processing for wireless delivery, file e384c42e-1d9c-d4b2-e053-9f05fe0a1d67
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7
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Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study, file 5d4c072f-99a5-4e2d-a832-e508914037aa
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6
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The reliability of objective fatigue measures in Multiple Sclerosis Patients, file e384c431-4da0-d4b2-e053-9f05fe0a1d67
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6
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Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson’s Disease, file 92e876bc-46b9-4c11-ab6f-6ab5ff6abb00
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5
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Transparent encryption techniques for H.264/AVC and H.264/SVC compressed video, file e384c42e-0c85-d4b2-e053-9f05fe0a1d67
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5
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How resistant are levodopa-resistant axial symptoms? Response of freezing, posture and voice to increasing levodopa intestinal infusion rates in Parkinson's disease, file 6ab331fc-fab6-434a-83df-debfba3abbe9
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4
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Machine learning- and statistical-based voice analysis of Parkinson's disease patients: A survey, file 796ab5c0-2b82-4f86-9e7f-f5293fef8dd1
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4
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A Preliminary Comparison between Traditional and Gamified Leg Agility Assessment in Parkinsonian Subjects, file ab3ee412-dbab-4ab9-9e84-fcdc13252c90
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3
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DETECTION AND TRACKING OF ASTRAL MICROTUBULES IN FLUORESCENCE MICROSCOPY IMAGES, file e384c430-2253-d4b2-e053-9f05fe0a1d67
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3
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Detection of freezing of gait in people withParkinson’s disease using smartphones, file e384c432-3bf1-d4b2-e053-9f05fe0a1d67
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3
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Single-Channel EEG Detection of REM Sleep Behaviour Disorder: The Influence of REM and Slow Wave Sleep, file e384c434-da43-d4b2-e053-9f05fe0a1d67
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3
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Single-Channel EEG Detection of REM Sleep Behaviour Disorder: The Influence of REM and Slow Wave Sleep, file e384c434-e145-d4b2-e053-9f05fe0a1d67
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3
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Obesity and Gastro-Esophageal Reflux voice disorders: a Machine Learning approach, file 35fd0978-e76b-42cd-9388-4a931ff98882
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2
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Hallmarks of Parkinson’s disease progression determined by temporal evolution of speech attractors in the reconstructed phase-space, file 6fb10555-0571-480d-976d-300ea0f99946
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2
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Correlation between wearable inertial sensor data and standardised Parkinson's disease axial impairment measures using machine learning, file 83030685-2a3f-48c3-996a-3639d5c4a6dd
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2
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Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases, file da24fe2d-42bf-439a-b230-d803bd0b7fb8
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2
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Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review, file db53ad67-a3e7-440d-9892-a57026d6a294
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2
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Slice-level rate-distortion optimized multiple description coding for H.264/AVC, file e384c42e-18d9-d4b2-e053-9f05fe0a1d67
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2
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Home monitoring of motor fluctuations in Parkinson’s disease patients, file ed9380b2-de30-4552-ba1f-95f33d18d5db
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2
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Objective Assessment of the Finger Tapping Task in Parkinson's Disease and Control Subjects using Azure Kinect and Machine Learning, file 13832bd0-695b-4322-945c-5df6c777529d
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1
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Machine learning- and statistical-based voice analysis of Parkinson's disease patients: A survey, file 14e6035c-02cf-44f0-b65c-91e756fb9d52
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1
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Objective Assessment of the Finger Tapping Task in Parkinson's Disease and Control Subjects using Azure Kinect and Machine Learning, file 221f975a-88e0-4faa-ba06-284deea64c80
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1
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Neuronal Spike Shapes (NSS): A straightforward approach to investigate heterogeneity in neuronal excitability states, file 471f8325-5e05-4e08-b6c8-f8ed93f88c2c
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1
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Electrodermal Activity in the Evaluation of Engagement for Telemedicine Applications, file 7f30c27f-d28d-41bb-89cf-5f8c6733cfcf
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1
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How resistant are levodopa-resistant axial symptoms? Response of freezing, posture and voice to increasing levodopa intestinal infusion rates in Parkinson's disease, file 8db51cf1-7c4b-442d-ac75-67626eadea28
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1
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Sleep Quality through Vocal Analysis: a Telemedicine Application, file 94fe004d-5988-46dc-94d5-0a597391bc12
|
1
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Electrodermal Activity in the Evaluation of Engagement for Telemedicine Applications, file a2599f50-7da9-4ec7-b6a7-477b945bb8bc
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1
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Hallmarks of Parkinson’s disease progression determined by temporal evolution of speech attractors in the reconstructed phase-space, file a87f9cbc-906f-4586-96d6-663023ee4d00
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1
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Real-time detection of freezing of gait in Parkinson’s disease using multi-head convolutional neural networks and a single inertial sensor, file b7a57450-85f8-4229-80ff-c67da448cdcc
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1
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3D graphics compression and rendering framework, file e384c42e-3481-d4b2-e053-9f05fe0a1d67
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1
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Concealment driven smart slice reordering for robust video transmission, file e384c42e-f8f1-d4b2-e053-9f05fe0a1d67
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1
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Image and video transmission: A comparison study of using unequal loss protection and multiple description coding, file e384c42f-0893-d4b2-e053-9f05fe0a1d67
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1
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Single-Channel EEG Classification of Sleep Stages Based on REM Microstructure, file e384c432-cf77-d4b2-e053-9f05fe0a1d67
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1
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Decoder driven adaptive distributed arithmetic coding, file e384c433-43e8-d4b2-e053-9f05fe0a1d67
|
1
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High-accuracy wearable detection of freezing of gait in Parkinson's disease based on pseudo-multimodal features, file fe31c87c-8363-48e9-974d-0ca523bad906
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1
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Totale |
1.654 |