The Experts below are selected from a list of 870408 Experts worldwide ranked by ideXlab platform
Nicolas Vuillerme - One of the best experts on this subject based on the ideXlab platform.
-
Actimetry@home: Actimetric Tele-surveillance and Tailored to the Signal Data Compression
Smart Homes and Health Telematics, 2015Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
-
ICOST - Actimetry@home: Actimetric Tele-surveillance and Tailored to the Signal Data Compression
Smart Homes and Health Telematics, 2014Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
-
actimetry home actimetric tele surveillance and tailored to the Signal Data compression
International Conference on Smart Homes and Health Telematics, 2014Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
Jacques Demongeot - One of the best experts on this subject based on the ideXlab platform.
-
Actimetry@home: Actimetric Tele-surveillance and Tailored to the Signal Data Compression
Smart Homes and Health Telematics, 2015Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
-
ICOST - Actimetry@home: Actimetric Tele-surveillance and Tailored to the Signal Data Compression
Smart Homes and Health Telematics, 2014Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
-
actimetry home actimetric tele surveillance and tailored to the Signal Data compression
International Conference on Smart Homes and Health Telematics, 2014Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
Henry X Liu - One of the best experts on this subject based on the ideXlab platform.
-
SMART-Signal Phase II: Arterial Offset Optimization Using Archived High-Resolution Traffic Signal Data
2013Co-Authors: Henry X LiuAbstract:Traditionally, offset optimization for coordinated traffic Signals is based on average travel times between intersections and average traffic volumes at each intersection, without consideration of the stochastic nature of field traffic. Using the archived high-resolution traffic Signal Data, this project developed a Data-driven arterial offset optimization model that will address two well-known problems with vehicle-actuated Signal coordination: the early return to green problem and the uncertain intersection queue length problem. To account for the early return to green problem, the authors introduce the concept of conditional distribution of the green start times for the coordinated phase. To handle the uncertainty of intersection queue length, the authors adopt a scenario-based approach that generates optimization results using a series of traffic-demand scenarios as the input to the offset optimization model. Both the conditional distributions of the green start times and traffic demand scenarios can be obtained from the archived high-resolution traffic Signal Data. Under different traffic conditions, queues formed by side-street and main-street traffic are explicitly considered in the derivation of intersection delay. The objective of this model is to minimize total delay for the main coordinated direction and at the same time it considers the performance of the opposite direction. Due to model complexity, a genetic algorithm is adopted to obtain the optimal solution. The performance of the optimized offsets are tested not only in a simulated environment but also in the field. Results from both experiments show that the proposed model can reduce travel delay of coordinated direction significantly without compromising the performance of the opposite approach.
-
Arterial offset optimization using archived high-resolution traffic Signal Data
Transportation Research Part C: Emerging Technologies, 2013Co-Authors: Henry X LiuAbstract:Traditionally, offset optimization for coordinated traffic Signals is based on average travel times between intersections and average traffic volumes at each intersection, without consideration of the stochastic nature of field traffic. Using the archived high-resolution traffic Signal Data, in this paper, we develop a Data-driven arterial offset optimization model which will take two well-known problems with vehicle-actuated Signal coordination into consideration: the early return to green problem and the uncertain intersection queue length problem. To account for the early return to green problem, we introduce the concept of conditional distribution of the green start times for the coordinated phase. To handle the uncertainty of intersection queue length, we adopt a scenario-based approach that generates optimal offsets using a series of traffic demand scenarios as the input to the optimization model. Both the conditional distributions of the green start times and traffic demand scenarios can be obtained from the archived high-resolution traffic Signal Data. Under different traffic conditions, queues formed by side-street and main-street traffic are explicitly considered in the derivation of intersection delay. The objective of this offset optimization model is to minimize total delay for the main coordinated direction and at the same time it considers the performance of the opposite direction. Due to the model complexity, a genetic algorithm is adopted to obtain the optimal solution. The proposed methodology was tested on a major arterial (TH55) in Minnesota. The results from the field implementation show that the proposed model can reduce travel delay of coordinated direction significantly without compromising the performance of the opposite approach.
-
identification of oversaturated intersections using high resolution traffic Signal Data
Transportation Research Part C-emerging Technologies, 2010Co-Authors: Henry X Liu, Douglas GettmanAbstract:Abstract Conceptually, an oversaturated traffic intersection is defined as one where traffic demand exceeds the capacity. Such a definition, however, cannot be applied directly to identify oversaturated intersections because measuring traffic demand under congested conditions is not an easy task, particularly with fixed-location sensors. In this paper, we circumvent this issue by quantifying the detrimental effects of oversaturation on Signal operations, both temporally and spatially. The detrimental effect is characterized temporally by a residual queue at the end of a cycle, which will require a portion of green time in the next cycle; or spatially by a spill-over from downstream traffic whereby usable green time is reduced because of the downstream blockage. The oversaturation severity index (OSI), in either the temporal dimension (T-OSI) or the spatial dimension (S-OSI) can then be measured using high-resolution traffic Signal Data by calculating the ratio between the unusable green time due to detrimental effects and the total available green time in a cycle. To quantify the T-OSI, in this paper, we adopt a shockwave-based queue estimation algorithm to estimate the residual queue length. S-OSI can be identified by a phenomenon denoted as “Queue-Over-Detector (QOD)”, which is the condition when high occupancy on a detector is caused by downstream congestion. We believe that the persistence duration and the spatial extent with OSI greater than zero provide an important indicator for measuring traffic network performance so that corresponding congestion mitigation strategies can be prepared. The proposed algorithms for identifying oversaturated intersections and quantifying the oversaturation severity index have been field-tested using traffic Signal Data from a major arterial in the Twin Cities of Minnesota.
Olivier Hansen - One of the best experts on this subject based on the ideXlab platform.
-
Actimetry@home: Actimetric Tele-surveillance and Tailored to the Signal Data Compression
Smart Homes and Health Telematics, 2015Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
-
ICOST - Actimetry@home: Actimetric Tele-surveillance and Tailored to the Signal Data Compression
Smart Homes and Health Telematics, 2014Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
-
actimetry home actimetric tele surveillance and tailored to the Signal Data compression
International Conference on Smart Homes and Health Telematics, 2014Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
Hana Hazgui - One of the best experts on this subject based on the ideXlab platform.
-
Actimetry@home: Actimetric Tele-surveillance and Tailored to the Signal Data Compression
Smart Homes and Health Telematics, 2015Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
-
ICOST - Actimetry@home: Actimetric Tele-surveillance and Tailored to the Signal Data Compression
Smart Homes and Health Telematics, 2014Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
-
actimetry home actimetric tele surveillance and tailored to the Signal Data compression
International Conference on Smart Homes and Health Telematics, 2014Co-Authors: Jacques Demongeot, Olivier Hansen, Hana Hazgui, Ali Hamié, Giuseppe Virone, Nicolas VuillermeAbstract:An early diagnosis of a neurodegenerative process like the Alzheimer’s disease needs a tele-surveillance at home based on the recording of pathologic Signals coming both from the cardiac activity (for detecting the loss of the sinus respiratory arrhythmia) and from the repetition of tasks of the daily life (signing a pathologic behavior called perseveration), whose non-invasive detection can lead to an early diagnosis, if it triggers secondly a battery of tests based on brain imaging, clinical neurology and cognitive sciences to confirm the suspicion of neuronal degeneration. For increasing the efficiency of alarms triggering these tests, we use dedicated tailored Data compression methods, whose two examples will be presented, the Dynalets method for quantitative compression of the physiologic Signals and the monotonic signature for qualitative compression.
