Introduction: Malignant sylvian infarction (MSI) is a type of ischemic stroke (ICS) usually affecting the entire territory of the middle cerebral artery (MCA) associated with significant cerebral edema and a mass. It ...Introduction: Malignant sylvian infarction (MSI) is a type of ischemic stroke (ICS) usually affecting the entire territory of the middle cerebral artery (MCA) associated with significant cerebral edema and a mass. It represents about 10% of all AICs, with a mortality of up to 80%. The objectives of our study were to describe the sociodemographic profile and the main clinical manifestations and identify the prognostic factors of ISM. Material and Methods: We conducted a retrospective descriptive study over a 2-year period. It included patients hospitalized for cerebral infarction involving 2/3 of the ACM territory with a NIHSS score ≥ 17 and/or a Glasgow score Results: We collected 223 patients hospitalized for ischemic stroke, of whom 21 patients (9.4%) presented with ISM. The mean age was 57.43 ± 24.24 years with a male predominance (52.4%). The mean admission time was 47 ± 0.87 hours, and hemiplegia was the frequent neurological sign (85.7%). HBP was the common cardiovascular risk factor (76.2%). The mean NIHSS at admission was 18.38 ± 12.29. Respiratory distress (p-value = 0.00015), aspiration pneumonia (p-value = 0.015) and brain herniation (p-value = 0.014) were the main complications associated with mortality. Conclusion: ISM is associated with poor prognosis in the absence of surgical treatment. Respiratory distress, aspiration pneumonia and brain herniation are associated with high mortality.展开更多
An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） s...An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.展开更多
Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide metho...Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to detect the position of car accident. The models consider GPS/INS-based navigation algorithm, calibration of navigational sensors, a de-nosing method as long as vehicle accident, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, the location-based accident detection model is tested in different scenarios. The results illustrate that under harsh environments with no GPS signal, location of accident can be detected. Also results confirm that calibration of sensors has an important role in position correction algorithm. Finally, the results present that the proposed accident detection algorithm can recognize accidents and related its positions.展开更多
文摘Introduction: Malignant sylvian infarction (MSI) is a type of ischemic stroke (ICS) usually affecting the entire territory of the middle cerebral artery (MCA) associated with significant cerebral edema and a mass. It represents about 10% of all AICs, with a mortality of up to 80%. The objectives of our study were to describe the sociodemographic profile and the main clinical manifestations and identify the prognostic factors of ISM. Material and Methods: We conducted a retrospective descriptive study over a 2-year period. It included patients hospitalized for cerebral infarction involving 2/3 of the ACM territory with a NIHSS score ≥ 17 and/or a Glasgow score Results: We collected 223 patients hospitalized for ischemic stroke, of whom 21 patients (9.4%) presented with ISM. The mean age was 57.43 ± 24.24 years with a male predominance (52.4%). The mean admission time was 47 ± 0.87 hours, and hemiplegia was the frequent neurological sign (85.7%). HBP was the common cardiovascular risk factor (76.2%). The mean NIHSS at admission was 18.38 ± 12.29. Respiratory distress (p-value = 0.00015), aspiration pneumonia (p-value = 0.015) and brain herniation (p-value = 0.014) were the main complications associated with mortality. Conclusion: ISM is associated with poor prognosis in the absence of surgical treatment. Respiratory distress, aspiration pneumonia and brain herniation are associated with high mortality.
基金co-supported by the National Key Research and Development Program of China(No.2016YFC0803103)Beijing Advanced Innovation Center for Future Urban Design(No.UDC2016050100)Beijing Postdoctoral Research Foundation
文摘An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.
文摘Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to detect the position of car accident. The models consider GPS/INS-based navigation algorithm, calibration of navigational sensors, a de-nosing method as long as vehicle accident, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, the location-based accident detection model is tested in different scenarios. The results illustrate that under harsh environments with no GPS signal, location of accident can be detected. Also results confirm that calibration of sensors has an important role in position correction algorithm. Finally, the results present that the proposed accident detection algorithm can recognize accidents and related its positions.
