Copper and Magnesium Deficiency are Associated with Osteoporosis in Southern Gaza Patients

Osteoporosis is a common disease in old ages, trace minerals are central components of bone density and hardness. The present study aims to measure copper, magnesium, iron, calcium and phosphorus levels in osteoporotic southern Gaza patients and control groups. A case –control study included 35 osteoporotic patients and 35 controls aged 40-70 years. Copper, magnesium iron, calcium and phosphorus levels were measured in the serum at PalestinianMedical Relieve Society-Gaza by absorption spectrophotometry method-XLFS Kit (Diasys Diagnostic System GmbH). Serum copper and magnesium levels in osteoporotic patients (74.3±9.8μg/dL 1.56±0.18mg/dl) respectively is significantly (p<0.001) lower than control (98.3±15.2μg/dL, 2.06±0.13mg/dl ). The present work indicated a positive correlation between copper and magnesium levels (r=0.627, p<0.00), positive correlation between copper and number of daily meals (r=0.263, p<0.030), and also positive correlation between calcium and daily exercises (r=0.449, p<0.010). In conclusion copper and magnesium levels are significantly lower in postmenopausal women and men with osteoporosis. Optimizing levels of those trace minerals in old people is beneficial in prevention of osteoporosis. Daily exercises and ingestion of food containing trace minerals is highly recommended for this age group

IMPROVED REFERENCE KEY FRAME ALGORITHM

The autonomous vehicles, such as wheeled robots and drones, efficiently contribute in the search and rescue operations. Specially for indoor environments, these autonomous vehicles rely on simultaneous localization and mapping approach (SLAM) to construct a map for the unknown environment and simultaneously to estimate the vehicle’s position inside this map. The result of the scan matching process, which is a key step in many of SLAM approaches, has a fundamental role of the accuracy of the map construction. Typically, local and global scan matching approaches, that utilize laser scan rangefinder, suffer from accumulated errors as both approaches are sensitive to previous history. The reference key frame (RKF) algorithm reduces errors accumulation as it decreases the dependency on the accuracy of the previous history. However, the RKF algorithm still suffers; as most of the SLAM approaches, from scale shrinking problem during scanning corridors that exceed the maximum detection range of the laser scan rangefinder. The shrinking in long corridors comes from the unsuccessful estimation of the longitudinal movement from the implemented RKF algorithm and the unavailability of this information from external source as well. This paper proposes an improvement for the RKF algorithm. This is achieved by integrating the outcomes of the optical flow with the RKF algorithm using extended Kalman filter (EKF) to overcome the shrinking problem. The performance of the proposed algorithm is compared with the RKF, iterative closest point (ICP), and Hector SLAM in corridors that exceed the maximum detection range of the laser scan rangefinder

LIDAR-INERTIAL LOCALIZATION WITH GROUND CONSTRAINT IN A POINT CLOUD MAP

Real-time localization is a crucial task in various applications, such as automatic vehicles (AV), robotics, and smart city. This study proposes a framework for map-aided LiDAR-inertial localization, with the objective of accurately estimating the trajectory in a point clouds map. The proposed framework addresses the localization problem through a factor graph optimization (FGO), enabling the fusion of homogenous measurements for sensor fusion and designed absolute and relative constraints. Specifically, the framework estimates the light detection and ranging (LiDAR) odometry by leveraging inertial measurement unit (IMU) and registering corresponding featured points. To eliminate the accumulative error, this paper employs a ground plane distance and a map matching error to constraint the positioning error along the trajectory. Finally, local odometry and constraints are integrated using a FGO, including LiDAR odometry, IMU pre-integration, and ground constraints, map matching constraints, and loop closure. Experimental results were evaluated on an open-source dataset, UrbanNav, with an overall localization accuracy of 2.29 m (root mean square error, RMSE)

COMPARISON AND ANALYSIS OF NONLINEAR LEAST SQUARES METHODS FOR VISION BASED NAVIGATION (VBN) ALGORITHMS

A robust scale and rotation invariant image matching algorithm is vital for the Visual Based Navigation (VBN) of aerial vehicles, where matches between an existing geo-referenced database images and the real-time captured images are used to georeference (i.e. six transformation parameters - three rotation and three translation) the real-time captured image from the UAV through the collinearity equations. The georeferencing information is then used in aiding the INS integration Kalman filter as Coordinate UPdaTe (CUPT). It is critical for the collinearity equations to use the proper optimization algorithm to ensure accurate and fast convergence for georeferencing parameters with the minimum required conjugate points necessary for convergence. Fast convergence to a global minimum will require non-linear approach to overcome the high degree of non-linearity that will exist in case of having large oblique images (i.e. large rotation angles).The main objective of this paper is investigating the estimation of the georeferencing parameters necessary for VBN of aerial vehicles in case of having large values of the rotational angles, which will lead to non-linearity of the estimation model. In this case, traditional least squares approaches will fail to estimate the georeferencing parameters, because of the expected non-linearity of the mathematical model. Five different nonlinear least squares methods are presented for estimating the transformation parameters. Four gradient based nonlinear least squares methods (Trust region, Trust region dogleg algorithm, Levenberg-Marquardt, and Quasi-Newton line search method) and one non-gradient method (Nelder-Mead simplex direct search) is employed for the six transformation parameters estimation process. The research was done on simulated data and the results showed that the Nelder-Mead method has failed because of its dependency on the objective function without any derivative information. Although, the tested gradient methods succeeded in converging to the relative optimal solution of the georeferencing parameters. In trust region methods, the number of iterations was more than Levenberg-Marquardt because of the necessity for evaluating the local minimum to ensure if it is the global one or not in each iteration step. As for the Levenberg-Marquardt method, which is considered as a modified Gauss-Newton algorithm employing the trust region approach where a scalar is introduced to assess the choice of the magnitude and the direction of the descent. This scalar determines whether the Gauss-Newton method direction or the steepest descent method direction will be used as an adaptive approach for both linear and non-linear mathematical models and it successfully converged and achieved the relative optimum solution. These five methods results are compared explicitly to the linear traditional least-squares approach, with detailed statistical analysis of the results, with emphasis on the UAV (VBN) applications

NEW COMBINED PIXEL/OBJECT-BASED TECHNIQUE FOR EFFICIENT URBAN CLASSSIFICATION USING WORLDVIEW-2 DATA

The new advances of having eight bands satellite mission similar to WorldView-2, WV-2, give the chance to address and solve some of the traditional problems related to the low spatial and/or spectral resolution; such as the lack of details for certain features or the inability of the conventional classifiers to detect some land-cover types because of missing efficient spectrum information and analysis techniques. High-resolution imagery is particularly well suited to urban applications. High spectral and spatial resolution of WorldView-2 data introduces challenges in detailed mapping of urban features. Classification of Water, Shadows, Red roofs and concrete buildings spectrally exhibit significant confusion either from the high similarity in the spectral response (e.g. water and Shadows) or the similarity in material type (e.g. red roofs and concrete buildings). This research study assesses the enhancement of the classification accuracy and efficiency for a data set of WorldView-2 satellite imagery using the full 8-bands through integrating the output of classification process using three band ratios with another step involves an object-based technique for extracting shadows, water, vegetation, building, Bare soil and asphalt roads. Second generation curvelet transform will be used in the second step, specifically to detect buildings' boundaries, which will aid the new algorithm of band ratios classification through efficient separation of the buildings. The combined technique is tested, and the preliminary results show a great potential of the new bands in the WV-2 imagery in the separation between confusing classes such as water and shadows, and the testing is extended to the separation between bare soils and asphalt roads. The Integrated band ratio-curvelet transform edge detection techniques increased the percentage of building detection by more than 30%

Registration of time of flight terrestrial laser scanner data for stop-and-go mode

Terrestrial Laser Scanners (TLS) are utilized through different data acquisition techniques such as Mobile Laser Scanning (MLS) and the output can be used in different applications such as 3D city modelling, cultural heritage documentations, oil and Gas as built, etc... In this research paper, we will investigate one of the modes of TLS on mobile mapping platform. Namely the Stop-and-Go (SAG) mode. Unlike the continuous mode, the Stop-and-Go mode does not require the use of IMU to estimate the TLS attitude and thus inturn it has an overall reduction in the system cost. Moreover, it decreases the time required for data processing in comparison with the continuous mode. For successful use of SAG mobile mapping in urban areas, it is preferred to use a long range time of flight laser scanner to cover long distances in each scan and minimize the registration error. The problem arise with Long range laser scanners is their low point cloud density. The low point cloud density affects the registration accuracy specially in monitoring applications. The point spacing between points is one of the issues facing the registration especially when the matching points are chosen manually. Since most of TLS nowadays are equipped with camera on-board we can utilize the camera to get an initial estimate of the registration parameters based on image matching. After having an initial approximation of the registration parameters we feed those parameters to the Iterative Closest Point algorithm to obtain more accurate registration result