Mohamed Elhabiby - Academia.edu (original) (raw)

Papers by Mohamed Elhabiby

MEJ. Mansoura Engineering Journal

 I. INTRODUCTION Global Navigation Satellite System (GNSS) is an absolute positioning technique ... more  I. INTRODUCTION Global Navigation Satellite System (GNSS) is an absolute positioning technique where the user receives transmitted data from at least four GNSS satellites to determine the position related to a fixed coordinate frame [1]. However, GNSS signals may be subjected to different error types that must be eliminated or

Sensors

Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, ... more Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, the surrounding environmental perception, and control. Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) is one of the significant components of any vehicle navigation system. However, GNSS has limitations in some operating scenarios such as urban regions and indoor environments where the GNSS signal suffers from multipath or outage. On the other hand, INS standalone navigation solution degrades over time due to the INS errors. Therefore, a modern vehicle navigation system depends on integration between different sensors to aid INS for mitigating its drift during GNSS signal outage. However, there are some challenges for the aiding sensors related to their high price, high computational costs, and environmental and weather effects. This paper proposes an integrated aiding navigation system for vehicles in an indoor environment (e.g., underground parking). This prop...

Sensors

Various high budget industries that utilize wheel-based vehicles rely on wheel odometry as an int... more Various high budget industries that utilize wheel-based vehicles rely on wheel odometry as an integral aspect of their navigation process. This research introduces a low-cost alternative for typical wheel encoders that are typically used to determine the on-track speed of vehicles. The proposed system is referred to as an Accelerometer-based Wheel Odometer for Kinematics determination (AWOK). The AWOK system comprises just a single axis accelerometer mounted radially at the center of any given wheel. The AWOK system can provide direct distances instead of just velocities, which are provided by typical wheel speedometers. Hence, the AWOK system is advantageous in comparison to typical wheel odometers. Besides, the AWOK system comprises a simple assembly with a highly efficient data processing algorithm. Additionally, the AWOK system provides a high capacity to handle high dynamics in comparison to similar approaches found in previous related work. Furthermore, the AWOK system is not ...

Mechatronic Systems and Control

The use of inertial sensors (accelerometers and gyroscopes) in characterizing essential tremor (E... more The use of inertial sensors (accelerometers and gyroscopes) in characterizing essential tremor (ET) motion is quite prevalent. For this reason, it is important to determine the following for accelerometers and gyroscopes: (i) whether frequency localization offered by utilizing wavelets provides significant advantages to a Fourier based approach of analyzing data (ii) whether accelerometers or gyroscopes can better distinguish ET motion from that of a control (iii) whether three axes of inertial sensor measurement are required to assess essential tremor motion. To examine the above mentioned areas of inquiry, triaxial inertial sensor signals were captured for patients using their dominant hand to direct a laser at targets on a computer screen whilst sitting in an upright position. Participants were asked to keep their arm extended in front of them with slight bend in their elbow; 7 ET patients (5 males, 2 females) with a mean age of 66 and 9 controls (4 males, 5 females) with a mean age of 64 took part. Analysis of the inertial data showed that both accelerometer and gyroscope sensors mounted along any arbitrary axis provide similar frequency of motion information and that frequency localization using wavelets offers some advantages to a Fourier based approach.

The International Conference on Electrical Engineering

GPS measurements can be modeled as a true range plus other errors such as orbital and clock biase... more GPS measurements can be modeled as a true range plus other errors such as orbital and clock biases, atmospheric residual, multipath, and observation noise. Modeling is one approach to deal with some of these errors, if their characteristics are known (e.g. troposphere and ionosphere errors). Another way to deal with these errors is filtering in the frequency domain, where all these errors have different frequency spectrum component. Each errors is characterized by a specific frequency band, e.g. the receiver noise can be characterized with high frequency components, multipath errors, which have low to medium frequency bands, while the ionospheric and tropospheric errors are at a lower frequency band. Wavelet spectral techniques can separate GPS signal into sub-bands where different errors can be separated and mitigated. This paper introduces two new wavelet spectral analysis techniques to mitigate DGPS errors in the frequency domain namely, cycle slip and multipath errors. The first approach in this paper, Wavelet de-trending, is introduced to remove the long wavelength carrier phase multipath error in the measurement domain. The presented wavelet-based trend extraction model is applied to GPS static baseline solutions. The second approach in this paper is introduced to detect and remove cycle slip error which can be seen as a singularity in the GPS data. The propagation of singularities between the wavelets levels of decomposition is different from the propagation of noise. This characteristic is used to identify the singularities from noise.

The International Conference on Electrical Engineering

In this research paper, a new implementation on the second generation curvelet transform in the e... more In this research paper, a new implementation on the second generation curvelet transform in the edge detection of coastline is presented and applied on WorldView-2 imagery, together with a comparison with the classical edge detection methods such as Canny operator and the traditional wavelet transforms. This implementation is aiming to compare this new approach to the traditional edge detection techniques. It is found that the curvelet proposed implementation performs better in detecting larger and elongated structures compared to the Canny and the wavelet transforms. However, Although this method is promising and efficient for edge detection, the quality of the edge detection is still a function of the pre-processing steps (the classification step in this research paper) , as any edge detector will suffer from the heterogeneity of the images especially when using very high resolution imagery.

Sensors (Basel, Switzerland), Jan 7, 2017

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operation... more Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions' environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of lin...

Can J Earth Sci, 2009

In carrier-phase measurements, which are the most precise observations for Global Positioning Sys... more In carrier-phase measurements, which are the most precise observations for Global Positioning System (GPS) relative positioning, multipath error is still a factor that interferes with achieving the desired accuracy. Various improvements in receiver and antenna technologies, as well as modeling strategies, have resulted in better ways of coping with this error source. However, errors caused by multipath can be as large as 5 cm, which is not an acceptable accuracy, especially in precise surveying applications like deformation monitoring. In this paper, a full assessment of different wavelets techniques that can be used in multipath mitigation is made to evaluate the optimum way of using wavelets to reduce or remove this type of error. Also, a new approach based on the wavelet detrending technique is introduced to remove carrier-phase multipath error in the measurement domain. To mitigate multipath, GPS double-difference observables are fed to an adaptive wavelet analysis procedure based on high- and low-pass filter decomposition with different levels of resolution. Consequently, the observable sequences are corrected; these corrected observables can then be used to reduce the ambiguity search volume during the initial float solution stage. Meanwhile, double-difference observations with multipath mitigation offer an efficient method for obtaining a better baseline solution.

International Journal of Computer Science & Engineering Survey, 2012

The use of a weighted-frequency Fourier linear combiner (WFLC) algorithm for assessment and atten... more The use of a weighted-frequency Fourier linear combiner (WFLC) algorithm for assessment and attenuation of movement disorder tremor (including essential tremor and Parkinson's tremor) is quite prevalent; indeed, this technique is likely the most popular for such applications. The novel work presented here applies this technique to accelerometer and gyroscope data describing six degree-of-freedom motion (three translational and three rotational degrees-of-freedom). Most analysis of tremor is based on observation of generally one to three degrees-of-freedom of motion. Six degree-of-freedom motion analysis is more difficult to accomplish because of the complexity of capturing such a large amount of motion data. As well, processing accelerometer and gyroscope data to yield six degree-of-freedom motion generally involves the use of a Kalman smoother (necessary because of signal noise and drift) to ensure that accelerometer signals are correctly compensated for the influence of gravity. After data are processed using a Kalman smoother and the WFLC algorithm is applied, results are interpreted using wavelet frequency spectrum analysis to determine the frequency content before and after processing the data. Results show that the WFLC algorithm can be successfully applied to all six degrees-of-freedom of motion to largely remove tremor.

Global array computations are greatly facilitated by Spherical Harmonic Transforms (SHTs) just as... more Global array computations are greatly facilitated by Spherical Harmonic Transforms (SHTs) just as planar array computations benefit from discrete Fast Fourier Transforms (FFTs). Multiresolution analysis and synthesis involve linear filtering or convolutions, decimation and dilation. Spherical convolutions in the continuous sense differ from planar ones because of the noncommutativity of spherical rotations and hence are restricted to isotropic filtering. However in discrete computations, the situation is different which can be exploited in practical applications. Different spherical strategies for multiresolution analysis based on regular grids (e.g. first generation wavelet analysis), Reuter grids (spherical wavelet analysis) and tessellations are presented with numerical simulations to exhibit their respective advantages and disadvantages. Concluding remarks with some recommendations for various geoscience applications are also included.

Cloud-related shadows represent areas with low illumination conditions that affect remote sensing... more Cloud-related shadows represent areas with low illumination conditions that affect remote sensing image quality. In this research, a wavelet-based image sharpening algorithm was developed to enhance shadow areas independently using the defected cloudy image information. The developed algorithm is applied locally by boosting the image high frequency content in the shadow areas using the defected image de-noised wavelet coefficients. Image

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012

. The new advances of having eight bands satellite mission similar to WorldView-2, WV-2, give the... more . 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%.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014

3D model of indoor environments provide rich information that can facilitate the disambiguation o... more 3D model of indoor environments provide rich information that can facilitate the disambiguation of different places and increases the familiarization process to any indoor environment for the remote users. In this research work, we describe a system for visual odometry and 3D modeling using information from RGB-D sensor (Camera). The visual odometry method estimates the relative pose of the consecutive RGB-D frames through feature extraction and matching techniques. The pose estimated by visual odometry algorithm is then refined with iterative closest point (ICP) method. The switching technique between ICP and visual odometry in case of no visible features suppresses inconsistency in the final developed map. Finally, we add the loop closure to remove the deviation between first and last frames. In order to have a semantic meaning out of 3D models, the planar patches are segmented from RGB-D point clouds data using region growing technique followed by convex hull method to assign boundaries to the extracted patches. In order to build a final semantic 3D model, the segmented patches are merged using relative pose information obtained from the first step.

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014, 2014

ABSTRACT This paper presents a monocular camera and inertial measurement unit (IMU) fusion techni... more ABSTRACT This paper presents a monocular camera and inertial measurement unit (IMU) fusion technique using Extended Kalman Filter (EKF) with delay in landmark initialization to address the simultaneous localization and mapping (SLAM) problem for single smartphone. The dynamic model of the EKF is chosen to be constant acceleration while the velocity of the system is constantly monitored in order to have enough overlap between consecutive camera frames. Moreover inconsistency in SLAM algorithm due to heading error is removed by utilizing magnetometer measurement model. The use of data association technique ensures that the final map solution is robust and consistent even in complex environment. For fast and robust features matching, the Speed-Up Robust Features (SURF) extraction algorithm followed by random sample consensus (RANSAC) method is applied on camera frames. The extracted features from SURF algorithm are related to ground plane, since the system moves parallel to the ground. The experimental results illustrate the performance of the monocular-IMU SLAM over long walked trajectories in indoor environment.

International Association of Geodesy Symposia, 2008

A wavelet transform algorithm combined with a conjugate gradient method is used for the inversion... more A wavelet transform algorithm combined with a conjugate gradient method is used for the inversion of Poisson's integral (downward continuation), used in airborne gravimetry applications. The wavelet approximation is dependent on orthogonal wavelet base functions. The integrals are approximated in finite multiresolution analysis subspaces. Mallat's algorithm is used in the multiresolution analysis of the kernel and the data. The full solution with all equations requires large computer memory, therefore, the multiresolution properties of the wavelet transform are used to divide the full solution into parts at different levels of wavelet multiresolution decomposition. Global wavelet thresholding is used for the compression of the kernel and because of the fast decrease of the kernel towards zero, high compression levels are reached without significant loss of accuracy. Hard thresholding is used in the compression of the kernel wavelet coefficients matrices. A new thresholding technique is introduced. A first-order Tikhonov regularization method combined with the L-curve is used for the regularization of this problem. First, Poisson's integral is inverted numerically with the full matrix without any thresholding. The solution is obtained using the conjugate gradient method after 28 iteration steps with a root mean square error equal to 5.58 mGal in comparison to the reference data. Second, the global hard thresholding solution achieved a 94.5% compression level with less than 0.1 mGal loss in accuracy. These high compression levels lead to large savings in computer memory and the ability to work with sparse matrices, which increases the computational speed.

Communications in Computer and Information Science, 2013

ABSTRACT A weighted-frequency Fourier linear combiner (WFLC) filter is used for removal of tremor... more ABSTRACT A weighted-frequency Fourier linear combiner (WFLC) filter is used for removal of tremor motion for data captured from movement disorders subjects with essential tremor (ET) and Parkinson’s disease (PD). This technique is applied here in six degrees-of-freedom and data are filtered so that a comparison can be made before and after filtering to assess the extent to which the WFLC filter removed tremor. A wavelet spectral analysis is employed to determine the effectiveness of the filter in removing tremor in the 3-12 Hz band of interest. A Kalman filter is employed to improve data processing so that six degree-of-freedom tremor motion can be accurately rendered for subsequent filtering; such accurate rendering is needed so that full tremor motion can be adequately described. A Fourier coherence based technique is utilized so that relationships for interrelated tremors for the different six degrees-of-freedom can be identified. Much of the analysis shown is novel.

International Journal of Computer Science & Engineering Survey, 2012

Edge detection is an important assignment in image processing, as it is used as a primary tool fo... more Edge detection is an important assignment in image processing, as it is used as a primary tool for pattern recognition, image segmentation and scene analysis. Simply put, an edge detector is a high-pass filter that can be applied for extracting the edge points within an image. Edge detection in the spatial domain is accomplished through convolution with a set of directional derivative masks in this domain. On one hand, the popular edge detection spatial operators such as; Roberts, Sobel, Prewitt, and Laplacian are all defined on a 3 by 3 pattern grid, which is efficient and easy to apply. On the other hand, working in the frequency domain has many advantages, starting from introducing an alternative description to the spatial representation and providing more efficient and faster computational schemes with less sensitivity to noise through high filtering, de-noising and compression algorithms. Fourier transforms, wavelet and curvelet transform are among the most widely used frequency-domain edge detection from satellite images. However, the Fourier transform is global and poorly adapted to local singularities. Some of these draw backs are solved by the wavelet transforms especially for singularities detection and computation. In this paper, the relatively new multi-resolution technique, curvelet transform, is assessed and introduced to overcome the wavelet transform limitation in directionality and scaling. In this research paper, the assessment of second generation curvelet transforms as an edge detection tool will be introduced and compared to traditional edge detectors such as wavelet transform and Canny Edge detector. Second generation curvelet transform provides optimally sparse representations of objects, which display smoothness except for discontinuity along the curve with bounded curvature. Preliminary results show the power of curvelet transform over the wavelet transform through the detection of nonvertical oriented edges, with detailed detection of curves and circular boundaries, such as non straight roads and shores. Conclusions and recommendations are given with respect to the suitability; accuracy and efficiency of the curvelet transform method compared to the other traditional methods

MEJ. Mansoura Engineering Journal

 I. INTRODUCTION Global Navigation Satellite System (GNSS) is an absolute positioning technique ... more  I. INTRODUCTION Global Navigation Satellite System (GNSS) is an absolute positioning technique where the user receives transmitted data from at least four GNSS satellites to determine the position related to a fixed coordinate frame [1]. However, GNSS signals may be subjected to different error types that must be eliminated or

Sensors

Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, ... more Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, the surrounding environmental perception, and control. Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) is one of the significant components of any vehicle navigation system. However, GNSS has limitations in some operating scenarios such as urban regions and indoor environments where the GNSS signal suffers from multipath or outage. On the other hand, INS standalone navigation solution degrades over time due to the INS errors. Therefore, a modern vehicle navigation system depends on integration between different sensors to aid INS for mitigating its drift during GNSS signal outage. However, there are some challenges for the aiding sensors related to their high price, high computational costs, and environmental and weather effects. This paper proposes an integrated aiding navigation system for vehicles in an indoor environment (e.g., underground parking). This prop...

Sensors

Various high budget industries that utilize wheel-based vehicles rely on wheel odometry as an int... more Various high budget industries that utilize wheel-based vehicles rely on wheel odometry as an integral aspect of their navigation process. This research introduces a low-cost alternative for typical wheel encoders that are typically used to determine the on-track speed of vehicles. The proposed system is referred to as an Accelerometer-based Wheel Odometer for Kinematics determination (AWOK). The AWOK system comprises just a single axis accelerometer mounted radially at the center of any given wheel. The AWOK system can provide direct distances instead of just velocities, which are provided by typical wheel speedometers. Hence, the AWOK system is advantageous in comparison to typical wheel odometers. Besides, the AWOK system comprises a simple assembly with a highly efficient data processing algorithm. Additionally, the AWOK system provides a high capacity to handle high dynamics in comparison to similar approaches found in previous related work. Furthermore, the AWOK system is not ...

Mechatronic Systems and Control

The use of inertial sensors (accelerometers and gyroscopes) in characterizing essential tremor (E... more The use of inertial sensors (accelerometers and gyroscopes) in characterizing essential tremor (ET) motion is quite prevalent. For this reason, it is important to determine the following for accelerometers and gyroscopes: (i) whether frequency localization offered by utilizing wavelets provides significant advantages to a Fourier based approach of analyzing data (ii) whether accelerometers or gyroscopes can better distinguish ET motion from that of a control (iii) whether three axes of inertial sensor measurement are required to assess essential tremor motion. To examine the above mentioned areas of inquiry, triaxial inertial sensor signals were captured for patients using their dominant hand to direct a laser at targets on a computer screen whilst sitting in an upright position. Participants were asked to keep their arm extended in front of them with slight bend in their elbow; 7 ET patients (5 males, 2 females) with a mean age of 66 and 9 controls (4 males, 5 females) with a mean age of 64 took part. Analysis of the inertial data showed that both accelerometer and gyroscope sensors mounted along any arbitrary axis provide similar frequency of motion information and that frequency localization using wavelets offers some advantages to a Fourier based approach.

The International Conference on Electrical Engineering

GPS measurements can be modeled as a true range plus other errors such as orbital and clock biase... more GPS measurements can be modeled as a true range plus other errors such as orbital and clock biases, atmospheric residual, multipath, and observation noise. Modeling is one approach to deal with some of these errors, if their characteristics are known (e.g. troposphere and ionosphere errors). Another way to deal with these errors is filtering in the frequency domain, where all these errors have different frequency spectrum component. Each errors is characterized by a specific frequency band, e.g. the receiver noise can be characterized with high frequency components, multipath errors, which have low to medium frequency bands, while the ionospheric and tropospheric errors are at a lower frequency band. Wavelet spectral techniques can separate GPS signal into sub-bands where different errors can be separated and mitigated. This paper introduces two new wavelet spectral analysis techniques to mitigate DGPS errors in the frequency domain namely, cycle slip and multipath errors. The first approach in this paper, Wavelet de-trending, is introduced to remove the long wavelength carrier phase multipath error in the measurement domain. The presented wavelet-based trend extraction model is applied to GPS static baseline solutions. The second approach in this paper is introduced to detect and remove cycle slip error which can be seen as a singularity in the GPS data. The propagation of singularities between the wavelets levels of decomposition is different from the propagation of noise. This characteristic is used to identify the singularities from noise.

The International Conference on Electrical Engineering

In this research paper, a new implementation on the second generation curvelet transform in the e... more In this research paper, a new implementation on the second generation curvelet transform in the edge detection of coastline is presented and applied on WorldView-2 imagery, together with a comparison with the classical edge detection methods such as Canny operator and the traditional wavelet transforms. This implementation is aiming to compare this new approach to the traditional edge detection techniques. It is found that the curvelet proposed implementation performs better in detecting larger and elongated structures compared to the Canny and the wavelet transforms. However, Although this method is promising and efficient for edge detection, the quality of the edge detection is still a function of the pre-processing steps (the classification step in this research paper) , as any edge detector will suffer from the heterogeneity of the images especially when using very high resolution imagery.

Sensors (Basel, Switzerland), Jan 7, 2017

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operation... more Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions' environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of lin...

Can J Earth Sci, 2009

In carrier-phase measurements, which are the most precise observations for Global Positioning Sys... more In carrier-phase measurements, which are the most precise observations for Global Positioning System (GPS) relative positioning, multipath error is still a factor that interferes with achieving the desired accuracy. Various improvements in receiver and antenna technologies, as well as modeling strategies, have resulted in better ways of coping with this error source. However, errors caused by multipath can be as large as 5 cm, which is not an acceptable accuracy, especially in precise surveying applications like deformation monitoring. In this paper, a full assessment of different wavelets techniques that can be used in multipath mitigation is made to evaluate the optimum way of using wavelets to reduce or remove this type of error. Also, a new approach based on the wavelet detrending technique is introduced to remove carrier-phase multipath error in the measurement domain. To mitigate multipath, GPS double-difference observables are fed to an adaptive wavelet analysis procedure based on high- and low-pass filter decomposition with different levels of resolution. Consequently, the observable sequences are corrected; these corrected observables can then be used to reduce the ambiguity search volume during the initial float solution stage. Meanwhile, double-difference observations with multipath mitigation offer an efficient method for obtaining a better baseline solution.

International Journal of Computer Science & Engineering Survey, 2012

The use of a weighted-frequency Fourier linear combiner (WFLC) algorithm for assessment and atten... more The use of a weighted-frequency Fourier linear combiner (WFLC) algorithm for assessment and attenuation of movement disorder tremor (including essential tremor and Parkinson's tremor) is quite prevalent; indeed, this technique is likely the most popular for such applications. The novel work presented here applies this technique to accelerometer and gyroscope data describing six degree-of-freedom motion (three translational and three rotational degrees-of-freedom). Most analysis of tremor is based on observation of generally one to three degrees-of-freedom of motion. Six degree-of-freedom motion analysis is more difficult to accomplish because of the complexity of capturing such a large amount of motion data. As well, processing accelerometer and gyroscope data to yield six degree-of-freedom motion generally involves the use of a Kalman smoother (necessary because of signal noise and drift) to ensure that accelerometer signals are correctly compensated for the influence of gravity. After data are processed using a Kalman smoother and the WFLC algorithm is applied, results are interpreted using wavelet frequency spectrum analysis to determine the frequency content before and after processing the data. Results show that the WFLC algorithm can be successfully applied to all six degrees-of-freedom of motion to largely remove tremor.

Global array computations are greatly facilitated by Spherical Harmonic Transforms (SHTs) just as... more Global array computations are greatly facilitated by Spherical Harmonic Transforms (SHTs) just as planar array computations benefit from discrete Fast Fourier Transforms (FFTs). Multiresolution analysis and synthesis involve linear filtering or convolutions, decimation and dilation. Spherical convolutions in the continuous sense differ from planar ones because of the noncommutativity of spherical rotations and hence are restricted to isotropic filtering. However in discrete computations, the situation is different which can be exploited in practical applications. Different spherical strategies for multiresolution analysis based on regular grids (e.g. first generation wavelet analysis), Reuter grids (spherical wavelet analysis) and tessellations are presented with numerical simulations to exhibit their respective advantages and disadvantages. Concluding remarks with some recommendations for various geoscience applications are also included.

Cloud-related shadows represent areas with low illumination conditions that affect remote sensing... more Cloud-related shadows represent areas with low illumination conditions that affect remote sensing image quality. In this research, a wavelet-based image sharpening algorithm was developed to enhance shadow areas independently using the defected cloudy image information. The developed algorithm is applied locally by boosting the image high frequency content in the shadow areas using the defected image de-noised wavelet coefficients. Image

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012

. The new advances of having eight bands satellite mission similar to WorldView-2, WV-2, give the... more . 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%.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014

3D model of indoor environments provide rich information that can facilitate the disambiguation o... more 3D model of indoor environments provide rich information that can facilitate the disambiguation of different places and increases the familiarization process to any indoor environment for the remote users. In this research work, we describe a system for visual odometry and 3D modeling using information from RGB-D sensor (Camera). The visual odometry method estimates the relative pose of the consecutive RGB-D frames through feature extraction and matching techniques. The pose estimated by visual odometry algorithm is then refined with iterative closest point (ICP) method. The switching technique between ICP and visual odometry in case of no visible features suppresses inconsistency in the final developed map. Finally, we add the loop closure to remove the deviation between first and last frames. In order to have a semantic meaning out of 3D models, the planar patches are segmented from RGB-D point clouds data using region growing technique followed by convex hull method to assign boundaries to the extracted patches. In order to build a final semantic 3D model, the segmented patches are merged using relative pose information obtained from the first step.

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014, 2014

ABSTRACT This paper presents a monocular camera and inertial measurement unit (IMU) fusion techni... more ABSTRACT This paper presents a monocular camera and inertial measurement unit (IMU) fusion technique using Extended Kalman Filter (EKF) with delay in landmark initialization to address the simultaneous localization and mapping (SLAM) problem for single smartphone. The dynamic model of the EKF is chosen to be constant acceleration while the velocity of the system is constantly monitored in order to have enough overlap between consecutive camera frames. Moreover inconsistency in SLAM algorithm due to heading error is removed by utilizing magnetometer measurement model. The use of data association technique ensures that the final map solution is robust and consistent even in complex environment. For fast and robust features matching, the Speed-Up Robust Features (SURF) extraction algorithm followed by random sample consensus (RANSAC) method is applied on camera frames. The extracted features from SURF algorithm are related to ground plane, since the system moves parallel to the ground. The experimental results illustrate the performance of the monocular-IMU SLAM over long walked trajectories in indoor environment.

International Association of Geodesy Symposia, 2008

A wavelet transform algorithm combined with a conjugate gradient method is used for the inversion... more A wavelet transform algorithm combined with a conjugate gradient method is used for the inversion of Poisson's integral (downward continuation), used in airborne gravimetry applications. The wavelet approximation is dependent on orthogonal wavelet base functions. The integrals are approximated in finite multiresolution analysis subspaces. Mallat's algorithm is used in the multiresolution analysis of the kernel and the data. The full solution with all equations requires large computer memory, therefore, the multiresolution properties of the wavelet transform are used to divide the full solution into parts at different levels of wavelet multiresolution decomposition. Global wavelet thresholding is used for the compression of the kernel and because of the fast decrease of the kernel towards zero, high compression levels are reached without significant loss of accuracy. Hard thresholding is used in the compression of the kernel wavelet coefficients matrices. A new thresholding technique is introduced. A first-order Tikhonov regularization method combined with the L-curve is used for the regularization of this problem. First, Poisson's integral is inverted numerically with the full matrix without any thresholding. The solution is obtained using the conjugate gradient method after 28 iteration steps with a root mean square error equal to 5.58 mGal in comparison to the reference data. Second, the global hard thresholding solution achieved a 94.5% compression level with less than 0.1 mGal loss in accuracy. These high compression levels lead to large savings in computer memory and the ability to work with sparse matrices, which increases the computational speed.

Communications in Computer and Information Science, 2013

ABSTRACT A weighted-frequency Fourier linear combiner (WFLC) filter is used for removal of tremor... more ABSTRACT A weighted-frequency Fourier linear combiner (WFLC) filter is used for removal of tremor motion for data captured from movement disorders subjects with essential tremor (ET) and Parkinson’s disease (PD). This technique is applied here in six degrees-of-freedom and data are filtered so that a comparison can be made before and after filtering to assess the extent to which the WFLC filter removed tremor. A wavelet spectral analysis is employed to determine the effectiveness of the filter in removing tremor in the 3-12 Hz band of interest. A Kalman filter is employed to improve data processing so that six degree-of-freedom tremor motion can be accurately rendered for subsequent filtering; such accurate rendering is needed so that full tremor motion can be adequately described. A Fourier coherence based technique is utilized so that relationships for interrelated tremors for the different six degrees-of-freedom can be identified. Much of the analysis shown is novel.

International Journal of Computer Science & Engineering Survey, 2012

Edge detection is an important assignment in image processing, as it is used as a primary tool fo... more Edge detection is an important assignment in image processing, as it is used as a primary tool for pattern recognition, image segmentation and scene analysis. Simply put, an edge detector is a high-pass filter that can be applied for extracting the edge points within an image. Edge detection in the spatial domain is accomplished through convolution with a set of directional derivative masks in this domain. On one hand, the popular edge detection spatial operators such as; Roberts, Sobel, Prewitt, and Laplacian are all defined on a 3 by 3 pattern grid, which is efficient and easy to apply. On the other hand, working in the frequency domain has many advantages, starting from introducing an alternative description to the spatial representation and providing more efficient and faster computational schemes with less sensitivity to noise through high filtering, de-noising and compression algorithms. Fourier transforms, wavelet and curvelet transform are among the most widely used frequency-domain edge detection from satellite images. However, the Fourier transform is global and poorly adapted to local singularities. Some of these draw backs are solved by the wavelet transforms especially for singularities detection and computation. In this paper, the relatively new multi-resolution technique, curvelet transform, is assessed and introduced to overcome the wavelet transform limitation in directionality and scaling. In this research paper, the assessment of second generation curvelet transforms as an edge detection tool will be introduced and compared to traditional edge detectors such as wavelet transform and Canny Edge detector. Second generation curvelet transform provides optimally sparse representations of objects, which display smoothness except for discontinuity along the curve with bounded curvature. Preliminary results show the power of curvelet transform over the wavelet transform through the detection of nonvertical oriented edges, with detailed detection of curves and circular boundaries, such as non straight roads and shores. Conclusions and recommendations are given with respect to the suitability; accuracy and efficiency of the curvelet transform method compared to the other traditional methods