Mohamed S. Shehata

Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey

Structural health monitoring (SHM) using wireless sensor networks (WSNs) has gained research interest due to its ability to reduce the costs associated with the installation and maintenance of SHM systems. SHM systems have been used to monitor critical infrastructure such as bridges, high-rise buildings, and stadiums and has the potential to improve structure lifespan and improve public safety. The high data collection rate of WSNs for SHM pose unique network design challenges. This paper presents a comprehensive survey of SHM using WSNs outlining the algorithms used in damage detection and localization, outlining network design challenges, and future research directions. Solutions to network design problems such as scalability, time synchronization, sensor placement, and data processing are compared and discussed. This survey also provides an overview of testbeds and real-world deployments of WSNs for SH.

A semi-automated technique for internal jugular vein segmentation in ultrasound images using active contours

The assessment of the blood volume is crucial for the management of many acute and chronic diseases. Recent studies have shown that circulating blood volume correlates with the cross-sectional area (CSA) of the internal jugular vein (IJV) estimated from ultrasound imagery. In this paper, a semi-automatic segmentation algorithm is proposed using a combination of region growing and active contour techniques to provide fast and accurate segmentation of IJV ultrasound videos. The algorithm is applied to track and segment the IJV across a range of image qualities, shapes and temporal variation. The experimental results show that the algorithm performs well compared to expert manual segmentation and outperforms several published algorithms incorporating speckle tracking.

Moving object detection from moving platforms using Lagrange multiplier

Moving object detection is the first key step for many automated vision analysis applications. One of the major challenges to achieve accurate moving object detection is detecting moving objects in videos captured by moving camera platforms, also called active cameras, where both interest objects and background elements are moving. This paper presents a novel algorithm for moving objects detection from active cameras. The proposed method decomposes a video from an active camera into three components: background, moving objects, and transformation matrix between consecutive frames. The proposed method formulates the problem as a robust principle component analysis (PCA) problem (low rank matrix optimization problem) and solves it using inexact augmented Lagrange multiplier (IALM). In the proposed method, the background represents the low rank matrix, and the moving objects and transformation matrix are treated as added corruption. The robustness of the proposed method is demonstrated using a challenging dataset captured by camera mounted on unmanned air vehicle. The obtained results show that the proposed method achieves best results compared to other current state-of-the-art relevant methods.

Physiological Features of the Internal Jugular Vein from B-Mode Ultrasound Imagery

Traditional methods of capturing vital signs by monitoring electrical impulses are quite effective however this data has the potential to be extracted from alternative technology. Non-invasive monitoring using low-cost ultrasound imaging of arterial and venous vasculature has the potential to detect standard vital signs such as heart and respiratory rate as well as additional parameters such as relative changes in circulating blood volume. This paper explores the feasibility of using ultrasound to monitor these signals by detecting spatial and temporal changes in the internal jugular vein (IJV). Ultrasound videos of the jugular in the transverse plane were collected from a subset of healthy subjects. Frame-by-frame segmentation of the IJV demonstrates frequency characteristics similar to certain physiological systems. Heart and respiratory rate appear to be present in IJV cross-sectional area variations in select ultrasound clips and may provide information regarding the severity of a patient’s illness.

Performance Evaluation of 3D Keypoints and Descriptors

This paper presents a comprehensive evaluation of the performance of common 3D keypoint detectors and descriptors currently available in the Point Cloud Library (PCL) to recover the transformation of 300 real objects. Current research on keypoints detectors and descriptors considers their performance individually in terms of their repeatability or descriptiveness, rather than on their overall performance at multi-sensor alignment or recovery. We present the data on the performance of each pair under all transformations independently: translations and rotations in and around each of the x-, y- and z-axis respectively. We provide insight into the implementation of the detectors and descriptors in PCL leading to abnormal or unexpected performance. The obtained results show that the ISS/SHOT and ISS/SHOTColor detector/descriptor pair works best at 3D recovery under various transformations.

A novel method for segmenting moving objects in aerial imagery using matrix recovery and physical spring model

Aerial imagery applications have gained a great interest especially in the area of comprehensive ground activities analysis. One of the key tasks in such applications is moving objects segmentation. Although many efforts have been presented in the literature that claim high true object detection rates, they still suffer from high false positive rates. This paper focuses on maintaining a high true positive detection rates while significantly reducing the false positive detection rates. To achieve this goal, this paper proposes a novel method that integrates matrix recovery concept with physical spring model to drastically reduce false detections. The proposed method segment all candidate moving objects by recovering the low rank matrix, which normally results high false positive detection. To reject false detections, each candidate moving object is modelled as a mass suspended by system of springs, such that the forces of springs attached to false detections is negligible whereas the forces of springs attached to a true moving object will be significant in response to the object motion. The results show that the proposed method, compared to other current state-of-the-art methods, achieved better true positive rates while drastically lowering the false positive rates.

UT-MARO: Unscented Transformation and Matrix Rank Optimization for Moving Objects Detection in Aerial Imagery

Aerial imagery is widely used in many civilian and military applications, as it provides a comprehensive view and real-time surveillance. Automated analysis is an essential task of aerial imagery to detect moving objects, however, the shakiness of these images and the small size of the moving objects are major challenges facing such task. This paper proposes UT-MARO, a novel moving object detection technique. UT-MARO achieves high accurate detection of small-size moving objects in shaky aerial images with low computation complexity and is composed of two phases: (1) UT-alignment and (2) MARO-extraction. UT-alignment utilizes unscented transformation to first align shaky images, then in the second phase, MARO-extraction detects small moving objects by extracting the background using low rank matrix optimization. The robustness of the proposed technique is tested on DARPA and UCF aerial images datasets; and the obtained results prove that UT-MARO has the best performance with lowest complexity compared to relevant current state of the art techniques.

MARO: matrix rank optimization for the detection of small-size moving objects from aerial camera platforms

Moving objects detection from aerial camera platforms is a very challenging problem due to the small-size of the moving objects and the false motion of the static background elements. Although many methods have been proposed in this domain, they always have a trade-off between true detections and false detections. This paper proposes a novel solution called matrix rank optimization method (MARO) to achieve high true detections with low false detections. In MARO, the detection problem is formulated as a principal component pursuit with a transformation domain. The novelty of MARO is that it solves this problem by using the inexact Newton method and a backtracking behaviour in inexact augmented Lagrange multiplier. MARO has been extensively evaluated using DARPA VIVID, UCF aerial action, and VIRAT aerial datasets. The results show that MARO outperforms current-state-of-the-art methods, as well as lowers the execution time without sacrificing the accuracy.

A Multiple Classifier System to improve mapping complex land covers: a case study of wetland classification using SAR data in Newfoundland, Canada

ABSTRACT There are currently various classification algorithms, each with its own advantages and limitations. It is expected that fusing different classifiers in a way that the advantages of each are selected can boost the accuracy in the classification of complex land covers, such as wetlands, compared to using a single classifier. Classification of wetlands using remote-sensing methods is a challenging task because of considerable similarities between wetland classes. This fact is more important when utilizing synthetic aperture radar (SAR) data, which contain speckle noise. Consequently, discriminating wetland classes using only SAR data is generally not as accurate as using some other satellite data, such as optical imagery. In this study, a new Multiple Classifier System (MCS), which combines five different algorithms, was proposed to improve the classification accuracy of similar land covers. This system was then applied to classify wetlands in a study area in Newfoundland, Canada, using multi-source and multi-temporal SAR data. The results demonstrated that the proposed MCS was more accurate for the classification of wetlands in terms of both overall and class accuracies compared to applying one specific algorithm. Therefore, it is expected that the proposed system improves the classification accuracy of other complex landscapes.

Estimation and tracking of AP-diameter of the inferior vena cava in ultrasound images using a novel active circle algorithm

Medical research suggests that the anterior-posterior (AP)-diameter of the inferior vena cava (IVC) and its associated temporal variation as imaged by bedside ultrasound is useful in guiding fluid resuscitation of the critically-ill patient. Unfortunately, indistinct edges and gaps in vessel walls are frequently present which impede accurate estimation of the IVC AP-diameter for both human operators and segmentation algorithms. The majority of research involving use of the IVC to guide fluid resuscitation involves manual measurement of the maximum and minimum AP-diameter as it varies over time. This effort proposes using a time-varying circle fitted inside the typically ellipsoid IVC as an efficient, consistent and novel approach to tracking and approximating the AP-diameter even in the context of poor image quality. In this active-circle algorithm, a novel evolution functional is proposed and shown to be a useful tool for ultrasound image processing. The proposed algorithm is compared with an expert manual measurement, and state-of-the-art relevant algorithms. It is shown that the algorithm outperforms other techniques and performs very close to manual measurement.