Nathir A. Rawashdeh

A Low-Cost Control System for a High-Altitude UAV

The BIG BLUE project at the University of Kentucky is a test bed UAV for Mars airplane technology. A major focus of the BIG BLUE effort has been the development of a low-cost and light-weight avionics, control, and communication system to manage the aircraft and correspond with ground stations. BIG BLUE I, launched in May 2003, achieved the first successful deployment of inflatable/rigidizable wings at altitude. BIG BLUE II, launched in May 2004, had a flight-ready fuselage and control system. This paper describes the BIG BLUE project detailing the design and implementation of the avionics, control, and communication system

Printer banding estimation using-the Generalized Spectrum

This paper compares multi-step algorithms for estimating banding arameters of a harmonic signature model. The algorithms are based on two different spectral measures, the power spectrum (PS) and the collapsed average (CA) of the generalized spectrum. The generalized spectrum has superior noise reduction properties and is applied for the first time to this application. Monte Carlo simulations compare estimation performances of profile (or coherent) averaging and non-coherent spatial averaging for estimating banding parameters in grain noise. Results demonstrate that profile averaging has superior noise reduction properties, but is less flexible in applications with irregular banding patterns. The PS-based methods result in lower fundamental frequency estimation error and greater peak height stability for low SNR values, with coherent averaging being significantly superior to non-coherent averaging. The CA has the potential of simplifying the detection of multiple simultaneous banding patterns because its peaks are related to intra-harmonic distances; however, good CA estimation performance requires sufficiently regular harmonic phase patterns for the banding harmonics so as not to undergo reduction along with the noise. In addition to the simulations, the algorithms are applied to samples from inkjet and laser printers to demonstrate the ability of the harmonic signature model in separating banding from grain and other image artifacts. Good results from experimental data are demonstrated based on visual inspection of examples where banding and grain have been separated.

Mult-sensor input path planning for an autonomous ground vehicle

Autonomous Unmanned Ground Vehicles (UGVs) are mobile platforms that serve a wide range of specialized applications in urban, military, domestic, and industrial settings. UGVs can be remotely operated or autonomous and usually include a variety of sensors and manipulators that are used to solve specific investigation tasks. They also include sensory input for use in autonomous navigation algorithms. For example, radio activity or explosive sensors can help the remote assessment of a dangerous area. In the case of autonomous navigation, a UGV usually employs Light Detection and Ranging (LIDAR) sensors a. k. a. laser range finders, ultra sonic sensors, cameras, and Global Positioning System (GPS) receivers to avoid obstacles and follow a set of GPS waypoints that define a path for the UGV to cover. This paper presents the development of autonomous path planning in a UGV that uses various sensors including, a laser range finder, a digital compass, a GPS receiver, and computer vision. The sensor data is fused in a “cost matrix” that assigns positive numerical values to obstacles detected using the various sensors. Negative value contributions are added to the cost matrix is areas corresponding to the desired heading dictated by GPS waypoint navigation. A cost function is implemented by adding cost matrix values over several possible paths crossing the matrix, causing the lowest-cost path to be selected as the UGVs next heading. The algorithm was tested on a grassy path with white lines defining an allowable path that includes various physical obstacles.

Vision-based sensing of UAV attitude and altitude from downward in-flight images

Autonomous unmanned aerial vehicles (UAVs) often carry video cameras as part of their payload. Outdoor video captured by such cameras can be used to estimate the attitude and altitude of the UAV by detecting the location of the horizon in the video frames. This paper presents a video frame processing algorithm for estimating the pitch and roll of a UAV, as well as its altitude. The frames are obtained from a downward pointing video camera equipped with a fisheye lens. These open-loop estimates can serve as redundant data used to implement graceful-degradation in the event that the main closed-loop control sensors fail, or for fault-tolerance purposes to augment inertial sensors for increased accuracy. The estimated values had a mean error of ±0.7 angular degrees for roll and ±0.9 angular degrees for pitch, while the altitude estimation from the video had a mean error of ±0.9 meters. The results are presented and compared to actual attitude and altitude values obtained from a traditional inertial measurement unit and, in the case of altitude comparison, an absolute air pressure sensor. The algorithm was developed on a personal computer to work at 10 frames per second and uses only simple image processing functions that can be deployed using open source libraries on lightweight computing boards capable of image processing.

Establishment of mechatronics engineering professional groups in Jordan

This paper describes the motivation and work performed to establish ways to link between academia and industry in Jordan, in the field of mechatronics engineering. Two efforts are described. First, the creation of an on-line forum, called the Jordan Mechatronics Network (JMN), for mechatronics professionals inside and outside of Jordan to share knowledge and facilitate cooperation, considering that many Jordanian engineers find employment in Arab Gulf states. This forum operates in the English language, is open, free, and user driven. This setup helps sustain JMN over time with minimal cost and overhead. The second effort is placed into the establishment of a society for mechatronics engineers in Jordan under the umbrella of the Jordan Engineers Association (JEA). The ongoing work currently finalizes the rules and regulations of the society, which is named Jordan Mechatronics Engineers Society (JMES). The JEA was established in 1958 and currently has over 81000 members. The JEA and JMES follow a traditional rigorous setup in the Arabic language with regulations for elections, membership, funding, investment, social and political activities, etc.

Artificial neural networks for dihedral angles prediction in enzyme loops: a novel approach

Structure prediction of proteins is considered a limiting step and determining factor in drug development and in the introduction of new therapies. Since the 3D structures of proteins determine their functionalities, prediction of dihedral angles remains an open and important problem in bioinformatics, as well as a major step in discovering tertiary structures. This work presents a method that predicts values of the dihedral angles φ and ψ for enzyme loops based on data derived from amino acid sequences. The prediction of dihedral angles is implemented through a neural network based mining mechanism. The amino acid sequence data represents 6342 enzyme loop chains with 18,882 residues. The initial neural network input was a selection of 115 features and the outputs were the predicted dihedral angles φ and ψ. The simulation results yielded a 0.64 Pearsons correlation coefficient. After feature selection through determining insignificant features, the input feature vector size was reduced to 45, while maintaining close to identical performance.

A Masters Programme in telecommunications management – demand-based curriculum design

This paper presents a curriculum design approach for a Masters Programme in Telecommunications Management based on demand data obtained from surveying the needs of potential students of the proposed programme. Through online surveys disseminated at telecom companies in Jordan, it was possible to measure the demand for such a programme and to determine the required programme contents and specifications. The curriculum design is based on definition of programme outcomes and on using a house of quality approach (HOQ) to determine the list of courses required in the programme. Surveyed competencies are mapped to a long list of proposed courses in a HOQ in order to determine the importance of each of these courses. A final list of core and elective courses is then developed considering the contribution to programme outcomes and the academic standards.

Development of a Jordanian-European double degree master program in mechatronics engineering

This paper describes the procedure followed to implement a double-degree master program in mechatronics engineering between Jordanian and European universities. Initially, a study of world-wide master curricula was carried out, followed by a survey of Jordanian industry and academia. This helped clarify the structure and content of the desired master program, such that it addressed the needs of the perspective students and the job market in Jordan and the region. To formulate a final degree structure and course content, it was necessary to work closely with European partners in an iterative approach. Some of the problematic questions that had to be answered pertained to: varying course credit systems; amount of time students spend at each university; student academic background; country-specific accreditation rules; university specific needs. This work is one result of a three-year European funded TEMPUS project between October 2011 and October 2014. The first double degree students enrolled in the developed program at Philadelphia University, in Amman - Jordan, in October 2013 with two German universities as partners.

An image quality motivated complex content classifier

Image quality loss is often determined by the nature and level of image artifacts along with the image context they appear in. For example, grain may be masked by texture, and blur is tolerable in flat fields, but offensive in regions of edges and structure. This paper develops image region classifiers for complex (real life) images. Based on the contents structure, the classes of interest are: a random field (such as sky or painted surfaces); textured regions (such as grass or line textures); regions with transients (such as edges on buildings). The linear classifiers examined use features from the optical density histogram (ODH), the cortex transform, and the co-occurrence matrix. The performance testing of the classifiers show that the best feature set size is four. Larger sets show no classification error reduction and tend to suffer from overfitting. The best performance is 3.3% misclassification, and is achieved using four features from the ODH and cortex transform. A misclassification rate of 10% is achieved using only co-occurrence matrix features. This rate drops to 4.4%, when ODH, cortex transform, and co-occurrence features are combined. The classifiers were trained on image regions assigned to each of the three classes by human observers, then tested on a larger non-overlapping image region set.

A video-based object detection and tracking system for weight sensitive UAVs

The capability of detecting and tracking targets can play a significant role in mobile robot navigation systems. Visual tracking systems may control the direction and speed of motion of a robot to keep the target in its field of view either by moving the robot itself or the vision sensors. In this work, a compact size tracking and guiding system that could be mounted on weight-sensitive UAV platforms is presented. The system combines a motion detection technique that could be used with non-static cameras in addition to color filtering to detect and track objects in the field of view of a UAV. This hybrid system provides a reliable tracking system for low resolution images taken by a UAV camera. The proposed system implements keypoint detection algorithms including SIFT, SURF and FAST, a motion detection method using frame subtraction and object detection algorithms using color back projection in a hybrid approach that utilizes the best of each algorithm and avoids heavy usage of computing resources. Keypoint detectors SURF, SIFT and FAST are tested and implemented for the purpose of image alignment and frame subtractions. Experimental tests showed the system’s ability to detect and track low detailed targets. The system is tested on a UAV using a Raspberry Pi 2 mini-computer running OpenCV libraries and was able to process eleven frames per second implementing object detection and tracking. The test objects were mainly cars monitored from different altitudes through a UAV downward pointing camera.