Rida M. Hamza

Runway Positioning and Moving Object Detection Prior to Landing

Safe navigation of both manned and unmanned aircraft requires a robust runway identification process and reliable obstacle detection to determine the runway status before landing. The navigation data extracted from multiple sources of current synthetic navigation sources are not adequate for positioning an aircraft, and it cannot detect moving obstacles on runways, especially during reduced visibility conditions. The enhanced vision system (EVS) described in this article can augment current synthetic vision database capabilities by providing more accurate positioning of a runway and detecting moving objects on it from an onboard infrared sensor. Our EVS is based on a two-step process. We first analyze the sensor image to identify and segment the runway coordinates. These estimates are then used to locate the runway structure and detect moving obstacles. In the segmentation process, we apply an adaptive thresholding technique to calculate the edges of a runway based on the predicted synthetic data. To match a runway template on the edges, we examine alternative fitting models. The predicted coordinates and the detected edges are then correlated to determine the location of the actual runway coordinates within the sensor image. These coordinate estimates are fed to the dynamic stabilization of the image sequence in the obstacle detection process. We also use feature points beyond the estimated runway coordinates to further improve the stabilization process. Next, we normalize the stabilized sequence to compensate for the global intensity variations caused by the gain control of the infrared sensor. We then create a background model to create an appearance model of the runway. Finally, we identify moving objects by comparing the image sequence with the background model. We have tested our EVS and reported significant improvements over the synthetic navigation data. We have been able to detect distant moving objects around the runway.

Standoff iris recognition using non-iterative polar based segmentation

Recently, the iris of the human eye has been used as a biometric indicator for identification. We have witnessed wide-scale deployment of iris technology across many product categories. However, these iris recognition solutions do not reflect the full potential of the technology. The robustness of the standoff iris segmentation approach relies heavily on accurate iris segmentation techniques. Computing iris features requires a high quality segmentation process that focuses on the subjects iris and properly extracts its boundaries. Because iris segmentation is sensitive to the acquisition conditions, it is a very challenging problem. In this paper, we describe a standoff iris recognition system to identify non-cooperative subjects. We introduce a novel iris segmentation approach that takes the analysis of edges into the polar domain at an earlier stage and uses non-iterative polar differential operator to locate the inner and outer borders of the iris. The approach is proven to be very effective for non-ideal gazed and obscured irises while providing comparable results to top performing algorithms on frontal iris images.

Virtual moire interference approach for an industrial safety monitoring system

The objective of this work was to determine the feasibility and reliability of using the moire interference phenomenon as a means to detect human intrusion within a monitored zone. We applied moire interference principle for use in low-cost, safety-critical industrial monitoring applications. Moire interference is usually applied in the context of industrial applications for shape measurements. In this framework, we show how we can apply this concept to build a new safety product that detects human intrusion into dangerous areas on the factory floor. We demonstrate that a solution based on moire interference offers the potential for detection true 3D objects while preventing false alarms due to lighting variations or shadows and simplifying the image processing software. In addition, our prosed approach is advantageous in the product certification process because it is an active detection method.

Clock multiplier with a range up to 370 MHz for video/display signal processing

This paper describes the design of a clock generation circuitry to be used as part of an affordable gigabit module head mounted display. A self-calibrated tapped delay line is used to generate different clock signals, which are then passed through logical function to produce an integral- multiple of an input clock. The system is fabricated on 0.8 micrometers CMOS triple layer using MOSIS CMOS process. All processes technology can operate at 3.3 V or 5.0 V. Experimental results show a realization of 4 times clock multiplier circuit with an output range of up to 370 MHz with almost zero-clock skew. The proposed clock multiplier circuitry is simple, temperature independent, uses a very small number of transistors and hence requires less area and power dissipation than earlier realizations.

Adaptive target recognition

This paper presents an ATR design paradigm that self configures and adapts to the diverse scenarios encountered during a mission. Todays ATR is constructed via inefficient and sub-optimal system configuration and training, whose process is very labor intensive, subjective and inaccurate. The resulting ATR is only capable of a limited amount of adaptation to changes in the environment. Moreover, the operation of such ATR systems require a user with expert algorithmic knowledge. Addressing the above-mentioned problems, the Honeywell effort is producing a self-adaptive ATR system. The system employs a Genetic Algorithm to autonomously and optimally perform configuration and training; the system also includes a specific knowledge capture mechanism, the Context Capture tool, which ties the context of the mission with an optimal configuration. Lastly, the system employs Case Based Reasoning to dynamically configure and control the ATR system based on the changing context during an ATR mission.