Jumoke Ladeji-Osias

Network intrusion visualization with NIVA, an intrusion detection visual and haptic analyzer

The rapid growth of malicious activities on worldwide communication networks, such as the Internet, has highlighted the need for efficient intrusion detection systems. The efficiency of traditional intrusion detection systems is limited, in part, by their inability to relay effectively relevant information due to their lack of interactive/immersive technologies. In this paper, we explore several network visualization techniques geared toward intrusion detection on small- and large-scale networks. We also examine the use of haptics in network intrusion visualization. By incorporating concepts from electromagnetics, fluid dynamics, and gravitational theory, we show that haptic technologies can provide another dimension of information critical to the efficient visualization of network intrusion data. Furthermore, we explore the applicability of these visualization techniques in conjunction with commercial network intrusion detectors. Finally, we present a network intrusion visualization application with haptic integration, NIVA, which allows the analyst to interactively investigate as well as efficiently detect structured attacks across time and space using advanced interactive three-dimensional displays.

Fusion of infrared and visible images using empirical mode decomposition and spatial opponent processing

Infrared (IR) cameras capture thermal radiations emitted by objects in a scene while Electro-Optical (EO) cameras picture reflected colors from a scene. These sources of different modalities produce complementary data of the same panorama. In aeronautics, medical imaging and rescue operations, these sensors are often utilized simultaneously. The objective of this work is to integrate the content of both streams in order to deliver a unique image presenting more visual cues than any of the originals taken separately. To reach that goal, the synthesis of a robust algorithm is required. The Empirical Mode Decomposition (EMD) decomposes image signals into Intrinsic Mode Functions (IMFs). In this paper, we proposed a novel approach that integrates the IR and EO IMFs using principles of neural science for multi-spectral fusion. We show how to integrate IR and EO information utilizing spatial opponent processing. We report the performance on images from Octec ltd and compare our result to their Wavelet-based fusion output. Mutual Information is the metric employed for fusion quality assessment in this work. Positive results have been obtained for tests conducted on IR and EO images containing complementary information.

Improving and Expanding Engineering Education in the Middle East and Africa Using Mobile Learning Technology and Innovative Pedagogy

Recent innovations in inexpensive and portable laboratory instruments have enabled new pedagogical approaches in the teaching of theoretical concepts and design practices in electrical engineering (EE). Faculty members at six universities in the USA have pioneered the use of these new tools to incorporate hands-on experimental activities into existing lecture courses. This has led to restructured EE courses with a focus on student-centered learning and not instructor-centered lectures. The goal of this effort has been to evaluate whether a more student-centered learning environment can stimulate a deeper understanding of EE principles and increase student engagement. The use of hands-on experiments started with an introductory electric circuits course and has expanded into physics, biology, and higher level EE courses. Several modes of instruction using this technology and pedagogy have been implemented at different institutions. In the blended approach, the classroom experience is a combination of lectures and hands-on activities using the mobile laboratory instruments to reinforce theoretical concepts. For the second instructional model, the inverted or flipped classroom, students are expected to read material at home, prior to their investigation of the concepts via hands-on activities in the classroom. A third model uses the portable laboratory instruments to complete hands-on activities outside of the classroom as homework problems, design projects, and/or a nontraditional laboratory component.

Bio-empirical mode decomposition: visible and infrared fusion using biologically inspired empirical mode decomposition

Abstract. Bio-EMD, a biologically inspired fusion of visible and infrared (IR) images based on empirical mode decomposition (EMD) and color opponent processing, is introduced. First, registered visible and IR captures of the same scene are decomposed into intrinsic mode functions (IMFs) through EMD. The fused image is then generated by an intuitive opponent processing the source IMFs. The resulting image is evaluated based on the amount of information transferred from the two input images, the clarity of details, the vividness of depictions, and range of meaningful differences in lightness and chromaticity. We show that this opponent processing-based technique outperformed other algorithms based on pixel intensity and multiscale techniques. Additionally, Bio-EMD transferred twice the information to the fused image compared to other methods, providing a higher level of sharpness, more natural-looking colors, and similar contrast levels. These results were obtained prior to optimization of color opponent processing filters. The Bio-EMD algorithm has potential applicability in multisensor fusion covering visible bands, forensics, medical imaging, remote sensing, natural resources management, etc.

Integrity Monitoring of Digital Elevation Models using Corner Edge Detection of Accumulated X-Band Radials

Synthetic vision systems (SVS) provides pilots with displays of stored geo-spatial data representing terrain, obstacles and cultural features. This system has the potential to improve flight safety by providing situational awareness and reducing the likelihood of controlled flight into terrain (CFIT). In order to enable the safe use of SVS at low altitudes, real-time range-to-terrain measurements may be necessary to ensure integrity of terrain data for civil aviation applications. This paper describes an integrity monitor which uses a novel approach to check the consistency between a terrain elevation profile synthesized from an X-band weather radar (WxR) sensor and the profile given in a digital elevation model (DEM). Features, in the form of edge locations and associated curvature strengths, are extracted and placed in a graph representation. A comparison is performed on the relationships that are drawn from the node attributes using the approximate graph matching technique in order to confirm the integrity of the terrain dataset. Terrain scans from NASAs integrated intelligent flight deck (IIFD) program are used to validate the proposed integrity monitoring approach

The Impact of Undergraduate Research in STEM at Morgan State University on the Production of Doctoral Degrees in Engineering and the Sciences

Abstract Morgan State University (Morgan) is a leading undergraduate institution for black science and engineering doctoral degree recipients. Morgan also is a leader in the production of black engineering degree recipients in the United States. This chapter provides a historic overview of the major programs with a tie to the impact on the institutional metrics, a discussion of the process for developing researchers in science and engineering, and alumni perspectives. The undergraduate research development models used in engineering at Morgan are compared and contrasted with the life sciences and physical sciences. The programs focus on developing communities of engineering practice and communities of science, thereby enhancing students’ self-efficacy and resilience, shaping disciplinary identity, and creating learning communities. These approaches are critical for the success of minority students and are supported by the social science literature. Best practices have been adopted at varying levels by the School of Engineering, the School of Computer Mathematics and Natural Science and the Behavioral Science departments that have netted these Ph.D. outcomes including multiyear mentored research, research training courses, and participation in professional meetings. Multiple approaches to student development, when matched with the disciplinary culture, are shown to result in national impact.

Implementing a shadow detection algorithm for synthetic vision systems in reconfigurable hardware

The integrity monitor for synthetic vision systems provides pilots with a consistency check between stored Digital Elevation Models (DEM) and real-time sensor data. This paper discusses the implementation of the Shadow Detection and Extraction (SHADE) algorithm in reconfigurable hardware to increase the efficiency of the design. The SHADE algorithm correlates data from a weather radar and DEM to determine occluded regions of the flight path terrain. This process of correlating the weather radar and DEM data occurs in two parallel threads which are then fed into a disparity checker. The DEM thread is broken up into four main sub-functions: 1) synchronization and translation of GPS coordinates of aircraft to the weather radar, 2) mapping range bins to coordinates and computing depression angles, 3) mapping state assignments to range bins, and 4) shadow region edge detection. This correlation must be done in realtime; therefore, a hardware implementation is ideal due to the amount of data that is to be processed. The hardware of choice is the field programmable gate array because of programmability, reusability, and computational ability. Assigning states to each range bin is the most computationally intensive process and it is implemented as a finite state machine (FSM). Results of this work are focused on the implementation of the FSM.