As wireless networking in the enterprise has gained popularity within recent years, the demand for technical talent has increased in direct proportion to that demand. This has occurred partially due to the complexity of troubleshooting and security issues. Professional wireless networking certification programs have also become popular as a result of the financial incentives associated with this demand. Since the content taught in these professional certifications is an appropriate reflection of the challenges faced in the real world as reported by Fortune magazine and the ChannelWeb network [11], it makes sense to align the content of undergraduate wireless networking courses with that of these certifications.

University professors have often taken the approach of teaching 802.11 wireless networks starting from the signal processing layer and immediately transitioning to the higher layers. This process bypasses the Media Access Control (MAC) layer in consequence. Understanding the MAC layer is of utmost importance for understanding wireless network security because it contains the management frames that control both authentication and encryption.

In this paper, course modules were created for undergraduates that focus on the 802.11 and 802.3 MAC layers and can be used to facilitate teaching troubleshooting and security concepts for wireless networking with the help of packet sniffers. These modules provide students with the hands-on experience of what is generally illustrated in only text for Wired Equivalent Privacy (WEP), Wi-Fi Protected Access (WPA) and Virtual Private Networking (VPN) as well as troubleshooting skills.

Recent events in both the health care and research community have increased the opportunities available for information technology and systems integration professionals. In health care, mandatory performance specifications in electronic health care records set forth by the United States federal government have placed essentially all aspects of information technology center stage within healthcare. Similarly in scientific research, the completion of the human genome in 2001 has forced researchers to become dependent upon the capabilities of information sciences and technology to convert genomic data into new knowledge regarding human disease, diagnostics and drug discovery. This manuscript describes our next step in the development of a fully integrated Biomedical Informatics curriculum within the realm of information technology, describing three distinct courses developed for computer and information technology students.

In this paper we describe some of the security issues in streaming video over the Internet. If high quality video sequences will be delivered to computers and digital television systems over the Internet in our “digital future” this material must be protected.

This paper presents an unsupervised color segmentation technique to divide skin detected pixels into a set of homogeneous regions which can be used in face detection applications or any other application which may require color segmentation. The algorithm is carried out in a two stage processing, where the chrominance and luminance information are used consecutively. For each stage a novel algorithm which combines pixel and region based color segmentation techniques is used. The algorithm has proven to be effective under a large number of test images

The objective of this work is to provide a simple and yet efficient tool to detect human faces in video sequences. This information can be very useful for many applications such as video indexing and video browsing. In particular the paper focuses on the significant improvements made to our face detection algorithm presented by Albiol, Bouman and Delp (see IEEE Int. Conference on Image Processing, Kobe, Japan, 1999). Specifically, a novel approach to retrieve skin-like homogeneous regions is presented, which is later used to retrieve face images. Good results have been obtained for a large variety of video sequences

The objective of this work is to provide a simple and yet efficient tool to detect human faces in video sequences. This information can be very useful for many applications such as video indexing and video browsing. In particular the paper focuses on the significant improvements made to our face detection algorithm presented by Albiol, Bouman and Delp (see IEEE Int. Conference on Image Processing, Kobe, Japan, 1999). Specifically, a novel approach to retrieve skin-like homogeneous regions is presented, which is later used to retrieve face images. Good results have been obtained for a large variety of video sequences

In this paper, we describe a framework of analyzing programs belonging to different TV program genres Hidden Markov Models and pseudo-semantic feature s derived from video shots. Clustering using Gaussian mixture models is used to determine the order of the modes. Results for initial genre classification experiments using two simple features derived from video shots are given.

With the increased need of data sharing among multiple organizations like government organizations, financial corporations, medical hospitals and academic institutions, it is critical to ensure data integrity so that effective decisions can be made based on these data. An important component of any solution for assessing data integrity is represented by techniques and tools to evaluate the trustworthiness of data provenance. However, few efforts have been devoted to investigate approaches for assessing how trusted the data are, based in turn on an assessment of the data sources and intermediaries. To bridge this gap, we propose a data provenance trust model. Our trust model takes into account various factors that may affect the trustworthiness and, based on these factors, assigns trust scores to both data and data providers. Such trust scores represent key information based on which data users may decide whether to use the data and for which purposes.

The objective of this work is the integration and optimization of an automatic face detection and recognition system for video indexing applications. The system is composed of a face detection stage presented previously which provides good results while maintaining a low computational cost (see Albiol, A. et al., Proc. IEEE Int. Conf. on Image Proc., vol.2, p.239-42, 2000). The recognition stage is based on the principal component analysis (PCA) approach which has been modified to cope with the video indexing application. After the integration of the two stages, several improvements are proposed which increase the face detection and recognition rate and the overall performance of the system. Good results have been obtained using the MPEG-7 video content set used in the MPEG-7 evaluation group.

We describe a video indexing system that automatically searches for a specific person in a news sequence. The proposed approach combines audio and video confidence values extracted from speaker and face recognition analysis. The system also incorporates a shot selection module that seeks for anchors, where the person on the scene is likely speaking. The system has been extensively tested on several news sequences with very good recognition rates.