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In multiuser wireless networks, opportunistic scheduling can improve the system throughput and thus reduce the total completion time. In this paper, we explore the possibility of reducing the completion time further by incorporating traffic information into opportunistic scheduling. More specifically, we first establish convexity properties for opportunistic scheduling with file size information. Then, we develop new traffic aided opportunistic scheduling (TAOS) schemes by making use of file size information and channel variation in a unified manner. We also derive lower bounds and upper bounds on the total completion time, which serve as benchmarks for examining the performance of the TAOS schemes. Our results show that the proposed TAOS schemes can yield significant reduction in the total completion time. The impact of fading, file size distributions, and random arrivals and departures, on the system performance, is also investigated. In particular, in the presence of user dynamics, the proposed TAOS schemes perform well when the arrival rate is reasonably high.

This paper reports an effort to develop a neural network-based system that is capable of analyzing images for the presence of particular features. In particular, the system would automatically analyze X-ray images as part of a non-destructive evaluation (NDE) function. Of particular interest is the development of neural network-based systems that can easily and inexpensively be retrained when design changes are made in parts to be inspected. Backpropagation networks with one hidden layer, based on classification and image compression concepts, were designed and tested with X-rays of Army M764 fuzes.

In multiuser wireless networks, opportunistic scheduling can improve the system throughput and thus reduce the total completion time. In this paper, we explore the possibility of reducing the completion time further by incorporating traffic information into opportunistic scheduling. In particular, we first establish general properties for opportunistic scheduling with file size information. Then, we develop new traffic aided opportunistic scheduling (TAOS) schemes by making use of file size information and channel variation in a unified manner. We also derive lower and upper bounds on the total completion time. Our results show that the proposed TAOS schemes can yield significant reduction in the total completion time

We study opportunistic scheduling in a multiuser wireless network where there are K users, each with a backlogged message for transmission. A key goal is to minimize the total completion time via a cross-layer design approach. Our main contribution consists of two size-aided opportunistic scheduling (SAOS) schemes. The SAOS schemes use both the traffic information and channel variation to reduce the completion time. Our results show that the SAOS schemes can yield significant reduction in the total completion time. We also examine two extreme cases, i.e., wireless shortest processing time first (SRPT) and "riding on the channel peak", which utilize only either the file size information or the instantaneous channel conditions.

Sensory engineering is defined to be the science and technology of synthetic environments. This emerging discipline incorporates such technologies as virtual environments and virtual reality, data visualization, human sensory system modeling, human-machine interface, and perception, cognition and performance characterization. An educational curriculum requires basic science in the fields of computer science, electrical engineering, psychology, physiology, the biomedical sciences, and mathematics. The authors present the plan for a multidivisional graduate education program in sensory engineering, being developed at the Johns Hopkins University, within the Schools of Arts and Sciences, Engineering, Medicine, and the Applied Physics Laboratory. >

Summary form only given, as follows. The automated analysis of X-ray images of mechanical fuses for nondestructive evaluation (NDE) requires that objects within the images be detected and recognized and that the orientations and relative positions of these objects be computed. Neural-network-based adaptive spatial filters for performing these functions are advantageous for many reasons, including robustness, flexibility, and adaptability to changes in fuse design. A research project has been started to investigate the use of feedforward neural networks to improve the performance of an NDE image analysis system. In particular, the objective was to develop a system which learns the rules for image understanding and could be applied to new or modified fuse designs with a minimum of software modification. >

We present a neural network architecture that can provide a large number of flexible decision bundarie for pattern recognition using a minimal number of parameters. A biologically compatible implementation of the network leads to greater discrimination power.

In this paper, the authors discuss the compelling power of stories and storytelling. After reviewing some of the most important literature about storytelling, they focus on four principal reasons for the power of story. First, stories are universal, crossing boundaries of language, culture and age. Second, they mirror human thought. All evidence from neurology and psychology leads to the conclusion that humans think in narrative structures. Concepts conveyed in story form more than ideas explained with logic and analysis imprint themselves naturally into human minds. Third, stories define who we are. Our sense of identity is forged by the stories we tell ourselves, the ones we come to believe and those we choose to dismiss. Fourth, stories build and preserve a group's sense of community. Stories align and motivate by portraying the world in vivid terms that build emotional connections among constituents, giving them a sense of shared purpose.

Reducing the burden of interacting with complex systems has been a long standing goal of user interface design. In our approach to this problem, we have been developing user interfaces that allow users to interact with complex systems in a natural way and in high-level, task-related terms. These capabilities help users concentrate on making important decisions without the distractions of manipulating systems and user interfaces. To attain such a goal, our approach uses a unique combination of multi-modal interaction and interaction planning. In this paper, we motivate the basis for our approach, we describe the user interface technologies we have developed, and briefly discuss the relevant research and development issues.