Certain tasks undertaken by groups using Group Decision Support Systems (GDSS) can be viewed as search problems. These tasks involve arriving at a solution or decision where the problem is complex enough to warrant the use of computerized decision support tools. For these types of GDSS tasks, we propose to model the information exchange and convergence toward a solution by the group as a simple genetic algorithm. The simple genetic algorithm is a generalized search technique that is based on the principles of evolution and natural selection. Simply put, the best points in the current population are more likely to be selected and combined through genetic operators to determine new points. We propose that groups using GDSS to address certain tasks behave like a simple genetic algorithm in the manner in which possible solutions are generated, enhanced and altered in attempting to reach a decision or consensus.

Most experimental uses of group decision support systems (GDSS) are associated with relatively unrestricted domains, for example, idea generation and preference specification, where few restrictions on potential solutions exist. However, an important GDSS task is that of resource allocation across functional areas of the organization, including supply chain applications. These types of tasks, such as budget planning and production planning, are typically highly constrained and difficult to solve optimally, necessitating the use of decision aids, such as those found in GDSS. We use a model based on adaptive search of a genetic algorithm as the analogy for the group decision making process. We apply this model to experimental data gathered from GDSS groups solving a production planning task. The results indicate very low estimated crossover rates in the experimental data. We also run computational experiments based on adaptive search to mimic the GDSS data and find that the low estimated crossover rate might be due to the highly constrained search space explored by the decision making groups. The results suggest further investigation into the presumed beneficial effects of group interaction in such highly constrained task domains, as it appears very little true information exchange occurs between group members in such an environment. Furthermore, the simulation technique can be used to help predict certain GDSS behaviors, thus improving the entire GDSS process.

Groups using group decision support systems (GDSS) for addressing organizational problems is an evolutionary process. An analytical model incorporating evolutionary processes exists, capturing this adaptation in the group decision-making process. This model is based on the genetic algorithm (GA) and can be used to estimate GA parameter values from experimental data. This research effort examines possible relationships between the GA crossover and mutation parameters and the group context variables of leadership. Both the presence of and the activity level of group leaders are considered. Particular attention is paid to model implementation for a specific instance of GDSS use. The results of this effort are generally encouraging, hinting at the need to conduct further research in this area.

Groups using group decision support systems (GDSS) for addressing organizational problems is an evolutionary process. An analytical model incorporating evolutionary processes exists, capturing this adaptation in the group decision-making process. This model is based on the genetic algorithm (GA) and can be used to estimate GA parameter values from experimental data. This research effort examines possible relationships between the GA crossover and mutation parameters and the group context variables of leadership. Both the presence of and the activity level of group leaders are considered. Particular attention is paid to model implementation for a specific instance of GDSS use. The results of this effort are generally encouraging, hinting at the need to conduct further research in this area.

This paper presents Value at Risk (VAR), a new methodology for Information Security Risk Assessment.  VAR summarizes the worst loss due to a security breach over a target horizon,  with a given level of confidence.  More formally, VAR describes the quantile of the projected distribution of losses over a given time period.  Most of the tools that are used for ISEC risk assessment are qualitative in nature and are not grounded in theory. VAR is a useful tool in the hands of an ISEC expert as it provides a theoretically based, quantitative measure of information security risk.  Using this measure of risk, the best possible balance between risk and cost of providing security can be achieved.  Most organizations, especially those heavily invested in eBusiness, already have determined the acceptable level of risk.  The dollar amount of this risk is then computed.  When the total VAR of an organization exceeds this amount, the organization is alerted to the fact that an increased security investment is required.

Certain tasks undertaken by groups using Group Decision Support Systems (GDSS) can be viewed as search problems. These tasks involve arriving at a solution or decision where the problem is complex enough to warrant the use of computerized decision support tools. Also, the task or situation must require more than one person to adequately address the problem. For these types of GDSS tasks, we propose to model the brainstorming, negotiating and learning processes undertaken by the group as a simple genetic algorithm. The simple genetic algorithm is a generalized search technique that is based on the principles of evolution and natural selection. Simply put, the best points in the search space are more likely to be selected and combined through genetic operators to determine new points. We propose that groups using GDSS to address certain tasks behave like a simple genetic algorithm in the manner in which possible solutions are generated, enhanced and altered in attempting to reach a decision or consensus

In this paper, we develop a framework for efficiently encoding predictive error frames (PEF) as part of a rate scalable, wavelet-based video compression algorithm. We investigate the use of rate-distortion analysis to determine the significance of coefficients in the wavelet decomposition. Based on this analysis, we allocate the bit budget assigned to a PEF to the coefficients that yield the largest reduction in distortion, while maintaining the embedded and rate scalable properties of our video compression algorithm.

In this paper we explore a wavelet compression scheme for color images that uses binary vector morphology to aid in the encoding of the locations of the wavelet coefficients. This is accomplished by predicting the significance of coefficients in the sub-bands. This approach fully exploits the correlation between color components and the correlation between and within subbands of the wavelet coefficients. This compression scheme produces images that are comparable in quality to those of color zerotree tree encoders at the same data rate but is computationally less complex.

Rate scalable video compression is appealing for low bit rate applications, such as video telephony and wireless communication, where bandwidth available to an application cannot be guaranteed. In this paper, we investigate a set of strategies to increase the performance of SAMCoW, a rate scalable encoder [1, 2]. These techniques are based on based on wavelet decomposition, spatial orientation trees, and motion compensation.

The characteristics of \non natural” images, such as predictive error frames used in video compression, present a challenge for traditional compression techniques. Particularly difficult are small images, such as QCIF, where compression artifacts at low data rates are more noticeable. In this paper, we investigate techniques to improve the performance of a wavelet-based, rate scalable video codec at low data rates. These techniques include preprocessing and postprocessing stages to enhance the quality and reduce the compression artifacts of decoded images.

In this talk we will describe embedded image and video compression techniques. We describe an embedded zero tree-like approach that exploits the interdependency between color components that is known as Color Embedded Zero Tree Wavelet (CEZW). We will also present a video compression technique, Scalable Adaptive Motion Compensated Wavelet (SAMCoW) compression, that uses the CEZW data structure described above. We show that in addition to providing a wide range of rate scalability, SAMCoW achieves comparable performance to the more traditional hybrid video coders.

Biological, chemical, and radiological agents can tamper with the activities of medical care providers, patient samples, and medicine administration.  This results in a shut down of all medical care, leaving patients at a major risk.  The technical challenge is to develop sensors to detect and monitor any violations in the medical care environment before threat to life occurs.  Wireless devices must communicate multimedia data such as patient information, laboratory results, prescriptions, and X-ray and EKG reports.  The reliability, security, and accuracy of these sensors and wireless devices can affect the timeliness access to information for patient monitoring.  In addition, data can be corrupted, computer information systems can fail, and communication networks may experience denial of service attacks leading to complete failure of proper patient care.  In this paper, we discuss security and safety issues in medical environment, the technology, types, and characteristics of sensors, and research issues in smart antennas, denial of service, fault tolerant authentication, privacy issues, and energy considerations.  A discussion of sensors in patient rooms, clinics/wards, hospitals, and measurements of safety and security is presented.  The available devices for sensor and wireless communication are also briefly included.