A schema for cryptographic keys generation using hybrid biometrics

Biometric identifiers refer to unique physical properties or behavioural attributes of individuals. Some of the well known biometric identifiers are voice, finger prints, retina or iris, facial structure etc. In our daily interaction with others directly or indirectly, we implicitly use biometrics to know, distinguish and trust people. Biometric identifiers represent the concept of "who a person is" by gathering vital characteristics that don't correspond to any other person. The human brain to some extent is able to ascertain disparities or variation in certain physical attributes and yet verify the authenticity of a person. But this is difficult to be implemented in electronic systems due to the intense requirements of artificial decision making and hard-coded logic. This paper examines the possibility of using a combination of biometric attributes to overcome common problems in having a single biometric scheme for authentication. It also investigates possible schemes and features to deal with variations in Biometric attributes. The material presented is related to ongoing research by the Computer Communications Research Group at Leeds Metropolitan University. We use this paper as a starting step and as a plan for advanced research. It offers ideas and proposition for implementing hybrid biometrics in conjunction with cryptography. This is work in progress and is in a very preliminary stage

The analysis of user behaviour of a network management training tool using a neural network

A novel method for the analysis and interpretation of data that describes the interaction between trainee network managers and a network management training tool is presented. A simulation based approach is currently being used to train network managers, through the use of a simulated network. The motivation is to provide a tool for exposing trainees to a life like situation without disrupting a live network. The data logged by this system describes the detailed interaction between trainee network manager and simulated network. The work presented here provides an analysis of this interaction data that enables an assessment of the capabilities of the trainee network manager as well as an understanding of how the network management tasks are being approached. A neural network architecture is implemented in order to perform an exploratory data analysis of the interaction data. The neural network employs a novel form of continuous self-organisation to discover key features in the data and thus provide new insights into the learning and teaching strategies employed

Central Nervous System Symptoms and Brain Damage Associated with Common Ruminant Digestive Disturbances

With the exception of the optic papilla, the nervous system is not subject to direct examination. Diagnosis of diseases of the nervous system must, therefore, be made by interpretation of symptoms. These symptoms are manifested as disturbances of consciousness, locomotion, senses, incoordination and motor irritation

"Body-In-The-Loop": Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost

This paper demonstrates methods for the online optimization of assistive robotic devices such as powered prostheses, orthoses and exoskeletons. Our algorithms estimate the value of a physiological objective in real-time (with a body “in-the-loop”) and use this information to identify optimal device parameters. To handle sensor data that are noisy and dynamically delayed, we rely on a combination of dynamic estimation and response surface identification. We evaluated three algorithms (Steady-State Cost Mapping, Instantaneous Cost Mapping, and Instantaneous Cost Gradient Search) with eight healthy human subjects. Steady-State Cost Mapping is an established technique that fits a cubic polynomial to averages of steady-state measures at different parameter settings. The optimal parameter value is determined from the polynomial fit. Using a continuous sweep over a range of parameters and taking into account measurement dynamics, Instantaneous Cost Mapping identifies a cubic polynomial more quickly. Instantaneous Cost Gradient Search uses a similar technique to iteratively approach the optimal parameter value using estimates of the local gradient. To evaluate these methods in a simple and repeatable way, we prescribed step frequency via a metronome and optimized this frequency to minimize metabolic energetic cost. This use of step frequency allows a comparison of our results to established techniques and enables others to replicate our methods. Our results show that all three methods achieve similar accuracy in estimating optimal step frequency. For all methods, the average error between the predicted minima and the subjects’ preferred step frequencies was less than 1% with a standard deviation between 4% and 5%. Using Instantaneous Cost Mapping, we were able to reduce subject walking-time from over an hour to less than 10 minutes. While, for a single parameter, the Instantaneous Cost Gradient Search is not much faster than Steady-State Cost Mapping, the Instantaneous Cost Gradient Search extends favorably to multi-dimensional parameter spaces

Observation of intermittency in wave turbulence

We report the observation of intermittency in gravity-capillary wave turbulence on the surface of mercury. We measure the temporal fluctuations of surface wave amplitude at a given location. We show that the shape of the probability density function of the local slope increments of the surface waves strongly changes across the time scales. The related structure functions and the flatness are found to be power laws of the time scale on more than one decade. The exponents of these power laws increase nonlinearly with the order of the structure function. All these observations show the intermittent nature of the increments of the local slope in wave turbulence. We discuss the possible origin of this intermittency.Comment: new version to Phys. Rev. Let

Highly resolved observations and simulations of the ocean response to a hurricane

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 34 (2007): L13604, doi:10.1029/2007GL029679.An autonomous, profiling float called EM-APEX was developed to provide a quantitative and comprehensive description of the ocean side of hurricane-ocean interaction. EM-APEX measures temperature, salinity and pressure to CTD quality and relative horizontal velocity with an electric field sensor. Three prototype floats were air-deployed into the upper ocean ahead of Hurricane Frances (2004). All worked properly and returned a highly resolved description of the upper ocean response to a category 4 hurricane. At a float launched 55 km to the right of the track, the hurricane generated large amplitude, inertially rotating velocity in the upper 120 m of the water column. Coincident with the hurricane passage there was intense vertical mixing that cooled the near surface layer by about 2.2°C. We find consistent model simulations of this event provided the wind stress is computed from the observed winds using a high wind-speed saturated drag coefficient.The development of the EM-APEX float system was supported by the Office of Naval Research through SBIR contract N00014-03-C-0242 to Webb Research Corporation and with a subcontract to APL-UW

Microwave Gaseous Discharges

Contains reports on three research projects