Face Orientation Normalization Using Eye Positions

Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps： during the first step detection of a face is done by finding exact position of it in a complex background （various lightning condition）, and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used： color based method, mean eye template and SVM （Support Vector Machine） technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position.     

Modelling and analysing cognitive causes of security breaches

In this paper we are concerned with security issues that arise in the interaction between user and system. We focus on cognitive processes that affect security of information flow from the user to the computer system and the resilience of the whole system to intruder attacks. For this, we extend our framework developed for the verification of usability properties by introducing two kinds of intruder models, an observer and an active intruder, with the associated security properties. Finally, we consider small examples to illustrate the ideas and approach. These examples demonstrate how our framework can be used (a) to detect confidentiality leaks, caused by a combination of an inappropriate design and certain aspects of human cognition, and (b) to identify designs more susceptible to cognitively based intruder attacks.     

Hybrid modelling of yeast production processes – combination of a priori knowledge on different levels of sophistication

Process models are used to formulate knowledge about process behaviour. They are applied, e.g., to predict the process' future behaviour and for state estimation when reliable on-line measuring techniques to monitor the key variables of the process are not available. There are different sources of information available for modelling, which provide process knowledge in different representations. Some elements or aspects may be described by physically based mathematical models and others by heuristically obtained rules of thumb, while some information may still be hidden in the process data recorded during previous runs of the process. Heuristic rules are conveniently processed with fuzzy expert systems, while artificial neural networks present themselves as a powerful tool for uncovering the information within the process data without the need to transform the information into one of the other representations. Artificial neural networks and fuzzy technology are increasingly being employed for modelling biotechnological processes, thus extending the traditional way of process modelling by mathematical equations. However, a sufficiently comprehensive combination of all these techniques has not yet been put forward. Here, we present a simple way of combining all the available knowledge relating to a given process. In a case study, we demonstrate the development of a hybrid model for state estimation and prediction on the example of a yeast production process. The model was validated during a cultivation performed in a standard pilot-scale fermenter.     

Design of a stable adaptive controller for driving aerobic fermentation processes near maximum oxygen transfer capacity

In many industrial fermentation processes oxygen availability is the main limiting factor for product production. Typically the dissolved oxygen (DO) concentration decreases continuously at the beginning of the batch until it reaches a critical level where the oxygen transfer rate is very close to the vessel's maximum transfer capacity. The process may be further driven close to this sensitive operating point with a controller that manipulates the carbon source feed rate. This operating strategy is linked with important productivity issues and is still frequently realised in open-loop at production scale. The main purpose of the present study is to derive an effective closed-loop control solution and to demonstrate its economical advantage in relation to the open-loop form of operation. A stable model reference adaptive controller (MRAC) was designed based on a phenomenological model of the process. The implementation requires two on-line measurements: the DO tension and oxygen transfer rate (OTR) between gas–liquid phases, which are nowadays standard and easily available in production facilities. The controller performance is accessed with a simulation case study. The main results show that the adaptive controller is precise, stable and robust to disturbances and to inaccuracies like variability in raw materials typical in fermentations run in complex media. The controller is simple, easy to implement, and could possibly improve productivity in processes for which oxygen transfer capacity is limiting growth and product production.     

Combining human error verification and timing analysis: a case study on an infusion pump

The design of a human–computer interactive system can be unacceptable for a range of reasons. User performance concerns, for example the likelihood of user errors and time needed for a user to complete tasks, are important areas of consideration. For safety-critical systems it is vital that tools are available to support the analysis of such properties before expensive design commitment has been made. In this work, we give a unified formal verification framework for integrating two kinds of analysis: (1) predicting bounds for task-completion times via exhaustive state-space exploration, and (2) detecting user-error related design issues. The framework is based on a generic model of cognitively plausible behaviour that captures assumptions about cognitive behaviour decided through a process of interdisciplinary negotiation. Assumptions made in an analysis, including those relating to the performance consequences of users recovering from likely errors, are also investigated in this framework. We further present a novel way of exploring the consequences of cognitive mismatches, on both correctness and performance grounds. We illustrate our analysis approach with a realistic medical device scenario: programming an infusion pump. We explore an initial pump design and then two variations based on features found in real designs, illustrating how the approach identifies both timing and human error issues.     

Influence of pozzolana on sulfate attack of cement stone affected by chloride ions

This study investigated the influence of natural pozzolana (opoka) additive on the hydration of Portland cement and the effects of pozzolana on sulfate attack of cement stone affected by chloride ions. In the samples, 25 % (by weight) of the Portland cement was replaced with pozzolana. The specimens were hardened for 28 days in water, and then one batch was soaked in a saturated NaCl solution and another in a 5 % Na₂SO₄ solution for 3 months at 20 °C. After being kept for 3 months in a saturated NaCl solution, samples were transferred to a 5 % Na₂SO₄ solution and kept under these conditions for 3 months. It was estimated that under normal conditions, pozzolana additive accelerated the hydration of calcium silicates and initiated the formation of CO₃ ^2?–AFm; opoka also decreased the threshold pore diameter of hardened Portland cement paste. It was found that Cl^– ions penetrate to monosulfoaluminate, form Friedel’s salt, and release SO₄ ^2? ions, which react with unaffected monosulfoaluminate and form extra ettringite; when samples were transferred to the 5 % Na₂SO₄ solution, a greater quantity of new ettringite was formed. Meanwhile, pozzolana additive reduced the penetration of chloride and sulfate ions into the structure of Portland cement hydrates and inhibited sulfate attack of cement stone treated in a saturated NaCl solution.     

DESIGN OF RECOMBINANT PROTEIN PRODUCTION PROCESSES

A model-aided process design and supervision technique is proposed. Its efficiency is demonstrated by a nontrivial example, the development of a production process for the light chain of the antibody MAK33. This protein was expressed by the genetically modified E. coli B pUBS520 p12023 bacteria under the control of the tac promoter where lactose was used for induction. It is shown in this example that data from a few experiments are sufficient to develop a satisfactory production process. The development of process models, which are prerequisite for a systematic optimization of protein production processes, as well as their use for the determination of quasi-optimal feed-forward control profiles, is discussed. Model-supported closed-loop control is shown to lead to another improvement. Further performance enhancements can be obtained by changing the operational mode from the usually employed fed-batch procedure to a repeated fed-batch mode.     

Learning resistance to temptation through observation.

An adult model and 48 4–5 yr olds were tempted to deviate by a "talking table." On both an immediate and a delayed test, Ss who had seen the model yield deviated more quickly and for a longer period of time than control Ss who had not seen a model. Ss who had seen the model resist deviated less quickly and worked longer at a boring task. In a 2nd study with 80 5–8 yr olds, the tempting was done by a young woman. On several measures, only resisting models who rationalized their behavior were effective, and sometimes only with girls. Ss in the yielding model condition were less generous. It is argued that the procedures employed to study the effects of resisting and yielding models are important in determining their relative effectiveness. (15 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Branching blend of natural quadrics based on surfaces with rational offsets

A new branching blend between two natural quadrics (circular cylinders/cones or spheres) in many positions is proposed. The blend is a ring shaped patch of a PN surface (surface with rational offset) parametrized by rational bivariant functions of degree (6,3). The general theory of PN surfaces is developed using Laguerre geometry and a universal rational parametrization of the Blaschke cylinder. The construction is extended via inversion to a PN branching blend of degree (8,4) between Dupin cyclide and a natural quadric.     

MIMO Channel Reconstruction from Lower Dimensional Multiple Antenna Measurements

Wireless Personal Communications - A method for reconstructing multiple-input multiple-output (MIMO) channel correlation matrices from lower dimensional channel measurements is presented....