Randomized mutual exclusion on a multiple access channel

In this paper we consider the mutual exclusion problem on a multiple access channel. Mutual exclusion is one of the fundamental problems in distributed computing. In the classic version of this problem, n processes execute a concurrent program that occasionally triggers some of them to use shared resources, such as memory, communication channel, device, etc. The goal is to design a distributed algorithm to control entries and exits to/from the shared resource (also called a critical section), in such a way that at any time, there is at most one process accessing it. In our considerations, the shared resource is the shared communication channel itself (multiple access channel), and the main challenge arises because the channel is also the only mean of communication between these processes. We consider both the classic and a slightly weaker version of mutual exclusion, called \(\varepsilon \)-mutual-exclusion, where for each period of a process staying in the critical section the probability that there is some other process in the critical section is at most \(\varepsilon \). We show that there are channel settings, where the classic mutual exclusion is not feasible even for randomized algorithms, while the \(\varepsilon \)-mutual-exclusion is. In more relaxed channel settings, we prove an exponential gap between the makespan complexity of the classic mutual exclusion problem and its weaker \(\varepsilon \)-exclusion version. We also show how to guarantee fairness of mutual exclusion algorithms, i.e., that each process that wants to enter the critical section will eventually succeed.     

A learning-based colour image segmentation with extended and compact structural tensor feature representation

In this paper a novel Tensor-Based Image Segmentation Algorithm (TBISA) is presented, which is dedicated for segmentation of colour images. A purpose of TBISA is to distinguish specific objects based on their characteristics, i.e. shape, colour, texture, or a mixture of these features. All of those information are available in colour channel data. Nonetheless, performing image analysis on the pixel level using RGB values, does not allow to access information on texture which is hidden in relation between neighbouring pixels. Therefore, to take full advantage of all available information, we propose to incorporate the Structural Tensors as a feature extraction method. It forms enriched feature set which, apart from colour and intensity, conveys also information of texture. This set is next processed by different classification algorithms for image segmentation. Quality of TBISA is evaluated in a series of experiments carried on benchmark images. Obtained results prove that the proposed method allows accurate and fast image segmentation.     

On Constructing Unit Triangular Matrices with Prescribed Singular Values

We propose an efficient algorithm for computing a unit lower triangular n×n matrix with prescribed singular values of O(n ²) cost. This is a solution of the question raised by N. J. Higham in [4, Problem 26.3, p. 528]. Received July 21, 1999; revised November 4, 1999     

Optimal Average Case Estimation in Hilbert Norms

In contrast to the worst case approach, the average case setting provides less conservative insight into the quality of estimation algorithms. In this paper we consider two local average case error measures of algorithms based on noisy information, in Hilbert norms in the problem element and information spaces. We define the optimal algorithm and provide formulas for its two local errors, which explicitly exhibit the influence of factors such as information, information (measurement) errors, norms in the considered spaces, a subset where approximations are allowed, and “unmodeled dynamics.” Based on the error expression, we formulate in algebraic language the problem of selecting the optimal approximating subspace. The solution is given along with the specific formula for the error, which depends on the eigenvalues of a certain matrix defined by information and norms under consideration. Date received: November 25, 1999. Date revised: May 30, 2000.     

Human factors in quality management

This article shows the results of research regarding the importance and the role of human factors in quality management in production enterprises. In creating the concept of systematizations of human factors in quality management an anthropocentrism rule was accepted and resulting from it a category of life quality. Acceptance of this rule led to a multiaspect and comprehensive approach to human factors in quality management. An interpretation of the notion of quality was made, adequate to needs, goals, and requirements of a human. Additionally, a cybernetic model of quality management system was elaborated. Human factors was exhibited in the manager's and executor's roles in quality management systems in enterprise as well as to the roles of creators, producers, and users (customers) in product life cycles. An existence of human factors in ISO norms regarding quality management, work safety, and hygiene management and environment management was also shown. The objective of this article is a comprehensive identification and systematization of aspects of human factors in quality management. © 2008 Wiley Periodicals, Inc.     

Identification of Human Postural Sway

The output of the human postural control system is studied by means of linear system theory. It is assumed that the act of maintaining an erect posture can be treated as an autoregressive system with white noise on input. The identification is performed on the basis of the centre of pressure trajectory and it is shown that the most important features of the postural signal are sufficiently well reproduced by a low order linear autoregressive model. It is shown that the parameters of a model depend on the human subject and in some way characterize his state. Poles of the transmitancy function lying close to the unit circle are discussed as parameters describing the response function and human reactions to external perturbations. In additionf, an analysis of the correlation function within the autoregressive model is performed and its scaling exponents are computed and compared to the experimental results.     

A Chamber for Testing the Release of Volatile Substances Secreted by Animals,Especially Mammals,as Exemplified by Substances Released by Rats in Response to Stress

Abstract

The success of the testing of volatile organic compounds emitted by animals is dependent on the creation of appropriate conditions for air sampling subsequently used to assay and identify the compounds. These conditions play a particularly important role in the investigation of pheromones, which are secreted in extremely low concentrations. The authors have not come across any previous work which offers constructional solutions, which would allow avoidance of contamination of the air samples containing volatile substances secreted by animals.

A constructional solution was developed, which provides optimal conditions for their sampling and isolation. Its main advantages are as follows: the exposure chamber (the chamber in which the animal is studied) is filled with synthetic air; the exposure chamber is separated from the atmospheric air with a synthetic air “jacket”; the exposure chamber has been constructed using materials which do not release chemicals and absorb them in trace quantities.