According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

How to Do Things With Words

Building Leadership Effectiveness This program encourages leaders to develop practices that transform values into action, vision into realities, obstacles into innovations, and risks into rewards. Participants will be introduced to the five practices of exemplary leadership: modeling the way, inspiring a shared vision, challenging the process, enabling others to act, and encouraging the heart Coaching & Communicating for Performance Coaching & Communicating for Performance is a highly interactive program that will give supervisors and managers the opportunity to build skills that will enable them to share expectations and set objectives for employees, provide constructive feedback, more effectively engage in learning conversations, and coaching opportunities. Skillful Conflict Management for Leaders As a leader, it is important to understand conflict and be effective at conflict management because the way conflict is resolved becomes an integral component of our university’s culture. This series of conflict management sessions help leaders learn and put into practice effective strategies for managing conflict.

기독교 사역과 Leadership

A maximum likelihood (ML) estimator is developed for determining time delay between signals received at two spatially separated sensors in the presence of uncorrelated noise. This ML estimator can be realized as a pair of receiver prefilters followed by a cross correlator. The time argument at which the correlator achieves a maximum is the delay estimate. The ML estimator is compared with several other proposed processors of similar form. Under certain conditions the ML estimator is shown to be identical to one proposed by Hannan and Thomson [10] and MacDonald and Schultheiss [21]. Qualitatively, the role of the prefilters is to accentuate the signal passed to the correlator at frequencies for which the signal-to-noise (S/N) ratio is highest and, simultaneously, to suppress the noise power. The same type of prefiltering is provided by the generalized Eckart filter, which maximizes the S/N ratio of the correlator output. For low S/N ratio, the ML estimator is shown to be equivalent to Eckart prefiltering.

The generalized correlation method for estimation of time delay

This paper focuses on the class of speech enhancement systems which capitalize on the major importance of the short-time spectral amplitude (STSA) of the speech signal in its perception. A system which utilizes a minimum mean-square error (MMSE) STSA estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the "spectral subtraction" algorithm. In this paper we derive the MMSE STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables. We analyze the performance of the proposed STSA estimator and compare it with a STSA estimator derived from the Wiener estimator. We also examine the MMSE STSA estimator under uncertainty of signal presence in the noisy observations. In constructing the enhanced signal, the MMSE STSA estimator is combined with the complex exponential of the noisy phase. It is shown here that the latter is the MMSE estimator of the complex exponential of the original phase, which does not affect the STSA estimation. The proposed approach results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise. The complexity of the proposed algorithm is approximately that of other systems in the discussed class.

Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator

Publisher Summary This chapter discusses various ways in which homomorphic filtering has been explored for use with seismic data processing. Homomorphic filtering represents an alternative approach to the deconvolution of the seismic signal. This class of filtering techniques represents a generalization of linear filtering to encompass a variety of nonlinear filtering problems. One particular class of nonlinear filtering problems to which it has been successfully applied in a number of applications is that of deconvolution. Within the context of seismic signal processing, a number of approaches have been explored for the application of this class of nonlinear filtering techniques. One consists of applying the method directly to extract the reflector series from the received signal. A second corresponds to using homomorphic filtering to estimate the source pulse. From this estimate, an inverse filter is then designed using one of several possible techniques including Wiener inverse filter design.

Application of homomorphic filtering to seismic data processing

Enterprise representations can be used to improve the organizational self-awareness, allowing the communication of knowledge about these concerns among different organizational actors. In order to achieve this goal, the model must provide an updated, trustworthy and reliable representation. However, the typical usage of the enterprise model is restricted to support some organizational activities for limited time intervals. The recognized difficulty in maintaining it updated and aligned with the reality has prevented the usage of enterprise models as a repository of the organizational knowledge relevant for the daily activities execution. The present work proposes a process for continuously update the enterprise model trough the annotation mechanism. Annotations enable interaction contexts that allow actors to make explicit their knowledge about their activities through graphic representations. In this process, actors act as active updaters of the as-is model, through the comparison between the modeled activities and the ongoing real executed activities. This process is modeled by DEMO methodology in order to depict the essential transactions between actors of operational and model updating processes.

Business Process Model Dynamic Updating

Enterprises are dynamical systems, formed by a semantic web of active servers of two kinds: Carbon-based servers, normally called Humans, and silicon-based servers, called Computers. These active elements change the state of the world through their individual and collective orchestrated networked actions, in real time. All an enterprise "does" is the sum total of the actions of its active servers. No more, no less! An Enterprise is an entity by itself, whose existence is associated with intentions, missions, goals and purposes that are in some degree shared by its active elements and inform the prescribed organizational elements of its structures. An Enterprise is under permanent change, due to external and internal conditions, and the enacted changes occur either by spontaneous actions of its active servers or by intentional systemic change that propagates top-down and is purposefully adopted by the baseline servers. This talk will show how relevant is the Engineering Body of Knowledge of Systems Theory and Dynamic Systems Control and the formal principles and methods of Enterprise Engineering to model, design and operate Enterprises and in particular, how to steer top-down strategic transformations and to combine them with adaptive bottom-up emergent adaptive phenomena.

http://ieeexplore.ieee.org/iel7/7052447/7077084/07077104.pdf

An engineering approach to natural enterprise dynamics: From top-down purposeful systemic steering to bottom-up adaptive guidance control

Enterprise Operating System: the enterprise (self) governing system

Current models for isolated word recognition perform very well on small vocabularies of distinctly different sounding words. However, when we are confronted with vocabularies of similar sounding words (e.g., the letters of the alphabet), the performance of isolated word recognizers decreases dramatically. By carefully reexamining the model used for isolated word recognition we have identified some of the inherent deficiencies. In this paper we propose an improved word-recognition model that is inherently capable of accurately recognizing words from almost any vocabulary. We have investigated a simple implementation of the model that preserves most of the structure of a linear predictive coding (LPC)-based version of the canonic isolated word model. In an experimental evaluation of the improved model, using an alpha-digit vocabulary, recognition accuracy improvements of from 1 to 5.7 percent were obtained for four talkers. The improvements were due to changes in both the analysis model and the decision procedure. The strengths and weaknesses of the improved model are discussed.

José Tribolet

Papers

Application of homomorphic filtering to seismic data processing

Business Process Model Dynamic Updating

An engineering approach to natural enterprise dynamics: From top-down purposeful systemic steering to bottom-up adaptive guidance control

Enterprise Operating System: the enterprise (self) governing system

An improved model for isolated word recognition