Error resilience

Error resilience Depending on the architecture of the bitstream, perceptual coders are more or less susceptible to single or burst errors on the transmission channel. This can be overcome by application of error-correction codes, but with more or less cost in terms of decoder complexity and/or decoding delay. 

A. Panayides, Z. Antoniou, Y. Mylonas, M.S. Pattichis, A. Pitsihides, and C.S. Pattichis, "High-resolution, low-delay, and error-resilient medical ultrasound video communication using H.264/AVC over mobile WiMAX networks," IEEE J. Biomed. Health Informat., vol. 17, no. 3, pp. 619-628, May 2013. 

Usually soft-error tolerant hardware uses replication of large hardware parts and voting for error detection and correction [38,27,4]. Currently research efforts include more sophisticated approaches than simple replication. [18] reuses testing circuitry for error detection and correction and [17] extends hardware with built-in soft error resilience which is able to detect and correct soft errors and even to predict a soon hardware failure. The hardware design presented in [26] on-the-fly replicates executed instructions. [23] checks consistency of data independent parts of instruction fetch and decoding for repeated traces within an application. For this to be useful, it is required that an application consists to large parts of traces which are repeated often. All those approaches only aim... 

Mitra, S., Seifert, N., Zhang, M., Shi, Q., Kim, K.S. Robust system design with built-in soft-error resilience. Computer 38(2), 43-52 (2005)... 

Previous methods on nonlinear sensor network design mrniniize cost subject to a variety of constraints linked to the network performance precision, residual precision, error detectability and resilience. In recent work, the use of accuracy as an attribute of a network that can replace precision, error detectability and resihence more effectively has been considered. In this paper, we propose a sensor network design methodology based on accuracy thresholds. 

In contrast with the use of objective functions such as observability or reliability that had been used, Bagajewicz (1997, 2000) formulated a mixed integer nonlinear programming to obtain sensor networks satisfying the constraints of residual precision, resilience, error detectability at minimal cost. A tree enumeration was proposed where at each node the optimization problem of the different characteristics are solved. 

AU the above work focused on minimizing network cost using precision, residual precision, error detectability and resilience constraints. In this work we also minimize network cost, but we use software accuracy (as defined by Bagajewicz, 2005), which replaces all the above network attributes, as a constraint. We first review accuracy and we then discuss issues of the methodology involved to calculate accuracy at each node. We finish with an example. 

Some passive controls will live outside the user interface and may not be apparent to day-to-day operators. For example, HIT systems typically need to exhibit resiliency in their architecture whether brought about through redundancy or other systematic means. These design features represent active engineered controls. However it is common for this to be supported by other more passive controls which require some degree of human intervention. The platforms on which systems reside can often be monitored for availability and performance. In some cases systems may be specifically instrumented to provide metrics on the execution of specific functions or the success of database transactions. Similarly systems may log errors or failed messages which are then made available for inspection by service management personnel. 

Woods, David D. Lessons from beyond human error Designing for resilience in the face of diange and surprise. Design for Safety Workshop, NASA Ames Research Center, October 8-10,2000. 

The performance of an ANN is measured by the root-mean-square error (RMSE) which is also the function to be minimised. The Levenberg-Marquardt optimization algorithm (Marquardt, 1963) and resilient propagation algorithm (RPROP) (Riedmiller Braun, 1993) were used to train the neural networks in this study. 

Clinicians, like everyone else, vary in temperament, resilience and attitude to their own errors. Jenny Firth-Cozens (1997) has found that a tendency to self criticism is predictive of stress this tendency may be rooted in earher relationships, which in turn may find an echo in relationships with senior colleagues. For a highly self critical person, errors and mistakes will he particularly disturbing in serious cases the clinician may enter a vicious downward spiral of anxiety, shame and deteriorating performance. There is a fine balance to be struck between personal high standards and undue self criticism. The high personal standards of excellent clinicians may in fact make them particularly vulnerable to the impact of mistakes. 

Clinicians are resilient people, but anyone may be vulnerable, because of personality, position or circumstance, to distressingly severe reaaions to error. While younger clinicians may be more vulnerable, anyone can be affected at any stage in their career, unless they have become so arrogant or damaged as to be insensitive to the impact of mistakes on their patients. 

Confidentiality is maintained with respect to the patient and the providers who were involved, but a case study is created to inform others about the risks and lessons learned so that greater resilience can be introduced to prevent error from reaching a patient again. The analysis is introduced to participants through the introductory protocol shown in Exhibit 7.1. In addition, protocols (described in the following sections) have been developed for explicit guidance of the notification process after a medical accident. 

We used to think that studying medical accidents could create harm-free care. We learned instead that risks, near misses, hazards, and the wisdom of front-line workers teach equally important lessons about improving safety, without aU the emotion and trauma of accidents. Recovery from error and resilience in a system are as important as prevention in creating safety for patients. 

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