S-SETA: Selective Software-Only Error-Detection Technique Using Assertions

Software-based techniques offer several advantages to increase the reliability of processor-based systems at very low cost, but they cause performance degradation and an increase of the code size. To meet constraints in performance and memory, we propose SETA, a new control-flow software-only technique that uses assertions to detect errors affecting the program flow. SETA is an independent technique, but it was conceived to work together with previously proposed data-flow techniques that aim at reducing performance and memory overheads. Thus, SETA is combined with such data-flow techniques and submitted to a fault injection campaign. Simulation and neutron induced SEE tests show high fault coverage at performance and memory overheads inferior to the state-of-the-art.This work was supported in part by CNPq and CAPES, Brazilian agencies

Reliability on ARM Processors Against Soft Errors Through SIHFT Techniques

ARM processors are leaders in embedded systems, delivering high-performance computing, power efficiency, and reduced cost. For this reason, there is a relevant interest for its use in the aerospace industry. However, the use of sub-micron technologies has increased the sensitivity to radiation-induced transient faults. Thus, the mitigation of soft errors has become a major concern. Software-Implemented Hardware Fault Tolerance (SIHFT) techniques are a low-cost way to protect processors against soft errors. On the other hand, they cause high overheads in the execution time and memory, which consequently increase the energy consumption. In this work, we implement a set of software techniques based on different redundancy and checking rules. Furthermore, a low-overhead technique to protect the program execution flow is included. Tests are performed using the ARM Cortex-A9 processor. Simulated fault injection campaigns and radiation test with heavy ions have been performed. Results evaluate the trade-offs among fault detection, execution time, and memory footprint. They show significant improvements of the overheads when compared to previously reported techniques.This work was supported in part by CNPq and CAPES, Brazilian agencies

Role of age and comorbidities in mortality of patients with infective endocarditis

Purpose: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. Methods: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015. Patients were stratified into three age groups:<65 years, 65 to 80 years, and = 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. Results: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 = 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients =80 years who underwent surgery were significantly lower compared with other age groups (14.3%, 65 years; 20.5%, 65-79 years; 31.3%, =80 years). In-hospital mortality was lower in the <65-year group (20.3%, <65 years;30.1%, 65-79 years;34.7%, =80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%, =80 years; p = 0.003).Independent predictors of mortality were age = 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI = 3 (HR:1.62; 95% CI:1.39–1.88), and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared, the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. Conclusion: There were no differences in the clinical presentation of IE between the groups. Age = 80 years, high comorbidity (measured by CCI), and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

A DSP implementation of an AOM and its application to defects detection in textile material

This paper explains a method of defects detection in textile material using a DSP. This Supervised Learning method will allow the detection of defects in anyone of the phases of production. An algorithm of pattern classification based on minimum distance is used to carry out this method. Scalar distance in an Associative Orthogonal Memory (AOM network) is used to provide a measure of the angle which form the 2 compared vectors too. In our system, we can appreciate that the method doesn't require an excessive processing time, so we can implement it for real time processing. Other advantage of the system is that it is applied to different types of clothes and defects (In general, other approaches are centred in only one type of defect). In the other hand, our algorithms produce rates of success around 94%. These results are quite encouraging if we keep in mind that it has been analysed some complex cloth types (such as lined cloth). To finish, and since the results obtained both in error rate and in execution times have been quite good, the application of this method can be very advantageous, moreover knowing that the development environment used is relatively simple