Fault Tolerance Mechanisms for FPGA-Based Regular Expression Matching

Traditional fault-tolerance techniques relying on spatial and temporal redundancy typically imply high power, delay, and area overheads. Cost-effective solutions often depend on system’s design and hardware platform at hand. Particularly for Field-Programmable Gate Arrays (FPGAs), soft errors on the configuration memory are a significant dependability threat. In this work, we present an extended and comprehensive fault tolerance mechanism especially suited for dealing with configuration faults on FPGA-based systems that must deal multiple failure modes. Each failure mode may present different criticality and probability of occurrence, and these properties are measured and exploited to provide low-cost solutions when compared to standard approaches such as triple modular redundancy. The exploited properties are typically found in critical monitoring systems that may trigger security- or safety-critical alarms and warnings in general. In such systems, failing to trigger an alarm when necessary is frequently regarded as more critical than providing an occasional false alarm. For instance, Regular Expression Matching (REM), a compute-intensive mechanism heavily used to perform Deep Packet Inspection in critical network applications, presents such properties, and it can be greatly accelerated by FPGAs to meet performance constraints in high-throughput networks. Therefore, we use FPGA-based REM engines as a case study to demonstrate the effectiveness of the proposed techniques. Additionally, a mutually-aware placement and scrubbing mechanism is introduced to reduce the repair time, improving the system reliability and availability. Experimental results show that the failure rate and the repair time can be reduced by 95 and 90% respectively while avoiding the costs of triplication.     

Thixoforging of 332 aluminum alloy in a mechanical eccentric press

Journal of Materials Science - This study sought to analyze the semi-solid behavior of commercial 332 aluminum alloy during thixoforming in a mechanical eccentric press (thixoforging). The...     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

Mathematical Morphology on the Spherical CIELab Quantale with an Application in Color Image Boundary Detection

Mathematical morphology is a theory with applications in image processing and analysis. This paper presents a quantale-based approach to color morphology based on the CIELab color space in spherical coordinates. The novel morphological operations take into account the perceptual difference between color elements by using a distance-based ordering scheme. Furthermore, the novel approach allows for the use of non-flat structuring elements. An illustrative example reveals that non-flat dilations and erosions may preserve more features of a color image than their corresponding flat operations. Furthermore, the novel non-flat morphological operators yielded promising results on experiments concerning the detection of the boundaries of objects on color images.     

Overview of the scientific production in the Pharmacy area in Brazil: profile and productivity of researchers granted with fellowships by the National Council for Scientific and Technological Development

In Brazil, the National Council for Scientific and Technological Development (CNPq) distributes productivity fellowships in research (RS) as a recognition to individuals with outstanding productivity levels in their areas. The aim of this study is to evaluate the scientific production of the Brazilian Pharmacy area, one division of the Health Sciences Great Area of CNPq, considering the profile and productivity levels of RS fellows. The results showed that most of the 156 active RS fellows in 2015 were female, with doctorate completed in the Southeast region (mainly in University of São Paulo) and with research activities developed in the South and Southeast regions. Most of their work was published in journals classified as B1 and B2 Qualis in Pharmacy by the Coordination for the Improvement of Higher Education Personnel (CAPES), with high prevalence of publications in local journals and/or specialized on medicinal plants. Besides, they featured much dependence on advising and productivity indexes related to the category and level of RS fellowship. The evolution of such data must be continually evaluated to determine the influence of CNPq productivity fellowships on performance and stratification of researchers in the Pharmacy area in Brazil.     

The renewable energy market in Brazil: Current status and potential

Renewable energy offers a range of options with which to meet the growing demand for energy, particularly in the context of the pursuit (especially in developing countries) of economic development which takes into account social and environmental issues. Brazil has abundant natural sources of renewable energy, such as wind and solar power, hydraulic energy, small hydroelectric plants, ethanol and bio-diesel. These sources form part of the Brazilian strategy aimed at satisfying the demand for 6300 MW of fresh capacity per year arising out of projected economic growth of 5.1% per year over the next 10 years. Renewable energy sources currently provide 47.2% of the internal supply of primary energy in Brazil. Brazil has been pursuing a strategy of maintaining its renewable energy matrix and developing and providing incentives for further low carbon initiatives.In this study we set out an overview of the renewable energy options available in Brazil, their current status, the main positive results obtained to date and future potential. We describe the market for renewable energy in Brazil and examine specific public policies aimed at overcoming barriers to this market, thereby promoting its consolidation and expansion.     

Sustained release of potassium diclofenac from a pH‐responsive hydrogel based on gum arabic conjugates into simulated intestinal fluid

This work describes the preparation, the swelling properties and the potassium diclofenac (KDF) release profile of hydrogels of gum arabic (GA), N′,N′‐dimethylacrylamide, and methacrylic acid. In order to convert GA into a hydrogel, the polysaccharide was vinyl‐modified with glycidyl methacrylate. The hydrogels showed pH‐responsive swelling changes, which were more expressive in the basic environment. Release data of KDF were adjusted to a diffusion‐based kinetic model that provides an important insight on affinity of the drug for hydrogel and solvent, which may be the leading parameter for release of guest molecules from polymers. The KDF release from the hydrogels into simulated intestinal fluid decreases when the amount of modified GA increases. This was demonstrated to be due to the higher affinity of KDF for GA‐richer hydrogel, which makes the anti‐inflammatory release less favorable. The analysis of released drug half‐time (t_1/2 = 16.10 and 21.51 h) indicated sustained release characteristics. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43319.     

Microstructure and mechanical properties of ternary mortars with brick powder,glass powder,slag, fly ash,and limestone

The objective of this research is to study the effects produced by ternary binders which combine the addition of waste brick powder with fly ash, limestone, ground granulated blast furnace slag or waste glass powder in the microstructure and mechanical properties of mortars. In these ternary binders, the ordinary Portland cement was partially replaced by 10% of waste brick powder and 10% of another of the abovementioned additions. Mortars prepared with ordinary Portland cement without additions were also prepared. The microstructure was characterized with mercury intrusion porosimetry, electrical resistivity, and thermogravimetric analyses. Ultrasonic pulse velocity, compressive and flexural strengths were also determined. Mortars made using ternary binders with two active additions showed higher pore refinement and higher electrical resistivity at 250 days. Furthermore, their compressive strength and ultrasonic pulse velocity were relatively similar or even higher than that noted for reference specimens.