Low-cost,Concurrent Checking of Pointer and Array Accesses in C Programs

Illegal pointer and array accesses are a major cause of failure for C programs. We present a technique called ‘guarding’ to catch illegal array and pointer accesses. Our implementation of guarding for C programs works as a source-to-source translator. Auxiliary objects called guards are added to a user program to monitor pointer and array accesses at run time. Guards maintain attributes to catch out of bounds array accesses and accesses to deallocated memory. Our system has found a number of previously unreported errors in widely-used Unix utilities and SPEC92 benchmarks. Many commonly used programs have bugs which may not always manifest themselves as a program crash, but may instead produce a subtly wrong answer. These programs are not routinely checked for run-time errors because the increase in execution time due to run-time checking can be very high. We present two techniques to handle the high cost of run-time checking of pointer and array accesses in C programs: ‘customization’ and ‘shadow processing’. Customization works by decoupling run-time checking from original computation. A user program is customized for guarding by throwing away computation not relevant for guarding. We have explored using program slicing for customization. Customization can cut the overhead of guarding by up to half. Shadow processing uses idle processors in multiprocessor workstations to perform run-time checking in the background. A user program is instrumented to obtain a ‘main process’ and a ‘shadow process’. The main process performs computations from the orignal program, occasionally communicating a few key values to the shadow process. The shadow process follows the main process, checking pointer and array accesses. The overhead to the main process which the user sees is very low – almost always less than 10%. © 1997 by John Wiley & Sons, Ltd.     

Manufacturing quality density,control and maintenance

The purpose of this paper is to define conceptually and operationally, a measure of quality density in manufacturing. This is expressed in terms of a manufacturing process propensity to ‘output’ units which are defectives, as a function of the manufactured production output, the manufacturing deterioration process, maintenance, quality control, and generally the risks a manufacturing process is subjugated to. Particularly, we assume that the production process induces ‘shocks’ which lead to poorer manufactured quality, while maintenance efforts are continually being employed to restore the process propensity to produce units of better quality. Quality control is used then to monitor the output quality of units produced. Using a methodology of level crossing in stochastic point processes, the stationary probability distribution of a manufactured output quality is defined. Applications to production quality management, combined quality and maintenance management, as well as quality control are considered. Finally, an exponential shock model is resolved analytically.     

FILTER INLET VELOCITY REDISTRIBUTION WITH FILTER LOADING

ABSTRACT

Previous flow visualizations and laser Doppler anemometer velocity measurements have shown that clean engine air filters are presented with very non-uniform velocity distributions when tested in the SAE universal panel Filter test housing. Experiments were conducted to measure the changes that occur in the velocity distribution in the plane 12.7 mm upstream of the filter as it is loaded with dust. Laser Doppler anemometer measurements of velocity profiles were performed for a production engine air Filter in the SAE universal panel filter test housing. Test conditions corresponded to a clean Filter, and dust-loaded Filters at additional pressure drops corresponding to 50 percent, 100 percent, and 150 percent of the design terminating pressure value. The results show that dust loading does make the velocity profiles less non-uniform, but that the changes are not dramatic. The inlet velocity profiles for the design capacity Filter remain very non-uniform.     

Specific Heat, Thermal Conductivity, Density, and Thermal Diffusivity of Mexican Tortillas Dough

A split-split unit experiment was conducted and analyzed to determine the specific heat, thermal conductivity, density, and thermal diffusivity of Mexican corn-based tortillas dough (MCTD) as a function of processing time, temperature, and moisture content (wb). The data indicated that thermal conductivity and thermal diffusivity were significantly influenced by processing time, and respective components of temperature and moisture content. Specific heat and density varied with levels of moisture content and temperature. Thermal conductivity, specific heat, density, and thermal diffusivity could each be predicted separately by a polynomial equation.     

PREDICTION OF FLOUR EXTRACTION RATE IN HARD RED WINTER WHEAT USING THE SINGLE KERNEL CHARACTERIZATION†

Flour extraction rate is a key determinant in milling efficiency and profitability and can be useful in projecting flour output from a mill. Wheat is generally purchased based upon a small group of traditionally measured physical characteristics. This article explores the use of the Single Kernel Characterization System (SKCS) to predict flour extraction rate. Regression analysis was performed using the SKCS parameters and test weight against flour extraction rate for over 600 observations from multiple years from the hard red winter wheat production areas of the U.S. The regression equation had an R²of 0.81. The data suggest that the SKCS 4100 and test weight can be used to predict flour extraction rate in hard red winter wheat. Using the regression equation as a tool, mill buyers may be able to make better decisions regarding their wheat purchases and prediction of flour output.     

Reduction of Bitterness and Tartness in Grapefruit Juice with Florisil

Treatment of commercial grapefruit juice with Florisil (activated magnesium silicate) simultaneously reduced the content of citric acid and the bitter compounds limonin and naringin. Ascorbic acid concentration and °Brix (total soluble solids) were not altered by the Florisil treatment. Experienced taste panelists were able to differentiate between the nontreated and Florisil-treated juice on the basis of bitterness and tartness (acidity). The panelists indicated a preference for the Florisil-treated juice.     

Stability of Non-Pasteurized, Refrigerated Muscadine Grape Juice

The stability of nonpasteurized muscadine grape juice processed with and without 100 mg/L potassium metabisulfite was monitored during storage at 3°C. The muscadine flavor intensity, sweetness, off-flavor levels, and color of white juices remained stable, and ethanol levels remained low for 7 wk. However, the flavor intensity and sweetness of nonsulfited white juice decreased, and off-flavor and ethanol levels increased after 7 wk. Sulfite lowered microbial levels in white juice throughout 9 wk. Red juices did not develop significant levels of off-flavors or ethanol during 9 wk, although the flavor intensity declined, especially in nonsulfited red juice. Sulfite lightened the color of red juice, but the color was stable in both sulfited and nonsulfited red juices.     

Rheological Properties of Solutions and Emulsions Stabilized with Xanthan Gum and Propylene Glycol Alginate

The rheological effects of propylene glycol alginate (PGA) added to solutions and model emulsions containing xanthan gum (XG) were studied using controlled stress rheometry with concentrations appropriate for salad dressings. For samples with XG and XG + PGA blends, solutions and emulsions showed a Newtonian plateau at low shear stresses. The Newtonian plateau of a solution accurately predicted (r²= 1.00) the Newtonian plateau for an emulsion of equivalent gum concentration. Addition of PGA to constant levels of XG showed a more than additive increase in the Newtonian plateau viscosity for solutions and emulsions. For XG aqueous solutions, pseudoplasticity decreased upon addition of PGA. Storage and loss moduli increased with addition of PGA to XG for solutions and emulsions, although G’ for solutions of PGA alone were negligible.