Estimating and reducing the memory requirements of signal processing codes for embedded systems

Most embedded systems have limited amount of memory. In contrast, the memory requirements of the digital signal processing (DSP) and video processing codes (in nested loops, in particular) running on embedded systems is significant. This paper addresses the problem of estimating and reducing the amount of memory needed for transfers of data in embedded systems. First, the problem of estimating the region associated with a statement or the set of elements referenced by a statement during the execution of nested loops is analyzed. For a fixed execution ordering, a quantitative analysis of the number of elements referenced is presented; exact expressions for uniformly generated references and a close upper and lower bound for nonuniformly generated references are derived. Second, in addition to presenting an algorithm that computes the total memory required, this paper also discusses the effect of transformations (that change the execution ordering) on the lifetimes of array variables, i.e., the time between the first and last accesses to a given array location. The term maximum window size is introduced, and quantitative expressions are derived to compute the maximum window size. A detailed analysis of the effect of unimodular transformations on data locality, including the calculation of the maximum window size, is presented.     

Reliability models for computer systems: An overview including dataflow graphs

The reliability of a system is the probability that the system will perform its intended mission under given conditions. This paper provides an overview of the approaches to reliability modelling and identifies their strengths and weaknesses. The models discussed include structure models, simple stochastic models and decomposable stochastic models. Ignoring time-dependence, structure models give reliability as a function of the topological structure of the system. Simple stochastic models make direct use of the properties of underlying stochastic processes, while decomposable models consider more complex systems and analyse them through subsystems. Petri nets and dataflow graphs facilitate the analysis of complex systems by providing a convenient framework for reliability analysis.     

Quantitation of Sulfacetamide, Sulfadiazine, Sulfamerazine and Sulfame Thazine in Various Cominations Dsing High-Performance Liquid Chromatography

A reverse phase high-performance liquid chromatography method for the quantitation of sulfacetamide, sulfadiazine, sulfamerazine, and sulfamethazine in various combinations has been developed. The method is simple, accurate, precise and reproducible. The percent relative standard deviations based on 6 injections were 2.1, 0.6, 1.9, and 1.6 for sulfacetamide, sulfadiazine, sulfamerazine, and sulfamethazine, respectively. The ratio of peak heights (drug/internal standard) wer closely related (r value 0.99 or better) to concentrations (± 20% of the standard solution concentrations). The results of synthetic mixtures showed quantitative recovery and method was successfully applied to commercial dosage forms (tablets and suspension). Extraction of sulfa drugs from the dosage forms required a very simple procedure.     

A Study of The Moisture-Uptake Kinetics of a Hygroscopic Pharmaceutical Powder

The moisture-uptake kinetics of a hygroscopic powder, sodium heparin contained in a cylindrical container, was determined using a novel moisture-uptake measuring device under a constant convective air flow. The amount of moisture uptake increased with the increase in the relative humidity of the air. The effect of powder-bed height on the total amount of moisture uptake was found to be significant only at the highest relative humidity (75%) evaluated in this study. However, the percent of weight increase of the powder as a result of moisture uptake decreases as the height of the powder bed increases. The results of this study are explained by the dynamic nature of the moisture-uptake process associated with the instrument.     

Adenosine A1 receptor stimulation antagonizes the negative inotropic effects of the PKC activator dioctanoylglycerol

It has been suggested that adenosine cardioprotection occurs via adenosine A1 receptor-mediated activation of protein kinase C (PKC). However, adenosine has well-known vasodilatory effects in the myocardium, whereas PKC is a vasoconstrictor. This study examined whether adenosine A1 receptor activation alters the effects of the PKC activator. 1,2-dioctanoyl-s,n-glycerol (DOG) in isolated perfused rat hearts (left-ventricular developed pressure) and rat ventricular myocytes ([Ca2+]i and cell shortening). Exposure to DOG decreased left-ventricular developed pressure by 30%, an effect that was completely reversible. Pretreatment of isolated hearts with either the PKC inhibitor chelerythrine or the adenosine A1 agonist 2-chloro-N6-cyclo-cyclo-isolated pentlyadenosine (CCPA) attenuated the negative inotropic effects of DOG. In the isolated myocytes, DOG decreased [Ca2+]i and cell shortening by 25 and 28%, respectively, effects that were attenuated by both chelerythrine and CCPA. The CCPA attenuation of the DOG-induced decrease in [Ca2+]i and cell shortening was blocked by pretreating the myocytes with the adenosine A1 antagonist, 8-cyclopentyl-1,3-dipropylxanthine (DPCPX). These results indicate that in rat ventricular myocardium, adenosine A1 receptor activation attenuates the apparent PKC-dependent negative inotropic effects of DOG via preservation of [Ca2+]i levels.     

The effects of stress on memory and the hippocampus throughout the life cycle: implications for childhood development and aging

Studies in animals showing hippocampal atrophy and associated memory deficits in stress and aging have implications for stress and aging in humans. Clinical studies in traumatized human populations with posttraumatic stress disorder (PTSD) have replicated studies in animals, showing reduction in volume of the hippocampus measured with magnetic resonance imaging and associated memory deficits. Trauma at different stages of development (early childhood abuse versus trauma in later life due to combat) may influence the nature of memory deficits and hippocampal atrophy. Studies in aging human subjects are consistent with animal studies, although future research is needed in this area. The similarities between biological findings related to cortisol and the hippocampus in stress and aging in both animal and human studies raises the question of whether PTSD can be seen as a form of accelerated aging. Evidence that stress affects the hippocampus and the capacity for learning has broad implications for public health policy, underlying the need for additional resources in this important area and a reexamination of our understanding of factors influencing academic achievement.     

Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.