Synthesis, structure and power of systolic computations

AbstractA variety of problems related to systolic architectures, systems, models and computations are discussed. The emphases are on theoretical problems of a broader interest. Main motivations and interesting/important applications are also presented. The first part is devoted to problems related to synthesis, transformations and simulations of systolic systems and architectures. In the second part, the power and structure of tree and linear array computations are studied in detail. The goal is to survey main research directions, problems, methods and techniques in not too formal a way

Nonacceptability criteria and closure properties for the class of languages accepted by binary systolic tree automata

AbstractIn this paper a contribution is given to the solution of the problem of finding an inductive characterization of the class of languages accepted by binary systolic tree automata, L(BSTA), in terms of the closure of a class of languages with respect to certain operations. It is shown that L(BSTA) is closed with respect to some new operations: selective concatenation, restricted concatenation and restricted iteration. The known nonclosure of L(BSTA) with respect to classical language operations, like concatenation and Kleene iteration is proved here by using a new nonacceptability criterion

Effectiveness of diabetes conversation Map™ on insulin acceptance among insulin refusal patients of type 2 diabetes mellitus

Background: Diabetes education is a very crucial aspect of management for a diabetic patient. Educations can be delivered either by group or individual. For busy health centres, group education is the most practical approach in the setting. However, in most of the public hospital setting, the group-based diabetes education is still not yet well established. In recent years, group-based diabetes education using the diabetes conversation maps (DCM) endorsed by the International Diabetes Federation has been popularised ever since the complete set was translated into malay language. However, the effectiveness of this diabetes conversation map is still not known in our country. Objectives: To compare the percentage of insulin acceptance between the intervention group (those who receive DCM education) and control group (those who receive standard counselling) and associated factors of insulin acceptance among uncontrolled type 2 diabetic patients, who refused insulin initiation attending Klinik Rawatan Keluarga HUSM. Methodology: An interventional study was carried out where a total of 88 Type 2 diabetic adults from Klinik Rawatan Keluarga, HUSM with glycosylated haemoglobin (HbA1c) concentrations of 8% and or more and refused insulin treatment were randomised into intervention(those who receive DCM education) and control groups(those who receive standard individual education). Post sessions, participants were reviewed by the investigator to find out their acceptance towards insulin initiation. Logistic regression was done to look at the factors associated with the insulin acceptance. Results: The response rate was 97.7%. There was a significant difference in insulin acceptance between the intervention group education using DCM as compared to the control group using standard individual education module (86% vs 11%, p < 0.001). There was a significant association between history of relatives use insulin and insulin acceptance (AOR: 6.96; 95% CI: 2.30, 21.03; p=0.001). Conclusion: Group education using Diabetes Conversation Map (DCM) is effective in increasing insulin acceptance among patient who initially refused insulin treatment. We recommend using DCM in primary care centres for diabetic patients who had difficulties in accepting insulin. Having relative that use insulin is a significant associated factor among patient who accepts insulin

An instruction systolic array architecture for multiple neural network types

Modern electronic systems, especially sensor and imaging systems, are beginning to incorporate their own neural network subsystems. In order for these neural systems to learn in real-time they must be implemented using VLSI technology, with as much of the learning processes incorporated on-chip as is possible. The majority of current VLSI implementations literally implement a series of neural processing cells, which can be connected together in an arbitrary fashion. Many do not perform the entire neural learning process on-chip, instead relying on other external systems to carry out part of the computation requirements of the algorithm. The work presented here utilises two dimensional instruction systolic arrays in an attempt to define a general neural architecture which is closer to the biological basis of neural networks - it is the synapses themselves, rather than the neurons, that have dedicated processing units. A unified architecture is described which can be programmed at the microcode level in order to facilitate the processing of multiple neural network types. An essential part of neural network processing is the neuron activation function, which can range from a sequential algorithm to a discrete mathematical expression. The architecture presented can easily carry out the sequential functions, and introduces a fast method of mathematical approximation for the more complex functions. This can be evaluated on-chip, thus implementing the entire neural process within a single system. VHDL circuit descriptions for the chip have been generated, and the systolic processing algorithms and associated microcode instruction set for three different neural paradigms have been designed. A software simulator of the architecture has been written, giving results for several common applications in the field

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 144

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1975