EAT117 Electronic Principles - Assignment Example

Number: EAT117 Electronic Principles Logic systems mainly use reasoning techniques such as induction and deductions. Logic systems help in the implementation of knowledge-based and artificial intelligence systems. Computer systems usually automate logic systems in them. Logic systems usually operate complex types of reasoning required in operation of electronic tasks. Logic systems have a wide range of application. Some of the applications are problem solving, predictive analytics, natural language processing, scheduling, and complex event processing. The first logic systems were systems and theorem provers. Early systems such as General Problem Solver mainly assisted in solving general tasks. They provide a generic planning engine useful in solving structured problems (Jablonski 66). The early logic systems required knowledge of reasoning to operate. The firs practical application of logic systems was expert systems. Expert systems emphasized on more specific domains rather than general problem solving. There was also much limitation of expert systems on logic. Focusing on specific domains improved the performance of logic systems. Inference engines were the engines specifically used with expert systems. Inference engines are opposed to theorem provers which had general inference techniques. Logic systems encompass application of reasoning in decision systems. The most common application of the systems is in computers. Varied implementations of reasoning demonstrate different uses of logics. Logic systems mainly use deductive reasoning to extract inferences from existing knowledge. Inference engines are able to carry out forward or backward reasoning to establish a conclusion. Inferences of conclusions come as a result of modus ponens. The most common types of reasoning include deductive, inductive, defeasible, heuristic, and abductive reasoning. Logic systems can employ open world assumption, OWA, or closed world assumption, CWA. Logic systems in computers use logic programming express rules and facts about a task domain. Some of the logic programming families include Answer Set Programming, Datalog, and Prolog (Ambler 79). They provide descriptions of procedures and rules of reasoning by the use of clauses. Application of logic systems is in rule engines which operate using discrete rules. Separate application of logic sets can occur to functionality. Rule engines mainly run reasoning capabilities. Forward and backward chining are common approaches in implementations of production systems. Rule engine as a component of logic systems is widely applicable in business rules where they assist in automating decision-making processes. Also important in logic systems is deductive classifiers. It was a new type of intelligence knowledge after the invention of rule engines. It uses frame language to perform tasks. Frame languages gives descriptions of domains as sets of many classes. It has a close relationship with object-oriented designs. Frame languages differ from object-oriented by the fact that it uses formal semantics which depend on first order logics. Frame languages apply semantics to generate input to deductive classifiers. Classifiers can then a model to find out relationships that are consistent I it. For example, it can ascertain if an object falls in the classes that the user gives. Another technology in logic systems is machine learning systems. It involves performing reasoning basing on the existing example data or the observed events for the purposes of training. Machine learning systems, for example, are able to use inductive reasoning to develop hypotheses on the existing data or observed facts. The systems entail the use of generalized searching rules as well as functions to obtain results according to observations. It can then use the generalization for performance of future tasks. Close to machine learning systems is Case-based reasoning (Ambler 58). Case-based reasoning systems generate solutions by making comparisons of a particular task to other tasks whose solutions already exist. Basing on case histories, CBR systems infer solutions by the use of analogical reasoning. The systems are manly applicable in technical or customer support centers. There are also procedural reasoning systems, PRS, which use procedural knowledge base to arrive to plans with the help of a number of reasoning techniques. Plans describe a set of actions for performing various tasks. PBR uses models such as belief-desire-intention model to get to appropriate plans. Logic systems use formal language to execute tasks. A formal language is a collection of sentences that are used to develop specific formulas. One can define the syntax of a formal language without necessarily referring to interpretation. There are cases where formal languages contain two sets of alphabets. Logic systems consist of logic families (Woods 235). Logic families encompass electronic logic gates. Several vacuum-tube and solid-state logic systems were in use before the invention of integrated circuits. Otherwise, the early systems lacked proper standardization. In addition, they were not inter-operatable. The earliest class of digital circuits made using resistors is Resistor-transistor logic, RTL. It uses logical negation to operate. It had the advantage of using minimum number of transistors. The number of transistors was a key consideration before the invention of integrated circuit technology. The earlier technologies mainly used discrete components. The preference for the minimum use of transistors was due to that transistors were the most expensive to produce (Jablonski 65). However, RTL had certain limitations. It requires a high supply of current. When the transistor is on, it has high power dissipation due to the base resistor in contact with logical 1. There has been much development on the transistor switching speed in RTL. The use of higher-frequency transistor has enabled the achievement of high speeds. Another technique to enhance speed of transistors is to put capacitors in parallel with input resistors to reduce the time for driving forward bias. An improvement occurred to RTL to form Direct-Coupled Transistor Logic. DCTL gate do not have base resistors. The connection of transistors is direct to inputs. They are advantageous due to their simplicity as well as the economical aspect. Fabricating them to integrated circuits is relatively easier than RTL gates. However, they have characteristic limitations. They need consistent transistor characteristics as a result of their susceptibility to ground noise. Apart from RTL, there is Diode-transistor logic, DTL. The logic gating function for DTL takes place by the use of a diode network. A transistor conducts the amplification function. For situations when noise is very high, scientists introduced High Threshold, HTL, which is a variant of DTL. HTL has increased noise margin. Because of its high value resistors, its speed is slow. It is mainly applicable in industrial environments. There is Emitter-coupled logic, ECL. The transistors of ECL can never get into saturation. It has the advantage of low gate delays. However, ECL uses much power. Each gate draws current continuously. Close to RTL is Transistor-transistor Logic, TTL. Its transistor conducts both functions of amplification and logic gating. However, it is power intensive especially at a slow speed (Jablonski, 67). TTL is an important foundation for many electronic devices. Work cited Ambler, Scott W. The Object Primer: Agile Modeling-Driven Development with Uml 2. New York: Cambridge, 2004. Print. Jablonski, Stefan. Guide to Web Application and Platform Architectures. Berlin: Springer, 2004. Print. Woods, Roger. Fpga-based Implementation of Signal Processing Systems. Chichester, U.K: John Wiley & Sons, 2008. Print. Read More