Overview

Easy to understand; covers H446 course in great detail. See more: https://bytepublishers.company.site . OCR A level: broad, deep, challenging to learn & teach. Written in response to issues encountered teaching the subject. Follows H446 spec. section by section; ensures teachers & students know that all subject matter has been covered. Content is covered in a compact, clear style with colour used throughout to highlight different sections and key content. Shown below is the first part of the contents, as you can see it follows the specification exactly: Component 1: Content of Computer systems 1.1 Characteristics of contemporary processors, input, output & storage devices 1.1.1 Structure & function of the processor (a) Main components of processor (b) Fetch-Decode-Execute Cycle including its effects on registers (c) Factors affecting performance of CPU (d) Use of pipelining in a processor to improve efficiency.

Full Product Details

Author:   Priscilla Huby
Publisher:   Byte Publishers
Imprint:   Byte Publishers
ISBN:  

9781527298224

ISBN 10:   1527298221
Pages:   308
Publication Date:   31 August 2021
Audience:   College/higher education ,  A / AS level
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

COMPONENT 1: CONTENT OF COMPUTER SYSTEMS 1 1.1 Characteristics of contemporary processors, input, output & storage devices 4 1.1.1 Structure & function of the processor 4 (a) Main components of processor 6 (b) Fetch-Decode-Execute Cycle including its effects on registers 10 (c) Factors affecting performance of CPU 11 (d) Use of pipelining in a processor to improve efficiency. 13 (e) Von Neumann, Harvard & contemporary processor architecture. 15 1.1.2 Types of processor 17 (a) Differences between & uses of CISC & RISC processors. 17 (b) GPUs & their uses (including those not related to graphics). 21 (c) Multicore & Parallel systems. 23 1.1.3 Input, output & storage 24 (a) How different input, output & storage devices can be applied to solution of different problems 24 (b) Uses of magnetic, flash & optical storage devices. 27 (c) RAM & ROM. 29 (d) Virtual storage. 30 1.2 Software & software development 31 1.2.1 Systems Software 31 (a) Need for, function & purpose of operating systems. 31 (b) Memory Management (paging, segmentation & virtual memory). 34 (c) Interrupts, role of interrupts & Interrupt Service Routines (ISR), role within Fetch-Decode-Execute Cycle. 39 (d) Scheduling: round robin, 1st come 1st served, multi-level feedback queues, shortest job 1st & shortest remaining time. 41 (e) Distributed, embedded, multi-tasking, multi-user & Real Time operating systems. 43 (f) BIOS 45 (g) Device drivers. 46 (h) Virtual machines (software used to take on function of machine, including executing intermediate code or running OS within another). 47 1.2.2 Applications Generation 49 (a) Nature of applications, justifying suitable applications for a specific purpose 49 (b) Utilities 50 (c) Open source vs closed source. 53 (d) Translators: Interpreters, compilers & assemblers. 55 (e) Stages of compilation (lexical analysis, syntax analysis, code generation & optimisation). 57 (f) Linkers & loaders & use of libraries. 60 1.2.3 Software Development 62 (a) Understand waterfall lifecycle, agile methodologies, extreme programming, spiral model & rapid application development. 63 (b) Relative merits & drawbacks of different methodologies & when they might be used. 70 (c) Writing & following algorithms. 77 1.2.4 Types of Programming Language 80 (a) Need for & characteristics of variety of program 80 (b) Procedural languages. 83 (c) Assembly language (includes following & writing simple programs with Little Man Computer instruction set) 85 (d) Modes of addressing memory (immediate, direct, indirect & indexed) 92 (e) Object-oriented languages with an understanding of classes, objects, methods, attributes, inheritance, encapsulation & polymorphism. 93 1.3 Exchanging data 99 1.3.1 Compression, Encryption & Hashing 99 (a) Lossy vs lossless compression. 99 (b) Run length encoding & dictionary coding for lossless compression. 102 (c) Symmetric & asymmetric encryption. 104 (d) Different uses of hashing. 106 1.3.2 Databases 107 (a) Relational database, flat file, primary key, foreign key, secondary key, entity relationship modelling, normalisation & indexing.. 107 (b) Methods of capturing, selecting, managing & exchanging data. 113 (c) Normalisation to 3NF. 117 (d) SQL - Interpret & modify 120 (e) Referential integrity. 124 (f) Transaction processing, ACID (Atomicity, Consistency, Isolation, Durability), record locking & redundancy. 125 1.3.3 Networks 127 (a) Characteristics of networks & importance of protocols & standards. 127 (b) Internet structure: * TCP/IP Stack. * DNS * Protocol layering. * LANs & WANs. * Packet & circuit switching. 133 (c) Network security & threats, use of firewalls, proxies & encryption 141 (d) Network hardware. 144 (e) Client-server & peer to peer. 148 1.3.4 Web Technologies 149 (a) HTML, CSS & JavaScript 149 (b) Search engine indexing. 157 (c) PageRank algorithm. 158 (d) Server & client side processing. 160 1.4 Data types, data structures & algorithms. 162 1.4.1 Data Types 162 (a) Primitive data types, integer, real/floating point, character, string & Boolean. 162 (b) Represent positive integers in binary. 163 (c) Use of sign & magnitude & two's complement to represent negative nos. in binary. 164 (d) Addition & subtraction of binary integers. 166 (e) Represent positive integers in hexadecimal 168 (f) Convert positive integers between binary hexadecimal & denary. 169 (g) Representation & normalisation of floating point numbers in binary. 170 (h) Floating point arithmetic, positive & negative numbers, addition & subtraction. 174 (i) Bitwise manipulation & masks: shifts, combining with AND, OR, & XOR. 176 (j) How character sets (ASCII and UNICODE) are used to represent text. 179 1.4.2 Data Structures 181 (a) Arrays (of up to 3 dimensions), records, lists, tuples. 181 (b) Following structures to store data: linked-list, graph (directed & undirected), stack, queue, tree, binary search tree, hash table. 185 (c) How to create, traverse, add data to & remove data from data structures mentioned above. 192 1.4.3 Boolean Algebra 207 (a) Define problems using Boolean logic. 207 (b) Manipulate Boolean expressions, including using Karnaugh maps to simplify Boolean expressions. 209 (c) Use following rules to derive or simplify statements in Boolean algebra: De Morgan's Laws, distribution, association, commutation, double negation. 214 (d) Using logic gate diagrams & truth tables. 216 (e) logic associated with D type flip flops, half & full adders. 218 1.5 Legal, moral, cultural & ethical issues 221 1.5.1 Computing related legislation 221 (a) Data Protection Act 1998. 221 (b) Computer Misuse Act 1990. 222 (c) Copyright Design & Patents Act 1988 (CPDA) 223 (d) Regulation of Investigatory Powers Act 2000. (RIPA) 224 1.5.2 Moral & ethical Issues 225 COMPONENT 2: CONTENT OF ALGORITHMS & PROGRAMMING 233 2.1 Elements of computational thinking. Understand what is meant by computational thinking 233 2.1.1 Thinking abstractly 234 (a) The nature of abstraction. 234 (b) The need for abstraction. 235 (c) Differences between an abstraction & reality. 236 (d) Devise an abstract model for a variety of situations. 237 2.1.2 Thinking ahead 238 (a) Identify inputs & outputs for a given situation. 238 (b) Determine preconditions for devising a solution to a problem. 238 (c) Nature, benefits & drawbacks of caching. 239 (d) Need for reusable program components. 240 2.1.3 Thinking procedurally 241 (a) Identify components of a problem 241 (b) Identify components of solution to a problem 242 (c) Determine order of steps needed to solve a problem. 243 (d) Identify sub-procedures necessary to solve a problem. 243 2.1.4 Thinking logically 244 (a) Identify points in a solution where a decision has to be taken. 244 (b) Determine logical conditions that affect the outcome of a decision. 245 (c) Determine how decisions affect flow through a program. 245 2.1.5 Thinking concurrently 246 (a) Determine the parts of a problem that can be tackled at the same time. 246 (b) Outline the benefits & trade offs that might result from concurrent processing in a particular situation. 247 2.2 Problem solving & programming 248 2.2.1 Programming techniques 248 (a) Programming constructs: sequence, iteration, branching. 248 (b) Recursion, how it can be used & compares to an iterative approach. 253 (c) Global & local variables. 254 (d) Modularity, functions & procedures, parameter passing by value & by reference. 255 (e) Use of IDEs to develop/debug a program. 259 (f) Use of object oriented techniques. 261 2.2.2 Computational methods 262 (a) Features that make a problem solvable by computational methods. 262 (b) Problem recognition. 263 (c) Problem decomposition. 263 (d) Use of divide & conquer. 264 (e) Use of abstraction. 264 (f) Learners should apply their knowledge of backtracking * data mining * heuristics * performance modelling * pipelining * visualisation to solve problems. 265 2.3 Algorithms: Use of algorithms to describe problems & standard algorithms 269 2.3.1 Algorithms 269 (a) Analysis & design of algorithms for a given situation. 269 (b) Suitability of different algorithms for given task & data set, in terms of execution time & space. 270 (c) Measures & methods to determine efficiency of different algorithms, Big O notation (constant, linear, polynomial, exponential & logarithmic complexity). 271 (d) Comparison of complexity of algorithms. 274 (e) Algorithms for main data structures, (stacks, queues, trees, linked lists, depth-first (post-order) & breadth-first traversal of trees). 277 (f) Standard algorithms (bubble sort, insertion sort, merge sort, quick sort, Dijkstra's shortest path algorithm, A* algorithm, binary search and linear search). 279 APPENDIX: PSEUDOCODE 292 INDEX 295

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