OCR GCSE Computer Science J277 - 1.2 Memory and Storage

A complete deck of 63 vocabulary flashcards covering OCR GCSE Computer Science J277 Unit 1.2 Memory and Storage.

Primary Storage Need

The CPU needs fast access to data and instructions currently in use.

RAM

Volatile primary memory that stores programs and data currently in use.

Volatile Memory

Memory whose contents are lost when the power is switched off.

ROM

Non-volatile primary memory that stores instructions or data that should remain when power is off.

Non-volatile Memory

Memory whose contents are retained when the power is switched off.

RAM vs ROM

RAM is volatile and normally stores data/programs currently in use; ROM is non-volatile and stores data/instructions that should persist.

Virtual Memory

A section of secondary storage used as temporary memory when RAM is full.

Virtual Memory Operation

Data is transferred between RAM and secondary storage when there is not enough RAM.

Virtual Memory Speed

Secondary storage is slower to access than RAM.

Cache Memory

Very fast memory used to hold frequently or recently needed data and instructions for the CPU.

Secondary Storage Need

To store programs and data long term when power is off.

Secondary Storage Types

Magnetic, optical and solid state.

Magnetic Storage

Stores data magnetically; examples include hard disk drives and magnetic tape.

Optical Storage

Uses laser light to read/write data; examples include CDs, DVDs and Blu-ray discs.

Solid-State Storage

Stores data electronically with no moving parts; examples include SSDs and USB flash drives.

Storage Characteristics

Capacity, speed, portability, durability, reliability and cost.

Bit

A single binary digit: $0$ or $1$.

Nibble

$4$ bits.

Byte

$8$ bits.

Kilobyte (KB)

$1,000$ bytes for OCR calculations; $1,024$ may also be accepted.

Megabyte (MB)

$1,000\text{ KB}$ for OCR calculations.

Gigabyte (GB)

$1,000\text{ MB}$ for OCR calculations.

Terabyte (TB)

$1,000\text{ GB}$ for OCR calculations.

Petabyte (PB)

$1,000\text{ TB}$ for OCR calculations.

Binary Data Representation

Digital computers process and store data using two states, represented as $0$ and $1$.

OCR Denary Range

$0$ to $255$.

OCR 8-bit Binary Range

$00000000$ to $11111111$.

OCR Two-Digit Hexadecimal Range

$00$ to $\text{FF}$.

Hexadecimal

Base $16$ number system using digits $0\text{-}9$ and letters $\text{A-F}$.

Hexadecimal Utility

It represents binary values more compactly and is easier for humans to read.

Most Significant Bit (MSB)

The bit with the highest place value, normally the leftmost bit.

Least Significant Bit (LSB)

The bit with the lowest place value, normally the rightmost bit.

Overflow Error

Occurs when the result of a binary calculation is too large to fit in the available number of bits.

Left Binary Shift

Moves bits left and, for positive whole numbers without overflow, multiplies the value by $2$ for each place shifted.

Right Binary Shift

Moves bits right and, for positive whole numbers, divides the value by $2$ for each place shifted, discarding shifted-out bits.

Character Set

A defined collection of characters and the binary codes used to represent them.

ASCII

A character set used to represent characters with binary codes.

Unicode

A character set designed to represent a much larger range of characters than ASCII.

Bits per Character

More bits allow more different characters to be represented.

Character Code Memorisation

Students do not need to memorise ASCII or Unicode character codes, but need to understand how character sets work.

Pixel

The smallest individual element of a digital image; each pixel is represented by a binary colour code.

Image Resolution

The number of pixels used to represent an image, often described by width x height.

Colour Depth

The number of bits used to represent the colour of each pixel.

Increasing Image Resolution

Image quality/detail can increase, but file size also increases.

Increasing Colour Depth

More colours can be represented and quality can improve, but file size increases.

Image Metadata

Additional information stored about an image, such as its height and width.

Image File-Size Relationship

$$\text{File size in bits} = \text{colour depth} \times \text{image height in pixels} \times \text{image width in pixels}$$

Digital Sound Storage

The sound wave is sampled at regular intervals and the samples are represented in binary.

Sample Rate

The number of sound samples taken per second, measured in hertz (Hz).

Sound Bit Depth

The number of bits available to store each sound sample.

Audio Duration

How many seconds of audio the sound file contains.

Increasing Sample Rate

Playback quality can improve, but file size increases.

Increasing Sound Bit Depth

Each sample can be represented more precisely, which can improve quality, but file size increases.

Sound File-Size Relationship

$$\text{File size in bits} = \text{sample rate} \times \text{duration in seconds} \times \text{bit depth}$$

Text File-Size Relationship

$$\text{File size in bits} = \text{bits per character} \times \text{number of characters}$$

Compression

Reducing the size of a file.

Need for Compression

To use less storage space or reduce the amount of data that must be transmitted.

Lossy Compression

Reduces file size by permanently removing some data; the original file cannot be perfectly reconstructed.

Lossless Compression

Reduces file size without permanently losing data; the original file can be reconstructed.

Importance of Lossless Compression

When no data can be lost, such as text, program files or other data requiring exact reconstruction.

Suitability of Lossy Compression

For media such as images, audio or video where some loss of detail may be acceptable for a smaller file.

Lossy Compression Trade-off

Usually achieves a smaller file but sacrifices some quality or data.

Lossless Compression Trade-off

Preserves all original data but usually does not reduce file size as much as lossy compression.