How to run your first Assembly Language Program using 8086 MASM Assembler and Dosbox at windows

If you are a beginner in assembly language, running your first program on a 64-bit Windows operating system may be difficult. However, I can help you with this. To begin with, download MASM and DosBox software.

1. MASM

2. DosBox

After that, extract the MASM file into the 'C' drive, which will create a folder named 8086, and then install DosBox.

Once you have completed the installation, open the DosBox application from your desktop screen. You will see two files:

Now, type 'mount c c:\8086' and press Enter, followed by 'c:' and press Enter again. You are now ready to write your first program. Open an editor by typing 'EDIT program_name.ASM', and a new window will appear as shown below where you can write your program:

For example, I have provided a sample program for you to try. 

.................................................................................................>

.MODEL SMALL

.STACK 100H

.DATA

MSG DB 'HELLO!$'

.CODE

MAIN PROC

MOV AX,@DATA

MOV DS,AX

LEA DX,MSG

MOV AH,9

INT 21h

MOV AH,4CH

INT 21h

MAIN ENDP

END MAIN

....................................................>

You need to save the file with an extension '.asm', such as 'hello.asm.' 

Once you have saved the program, exit the editor, and you will be returned to the previous DOSBox window. You should see 'C:>'.

Now you will write the following command:  

masm  program-name.asm

In our example, I will use the following command:

masm hello.asm

Then press Enter. You will see the following:

Now, press Enter again. You will see the following:

Press Enter again. You will see the following:

If there are any errors or warnings, you will be notified of them, and you need to correct them by typing 'EDIT program_name.ASM.' Once you have fixed the issues, type 'link program_name_without_extension'. In our example, we use the following:

LINK  hello

Press Enter. You will see the following:

Finally, type 'program-name.exe' .For our example, it would be: 

hello.exe

To exit the DOSBox window, type 'exit'.

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Digital signal processing: A brief Introduction

A signal   is formally defined as a function of one or more independent variables that conveys information on the nature of a physical phenomenon. For example- any physical quantity that varies with time, space or any other independent variables such as  voice, image, light etc .

Any signal generation is usually associated with a system that responds to a stimulus or force. The stimulus (like human voice) in combination with the system (like carbon microphone) is called a signal source (like sound signal source ). 

After generating when we pass a signal through a system (physical device), it performs an operation on the signal for proper information transmission. Such operations are usually referred to as signal processing (DSP).If the system is digital in nature , the operations are called as a digital signal processing. 

A digital system can be constructed as a combination of digital hardware and software, each of which performs its own set of specified rules. The method or set of rules for implementing the system is called an algorithm.

In practice we have an interest in devising algorithms that are computationally efficient, fast and easily implemented. Thus a major topic in our study of digital signal processing is the discussion of efficient algorithms for performing such operations as filtering, correlation, and spectral analysis.

The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, radar, sonar, financial signal processing, seismology and bio-medicine. Specific examples are speech compression and transmission in digital mobile phones, room correction of sound in hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, medical imaging such as CAT scans and MRI, MP3 compression, computer graphics, image manipulation, hi-fi loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers.

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Meaning of Systematic and Non-systematic code

In coding theory, systematic and non-systematic codes refer to ways of organizing the bits of a message in a code.

Systematic code: If the check bits (the redundancy bits added with message bits to create codeword) and the message bits can be separated or identified from the codewords then it is called systematic code. Examples of systematic codes include Hamming codes, Reed-Solomon codes, and convolutional codes.

Non-systematic code: If the check bits (the redundancy bits added with message bits to create codeword) and the message bits can not be separated or identified from the codewords then it is called non-systematic code. They are mixed in the block of the codeword. Examples of non-systematic codes include cyclic codes and turbo codes.

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Line code or coding: their properties

What is Line Coding?

Generally in a digital communication system, the data transmission capacity of a practical channel is much larger than the individual sources. To utilize the channel capacity effectively , we combine several sources through a digital multiplexer using the process of interleaving. The output of a multiplexer is coded into electrical pulses or waveform to transmit data over the channel. This process is called line code/coding or transmission coding. 

Properties of various Line Coding: 

Digital data signal can be transmitted by various line coding. A line code should have the following properties:

1. Transmission power and bandwidth should be as small as possible.

2. It should be possible to detect and correct errors.

3. It should be possible to extract timing or clock information from the signal.

4. The code must be transparent ( receiving every possible sequence of data of the coded signal faithfully).  

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What is Aperture effect in digital communication?

The term "aperture effect" is often used in the context of digital communication and refers to the phenomenon where a signal's frequency components are lost during the process of sampling and reconstruction.

In digital communication, analog signals are converted into digital signals through a process called sampling. During sampling, the analog signal is measured at regular intervals, and each sample is represented as a binary code. The samples are then reconstructed into an analog signal at the receiver using a process called reconstruction.

However, there are limitations to the accuracy of the reconstructed signal due to the finite number of samples used. The accuracy is affected by the sampling rate, the bandwidth of the signal, and the reconstruction filter used.

The aperture effect occurs when the bandwidth of the signal exceeds the Nyquist frequency, which is half the sampling rate. In this situation, the high-frequency components of the signal are lost during sampling, resulting in a distorted reconstructed signal. This distortion is known as the aperture effect.

The aperture effect is caused by high-frequency components of the analog signal that are lost during the sampling process when the signal's bandwidth exceeds the Nyquist frequency. 

To reduce or remove the aperture effect, an anti-aliasing filter can be used to remove the high-frequency components of the signal before sampling.

An anti-aliasing filter is a low-pass filter that is placed before the analog-to-digital converter (ADC) in the signal chain. Its purpose is to attenuate the high-frequency components of the analog signal above the Nyquist frequency, so that they do not interfere with the sampling process. By removing these high-frequency components, the anti-aliasing filter ensures that the analog signal is properly sampled and reconstructed with minimal distortion.

The cutoff frequency of the anti-aliasing filter should be set to slightly below the Nyquist frequency of the sampling system. The filter should also have a steep roll-off characteristic to ensure that it effectively removes high-frequency components while minimizing the attenuation of lower frequency components.

In summary, the aperture effect can be reduced or removed by using an anti-aliasing filter to remove high-frequency components of the analog signal before it is sampled. The filter should be designed with a cutoff frequency slightly below the Nyquist frequency and a steep roll-off characteristic to ensure optimal performance.

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What is Aliasing in digital communication ?

When the signals are sampled at the rate less than Nyquist rate ( sampling frequency > 2W ), then aliasing take place. [ The sampling rate of 2W samples per second, for a signal bandwidth of W Hz, is called the Nyquist rate; its reciprocal .5W (measured in seconds) is called the Nyquist interval.] Frequencies higher than 'W' takes the form of lower frequencies in sampled spectrum. This is called aliasing.

Aliasing can be reduced by sampling at a rate higher than Nyquist rate.

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Different types of noises and signals in communication system

What is noise?   

The noise is the unwanted signals that tend to disturb the operation of a system and over which we have incomplete control.

Two broadly used categories of noise are given below – 

External noises: The noise whose sources are external to the communication system is known as external noise. Example- atmospheric noises, extraterrestrial noises, man-made noises etc.

Internal noises: The noise which get generated within the communication system is known as internal noise. Example- shot noise, thermal noise, transit-time noise, miscellaneous internal noises etc.        

What is Signal?

A signal is formally defined as a function of one or more variables that conveys information on the nature of a physical phenomenon.

Several types of signal’s are given below-

Continuous-time signal: A signal that is specified for every value of time is a continuous time signal. Example- telephone and video camera output.

Discrete-time signal: A signal that is specified for only discrete values of time, is a discrete-time signal. Example- monthly sales of a corporation.

Analog signal: A signal whose amplitude can take on any value in a continuous range is an analog signal.

Digital signal: A signal whose amplitude can take on only a finite number of values is known as digital signal.

Periodic signal and aperiodic signal:- A signal g(t) is said to be periodic if for some positive constant T0 , 

                                              g(t) = g(t + T0 )

The smallest value of T0 is the period of g(t). A signal is aperiodic if it is not periodic.

Deterministic signal:- A signal whose physical description is known in either a mathematical form or a graphical form is a deterministic signal.

Random signal:-A signal which is known in terms of probabilistic description such as mean value, mean squared value and so on rather than its complete mathematical or graphical description is a random signal.

Energy signal:- A signal with finite energy is an energy signal. In other words, a signal g(t) is an energy signal if  

                                            

Power signal:- A signal with finite power is a power signal. In other words, a signal g(t) is a power signal if                                    

Causal signal:- A signal that does not start before  t=0 is called a causal signal.

                                g(t) = 0    ; where t less than zero

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