Signal Processing Projects Using Matlab

Signal processing facilitates to apply the mathematical operations on signals captured from the real environment for abstracting useful information. In general, signals are in the form of audio, image, location, temperature, and video. Prior to performing arithmetic operations, convert the raw signal into a digital signal (if analog) or directly manipulate the signal. This page describes to you the top-quality research notions for Signal Processing Projects Using Matlab.

In general, DIP signals are analyzed for the purpose of improving signal quality, representation, interpretation, and visualization. Further, it can also be processed for transforming the signal from one format to another.

In analog signal processing, the analog hardware senses the signals like sound, light from the real world and performs the required operations. Then, it uses an external signal converter for converting the signals into analog-to-digital for real-world implementation.

Digital signal processing, collects and processes the digital signal/data through digital techniques in a digital framework. Also, it yields output as a digital signal for real-world implementation. In some cases, it produces analog signals as output by using a digital-to-analog converter. All these processes are rapidly performed rather than analog processing. Next, we can see the major classification of signals. 

Implementing Digital Signal Processing Projects using Matlab

Classification of Signals 

  • Discrete-Time
  • Continuous-Time

Discrete-time signals will pass the signal at certain time intervals which measure discrete time instants, possibly each day, second, or millisecond. Similarly, continuous-time signals will pass the signal without breaks which measure each time instant from beginning to end. Further, we have given some main benefits to using digital signal processing projects using matlab software in both real and non-real applications. 

Advantages of Digital Signal Processing

  • Assure the fault tolerance
  • Scalable to reproduce the large volume of a signal at a reasonable cost
  • Easy to import advanced image processing techniques and approaches
  • DSP operations can easily be altered by modifying the program
  • Due to the offline capability, the signals can be transported effortlessly
  • DSP system enables the continuous workflow without any time delay issues
  • In comparison with analog system, it has improved accuracy  

What is digital signal processing used for?

Nowadays, DSP is spread over in every part of the world due to its extensive advancements in digital communication. In our day-to-day life, we can find DSP systems in any form of applications that largely depend on the areas of speech / audio signal processing, voice recognition, RADAR, remote sensing, SONAR, and other trade signals. Moreover, we have itemized some interesting facts on the significance of digital signal processing. 

Why is DSP important? 

  • Make you know and apprehend the design of digital filters (IIR and FIR)
  • Make you study the multirate signal processing fundamental functions
  • Make you control the frequency domain based discrete-time signals by the following methods,
    • Fast Fourier Transform (FFT)
    • Discrete Fourier Transforms (DFT)
    • Z- Transform
  • Make you know the DSP basics along with working principles of linear systems

Next, we can see about the essential characteristics of signal processing which stimulate the scholars to identify new research ideas for Signal Processing Projects Using Matlab. Our research team has collected an infinite number of research notions for serving you in all aspects of signal processing. 

What are the Key Features of DSP?

  • Mean and Standard Deviation of Skewness, and Variance
  • Frequency and Phase Variations
  • Coefficients of Discrete Cosine Transform (DCT)
  • Log of Fourier Transforms (Fast-FT, Discrete-FT, and Inverse-DFT)
  • The amplitude of Power Spectrum

As a matter of fact, each process in DSP has different actions to be performed. For obtaining the best result, we need to find the appropriate techniques to make that process work effectively. Here, we have listed the important algorithms and techniques used in the feature extraction process of signal processing projects using python. 

How to Extract the Features of DSP?

  • Wavelet Transform (WT)
  • Fourier Transforms (FFT)
  • Eigenvector Methods (EM)
  • Time-frequency distributions (TFD)
  • Principal Component Analysis (PCA)
  • Kernel-PCA
  • Multifactor Dimensionality Reduction (MDR)
  • Independent Component Analysis (ICA- Non-linear and Linear)
  • And many more

For your information, we have given you the other additional processes involved in the Signal Processing Projects Using Matlab. More than this, there are several processes available in processing analog and digital signals. 

Process of DSP 
  • Signal Quality Analysis
  • Filtering
  • Preprocessing
  • Feature Selection
  • Feature Extraction
  • Decision making / Classification
  • Post Processing / Optimization

So far, we have completely debated on the digital signal processing merits, importance, purpose, characteristics, and processes. Now, let’s see about the current research areas in digital signal processing. 

Research Areas in DSP 
  • Human-Machine Interactive System
  • Signal Processing in IoT Applications
  • Seismic data-based Geophysical Signal Processing
  • Signal Processing in Integrated Environment
  • Multimodal Intelligent User Interfaces

In addition, we have given you the list of the latest research ideas of digital signal processing which are collected from recent research areas. Once, you make a bond with us, we let you know other noteworthy PhD research topics.

Latest Digital Signal Processing Projects Using Matlab

  • Advanced Network Security Approaches for Drone Technology
  • Recent Applications of Medical Imaging
  • Emotion Recognition and Classification in Single-Channel EEG
  • Haptic Preferences Gamma Correlation in EEG Signals
  • Effective Multi-Bit Flip-Flops Technique for Power Reduction
  • LSM based Noise Reduction in ECG Signal
  • Adaptive Filtering and Control Design using Current-Mode Boost Converter
  • Collaborative Auto-stage Classification using SVM Algorithm
  • Adaptive FPGA Model Design and Architecture

Here, we have mentioned some commonly used Matlab functions in the time practically implementing the digital signal processing projects using matlab. Our developers have long-term experience in performing mathematical or numerical analysis for developing new solutions. So, we will tackle any kind of challenging research topic.   

Matlab Functions for DSP 

  • var* – Compute the Variance
  • xcov – Compute the cross-covariance
  • xcorr* – Compute the cross-correlation
  • Upsample – Based on the integer factor, increase the sample rate
  • vmd* – Determine the Variational mode decomposition
  • wvd* – Calculate both the smoothed pseudo wigner-ville distribution and wigner-ville distribution
  • zp2tf – Transform the filter parameter from zero-pole-gain to transfer function form
  • xcorr2 – Determine the cross-correlation of 2-D models
  • Upfirdn – Perform 3 processes and they are: upsample, FIR filter, and downsample
  • xwvd* – compute the cross smoothed pseudo wigner-ville distribution
  • yulewalk* – Co-efficient of the transfer function (IIR Filter)
  • xspectrogram* – Calculate cross-spectrogram based on STFT
  • zp2ss – Transform the filter parameters from zero-pole-gain to state-space form

Furthermore, we have highlighted the future scope of digital signal processing. The future research directions mainly rely on these areas.  

Future of Digital Signal Processing                 

  • Signal Processing For Multiple Usages 
    • In truth, signal processing is gradually increasing its growth in many real-world apps/software because of its broad-size storage and low computing power characteristics. In addition, it springs with new digital advancements which become the main contribution to society. Hence, it offers an enriched platform for solving challenging global issues.
  • Signal Processing for Industry 
    • Basically, signals are used to transfer digital information over the wireless medium. Due to the winning digital era, it is widely employed in many industrial sectors. Also, it is largely expanding in the medical field for health care monitoring systems. For instance: it processes the signal/data from MRIs, X-rays, CT scans, and X-rays through digital methods. 

QRS Detection in Real-Time ECG Signals 

For illustrative purpose: here we are going to discuss the one latest topic. That is, how to identify the QRS complex in biomedical signal (ECG). Through this, the physician can examine the patient’s cardiac functions based on ECG signals for further medical treatment. Generally, the model-based system is utilized to build, deploy, develop, and test the proposed algorithms. 

In a realistic ECG signal, it is a quite difficult job to identify the QRS complex. Since the noise and QRS complex continuously vary in the raw signal. Her, we have included some noise sources which affect the raw signals,

  • Motion Artifacts in Electrode
  • Baseline wander
  • Electrode Contact Noise
  • Muscular activity or EMG
  • PLI-Power Line Interference (50 Hz / 60 Hz)

Next, we have listed out the common sources that where we collect the ECG signals for modeling and testing biosignal processing algorithms: 

  • Real-time ECG signal collection
  • ECG simulator
  • Pre-recorded ECG signal
  • Biomedical databases

For instance, we have given the following pre-recorded ECG signals for simulation and analysis purposes with its dataset information.

  • 82 bpm mean heart rate – 1 ECG signals dataset
  • 45 to 220 bpm mean heart rates – 4 synthesized ECG datasets

Then for synthesizing the ECG signal, we need to do the following setting in the configuration process:

  • Sampling frequency: 360 Hz
  • Heart Rate: 1 bpm (Standard deviation)
  • Additive Noise: 0.005 mV (in uniformly distribution)

In the real-world ECG signal application, the QRS detection model identifies filtered signal peaks. Depending on the average noise and QRS peak value, the detection threshold is altered at run-time. Then, classify the peak as noise or QRS based on the detection threshold. Here, we have given the set of instructions for detecting the QRS using the PIC method.

  • Rule 1: Eliminate all the peaks that go before or after the higher peaks (< 196 ms /306bpm)
  • Rule 2: When the peak exists, verify the presence of the negative and positive slopes of the signal. Once the slope is presented, then inform there is a peak or else inform baseline shift
  • Rule 3:if (peak > detection threshold)


Print (“Classify as QRS complex”);




Print (“Classify as Noise”);


  • Rule 4: When the peak exists <=360ms, but the QRS is not in the 1.5 R-to-R intervals, then classify as QRS complex.

Further, if you are looking for any research development in the signal processing projects using matlab research, then tie-up with us. Let’s create amazing research dsp research proposal, dsp project work by holding our hands together.


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