This page shows you the groundbreaking research topics for Digital Signal Processing Projects Using Matlab with its current research areas. Signals are refined for fulfilling the need of various drives to create useful and improved information. For this purpose, it eliminates the undesired noise and fixes the distortion in the raw signal gathered from the real world. 

Further, we can analyze the signal through various techniques and algorithms based on the signal nature. And, some of the common methods are given as follows,

  • Wavelet Domain
  • Time domain (One- Dimensional signals)
  • Cross-correlation Domain
  • Discrete Frequency Domain
  • Spatial domain (Multi-Dimensional signals)
  • Auto-correlation Domain

Digital signal processing (DSP) is the practice of processing signals in the digital domain through digital methods in the digitalized assisted tool. DSP is widely found out in many real and non-real applications/systems. For instance: automation systems, image processing, sensor array system, control system, healthcare application, data communication, and many more.

Hence, it is considered to be a wide research area for scholars. Majorly, the signal processing applications fall under any of the following three classifications: 

  • Wireless Network Systems
  • Digital Data Communication System
  • Transducer Sensor and Control System

By knowing the demand for DIP applications, scholars are willing to develop advanced DSP algorithms and computer-assisted technologies. There is always a huge seek for implementing systems with maximum speed, i.e., greater than 100Gbps and minimum power. So, our resource team is currently focusing on designing new DSP system architecture, algorithms, tools, and techniques. Next, we can see the embedded DSP features that help you identify the importance of DSP projects in research society.

Characteristics of Digital Signal Processing Projects Using Matlab

  • Intensive computations
  • Compare to high-performance applications, it requires low arithmetic operations. For instance: scientific computing
  • Support parallel processor in the time of execution
  • Assure the data independence and maximum degree of parallelism (DoP)

As a matter of fact, digital inputs and outputs take place by the following step-by-step operations. Also, it may include extra processes that depend on the application requirements. 

  • Reading the input signals from the digital lines or ports
  • Writing the signals to digital lines or ports
  • Store the signal in memory when the signal are in binary format
  • Perform filtering process by FIR or IIR filters
  • Do the logic and arithmetic computation
  • Modulate or demodulate the signal
  • Transfer the signal to specific destination

Next, we can see about the current happening research areas that scholars are highly demanding to start their research career for Digital Signal Processing Projects Using Matlab.

The following are listed the technical experts suggest areas based on their recent research work.

Research Digital Signal Processing Projects Using Matlab

Latest Research Areas in DSP

  • Impact of VoIP in Digital Signal Processing
  • Implementation of different Medical Applications
    • EEG based Brain Monitoring and Seizure Prediction
    • Automated Fundus Image Classification and Analysis (based on Retinal image)
  • Enhance the System Security using Reverse Engineering and PUFs Mechanisms

More than the areas mentioned above, we also direct you to other significant research areas of digital signal processing. Below, we have listed some sample current research areas which give you noteworthy research outcome. We help to create innovative DSP projects using matlab.

Research Issues in DSP

  • Design custom / mixed integrated circuits
  • Development of  advance wireless communication models
  • Bistream codecs design and manipulation of for mobile applications
  • Design ultra-lowpower digital hearing assistances using digital signal processors
  • Modeling of wideband microwave power amplifier based on lower distortion
  • Implementation of balanced IIR filter model and techniques
  • Design of advance GPS receiver systems
  • Apply polyphase allpass filters for high-fidelity decimators chip Low-power signal processors design based on asynchronous logics
  • Design of flexible RF architectures for SDR applications
  • Nyquist barrier Sampling based on non-equispaced sampling approach
  • Development of microwave and RF Computer-aided techniques
  • Harmonic distortion analysis and assessment in microwave transmission systems
  • Modeling of fractional-delay filters for beamforming and sample rate conversions
  • Implementation of adaptive digital filtering using advance natural algorithm
  • Deployment of sigma-delta convertor for bandpass and baseband applications
  • Enhancement of image quality for urban traffic management and biomedical applications
  • Detection of harmonic interface using adaptive notch filters assessment in main power systems

Next, we can talk about the development of DSP applications/software. In general, the digital signal processing system will meet any of the following three groups,

DSP Software Development

  • Off-line synthesis and analysis in signal processing
  • Developing DSP programming chips for embedded devices
  • Real-time signal processing based on surrounding events

Further, you also need to be aware of the programming languages used for developing DSP applications. Language also plays a major role in development because it makes you code algorithms easily. Some of the languages in DSP that bring the best results with dsp projects using Python, C++, Java, C, and more. The followings are the basics that the programing language should ensure.

  • Define and Declare the dissimilar data and variables
  • Explain the signal manipulation operation to be performed
  • On using the acquired outcome, control the operations and workflow
  • Order the operations based on the workflow and run the programs
  • Allow the real-time data movement for external users and DSP Software Packages

Next, from the perspective of DSP supportive tools and libraries, this field offers an infinite number of packages such as Matlab, Python, OpenCV, Simulink, Python, Maplesoft, Scilab, Verilog HDL, Mathcad, and more. For instance, we can see what are the important characteristics that the MATLAB tool comprise for processing digital signals,

DSP Projects Using Matlab

Key features of MATLAB in DSP

  • Support transforming methods (wavelet, discrete cosine and fast fourie)
  • Massive functions for processing signals and designing filters
  • Include functions for analyzing and modeling parametric based time-series
  • Easy to develop GUI application (signal design, analyze and display)
  • Simple to design and analyze filter (FIR, IIR and analog filter)
  • Provide sophisticated tool for spectral analysis and statistical signal processing
  • Give procedure for generating waveform (Pulse train generator, Periodic sinc generator and Gaussian pulse generator)

In addition, we are familiar with several signal processing tools and algorithms. The toolboxes of MATLAB system objects, Simulink block, and functions will give a wide range of DSP designing techniques for dedicated multi-rate processing, FFTs, and filters. As a result, it helps us to design any kind of real-time system and process stream data.

 Further, it also comprises tools for acquiring I/O signals, responsive visualization, signal generation, and spectral analysis. On the whole, it let you monitor the efficiency of the system’s performance and behavior. It also offers HDL code generation for the fast development of the embedded system. Here, we have listed the hardware that helps you to execute the algorithms: 

  • Stream Processors
  • Digitalized Computers
  • Industrial Digital Controllers
  • Microprocessor FPGAs
  • General-purpose Microprocessors
  • Digital Signal / Sound Processors
  • Application-specific Integrated Circuits

For your add-on information, we have also listed a few popularly used methods to perform key operations of digital signal processing.

Some functions in MATLAB for signal processing

  • filter – Filter the data
  • inv – Inverse the matrix form
  • cos – Calculate the cosine
  • sign – Execute signum function
  • sum – Quantify the elements in vector
  • exp – Calculate the exponential
  • roos – Determines the polynomial roots
  • max,min – Get extreme high or extreme low values
  • abs – Calculate the absolute value
  • conv – Multiply the two polynomials values
  • log 10 – Calculate the natural logarithm

Here, we have given you the Matlab syntax for reading an MS wave or music file which supports only PCM format.

How to read WAV file in MATLAB?

Y=wavread(file)

[y, fs, nbits] = wavread(file)

[…] = wavread(file, n)

[…] = wavread(file, [n1, n2])

[y, fs, nbits, opts] = waveread(file)

Furthermore, we can see the most promising Matlab toolboxes that give all sorts of functionalities to apply modern techniques and algorithms of DSP. And, some of them are 

  • Signal Processing Toolbox
  • Statistics and Machine Learning Toolbox
  • Deep Learning Toolbox
  • Wavelet Toolbox

In addition, we have given the block sets used in Matlab for processing digital signals.

Signal Processing Blockset in Matlab

  • Quantizer:
    • Uniform encoder and decoder and Quantizer
  • Operations
    • Zero padding, integer delay, convolution, window function, unwrap, and down sample
  • Transforms
    • Discrete Cosine Transform
    • Real and Complex Cepstrum
    • Fast Fourier Transform
  • Statistics
    • RMS, Correlation, mean and maximum
    • Sink and Signal Source
  • Filtering
    • Adaptive Digital Filter Model and Execution

Deep Learning and Machine Learning for Signal Processing

Both ML and DL algorithms and approaches play a key player in designing and developing signal applications over dynamic time-series information in signal processing. By the by, these applications are range from healthcare systems to advanced autonomous vehicle systems. For your information, we will let you understand the newly developed MATLAB techniques for real-time applications: predictive models. Below, we have specified the things that we are backing you in Digital Signal Processing Projects Using Matlab.

  • We give guidance on data preprocess,  developing signal datasets and signal labeling
  • We support you to design accurate AI models through several feature extraction approaches
  • We make you to know and understand the network types and how the DL methods are applied to train the models and how they are installed in real-time environment with embedded devices.

Compared to image processing, signal processing demands DL-based techniques for preprocessing, extract, and transform signal data. In addition, it is essential to focus on missing samples, data variability, interference, jitter, non-linear, and phase distortion for dealing with huge-scale high-quality image datasets. Below, we have given you some key points that support you to train the deep model effectively regardless of large set usage.

Highlights of Deep Learning in DSP

  • GPU-Acceleration Training
  • Include several feature extraction Methodologies
  • Implementation of Embedded Device (Raspberry Pi)
  • Simple dataset management system based on data stores
  • Utilization of Signal Labeler and Analyzer App for Radar Signal Processing

Next, we can discuss the processes involved in signal dataset creation, data preprocessing, transforming, developing, and deploying models. These processes are basic for all digital signal processing systems, and further, they vary based on the need for the project.

  • Create and Access Datasets
    • Collect the data from different sources
    • Augment the data for simulation
    • Label the data for access
  • Preprocess and Transform Data
    • Remove the uncertain data
    • Perform the data conversion process
    • Extract the essential features
  • Develop Predictive Models
    • Add the reference models / Create the system from scratch
    • Design and train the hardware accelerated systems
    • Carry out the tuning process of hyper-parameters
  • Accelerate and Deploy
    • Deploy the desktop Apps
    • Deploy the business applications
    • Install the required system hardware and embedded device

To sum up, our resource team has shared a few novel machine learning-based research notions in the categories of signal analysis, speech recognition, neural network/ deep learning, audio classification, communication, and music information retrieval.

Recent research ideas in DSP

  • Signal Analysis (ML)
    • Attention based Neural Network Models
    • Attribute-based Classification and Transfer Learning
    • Deep Cross-Domain Transfer Learning
  • Speech Recognition (ML)
    • Weighted Finite State Models and Techniques
    • Dynamic Programming Algorithms
    • Hidden Markov Models Applications
  • Neural Networks and Deep Learning
    • Non-Linear Objective Function
    • Different kinds of Learning Approaches
    • Multi-Nominal and Multi-Label Classification
  • Audio Classification (ML)
    • CNN Model Architecture
    • RNN based Bidirectional LSTM
    • Multi-Objective based Time Series Matching
  • Communication (ML)
    • Deep Neural Network Driven Wireless Applications
  • Music Information Retrieval (ML)
    • Matrix Factorization based Recommendation System
    • Music / Signal Source Separation
    • Theory of Dynamic Latent Variable Models

Further, if you want more data on recent research gaps and other new Digital Signal Processing Projects Using Matlab, communicate with our team.