What are the latest Research DSP Topics for my research work?  Are you interested in selecting novel digital signal processing research topics?  Get to know about the major issues in DSP. Digital Signal Processing (DSP) is a platform with various methods for analyzing the signal from multiple sources for better signal interpretation. Basically, the signals are acquired from different constrained hardware systems. So, the possibility of noise in the raw input signal is very common. For instance: LIDAR systems, audio recording systems, medical signal acquisition systems (ECG and EEG), and more. This DSP technique is intended to remove the signal’s undesired noises and improve signal quality.  

What is DSP subject?

  • Digital [D]
    • Processing digital type of input through digital technologies in digital software
  • Signal [S]
    • A function of  a set of input values to determine dependent values
    • Computer the discrete time and continuous time in signal space
  • Processing [P]
    • Manipulate the signal through various methods for important information
    • Extract the essential features
    • Improve the signal/image quality (speech intelligibility or image visualization)

As a matter of fact, Analogue and Digital Signal Processing research fields are originated from Signal Processing. Nowadays, the increasing growth of wireless digital communication in the digital world increases digital signal processing utilization. Also, it supports both static (stored) and streaming (dynamic) large data for processing. As a result, a tremendous amount of researchers are moving towards this field for best DSP Topics. Here, we have given you some vastly expecting titles from scholar’s perception. 

Research DSP Topics

Top 10 Research DSP Topics 

  • Linear Phase Response Systems
  • Signal Sampling and Aliasing
  • Fast Fourier Transform (FFT) in Signal Processing
  • Frequency Response Analysis
  • Fourier Transforms in Discrete-Time Signals
  • Discrete Fourier Transform (DFT) in Signal Processing
  • Impulse Response and Convolution Transfer Functions
  • Efficient Deign and Execution of Digital Filters (IIR and FIR)
  • Finite Impulse Response (FIR) And Infinite Impulse Response (IIR) System Design

Currently, our research team is performed a thorough survey on recent digital signal processing research journal papers, articles, magazines, and more for collecting information about recent developments and original DSP topics. From this deep study, we have listed followed topics as the latest research areas.

  • Finite Difference Approximation in Z -transforms
  • Discrete-time Signals Classification
  • Signal Processing Block Diagrams
  • Discrete-time Fourier transform Solutions for Signal Processing
  • Realization of Digital Filters Structures
  • Linear time-invariant systems (LTI) systems for Signal Processing
  • Design of Filters: FIR, Ideal and IIR
  • Application of Convolution
  • Digital Filter Modelling and Applications
  • Signal and Data Flow-Graphs in Filters
  • Discrete Fourier Transform (DFT) in Discrete Signals
  • Continuous-Time Sampling and Processing
  • Frequency Analysis of Signal System (by Impulse and frequency response)

In addition, we have given the major features of digital signal processing, which gain special attention in both research and development phases. When we choose DSP Topics, one should notice that any of the below characteristics are employed in the solutions.

Characteristics of DSP

  • Linear and Non-linear signal system properties identification and evaluation 
  • Easy to remove the noisy frequency using filtering techniques
  • Transform Domain Prediction (For instance: Wavelet And Fourier Transform)
  • Efficient in time and accuracy in handling stream data
  • For smooth real-time processing, it utilizes Ticker interface and MBED TIMER to attain the time-series data
  • Digital realization of logic circuits and other hardware for signal processing
  • Real-time differentiation and integration of analogue/digital signals
  • Manipulation of signals (Equalization, Interpolation, Filtering, Denoising, etc.)
  • Enable signal (Video / Audio) operation and compression using mathematical functions and procedures
  • Computation of correlation, standard deviation, means value, PDF, and many more
  • Assure effective buffering and data management to overcome data overflow and time problems
  • Signal operation software realizations in digital systems
  • Flexible to interact with outside signals through A/D and D/A convertors, Hence, the analogue entities has special care in digital processing

For scholar’s benefit, next, we can see the current budding research ideas of digital signal processing. Regardless of signal type, the following issues will be primarily concerned in most of the DSP topics.

Research Areas in Digital Signal Processing Topics

Major Research Issues in DSP

  • Signal Modeling
  • Signal Separation
  • Signal Representation
  • Signal Modification
  • Signal Enhancement

We can now see what makes scholars move towards the digital signal processing research field and how the DSP is processed in realistic environments. The following points show the ability of the DSP in both real and non-real-time applications.

What Can DSP is used for?
  • Maximum accuracy attainment in digital data processing
  • Long-distance packet distribution in ultra-speed
  • Enhancement of images, videos, audios, signals through digitalized approaches
  • Improve the signal interpretation and operations for efficiency
  • Automatic process of control system data
  • Sense data from real-world environment (For instance: echo cancellation)
  • Efficient data transmission using data compression methods

In addition, we have given the list of real-time applications of digital signal processing. Due to the valuable characteristics of DSP, it spreads in many research fields that rely on digital telecommunication and wireless networks. Let’s see a few of the application below,

What are the applications of digital signal processing?
  • Trends in Smartphone Industry
  • Real-time Data Compression in Digital Communication
  • Optical Character Recognition (OCR) in Defence System
  • Digital Speech Processing in Smart Home Aids
  • Advance Consumer Electronics in Industrial Applications
  • Radar Applications for Surveillance System
  • Audio and Video Signal Processing in Gaming Applications
  • Medical Signal Processing in Biomedical Applications (Hearing Aids, BP Testing and EEG Test)
  • Digital Image Processing in Computer Vision Application
  • Smart Clinical and Wearable Devices in Healthcare Applications
  • Wavelet based Multi-Resolution Signal Analysis and Processing

Most importantly, the efficiency of the many signal processing applications is assessed through certain metrics and methods based on the input data. Here, we have listed the applications along with their main digital signal processing using matlab techniques for your reference.

Digital Signal Processing Techniques

  • Model-based Sparse Component Analysis
    • Source and Multi-speaker Speech Localization
  • Pseudo-coherent vector
    • Speech Enhancement (by node selection)
  • Non-negative Matrix Factorization (NMF)
    • Microphone Array Calibration
  • Reverberation Times (RT)
    • 3D Geometrical Room Geometry Estimation
  • Room Impulse Response (RIR)
    • Microphone Clustering
  • Cepstral Coefficient Features
    • Noise Suppression
  • Pseudo-Coherence Vector (PCV)
    • Multi-Channel Speech Detection
  • Kurtosis of the LPC Residuals
    • Microphone Clustering
  • Magnitude Squared Coherence (MSC)
    • Multi-Microphone Detection
  • Time Delay and Attenuation
    • Blind Source Separation
  • Coherent-to-diffuse Ratio (CDR)
    • Multi-Speakers Talk Detection
  • Reverberation
    • Directional of Arrival (DOA) Approximation
  • Euclidean Distance Matrix (EDM)
    • 3D Sound Localization
  • Signal-to-Interference Ratio (SINR)
    • Adaptive Noise Reduction
  • Low Power Digital FIR Filters
    • ICD, Pacemaker and Hearing Aids
  • Multi-stage Sample Rate Conversion
    • Audio and Speech Signal Processing
  • Coherent Noise
    • Clustered Blind Beamforming
  • Signal Power
    • Digital Acoustic Emission Monitoring
  • Discrete Wavelet Transforms
    • Signal Analysis and Compression
  • Time-Frequency Analysis
    • Vibration Signal Prediction and Analysis
  • Adaptive Fault-Tolerance Parallel Filters
    • Defence, Aerospace, Biomedical and Banking
  • Signal Energy and Power
    • Sound Microphone Localization and Sound Source Localization
  • Power Spectrum Analysis and Estimation
    • Biomedical Signal Processing and Analysis

In general, handling signals is a quite challenging task when we work with a complex system. At that time, it is necessary to choose the optimal problem-solving techniques that perform mathematical functions to untie the efficient way. Here, we have listed a few mathematical algorithms/ functions that are largely used in DSP.

Mathematical Methods for DSP

  • Representation of Signal:
    • Mathematical Methods and Transform Theory (Fourier Transform and Karhunen-Loeve)
    • Other Technological Convergence in Transforms
  • Fundamentals of Sampling Theorems and Signal Processing:
    • Adaptive Multirate Filter Banks
    • Aliasing in Sampling
    • Sampling Rate Conversion (based on Rational and Integer factors)
    • Digital Signal Interpolation Filter and Decimator
    • Oversampled Analogue to Digital (A/D) and Digital to Analogue (D/A) Processing
  • Transformation Techniques:
    • Wavelet Transform (Discrete and Continuous)
    • Wavelet Multi-Resolution Analysis (MRA)
    • Wavelets Characterization and Analysis
  • Recent Evaluation of Fundamental Signals, and Systems
    • Orthogonality in Optical Signals
    • Vector Spaces and Signal Space
    • 1-Dimentional Signals (Analogue and Digital)
    • Discrete Random Signals Processing
    • Inner Product Spaces Representation
    • Multi-Dimensional System and Signals

Next, we can see the significant research areas of digital signal processing, which act as the repository for current research DSP topics. There offers a wide-range research platform for scholars who are looking for remarkable research ideas for DSP Topics.

Research Areas in DSP [Digital Signal Processing]

  • Industrial Internet of Things Signal processing
  • Advanced Seismic Data¬† / Signal Processing and Analysis
  • Enhanced MUMIO and Massive MIMO Wireless System
  • Joint Resource and Power Spectrum allocation for D2D Networks
  • Signal Processing in Cellular-Wireless Body Area Networks
  • Real-time Digital Signal Propagation and Modulation
  • Wireless Multi-channel Signal Processing System
  • Learning based Signal Authentication for Cybersecurity Systems
  • Cepstrum Analysis in Homographic signal processing
  • Digital Beam alignment and Beamforming or mmWave Signals
  • Real-time Blind Source Separation and Extraction Application
  • Pilot Signal Transmission based on Waveform and Modulation Schemes
  • Digital Speech Recognition and Speech Synthesis in Signal Processing
  • Channel modelling and Channel characteristic in Signal Processing

Next, we also itemized the few new promising research ideas from important research areas of DSP. Beyond this, our research team has identified an infinite number of up-to-date research notions for creating masterpiece research work in every handhold of scholars.  

Emerging Digital Signal Processing [DSP Topics]

  • Smart IoT Signal Processing
  • Self-directed Perception and Control for Vehicular System
  • Adaptive and Distributed Power Allocation by Optimal Techniques
  • Programming Models for High-Performance Computing (HPC) Software
  • Radio Frequency and Digital signal Processing and Applications
  • Nano-Scale Signal Processing and Communication Technology
  • Direct RF Front-End Digitization and Processing
  • Single-Chip enabled Digital Signal Processing
  • Learning based Knowledge-Aided Signal Processing
  • New Signal Processing Applications (Smart 3D TV, TV, UHD TV and 4K-TV)
  • Cognitive Radar  and Multi-functional Reconfigurable Antenna for Signal Processing
  • Cloud Computing Trends in Signal Processing
  • Developments in Diversity Technology (Frequency / Embedded / Time / Spatial Domains )

Next, our developers have given the commonly used databases for digital ECG signal processing systems for illustration purposes. The selection of a dataset/database is also an important task in developing DSP projects.

ECG DATABASES in DSP
  • Fantasia
    • Number of Records: 40
    • Duration: 120 min
    • Bits / Sample: 16/4
    • Number of Leads: 1
    • Sampling Frequency: 250 Hz
    • Heartbeat Type: NSR
    • Noise Types: BW, PLI and MA
    • Remarks: Highly no noise
  • MIHBIHNST
    • Number of Records: 2
    • Duration: 30 min
    • Bits / Sample: 11
    • Number of Leads: 2
    • Sampling Frequency: 360
    • Heartbeat Type: NSR, RBBB, APC, Blocked APC and PVC, 
    • Noise Types: – EM, BW, and MA
    • Remarks: – Add noise at various SNRs
  • PICC
    • Number of Records: 2000
    • Duration: 10 sec
    • Bits / Sample: 11
    • Number of Leads: 12
    • Sampling Frequency: 360 Hz
    • Heartbeat Types: –
    • Noise Types: MA, FL, EM, BW, Spk, PLI and AB
    • Remarks: Embrace both suitable and objectionable information
  • TELE
    • Number of Records: 250
    • Duration: 48 min
    • Bits / Sample: 12
    • Number of Leads: 1
    • Sampling Frequency: 500
    • Heartbeat Type: Pathological Q-waves
    • Noise Types: EM and MA
    • Remarks: Comprises Huge poor quality signals set
  • MIT-BIHSTC
    • Number of Recor ds: 28
    • Duration: 24 min
    • Bits / Sample: 12
    • Number of Leads: 2
    • Sampling Frequency: 360 Hz
    • Heartbeat Types: Myocarditis, heart valve disease, Myocardinal hypertrophy, Healthy controls and Miscellaneous
    • Noise Types: BW, MA, and PLI
    • Remarks: Zero-percentage of noise
  • MIMIC-II
    • Number of Records: >800
    • Duration: –
    • Bits / Sample: 10
    • Number of Leads: –
    • Sampling Frequency: 125
    • Heartbeat Type: severe tachycardia, alarms annotation, ventricular tachycardia (V-tach), severe bradycardia and asystole alarms
    • Noise Types: –
    • Remarks: –
  • MITBIHA
    • Number of Records: 48
    • Duration: 30 min
    • Bits / Sample: 11
    • Number of Leads: 2
    • Sampling Frequency: 360 Hz
    • Heartbeat Types: Q, APC, P,  NSR, F, RBBB, PVC, x, VF, LBBB, f and j
    • Noise Types: MA, EM, BW, PLI, AB
    • Remarks: Many of the records are free of noise
  • MACE
    • Number of Records: 27
    • Duration: 8 sec
    • Bits / Sample: 16
    • Number of Leads: 1
    • Sampling Frequency: 500 Hz
    • Heartbeat Type: NSR
    • Noise Types: Motion artifact
    • Remarks: Include movement artefact in several activities

If you are interested in learning new thought-provoking topics that highlight the DSP core concepts, then communicate with us. Well, it will help you to develop the optimum signal processing applications and help choosing trending DSP Topics for your research work.