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42 Digital Signal Processing Project Ideas to Explore

Explore a comprehensive list of 42 intriguing project ideas in Digital Signal Processing, spanning from radar signal processing and ECG

Digital Signal Processing (DSP) Project Ideas encompass a dynamic field with vast applications across industries, from telecommunications to biomedical engineering . If you’re seeking inspiration for your next DSP project, here are 42 intriguing ideas to consider from ilovephd.com .

42 Digital Signal Processing (DSP) Project Ideas with Examples and Applications

1. speech recognition system.

Develop a system capable of recognizing spoken words and converting them into text.

Example: Building a voice-controlled virtual assistant like Siri or Alexa.

2. Image Denoising

Create algorithms to remove noise from digital images, enhancing their quality.

Example: Implementing a denoising filter to improve the clarity of medical MRI scans.

3. Audio Equalization

Design an audio equalizer to adjust the frequency response of audio signals.

Example: Developing software for a music player with customizable equalization settings.

4. Radar Signal Processing

Explore techniques to process radar signals for target detection and tracking. Example: Building a radar system for monitoring air traffic.

5. ECG Signal Processing

Analyze electrocardiogram (ECG) signals to detect abnormalities and heart conditions. Example: Developing software for real-time monitoring of cardiac health.

6. Image Compression

Develop algorithms to reduce the size of digital images while preserving their quality. Example: Creating a compression technique for storing large volumes of satellite images efficiently.

7. Speech Synthesis

Generate artificial speech from text input using synthesis techniques. Example: Designing a system for converting written text into spoken words for accessibility purposes.

8. Biometric Identification

Implement algorithms for recognizing individuals based on biometric traits such as fingerprints or facial features. Example: Developing a fingerprint recognition system for secure access control.

9. Adaptive Noise Cancelling

Build systems capable of removing unwanted noise from audio signals in real time. Example: Creating noise-canceling headphones that adapt to different environments.

10. Wireless Communication Systems

Design signal processing algorithms for improving the performance of wireless communication networks. Example: Developing protocols for efficient data transmission in 5G networks.

11. Image Registration

Align multiple images to a common coordinate system for analysis and comparison. Example: Registering medical images from different modalities for accurate diagnosis.

12. Audio Source Separation

Separate mixed audio signals into individual sources, such as voices and background music. Example: Extracting the vocals from a music track for remixing purposes.

13. Sonar Signal Processing

Process acoustic signals for underwater sensing and navigation applications. Example: Building a sonar system for mapping the ocean floor.

14. Heart Rate Monitoring

Develop algorithms for measuring and analyzing heart rate variations from physiological signals. Example: Creating a wearable device for continuous heart rate monitoring during exercise.

15. Video Enhancement

Improve the quality of digital video by reducing noise and enhancing details. Example: Enhancing surveillance footage for better identification of objects and individuals.

16. Digital Filter Design

Design digital filters for various signal processing applications, such as smoothing or sharpening. Example: Implementing a low-pass filter to remove high-frequency noise from sensor data.

17. Time-Frequency Analysis

Analyze signals in both the time and frequency domains to extract meaningful information. Example: Using wavelet transforms for analyzing non-stationary signals in biomedical research.

18. Voice Activity Detection

Detect the presence of speech in audio recordings for applications such as automatic transcription. Example: Building a system to detect and filter out background noise during phone calls.

19. Seismic Signal Processing

Process seismic data for oil exploration, earthquake monitoring, and structural health monitoring. Example: Analyzing seismic waves to identify potential oil reserves underground.

20. Gesture Recognition

Develop algorithms to recognize and interpret gestures from motion sensor data. Example: Creating a gesture-based interface for controlling electronic devices.

21. Digital Signal Processing Project Ideas

Process speech signals for tasks such as speech coding, recognition, and synthesis. Example: Implementing a codec for compressing and transmitting speech over a network.

22. Spectral Analysis

Analyze the frequency content of signals to identify underlying patterns and structures. Example: Using Fourier analysis to characterize the harmonic components of musical instruments.

23. Biomedical Signal Processing

Process physiological signals for medical diagnosis and monitoring. Example: Analyzing EEG signals to detect abnormalities associated with neurological disorders.

24. Image Segmentation

Divide digital images into meaningful regions for analysis and interpretation. Example: Segmenting medical images to identify and quantify tumors for cancer diagnosis.

25. Doppler Signal Processing

Process Doppler radar signals for velocity measurement and motion detection. Example: Building a speed detection system for traffic monitoring and law enforcement.

26. Audio Effects Processing

Apply digital effects to audio signals for creative purposes or sound enhancement. Example: Implementing reverb effects for simulating different acoustic environments in music production.

27. Wireless Sensor Networks

Develop signal processing algorithms for energy-efficient communication and data processing in sensor networks. Example: Designing algorithms for distributed sensor fusion in environmental monitoring systems.

28. Motion Detection

Detect and track moving objects in video streams for surveillance and security applications. Example: Building a motion detection system for monitoring activity in a smart home.

29. Data Compression

Develop algorithms for compressing and decompressing digital data to reduce storage or transmission bandwidth. Example: Creating a compression algorithm for storing large datasets collected by IoT devices.

30. Adaptive Beamforming

Adjust the directionality of antenna arrays to enhance signal reception and rejection of interference. Example: Implementing beamforming techniques for improving the performance of wireless communication systems.

31. Remote Sensing Image Processing

Analyze satellite or aerial images for environmental monitoring, agriculture, and urban planning. Example: Developing algorithms for land cover classification using remote sensing data.

32. Audio Watermarking

Embed imperceptible signals into audio files for copyright protection or content authentication. Example: Adding digital watermarks to music tracks to track unauthorized distribution.

33. Digital Speech Enhancement

Enhance the intelligibility and quality of speech signals corrupted by noise or channel distortion. Example: Developing software for cleaning up audio recordings in noisy environments.

34. Optical Character Recognition (OCR)

Convert scanned documents or images into editable text using pattern recognition techniques. Example: Building software for extracting text from scanned books or documents.

35. Signal Reconstruction

Reconstruct missing or corrupted parts of signals using interpolation or estimation techniques. Example: Recovering missing samples in audio recordings for seamless playback.

36. Channel Equalization

Compensate for channel distortions in communication systems to improve signal quality. Example: Implementing equalization techniques to mitigate multipath fading in wireless channels.

37. Digital Modulation Techniques

Design modulation schemes for transmitting digital data over communication channels. Example: Implementing phase-shift keying (PSK) modulation for high-speed data transmission in wireless networks.

38. Brain-Computer Interfaces (BCI)

Develop systems for translating brain activity into control signals for external devices. Example: Creating a BCI system that allows paralyzed individuals to control robotic limbs with their thoughts.

39. Adaptive Filtering

Adjust filter parameters in real time to adapt to changing signal characteristics or environmental conditions. Example: Implementing adaptive noise cancellation for improving speech quality in noisy environments.

40. Time Series Analysis

Analyze sequential data to identify patterns, trends, and anomalies over time. Example: Using autoregressive integrated moving average (ARIMA) models for forecasting stock prices.

41. Image Morphing

Transform one image into another through a series of intermediate images. Example: Creating visual effects in movies to transform one character into another.

42. Array Signal Processing

Process signals received by sensor arrays to estimate the direction of arrival of incoming signals. Example: Implementing beamforming techniques for localizing sound sources in audio recordings.

Digital Signal Processing (DSP) offers a wide range of project opportunities, from analyzing medical data to improving communication systems. With 42 Digital Signal Processing Project Ideas to explore, researchers and students can dive into DSP’s dynamic world, contributing to advancements in technology and science. So, pick a project, delve in, and embark on a rewarding journey of discovery in the fascinating realm of DSP.

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SIGNAL PROCESSING PROJECTS

This Blog is all about research resource on signal processing projects , explained with 25+ project ideas, fault diagnosis method and much more. Signal processing is the analysis method, modification, and synthesis of signals ( images, audio, and other measured scientific data). To be precise as the word signal includes the following.

• Pressure and temperature changes
• Any data that can be measured

WHAT DOES SIGNAL PROCESSING DO?

Upon these data, mathematical manipulations like addition, subtraction, multiplication, and division are performed for either signal enhancement or getting some essential feature information from it. The increased choice for Signal processing projects among researchers can be attributed to its growing demand in various applications. The following properties of digital signals make them crucial.

• Discrete and finite
• Containing proper set of values
• Regular sampling
• Representation by using numbers
• Easily processed
• Readily manipulated
• Easy to store

Hence digital signals are easier to handle and use than analog. And so, the use of digital signal processing has increased tremendously over the years. We are being a part of meeting the demands of such growing use of signal processing. Here is a complete picture of signal processing and doing projects in it. Let us first start with acquiring data for signal processing.

WHAT IS DATA ACQUISITION AND SIGNAL PROCESSING?

Acquiring signals is the first step for any signal processing techniques.

• Digital signals can also be easily derived from its analogue counterpart.
• Directly obtaining digital signals is also comparatively easier.

Data acquisition, commonly called DAS or DAQ , is the method by which favorable information is obtained from real-world data by the method of sampling. For this purpose, there are a wide variety of components available.

• These components include proper sensors which are specific to the signal under study.
• The primary function of sensors is to convert the input signal of any form into electrical signals.

Our experts and engineers have designed customized sensors and the proper algorithms suitable to extract information from them. This has led our customers to readily adopt the sensors that we designed into their system of signal processing . So such successful attempts had made us popular among researchers working on signal processing all across the world.

We are also the backbone behind many successful signal processing projects . We provide one of the best world-class research guidance services online . So you can approach us for any of your research needs. Our team of experts is always ready to help you. Now let us see about the main characteristics of signal processing.

KEY FEATURES OF SIGNAL PROCESSING

The following are the main features of signal processing.

• Software re – programmability (the information required can be of time just by making changes in the algorithm and not in hardware)
• Determined operations (time for execution is accurately determined)
• Increased throughput (data of high speed streaming is easily processed)
• Ability to process real time signals (digital signal processing time should be less so that the system is considered efficient)

The above key features associated with signal processing are readily employed for advanced applications in many disciplines of day-to-day science and technology. This does not mean that signal processing is devoid of any issues. Yes, there are some issues in Signal processing prevailing even today for researchers like you to solve. In the next section, let us see about some open issues in signal processing.

OPEN ISSUES IN SIGNAL PROCESSING PROJECTS

With growing technology, the problems in signal processing can be readily overcome. It demands greater study and deep research . The following are the open issues in signal processing.

• Insensitive to temperature changes
• Limitations to frequency range
• Inability to pass power
• Insensitivity to some variables that are not important
• Understandable issues (physical terms)
• Computational measurement that are less expensive
• Response that is reproducible
• Requirement of power supply
• No correlation with certain characteristics (domain features)
• Ability to be determined perfectly (by mathematics)

These issues can be easily overcome with the support of our research experts. Because they have gained ample experience in solving research problems and questions in signal processing .

Once you talk to our experts, you get a clear picture of the methods they follow to find solutions to solve existing signal processing research problems . You can also use those methods to search for a solution to your problem first hand.

 Later you can go for advanced studies to devise a novel solution to it. This can significantly reduce your burden. Now let us look into some of the processing requirements and the hardware implementations for signal processing techniques.

• Direct memory access
• Architecture with increase the memory (high bandwidth)
• Specific modes (addressing purposes)
• Broad accumulator registers
• Centered at MAC
• Architectures like SIMD, VLIW (parallel)
• Equipped with proper hardware
• Shadow registers
• Loops (zero – overhead)

From the above points, we can surely conclude that there are wide-ranging applications of signal processing that require vast processing methods and hardware for efficient implementation.

Preventing the research issues to arise is precisely how you must direct your work. By sticking to this objective, you can double the creativity and performance of your system at execution. You need not worry at all as we will surely guide you in this regard. Now we will address some of the frequently asked questions in signal processing.

FAQs IN SIGNAL PROCESSING

Our experts have attempted to answer most of the frequently asked questions in signal processing. You can refer to them for your project needs.

The usage of optimal filters can prevent noise from intervening into the signal

The following components make Shannon entropy the best tool for feature extraction and reducing dimension. 1.Python 3.7 2.Wavelet coefficients 3.Imported Pandas library

1.First you should understand that RSSI is influenced by different factors. So measuring distance using it is not always reliable. 2.For discovering errors in distance measurement using RSSI you can do the following. 2.1 Distance for various values of RSSI values are first determined using the prevailing models. 2.2 Actual distance is then measured (simultaneously with RSSI) 2.3 RMS errors play a significant role in this aspect Comparison of data can then allow you to find out the errors in distance measurement. 2.4 Do note that new statistical methods are being developed for this purpose.

An algorithm is said to be optimistic when it deals with each and every static and dynamic characteristic possessing topological and physical properties. Bellman’s optimality principle for Dynamic programming or DP is one of the best algorithms to solve Signal processing problems.

If we are correct, then the above FAQs should have surely guided you to the most. We would like to provide you with a complete list of expert-answered FAQs so that we can make your work easier. Connect with us to grab the expert answers for your queries. Now let us have some insight into methods used for diagnosing faults in signal processing.

SIGNAL PROCESSING FAULT DIAGNOSIS METHODS

There are some established and properly devised methods to detect a fault in signal processing. Those methods for fault diagnosis and Signal processing are listed below.

• Identification of system
• Method to estimate parameters
• Physical modelling
• Vibration analysis
• Monitoring noise
• Monitoring torque
• Impedance (inverse sequence)
• Weight fusion
• Fuzzy logic
• Neural networks
• Kalman filter
• Algorithm (generic)
• Expert system
• Methods devised by combining the knowledge based and model based fault diagnosis techniques

The above methods are widely used to detect faults in signal processing . Our experts have been consistently working on these methods. You might have been using some of these techniques, and only you are aware of how efficiently you use them. If you need any methods to enhance your working on these, then don’t hesitate to contact our experts. We will guide you in the best possible way. Now let us see about the matrix methods in signal processing.

SIGNAL PROCESSING MATRIX METHODS

The Matrix method in signal processing works primarily on linear algebra. What does it actually depend on?

• Probability
• Statistical methods
• Optimization techniques

Deep learning methods are the final stop where all the aspects of matrix methods reside. You may be an expert in handling these techniques. Even then, you should know that some issues are waiting to come your way. We will readily present you with the necessary solution after figuring out those existing research problems. Our experience is our motivation. So you can reach out to us, and we are sure to solve all your queries. Now let us have some idea about Signal processing projects.

TOP 13 RESEARCH IDEAS FOR SIGNAL PROCESSING PROJECTS

The following are the most important areas of research in signal processing . Do have the following list for reference.

• Quantum signal processing
• Process in multi channel signals (array of sensors)
• Processing Network and communication signal
• Theoretical and practical methods associated with signal processing
• Compressive sensing
• Optimistic designs for execution of signal processing
• Dictionary learning
• Enhanced security (information forensics)
• Network Security
• Processing speech (also language)
• Signal processing in education sector
• Industrial applications
• Graph signal processing
• Processing of multimedia signals

We provide you technical details essential for these signal processing topics from reliable and authentic sources of data used by researchers in signal processing . Handling these technicalities won’t be a big problem for you as you have already got background knowledge in signal processing. However, you have some queries feel free to connect with our experts. Let us now have some understanding of parameters for signal processing.

SIGNAL PROCESSING PARAMETERS

Certain parameters are involved in signal processing. Those signal parallel processing parameters are listed below.

• RMS or Root mean square
• SMV or Signal vector magnitude
• SMA or Mean signal magnitude area
• STD or Standard deviation

These parameters play an important role in determining the accuracy in classification on the basis of feature vectors. Our projects worked perfectly by showing greater synchronization of all these parameters. Now let’s have a look into simulation setting parameters in signal processing.

SIMULATION SETTING PARAMETERS IN DSP

The following are the parameters associated with simulation settings in signal processing . The parameters are provided along with the efficient parameter value for your reference.

• Window function (Boxcar – no window)
• Frequency lines (800)
• Resolution of sampling frequency (1000 Hz)
• Bandwidth (frequency) – 200 Hz
• Averages (5)
• Sample quantity (2048)
• Desired frequency range (60 to 200 Hz)
• Frequency resolution (0.25 Hz)
• Time for acquiring data (4.0 s)

You need to know these simulation parameters in great detail while you design your system. Share your views with us on designing and executing signal processing projects ensuring these parameters so that you can rectify any doubts if you encounter them. We will now give you some of the configuration parameters used in the digital signal processing system module.

DSP SYSTEM MODULE CONFIGURATION PARAMETERS

The configuration parameters in Digital Signal processing project designs include the following.

• Enabling and disabling
• Time segments of these parameters can be set with various bandwidth
• Choice for discriminators is provided
• You can either enable or disable it
• Range – 0 to 20 ms
• Quadrature (sampling methods)
• Tong search parameters (configurable)
• Either enabled or disabled
• Can be associated with various values
• Provisions for using filters (both enabling and disabling)

These parameters are of utmost importance in Digital Signal processing projects . You might probably have the awareness and the essential tools to carry out signal processing techniques for various applications. We take steps to present to you the most enjoyable research journey. Connect with us for ultimate research support.

Digital Signal Processing Topics

This page is about the most interesting innovation in the digital signal processing topics with its forthcoming research areas. Digital Signal Processing is shortly termed the DSP. Basically, it is intended to enhance the performance of digital telecommunication by applying different technologies. As a result, it assures the consistency and precision of digital data transmission. 

In another way, DSP is defined as the implementation of mathematical operations on real-world signals for synthesizing to improve their efficiency . As a signal, it employs voice, video, audio, temperature and etc. For instance: real SNR reduces due to full-scale input/output, noise in the raw signal, and other noise in the conversion of analog to digital.

Key Components of DSP

• Program Memory : Collection of process or programs for data compress and operations
• Data Memory : Once the data is processed, then stores the processed data into memory
• Computing Engine: Program perform arithmetic and numerical operations on input data
• I/O : Support different things used in DSP For instance:  timers, serial / external ports that link the outsiders

For the most part, DSP offers robust engineering techniques for mining collected signals from the sensor. In general, signals are classified into different forms as odd, even, aperiodic, periodic, power, energy, stochastic and deterministic . In addition, we have given the other DSP classification for your reference.

DSP System Classification

• Time-invariant and Time-variant
• Non-Linear and Linear
• Discrete and Continuous
• Non-invertible and Invertible
• Unstable and Stable
• Non-Causal, Anti-causal and Causal
• Distributed and Lumped
• Static (Memory less) and Dynamic (memory)

Next, we can see some key features of digital signal processing that spring a new dimension of research in current DSP exploration . Also, these features gain the attention of the active scholars and make everyone move towards DSP.

What are characteristics of DSP?

• Linear phase
• Auto-upgrade of System
• Large-scale data storage
• Zero drift (temperature and time)
• Increased Scalability
• Improved Accuracy
• Reproductivity

In order to identify the system performance before directly deploying it in a real-world environment, we choose a simulation platform. Since it saves real-time implementation time, effort, and cost, here, this modeling technique motivates us to analyze the actual working process in DSP . Also, it helps to address the noise, signal, distortion, and measure quantity . Though it has so many benefits, it still has some limitations, which are given below,

Modeling Issues of Digital Signal Processing

• Computation Cost
• Complex modeling may affect the performance
• Poor model will give inefficient performance

How to implement the Digital Signal Systems?

In the general implementation process of the DSP system, we follow three stages . And they are input, process, and output . At first, it gets the analog signal as input. Then, it converts the analog into digital format. After that, it performs some digital operations for the required outcome. At last, it gives the digital data as the output.

Before getting into the Digital Signal Processing Topics in detail, we need to know some key terms that are frequently used in DSP research. And, it is categorized into applications, hardware, and software.

Major Terminologies of DSP

• Examining specific signal
• System input and output
• Developing particular process
• DSP, GPP, PLD and FPGA
• C, C++, Pascal and many more
• Dedicated Technologies like Filter Design Software
• Mathematical, Matlab, Mathcad and others

As a matter of fact, signal processing offers an interdisciplinary platform for performing smart operations of signal modification, analyzing, and synthesizing . Here, we have given the few processes involved in signal processing applications.

• For instance: RADAR and SONAR
• Decomposition
• Turbo Coding
• Speech / Audio Recognition
• Biometric Recognition (finger print and Iris)
• Computer / Machine Vision

Trending Digital Signal Processing Topics [Domain]

• Polarization
• Motion Artifacts
• Analog Signal Processing
• Statistics and Probability
• Theory of Communication
• Analog and Digital Electronics

Modules in Digital Signal Processing

Here, we have included the essential entities that we use in implementing Digital Signal Processing Topics using sophisticated simulation tools and technologies. In this, we have given basic components of DSP projects using python .

• Implantable Devices
• Wearable Devices and Sensors
• Electromagnetic (EM) Field

For illustration purposes, here, our developers have given some designing ideas for digital filers in DSP . More than this, we have numerous exciting novel ideas for modeling filters.  

Design of Digital Filters Ideas

• Analog Digital Filters Design
• FIR Frequency Transformation
• Linear-Phase Filters (IIR and FIR)
• Analog IIR Filters Design
• FIR and IIR filters Comparative Study
• FIR filters Design and Properties (using windows)
• Digital filters Design (using least-squares)

Project Process Flow for Signal Processing

The quality of the research mainly relies on the workflow of the research idea implementation. Based on the selected research topic, the workflow may include some additional processes. Here, we have listed put the basic structure of the Digital Signal Processing project workflow. 

• Measure the signal values and observe the inputs
• Apply signal conditioners
•  Signals Classification, Analysis and Processing

Further, our research team is willing to let you know few upcoming Digital Signal Processing Topics. Further, we also support you in other newly developed integrated research areas. If you have your own research topic, then we ready to give assistance in that topic also. Also, we suggest an appropriate research solution for any kind of research issue in spite of challenges. 

Research Ideas in Signal Processing

• Modeling of Digital Hearing Assistance
• EEG based Multiple Epileptic Seizure Detection
• Multimodal Speech Recognition and Processing
• Real-time Brain Activity Prediction and Analysis
• ML approaches for Bio-signals Analysis

So far, we have discussed the signal processing fundamentals, classifications, techniques. Now, we can see the creative research ideas that our experts have recently handpicked from current DSP research areas.

Digital Signal Processing Project Topics

• Modeling of Noise Channel
• EVM assisted DTMF Decoding System
• FIR Filter and IIR Filter Design
• Touch Tone Signaling and Detection
• DaDisp Software Development
• EVM based Note extraction on Live Music
• Soundcard based Sample filtering
• Audio Noise Filtering and Reduction in Automobiles
• Dynamic Signal Detection and Recognition
• Design, Analysis and Signal Processing of Digital Filter
• Enhanced Design of Sub-band Coder and Quadratic Mirror Filters
• LPC based Adaptive Filter Applications
• EVM based TMS320C30 Control System
• Design of Digital Filter and EVM with Improved Operations
• Modeling of EVM based PID Controller System
• CDMA based Spread Spectrum Processing
• Real-time Spread Spectrum Signals Detection and Applications
• Analog and Digital Interfacing and Controlling
• C/C++ based Fundamental  DSP Algorithms
• TLC32044 -Texas Instruments Configuration System

On the whole, make use of this opportunity and hold your hand with us for choosing Best Digital Signal Processing Topics to avail the best research in your PhD / MS study career.  

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In Digital Signal Processing, students experiment with sensors for creative projects

In Prof. Laura Balzano’s Digital Signal Processing and Analysis course ( EECS 351 ), students created projects with a variety of goals, from improving a non-invasive method of assessing traumatic brain injury patients to classifying spoken language to autonomously evaluating a musician’s piano playing. The Winter 2022 course featured an enrollment of approximately 70 students.

“The goal of EECS 351 is to learn the fundamentals of Digital Signal Processing and understand the myriad ways we represent and process signals and data on a computer,” Balzano said.

In the course, students to gain firsthand experience with sensors and many signal processing techniques. They design and carry out a data collection and analysis project on a topic of their choosing, which gives them experience on how algorithms work in real life on real data.

“The creativity and range of the projects was outstanding, as usual,” Balzano said.

The creativity and range of the projects was outstanding, as usual. Prof. Laura Balzano

The course incorporates image processing and machine learning techniques and is good preparation for students looking to take additional 400-level courses that build off the material, such as EECS 452 (Digital Signal Processing Design Laboratory), 453 (Principles of Machine Learning), 455 (Wireless Communications Systems), and 460 (Control Systems Analysis and Design).

Check out the summaries of all the projects below:

Audio Deconvolution   By Faulker Bodbyl-Mast, Jackson Carroll, and Kelman Wolfostin  Goal: Create an audio plugin that can isolate a source sound (i.e. human speech) from the acoustical sound, or reverberation, that voice excites in a room. This has many relevant applications across the audio industry, particularly for those working for the film and music industries.

Hand Detection   By Aditya Bhatnagar, Shahad Alsayyad, Sergio Goodwin, Ali Robinson, and Yiwei Zeng  Goal: Create a program that can operate lights in a house based on hand cues, similar to a smart home system. The number of fingers a user holds up to a live camera feed identifies the target light, and a thumbs up or thumbs down gesture changes the state of the light.    

AMP Simulation Project By Logan Kibler, Sam Smith, and Hunter Adams  Goal: Create an accurate recreation of the sound you hear running a guitar signal through a AB763 Fender Twin Reverb guitar amplifier. Guitar amps such as this one can weigh up to 100 pounds and can be tedious to move around. Also, audio engineers and producers may want to try out the sound of an amplifier that they do not actually own, and a virtual recreation would be a cost-effective and quick way to do so.

Detecting Noise in Eye-Impedance During Traumatic Brain   By Parker Stogdill, Sean O’Connell, Wes Cummings, and Eason Chang  Goal: Detect and remove impulse noise for improved non-invasive evaluation of traumatic brain injury patients. The condition of traumatic brain injury patients is traditionally determined by drilling through the skull and inserting a sensor into the brain to measure the blood pressure of the brain. As an alternative the Tiba Lab at Michigan Medicine is doing research into using eye-impedance, a non-invasive measure, to track the patient’s condition. This team is working to improve the success of this non-invasive procedure.

Computer-Aided Melanoma Detection   By Kabir Deol, Dominic Dadabbo, Ryan Downey, and Joseph Wendt  Goal: Accurately detect melanoma on a person’s skin through a computer-aided algorithm people can use in their own home. This could help save costly visits to the dermatologist while helping improve early detection of melanomas. In addition, machine learning algorithms have historically excluded people of color when training their datasets. This team trained their data to detect melanoma on a variety of skin pigmentations, ensuring no groups would be left out.

Handwriting to LaTex   By Emilia Psacharopoulos, Enakshi Deb, Maura Mulligan, James Wishart, and Ritika Pansare  Goal: Translate written characters from a whiteboard into a well-formatted LaTeX document. Many lectures are recorded for students who are unable to attend in person. However, it can be difficult to read writing on whiteboards via the recording. By translating the written content automatically, this program improves accessibility for all.

MP3 to MIDI Converter   By Cody Dempster, Wes Mackey, Kaitlyn Nowak, Laurel Saxe, and David Suh  Goal: Take any MP3 audio file and convert it to the MIDI file format. The MIDI file format can then be used to obtain the sheet music for the song. The converter would be most helpful for beginner musicians who would like to learn to play a song that they heard.

Classifying Dogs vs Cats from Images   By Iman El-Bawab, Ankita Maahajan, and Shwera Pati  Goal: Create a highly accurate model that can predict whether an image is a cat or a dog. The model can then be expanded to more complex systems, such as identifying emotion based on facial expressions in pictures, or distinguishing between pedestrians/hazardous objects in the road scene for autonomous vehicles.

Noise Cancellation Project   By Sandilya Sai Garimella, Andrew Elliott, and Jocelyn Gu  Goal: Use active noise reduction to remove loud, unwanted noise, so as to create less noisy conditions for work, study, and daily life. The active noise reduction function is to generate reverse sound waves equal to the external noise through the noise reduction system, neutralize the noise, and achieve the effect of noise reduction.

Image Sonfication   By Servando Garza, Zion Studivant, Edward Ted Ivanac, Marco Tulio Giachero Pajaro, and Matthew Perez  Goal: Understand the different methods in which images can undergo audio-based sonification and create a musical application that can be made of the time-varying data from the images. Image sonification is the process of converting non-time varying data in images into a time-varying form.

Rating Practice Piano Pieces   By Stefanos A Frilingos, Blake A Hall, Wentao Xu, Xiangdong Wei, and Haoliang Cheng  Goal: Adopt multiple features of the music signal processing models to automate the evaluation of a large set of piano pieces. Autonomous rating of music performance could better the performers’ understanding of playing techniques and provide a flexible tool for the evaluation of autonomous music generation

Language Classification   By Aashish Karikrishnan, Daniel Li, Andrew Lyandar, and Shantanu Purandare  Goal: Classify spoken languages by using a multi-language identification system and audio recordings from at least three languages. In addition to classifying distinct languages, the model is used to sort each language and dialect into categories. 

Wordle Helpers   By Jackson Muller, Andy Zhang, Dawson Hartman, and Justin Yu  Goal: Assisting people in solving Wordle. In Wordle, players have six tries to guess a randomly chosen five letter word. ​The team created a program that helps people evaluate the quality of their starter word. They also created a User Interface that assists the player by suggesting words based on the player’s previous entry.

Using DSP to remaster old songs   By Richard Shen, Michael Sun, and Hyugo Weicht  Goal: Digitally remaster songs by employing filtering techniques to remove noise and amplify the original song. The team also implemented a general equalizer where a user can adjust the “low’s, mid’s, and high’s” of the filtered recording manually. This will improve the preservation of old recordings and ensure the artistry of past musicians can continue to be enjoyed.

Classification of Underwater Mammals   By Izzi Nolan, Jason Ribbentrap, and Sebastian Sulborski  Goal: Identify dolphins and whales based on their underwater songs. The team created filters to minimize noise from external factors to improve the accuracy of identification. This program can be used to alert motorized boats to the presence of dolphins and whales.

Noice Cancellation   By Qianxu Li, Tianwei Liu, and Raj Patel Goal: Create a system that can effectively isolate the audio of individual speakers and remove noise accordingly. This system can be used in various applications such as auto caption, noise cancellation for zoom, real-time audio selection, and more.

Low-Resolution Image Processing with a Quadcopter   By David Li, David Engel, and Hengrui Tian  Goal: Test image processing of unmanned aerial vehicles (UAV) through red color detection, circle detection, and chessboard coordinates determination. Image processing enables UAV’s to make decisions in real time based on the environment. For example, an UAV can use its camera to detect objects, which makes drone delivery a possibility.

Stem Player   By Jedidah Pienkny, David Pulido, and Erik Radakovich Goal: Create a stem player, which takes digital music files and manipulates them in order to isolate the various parts of their makeup: drums, bass, vocals, and more. This is useful for music enthusiasts who like remixing their favorite songs, or for testing sound systems to determine optimal or sub-optimal performance in specific ranges. 

School of Electrical and Computer Engineering

College of engineering, digital signal processing.

Digital Signal Processing (DSP) involves the representation, processing, modeling, and analysis of signals, information, and physical phenomena. DSP interprets the captured data and enables visualization, analysis, manipulation, and control. DSP lies at the core of modern artificial intelligence (AI) and machine learning algorithms.

Digital Signal Processing is at the core of virtually all of today's information technology, and its impact is felt everywhere -- in telecommunications, medical technology, radar and sonar, and in seismic data analysis. The Digital Signal Processing Group at ECE operates the largest educational and research programs in the world in both size and impact. Nearly half of the DSP faculty hold the prestigious title of IEEE Fellow, and are world renowned for their leadership and expertise. DSP's faculty have authored or co-authored over 25 textbooks on DSP, which are studied at universities around the world.

The research at ECE's DSP group is supported by government and industry grants totaling over $4 million annually and encompasses virtually all areas in the theory and implementation of DSP systems. This research is supported by a network of state-of-the-art research laboratories. These resources offer students abundant opportunities to engage in cutting-edge research with DSP's world-renowned faculty

ECE's DSP group receives research support from a variety of funding sources including the Georgia Research Alliance, the National Science Foundation, the Defense Advanced Research Projects Agency, the U.S. Army Research Office, the Ballistic Missile Defense Organization, the National Aeronautical and Space Administration, John and Mary Franklin Foundation, Hewlett Packard, Texas Instruments, IBM, Analog Devices, Intel, NCR, Lanier Worldwide, COMPASS Design Automation, Kodak, and the Region of Lorraine, France.

Digital Signal Processing research falls within the following major areas:

• DSP and Machine Learning Theory
• High-dimensional Statistics
• Conversational Systems
• Robust and Explainable AI  
• Active and Reinforcement Learning 
• Image, Speech, Audio, and Video Processing and Learning 
• Radar and Array Processing

ECE offers the largest undergraduate and graduate DSP academic programs in the country. These programs provide a strong foundation in all aspects of digital signal processing from which students can continue graduate studies, work in industry, or enter schools of medicine, business or law. DSP faculty has played a major role in ECE's Computer Enhanced Education initiatives, and now an on-line master's program.  View the course listing .

Continuing Education Program

DSP faculty, in conjunction with industry experts, offer a variety of  continuing education courses  to practicing engineers on topics of current interest including fundamentals of digital signal processing, signal processing for telecommunications, multimedia signal processing, and video processing and compression.

Digital Signal Processing Faculty Members 

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Signal processing articles from across Nature Portfolio

Signal processing is the transmission of information with a modification, so that the new form of information may be exploited by downstream devices, as in sound converted to nerve impulses from ear to neurons. The scientific study of signal processing implicates information theory and is key to telecommunications or biology.

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Signal processing focuses on algorithms and hardware for analyzing, modifying and synthesizing signals and data, across a wide variety of application domains. As a technology it plays a key role in virtually every aspect of modern life including for example entertainment, communications, travel, health, defense and finance.

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Doctoral thesis: interactive spin dynamics in magnon and quantum spin systems, doctoral thesis: learning to improve clinical decisions and ai safety by leveraging structure, doctoral thesis: guiding deep probabilistic models.

Department of Electrical and Electronic Engineering

Digital signal processing

Our researchers.

• Emad Alsusa
• Alex Casson
• Fumie Costen
• David Foster
• Patrick Gaydecki
• Peter R Green
• Krikor Ozanyan
• Tony Peyton
• Paul Wright
• Wuliang Yin

We make sensor instrumentation and signal processing algorithms for extracting information from raw sensor data.

We have a particular specialism for real-time signal analyses and hardware acceleration for fast signal processing in portable and power constrained situations.

Our technologies have many applications in non-destructive testing and biomedical engineering, and our researchers are always exploring innovative approaches that will revolutionise the technology we use every day.

Research focus

We are experts in this field and cover all aspects related to digital signal processing in a range of environments.

Some specific areas we focus on are:

Image recognition and machine learning

Image recognition replies on innovative techniques in pattern recognition and machine learning and is vital in many applications from security to robotics and precision agriculture. We’re driving forward recent advances in deep learning and GPUs to make vision-based solutions more intelligent, robust and deploying them in real-time applications.

Low-power sensor nodes

Very low power consumption sensor nodes are at the heart of the emerging Internet of Things. These sensors need to process data and we make ultra-low power consumption signal processing (just nano-Watts!) for embedding in sensor nodes to enhance their functionality and battery life.

Neurotechnology

Brain monitoring used to be confined to lab environments. Wearable EEG means this is no longer the case: we can monitor the brain easily, in a range of situations. We are using wearable EEG to explore the new applications of brain interfacing that are now possible.

Real-time signal processing

Real-time signal processing based on fast digital signal processors started in the 1970s and is now one of the fastest growing areas in the field of digital technology. We create integrated DSP instruments for designing, downloading and running very high performance audio-bandwidth filters in real-time.

Research highlights

Check out some of our research highlights below:

• Signal Wizard
• Non-destructive testing of materials
• Analysis of cardiovascular and autonomic function
• Neurotechnology (EEG and transcranial stimulation)
• Low power sensor nodes

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Latest Projects Based on Digital Signal Processing

The following projects are based on digital signal processing. This list shows the latest innovative projects which can be built by students to develop hands-on experience in areas related to/ using digital signal processing.

1. DSP Implementation of Moving object detection and tracking based on MPEG-4

Moving object detection is widely used in fields like aviation, marine, road monitoring for video surveillance and safety purposes. In this Digital signal processing project, we are going to discuss MPEG-4 based moving object detection and tracking system.

MPEG - 4 Moving Picture Experts Group is an organization who is responsible for video encoding standard. When compared to MPEG-1 and MPEG-2, MPEG - 4 encodes the video file in a smaller size which is why it is used widely in online streaming platforms and media file transfer. MPEG 2 is used in DVD’s and it has good video quality compared to MPEG 4.

2. Improved Speech communication in a car using DSP

In newly manufactured cars a lot of effort is put on reducing the background noise so that the person in the front seat can hear the voice from the rear seat. The main intension of this DSP project is to increase the audibility between the driver and the rear seat persons. This system is implemented in DSP –system in a test car, a microphone is placed in front of the driver to collect the speech and this microphone filters and remove the main part of the background noise. And loudspeakers are placed in the rear for the passenger to listen from the driver, there is a delay in sound of the driver to reach the passenger which makes the right voice of the driver. This evaluated system shows that there is increased audibility along with the no change in background noise.

3. Gearbox diagnosis using DSP

This DSP project is based on vibration analysis procedure. This method has a quick way to implement in the existing method by attaching accelerometers outside the gearbox. By using this method we can easily identify the condition of gearbox. This method completely depends on the vibration frequencies in the gearbox.

4. Pre-Encoded Multipliers Based on Non-Redundant Radix-4 Signed-Digit Encoding for DSP Applications

Overview: A enormous number of multiplications with coefficients that don’t alter during the strangulation of Digital Signal Processing (DSP) and intermedia applications like Fast Fourier Transform (FFT) and audio/video pamphlets whose toted out it. The optimized architecture block of the multiplier is very necessary because this is the vital component for realizing computationally protracted applications and it’s influenced complete system performance.

By the use of Canonic Signed Digit (CSD) delineation, regular coefficients can be ciphered to enclose the least non-zero values. CSD multipliers cover the slightest non-zero fractional products which in turn reduces their switching motion. Folding technique is a very important process to decrease the silicon area by time manifold.

Although the CSD multipliers are compact wired to explicit coefficients, functional blocks like adders and subtractor are not profitable to it. Since the explicit products formation unit is produced uniquely for a group of coefficients and cannot be rephrased for another group, this multiplier design misses flexibility.

5. Flexible DSP Accelerator Architecture Exploiting Carry-Save Arithmetic - VLSI Project

Current electronic systems which primarily consist of embedded circuitry focus on high-end application streams. It is prescribed for the productive utilization of computationally demanding digital signal processing (DSP) functions. For the digital signal processing (DSP) stream, hardware stimulation has been tested as an excessively auspicious implementation technique. The fusion of heterogeneity through functional hardware stimulators upgrades performance and decreases energy expenditure. 

As the multiple instantiated application-specific integrated circuits (ASICs) are required to quicken several kernels, ASIC creates the optimal accelerator solution in charge of overall power and design performance and their obstinacy to expand silicon complexity. In the initial data-flow graph (DFG) of the kernels, soaring performance formative data paths have been visualized to precisely map primitive or linked operations. 

In a controlled evident template library, the templates of convoluted linked operations are either derived precisely from the kernel’s data flow graph. Design selections on the stimulator datapath hugely bump its efficiency. The utilization of architecture level expansions like enlarged instruction-level replicas has worked on obscene grained decomposable datapaths.

For obtaining a tailored design structure, the domain explicit architecture formation algorithms alter the type and number of estimation units. Handling of multiple ALUs with heterogeneous arithmetic appearances has adopted an intrusive operation which is the string to set up the computation of integrated subexpressions.

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Signal processing is a discipline that deals with the transformation and manipulation of signals for information extraction, signal estimation, and efficient representation of signals. Digital signal processing (DSP) has a wide range of applications and has become a critical component of almost all modern electronic devices. DSP finds applications in speech and audio signal processing, image processing, digital communications, control and robotics, medical devices, and many other industrial and consumer electronics. The application of DSP in speech and audio processing includes speech compression, speaker identification and verification, noise cancellation, music equalization and manipulation, etc.

DSP techniques have made a wide range of image processing applications possible, such as face recognition, image enhancement, and image compression. The recent rapid growth and expansion of wireless communication devices, systems, and networks are made possible because of the sophisticated DSP algorithms. Similarly, radar systems and robotics owe their existence and growth to DSP. Also, recently, biomedical DSP has spurred the development of many life-saving wearable medical devices.

The UW Bothell EE graduate curriculum includes a sequence of two courses on digital signal processing courses, B EE 511 and B EE 512, and a course on image processing, B EE 515. The DSP courses cover the basic signal processing concepts such as analysis of signals and systems, design of filters, multi-rate processing and design of polyphase filters, and advanced signal processing algorithms such as the design of optimum filters, adaptive filters, Kalman filters, and spectrum estimation. For deeper knowledge, students are encouraged to pursue B EE 600 Independent Student or B EE 700 Thesis with a faculty member.

Career pathways

Experiences from classes in this key focus area of Signal Processing will prepare students for wide employment opportunities in the field of signal processing. DSP engineers work in various industries, including telecommunications, aerospace, semiconductor, consumer electronics, etc. Students should be prepared for competitive consideration of employment positions such as:

• Signal & Image Processing Engineer
• Camera Algorithm Engineer
• Principle Active Noise Cancellation Engineer
• Camera Systems Engineer
• Financial Signal Processing Engineer
• Speed and Audio Processing Engineer
• Machine Learning and Artificial Intelligence Engineer

The Institute of Electrical and Electronics Engineers Signal Processing Society shared “ 3 Reasons Why Signal Processing is the Career of the Future ”:

• Signal Processing Plays a Key Role in Multiple Industries : Signals are used in finance, to send messages about and interpret financial date. The most exhilarating new movies are made possible by multi-resolution signal processing, making entertainment a lucrative market for people with this skill set.
• Signal Processing is the Technology of the Future : The latest breakthroughs in health care are enabled by signal processing engineers, who are developing ways to process medial images more quickly and accurately.
• Signal Processing Can Serve a Social Purpose : Signal processing allows for the expansion of computing power and data storage capabilities, making signal processing engineers indispensable for understanding and tackling our biggest global problems.

Faculty in Signal Processing

Harry Aintablian

Tadesse Ghirmai

Research at UW Bothell

Dr. Harry Aintablian’s research experience is in power electronics for space systems. At UW Bothell he has supervised several student research projects in power conversion, reliability analyses of electronic systems, and power management of photovoltaic systems with energy storage. His current research involves the development of high-voltage, high-frequency power supplies for electrohydrodynamic applications such as Unmanned Aerial Vehicles (UAVs) and a COVID respirator mask.

Dr. Kaibao Nie’s research focuses on signal processing in cochlear implants. A cochlear implant is an electronic device to restore hearing to people with profound hearing loss. It can convert sounds to electrical current pulses for directly stimulating the auditory nerve. The research topics on cochlear implants span a wide range of techniques including noise reduction, speech coding, collection of electrically evoked action potentials, cortical EEG processing and auditory modeling. View a conference paper Dr. Nie coauthored on speech processing in hearing devices , speech perception with cochlear implants , and keywords spotting .

Study Signal Processing

The curriculum reflects depth and breadth of faculty research expertise and provides graduate students with a solid foundation in signal processing and digital image processing. Relevant courses include:

• B EE 510 Probability and Random Process
• B EE 511 Signal Processing I
• B EE 512 Signal Processing II
• B EE 515 Digital Image Processing Applications
• B EE 520 Predictive Learning from Data

Learning objectives

In this technical area, students will learn:

• Industry standard simulation tools such as Matlab, Spiece, and LabView. Those tools are extensively used in industry as well as in academia for the simulation of design and analysis in several disciplines of engineering such as signal processing, circuit design, system fault analysis, and instrumentation interface.
• Basic probability and random processes and their applications to engineering.
• Digital signal processing techniques for analysis of systems and designing of digital filters.
• Statistical signal processing which deals with random signals, their modeling, characterization, and transformation to extract useful information about the underlying mechanism that generates them.
• Advanced image processing techniques, image filtering design, and its applications to images acquired from various imaging techniques.
• Concepts of predictive learning algorithms for supervised and unsupervised learning tasks.

Emphasis on project-based learning through class projects

Many of the courses listed above provide class projects that will enhance student learning. Particularly, students in a team-oriented project learn important skills such as collaboration, communication, and presentation. Both DSP courses (B EE 511 and 512) have significant design homework problems and projects, where MATLAB is extensively used as a tool.

[B EE 510] Probability and Random Process

Handwriting recognition is an important machine learning problem that has many practical applications. In this design project, students will develop learning and classification algorithms to identify the number on an image of a handwritten digit (0 to 9). Students create programs in Python or Matlab to perform handwritten digit recognition using the Naïve Bayes model and the Gaussian mixture model. The purpose of this project is to introduce students to the basic concepts of Bayes classifier and machine learning.

[B EE 511] Signal Processing I

Students can work on pre-processing and feature extraction of electrocardiogram (ECG) signals; design lowpass, highpass and notch filters to remove noise and power-line interference from the ECG signal; and develop algorithms to determine the average heart rate and heart rate variability from the ECG signal.

[B EE 512] Signal Processing II

Students can work on speech signal modeling and linear prediction. The first part of the project requires students to represent a speech signal using parameters of an all-pole filter and then synthesize back the speech signal from the filter parameters. In the second part of the project, students predict future stock values using a linear predictor.

Area of investment and support: Digital signal processing

The theory, algorithms and architectures for processing data and signals and the information they carry.

The scope and what we're doing

The theory, algorithms and architectures for processing data and signals and the information they carry – for example, audio, video, image, speech, sonar, radar, medical, sensor, graph signals, big data, network data – for applications across science, technology and media.

This research area includes the theory and techniques concerned with detection, estimation, coding, transmission, enhancement, analysis, representation, recording, reconstruction, transformation and interpretation of signals, data and information.

This strategy reflects growth, which means capacity is now commensurate with this area’s importance, as well as its broad applicability and relevance to many other areas. The area’s focus seems to have narrowed, however, due to an emphasis on defence challenges. There is a need to take opportunities offered by applications in other sectors and developments arising in mathematics, for example.

Digital signal processing is a key underpinning area. As society becomes increasingly connected and reliant on electronic devices everywhere from the home to healthcare, the efficient nature of those connections and safe, secure communication between them assumes growing importance. Signal processing is the medium by which most of this will be carried out.

There is well-coordinated activity in the defence sector resulting from investment in 2013 in the £6 million University Defence Research Collaboration (UDRC). As a result, there has been a focus on priorities for defence-related research in this area, but there is an opportunity to address many other potential application areas, including health, autonomous systems, robotics, manufacturing, aerospace, security, communications, broadcasting and home entertainment, biology and environmental sciences, and the creative industries.

We aim to have stronger connections between researchers in this area and those across a range of application areas. This includes the research and user communities in communications and networks, vision, hearing and imaging, human-computer interaction, music and entertainment, healthcare, manufacturing and environment.

Enabling this will require links between digital signal processing researchers and those working in areas that could contribute to its future development – for example, machine learning, artificial intelligence, optical sensing and mathematics.

Why we're doing it

The growing availability of cheap and portable sensors is leading to an explosion of data in a wide variety of sectors. The challenge of handling, analysing and making sense of this data will require new technologies. Separating weak sensor-signals from background and other noise and then analysing them will present challenges for the signal processing community to solve if this potential is to be realised. The community has a key role to play and linking theory to practice will be important.

Digital signal processing in the UK has a vibrant, world-class research community and there is industry demand for PhDs with solid digital signal processing and machine learning skills.

Priorities for defence-related research in this area have been the subject of scrutiny with respect to the development of and support for the University Defence Research Collaboration (UDRC). There is, however, a need to recognise and understand the opportunities created by other sectors.

View evidence sources used to inform our research strategies .

Past projects, outcomes and impact

Visualising our portfolio (VoP) is a tool for users to visually interact with the EPSRC portfolio and data relationships. Find out more about research area connections and funding for digital signal processing .

Find previously funded projects on Grants on the Web .

Last updated: 21 December 2022

This is the website for UKRI: our seven research councils, Research England and Innovate UK. Let us know if you have feedback or would like to help improve our online products and services .

• Frontiers in Signal Processing
• Signal Processing for Communications
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Emerging Optimization, Learning and Signal Processing for Next Generation Wireless Communications and Networking

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Next-generation wireless systems conceptualize a transformative leap forward in connectivity, promising unprecedented speeds, ultra-low latencies, and ubiquitous service. Leveraging advanced technologies such as mmWaves, THz frequencies and massive MIMO, as well as intelligent network orchestration, next-gen wireless envisions seamless streaming of high-definition content, immersive virtual and augmented reality, and real-time collaboration on a global scale. Next-gen wireless will ultimately support a variety of interconnected devices, from smart cities and autonomous vehicles to IoT sensors and wearables, introducing a new era of digital transformation. While next-gen wireless presents a high potential for societal and economic growth, operation of such systems requires not only to be efficient and driven by actual, observable data, but also to meet certain (often strict) design specifications. To achieve these goals, optimal or at least effective wireless system design (across layers) arises as a fundamental, timely and perpetual task. Recent advances in technical areas such as optimization, learning and signal processing have attracted significant interest among researchers due to their potential to tackle previously untouchable challenges in wireless system design. For instance, by harnessing the expressive power of learning representations such as neural networks (and beyond), pervasive tasks such as channel estimation, equalization, and interference cancellation can be successfully handled in ways that were not achievable with traditional methods. Moreover, novel techniques within the realms of nonconvex optimization and reinforcement learning enable adaptive and autonomous systems capable of dynamically adjusting to changing conditions. Nevertheless, integration and deployment into real-world practical wireless communication systems is challenging. For instance, balancing the need for accuracy and efficiency while ensuring low-latency operation remains a critical issue. Further, robustness and reliability at the presence of dynamic and unpredictable wireless environments still need to be thoroughly validated to guarantee consistent performance under diverse conditions. Additionally, there are concerns regarding the interpretability and transparency of black-box machine learning models, particularly in critical or high-stakes applications. These are just some examples of the challenges towards the realization of the full potential of modern optimization, learning and signal processing approaches in shaping the future of wireless communications and networking. We solicit high-quality original research papers broadly on themes including, but not limited to: > Resource allocation (deterministic, stochastic, constrained, etc.), > Random and multiple access, > Beamforming and Interference Management, > Scheduling and routing algorithms, > Cross-layer design and optimization for next-gen wireless, > AI and machine learning for wireless communications and networking (including approaches relying on deep learning, reinforcement learning, and federated learning in wireless), > Cutting-edge technologies such as Integrated Sensing and Communications (ISAC), Intelligent Reflecting Surfaces (IRSs), mobility-enabled communication, and Movable Antennas (MAs), > Fair, Reliable, Robust and Aware wireless communications and networking. > Short/long-term, or multi-time scale optimization of wireless systems.

Keywords : Emerging Optimization; Wireless Communications; Networking

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Physics > Optics

Title: digital signal processing techniques for noise characterization of lasers and optical frequency combs: a tutorial.

Abstract: Performing noise characterizations of lasers and optical frequency combs on sampled and digitized data offers numerous advantages compared to analog measurement techniques. One of the main advantages is that the measurement setup is greatly simplified. Only a balanced detector followed by an analog-to-digital converter is needed, allowing all the complexity to be moved to the digital domain. Secondly, near-optimal phase estimators are efficiently implementable, providing accurate phase noise estimation in the presence of the measurement noise. Finally, joint processing of multiple comb lines is feasible, enabling computation of phase noise correlation matrix, which includes all information about the phase noise of the optical frequency comb. This tutorial introduces a framework based on digital signal processing for phase noise characterization of lasers and optical frequency combs. The framework is based on the extended Kalman filter (EKF) and automatic differentiation. The EKF is a near-optimal estimator of the optical phase in the presence of measurement noise, making it very suitable for phase noise measurements. Automatic differentiation is key to efficiently optimizing many parameters entering the EKF framework. More specifically, the combination of EKF and automatic differentiation enables the efficient optimization of phase noise measurement for optical frequency combs with arbitrarily complex noise dynamics that may include many free parameters. We show the framework's efficacy through simulations and experimental data, showcasing its application across various comb types and in dual-comb measurements, highlighting its accuracy and versatility. Finally, we discuss its capability for digital phase noise compensation, which is highly relevant to free-running dual-comb spectroscopy applications.

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Design and Implementation of 5G Wireless Receiver Algorithms on the DSP Board

• Published: 20 May 2024

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• Mohammed Abdulzahra Ahmed Al-Dulaimi 1 &
• Mohammed Jawad AL-Dujaili   ORCID: orcid.org/0000-0002-3804-6667 1  

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With the advent of fifth Generation of cellular communication with its key features that superior over all the precedent generations. However, there are some challenges need to be taken into consideration. The main crucial part of communication system is the receiver and as it is known there are two main types of receivers which can be used to demodulate the signals and recover the transmitted data. In this paper, we used those two types of algorithm receivers which are the Non-Coherent and Coherent receivers. They are firstly demonstrated in details in accordance with receiver specification design and then moved to the practical side of implementation. In which novelty of our work is highlighted, by carrying out those different algorithms on the ADSP 21364 SHARC board where initially sampling the received DPSK signal, design the Bandpass and lowpass digital filtering respectively and all to be compiled by Analog Devices VisualDSP +  + software that is programmed by C language prior to download to the ADSP Board chip. The same algorithm is followed in the frame synchronization for both of them by detecting the message through distinguishing the preamble of the message “ +  +  +  + ” data which is before the 72 bytes (real message).Then that actual message will be stored and dumped to the memory and displayed to ensure from the received message whether it is corrupted with errors or not. However, it is clearly noticed that the Coherent receiver is more efficient than the Non-coherent, but it still a bit expensive because used double look-up tables for cosine and sine in IQ converter. Finally, coherent receiver with Costas loop algorithm was implemented as well to improve the previous differential detection scheme.

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The most datasets generated and/or analysed in this study are comprised in this submitted manuscript. The other datasets are available on reasonable request from the corresponding author with the attached information.

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The code used for analysis in this study is available on reasonable request from the corresponding author with the attached information.

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Mohammed Abdulzahra Al-dulaimi and Mohammed Jawad Al-Dujaili conceived and designed the study performed experiments and collected data analyzed and interpreted the data wrote the manuscript. reviewed and approved the final version.

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Al-Dulaimi, M.A.A., AL-Dujaili, M.J. Design and Implementation of 5G Wireless Receiver Algorithms on the DSP Board. Wireless Pers Commun (2024). https://doi.org/10.1007/s11277-024-11161-9

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Global Digital Signal Processor Market Size, Share, and COVID-19 Impact Analysis, By Type (32-bit Floating Point, 16-bit Fixed Point, and Others), By Core Type (Multi Core and Single Core), By Application (Audio Processing, Video Processing, Telecommunications, Industrial Automation, Healthcare, and Others), and By Region (North America, Europe, Asia-Pacific, Latin America, Middle East, and Africa), Analysis and Forecast 2023 - 2033.

Global Digital Processor Market Insights Forecasts to 2033

• The Global Digital Signal Processor Market Size was Valued at USD 11.47 Billion in 2023
• The Market Size is Growing at a CAGR of 6.97% from 2023 to 2033
• The Worldwide Digital Signal Processor Market Size is Expected to Reach USD 22.49 Billion by 2033
• Asia Pacific is Expected to Grow the fastest during the forecast period.

Get more details on this report -

The Global Digital Signal Processor Market Size is Anticipated to Exceed USD 22.49 Billion by 2033, Growing at a CAGR of 6.97% from 2023 to 2033.    

Market Overview

Digital signal processors (DSP) are specialist microprocessors that, unlike general-purpose microprocessors, have an architecture optimized for digital signal processing operations using voice, video, audio, temperature, and location signals. DSP normally uses certain memory structures that can retrieve multiple data or commands at the same time. Digital signal processors typically require a large number of numerical operations to be done quickly and continuously on a sequence of data samples. Signals are continuously translated from analog to digital, handled digitally, and then converted back to analog. Furthermore, the digital signal processor market is being fueled by a number of reasons, including the rising need for advanced signal processing in a variety of industries, the expansion of Internet of Things (IoT) technology, and the rising demand for high-quality audio and video processing. The market is also being driven by increased DSP adoption in emerging economies, as well as the development of new and innovative DSP technologies.

Report Coverage

This research report categorizes the market for the global digital signal processor market based on various segments and regions forecasts revenue growth and analyzes trends in each submarket. The report analyses the key growth drivers, opportunities, and challenges influencing the global digital signal processor market. Recent market developments and competitive strategies such as expansion, product launch, and development, partnership, merger, and acquisition have been included to draw the competitive landscape in the market. The report strategically identifies and profiles the key market players and analyses their core competencies in each sub-segment of the global digital signal processor market.

Global Digital Signal Processor Market Report Coverage

Driving Factors

The digital signal processor market continues to grow due to rapid technological improvements. As the demand for high-performance computing grows in areas such as telecommunications, automotive, consumer electronics, and healthcare, efficient signal processing solutions become critical. DSPs play an important role in satisfying these needs by providing improved capabilities for sophisticated algorithms and real-time processing jobs. Furthermore, the increasing use of the Internet of Things (IoT) and connected devices is driving up demand for the global digital signal processor market. DSPs are utilized in a variety of IoT applications, including smart home devices, smart appliances, and wearables, to process sensor data and generate actionable insights. Additionally, increased data traffic and the use of technologies such as Internet Protocol (IP) cameras for video surveillance are likely to drive up product demand globally. Furthermore, due to its cost advantages, digital signal processing has emerged as the preferred technique for constructing high-performance communication systems.

Restraining Factors

Designing and programming architectures for digital signal processors introduces complexities, which operate as a primary obstacle, limiting market growth. With the requirement to balance features such as low costs, minimal power consumption, high performance, optimal computing, and so on, chip designers find it difficult to design or program while adhering to these parameters, which limits market growth. On the other hand, the market for digital signal processors is being hindered by a number of factors, including currency fluctuations in different markets, a lack of skilled labor in the core electronics sector, difficulty in designing and programming complex DSP architecture, increased raw material costs, and new technology developed by competitors.

• Market Segmentation

The global digital signal processor market share is classified into type, core type, and application .

• The 32-bit floating point segment is expected to hold the largest share of the global digital signal processor market during the forecast period .   

Based on the type, the global digital signal processor market is divided into 32-bit floating point, 16-bit fixed point, and others. Among these, the 32-bit floating point segment is expected to hold the largest share of the global digital signal processor market during the forecast period. Real-time processing capabilities are in high demand in applications such as radar signal processing, autonomous vehicles, and medical imaging, which is driving the growth of the 32-bit floating point segment in the global digital signal processor market. For instance, in radar signal processing, floating-point DSPs enable the rapid and accurate execution of algorithms for target recognition, tracking, and classification, ensuring a fast response to changing conditions. Similarly, these processors help autonomous vehicles make real-time decisions by analyzing sensor data for activities such as object detection, path planning, and collision avoidance.    

• The multi core segment is expected to grow at the fastest CAGR in the global digital signal processor market during the forecast period.

Based on the core type, the global digital signal processor market is divided into multi core and single core. Among these, the multi core segment is expected to grow at the fastest CAGR in the global digital signal processor market during the forecast period. Multi-core DSPs can perform parallel processing, which means they can run numerous tasks at the same time. This capacity is particularly useful in applications requiring real-time execution of sophisticated algorithms, such as telecommunications, audio processing, and picture recognition. The parallel processing design improves the overall performance and efficiency of DSPs, making them ideal for demanding signal processing applications. Due to these factors, the multi core segment is growing rapidly in the global digital signal processor market.

• The audio processing segment is expected to grow at the fastest CAGR in the global digital signal processor market during the forecast period.    

Based on the application, the global digital signal processor market is divided into audio processing, video processing, telecommunications, industrial automation, healthcare, and others. Among these, the audio processing segment is expected to grow at the fastest CAGR in the global digital signal processor market during the forecast period. The audio processing segment is driven by the increasing demand in both the consumer and business sectors for immersive, high-fidelity audio experiences. DSPs are necessary for audio processing because digital audio has defects including distortion, noise, and other issues. They are excellent at reducing noise, equalizing (EQ) to make up for speaker limitations, canceling off echo to facilitate clear conversation, enhancing audio with effects like reverb and spatial audio, and compressing and decompressing data for effective transmission and storage. These factors are driving the rapid growth of the audio processing segment in the global digital signal processor market.

Regional Segment Analysis of the Global Digital Signal Processor Market

• North America (U.S., Canada, Mexico) 
• Europe (Germany, France, U.K., Italy, Spain, Rest of Europe)
• Asia-Pacific (China, Japan, India, Rest of APAC)
• South America (Brazil and the Rest of South America) 
• The Middle East and Africa (UAE, South Africa, Rest of MEA)

North America is anticipated to hold the largest share of the global digital signal processor market over the predicted timeframe.

North America is anticipated to hold the largest share of the global digital signal processor market over the predicted timeframe. The high presence of top semiconductor businesses and tech giants in North America is one of the main causes of the region's dominance in the digital signal processor market. Industry pioneers including Texas Instruments, Qualcomm Incorporated, NVIDIA Corporation, Analog Devices, Intel Corporation, and others are based in the region. These businesses are well-known for their inventiveness and proficiency in creating cutting-edge digital signal processor solutions for a variety of industries, such as consumer electronics, telecommunications, automotive, healthcare, aerospace, and defense. In addition, governments in a number of the region's nations are initiating programs to advance the manufacturing sector and revive the expansion of the electronics, semiconductor, and manufacturing industries nationally. 'CHIPS for America,' another initiative created by the federal government, encouraged homegrown manufacturing. The US government is currently developing incentives to essentially "reshoring" a term used to describe the return of US semiconductor manufacture.

Asia Pacific is expected to grow at the fastest pace in the global digital signal processor market during the forecast period. The region is a key market for electronics production, and digital signal processors are widely used in the electronics industry, which is projected to drive the region's digital signal processor market growth. Factors such as rising disposable incomes, urbanization, and increased awareness of smart home technology will drive market expansion. This surge in smart home adoption has resulted in increased demand for a wide range of smart devices in the safety, energy control, climate management, lighting, and voice-enabled categories, all of which rely heavily on DSPs for critical functionalities such as noise cancellation, image processing, and motor control.

Competitive Analysis:

The report offers the appropriate analysis of the key organizations/companies involved within the global digital signal processor market along with a comparative evaluation primarily based on their product offering, business overviews, geographic presence, enterprise strategies, segment market share, and SWOT analysis. The report also provides an elaborative analysis focusing on the current news and developments of the companies, which includes product development, innovations, joint ventures, partnerships, mergers & acquisitions, strategic alliances, and others. This allows for the evaluation of the overall competition within the market.

List of Key Companies

• Analog Devices Inc.
• Texas Instrument Incorporated
• Infineon Technologies AG
• NXP Semiconductors
• STMicroelectronics
• Broadcom Inc.
• Asahi Kasei Corporation
• Knowless electronics LLC
• Qualcomm Technologies Inc.
• ROHM Company Ltd.
• Cirrus Logic, Inc.
• Renesas Electronics Corp.
• Rockwell Automation Inc.
• Skyworks Solutions Inc.

Key Target Audience

• Market Players
• Government Authorities 
• Consulting And Research Firm
• Venture capitalists
• Value-Added Resellers (VARs)

Recent Developments

• In January 2024, Under the WORK PRO brand, Equipson announced the launch of the new Integra Series of digital signal processors. There are three models in the series eight, sixteen, and thirty-two that offer configurable per-channel DSP architecture, gain, EQ, compression, limiting, and other features and allow installation users to control audio output across multiple zones.
• In February 2022 , STMicroelectronics introduced the Intelligent Sensor Processing Unit (ISPU), which combines a DSP capable of executing AI algorithms and a MEMS sensor on the same silicon. Aside from decreasing the size and power consumption of system-in-package devices by up to 80%, combining sensors with AI allows for electronic decision-making at the application edge.

Market Segment

This study forecasts revenue at global, regional, and country levels from 2020 to 2033. Spherical Insights has segmented the global digital signal processor market based on the below-mentioned segments:

Global Digital Signal Processor Market, By Type

• 32-bit Floating Point
• 16-bit Fixed Point

Global Digital Signal Processor Market, By Core Type

• Single Core

Global Digital Signal Processor Market, By Application

• Audio Processing
• Video Processing
• Telecommunications
• Industrial Automation

Global Digital Signal Processor Market, Regional

• Rest of Europe
• South Korea
• Rest of Asia Pacific
• Rest of South America
• Saudi Arabia
• South Africa
• Rest of the Middle East & Africa
• Single User: $3550 Access to only 1 person; cannot be shared; cannot be printed
• Multi User: $5550 Access for 2 to 5 users only within same department of one company
• Enterprise User: $7550 Access to a company wide audience; includes subsidiary companies or other companies within a group of companies

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Three Expeditions in Computing Research Projects to Receive NSF Funding

The National Science Foundation is awarding three projects a total of $36 million under the NSF Expeditions in Computing program , an initiative that seeks to finance research that can accelerate innovation in various science and engineering domains.

The first project seeks to reduce the environmental impact of computer systems. Under the leadership of Harvard University, the five-year research initiative could result in a more sustainable way of manufacturing computer systems, the NSF said Thursday.

The second project seeks to develop computational decarbonization, a new field that aims to reduce the lifecycle of carbon emissions emanating from complex computing and societal infrastructure systems. The project is being led by University of Massachusetts Amherst.

The third project seeks to incorporate machine learning into operating systems in order to enable more dynamic hardware resource management. The project is being led by The University of Texas at Austin.

Regarding the awards, Dilma Da Silva , acting assistant director for the NSF Directorate for Computer and Information Science and Engineering, said, “Congratulations to these pioneering teams whose research will forge new pathways in computational decarbonization and in revolutionizing operating system design with machine learning.”