Digital Signal Processing (DSP) is composed with different digital techniques for performing signal operations and transformation for analyzing, transmitting and enhancing the quality of signals. As well as, in order to work with signal data, it avails extensive range of tools and technologies. In general, signal is the method to pass the continuous varying information in terms of independent variables such as frequency, space, time, and many more. For instances:
- Vehicle Speed
- Resistors Voltage
- System Pressure / Heat
- Image Luminance Intensity
Digital Signal Processing (DSP) collect the input data like image, signals, audio, temperature, video and more from real-time environs and manipulate the collected data and produce the digital output. In present the digital era, many of the wireless applications in digital communication use DSP as their backbone.
With an intention to deliver incredible DSP projects, our resource team passionately work on all vital DSP basic concepts, recent developments, current real applications and future research scopes. For any DSP system, the followings processes / functions are common to reach the research goal.
- Normalization: It creates the statistical noise response in most possible even series
- Detection: It detects the target signal from the surrounded noisy signals
- Display processing: It specifies the data workability and control issues in the signal system
- Parameter tracking ad valuation: It tracks the location of the target signal to assess the signal
- Classification: It classifies the target signal to distinguish it from unwanted signals
As mention earlier, signals are in the many forms. Here, we have specified image, video, and audio signals as the examples which are highly used digital signal processing and discussed about purpose and source.
What are the three types of signal processing?
- Image processing – For enhancing the visualization of image from digital systems, cameras, medical imaging devices.
- Video processing – For inferring the information in the moving images or streaming videos
- Audio signal processing – For improving the audio signals which comprises acoustic content like speech, voice, sound, music.
Why Use Digital Signal Processing?
Here, we have explained the two main digital signal processing applications for the illustration purpose. In specific, we exposed the practicality and adaptability of DSP system in real world implementation.
- Digital Signal Processing for Echo Location:
- For modern radar system, digital signal processing provide the strong support in working with large distanced data
- In specific, DSP is employed to increase the precision in detecting long-distance objects
- Further, it is used for pulse compression to increase the SNR and range resolution of radar models
- For that, it deploy DSP chip to reduce noise and let machinist to optimize the pulse and transmit RF pulses.
- Digital Audio Processing:
- For audio signal processing, the input will be in the form of speech / music.
- Based on the applied DSP techniques, the audio will be recorded from different sound sources to generate the absolute enhanced sound mix.
- For instance: In the music studio, initially the tracks are recorded in the analogue format. Then, it transforms those tracks into digital format for better manipulation.
- Here, DSP can perform different signal processing approaches depends on needs
- As a result, it integrates articulation simulation and audio recording in one place for improved human hearing.
Now, we can discuss about the common research problem in digital signal processing. Though numerous researches have done over these areas, still these areas are puzzling for current scholars. So, our research team has new research ideas with appropriate solutions for these problems.
Research Challenges in DSP
- Low CPU speed
- Noise Sensitivity
- Insufficient storage capacity
- Low precision because of nonlinearities and component tolerance occurrence
- Threat to loss data in signal sampling
- Not efficient to adapt run-time variations
- Existence of Quantization error (round-off)
- Lack of repeatability because of sudden environ changes (like vibration and temperature) and tolerance
- Need external mixed-signal devices for D/A and A/D signal conversion
- Low dynamic range for power, frequency and voltage
In specific, we have mentioned below issues as critical research problems for upcoming research scholars. Since, these issues are need to concerned while handpicking DSP project topics.
Four Common issues in Digital Signal Processing System
- One of the essential components in digital signal processor is transistors which consume more power while system execution. Deploying infinite number of transistors in a system will eventually increase the power consumption of the system.
- In general, digital signal processor is composed with many components. In addition, external signal convertors (A/D /D/A) and filers increase the intricacy of a system.
- Data Loss
- Based on the Rate-Distortion Theory, the data loss will occur when the quantization value go below the specific Hz
- Learning curve and System Design Duration
- Learning about the digital signal processing inputs and outputs is very necessary when we design the system. The lack of knowledge in that information will definitely increase the designing time.
Our have research teams to support you in all the aspects of digital signal processing. Our technical professionals have long-term experience in handling DSP projects. So, we assure you that our delivered project surely meet your expectation. For a sample, we have specified how we process the signal with real-time application.
How do we process the signal?
- Determine the frequency through transform algorithms (Discrete Fourier transforms)
- Detect the signal fully covered by noise through correlation approaches (cross-correlation)
- Remove the noise over the signal through filtering methods (FIR / IFR)
For instance: in the following the measurement application, maintaining the signal quality is quite difficult task.
- Signal Source: Off-The-Shelf Data Reading Device
- Signal Bandwidth – 1 MHz
- Sampling Rates – Million per Second
. Actually, it measures all the available signals in regardless of usefulness. So, it becomes to attain the precision in measurement. Further, when the below specified constraints are not taken in consideration then the performance is not good.
- low impedance – minimal noise
- high impedance – low signal interaction
Further, we have also listed other factors that affect while measuring quality of signals. These factors restrict the measurement performance in evaluating signal process efficiency.
The factors limiting measurement performance include the following:
- Changes in measurement process
- External source signals interaction
- Not fully eliminate the outside interference
- High impedance and sensitivity
When signal preprocessing required?
The advanced preprocessing techniques are applied once the signals are collected. It helps to reduce the size of the whole dataset by removing unwanted, noised and corrupted data. Below, we have discussed some circumstances that are apt to apply preprocessing techniques.
- Statistical Averaging
- Lack of Signal Compensation
- Signal Calibration (adjustment)
- Data Encryption and Compression
- Selection of related information
- Phase and Time Response Compensation
- Engineering Unit Conversion
- Long-Term Trends Correction and Analysis
- Elimination of Noisy Signals
Here, we have shared somefrequent questions asked by handhold scholars regarding the signal processing along with our expert’s answers for your references.
How do we process a signal?
- What are the features essential to concentrate?
- For instance, in the case of Event-based Potentials (ERPs), precise temporal data has more importance. Similarly, in the case of motor imagery classification, precise spatial data has more importance
- Are your signal analyzes methods executed online or offline?
- We don’t want to move towards costly approaches, if the preprocessing method is applied at the initial stage
- Example on sort of artifacts you used in data? And how you remove the unwanted ones and how you set flag to keep eye on?
- For instance, we can take eyelid movements in blinking as noise but we cannot set it as significant feature
And, our research team has shared some interesting research areas in digital signal processing that many scholars prefer to have best DSP project. More than this, we also serve you in other current research areas.
Research Areas in DSP
- Spice Analog Circuit Prototype Design and Simulation
- MIMO Propagation models for Multi-Antenna
- Designing of block based data flow for DSP system
- RF Wave Analysis based on Harmonic Balance (using frequency features)
- DSP Applications: GSM, IS-95 / W – CDMA and Live Video Broadcast
- Analog / RF based Circuit Envelope Simulation (using time features)
In addition, our developer team has given important information on source code that how the code is analyzed and tested for errors and how to increase the code performance. After that, we also mention you about the significant DSP tools, toolboxes and functions.
How to check a project source code?
- Lint tool for code violations count and Static code analysis tool: Use the auto-generated code and run it. Next, check the code errors. Then, sum the number of errors.
- Source Lines of code: Normalize the code through data type, syntax and methods for tracking of the code.
- Unit Test and Code Coverage: Percentage of overall paths which is executed in unit test suite. It is best to have high.
- Complexity of Cyclomatic: Total count of execution paths that present in a unit of code. It is best to have low.
Tools / Toolboxes for DSP Projects
Nearly from past half-century, signal processing is embedded with colossal collection of sophisticated tools for taking actions towards both the simplex and complex processing of raw signals. By the by, it includes the following numerical functions and algorithms to tackle the specific problem in digital signal processing.
- Convolution Method
- Wavelet Transforms
- Recursive Least Squares (RLS)
- Compressed Sensing (CS)
- Least Mean Squares (LMS)
- Auto-correlation / Serial-correlation
- Gradient Descent Method (GDM)
- Linear Estimators
- Discrete Fourier Transform (DFT)
Further, it offers different toolboxes to build different techniques for developing various DSP projects. In some cases, we prefer mixed tools to implement hybrid techniques for processing complex signal system.
- System Identification
- Noise reduction (in noisy time-series data)
- Design Easy Version of Complex System
- Dynamic Model Stimulation
- Interactive Environment
- Graphical Programming Interface
- Rapid Prototype Design
- Wavelet Toolbox
- Image Compression
- Image and Signal De-noising
- Image and Signal Synthesis
- Statistics Toolbox
- Model Complex Systems
- Build Custom Based Statistical Models
- Teach And Learn Statistical Theories
- Analyze History Trends
On the whole, if you need best research guidance in digital signal processing, then just make bond with us. Our experts are very friendly to support you in every step of your research.