Signal Processing

patent name : AUDIO EQUALIZER PROVIDING RECIPROCAL EQUALIZATION PLUS N FINITE-DEPTH NOTCH Audio equalizers are well-known in the art for boosting and cutting predetermined frequency ranges or bands. Ideally, an equalizer should have reciprocal, mirror image characteristics for boosting when compared to cutting. Moreover, an ideal equalizer should provide an infinite-depth notch. Audio equalizer circuit is used for adjusting the amount of boost and cut. The circuit uses a combination negative feedback, positive feedback, negative feed-forward and positive feed-forward resulting in boost and cut characteristics which are entirely reciprocal and which, nonetheless provides an infinite-depth notch. The equalizer is fabricated with an ordinary band pass filter, resistors, potentiometer and operational amplifiers. The invention relates to the field of audio equalizers and tone control. It will be obvious to one skilled in the art that the present invention may be practiced without th...

Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech processing, sonar, radar and other sensor array processing.  DSP can involve linear or nonlinear operations. Nonlinear signal processing is closely related to   nonlinear system identification   and can be implemented in the   time ,   frequency , and   spatio-temporal domains . The most important thing that this lab taught me was the difference between a DSP processor and a general p...

In the first experiment we were give the task of applying the concept of signals and system. First experiment was on convolution. A convolution is a mathematical operation that represents a signal passing through a Linear and Time-Invariant( LTI) system. We performed linear convolution & circular convolution by formula. In linear convolution signal may not be periodic but in circular convolution signal is assumed to be periodic . Then we performed Linear convolution & Circular convolution by Fast Fourier Transform( FFT) to check whether we are getting same outputs or not. In the end we finally performed Linear convolution by circular convolution formula. Also we performed convolution using OSM and OAM We performed all this convolution on DSP board .

Correlation is a measure of similarity between two signals. The concept were taught in the class and based on that practicals are performed. There are two types of correlation: Auto correlation: It is defined as correlation of a signal with itself. Auto correlation function is a measure of similarity between a signal & its time delayed version.  Cross correlation: Cross correlation is the measure of similarity between two different signals

In this experiment we performed Discrete Fourier Transform(DFT) and Fast Fourier Transform(FFT).  This topic was introduced in the class before the practical and hence was relatively easy to understand during the practical session. We understood the facts learned in theory. It is very important to understand the working of the code , only then can one be able to construct a function of his own. The code for FFT was found to be more  complex than DFT. We performed FFT on 4-point and 8-point and N-point sequence and displayed the output for the same.

Linear Finite Impulse Response is implemented using any of the two algorithms of OSM and OAM. Overlap Add & Overlap Save Methods are particularly used to perform convolution on longer input of data sequences by breaking actual input signal into smaller length of different signals and then performing convolution on individual data streams. .In OAM method, the values which get overlap were added together. In OSM method, the values which get overlap were discarded.In OAM, the individual output signals are added as it is, while in OSM, the individual output signals have their first “N-L” terms removed and then remaining terms be subtended one after another .

In this lab session we performed LED binary counter that was running from 0 to 15. At 0 (0000 in binary) not a single  led was ON but at 15 (1111) all led where ON. We route the code first in C language then we removed all the input and outputs line firm the code and then upload it on Code composer studio. From there the code was upload on to the DSP board. On which we observed blinking of LED. Also we implemented same program with minor changes to make the system work as stand alone using flash programming.

In this lab, we carried out different digital modulation techniques like BPSK, DPSK and QPSK using CCS simulator. BPSK conveys data by changing, or modulating, two different phases of a reference signal. In DPSK, there is no absolute carrier phase reference, instead transmitted signal is itself used as phase reference. In QPSK two bits are modulated at once, selecting one of four possible carrier phase shifts.

In this experiment we performed the real world application of the DSP using sensor interfacing. We interfaced a handful of sensors and then saw their respective outputs on the DSO.The Sensors were: LDR,Temperature Sensor,DHT 11,SHARP sensor. Optocoupler, Microphone.After implementing all on the board we came across how these sensors work and how is it's input/output mechanism

In this experiment we performed  ADC-DAC  on the DSP processor (TMS320).This processor has two 8 bit ADC's and one external 12 bit DAC. We generated different types of waves like sine wave, sawtooth wave, triangle wave & ramp-pedestal wave. We did this by mapping the given bits to the function thus getting the required values.We also used 10k pot to see the changing voltage. There are two 8 bit ADC's to increase the speed of processing.