DSP

In this experiment we did carried out different phase shift keying techniques and line coding technique. But first we saw harmonic generation where we see different harmonics of wave and their interactions with each other. We performed these experiments on CCS simulator generated the the values and plotted the values on QTplotter. BPSK, DPSK and QPSK are the types of phase shift keying we saw in these experiments. We realized the pros and cons of each of the techniques. Secondly we saw line coding techniques like Manchester coding, Duo-binary encoding, Modified Duo-binary and Duo-binary with a precoder. Here too we realized how each coding technique affects inter-symbol interference.

In this experiment, we performed auto-correlation and cross-correlation. Apart from this we also used Carl Pearson Correlation Coefficient calculation using the provided formula and using FFT for the same. Correlation is used for checking the similarity between two signals, and hence is an important tool. DSP is quite efficient in giving us the computational results.

In this experiment, we performed linear convolution, circular convolution and linear convolution by circular convolution and both using FFT. In circular convolution, we observed that the output signal was just the addition of elements of the linear convolution output. We can see that the linear convolution signal was overlapped to give circular convolution output. This is aliasing. Therefore, we made the following conclusions from the above experiment: ● Length of linear convolution output signal= L+M-1 ● Length of circular convolution=Max(L,M) ● Circular convolution gives aliased output

In this experiment we want to implement Linear filtering Tradition methods of time domain based convolution require the entire input sequence to be present before commutation. This can pose a problem in real time applications where the data coming is long and continuous. Overlap save method and overlap add method divide the input sequence into smaller blocks. The filtering of individual blocks which are smaller in length is done by DFT and IDFT and then fitted one after the other. The overlapped portion is discarded in OSM whereas it is added in OAM to get the required output sequence. This resultant is the same as that if the input had been processed via time domain convolution.

Fourier transform is an essential part of signal processing , since it transforms a signal from the time domain into the Frequency domain. When we have a discrete signal we need to use Discrete Fourier transform is DFT FFT is an algorithm to compute DFT of any input FFT is a proven faster method as compared to the DFT algorithm. Some of the similar complexities re-appeared in this code too (example - integration of complex numbers). Unlike the DFT, i could identify patterns in the butterfly diagram taught in the class. Formulating this pattern into a code format and making this code generic(for 4-point and 8-point) and efficient was the real highlight of this code. Though a generic N-point FFT code, still remains an ambitious task.

In this experiment we get a to implement flash programming to implement a LED counter on the DSP board of TI, we understood the basics of flash programming and proper use of JTAG. In the given experiment, we utilized the on board LEDs to show the binary counting action happening, from 0-15 in binary. Once the flash programming was done, even after the power was removed and external power source was used, the DSP board still contained and ran the same code, hence we saw that if we want the same code to run on the Board, without interruptions then Flash programming is our go-to choice.

In a digital signal processing application the sensors are crucial part of the system. sensors are the means by which the processor aquires the data from outside world. generally sensors give output voltage in a fixed range depending on the sensor parameters. In this practical we used different types of sensors and interfaced them with the DSP by using an a/d converter. this practical enabled us to aquire real world data by using various kinds of sensors. We used an LDR which detects light, Optocoupler which is used to detect block in a certain pathway of light, Ultrasonic sensor and detector used to measure distance, Hall effect sensor used to measure magnetic fields, Audio sensor(microphone) used to capture surrounding audio, Temperature sensor to get the ambient temperature. Using all these sensors we can build real time standalone systems

In this experiment we used ADC-DAC of the DSP processor (TMS320). It has two 8 bit ADCs and one external 12 bit DAC. We generated different types of waves like sine wave, sawtooth wave and triangle wave. We did this by mapping the given bits to the function thus getting the requiered values. We also used a 10k pot to see the changing voltage. There are two 8 bit ADCs to sample two inputs at once. Thus we can create and sample any waveform we want using the ADC-DAC hardware

In this experiment we used our DSP codes to solve a real world problem statement We created a DSP stand alone system with the TMS320F28335 We made a QPSK transmitter and receiver system using a ddsp.BPSK is Binary Phase Shift Keying. the input to the reciever is binary data. this data was entered through a computer. the dsp then uses this data to create a sine wave at the output. the phase of the sine wave is changed according to the bit that is to be transmitted. the on board dac ic was used to generate this sine wave output. the ouput of the transmitter is given to the input of the reciever. The receiver used the onchip adc to sample the bpsk input sine wave. this sampled data was used to decode the the bpsk signal and to retrive the information in bit format. this information can now be viewd on the computer screen by the user whenever he desires.

Signal Processing is such a ubiquitous subject that one wouldn't even realize one is using it daily. One of the most important parts of or body is a Signal Processor , namely the brain , without it everything else would be meaningless. Just like in Biological systems where we have different Inputs like the Eyes , the Ears , the Nose and all the Touch sensory inputs and a lot more , and all these signals need to be captured (sampled)  and then processed for the insight that they provide. Similarly for Digital Signals coming from different places , we need to sample them and extract the underlying information in them. That is the crux of DSP , and like the human brains specialised processing zones , DSP applications rely on specialised hardware of a DSP processor. A DSP processor is a more parallelized form of a General Purpose Processor and can effectively execute DSP algorithms in Parallel and thus faster In the DSP lab . we have worked on one such chip the TMS320F28335 ...