Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Subject: search.filters.subject.Fast Fourier Transform

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Address generation for DSP Kernels
    (2011) Ramesh Kini, M.; Sumam David, S.
    Performance of Signal Processing Algorithms implemented in hardware depend on efficiency of datapath, memory speed, and address computation. Pattern of data access in signal processing applications is complex and it is desirable to execute the innermost loop of a kernel every clock. This demands generation of typically three addresses per clock: two addresses for data sample/coefficient and one for storage of processed data. Presence of a set of dedicated, efficient Address Generator Units (AGU) helps in better utilization of the datapath elements by using them only for kernel operations; and will certainly enhance the performance. This paper focuses on design and implementation of Comprehensive Address Generator Unit (CAGU) for complex addressing modes required by DSP Kernels used in Multimedia Signal Processing. An 8 bit CAGU has been implemented using UMC 0.18 micron, 6 metal layers process, that occupies 21802 sq microns, consuming 2.95 mW and works with a clock period of 6 ns. © 2011 IEEE.
  • No Thumbnail Available
    Item
    Noise Cancellation by Fast Fourier Transform for Wav2Vec2.0 based Speech-to-Text System
    (Institute of Electrical and Electronics Engineers Inc., 2023) Gupta, S.P.; Spoorthy, V.; Koolagudi, S.G.
    Speech-To-Text (STT) systems are a part of the Speech Recognition domain in which speech is given as input, and it generates the transcript. The input speech sometimes disrupts the STT system and generates incorrect transcripts because of background noise. In this work, we have discussed a Fast Fourier Transform (FFT) based noise cancellation method for Hindi words with background noise and performed speech to text conversion using a fine-tuned and pre-trained Wav2Vec2.0 model. The background noise added to the audio samples is Gaussian white noise with three different intensity levels, 0.01, 0.03, and 0.05 units, indicated by the Gaussian distribution's standard deviation (STD). The model has been trained on the OpenSLR Hindi dataset. The proposed system is evaluated by the metric Character Error Rate (CER). The testing of the model is done using 20 Hindi words in both clean and noisy conditions. The results obtained proved that the noise cancellation was found effective in terms of CER, and on first level noise with an STD of 0.01, the CER is better after noise cancellation than its noisy counterpart. © 2023 IEEE.
  • No Thumbnail Available
    Item
    A Comparative Analysis between Sliding Mode Control and Super Twisting Sliding Mode Control Applied on a Quadcopter
    (Institute of Electrical and Electronics Engineers Inc., 2024) Vijapur, S.; Jungade, O.A.; Thomas, M.J.
    The use of quadcopters has grown exponentially in countless fields. With the ever-growing demand for automation in today's world, algorithms for the autonomous control of drones have become a major area of research. In this paper, we implement two control strategies, namely Sliding Mode Control (SMC) and Super Twisting Sliding Mode Control (STSMC), on a 6 Degree of Freedom quadcopter model and compare their performance. Using Fast Fourier Transforms, the two controllers are analysed to enable an effective comparison of the chattering in the control input by defining a suitable index. With this index, we observe a decrease of two orders of magnitude in the chattering present in the input given by the STSMC controller. © 2024 IEEE.