Aislyn Technologies Pvt Ltd

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Digital Signal Processing

Digital signal processing (DSP) is the numerical manipulation of signals, usually with the intention to measure, filter, produce or compress continuous analog signals. It is characterized by the use of digital signals to represent these signals as discrete time, discrete frequency, or other discrete domain signals in the form of a sequence of numbers or symbols to permit the digital processing of these signals. Theoretical analyses and derivations are typically performed on discrete-time signal models, created by the abstract process of sampling. Numerical methods require a digital signal, such as those produced by an analog-to-digital converter (ADC). The processed result might be a frequency spectrum or a set of statistics. But often it is another digital signal that is converted back to analog form by a digital-to-analog converter (DAC). Even if that whole sequence is more complex than analog processing and has a discrete value range, the application of computational power to signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression. Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, among others. DSP algorithms have long been run on standard computers, as well as on specialized processors called digital signal processors, and on purpose-built hardware such as application-specific integrated circuit (ASICs). Currently, there are additional technologies used for digital signal processing including more powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors, among others. Digital signal processing 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. Applications: The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, radar, sonar, financial signal processing, seismology and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room correction of sound in hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, medical imaging such as CAT scans and MRI, MP3 compression, computer graphics, image manipulation, hi-fi loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers


  1. Feature adapted convolutional neural networks for downbeat tracking
  2. Steganalysis of AAC using calibrated Markov model of adjacent codebook
  3. Steganalysis of AAC using calibrated Markov model of adjacent codebook
  4. Efficient target-response interpolation for a graphic equalizer
  5. Novel favorite music classification using EEG-based optimal audio features selected via KDLPCCA
  6. Optimizing DTW-based audio-to-MIDI alignment and matching
  7. Fusion Methods for Speech Enhancement and Audio Source Separation
  8. Foreground Speech Segmentation and Enhancement Using Glottal Closure Instants and Mel Cepstral Coefficients
  9. Classification of motor task execution speed from EEG data
  10. Robust blind source separation in a reverberant room based on beamforming with a large-aperture microphone array
  11. Enhancement and Noise Statistics Estimation for Non-Stationary Voiced Speech
  12. A Hierarchical Classification and Segmentation Scheme for Processing Sensor Data
  13. Text-Independent Phoneme Segmentation Combining EGG and Speech Data
  14. Channel Acquisition for Massive MIMO-OFDM With Adjustable Phase Shift Pilots
  15. Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels
  16. Iterative Methods for Subspace and DOA Estimation in Nonuniform Noise
  17. Performance Analysis of Full-Duplex-MRC-MIMO With Self-Interference Cancellation Using Null-Space-Projection
  18. ML estimation of time and frequency offset in OFDM systems
  19. Particle filters for positioning, navigation, and tracking
  20. Structured Compressed Sensing: From Theory to Applications
  21. Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation
  22. Compressed Sensing with Basis Mismatch: Performance Bounds and Sparse-Based Estimator
  23. Average SINR Calculation of a Persymmetric Sample Matrix Inversion Beamformer
  24. A Combined Rule-Based and Machine Learning Audio-Visual Emotion Recognition Approach
  25. User mobility impact on millimeter-wave system performance
  26. Opportunities for a more Efficient Use of the Spectrum based in Cognitive Radio
  27. Time-division multiple access based intra-body communication for wearable health tracker
  28. An effective handover scheme in heterogeneous networks
  29. Outage performance of opportunistic AF OFDM relaying over Rician fading channel
  30. LTCC passive components for matching circuits of cognitive radio antennas
  31. Received signal strength based localization in sectorized cellular networks
  32. Low-complexity approximations for LMMSE channel estimation in OFDM/OQAM