Mohammadreza Asghari Oskoei

Biomedical Engineering · 2007–2008

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

Whom Mohammadreza Asghari Oskoei trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Kevin B. Englehart
  2. Adrian D. C. Chan
  3. B. Hudgins
  4. Yonghong Huang
  5. Philip A. Parker

Who trusts Mohammadreza Asghari Oskoei overall

  1. Angkoon Phinyomark
  2. Chusak Limsakul
  3. Pornchai Phukpattaranont
  4. Rami N. Khushaba
  5. Nurhazimah Nazmi
  6. Jawad Nagi
  7. Rubana H. Chowdhury
  8. Franck Quaine
  9. U. Côté-Allard
  10. Dario Farina

Papers from Mohammadreza Asghari Oskoei's perspective

Ordered by how strongly the researchers Mohammadreza Asghari Oskoei trusts endorse each paper. Same corpus, a different order for every researcher.

  1. A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses (IEEE Transactions on Biomedical Engineering)
  2. A wavelet-based continuous classification scheme for multifunction myoelectric control (IEEE Transactions on Biomedical Engineering)
  3. Continuous myoelectric control for powered prostheses using hidden Markov models (IEEE Transactions on Biomedical Engineering)
  4. A robust, real-time control scheme for multifunction myoelectric control (IEEE Transactions on Biomedical Engineering)
  5. Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study (IEEE Transactions on Biomedical Engineering)
  6. Speaker identification and verification using Gaussian mixture speaker models
  7. Speaker Verification Using Adapted Gaussian Mixture Models
  8. Robust text-independent speaker identification using Gaussian mixture speaker models
  9. An overview of automatic speaker recognition technology (IEEE International Conference on Acoustics, Speech, and Signal Processing)
  10. Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal (IEEE Transactions on Biomedical Engineering)

Their papers