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tms320f28335 digital signal processing platform  (MathWorks Inc)


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    MathWorks Inc tms320f28335 digital signal processing platform
    Tms320f28335 Digital Signal Processing Platform, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 821 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/tms320f28335 digital signal processing platform/product/MathWorks Inc
    Average 96 stars, based on 821 article reviews
    tms320f28335 digital signal processing platform - by Bioz Stars, 2026-04
    96/100 stars

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    Figure 1. Complex activities such as playing a musical instrument present a great challenge to upper limb amputees. Tasks such as these require accurate slip control of multiple fingertips simultaneously across different surfaces. In this paper, we explore the potential for three amputees and nine non- amputees to simultaneously control the state of sliding contact at two fingertips simultaneously by integrating two channels of variable frequency vibrotactile haptic feedback into their motor control strategies. The rendering of the man in this image was licensed and modified for public display.

    Journal: Robotics

    Article Title: Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

    doi: 10.3390/robotics13070097

    Figure Lengend Snippet: Figure 1. Complex activities such as playing a musical instrument present a great challenge to upper limb amputees. Tasks such as these require accurate slip control of multiple fingertips simultaneously across different surfaces. In this paper, we explore the potential for three amputees and nine non- amputees to simultaneously control the state of sliding contact at two fingertips simultaneously by integrating two channels of variable frequency vibrotactile haptic feedback into their motor control strategies. The rendering of the man in this image was licensed and modified for public display.

    Article Snippet: The Slow and Fast vibrotactile activation signals for this test were created in Simulink using the ROS toolbox.

    Techniques: Control, Modification

    Figure 4. Characterization of the two vibration modes of the vibrotactile stimulators for haptic feedback to the human subjects from the index (I) and little (L) fingers. (A) The BioTac SP on the I finger of the Shadow Hand was used in this experiment to measure the Slow and Fast vibration modes of the vibrotactile stimulators that was conveyed to the human subjects. (B) Steady–state pressure (PDC) and (C) spectrogram of the steady-state pressure (PDC) measured by the BioTac corresponding to the Slow vibration mode. (D) The dynamic pressure signal (PAC) and the (E) corresponding spectrogram from the Slow vibration mode. (F) Steady–state pressure (PDC) and (G) spectrogram corresponding to the Fast vibration mode. (H) The dynamic pressure signal (PAC) and (I) the corresponding spectrogram from the Fast vibration mode.

    Journal: Robotics

    Article Title: Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

    doi: 10.3390/robotics13070097

    Figure Lengend Snippet: Figure 4. Characterization of the two vibration modes of the vibrotactile stimulators for haptic feedback to the human subjects from the index (I) and little (L) fingers. (A) The BioTac SP on the I finger of the Shadow Hand was used in this experiment to measure the Slow and Fast vibration modes of the vibrotactile stimulators that was conveyed to the human subjects. (B) Steady–state pressure (PDC) and (C) spectrogram of the steady-state pressure (PDC) measured by the BioTac corresponding to the Slow vibration mode. (D) The dynamic pressure signal (PAC) and the (E) corresponding spectrogram from the Slow vibration mode. (F) Steady–state pressure (PDC) and (G) spectrogram corresponding to the Fast vibration mode. (H) The dynamic pressure signal (PAC) and (I) the corresponding spectrogram from the Fast vibration mode.

    Article Snippet: The Slow and Fast vibrotactile activation signals for this test were created in Simulink using the ROS toolbox.

    Techniques:

    Figure 5. Training subjects for efferent control. (A) The amplified, filtered, and rectified EMG signals from six electrodes were normalized. (B) ANN classifier outputs for the Sim, I, L, and NM classes corresponding to the six EMG signals. (C) Subject S3 performing the EMG classifier training. The red and blue circles indicate where the vibrotactile stimulators for the index (I) and little (L) fingers were respectively placed for subject S3. The subject (S3) gave permission for the use of his image.

    Journal: Robotics

    Article Title: Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

    doi: 10.3390/robotics13070097

    Figure Lengend Snippet: Figure 5. Training subjects for efferent control. (A) The amplified, filtered, and rectified EMG signals from six electrodes were normalized. (B) ANN classifier outputs for the Sim, I, L, and NM classes corresponding to the six EMG signals. (C) Subject S3 performing the EMG classifier training. The red and blue circles indicate where the vibrotactile stimulators for the index (I) and little (L) fingers were respectively placed for subject S3. The subject (S3) gave permission for the use of his image.

    Article Snippet: The Slow and Fast vibrotactile activation signals for this test were created in Simulink using the ROS toolbox.

    Techniques: Control, Amplification

    Figure 6. Control system for multichannel sensorimotor integration. Six EMG signals were classified by subject-specific ANNs to specify which finger(s) the subjects wanted to control. The EMG signals were also used to specify the desired forces for the index (I) and little (L) fingers that were realized by hybrid force–velocity controllers. ANNs were used with the BioTac SPs on the I and L fingers to classify the 24 taxels at each fingertip into sensations of sliding contact, either up or down. These sensations of touch from each fingertip were encoded via the frequency of vibration and fed back to the subjects with the haptic armband. The red and blue circles indicate where the vibrotactile stimulators for the index (I) and little (L) fingers were respectively placed for subject S1. EMG electrodes were located under the black armband. The subject (S1) gave permission for the use of his image.

    Journal: Robotics

    Article Title: Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

    doi: 10.3390/robotics13070097

    Figure Lengend Snippet: Figure 6. Control system for multichannel sensorimotor integration. Six EMG signals were classified by subject-specific ANNs to specify which finger(s) the subjects wanted to control. The EMG signals were also used to specify the desired forces for the index (I) and little (L) fingers that were realized by hybrid force–velocity controllers. ANNs were used with the BioTac SPs on the I and L fingers to classify the 24 taxels at each fingertip into sensations of sliding contact, either up or down. These sensations of touch from each fingertip were encoded via the frequency of vibration and fed back to the subjects with the haptic armband. The red and blue circles indicate where the vibrotactile stimulators for the index (I) and little (L) fingers were respectively placed for subject S1. EMG electrodes were located under the black armband. The subject (S1) gave permission for the use of his image.

    Article Snippet: The Slow and Fast vibrotactile activation signals for this test were created in Simulink using the ROS toolbox.

    Techniques: Control

    Figure 7. Sample data illustrating robotic system operation for the four cases of simultaneous slip at the index (I) and little (L) fingers. Simultaneous haptic feedback had two different vibration frequencies depending upon the direction of sliding contact. Upward slip was encoded with Slow vibration, while the downward slip produced Fast vibration. Subjects were trained to permit upward slip but prevent downward slip using their six EMG signals to produce four different classes (Sim, I, L, NM) with the efferent ANN. (A) Simultaneous downward slip at each fingertip was created by (B) both stepper motors driving downward slip. (C) This caused both vibrotactile actuators to be activated in the Fast mode. (D) The subject perceived the simultaneously activated channels of haptic feedback and increased his EMG signals (E) to produce the Sim class with the efferent ANN. (F,G) Slip down at the I finger with slip up at the L finger caused the (H) vibrotactile stimulators to be actuated with the Fast and Slow modes, respectively. (I) The subject responded to this multichannel

    Journal: Robotics

    Article Title: Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

    doi: 10.3390/robotics13070097

    Figure Lengend Snippet: Figure 7. Sample data illustrating robotic system operation for the four cases of simultaneous slip at the index (I) and little (L) fingers. Simultaneous haptic feedback had two different vibration frequencies depending upon the direction of sliding contact. Upward slip was encoded with Slow vibration, while the downward slip produced Fast vibration. Subjects were trained to permit upward slip but prevent downward slip using their six EMG signals to produce four different classes (Sim, I, L, NM) with the efferent ANN. (A) Simultaneous downward slip at each fingertip was created by (B) both stepper motors driving downward slip. (C) This caused both vibrotactile actuators to be activated in the Fast mode. (D) The subject perceived the simultaneously activated channels of haptic feedback and increased his EMG signals (E) to produce the Sim class with the efferent ANN. (F,G) Slip down at the I finger with slip up at the L finger caused the (H) vibrotactile stimulators to be actuated with the Fast and Slow modes, respectively. (I) The subject responded to this multichannel

    Article Snippet: The Slow and Fast vibrotactile activation signals for this test were created in Simulink using the ROS toolbox.

    Techniques: Produced