Research on the Short-Time Bias Stability of the Solid Vibration Beam Gyroscope

In this paper, Short-term bias stability of solid state vibration beam gyroscope is studied. Based on the dynamical equation of solid vibration beam gyroscope, the elements effecting the zero stability which effects the short term bias stability was discussed. The computed result shows that the main factors for short-term bias stability is transient solution error. The output signal has been filtered by the low pass filter, according to the characteristic of transient solution error. Simulation results demonstrate that low-pass filter can effectively suppress the error of output signal. In addition, the signal of solid state vibrating gyroscope filtered by low-pass filter agrees well with the simulation result. From the simulation and experiment data, the validity of the model and the feasibility of the filtering programs has been proved.

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Myoelectric-Controlled Exoskeletal Elbow Robot to Suppress Essential Tremor: Extraction of Elbow Flexion Movement Using STFTs and TDNN

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2012.p0141 ◽

2012 ◽

Vol 24 (1) ◽

pp. 141-149 ◽

Cited By ~ 8

Author(s):

Takeshi Ando ◽

◽

Masaki Watanabe ◽

Keigo Nishimoto ◽

Yuya Matsumoto ◽

...

Keyword(s):

Signal Processing ◽

Essential Tremor ◽

Voluntary Movement ◽

Elbow Flexion ◽

Myoelectric Signal ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass ◽

Short Time ◽

Short Time Fourier Transformation

Essential tremor is the most common of all involuntary movements. Many patients with an upper-limb tremor have serious difficulties in performing daily activities. We developed a myoelectric-controlled exoskeletal robot to suppress tremor. In this article, we focus on developing a signal processing method to extract voluntary movement from a myoelectric in which the voluntary movement and tremor were mixed. First, a Low-Pass Filter (LPF) and Neural Network (NN) were used to recognize the tremor patient’s movement. Using these techniques, it was difficult to recognize the movement accurately because the myoelectric signal of the tremor patient periodically oscillated. Then, Short-Time Fourier Transformation (STFT) and NN were used to recognize the movement. This method was more suitable than LPF and NN. However, the recognition timing at the start of the movement was late. Finally, a hybrid algorithm for using both short and long windows’ STFTs, which is a kind of “mixture of experts,” was proposed and developed. With this type of signal processing, elbow flexion was accurately recognized without the time delay in starting the movement.

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Convolution Integral: How a Graphical Type of Solution Can Help Minimize Misconceptions

Journal of Electrical And Electronics Engineering ◽

10.33021/jeee.v3i2.1489 ◽

2021 ◽

Vol 3 (2) ◽

pp. 103

Author(s):

Hendra J. Tarigan

Keyword(s):

Impulse Response ◽

Physical System ◽

Convolution Integral ◽

Pass Filter ◽

Low Pass Filter ◽

Convolution Integrals ◽

Low Pass ◽

Simulation And Experiment ◽

Rc Circuit ◽

Set Up

A physical system, Low Pass Filter (LPF) RC Circuit, which serves as an impulse response and a square wave input signal are utilized to derive the continuous time convolution (convolution integrals). How to set up the limits of integration correctly and how the excitation source convolves with the impulse response are explained using a graphical type of solution. This in turn, help minimize the students’ misconceptions about the convolution integral. Further, the effect of varying the circuit elements on the shape of the convolution output plot is presented allowing students to see the connection between a convolution integral and a physical system. PSpice simulation and experiment results are incorporated and are compared with those of the analytical solution associated with the convolution integral.

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