The optically pumped magnetometer (OPM) operating in spin exchange relaxation-free (SERF) regime is a kind of magnetic sensor with ultra-high sensitivity, which attracts widespread attentions for biomagnetism measurements in recent years. SERF OPMs need to operate in a zero magnetic field environment, otherwise the sensors will have reduced sensitivity. In many applications, multi-channel OPMs should be utilized to inversely localize and quantify the magnetic source, in which case the magnetic fields of the OPMs interfere with each other. This paper proposes an array OPMs magnetic field compensation method based on an artificial neural network (ANN) algorithm. The transfer function between the compensation currents and the observed signals are derived, based on which a nonlinear multiparameter optimization problem is abstracted. Then, an ANN model is employed to optimize the compensation currents of the OPMs with the objective function minimizing the magnetic field at each sensor location. This method can effectively reduce the effect of the magnetic field crosstalk of multi-channel OPMs.

Aiming at real-time control of a powered prosthetic hand, this paper studies the real-time classification of intended hand motions from tactile feature patterns on the forearm surface caused by muscle contraction. Two sheets of Plyvynylidene Fluoride (PVDF) film were used as tactile sensors to detect the tactile features on the forearm caused by intended hand motions. Machine learning was applied to the classification of hand motion intentions using the tactile feature patterns. In this paper, we further studied the real-time motion classification methods in an online environment. We found that average classification accuracy for the 6 types of motion in 6 experiment participants was 82.8 %, showing that real-time motion classification is possible by using the support vector machine with simple training for several minutes.

The decrease in radiofrequency wavelengths poses a challenge of B1-field inhomogeneity for ultra-high-field MRI. This study introduces a novel solution—a planar metamaterial with a unique hybrid structure that is specifically designed for integration with a birdcage coil at 7T. Simulations have demonstrated that this new structure greatly enhances the transmit efficiency.

Humans perceive tactile sensations to discriminate objects’ properties and manipulate them. While tactile sensation has been attempted to be used in robot sensing and tactile presentation in Virtual Reality, the mechanism of tactile sensation is not clarified. Hardness is one of the tactile senses, and it is generally perceived when an object is deformed. Moreover, in previous research, it was shown that an object’s surface properties also change the hardness sensation. However, the cause of this change has not been identified. The purpose of this study is to investigate how the hardness sensation changes depending on the surface properties of an object, and what factors cause the difference in hardness sensation. In this study, a sensory evaluation of the hardness feeling and measurement of the contact state were conducted by using samples with five different roughnesses.