Tactile sensor arrays have attracted considerable attention for their use in diverse applications, such as advanced robotics and interactive human–machine interfaces. However, conventional tactile sensor arrays suffer from electrical crosstalk caused by current leakages between the tactile cells. The approaches that have been proposed thus far to overcome this issue require complex rectifier circuits or a serial fabrication process. This article reports a flexible tactile sensor array fabricated through a batch process using a mesh. A carbon nanotube–polydimethylsiloxane composite is used to form an array of sensing cells in the mesh through a simple “dip-coating” process and is cured into a concave shape. The contact area between the electrode and the composite changes significantly under pressure, resulting in an excellent sensitivity (5.61 kPa–1) over a wide range of pressure up to 600 kPa. The mesh separates the composite into the arranged sensing cells to prevent the electrical connection between adjacent cells and simultaneously connects each cell mechanically. Additionally, the sensor shows superior durability compared with previously reported tactile sensors because the mesh acts as a support beam. Furthermore, the tactile sensor array is successfully utilized as a Braille reader via information processing based on machine learning.

Background To develop a warning system that can prevent or minimize laser exposure resulting in kidney and ureter damage during retrograde intrarenal surgery (RIRS) for urolithiasis. Our study builds on the hypothesis that shock waves of different degrees are delivered to the hand of the surgeon depending on whether the laser hits the stone or tissue. Methods A surgical environment was simulated for RIRS by filling the body of a raw whole chicken with water and stones from the human body. We developed an acceleration measurement system that recorded the power signal data for a number of hours, yielding distinguishable characteristics among three different states (idle state, stones, and tissue–laser interface) by conducting fast Fourier transform (FFT) analysis. A discrete wavelet transform (DWT) was used for feature extraction, and a random forest classification algorithm was applied to classify the current state of the laser-tissue interface. Results The result of the FFT showed that the magnitude spectrum is different within the frequency range of < 2500 Hz, indicating that the different states are distinguishable. Each recorded signal was cut in only 0.5-s increments and transformed using the DWT. The transformed data were entered into a random forest classifier to train the model. The test result was only measured with the dataset that was isolated from the training dataset. The maximum average test accuracy was > 95%. The procedure was repeated with random signal dummy data, resulting in an average accuracy of 33.33% and proving that the proposed method caused no bias. Conclusions Our monitoring system receives the shockwave signals generated from the RIRS urolithiasis treatment procedure and generates the laser irradiance status by rapidly recognizing (in 0.5 s) the current laser exposure state with high accuracy (95%). We postulate that this can significantly minimize surgeon error during RIRS.

In this paper, we present algorithms for predicting a spatio-temporal random field measured by mobile robotic sensors under uncertainties in localization and measurements. The spatio-temporal field of interest is modeled by a sum of a time-varying mean function and a Gaussian Markov random field (GMRF) with unknown hyperparameters. We first derive the exact Bayesian solution to the problem of computing the predictive inference of the random field, taking into account observations, uncertain hyperparameters, measurement noise, and uncertain localization in a fully Bayesian point of view. We show that the exact solution for uncertain localization is not scalable as the number of observations increases. To cope with this exponentially increasing complexity and to be usable for mobile sensor networks with limited resources, we propose a scalable approximation with a controllable trade-off between approximation error and complexity to the exact solution. The effectiveness of the proposed algorithms is demonstrated by simulation and experimental results.

It has been demonstrated that radioactive iodine therapy (RIT) is very useful for ablating remnant thyroid tissue or metastatic lesions after total thyroidectomy in well-differentiated thyroid cancer. Epiphora as a result of lacrimal drainage system obstruction is a rare complication of RIT. Previous studies have shown that most patients with epiphora after RIT are old ages, females, and with high cumulative radioactive iodine dose. Here, we report on a case of epiphora with low-risk factors including young ages, male, and 5.5-GBq (150-mCi) dose in the first RIT.