Machine learning monitoring for laser osteotomy.

This work proposes a new online monitoring method for an assistance during laser osteotomy. The method allows differentiating the type of ablated tissue and the applied dose of laser energy. The setup analyzes the laser-induced acoustic emission, detected by an airborne microphone sensor.

Sensitivity Analysis of Acoustic Emission Detection Using Fiber Bragg Gratings with Different Optical Fiber Diameters.

Acoustic Emission (AE) detection and, in particular, ultrasound detection are excellent tools for structural health monitoring or medical diagnosis. Despite the technological maturity of the well-received piezoelectric transducer, optical fiber AE detection sensors are attracting increasing attention due to their small size, and electromagnetic and chemical immunity as well as the broad frequency response of Fiber Bragg Grating (FBG) sensors in these fibers. Due to the merits of their small size, FBGs were inscribed in optical fibers with diameters of 50 and 80 μm in this work.

Author Correction: Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance.

Laser welding is a key technology for many industrial applications. However, its online quality monitoring is an open issue due to the highly complex nature of the process. This work aims at enriching existing approaches in this field.

Characterization of Ablated Bone and Muscle for Long-Pulsed Laser Ablation in Dry and Wet Conditions.

Smart laser technologies are desired that can accurately cut and characterize tissues, such as bone and muscle, with minimal thermal damage and fast healing. Using a long-pulsed laser with a 0.5-10  ms pulse width at a wavelength of 1.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study.

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation.

Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones.

Background: Individuals with type 1 diabetes (T1D) have to count the carbohydrates (CHOs) of their meal to estimate the prandial insulin dose needed to compensate for the meal's effect on blood glucose levels. CHO counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control.

Method: The GoCARB system is a smartphone application designed to support T1D patients with CHO counting of nonpacked foods.