An Artificial Neural Network to Eliminate the Detrimental Spectral Shift on Mid-Infrared Gas Spectroscopy.

We demonstrate the successful implementation of an artificial neural network (ANN) to eliminate detrimental spectral shifts imposed in the measurement of laser absorption spectrometers (LASs). Since LASs rely on the analysis of the spectral characteristics of biological and chemical molecules, their accuracy and precision is especially prone to the presence of unwanted spectral shift in the measured molecular absorption spectrum over the reference spectrum. In this paper, an ANN was applied to a scanning grating-based mid-infrared trace gas sensing system, which suffers from temperature-induced spectral shifts. Using the HITRAN database, we generated synthetic gas absorbance spectra with random spectral shifts for training and validation. The ANN was trained with these synthetic spectra to identify the occurrence of spectral shifts. Our experimental verification unambiguously proves that such an ANN can be an excellent tool to accurately retrieve the gas concentration from imprecise or distorted spectra of gas absorption. Due to the global shift of the measured gas absorption spectrum, the accuracy of the retrieved gas concentration using a typical least-mean-squares fitting algorithm was considerably degraded by 40.3%. However, when the gas concentration of the same measurement dataset was predicted by the proposed multilayer perceptron network, the sensing accuracy significantly improved by reducing the error to less than ±1% while preserving the sensing sensitivity.

Secure Stream Processing for Medical Data.

Medical data belongs to whom it produces it. In an increasing manner, this data is usually processed in unauthorized third-party clouds that should never have the opportunity to access it. Moreover, recent data protection regulations (e.g., GDPR) pave the way towards the development of privacy-preserving processing techniques. In this paper, we present a proof of concept of a streaming IoT architecture that securely processes cardiac data in the cloud combining trusted hardware and Spark. The additional security guarantees come with no changes to the application's code in the server. We tested the system with a database containing ECGs from wearable devices comprised of 8 healthy males performing a standardized range of in-lab physical activities (e.g., run, walk, bike). We show that, when compared with standard Spark Streaming, the addition of privacy comes at the cost of doubling the execution time.

In-Vivo Electrical Impedance Measurement in Mastoid Bone.

Nerve monitoring is a safety mechanism to detect the proximity between surgical instruments and important nerves during surgical bone preparation. In temporal bone, this technique is highly specific and sensitive at distances below 0.1 mm, but remains unreliable for distances above this threshold. A deeper understanding of the patient-specific bone electric properties is required to improve this range of detection. A sheep animal model has been used to characterize bone properties in vivo. Impedance measurements have been performed at low frequencies (<1 kHz) between two electrodes placed inside holes drilled into the sheep mastoid bone. An electric circuit composed of a resistor and a Fricke constant phase element was able to accurately describe the experimental measurements. Bone resistivity was shown to be linearly dependent on the inter-electrode distance and the local bone density. Based on this model, the amount of bone material between the electrodes could be predicted with an error of 0.7 mm. Our results indicate that bone could be described as an ideal resistor while the electrochemical processes at the electrode-tissue interface are characterized by a constant phase element. These results should help increasing the safety of surgical drilling procedures by better predicting the distance to critical nerve structures.