Sensor System based on in-fiber Bragg Grats and Interrogator with Spectral Time Channel Separation

The principle of construction and a variant of the software and hardware implementation of the sensor system based on intra-fiber Bragg gratings (FBG) by the example of temperature measurement are proposed. The use of spectral-time separation of measuring channels, which takes into account the features of the functioning and placement of sensors based on FBGs, can significantly reduce the cost, dimensions, weight and power consumption of the interrogator, and neutralize the difficulties in setting up and periodic calibration typical for serial products. The test results of the system show the directions and prospects of using the developed equipment as part of portable and on-board monitoring and control systems.

155 A Machine Learning-Based Approach for Predicting Surgeons’ Subjective Experience and Skill Levels: Neuroimaging Study

Aim Measuring cognitive load is important for surgical education and patient safety. Traditional approaches of measuring cognitive load of surgeons utilise behavioural metrics to measure performance and surveys and questionnaires to collect reports of subjective experience. There is a need for more automated, more accurate and objective evaluation methods. Method Functional neuroimaging data was collected using wireless NIRS device from sixteen surgeons (11 attending surgeons and 5 surgery resident) and 17 students while they performed two laparoscopic tasks (Peg transfer and String pass). Participant’s subjective mental load was assessed using NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. Results The Prefrontal cortex (PFC) activations were greater in students who reported higher-than-median task load, as measured by the NASA-TLX survey. However, in the case of attending surgeons the opposite tendency was observed, namely higher activations in lower v higher task loaded subjects. We found response was greater in the left PFC of students particularly near dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict differences in skill and task load using machine learning while focusing on the effects of NIRS channel separation distance on the results. Our results showed that the classification of skill level and subjective task load could be predicted based on PFC activation with an accuracy of nearly 90%. Conclusions The high accuracy of results is encouraging and suggest the integration of the strategy developed in this study as a promising approach to design automated, more accurate and objective evaluation methods.

Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM

Aiming at the problem of insufficient separation accuracy of aliased signals in space Internet satellite-ground communication scenarios, a stacked long short-term memory network (Stacked-LSTM) separation method based on deep learning is proposed. First, the coding feature representation of the mixed signal is extracted. Then, the long sequence input is divided into smaller blocks through the Stacked-LSTM network with the attention mechanism of the SE module, and the deep feature mask of the source signal is trained to obtain the Hadamard product of the mask of each source and the coding feature of the mixed signal, which is the encoding feature representation of the source signal. Finally, characteristics of the source signal is decoded by 1-D convolution to to obtain the original waveform. The negative scale-invariant source-to-noise ratio (SISNR) is used as the loss function of network training, that is, the evaluation index of single-channel blind source separation performance. The results show that in the single-channel separation of spatially aliased signals, the Stacked-LSTM method improves SISNR by 10.09∼38.17 dB compared with the two classic separation algorithms of ICA and NMF and the three deep learning separation methods of TasNet, Conv-TasNet and Wave-U-Net. The Stacked-LSTM method has better separation accuracy and noise robustness.

Formal Proofs of Orthogonality for Class-Incremental Learning for Wireless Device Identification in IoT

<div> <div> <div> <p>This document provides a formal proof and supple- mentary information of the paper: Class-Incremental Learning for Wireless Device Identification in IoT. The original paper focuses on providing a novel and efficient incremental learning algorithm. In this document, we explicitly explain why the mem- ory representations (latent device fingerprints in our application) in Artificial Neural Networks approximate orthogonality with insights for the invention of our Channel Separation Incremental Learning algorithm. </p> </div> </div> </div>

Formal Proofs of Orthogonality for Class-Incremental Learning for Wireless Device Identification in IoT

<div> <div> <div> <p>This document provides a formal proof and supple- mentary information of the paper: Class-Incremental Learning for Wireless Device Identification in IoT. The original paper focuses on providing a novel and efficient incremental learning algorithm. In this document, we explicitly explain why the mem- ory representations (latent device fingerprints in our application) in Artificial Neural Networks approximate orthogonality with insights for the invention of our Channel Separation Incremental Learning algorithm. </p> </div> </div> </div>