Deep transfer learning for improving single-EEG arousal detection

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases.Comment: Accepted for presentation at EMBC202

NLOS Identification and Mitigation Using Low-Cost UWB Devices

[Abstract] Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters. In this work, machine learning (ML) techniques are employed to analyze several sets of real UWB measurements, captured in different scenarios, to try to identify the measurements facing non-line-of-sight (NLOS) propagation condition. Additionally, an ulterior process is carried out to mitigate the deviation of these measurements from the actual distance value between the devices. The results show that ML techniques are suitable to identify NLOS propagation conditions and also to mitigate the error of the estimates when there is LOS between the emitter and the receiver.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Antenna Controller for Low-latency and High Reliability Robotic Communications over Time-varying Fading Channels

In this paper we consider the problem in which a single antenna mobile robot (MR) is required to maintain reliable communication with a base station (BS) while following a given trajectory. The wireless communication channel is assumed to experience time-varying small-scale fading with time-varying coherence time. To achieve high reliability and low latency of the communication link, compensation for the small-scale fading is required. Since the MR has to stay on the predefined trajectory, we propose to compensate for the fading by continuously adjusting the position of the antenna on a revolving platform on-board the MR. This is done by a closed-loop antenna controller which optimises the position of the antenna at all times without modifying the trajectory to be followed by the MR. Results show that this technique can effectively compensate for the small-scale fading without introducing delays in data reception

Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems

[Abstract] Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Transmission of Analog Information Over the Multiple Access Relay Channel Using Zero-Delay Non-Linear Mappings

[Abstract]: We consider the zero-delay encoding of discrete-time analog information over the Multiple Access Relay Channel (MARC) using non-linear mapping functions. On the one hand, zero-delay non-linear mappings are capable to deal with the multiple access interference (MAI) caused by the simultaneous transmission of the information. On the other, the relaying operation is a Decode-and-Forward (DF) strategy where the decoded messages are merged into a single message using a specific continuous mapping depending on the correlation level of the source information. At the receiver, an approximated Minimum Mean Squared Error (MMSE) decoder is developed to obtain an estimate of the transmitted source symbols which exploits the information received from the relay node in combination with the messages received from the transmitters through the direct links. The resulting system provides better performance than the other alternative encoding strategies for the MARC with similar complexity and delay and also approaches the performance of theoretical strategies which require a significantly higher delay and computational cost.This work was supported in part by the Office of the Naval Research Global of United States under Grant N62909-15-1-2014, in part by the Xunta de Galicia under Grant ED431C 2016-045, Grant ED341D R2016/012, and Grant ED431G/01, in part by the Agencia Estatal de Investigación of Spain under Grant TEC2015-69648-REDC and Grant TEC2016-75067-C4-1-R, and in part by the ERDF funds of the EU (AEI/FEDER, UE).Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED341D R2016/012Xunta de Galicia; ED431G/0

Wideband User Grouping for Uplink Multiuser mmWave MIMO Systems With Hybrid Combining

[Abstract] Analog-digital hybrid precoding and combining schemes constitute an interesting approach to millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems due to the low hardware complexity and/or low power required for its deployment. However, the design of the hybrid precoders and combiners of a wideband multiuser (MU) mmWave MIMO system is challenging because the signal processing in the analog domain is constrained to be frequency flat. Furthermore, the number of radio frequency (RF) chains limits the number of individual streams that a common base station (BS) can simultaneously serve. This work jointly addresses the user scheduling, the user precoder design, and the BS hybrid combining design for the uplink of wideband MU mmWave MIMO systems. On the one hand, user precoding and BS hybrid combining are jointly designed to minimize the impact of having frequency-flat RF components. On the other hand, a number of users larger than the number of RF chains are served at the BS by employing a distributed quantizer linear coding (DQLC)-based non-orthogonal multiple access (NOMA) scheme. The use of this encoding strategy also allows exploiting the spatial correlation between the source information. Simulation results show remarkable performance gains of the proposed approaches for wideband mmWave MIMO hardware-constrained systems.10.13039/501100010801-Xunta de Galicia (Grant Number: ED431C 2020/15) 10.13039/501100010801-Centro de Investigación de Galicia CITIC (Grant Number: ED431G2019/01) 10.13039/501100011033-Agencia Estatal de Investigación of Spain (Grant Number: RED2018-102668-T and PID2019-104958RB-C42) European Regional Development Funds (ERDF) of the EU (ERDF Galicia 2014-2020 & AEI/ERDF programs, UE) Predoctoral (Grant Number: BES-2017-081955)Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G2019/0

Multi-Sensor Accurate Forklift Location and Tracking Simulation in Industrial Indoor Environments

[Abstract] Location and tracking needs are becoming more prominent in industrial environments nowadays. Process optimization, traceability or safety are some of the topics where a positioning system can operate to improve and increase the productivity of a factory or warehouse. Among the different options, solutions based on ultra-wideband (UWB) have emerged during recent years as a good choice to obtain highly accurate estimations in indoor scenarios. However, the typical harsh wireless channel conditions found inside industrial environments, together with interferences caused by workers and machinery, constitute a challenge for this kind of system. This paper describes a real industrial problem (location and tracking of forklift trucks) that requires precise internal positioning and presents a study on the feasibility of meeting this challenge using UWB technology. To this end, a simulator of this technology was created based on UWB measurements from a set of real sensors. This simulator was used together with a location algorithm and a physical model of the forklift to obtain estimations of position in different scenarios with different obstacles. Together with the simulated UWB sensor, an additional inertial sensor and optical sensor were modeled in order to test its effect on supporting the location based on UWB. All the software created for this work is published under an open-source license and is publicly available.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Improving semi-supervised learning for audio classification with FixMatch

Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement

HRTFs Measurement Based on Periodic Sequences Robust towards Nonlinearities in Automotive Audio

The head related transfer functions (HRTFs) represent the acoustic path transfer functions between sound sources in 3D space and the listener’s ear. They are used to create immersive audio scenarios or to subjectively evaluate sound systems according to a human-centric point of view. Cars are nowadays the most popular audio listening environment and the use of HRTFs in automotive audio has recently attracted the attention of researchers. In this context, the paper proposes a measurement method for HRTFs based on perfect or orthogonal periodic sequences. The proposed measurement method ensures robustness towards the nonlinearities that may affect the measurement system. The experimental results considering both an emulated scenario and real measurements in a controlled environment illustrate the effectiveness of the approach and compare the proposed method with other popular approaches