Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates

This work addresses the problem of block-online processing for multi-channel speech enhancement. Such processing is vital in scenarios with moving speakers and/or when very short utterances are processed, e.g., in voice assistant scenarios. We consider several variants of a system that performs beamforming supported by DNN-based voice activity detection (VAD) followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently in order to make the method applicable in highly dynamic environments. Owing to the short length of the processed block, the statistics required by the beamformer are estimated less precisely. The influence of this inaccuracy is studied and compared to the processing regime when recordings are treated as one block (batch processing). The experimental evaluation of the proposed method is performed on large datasets of CHiME-4 and on another dataset featuring moving target speaker. The experiments are evaluated in terms of objective and perceptual criteria (such as signal-to-interference ratio (SIR) or perceptual evaluation of speech quality (PESQ), respectively). Moreover, word error rate (WER) achieved by a baseline automatic speech recognition system is evaluated, for which the enhancement method serves as a front-end solution. The results indicate that the proposed method is robust with respect to short length of the processed block. Significant improvements in terms of the criteria and WER are observed even for the block length of 250 ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article accepted for publication in IET Signal Processing journal. Original results unchanged, additional experiments presented, refined discussion and conclusion

The long-term evolution of neutron star merger remnants - II. Radioactively powered transients

We use 3D hydrodynamic simulations of the long-term evolution of neutron star merger ejecta to predict the light curves of electromagnetic transients that are powered by the decay of freshly produced r-process nuclei. For the dynamic ejecta that are launched by tidal and hydrodynamic interaction, we adopt grey opacities of 10 cm$^2$/g, as suggested by recent studies. For our reference case of a 1.3-1.4 $M_\odot$ merger, we find a broad IR peak 2-4 d after the merger. The peak luminosity is $\approx 2\times 10^{40}$ erg/s for an average orientation, but increased by up to a factor of 4 for more favourable binary parameters and viewing angles. These signals are rather weak and hardly detectable within the large error box (~100 deg$^2$) of a gravitational wave trigger. A second electromagnetic transient results from neutrino-driven winds. These winds produce `weak' r-process material with $50 < A < 130$ and abundance patterns that vary substantially between different merger cases. For an adopted opacity of 1 cm$^2$/g, the resulting transients peak in the UV/optical about 6 h after the merger with a luminosity of $\approx 10^{41}$ erg/s (for a wind of 0.01 $M_\odot$) These signals are marginally detectable in deep follow-up searches (e.g. using Hypersuprime camera on Subaru). A subsequent detection of the weaker but longer lasting IR signal would allow an identification of the merger event. We briefly discuss the implications of our results to the recent detection of an nIR transient accompanying GRB 130603B.Comment: 14 pages, 11 figures, 5 tables, accepted to MNRA

Trial-to-trial latency variability of somatosensory evoked potentials as a prognostic indicator for surgical management of cervical spondylotic myelopathy

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Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201