Showing papers on "Signal published in 2018"
TL;DR: It is demonstrated that 2D metal carbide MXenes, which possess high metallic conductivity for low noise and a fully functionalized surface for a strong signal, greatly outperform the sensitivity of conventional semiconductor channel materials.
Abstract: Achieving high sensitivity in solid-state gas sensors can allow the precise detection of chemical agents. In particular, detection of volatile organic compounds (VOCs) at the parts per billion (ppb) level is critical for the early diagnosis of diseases. To obtain high sensitivity, two requirements need to be simultaneously satisfied: (i) low electrical noise and (ii) strong signal, which existing sensor materials cannot meet. Here, we demonstrate that 2D metal carbide MXenes, which possess high metallic conductivity for low noise and a fully functionalized surface for a strong signal, greatly outperform the sensitivity of conventional semiconductor channel materials. Ti3C2Tx MXene gas sensors exhibited a very low limit of detection of 50–100 ppb for VOC gases at room temperature. Also, the extremely low noise led to a signal-to-noise ratio 2 orders of magnitude higher than that of other 2D materials, surpassing the best sensors known. Our results provide insight in utilizing highly functionalized metallic...
979 citations
TL;DR: This review elaborates upon existing optical nanoprobes that exploit ratiometric measurements for improved sensing and imaging, including fluorescence, surface enhanced Raman scattering (SERS), and photoacoustic nanoprops, and their potential biomedical applications for targeting specific biomolecule populations.
Abstract: Exploring and understanding biological and pathological changes are of great significance for early diagnosis and therapy of diseases. Optical sensing and imaging approaches have experienced major progress in this field. Particularly, an emergence of various functional optical nanoprobes has provided enhanced sensitivity, specificity, targeting ability, as well as multiplexing and multimodal capabilities due to improvements in their intrinsic physicochemical and optical properties. However, one of the biggest challenges of conventional optical nanoprobes is their absolute intensity-dependent signal readout, which causes inaccurate sensing and imaging results due to the presence of various analyte-independent factors that can cause fluctuations in their absolute signal intensity. Ratiometric measurements provide built-in self-calibration for signal correction, enabling more sensitive and reliable detection. Optimizing nanoprobe designs with ratiometric strategies can surmount many of the limitations encountered by traditional optical nanoprobes. This review first elaborates upon existing optical nanoprobes that exploit ratiometric measurements for improved sensing and imaging, including fluorescence, surface enhanced Raman scattering (SERS), and photoacoustic nanoprobes. Next, a thorough discussion is provided on design strategies for these nanoprobes, and their potential biomedical applications for targeting specific biomolecule populations (e.g. cancer biomarkers and small molecules with physiological relevance), for imaging the tumor microenvironment (e.g. pH, reactive oxygen species, hypoxia, enzyme and metal ions), as well as for intraoperative image guidance of tumor-resection procedures.
509 citations
TL;DR: To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper.
Abstract: Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.
334 citations
TL;DR: The resolution bounds of NLOS imaging are quantified, its potential for real-time tracking is demonstrated, and efficient algorithms that incorporate image priors and a physically accurate noise model are derived.
Abstract: How to image objects that are hidden from a camera's view is a problem of fundamental importance to many fields of research, with applications in robotic vision, defence, remote sensing, medical imaging and autonomous vehicles. Non-line-of-sight (NLOS) imaging at macroscopic scales has been demonstrated by scanning a visible surface with a pulsed laser and a time-resolved detector. Whereas light detection and ranging (LIDAR) systems use such measurements to recover the shape of visible objects from direct reflections, NLOS imaging reconstructs the shape and albedo of hidden objects from multiply scattered light. Despite recent advances, NLOS imaging has remained impractical owing to the prohibitive memory and processing requirements of existing reconstruction algorithms, and the extremely weak signal of multiply scattered light. Here we show that a confocal scanning procedure can address these challenges by facilitating the derivation of the light-cone transform to solve the NLOS reconstruction problem. This method requires much smaller computational and memory resources than previous reconstruction methods do and images hidden objects at unprecedented resolution. Confocal scanning also provides a sizeable increase in signal and range when imaging retroreflective objects. We quantify the resolution bounds of NLOS imaging, demonstrate its potential for real-time tracking and derive efficient algorithms that incorporate image priors and a physically accurate noise model. Additionally, we describe successful outdoor experiments of NLOS imaging under indirect sunlight.
318 citations
TL;DR: In this paper, the authors proposed a cooperative AmBC (CABC) system in which the reader recovers information not only from the A-BD, but also from the RF source.
Abstract: Ambient backscatter communication (AmBC) enables a passive backscatter device to transmit information to a reader using ambient RF signals, and has emerged as a promising solution to green Internet-of-Things (IoT). Conventional AmBC receivers are interested in recovering the information from the ambient backscatter device (A-BD) only. In this paper, we propose a cooperative AmBC (CABC) system in which the reader recovers information not only from the A-BD, but also from the RF source. We first establish the system model for the CABC system from spread spectrum and spectrum sharing perspectives. Then, for flat fading channels, we derive the optimal maximum-likelihood (ML) detector, suboptimal linear detectors as well as successive interference-cancellation (SIC) based detectors. For frequency-selective fading channels, the system model for the CABC system over ambient orthogonal frequency division multiplexing carriers is proposed, upon which a low-complexity optimal ML detector is derived. For both kinds of channels, the bit-error-rate expressions for the proposed detectors are derived in closed forms. Finally, extensive numerical results have shown that, when the A-BD signal and the RF-source signal have equal symbol period, the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios, and when the A-BD symbol period is longer than the RF-source symbol period, the existence of backscattered signal in the CABC system can enhance the ML detection performance of the RF-source signal, thanks to the beneficial effect of the backscatter link when the A-BD transmits at a lower rate than the RF source.
252 citations
Posted Content•
TL;DR: It is argued that the visual and audio components of a video signal should be modeled jointly using a fused multisensory representation, and it is proposed to learn such a representation in a self-supervised way, by training a neural network to predict whether video frames and audio are temporally aligned.
Abstract: The thud of a bouncing ball, the onset of speech as lips open -- when visual and audio events occur together, it suggests that there might be a common, underlying event that produced both signals. In this paper, we argue that the visual and audio components of a video signal should be modeled jointly using a fused multisensory representation. We propose to learn such a representation in a self-supervised way, by training a neural network to predict whether video frames and audio are temporally aligned. We use this learned representation for three applications: (a) sound source localization, i.e. visualizing the source of sound in a video; (b) audio-visual action recognition; and (c) on/off-screen audio source separation, e.g. removing the off-screen translator's voice from a foreign official's speech. Code, models, and video results are available on our webpage: this http URL
252 citations
07 Aug 2018
TL;DR: PLoRa takes ambient LoRa transmissions as the excitation signals, conveys data by modulating an excitation signal into a new standard LoRa "chirp" signal, and shifts this new signal to a different LoRa channel to be received at a gateway faraway.
Abstract: This paper presents PLoRa, an ambient backscatter design that enables long-range wireless connectivity for batteryless IoT devices. PLoRa takes ambient LoRa transmissions as the excitation signals, conveys data by modulating an excitation signal into a new standard LoRa "chirp" signal, and shifts this new signal to a different LoRa channel to be received at a gateway faraway. PLoRa achieves this by a holistic RF front-end hardware and software design, including a low-power packet detection circuit, a blind chirp modulation algorithm and a low-power energy management circuit. To form a complete ambient LoRa backscatter network, we integrate a light-weight backscatter signal decoding algorithm with a MAC-layer protocol that work together to make coexistence of PLoRa tags and active LoRa nodes possible in the network. We prototype PLoRa on a four-layer printed circuit board, and test it in various outdoor and indoor environments. Our experimental results demonstrate that our prototype PCB PLoRa tag can backscatter an ambient LoRa transmission sent from a nearby LoRa node (20 cm away) to a gateway up to 1.1 km away, and deliver 284 bytes data every 24 minutes indoors, or every 17 minutes outdoors. We also simulate a 28-nm low-power FPGA based prototype whose digital baseband processor achieves 220 μW power consumption.
251 citations
TL;DR: It is shown that the metasurface nonlocality can be engineered to enable signal manipulation in the momentum domain over an ultrathin platform, paving the way towards fast and power-efficient ultrathIn devices for edge detection and optical image processing.
Abstract: Optical analog signal processing has been gaining significant attention as a way to overcome the speed and energy limitations of digital techniques. Metasurfaces offer a promising avenue towards this goal due to their efficient manipulation of optical signals over deeply subwavelength volumes. To date, metasurfaces have been proposed to transform signals in the spatial domain, e.g., for beam steering, focusing, or holography, for which angular-dependent responses, or nonlocality, are unwanted features that must be avoided or mitigated. Here, we show that the metasurface nonlocality can be engineered to enable signal manipulation in the momentum domain over an ultrathin platform. We explore nonlocal metasurfaces performing basic mathematical operations, paving the way towards fast and power-efficient ultrathin devices for edge detection and optical image processing.
249 citations
TL;DR: A flexible, lightweight and highly conductive porous graphene network is reported as the humidity sensor for respiration monitoring and shows the ability to detect physiological activities including skin moisture, speaking and whistle rhythm, which could be a promising electronic for clinical respiration Monitoring.
246 citations
TL;DR: Taking advantage of simplicity, enzyme-free, convenience, and sensitivity, MPDA@TP-linked immunosorbent assay has the potential for the application in scientific research and clinical diagnosis.
Abstract: In this work, an innovative enzyme-free colorimetric immunoassay was proposed for the sensitive detection of alpha-fetoprotein (AFP) by introducing thymolphthalein-modified metal-polydopamine framework (MPDA@TP) for the signal generation and amplification. Using zeolitic imidazolate framework (ZIF-67) as the template, the hollow-structured metal-polydopamine framework (MPDA) with high surface recovery and abundant groups was synthesized and functionalized with thymolphthalein (TP) molecules via typical π-stacking reaction. In the presence of target AFP, an MPDA@TP-linked immunosorbent assay (MLISA) was implemented on the capture antibody-modified microplate by using detection antibody-labeled MPDA@TP as the secondary antibody. Upon alkaline solution introduction, the coated hydrophobic TP on the MPDA was deprotonated into hydrophilic TP2– ion and dissolved in the solution, thereby resulting in the color change of the solution from nearly colorless to deep blue, and the increasing absorbance of the solutio...
242 citations
TL;DR: The proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies.
TL;DR: This work introduced a vibronic backbone that could facilely expand the Stokes shifts, emission wavelength, and photostability of 11 different fluorophores by more than 3-fold, and showed that the DQ derivative of hemicyanine generated 5-fold signal in mouse models over indocyanine green.
Abstract: Fluorescent dyes have enabled much progress in the broad range of biomedical fields. However, many commercially available dyes suffer from small Stokes shifts, resulting in poor signal-to-noise ratio and self-quenching on current microscope configurations. In this work, we have developed a general method to significantly increase the Stokes shifts of common fluorophores. By simply appending a 1,4-diethyl-decahydro-quinoxaline (DQ) moiety onto the conjugated structure, we introduced a vibronic backbone that could facilely expand the Stokes shifts, emission wavelength, and photostability of 11 different fluorophores by more than 3-fold. This generalizable method could significantly improve the imaging efficiency of commercial fluorophores. As a demonstration, we showed that the DQ derivative of hemicyanine generated 5-fold signal in mouse models over indocyanine green. Furthermore, the DQ-modified fluorophores could pair with their parent molecules to conduct one-excitation, multiple emission imaging, allow...
TL;DR: The results demonstrate that the flexible pressure sensor based on the functional-sponge is a promising candidate for healthcare monitoring and wearable circuitry in artificial intelligence.
Abstract: High-performance stretchable and wearable electronic skins (E-skins) with high sensitivity and a large sensing range are urgently required with the rapid development of the Internet of things and artificial intelligence. Herein, a reduced graphene oxide (rGO)/polyaniline wrapped sponge is prepared via rGO coating and the in situ synthesis of polyaniline nanowires (PANI NWs) on the backbones of sponge for the fabrication of pressure sensors. From the as-prepared flexible sensor, tunable sensitivity (0.042 to 0.152 kPa-1), wide working range (0-27 kPa), fast response (∼96 ms), high current output (∼300 μA at 1 V), frequency-dependent performance reliable repeatability (∼9000 cycle) and stable signal waveform output can be readily obtained. In addition to tiny physiological activities (voice recognition, swallowing, mouth opening, blowing and breath), robust human motions (finger bending, elbow movement and knee squatting-arising) can also be detected in real-time by the flexible sensors based on rGO/polyaniline wrapped sponge. All the results demonstrate that the flexible pressure sensor based on the functional-sponge is a promising candidate for healthcare monitoring and wearable circuitry in artificial intelligence.
Patent•
AT&T1
TL;DR: In this article, a system that facilitates receiving a plurality of ultra-wideband electromagnetic waves that propagates along a surface of a transmission medium without requiring an electrical return path is described.
Abstract: Aspects of the subject disclosure may include, a system that facilitates receiving a plurality of ultra-wideband electromagnetic waves that propagates along a surface of a transmission medium without requiring an electrical return path, wherein the plurality of ultra-wideband electromagnetic waves conveys a plurality of communication signals, obtaining, from the plurality of ultra-wideband electromagnetic waves, at least one communication signal from the plurality of communication signals, and distributing the at least one communication signal to at least one communication device. Other embodiments are disclosed.
TL;DR: In this article, a review of recent experimental advances in the study of strong-field dynamics in crystals and nanostructures is presented, along with several avenues toward measuring and controlling electronic processes up to petahertz frequencies.
Abstract: The advent of visible-infrared laser pulses carrying a substantial fraction of their energy in a single field oscillation cycle has opened a new era in the experimental investigation of ultrafast processes in semiconductors and dielectrics (bulk as well as nanostructured), motivated by the quest for the ultimate frontiers of electron-based signal metrology and processing. Exploring ways to approach those frontiers requires insight into the physics underlying the interaction of strong high-frequency (optical) fields with electrons moving in periodic potentials. This Colloquium aims at providing this insight. Introduction to the foundations of strong-field phenomena defines and compares regimes of field-matter interaction in periodic systems, including (perfect) crystals as well as optical and semiconductor superlattices, followed by a review of recent experimental advances in the study of strong-field dynamics in crystals and nanostructures. Avenues toward measuring and controlling electronic processes up to petahertz frequencies are discussed.
02 Jul 2018
TL;DR: An end-to-end wireless communication system in which DNNs are employed for all signal-related functionalities, including encoding, decoding, modulation, and equalization is developed, in which accurate instantaneous channel transfer function is necessary to compute the gradient of the DNN representing.
Abstract: In this article, we use deep neural networks (DNNs) to develop an end-to-end wireless communication system, in which DNNs are employed for all signal-related functionalities, including encoding, decoding, modulation, and equalization. However, accurate instantaneous channel transfer function, i.e., the channel state information (CSI), is necessary to compute the gradient of the DNN representing. In many communication systems, the channel transfer function is hard to obtain in advance and varies with time and location. In this article, this constraint is released by developing a channel agnostic end-to-end system that does not rely on any prior information about the channel. We use a conditional generative adversarial net (GAN) to represent the channel effects, where the encoded signal of the transmitter will serve as the conditioning information. In addition, in order to obtain accurate channel state information for signal detection at the receiver, the received signal corresponding to the pilot data is added as a part of the conditioning information. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) and Rayleigh fading channels, which opens a new door for building data-driven communication systems.
TL;DR: In this paper, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition, and the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm.
Abstract: This paper studies the problem of parameter estimation for frequency response signals For a linear system, the frequency response is a sine signal with the same frequency as the input sine signal When a multi-frequency sine signal is applied to a system, the system response also is a multi-frequency sine signal The signal modeling for multi-frequency sine signals is very difficult due to the highly nonlinear relations between the characteristic parameters and the model output In order to obtain the parameter estimates of the multi-frequency sine signal, the signal modeling methods based on statistical identification are proposed by means of the dynamical window discrete measured data By constructing a criterion function with respect to the model parameters to be estimated, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition Moreover, the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm The simulation results show the effectiveness of the proposed methods
TL;DR: It is shown how topological acoustic valley transport can be designed to enable a unique beamforming mechanism that renders a superdirective needle-like sound radiation and reception pattern.
Abstract: Realizing directional acoustic signal transmittance and reception robust against surrounding noise and competing signals is crucial in many areas such as communication, navigation, and detection for medical and industrial purposes. The fundamentally wide-angled radiation pattern of most current acoustic sensors and transducers displays a major limitation of the performance when it comes to precise targeting and probing of sound particular of interest in human speaking and hearing. Here, it is shown how topological acoustic valley transport can be designed to enable a unique beamforming mechanism that renders a superdirective needle-like sound radiation and reception pattern. The strategy rests on out-coupling valley-polarized edge states, whose beam is experimentally detected in the far-field with 10° width and a sound-intensity enhancement factor ≈10. Furthermore, anti-interference communication is proposed where sound is received from desired directions, but background noise from other directions is successfully suppressed. This type of topological acoustic antenna offers new ways to control sound with improved performance and functionalities that are highly desirable for versatile applications.
TL;DR: In this article, the authors revisited the global 21 cm signal calculation incorporating a possible radio background at early times, and found that the global21 cm signal shows a much stronger absorption feature, which could enhance detection prospects for future 21 cm experiments.
Abstract: We revisit the global 21 cm signal calculation incorporating a possible radio background at early times, and find that the global 21 cm signal shows a much stronger absorption feature, which could enhance detection prospects for future 21 cm experiments. In light of recent reports of a possible low-frequency excess radio background, we propose that detailed 21 cm calculations should include a possible early radio background.
TL;DR: The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation.
Abstract: Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications.
TL;DR: The production of gravitational waves by an electroweak first-order phase transition is reviewed and a good candidate for detection at next-generation gravitational wave detectors, such as LISA, is reviewed.
Abstract: We review the production of gravitational waves by an electroweak first-order phase transition. The resulting signal is a good candidate for detection at next-generation gravitational wave detector...
TL;DR: The developed device can work as a multifunctional and dynamically reconfigurable signal directional coupler performing the functions of a waveguide crossing element, tunable power splitter, frequency separator, or multiplexer.
Abstract: Spin waves, and their quanta magnons, are prospective data carriers in future signal processing systems because Gilbert damping associated with the spin-wave propagation can be made substantially lower than the Joule heat losses in electronic devices. Although individual spin-wave signal processing devices have been successfully developed, the challenging contemporary problem is the formation of two-dimensional planar integrated spin-wave circuits. Using both micromagnetic modeling and analytical theory, we present an effective solution of this problem based on the dipolar interaction between two laterally adjacent nanoscale spin-wave waveguides. The developed device based on this principle can work as a multifunctional and dynamically reconfigurable signal directional coupler performing the functions of a waveguide crossing element, tunable power splitter, frequency separator, or multiplexer. The proposed design of a spin-wave directional coupler can be used both in digital logic circuits intended for spin-wave computing and in analog microwave signal processing devices.
TL;DR: A fully automatic, accurate method for determining the local resolution of a 3D map (MonoRes), which is computationally more rapid than existing methods in the field and offers the option of local filtering of the original map based on the calculated local resolution.
TL;DR: The concept of Primer Exchange Reaction (PER) cascades are introduced, which grow nascent single-stranded DNA with user-specified sequences following prescribed reaction pathways, providing a platform for engineering molecular circuits and devices with a wide range of sensing, monitoring, recording, signal processing, and actuation capabilities.
Abstract: DNA performs diverse functional roles in biology, nanotechnology and biotechnology, but current methods for autonomously synthesizing arbitrary single-stranded DNA are limited. Here, we introduce the concept of primer exchange reaction (PER) cascades, which grow nascent single-stranded DNA with user-specified sequences following prescribed reaction pathways. PER synthesis happens in a programmable, autonomous, in situ and environmentally responsive fashion, providing a platform for engineering molecular circuits and devices with a wide range of sensing, monitoring, recording, signal-processing and actuation capabilities. We experimentally demonstrate a nanodevice that transduces the detection of a trigger RNA into the production of a DNAzyme that degrades an independent RNA substrate, a signal amplifier that conditionally synthesizes long fluorescent strands only in the presence of a particular RNA signal, molecular computing circuits that evaluate logic (AND, OR, NOT) combinations of RNA inputs, and a temporal molecular event recorder that records in the PER transcript the order in which distinct RNA inputs are sequentially detected.
TL;DR: A new deep convolutional autoencoder (CAE) model for compressing ECG signals that can learn to use different ECG records automatically and allow secure data transfer in a low-dimensional form to remote medical centers is proposed.
TL;DR: The anatomy of a sensory-to-motor adaptive controller that transformed visual error vectors into motor-corrections was inferred, finding a timing code that links a Purkinje cell’s preference for error to its downstream projection on motor effectors that produce force to correct for that error.
Abstract: The primary output cells of the cerebellar cortex, Purkinje cells, make kinematic predictions about ongoing movements via high-frequency simple spikes, but receive sensory error information about that movement via low-frequency complex spikes (CS). How is the vector space of sensory errors encoded by this low-frequency signal? Here we measured Purkinje cell activity in the oculomotor vermis of animals during saccades, then followed the chain of events from experience of visual error, generation of CS, modulation of simple spikes, and ultimately change in motor output. We found that while error direction affected the probability of CS, error magnitude altered its temporal distribution. Production of CS changed the simple spikes on the next trial, but regardless of the actual visual error, this change biased the movement only along a vector that was parallel to the Purkinje cell's preferred error. From these results, we inferred the anatomy of a sensory-to-motor adaptive controller that transformed visual error vectors into motor-corrections.
26 Apr 2018
TL;DR: In this paper, the authors proposed an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transform (STFT), and its inverse are represented as layers within a deep network.
Abstract: This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transform (STFT), and its inverse are represented as layers within a deep network. Previous approaches, rather than computing a loss on the reconstructed signal, used a surrogate loss based on the target STFT magnitudes. This ignores reconstruction error introduced by phase inconsistency. In our approach, the loss function is directly defined on the reconstructed signals, which are optimized for best separation. In addition, we train through unfolded iterations of a phase reconstruction algorithm, represented as a series of STFT and inverse STFT layers. While mask values are typically limited to lie between zero and one for approaches using the mixture phase for reconstruction, this limitation is less relevant if the estimated magnitudes are to be used together with phase reconstruction. We thus propose several novel activation functions for the output layer of the T-F masking, to allow mask values beyond one. On the publicly-available wsj0-2mix dataset, our approach achieves state-of-the-art 12.6 dB scale-invariant signal-to-distortion ratio (SI-SDR) and 13.1 dB SDR, revealing new possibilities for deep learning based phase reconstruction and representing a fundamental progress towards solving the notoriously-hard cocktail party problem.
30 May 2018
TL;DR: A skin-like driving system that enables compact and reversible assembly of wirelessly activated, fully soft robots whose driving part can be softly, compactly, and reversibly assembled and highlights the effectiveness of the wireless inter-skin communication in providing universality for robotic actuation based on reversible assembly.
Abstract: Designing softness into robots holds great potential for augmenting robotic compliance in dynamic, unstructured environments. However, despite the body’s softness, existing models mostly carry inherent hardness in their driving parts, such as pressure-regulating components and rigid circuit boards. This compliance gap can frequently interfere with the robot motion and makes soft robotic design dependent on rigid assembly of each robot component. We present a skin-like electronic system that enables a class of wirelessly activated fully soft robots whose driving part can be softly, compactly, and reversibly assembled. The proposed system consists of two-part electronic skins (e-skins) that are designed to perform wireless communication of the robot control signal, namely, “wireless inter-skin communication,” for untethered, reversible assembly of driving capability. The physical design of each e-skin features minimized inherent hardness in terms of thickness (
TL;DR: Novel nanomaterials are introduced (e.g., carbon nanotube, graphene, indium tin oxide, nanowire and metallic nanoparticles) in order to construct a high-performance electrode and how to increase the number of signal tracers by employing nanommaterials as carriers and making the polymeric enzyme complex associated with redox cycling for signal amplification is described.
Abstract: An electrochemical immunosensor employs antibodies as capture and detection means to produce electrical charges for the quantitative analysis of target molecules. This sensor type can be utilized as a miniaturized device for the detection of point-of-care testing (POCT). Achieving high-performance analysis regarding sensitivity has been one of the key issues with developing this type of biosensor system. Many modern nanotechnology efforts allowed for the development of innovative electrochemical biosensors with high sensitivity by employing various nanomaterials that facilitate the electron transfer and carrying capacity of signal tracers in combination with surface modification and bioconjugation techniques. In this review, we introduce novel nanomaterials (e.g., carbon nanotube, graphene, indium tin oxide, nanowire and metallic nanoparticles) in order to construct a high-performance electrode. Also, we describe how to increase the number of signal tracers by employing nanomaterials as carriers and making the polymeric enzyme complex associated with redox cycling for signal amplification. The pros and cons of each method are considered throughout this review. We expect that these reviewed strategies for signal enhancement will be applied to the next versions of lateral-flow paper chromatography and microfluidic immunosensor, which are considered the most practical POCT biosensor platforms.
TL;DR: A simplified photonic reservoir computing scheme for data classification of severely distorted optical communication signals after extended fibre transmission is introduced, which demonstrates an improvement in bit-error-rate by two orders of magnitude compared to directly classifying the transmitted signal.
Abstract: Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheless, processing time-dependent high-speed signals can turn into an extremely challenging task, especially when these signals have been nonlinearly distorted. Recently, analogue hardware concepts using nonlinear transient responses have been gaining significant interest for fast information processing. Here, we introduce a simplified photonic reservoir computing scheme for data classification of severely distorted optical communication signals after extended fibre transmission. To this end, we convert the direct bit detection process into a pattern recognition problem. Using an experimental implementation of our photonic reservoir computer, we demonstrate an improvement in bit-error-rate by two orders of magnitude, compared to directly classifying the transmitted signal. This improvement corresponds to an extension of the communication range by over 75%. While we do not yet reach full real-time post-processing at telecom rates, we discuss how future designs might close the gap.