Showing papers on "Signal published in 2018"
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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
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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
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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.
215 citations
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02 Jul 2018TL;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.
195 citations
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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
190 citations
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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.
169 citations
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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.
135 citations
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AT&T1
TL;DR: In this paper, an analog surface wave repeater pair includes a first launcher configured to transmit and receive first guided electromagnetic waves that propagate on an outer surface of a first segment of a transmission medium.
Abstract: In accordance with one or more embodiments, an analog surface wave repeater pair includes a first launcher configured to transmit and receive first guided electromagnetic waves that propagate on an outer surface of a first segment of a transmission medium. A second launcher is configured to transmit and receive second guided electromagnetic waves that propagate on an outer surface of a second segment of the transmission medium. A first transceiver includes a first notch filter is configured to attenuate signals in a fourth generation (4G) wireless frequency band from the first microwave signal generated by the first launcher in response to receiving the first guided electromagnetic waves. A second transceiver includes a second notch filter configured to attenuate signals in the fourth 4G wireless frequency band from a second microwave signal generated by the second launcher in response to receiving the second guided electromagnetic waves.
123 citations
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TL;DR: This proposal lowers the requirement for wideband chaos generation and synchronization in high-speed long-distance chaotic optical communications, and fiber dispersion compensation can also be simplified, which has potential to be used in high speed long- distance secure optical communications.
Abstract: For the first time, to the best of our knowledge, we experimentally demonstrate a successful 30-Gb/s signal transmission of a duobinary message hidden in a chaotic optical carrier over 100-km fiber. Thanks to the duobinary modulation format with high spectral efficiency, the 30-Gb/s signal can be encrypted by a 10-GHz-wide chaotic carrier. A digital signal processing technique can be used to convert duobinary data into binary data on the receiver side. This proposal lowers the requirement for wideband chaos generation and synchronization in high-speed long-distance chaotic optical communications, and fiber dispersion compensation can also be simplified, which has potential to be used in high-speed long-distance secure optical communications.
117 citations
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28 Feb 2018
TL;DR: In this article, the authors present a crosspoint switch in a local data collection system having multiple inputs and multiple outputs including a first input connected to a first sensor and a second input connecting to a second sensor, each of which is configured to be individually assigned to any of the multiple outputs.
Abstract: The system generally includes a crosspoint switch in a local data collection system having multiple inputs and multiple outputs including a first input connected to a first sensor and a second input connected to a second sensor. The multiple outputs include a first output and a second output configured to be switchable between a condition in which the first output is configured to switch between delivery of a first sensor signal and a second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal and the second sensor signal. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. The local data collection system includes multiple data acquisition units each having an onboard card set configured to store calibration information and maintenance history. The local data collection system is configured to manage data collection bands.
117 citations
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16 Feb 2018
TL;DR: In this paper, the first data I/O block transmits input data supplied through a first pad to a first global I /O line, and further generates a write internal signal.
Abstract: A semiconductor memory device includes, in part, a first data I/O block and a second data I/O block. During a write operation, the first data I/O block transmits input data supplied through a first pad to a first global I/O line, and further generates a write internal signal. The second data I/O block transmits the write internal signal to a second pad in response to a monitor enable signal. During a read operation, the first data I/O block supplies data from the first global I/O line to a first pad, and further generates a read internal signal. The second data I/O block transmits the read internal signal to the second pad in response to a monitor enable signal.
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17 Jan 2018
TL;DR: In this paper, a wireless communications module is used to switch the processor from low-power mode to active mode in response to an activation trigger, receiving, from the one of the microphones and the camera, outbound audio and video signals.
Abstract: A device for communicating including a housing including a camera, a microphone, a speaker, a button, a battery, a sensor, non-volatile memory, a processor, and a wireless communications module, wherein the non-volatile memory stores code operable by the processor for switching the processor from low-power mode to active mode in response to an activation trigger, receiving, from the one of the microphone and the camera, outbound audio and video signals, then sending a signal to a server via the wireless communications module during active mode, the signal including one or more of an alert signal, a signal based on the outbound audio signal, and a signal based on the outbound video signal, receiving from the server an inbound audio signal and outputting a signal based on the inbound audio signal via the speaker, and switching the processor from active mode to low-power mode in response to a deactivation trigger.
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TL;DR: The chaos mask signal shows superior performance than that of the digital mask signals, however, similar prediction errors can be achieved for the chaos and colored-noise mask signals.
Abstract: We experimentally investigate delay-based photonic reservoir computing using semiconductor lasers with optical feedback and injection. We apply different types of temporal mask signals, such as digital, chaos, and colored-noise mask signals, as the weights between the input signal and the virtual nodes in the reservoir. We evaluate the performance of reservoir computing by using a time-series prediction task for the different mask signals. The chaos mask signal shows superior performance than that of the digital mask signals. However, similar prediction errors can be achieved for the chaos and colored-noise mask signals. Mask signals with larger amplitudes result in better performance for all mask signals in the range of the amplitude accessible in our experiment. The performance of reservoir computing is strongly dependent on the cut-off frequency of the colored-noise mask signals, which is related to the resonance of the relaxation oscillation frequency of the laser used as the reservoir.
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TL;DR: In this article, the authors investigated how different signal processing approaches (including and excluding approaches that partially correct for the possible signal contamination) affect the results of a typical functional neuroimaging study performed with functional near-infrared spectroscopy (fNIRS).
Abstract: Even though research in the field of functional near-infrared spectroscopy (fNIRS) has been performed for more than 20 years, consensus on signal processing methods is still lacking. A significant knowledge gap exists between established researchers and those entering the field. One major issue regularly observed in publications from researchers new to the field is the failure to consider possible signal contamination by hemodynamic changes unrelated to neurovascular coupling (i.e., scalp blood flow and systemic blood flow). This might be due to the fact that these researchers use the signal processing methods provided by the manufacturers of their measurement device without an advanced understanding of the performed steps. The aim of the present study was to investigate how different signal processing approaches (including and excluding approaches that partially correct for the possible signal contamination) affect the results of a typical functional neuroimaging study performed with fNIRS. In particular, we evaluated one standard signal processing method provided by a commercial company and compared it to three customized approaches. We thereby investigated the influence of the chosen method on the statistical outcome of a clinical data set (task-evoked motor cortex activity). No short-channels were used in the present study and therefore two types of multi-channel corrections based on multiple long-channels were applied. The choice of the signal processing method had a considerable influence on the outcome of the study. While methods that ignored the contamination of the fNIRS signals by task-evoked physiological noise yielded several significant hemodynamic responses over the whole head, the statistical significance of these findings disappeared when accounting for part of the contamination using a multi-channel regression. We conclude that adopting signal processing methods that correct for physiological confounding effects might yield more realistic results in cases where multi-distance measurements are not possible. Furthermore, we recommend using manufacturers' standard signal processing methods only in case the user has an advanced understanding of every signal processing step performed.
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TL;DR: In this article, the packet feature extraction in vibration signals was applied to correlate the sensor signals to measured surface roughness, and the best packets were found in the medium-high frequency DDA (6250-9375 Hz) and high frequency ADA (9375-12500 Hz) ranges.
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TL;DR: The objective of this study is to give the reader a bird's eye view of the biomedical signal processing world with a zoomed-in perspective of feature extraction methodologies which form the basis of machine learning and hence, artificial intelligence.
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01 Jan 2018TL;DR: This study investigates how different signal processing approaches (including and excluding approaches that partially correct for the possible signal contamination) affect the results of a typical functional neuroimaging study performed with fNIRS and recommends adopting signal processing methods that correct for physiological confounding effects in cases where multi-distance measurements are not possible.
Abstract: Even though research in the field of functional near-infrared spectroscopy (fNIRS) has been performed for more than 20 years, consensus on signal processing methods is still lacking. A significant knowledge gap exists between established researchers and those entering the field. One major issue regularly observed in publications from researchers new to the field is the failure to consider possible signal contamination by hemodynamic changes unrelated to neurovascular coupling (i.e., scalp blood flow and systemic blood flow). This might be due to the fact that these researchers use the signal processing methods provided by the manufacturers of their measurement device without an advanced understanding of the performed steps. The aim of the present study was to investigate how different signal processing approaches (including and excluding approaches that partially correct for the possible signal contamination) affect the results of a typical functional neuroimaging study performed with fNIRS. In particular, we evaluated one standard signal processing method provided by a commercial company and compared it to three customized approaches. We thereby investigated the influence of the chosen method on the statistical outcome of a clinical data set (task-evoked motor cortex activity). No short-channels were used in the present study and therefore two types of multi-channel corrections based on multiple long-channels were applied. The choice of the signal processing method had a considerable influence on the outcome of the study. While methods that ignored the contamination of the fNIRS signals by task-evoked physiological noise yielded several significant hemodynamic responses over the whole head, the statistical significance of these findings disappeared when accounting for part of the contamination using a multi-channel regression. We conclude that adopting signal processing methods that correct for physiological confounding effects might yield more realistic results in cases where multi-distance measurements are not possible. Furthermore, we recommend using manufacturers' standard signal processing methods only in case the user has an advanced understanding of every signal processing step performed.
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TL;DR: A nanopore based on the bacterial toxin ClyA is developed, in conjunction with binding proteins for glucose and asparagine, to detect these biomolecules simultaneously from a variety of unprocessed, diluted body fluids.
Abstract: Crucial steps in the miniaturisation of biosensors are the conversion of a biological signal into an electrical current as well as the direct sampling of bodily fluids. Here we show that protein sensors in combination with a nanopore, acting as an electrical transducer, can accurately quantify metabolites in real time directly from nanoliter amounts of blood and other bodily fluids. Incorporation of the nanopore into portable electronic devices will allow developing sensitive, continuous, and non-invasive sensors for metabolites for point-of-care and home diagnostics.
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TL;DR: In this paper, an ultrafast high-sensitivity refractive index (RI) and temperature-sensing system based on an optoelectronic oscillator (OEO) is proposed and demonstrated.
Abstract: An ultrafast high-sensitivity refractive index (RI) and temperature-sensing system based on an optoelectronic oscillator (OEO) is proposed and demonstrated in this paper. A Fabry–Perot fiber Bragg grating (FP-FBG), which combines a gap with two FBGs in a silica V-shaped slot and characterizes a narrow notch in the reflection spectrum, is incorporated in the OEO to implement a microwave photonic filter and perform oscillating frequency selection. A microwave signal is generated by the OEO, whose oscillating frequency is determined by the center frequency of the FP-FBG notch, which varies with the surrounding environments. The RI or the temperature change can be accordingly measured by monitoring the frequency shift of the microwave signal using an electrical spectrum analyzer or a digital signal processor. An experiment is performed to verify the proposal. An RI sensitivity of 413.8 MHz/0.001RIU and a temperature sensitivity of 2516 MHz/°C are successively demonstrated.
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TL;DR: The proposed novel noncontact heart-beat signal modeling and estimation algorithm using a compact 2.4-GHz Doppler radar is accurate, robust, and simple, and demonstrates an average heart- Beat detection accuracy of more than 90% at a distance of 1.5 m away from the subjects.
Abstract: This paper presents the theoretical and experimental study of a novel noncontact heart-beat signal modeling and estimation algorithm using a compact 2.4-GHz Doppler radar. The proposed technique is able to accurately reconstruct the heart-beat signal and generates heart rate variability indices at a distance of 1.5 m away from the human body. The feasibility of the proposed approach is validated by obtaining data from eight human subjects and comparing them with photoplethysmography (PPG) measurements. A Gaussian pulse train model is suggested for the heart-beat signal along with a modified-and-combined autocorrelation and frequency-time phase regression technique for high-accuracy detection of the human heart-beat rate. The proposed method is accurate, robust, and simple, and demonstrates an average heart-beat detection accuracy of more than 90% at a distance of 1.5 m away from the subjects. In addition, the average beat-to-beat time intervals extracted from the proposed model and signal reconstruction method show less than 2% error compared to PPG measurements. Bland–Altman analysis further validated the accuracy of the proposed approach in comparison with reference data.
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TL;DR: A direct signal demodulation method is proposed, in which, the LPF is removed to improve system stability and dynamic property and a novel magnetic polarity detection method is put forward based on the magnetic saturation, which has a high signal-to-noise ratio.
Abstract: In a permanent magnet synchronous motor (PMSM) drive system, high-frequency (HF) pulsating voltage signal injection has demonstrated high accuracy to estimate the initial rotor position. However, a conventional signal demodulation method may face the problems of long convergence time and limited system stability, owing to the low-pass filter (LPF) used in signal demodulation process. Thus, a direct signal demodulation method is proposed, in which, the LPF is removed to improve system stability and dynamic property. A direct demodulation collection is generated to extract the position deviation signal from estimated q -axis HF current. Recursive discrete Fourier transform is employed to calculate the amplitude of estimated d -axis HF current, thus an amplitude normalized technique is implemented to reduce the effects of HF signal and PMSM. Meanwhile, the stability and dynamic property are compared between the conventional and proposed method, with the aid of D-partition technique and amplitude–frequency characteristics diagram. Furthermore, a novel magnetic polarity detection method is put forward based on the magnetic saturation, which has a high signal-to-noise ratio. Finally, the experimental results on three PMSM drive systems prove that the proposed method is practicable and effective.
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TL;DR: A novel method is proposed to extract fault features from non-stationary vibration signals of gearboxes using the techniques of signal sparse decomposition and order tracking and an improved matching pursuit algorithm on segmental signal is designed to solve sparse coefficients and reconstruct steady- type fault components and impact-type fault components.
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01 Jan 2018TL;DR: A new, noise-free, broadband light storage scheme is implemented, opening the way to faithful multiphoton synchronization, and a fast ladder memory (FLAME) mapping the optical field onto the superposition between electronic orbitals of rubidium vapor is demonstrated.
Abstract: Future quantum photonic networks require coherent optical memories for synchronizing quantum sources and gates of probabilistic nature. Room temperature operation is also desirable for ease of scaling up. Until now, however, room-temperature atomic memories have suffered from an intrinsic read-out noise due to spontaneous four-wave-mixing. Here we demonstrate a new scheme for storing photons at room temperature, the fast ladder memory (FLAME). In this scheme, stimulated two-photon absorption is used instead of the previously used stimulated Raman scattering. As here the competing spontaneous processes would require spontaneous absorption of an optical photon, rather than emission, the noise is greatly suppressed. Furthermore, high external efficiency can be achieved as the control is well separated in frequency from the signal, and could be filtered out using highly efficient interference filters. We run the protocol in rubidium vapour, both on and off single-photon resonance, demonstrating a ratio of 50 between storage time and signal pulse width, an external total efficiency of over 25%, and only 2.3 × 10−4 noise photons per extracted signal photon. This paves the way towards the efficient synchronization of probabilistic gates and sources at room temperature, and the controlled production of large quantum states of light.
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TL;DR: The results prove that spin-torque nano-oscillators offer an interesting platform to implement different computing schemes leveraging their rich dynamical features.
Abstract: Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent works show that the non-linearity of their oscillation amplitude can be leveraged to achieve waveform classification for an input signal encoded in the amplitude of the input voltage. Here we show that the frequency and the phase of the oscillator can also be used to recognize waveforms. For this purpose, we phase-lock the oscillator to the input waveform, which carries information in its modulated frequency. In this way we considerably decrease amplitude, phase and frequency noise. We show that this method allows classifying sine and square waveforms with an accuracy above 99% when decoding the output from the oscillator amplitude, phase or frequency. We find that recognition rates are directly related to the noise and non-linearity of each variable. These results prove that spin-torque nano-oscillators offer an interesting platform to implement different computing schemes leveraging their rich dynamical features.
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TL;DR: In this paper, photoluminescence images are acquired using the sun as the sole illumination source by separating the weak luminescence signal from the much stronger ambient sunlight signal, which is done by using an appropriate choice of optical filtering and modulation of the cells' bias between the normal operating point and open circuit condition.
Abstract: To operate photovoltaic power plants at maximum capacity, it is desirable to identify cell or module failures in the field at the earliest possible stage. Currently used field inspection methods cannot detect many of the electronic defects that can be revealed with luminescence-based techniques. In this work, photoluminescence images are acquired using the sun as the sole illumination source by separating the weak luminescence signal from the much stronger ambient sunlight signal. This is done by using an appropriate choice of optical filtering and modulation of the cells' bias between the normal operating point and open circuit condition. The switching is achieved by periodically changing the optical generation rate of at least one cell within the module. This changes the biasing condition of all other cells that are connected to the same bypass diode. This method has the advantage that it can deliver high quality images revealing electrical defects in individual cells and entire modules, without requiring any changes to the electrical connections of the photovoltaic system.
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25 Jun 2018TL;DR: In this article, an in-vessel molecular communication testbed using magnetic nanoparticles dispersed in an aqueous suspension is presented, where an electronic pump for injection via a Y-connector provides a background flow for signal propagation.
Abstract: Simple and easy to implement testbeds are needed to further advance molecular communication research. To this end, this paper presents an in-vessel molecular communication testbed using magnetic nanoparticles dispersed in an aqueous suspension as they are also used for drug targeting in biotechnology. The transmitter is realized by an electronic pump for injection via a Y-connector. A second pump provides a background flow for signal propagation. For signal reception, we employ a susceptometer, an electronic device including a coil, where the magnetic particles move through and generate an electrical signal. We present experimental results for the transmission of a binary sequence and the system response following a single injection. For this flow-driven particle transport, we propose a simple parameterized mathematical model for evaluating the system response.
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TL;DR: In this paper, a gear fault diagnosis method based on structured sparsity time-frequency analysis (SSTFA) is proposed, which utilizes mixed-norm priors on timefrequency coefficients to obtain a fine match for the structure of signals.
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TL;DR: Simulation results demonstrate the effectiveness of the proposed wave-domain adaptive algorithms, more specifically the convergence speed and the noise cancellation performance in terms of the noise reduction level and acoustic potential energy reduction level over the entire spatial region.
Abstract: Noise control and cancellation over a spatial region is a fundamental problem in acoustic signal processing. In this paper, we utilize wave-domain adaptive algorithms to iteratively calculate the secondary source driving signals and to cancel the primary noise field over the control region. We propose wave-domain active noise control algorithms based on two minimization problems: first, minimizing the wave-domain residual signal coefficients, and second, minimizing the acoustic potential energy over the region, and derive the update equations with respect to two variables, the loudspeaker weights and wave-domain secondary source coefficients. Simulation results demonstrate the effectiveness of the proposed algorithms, more specifically the convergence speed and the noise cancellation performance in terms of the noise reduction level and acoustic potential energy reduction level over the entire spatial region.
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04 Oct 2018
TL;DR: In this article, a portable P-UE (20) receives a signal transmitted from another UE, specifies a first determination subject signal received from a VUE (10) provided to a vehicle, determines whether there is the first determination-subject signal which satisfies a predetermined condition or not among the reception signals, transmits a signal to the V-UE(10) in the case where there is no first-determinate subject signal that satisfies the predetermined condition.
Abstract: To increase a use efficiency of a wireless resource in Sidelink V2P communication, a portable P-UE (20) receives a signal transmitted from another UE, specifies a first determination subject signal received from a V-UE (10) provided to a vehicle, determines whether there is the first determination subject signal which satisfies a predetermined condition or not among the reception signals, transmits a signal to the V-UE (10) in the case where there is no first determination subject signal which satisfies the predetermined condition, and suppresses or stops the transmission of the signal to the V-UE (10) in the case where there is the first determination subject signal which satisfies the predetermined condition.
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AT&T1
TL;DR: In this article, a system for receiving electromagnetic waves that propagate along a transmission medium, responsive to a signal quality of the electromagnetic waves satisfying a threshold is described, by conditioning the electromagnetic wave without modifying digital signals conveyed by the electromagnetic signals and inducing propagation of the updated electromagnetic waves along the transmission medium.
Abstract: Aspects of the subject disclosure may include, a system for receiving electromagnetic waves that propagate along a transmission medium, responsive to a signal quality of the electromagnetic waves satisfying a threshold: generating updated electromagnetic waves by conditioning the electromagnetic waves without modifying digital signals conveyed by the electromagnetic waves and inducing propagation of the updated electromagnetic waves along the transmission medium. Other embodiments are disclosed.