Showing papers on "Sampling (signal processing) published in 2013"

Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors

Abstract: The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite number of bits; moreover, there is an inverse relationship between the achievable sampling rate and the bit depth. In this paper, we investigate an alternative CS approach that shifts the emphasis from the sampling rate to the number of bits per measurement. In particular, we explore the extreme case of 1-bit CS measurements, which capture just their sign. Our results come in two flavors. First, we consider ideal reconstruction from noiseless 1-bit measurements and provide a lower bound on the best achievable reconstruction error. We also demonstrate that i.i.d. random Gaussian matrices provide measurement mappings that, with overwhelming probability, achieve nearly optimal error decay. Next, we consider reconstruction robustness to measurement errors and noise and introduce the binary e-stable embedding property, which characterizes the robustness of the measurement process to sign changes. We show that the same class of matrices that provide almost optimal noiseless performance also enable such a robust mapping. On the practical side, we introduce the binary iterative hard thresholding algorithm for signal reconstruction from 1-bit measurements that offers state-of-the-art performance.

Ultrasound Indoor Positioning System Based on a Low-Power Wireless Sensor Network Providing Sub-Centimeter Accuracy

Abstract: This paper describes the TELIAMADE system, a new indoor positioning system based on time-of-flight (TOF) of ultrasonic signal to estimate the distance between a receiver node and a transmitter node. TELIAMADE system consists of a set of wireless nodes equipped with a radio module for communication and a module for the transmission and reception of ultrasound. The access to the ultrasonic channel is managed by applying a synchronization algorithm based on a time-division multiplexing (TDMA) scheme. The ultrasonic signal is transmitted using a carrier frequency of 40 kHz and the TOF measurement is estimated by applying a quadrature detector to the signal obtained at the A/D converter output. Low sampling frequencies of 17.78 kHz or even 12.31 kHz are possible using quadrature sampling in order to optimize memory requirements and to reduce the computational cost in signal processing. The distance is calculated from the TOF taking into account the speed of sound. An excellent accuracy in the estimation of the TOF is achieved using parabolic interpolation to detect of maximum of the signal envelope at the matched filter output. The signal phase information is also used for enhancing the TOF measurement accuracy. Experimental results show a root mean square error (rmse) less than 2 mm and a standard deviation less than 0.3 mm for pseudorange measurements in the range of distances between 2 and 6 m. The system location accuracy is also evaluated by applying multilateration. A sub-centimeter location accuracy is achieved with an average rmse of 9.6 mm.

Systems and methods for extending dynamic range of imager arrays by controlling pixel analog gain

Abstract: Array cameras and imager arrays configured to capture high dynamic range light field image data and methods of capturing high dynamic range light field image data in accordance with embodiments of the invention are disclosed. Imager arrays in accordance with many embodiments of the invention include multiple focal planes with associated read out and sampling circuitry. The sampling circuitry controls the conversion of the analog image information into digital image data. In certain embodiments, the sampling circuitry includes an Analog Front End (AFE) and an Analog to Digital Converter (ADC). In several embodiments, the AFE is used to apply different amplification gains to analog image information read out from pixels in a given focal plane to provide increased dynamic range to digital image data generated by digitizing the amplified analog image information. The different amplifications gains can be applied in a predetermined manner or on a pixel by pixel basis.

Technical Foundation and Calibration Methods for Time-of-Flight Cameras

Abstract: Current Time-of-Flight approaches mainly incorporate an continuous wave intensity modulation approach. The phase reconstruction is performed using multiple phase images with different phase shifts which is equivalent to sampling the inherent correlation function at different locations. This active imaging approach delivers a very specific set of influences, on the signal processing side as well as on the optical side, which all have an effect on the resulting depth quality. Applying ToF information in real application therefore requires to tackle these effects in terms of specific calibration approaches. This survey gives an overview over the current state of the art in ToF sensor calibration.

Continuous Time Level Crossing Sampling ADC for Bio-Potential Recording Systems

Abstract: In this paper we present a fixed window level crossing sampling analog to digital convertor for bio-potential recording sensors. This is the first proposed and fully implemented fixed window level crossing ADC without local DACs and clocks. The circuit is designed to reduce data size, power, and silicon area in future wireless neurophysiological sensor systems. We built a testing system to measure bio-potential signals and used it to evaluate the performance of the circuit. The bio-potential amplifier offers a gain of 53 dB within a bandwidth of 200 Hz-20 kHz. The input-referred rms noise is 2.8 μV. In the asynchronous level crossing ADC, the minimum delta resolution is 4 mV. The input signal frequency of the ADC is up to 5 kHz. The system was fabricated using the AMI 0.5 μm CMOS process. The chip size is 1.5 mm by 1.5 mm. The power consumption of the 4-channel system from a 3.3 V supply is 118.8 μW in the static state and 501.6 μW with a 240 kS/s sampling rate. The conversion efficiency is 1.6 nJ/conversion.

Energy harvesting receivers: Finite battery capacity

Abstract: When receivers rely on energy harvesting, energy outages will constrain reliable communication. To model the harvesting receiver, we decompose the processing tasks in two parts: first is sampling or Analog-to-Digital-Conversion (ADC) stage which includes all RF front-end processing, and second is decoding. We propose a model in which, for a given code rate, channel capacity, and battery size, the receiver can choose the sampling rate to balance the sampling and decoding energy costs. We then characterize the maximum reliable communication rate over the choice of sampling rate and code rate and we verify that the sampling rate should be maximized. In addition, we consider the fixed-timing transmission system and show that under some conditions the same rates can also be achieved.

Monitoring the propagation of mechanical waves using an optical fiber distributed and dynamic strain sensor based on BOTDA

Abstract: We report a Brillouin-based fully distributed and dynamic monitoring of the strain induced by a propagating mechanical wave along a 20m long composite strip, to which surface a single-mode optical fiber was glued. Employing a simplified version of the Slope-Assisted Brillouin Optical Time Domain Analysis (SA-BOTDA) technique, the whole length of the strip was interrogated every 10ms (strip sampling rate of 100Hz) with a spatial resolution of the order of 1m. A dynamic spatially and temporally continuous map of the strain was obtained, whose temporal behavior at four discrete locations was verified against co-located fiber Bragg gratings. With a trade-off among sampling rate, range and signal to noise ratio, kHz sampling rates and hundreds of meters of range can be obtained with resolution down to a few centimeters.

Organic light emitting diode display device for sensing pixel current and method for sensing pixel current thereof

Abstract: PURPOSE: An organic light emitting diode display device for measuring pixel current and a pixel current measuring method thereof are provided to compensate brightness deviation between pixels by measuring the current of each pixel in a high speed with a simple structure. CONSTITUTION: A display panel(20) includes a pixel array. A data driver(10) operates a data line. A digital to analog converter(DAC)(12) is connected to an output channel by channels. A sampling and holding(S/H) circuit(14) is connected to the output channel by channels. A first switch(SW1) is connected between the digital to analog converter and the output channel by channels. A second switch(SW2) is connected between the output channel and the sampling and holding circuit by channels. A capacitor(Ch) is connected to the input terminal of the sampling and holding circuit by channels in parallel.

Edge-Preserving Texture Suppression Filter Based on Joint Filtering Schemes

Abstract: Obtaining a texture-smoothing and edge-preserving filtered output is significant to image decomposition. Although the edge and the texture have salient difference in human vision, automatically distinguishing them is a difficult task, for they have similar intensity difference or gradient response. The state-of-the-art edge-preserving smoothing (EPS) based decomposition approaches are hard to obtain a satisfactory result. We propose a novel edge-preserving texture suppression filter, exploiting the joint bilateral filter as a bridge to achieve the purpose of both properties of texture-smoothing and edge-preserving. We develop the iterative asymmetric sampling and the local linear model to produce the degenerative image to suppress the texture, and apply the edge correction operator to achieve edge-preserving. An efficient accelerating implementation is introduced to improve the performance of filtering response. The experiments demonstrate that our filter produces satisfactory outputs with both properties of texture-smoothing and edge-preserving, while compared with the results of other popular EPS approaches in signal, visual and time analysis. Finally, we extend our filter to a variety of image processing applications.

Differential Sampling Measurement of a 7 V RMS Sine Wave With a Programmable Josephson Voltage Standard

Abstract: A 10 V programmable Josephson voltage standard has enabled sine waves with voltages up to 7 V RMS to be accurately measured with a differential sampling measurement technique. Expanding the voltage range for this technique enables the direct calibration of the low-frequency ranges of commercial calibrators in the ac voltage mode. This paper reviews the practically achievable performance and challenges of the differential sampling measurement technique that arise when measuring RMS voltages greater than a few volts. A relative Type A uncertainty of 4 parts in $10^{7}$ was achieved with the technique when measuring a 7 V RMS sine wave generated by a calibrator at 62.5 Hz.

S 2 KF: The Smart Sampling Kalman Filter

Abstract: An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems called Smart Sampling Kalman Filter (S2KF) is introduced. It is based on a new low-discrepancy Dirac Mixture approximation of Gaussian densities. The approximation comprises an arbitrary number of optimally and deterministically placed samples in the entire state space, so that the filter resolution can be adapted to either achieve high-quality results or meet computational constraints. For two samples per dimension, the S2KF comprises the UKF as a special case. With an increasing number of samples, the new filter quickly converges to the (typically infeasible) exact analytic LRKF. The S2KF can be seen as the ultimate generalization of all sample-based LRKFs such as the UKF, sigma-point filters, higher-order variants etc., as it homogeneously covers the state space with an arbitrary number of samples. It is evaluated by performing extended target tracking.

Can oversampling improve the dynamics of PWM controls

Abstract: Regular Sampling is the state of the art when employing digital discrete-time control to pulse width-modulated systems. However, the fact that the dynamics of discrete-time control is below that of an analog continuous-time control is often not being aware. The paper shows that digital control dynamics can be improved with oversampling, i.e. employing a higher rate for sampling and controller updating as with Regular Sampling without increasing the pulse frequency. As a result, it is shown that already an oversampling rate of about 8 to 16 provides approximately the same dynamics of a continuous-time control. The benefits of even higher oversampling rates are of minor significance, which can be understood as a clear design recommendation. Though the consideration is carried out with a simplified circuitry, the results are of general meaning and can be transferred easily to application in the area of drive control or switched-mode power supplies.

Stochastic Modeling of the PPG Signal: A Synthesis-by-Analysis Approach With Applications

Abstract: In this paper, we propose a stochastic model of photoplethysmographic signals that is able to synthesize an arbitrary number of other statistically equivalent signals to the one under analysis. To that end, we first preprocess the pulse signal to normalize and time-align pulses. In a second stage, we design a single-pulse model, which consists of ten parameters. In the third stage, the time evolution of this ten-parameter vector is approximated by means of two autoregressive moving average models, one for the trend and one for the residue; this model is applied after a decorrelation step which let us to process each vector component in parallel. The experiments carried out show that the model we here propose is able to maintain the main features of the original signal; this is accomplished by means of both a linear spectral analysis and also by comparing two measures obtained from a nonlinear analysis. Finally, we explore the capability of the model to: 1) track physical activity; 2) obtain statistics of clinical parameters by model sampling; and 3) recover corrupted or missing signal epochs by synthesis.

Ultrahigh-Resolution Photonic-Assisted Microwave Frequency Identification Based on Temporal Channelization

Abstract: Real-time ultrahigh-resolution microwave frequency identification is paramount for widespread applications, such as communications, radar, and electronic warfare Photonics-assisted microwave frequency identification can be achieved using an optical channelizer While this technique enables simultaneous measurement of multiple frequencies, it has poor measurement resolution due to the large channel spacing, which is usually greater than 1 GHz Here, we introduce a new channelizer-based microwave frequency measurement technique that offers nearly 500 times higher spectral resolution This method employs largely dispersed broadband optical pulses to encode the time-domain characteristics of the modulating signal to the optical spectral domain An optical channelizer is employed to slice the spectrum, which is equivalent to performing temporal sampling of the time-domain waveform The unknown microwave signal is then reconstructed and its spectral distribution is analyzed by a digital processor To evaluate the proposed technique, frequency measurements of a single-tone, a multiple-tone, and a frequency-hopping microwave signal are demonstrated A measurement resolution as high as 55 MHz is achieved using an optical channelizer with a channel spacing of 25 GHz

Compressed sensing-based MRI reconstruction using complex double-density dual-tree DWT

Abstract: Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS has great potential in reducing data acquisition time in MRI. In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a basis, usually wavelet transform or total variation. In this paper, we propose an improved compressed sensing-based reconstruction method using the complex double-density dual-tree discrete wavelet transform. Our experiments demonstrate that this method can reduce aliasing artifacts and achieve higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.

Systems and methods for sampling in an input network of a delta-sigma modulator

Abstract: In accordance with systems and methods of the present disclosure, an input network for a delta-sigma modulator having at least one integrator stage and a feedback digital-to-analog stage, may be configured to, during a first period of a first phase of a clock signal, drive an analog feedback signal proportional to a digital feedback signal of the feedback digital-to-analog stage onto an input plate of a sampling capacitor integral to the input network. The input network may further be configured to, during a second period of the first phase of the clock signal, sample an analog input signal onto the input plates of the sampling capacitor.

A New Digital Predistortion for Wideband Power Amplifiers With Constrained Feedback Bandwidth

Abstract: This letter presents a new digital predistortion (DPD) solution for wideband power amplifiers (PAs) with restricted feedback bandwidth and relatively low sampling rate. By integrating a microwave cavity filter into the feedback path, the feedback bandwidth is reduced efficiently. A PA parameter extraction method is then proposed to identify the PA model, using the bandwidth-constrained signals. By applying the extracted parameters to the direct learning architecture, the DPD function can be obtained, which can linearize nonlinear distortion over the sampling bandwidth. Experiments demonstrate that a 22 dB adjacent channel leakage ratio improvement is acquired for a 100 MHz Long Term Evolution-advanced signal, even when the feedback bandwidth is restricted from 500 MHz to 100 MHz, which remarkably reduces the ADC sampling rate from 1105.92 Msps to 368.64 Msps.

Compressive joint angular-frequency power spectrum estimation

Abstract: We introduce a new compressive power spectrum estimation approach in both frequency and direction of arrival (DOA). Wide-sense stationary signals produced by multiple uncorrelated sources are compressed in both the time and spatial domain where the latter compression is implemented by activating only some of the antennas in the underlying uniform linear array (ULA).We sample the received signal at every active antenna at sub-Nyquist rate, compute both the temporal and spatial correlation functions between the sub-Nyquist rate samples, and apply least squares to reconstruct the full-blown two-dimensional power spectrum matrix where the rows and columns correspond to the frequencies and the angles, respectively. This is possible under the full column rank condition of the system matrices and without applying any sparsity constraint on the signal statistics. Further, we can estimate the DOAs of the sources by locating the peaks of the angular power spectrum. We can theoretically estimate the frequency bands and the DOAs of more uncorrelated sources than active sensors using sub-Nyquist sampling.

A novel method based solely on FPGA units enabling measurement of time and charge of analog signals in Positron Emission Tomography

Abstract: This article presents a novel technique for precise measurement of time and charge based solely on FPGA (Field Programmable Gate Array) device and few satellite discrete electronic components used in Positron Emission Tomography (PET). Described approach simplifies electronic circuits, reduces the power consumption, lowers costs, merges front-end electronics with digital electronics and also makes more compact final design. Furthermore, it allows to measure time when analog signals cross a reference voltage at different threshold levels with a very high precision of $\sim$ 10ps (rms) and thus enables sampling of signals in a voltage domain.

Consistent iterative hard thresholding for signal declipping

Abstract: Clipping or saturation in audio signals is a very common problem in signal processing, for which, in the severe case, there is still no satisfactory solution. In such case, there is a tremendous loss of information, and traditional methods fail to appropriately recover the signal. We propose a novel approach for this signal restoration problem based on the framework of Iterative Hard Thresholding. This approach, which enforces the consistency of the reconstructed signal with the clipped observations, shows superior performance in comparison to the state-of-the-art declipping algorithms. This is confirmed on synthetic and on actual high-dimensional audio data processing, both on SNR and on subjective user listening evaluations.

Shannon Meets Nyquist: Capacity of Sampled Gaussian Channels

Abstract: We explore two fundamental questions at the intersection of sampling theory and information theory: how channel capacity is affected by sampling below the channel's Nyquist rate, and what sub-Nyquist sampling strategy should be employed to maximize capacity. In particular, we derive the capacity of sampled analog channels for three prevalent sampling strategies: sampling with filtering, sampling with filter banks, and sampling with modulation and filter banks. These sampling mechanisms subsume most nonuniform sampling techniques applied in practice. Our analyses illuminate interesting connections between undersampled channels and multiple-input multiple-output channels. The optimal sampling structures are shown to extract out the frequencies with the highest SNR from each aliased frequency set, while suppressing aliasing and out-of-band noise. We also highlight connections between undersampled channel capacity and minimum mean-squared error (MSE) estimation from sampled data. In particular, we show that the filters maximizing capacity and the ones minimizing MSE are equivalent under both filtering and filter-bank sampling strategies. These results demonstrate the effect upon channel capacity of sub-Nyquist sampling techniques, and characterize the tradeoff between information rate and sampling rate.

A New Approach to Theoretical Analysis of Harmonic Content of PWM Waveforms of Single- and Multiple-Frequency Modulators

Abstract: This paper presents a new approach to the spectral analysis of a carrier-based pulse width modulation (PWM) signal with continuous modulating function, and natural or regular sampling. The method is based on 1-D Fourier series and an expansion of the implicitly defined functions of the pulse edge angles of a PWM signal to power series. Expressions for the individual harmonics are given in closed form, allowing direct the calculation of the harmonic components, and can readily be implemented by a computer software package, such as MATLAB. Similarly, analytical expressions for the edges of the naturally sampled output PWM signal are determined. Cases of single- and multiple-modulation wave are analyzed in detail. As modulation of the half-bridge, single-phase inverter and the resulting spectrum constitute the basic building block from which the spectrum content of modulated multiphase, multilevel converters can readily be discerned, the spectrum of harmonics of the output PWM signal of an ideal half-bridge, single-phase inverter is analyzed. The method is evaluated and validated by comparing the spectrum obtained using the present method with those obtained by applying double Fourier series or by direct numerical procedure.

Applications of Compressed Sensing in Communications Networks

Abstract: This paper presents a tutorial for CS applications in communications networks. The Shannon's sampling theorem states that to recover a signal, the sampling rate must be as least the Nyquist rate. Compressed sensing (CS) is based on the surprising fact that to recover a signal that is sparse in certain representations, one can sample at the rate far below the Nyquist rate. Since its inception in 2006, CS attracted much interest in the research community and found wide-ranging applications from astronomy, biology, communications, image and video processing, medicine, to radar. CS also found successful applications in communications networks. CS was applied in the detection and estimation of wireless signals, source coding, multi-access channels, data collection in sensor networks, and network monitoring, etc. In many cases, CS was shown to bring performance gains on the order of 10X. We believe this is just the beginning of CS applications in communications networks, and the future will see even more fruitful applications of CS in our field.

Apparatus and method for space-division multiplexing optical coherence tomography

Abstract: A space-division multiplexing optical coherence tomography apparatus and system is provided. In one embodiment, the system includes a light source, a reference arm, and a sample arm. The sample arm splits the sampling light into a plurality of sampling beams which may be scanned simultaneously onto a surface of a sample. An optical delay may be introduced into the sampling beams before scanning. A plurality of reflected light signals returned from the sample is collected. In one arrangement, the signals may be combined to produce a single reflected light signal. The reflected light signal(s) and a reference signal are combined to produce an interference signal comprising data representative of digitized images captured of the actual object. In one embodiment, a single sample arm may be used for scanning and collecting image data. A related method is also provided.

Redundant Sensing Element Sampling

Abstract: A sensor system and method that adjusts sensor data to account for the presence of noise that causes variations in signal amplitude between sensor blocks and between sensor rows. In order to account for the presence of noise in a sensor apparatus, various embodiments apply a first adjustment to the sensor data to account for variations in signal amplitude that occur from block to block. Various embodiments may also apply a second adjustment to the sensor data to account for variations in signal amplitude that occur from row to row.

A microwave contactless heart rate sensor

Abstract: A heart-rate sensor for detecting artery blood-flow volume per unit length change in a human or animal subject, which comprises an antenna for sensing the instantaneous volume of blood in the artery of the subject, to be measured; a RADAR unit for transmitting microwave signals into a subject's body part or limb representing tissue targets. The output of the RADAR unit includes a superposition of signals each of which corresponding to a different tissue target with amplitudes that relate to the target's reflection strength; a sampling circuitry for converting reflected signals to digital; a window function circuitry for suppressing unwanted spectral sidebands originating from the subsequent processor operating on time truncated data; an FFT processor following the window function circuitry, for splitting the superposition according to its relative frequency into a multiplicity of bins, each of which with an amplitude that represents the reflection magnitude of a target at a specific distance from the antenna; a signal processor for filtering out the effect of the sensor movement with respect to the subject body part, or the movement of the body part, and for generating a signal, the amplitude of which is proportional to the artery varying dilatation representing the heart-rate; a heart-rate estimator for measuring the frequency of the artery dilatation variations and for canceling the interference of the amplitude of any signal that does not originate from the artery; a battery for powering the sensor.

Analytical mode decomposition of time series with decaying amplitudes and overlapping instantaneous frequencies

Abstract: In this study, the recently developed analytical mode decomposition with Hilbert transform was extended to the decomposition of a non-stationary and nonlinear signal with two or more amplitude-decaying and frequency-changing components. The bisecting frequency in the analytical mode decomposition became time-varying, and could be selected between any two adjacent instantaneous frequencies estimated from a preliminary wavelet analysis. The mathematical foundation for this new extension was integration of the bisecting frequency over time so that the original time series is actually decomposed in the phase domain. Parametric studies indicated that the analytically derived components are insensitive to the selection of bisecting frequency and the presence of up to 20% noise, sufficiently accurate when the sampling rate meets the Nyquist–Shannon sampling criterion, and applicable to both narrowband and wideband frequency modulations even when the signal amplitude decays over time. The proposed analytical mode decomposition is superior to the empirical mode decomposition and wavelet analysis in the preservation of signal amplitude, frequency and phase relations. It can be directly applied for system identification of buildings with time-varying stiffness.

Compressive Acquisition of Linear Dynamical Systems

Abstract: Compressive sensing (CS) enables the acquisition and recovery of sparse signals and images at sampling rates significantly below the classical Nyquist rate. Despite significant progress in the theory and methods of CS, little headway has been made in compressive video acquisition and recovery. Video CS is complicated by the ephemeral nature of dynamic events, which makes direct extensions of standard CS imaging architectures and signal models difficult. In this paper, we develop a new framework for video CS for dynamic textured scenes that models the evolution of the scene as a linear dynamical system (LDS). This reduces the video recovery problem to first estimating the model parameters of the LDS from compressive measurements and then reconstructing the image frames. We exploit the low-dimensional dynamic parameters (the state sequence) and high-dimensional static parameters (the observation matrix) of the LDS to devise a novel compressive measurement strategy that measures only the time-varying paramet...