Showing papers on "Noise measurement published in 2011"

Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter

Abstract: A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

Exemplar-Based Sparse Representations for Noise Robust Automatic Speech Recognition

Abstract: This paper proposes to use exemplar-based sparse representations for noise robust automatic speech recognition. First, we describe how speech can be modeled as a linear combination of a small number of exemplars from a large speech exemplar dictionary. The exemplars are time-frequency patches of real speech, each spanning multiple time frames. We then propose to model speech corrupted by additive noise as a linear combination of noise and speech exemplars, and we derive an algorithm for recovering this sparse linear combination of exemplars from the observed noisy speech. We describe how the framework can be used for doing hybrid exemplar-based/HMM recognition by using the exemplar-activations together with the phonetic information associated with the exemplars. As an alternative to hybrid recognition, the framework also allows us to take a source separation approach which enables exemplar-based feature enhancement as well as missing data mask estimation. We evaluate the performance of these exemplar-based methods in connected digit recognition on the AURORA-2 database. Our results show that the hybrid system performed substantially better than source separation or missing data mask estimation at lower signal-to-noise ratios (SNRs), achieving up to 57.1% accuracy at SNR = -5 dB. Although not as effective as two baseline recognizers at higher SNRs, the novel approach offers a promising direction of future research on exemplar-based ASR.

The Noise-Sensitivity Phase Transition in Compressed Sensing

Abstract: Consider the noisy underdetermined system of linear equations: y = Ax0 + z, with A an n × N measurement matrix, n <; N, and z ~ N(0, σ2I) a Gaussian white noise. Both y and A are known, both x0 and z are unknown, and we seek an approximation to x0. When x0 has few nonzeros, useful approximations are often obtained by l1-penalized l2 minimization, in which the reconstruction x1,λ solves min{||y - Ax||22/2 + λ||x||1}. Consider the reconstruction mean-squared error MSE = E|| x1,λ - x0||22/N, and define the ratio MSE/σ2 as the noise sensitivity. Consider matrices A with i.i.d. Gaussian entries and a large-system limit in which n, N → ∞ with n/N → δ and k/n → ρ. We develop exact expressions for the asymptotic MSE of x1,λ , and evaluate its worst-case noise sensitivity over all types of k-sparse signals. The phase space 0 ≤ 8, ρ ≤ 1 is partitioned by the curve ρ = ρMSE(δ) into two regions. Formal noise sensitivity is bounded throughout the region ρ = ρMSE(δ) and is unbounded throughout the region ρ = ρMSE(δ). The phase boundary ρ = ρMSE(δ) is identical to the previously known phase transition curve for equivalence of l1 - l0 minimization in the k-sparse noiseless case. Hence, a single phase boundary describes the fundamental phase transitions both for the noise less and noisy cases. Extensive computational experiments validate these predictions, including the existence of game-theoretical structures underlying it (saddlepoints in the payoff, least-favorable signals and maximin penalization). Underlying our formalism is an approximate message passing soft thresholding algorithm (AMP) introduced earlier by the authors. Other papers by the authors detail expressions for the formal MSE of AMP and its close connection to l1-penalized reconstruction. The focus of the present paper is on computing the minimax formal MSE within the class of sparse signals x0.

Dealing with noise in defect prediction

Abstract: Many software defect prediction models have been built using historical defect data obtained by mining software repositories (MSR). Recent studies have discovered that data so collected contain noises because current defect collection practices are based on optional bug fix keywords or bug report links in change logs. Automatically collected defect data based on the change logs could include noises. This paper proposes approaches to deal with the noise in defect data. First, we measure the impact of noise on defect prediction models and provide guidelines for acceptable noise level. We measure noise resistant ability of two well-known defect prediction algorithms and find that in general, for large defect datasets, adding FP (false positive) or FN (false negative) noises alone does not lead to substantial performance differences. However, the prediction performance decreases significantly when the dataset contains 20%-35% of both FP and FN noises. Second, we propose a noise detection and elimination algorithm to address this problem. Our empirical study shows that our algorithm can identify noisy instances with reasonable accuracy. In addition, after eliminating the noises using our algorithm, defect prediction accuracy is improved.

Effects of Noise Power Estimation on Energy Detection for Cognitive Radio Applications

Abstract: An uncertain knowledge of the noise power level can severely limit the energy detector (ED) spectrum sensing capability. In some situations this uncertainty can cause signal-to-noise ratio (SNR) penalties or even the rise of the SNR wall phenomenon. In this paper we analyze the performance of the ED with estimated noise power (ENP), addressing the threshold design and giving the conditions for the existence of the SNR wall. We derive analytical expressions for the design curves (SNR vs. observation time for a target performance) for the ENP-ED. Then we apply our analysis to cognitive radio (CR) systems where energy detection is used for fast sensing. For example it is shown that the SNR penalty with respect to ideal ED is of 5 log10(1+λ/λ) dB, when the time dedicated to noise power estimation is a multiple λ of the ED observation interval.

A 2.9–4.0-GHz Fractional-N Digital PLL With Bang-Bang Phase Detector and 560- ${\rm fs}_{\rm rms}$ Integrated Jitter at 4.5-mW Power

Abstract: This paper introduces a ΔΣ fractional-N digital PLL based on a single-bit TDC. A digital-to-time converter, placed in the feedback path, cancels out the quantization noise introduced by the dithering of the frequency divider modulus and permits to achieve low noise at low power. The PLL is implemented in a standard 65-nm CMOS process. It achieves - 102-dBc/Hz phase noise at 50-kHz offset and a total absolute jitter below 560 fsrms (integrated from 3 kHz to 30 MHz), even in the worst-case of a -42-dBc in-band fractional spur. The synthesizer tuning range spans from 2.92 GHz to 4.05 GHz with 70-Hz resolution. The total power consumption is 4.5 mW, which leads to the best jitter-power trade-off obtained with a fractional-N synthesizer. The synthesizer demonstrates the capability of frequency modulation up to 1.25-Mb/s data rate.

Framelet Algorithms for De-Blurring Images Corrupted by Impulse Plus Gaussian Noise

Abstract: This paper studies a problem of image restoration that observed images are contaminated by Gaussian and impulse noise. Existing methods for this problem in the literature are based on minimizing an objective functional having the l1 fidelity term and the Mumford-Shah regularizer. We present an algorithm on this problem by minimizing a new objective functional. The proposed functional has a content-dependent fidelity term which assimilates the strength of fidelity terms measured by the l1 and l2 norms. The regularizer in the functional is formed by the l1 norm of tight framelet coefficients of the underlying image. The selected tight framelet filters are able to extract geometric features of images. We then propose an iterative framelet-based approximation/sparsity deblurring algorithm (IFASDA) for the proposed functional. Parameters in IFASDA are adaptively varying at each iteration and are determined automatically. In this sense, IFASDA is a parameter-free algorithm. This advantage makes the algorithm more attractive and practical. The effectiveness of IFASDA is experimentally illustrated on problems of image deblurring with Gaussian and impulse noise. Improvements in both PSNR and visual quality of IFASDA over a typical existing method are demonstrated. In addition, Fast_IFASDA, an accelerated algorithm of IFASDA, is also developed.

Signal-Dependent Noise Modeling and Model Parameter Estimation in Hyperspectral Images

Abstract: In this paper, a novel method to characterize random noise sources in hyperspectral (HS) images is proposed. Noise is described using a parametric model that accounts for the dependence of noise variance on the useful signal. Such model takes into account the photon noise contribution and is therefore suitable for noise characterization in the data acquired by new-generation HS sensors where electronic noise is not dominant. A new algorithm is developed for the estimation of noise parameters which consists of two steps. First, the noise and signal realizations are extracted from the original image by resorting to the multiple-linear-regression-based approach. Then, the model parameters are estimated by using a maximum likelihood approach. The new method does not require the intervention of a human operator and the selection of homogeneous regions in the scene. The performance of the new technique is analyzed on simulated HS data. Results on real data are also presented and discussed. Images acquired with a new-generation HS camera are analyzed to give an experimental evidence of the dependence of random noise on the signal level and to show the results of the estimation algorithm. The algorithm is also applied to a well-known Airborne Visible/Infrared Imaging Spectrometer data set in order to show its effectiveness when noise is dominated by the signal-independent term.

Noise Folding in Compressed Sensing

Abstract: The literature on compressed sensing has focused almost entirely on settings where the signal is noiseless and the measurements are contaminated by noise. In practice, however, the signal itself is often subject to random noise prior to measurement. We briefly study this setting and show that, for the vast majority of measurement schemes employed in compressed sensing, the two models are equivalent with the important difference that the signal-to-noise ratio (SNR) is divided by a factor proportional to p/n, where p is the dimension of the signal and n is the number of observations. Since p/n is often large, this leads to noise folding which can have a severe impact on the SNR.

Method and apparatus for suppressing wind noise

Abstract: The invention includes a method, apparatus, and computer program to selectively suppress wind noise while preserving narrow-band signals in acoustic data. Sound from one or several microphones is digitized into binary data. A time-frequency transform is applied to the data to produce a series of spectra. The spectra are analyzed to detect the presence of wind noise and narrow band signals. Wind noise is selectively suppressed while preserving the narrow band signals. The narrow band signal is interpolated through the times and frequencies when it is masked by the wind noise. A time series is then synthesized from the signal spectral estimate that can be listened to. This invention overcomes prior art limitations that require more than one microphone and an independent measurement of wind speed. Its application results in good-quality speech from data severely degraded by wind noise.

An Integrated Solution for Online Multichannel Noise Tracking and Reduction

Abstract: Noise statistics estimation is a paramount issue in the design of reliable noise-reduction algorithms. Although significant efforts have been devoted to this problem in the literature, most developed methods so far have focused on the single-channel case. When multiple microphones are used, it is important that the data from all the sensors are optimally combined to achieve judicious updates of the noise statistics and the noise-reduction filter. This contribution is devoted to the development of a practical approach to multichannel noise tracking and reduction. We combine the multichannel speech presence probability (MC-SPP) that we proposed in an earlier contribution with an alternative formulation of the minima-controlled recursive averaging (MCRA) technique that we generalize from the single-channel to the multichannel case. To demonstrate the effectiveness of the proposed MC-SPP and multichannel noise estimator, we integrate them into three variants of the multichannel noise reduction Wiener filter. Experimental results show the advantages of the proposed solution.

An Adaptive Kalman Filter for ECG Signal Enhancement

Abstract: The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a Bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the Bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.

Performance of Eigenvalue-Based Signal Detectors with Known and Unknown Noise Level

Abstract: In this paper we consider signal detection in cognitive radio networks, under a non-parametric, multi-sensor detection scenario, and compare the cases of known and unknown noise level. The analysis is focused on two eigenvalue-based methods, namely Roy's largest root test, which requires knowledge of the noise variance, and the generalized likelihood ratio test, which can be interpreted as a test of the largest eigenvalue vs. a maximum-likelihood estimate of the noise variance. The detection performance of the two considered methods is expressed by closed-form analytical formulas, shown to be accurate even for small number of sensors and samples. We then derive an expression of the gap between the two detectors in terms of the signal-to-noise ratio of the signal to be detected, and we identify critical settings where this gap is significant (e.g., low number of sensors and signal strength). Our results thus provide a measure of the impact of noise level knowledge and highlight the importance of accurate noise estimation.

Performance of LDPC Codes Under Faulty Iterative Decoding

Abstract: Departing from traditional communication theory where decoding algorithms are assumed to perform without error, a system where noise perturbs both computational devices and communication channels is considered here. This paper studies limits in processing noisy signals with noisy circuits by investigating the effect of noise on standard iterative decoders for low-density parity-check (LDPC) codes. Concentration of decoding performance around its average is shown to hold when noise is introduced into message-passing and local computation. Density evolution equations for simple faulty iterative decoders are derived. In one model, computing nonlinear estimation thresholds shows that performance degrades smoothly as decoder noise increases, but arbitrarily small probability of error is not achievable. Probability of error may be driven to zero in another system model; the decoding threshold again decreases smoothly with decoder noise. As an application of the methods developed, an achievability result for reliable memory systems constructed from unreliable components is provided.

Noise power estimation based on the probability of speech presence

Abstract: In this paper, we analyze the minimum mean square error (MMSE) based spectral noise power estimator [1] and present an improvement. We will show that the MMSE based spectral noise power estimate is only updated when the a posteriori signal-to-noise ratio (SNR) is lower than one. This threshold on the a posteriori SNR can be interpreted as a voice activity detector (VAD). We propose in this work to replace the hard decision of the VAD by a soft speech presence probability (SPP). We show that by doing so, the proposed estimator does not require a bias correction and safety-net as is required by the MMSE estimator presented in [1]. At the same time, the proposed estimator maintains the quick noise tracking capability which is characteristic for the MMSE noise tracker, results in less noise power overestimation and is computationally less expensive.

Optimal signal-to-noise ratio for silicon nanowire biochemical sensors.

Abstract: The signal-to-noise ratio (SNR) for silicon nanowire field-effect transistors operated in an electrolyte environment is an essential figure-of-merit to characterize and compare the detection limit of such devices when used in an exposed channel configuration as biochemical sensors. We employ low frequency noise measurements to determine the regime for optimal SNR. We find that SNR is not significantly affected by the electrolyte concentration, composition, or pH, leading us to conclude that the major contributions to the SNR come from the intrinsic device quality. The results presented here show that SNR is maximized at the peak transconductance.

On noise modeling for power line communications

Abstract: This paper reviews existing noise models including both background and impulsive noise for the in-home PLC scenario, highlighting similarities and differences. With reference to the impulsive noise, it is shown that a simple model, in the frequency band up to 100 MHz, can be derived by considering the noise generated at the source and taking into account the effect of the channel. Capacity considerations are then made, comparing erasure decoding strategies or full decoding strategies.

Multiobjective Optimization of OFDM Radar Waveform for Target Detection

Abstract: We propose a multiobjective optimization (MOO) technique to design an orthogonal-frequency-division multiplexing (OFDM) radar signal for detecting a moving target in the presence of multipath reflections. We employ an OFDM signal to increase the frequency diversity of the system, as different scattering centers of a target resonate variably at different frequencies. Moreover, the multipath propagation increases the spatial diversity by providing extra “looks” at the target. First, we develop a parametric OFDM radar model by reformulating the target-detection problem as the task of sparse-signal spectrum estimation. At a particular range cell, we exploit the sparsity of multiple paths and the knowledge of the environment to estimate the paths along which the target responses are received. Then, to estimate the sparse vector, we employ a collection of multiple small Dantzig selectors (DS) that utilizes more prior structures of the sparse vector. We use the l1-constrained minimal singular value (l1-CMSV) of the measurement matrix to analytically evaluate the reconstruction performance and demonstrate that our decomposed DS performs better than the standard DS. In addition, we propose a constrained MOO-based algorithm to optimally design the spectral parameters of the OFDM waveform for the next coherent processing interval by simultaneously optimizing two objective functions: minimizing the upper bound on the estimation error to improve the efficiency of sparse-recovery and maximizing the squared Mahalanobis-distance to increase the performance of the underlying detection problem. We provide a few numerical examples to illustrate the performance characteristics of the sparse recovery and demonstrate the achieved performance improvement due to adaptive OFDM waveform design.

Estimators of the Magnitude-Squared Spectrum and Methods for Incorporating SNR Uncertainty

Abstract: Statistical estimators of the magnitude-squared spectrum are derived based on the assumption that the magnitude-squared spectrum of the noisy speech signal can be computed as the sum of the (clean) signal and noise magnitude-squared spectra. Maximum a posterior (MAP) and minimum mean square error (MMSE) estimators are derived based on a Gaussian statistical model. The gain function of the MAP estimator was found to be identical to the gain function used in the ideal binary mask (IdBM) that is widely used in computational auditory scene analysis (CASA). As such, it was binary and assumed the value of 1 if the local signal-to-noise ratio (SNR) exceeded 0 dB, and assumed the value of 0 otherwise. By modeling the local instantaneous SNR as an F-distributed random variable, soft masking methods were derived incorporating SNR uncertainty. The soft masking method, in particular, which weighted the noisy magnitude-squared spectrum by the a priori probability that the local SNR exceeds 0 dB was shown to be identical to the Wiener gain function. Results indicated that the proposed estimators yielded significantly better speech quality than the conventional minimum mean square error spectral power estimators, in terms of yielding lower residual noise and lower speech distortion.

An $\ell _{1}$ -Laplace Robust Kalman Smoother

Abstract: Robustness is a major problem in Kalman filtering and smoothing that can be solved using heavy tailed distributions; e.g., l1-Laplace. This paper describes an algorithm for finding the maximum a posteriori (MAP) estimate of the Kalman smoother for a nonlinear model with Gaussian process noise and l1 -Laplace observation noise. The algorithm uses the convex composite extension of the Gauss-Newton method. This yields convex programming subproblems to which an interior point path-following method is applied. The number of arithmetic operations required by the algorithm grows linearly with the number of time points because the algorithm preserves the underlying block tridiagonal structure of the Kalman smoother problem. Excellent fits are obtained with and without outliers, even though the outliers are simulated from distributions that are not l1 -Laplace. It is also tested on actual data with a nonlinear measurement model for an underwater tracking experiment. The l1-Laplace smoother is able to construct a smoothed fit, without data removal, from data with very large outliers.

The importance for speech intelligibility of random fluctuations in "steady" background noise.

Abstract: Spectrally shaped steady noise is commonly used as a masker of speech. The effects of inherent random fluctuations in amplitude of such a noise are typically ignored. Here, the importance of these random fluctuations was assessed by comparing two cases. For one, speech was mixed with steady speech-shaped noise and N-channel tone vocoded, a process referred to as signal-domain mixing (SDM); this preserved the random fluctuations of the noise. For the second, the envelope of speech alone was extracted for each vocoder channel and a constant was added corresponding to the root-mean-square value of the noise envelope for that channel. This is referred to as envelope-domain mixing (EDM); it removed the random fluctuations of the noise. Sinusoidally modulated noise and a single talker were also used as backgrounds, with both SDM and EDM. Speech intelligibility was measured for N = 12, 19, and 30, with the target-to-background ratio fixed at −7 dB. For SDM, performance was best for the speech background and worst for the steady noise. For EDM, this pattern was reversed. Intelligibility with steady noise was consistently very poor for SDM, but near-ceiling for EDM, demonstrating that the random fluctuations in steady noise have a large effect.

A Pitch Estimation Filter robust to high levels of noise (PEFAC)

Abstract: We present PEFAC, a fundamental frequency estimation algorithm that is able to identify the pitch of voiced frames reliably even at negative signal to noise ratios. The algorithm combines non-linear amplitude compression, to attenuate narrow-band noise components, with a comb-filter applied in the log-frequency power spectral domain, whose impulse response is chosen to attenuate smoothly varying noise components. We compare the performance of our algorithm with that of other widely used algorithms on a subset of the TIMIT database and demonstrate that it performs exceptionally well in both high and low levels of additive noise.

SAR Image Despeckling Using Bayesian Nonlocal Means Filter With Sigma Preselection

Abstract: Bayesian nonlocal (NL) (BNL) means filter, as an extension of the NL means algorithm, provides a general framework for image denoising when dealing with different noise. However, this approach makes a strong assumption that image patch itself provides a good approximation on the true parameter, which leads to the bias problem particularly under serious speckle noise. Another disadvantage of the BNL filter is that the commonly used patch preselection method cannot effectively exclude the outliers. In this letter, a new form of the BNL filter is presented for the purpose of synthetic aperture radar image despeckling, which incorporates the technique of sigma filter to cope with the bias problem. In addition, pixel preselection is adopted based on the refined sigma range, which greatly contributes to the preservation of the image details such as edges, texture, and the strong reflective scatters. Experimental results illustrate that the proposed BNL filter reaches the state-of-the-art performance on both the visual quality and evaluation indexes.

An Active Impulsive Noise Control Algorithm With Logarithmic Transformation

Abstract: To overcome the limitations of the existing algorithms for active impulsive noise control, an algorithm based on minimizing the squared logarithmic transformation of the error signal is proposed in this correspondence. The proposed algorithm is more robust for impulsive noise control and does not need the parameter selection and thresholds estimation according to the noise characteristics. These are verified by theoretical analysis and numerical simulations.

Boundary Artifact Reduction in View Synthesis of 3D Video: From Perspective of Texture-Depth Alignment

Abstract: 3D Video (3DV) with depth-image-based view synthesis is a promising candidate of next generation broadcasting applications. However, the synthesized views in 3DV are often contaminated by annoying artifacts, particularly notably around object boundaries, due to imperfect depth maps (e.g., produced by state-of-the-art stereo matching algorithms or compressed lossily). In this paper, we first review some representative methods for boundary artifact reduction in view synthesis, and make an in-depth investigation into the underlying mechanisms of boundary artifact generation from a new perspective of texture-depth alignment in boundary regions. Three forms of texture-depth misalignment are identified as the causes for different boundary artifacts, which mainly present themselves as scattered noises on the background and object erosion on the foreground. Based on the insights gained from the analysis, we propose a novel solution of suppression of misalignment and alignment enforcement (denoted as SMART) between texture and depth to reduce background noises and foreground erosion, respectively, among different types of boundary artifacts. The SMART is developed as a three-step pre-processing in view synthesis. Experiments on view synthesis with original and compressed texture/depth data consistently demonstrate the superior performance of the proposed method as compared with other relevant boundary artifact reduction schemes.

Lossless Linear Transformation of Sensor Data for Distributed Estimation Fusion

Abstract: In distributed estimation fusion, processed data from each sensor is sent to the fusion center. By taking linear transformation of the raw measurements of each sensor, two optimal distributed fusion algorithms are proposed in this paper. Compared with existing fusion algorithms, they have three nice properties. First, they are optimal in the sense that they are equivalent to the optimal centralized fusion. Second, their communication requirements from each sensor to the fusion center are equal to or less than those of the centralized and most existing distributed fusion algorithms. Third, they do not need the inverses of estimation error covariance matrices, which are assumed to exist in most existing algorithms but can not be guaranteed to exist. So the proposed algorithms can be applied in more cases. Pros and cons of these two new algorithms are analyzed. A possible way to reduce the computational complexity of the new algorithms, an extension to the case of a singular covariance matrix of measurement noise, and an extension to the reduced-rate communication case for some simple systems are also discussed.

A Low-Power Low-Noise Dual-Chopper Amplifier for Capacitive CMOS-MEMS Accelerometers

Abstract: This paper reports a novel dual-chopper amplifier (DCA) and its application to monolithic complementary metal-oxide semiconductor-microelectromechanical systems accelerometers. The DCA design minimizes the power consumption and noise by chopping the sensing signals at two clocks. The first clock is a high frequency for removing the flicker noise while the second clock is a significantly lower frequency to keep the unit gain bandwidth low. A monolithic three-axis accelerometer integrated with the DCA on the same chip has been successfully fabricated using a post-CMOS micromachining process. The measured noise floors are 40 μ g/√Hz in the x - and y -axis and 130 μ g/√Hz in the z -axis, and the power consumption is about 1 mW per axis.

Maximum-Likelihood Classification of Digital Amplitude-Phase Modulated Signals in Flat Fading Non-Gaussian Channels

Abstract: In this paper, we propose an algorithm for the classification of digital amplitude-phase modulated signals in flat fading channels with non-Gaussian noise. The additive noise is modeled by a Gaussian mixture distribution, a well-known model of man-made and natural noise that appears in most radio channels. The classifier utilizes a variant of the expectation-maximization algorithm to estimate the channel and noise parameters without the aid of training symbols. With these estimates, the signal is classified using a hybrid likelihood ratio test. Results are presented which show that the proposed classifier's performance approaches that of the ideal classifier with perfect knowledge of the channel state and noise distribution.

Noise Performance of Magnetometers With Resonant Thin-Film Magnetoelectric Sensors

Abstract: Sensors based on materials with a giant magnetoelectric (ME) effect may be used to measure biomagnetic fields at room temperature. It is necessary to know the noise behavior of the whole detection unit. The noise level of a thin-film ME sensor was measured at room temperature, and suitable types of low-noise amplifiers were investigated. Noise measurements were carried out at room temperature. Results show a sensitivity value of 5.4 pT/ √Hz at a resonance frequency of 330 Hz. Furthermore, the signal-to-noise ratio was investigated in order to improve the sensitivity of the sensor.

Time-frequency segmentation of bird song in noisy acoustic environments

Abstract: Recent work in machine learning considers the problem of identifying bird species from an audio recording. Most methods require segmentation to isolate each syllable of bird call in input audio. Energy-based time-domain segmentation has been successfully applied to low-noise, single-bird recordings. However, audio from automated field recorders contains too much noise for such methods, so a more robust segmentation method is required. We propose a supervised time-frequency audio segmentation method using a Random Forest classifier, to extract syllables of bird call from a noisy signal. When applied to a test data set of 625 field-collected audio segments, our method isolates 93.6% of the acoustic energy of bird song with a false positive rate of 8.6%, outperforming energy thresholding.