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In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.

Coherent seismic noise is usually difficult to attenuate due to the similar morphological patterns between noise and useful signals. To attenuate coherent noise, special preknowledge should be utilized in a state-of-the-art approach, which causes significant inconvenience. Here, we develop an automatic method to attenuate coherent noise based on the adaptive dictionary learning algorithm. The adaptive dictionary algorithm can learn the features of both signals and coherent noise and leave obvious morphological differences in the dictionary atoms. These differences in the dictionary atoms can be transformed into statistical differences, which can be measured and then used to distinguish between signal and noise atoms. We evaluate several statistical metrics in characterizing the dictionary atoms and their feasibilities in distinguishing between signal and noise atoms. We find that the kurtosis metric can best represent the differences between signal and noise atoms, and then we design a kurtosis-based filter to reject those high-kurtosis atoms and their corresponding coefficient vectors for suppressing the coherent noise. Synthetic and real data examples demonstrate the performance of the proposed algorithm.

Seismic exploration is a remote-sensing tool applied in a great many projects for engineering and resource-exploration purposes. Random noise suppression is one of the key steps in seismic-signal processing, especially those with important details and features. The threshold-shrinkage method based on Shearlet transform has been effectively applied in seismic-signal denoising. However, the method usually introduces the boundary effect, which influences the imaging quality. The denoising method of total generalized variation (TGV) is easy to produce `oil painting' effect, but it can effectively suppress the boundary effect. This paper proposes a denoising method based on Shearlet threshold-shrinkage and TGV for making full use of their characteristics, which can recover both edges and fine details much better than the existing regularization methods. First, we use the Shearlet threshold-shrinkage result as the input of TGV to obtain the primary denoising result and the residual profile. Second, we use the interactive iteration of Shearlet threshold-shrinkage and TGV to extract the signals efficiently from the residual profile and perform the effective signals stack continuously. During the processing, the adaptive-weight factor is combined for estimating the optimal denoising result. Last, the final estimated denoising result is obtained when the stopping criterion is met or the maximum number of iterations is reached. The synthetic and field results show that the proposed method can effectively suppress random noise. In addition, it can also remove the boundary effect and `oil painting' effect, which further improves the signal-to-noise ratio (SNR).

In many optical communication systems, in order to make the optical networking convenient and meet the application requirements, the optical terminals need to work in the same band and at the extremely closely adjacent working wavelengths. In practice, because of the light reflection and scattering of the wireless channels with the suspended particles, parts of the emitted optical signals will be reflected to the focal plane array (FPA). On the other hand, because the bandwidth of practical optical filters cannot be made narrow enough, or the sensitivity of FPAs is usually very high, the isolation between the transmitting and receiving optical paths in the terminal cannot be made high enough even if the optical filtering is used. Both the received optical signal from the remote terminal and the transmitted optical signal reflected by the terminal or wireless channels will be detected by the FPA at the same time. The reflected optical signal will interfere with the detection of the received optical signals from the far end, which seriously reduces the detection performance and the accuracy of the light spot centroid calculation. This paper proposes an integral control and increase-decrease counting statistical method for FPAs to eliminate the influence of the reflected optical signal on the signal detection and the noise mean value in the mean value of the total detection signal. The detection sensitivity of the received signal from the remote terminal, the isolation of the transmitting and receiving paths and the accuracy of the light spot centroid calculation are improved by this method. The Cramer-Rao lower bound is calculated and analyzed. In addition, the integral control and increase-decrease counting statistical algorithm is verified by the two software co-simulation, and the simulation results are given to illustrate the feasibility and performance of this method.

Repetitive motion planning (RMP) is a crucial issue encountered in studies on redundant robot manipulators. Numerous RMP schemes have been established in previous studies wherein simulations are assumed to be free of noise. However, noise is ubiquitous and can severely affect RMP schemes to the point of causing failure. This paper attempts address the limitations imposed by noise by providing the first RMP scheme with inherent noise-suppression capability. The new RMP scheme for redundant robot manipulators in a noisy environment is proposed on the basis of an equality criterion that is robust against additive noise. The equality criterion is established by incorporating the proportional and integral information of the desired end-effector path. The proposed scheme is reformulated as a quadratic program and is calculated by using a recurrent neural network. Comparative simulation results obtained with PA10 and four-link robot manipulators illustrate the effectiveness and superiority of the proposed RMP scheme over the traditional RMP scheme.

In this paper, the near-carrier enhancement of phase-noise (PN) at turnover temperature ( ${T} _{to}$ ) in a quasi-thickness-Lame (QTL) mode thin-film piezoelectric-on-silicon (TPoS) oscillator is reported for the first time. QTL-TPoS resonators fabricated on degenerately-doped n-type silicon offer a ${T} _{to}$ greater than 80°C and are suitable for implementation of highly-stable ovenized oscillators. In this work, a ~123MHz QTL-TPoS resonator is heated up to ${T} _{to}$ (~90°C) by injecting current through the silicon body of the resonator. It is experimentally observed that at turnover temperature, the phase-noise slope close to the carrier frequency ( $\Delta $ f 100Hz ) decreases substantially in contrast to the expected trends. A ~10dB improvement in phase noise at 10Hz offset and a ~25dB improvement at 1Hz offset is recorded when the oscillator is operating at ${T} _{to}$ compared to room temperature. [2020-0206]

FBMC-OQAM is a promising multi-carrier technique being considered for future wireless communication systems, owing to the flexibility it provides via the choice of prototype filters, robustness against asynchronous transmissions, and the absence of cyclic-prefix requirement. The performance of FBMC-OQAM systems is however limited by the accuracy of channel estimation due to the presence of so-called intrinsic interference. In this letter, we consider two well-known preamble-based channel estimation approaches for FBMC-OQAM, and propose generic preamble structures, followed by formulation of mathematical optimization problems to improve the resilience of both estimation schemes against this intrinsic interference. The proposed preambles, without incurring any additional run-time complexity at transceiver, lead to improved noise suppression, eventually leading to superior channel estimation performance. The benefit of the proposed preambles is shown not only numerically in terms of the relevant performance metrics, but also via link-level simulations under realistic transmission scenarios.

An artificial material made by kneading magnetic metal flakes in plastic sheets has been used widely in PCs or mobile phone handsets. This sheet is called a noise suppression sheet to reduce unwanted coupling or unwanted signals in the device. The sheet has strong frequency-dependent characteristics in the permeability. It is essential to evaluate these characteristics accurately to develop the sheets and to design the device mentioned above. Currently, the sheets have been evaluated by the transmission line methods, but the accuracy was not enough. The evaluation by the resonance cavity perturbation method is proposed in this article. Although the accuracy of the resonance cavity perturbation method is high, it works well at only one resonance frequency. The expansion of the measuring frequency by using a harmonic resonance cavity is proposed in this article. A comparison of the proposed method to the transmission line method is also discussed.

A nonlinear third-order extended state observer (TESO) is proposed for the rotor position and speed estimation for sensorless interior permanent magnet synchronous motor drives. The disturbance observation bandwidth of the proposed observer is expanded by deploying a gain optimization method. The measurement noise excited in the control loop is suppressed effectively by substituting the previous high gain configuration. Experimentally, both the steady-state and transient performance of the TESO are verified on testing rigs. In comparison with the performance of a conventional linear extended state observer, the TESO has a better capability of disturbance observation and noise suppression in a wide operating range.

In this paper, a hybrid uniplanar compact electromagnetic bandgap (UC-EBG) design is proposed with a wide-band noise suppression in power planes of printed circuit boards (PCBs). To achieve wide-band noise suppression, the proposed hybrid UC-EBG design employs a combination of various uniplanar EBG unit cells with different sizes which have different frequency ranges of noise suppression corresponding to the size of the cells. In addition, an analytic model of the EBG unit cell composed of cavity resonant model and meander line inductance is proposed and validated by 3-dimensional field simulation and measurement. To demonstrate the proposed design, a heterogeneous group of unit cells with different sizes was selected and the noise transfer function was compared with that of a conventional UC-EBG structure. The proposed hybrid UC-EBG design is experimentally verified by noise transfer function measurement with a significant expansion of the stopband up to 478 % in comparison to the conventional uniplanar EBG designs with the same area occupation.

Maritime images captured under low-light imaging condition easily suffer from low visibility and unexpected noise, leading to negative effects on maritime traffic supervision and management. To promote imaging performance, it is necessary to restore the important visual information from degraded low-light images. In this article, we propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. In particular, a hybrid regularized variational model, which combines L0-norm gradient sparsity prior with structure-aware regularization, is presented to refine the coarse illumination map originally estimated using Max-RGB. The adaptive gamma correction method is then introduced to adjust the refined illumination map. Based on the assumption of Retinex theory, a guided filter-based detail boosting method is introduced to optimize the reflection map. The adjusted illumination and optimized reflection maps are finally combined to generate the enhanced maritime images. To suppress the effect of unwanted noise on imaging performance, a deep learning-based blind denoising framework is further introduced to promote the visual quality of enhanced image. In particular, this framework is composed of two sub-networks, i.e., E-Net and D-Net adopted for noise level estimation and non-blind noise reduction, respectively. The main benefit of our image enhancement method is that it takes full advantage of the regularized illumination optimization and deep blind denoising. Comprehensive experiments have been conducted on both synthetic and realistic maritime images to compare our proposed method with several state-of-the-art imaging methods. Experimental results have illustrated its superior performance in terms of both quantitative and qualitative evaluations.

Solving dynamic Sylvester matrix equations is a prevalent research topic and many methods have been arisen to solve the dynamic Sylvester equation, but few of them consider the noise effect. To investigate the new approach which can suppress the noise effect, integration feedback is added in the conventional Newton-Raphson iterated (CNRI) algorithm to form the proposed integration-implemented Newton-Raphson iterated (IINRI) algorithm based on the control theorem. Besides, this paper transforms the dynamic Sylvester equation into a linear equation which turns into the zeroing finding problem in further by constructing the error function. According to the theoretical analyses and the simulation results, the IINRI algorithm has higher accuracy and strong robustness under different noises (e.g. the constant noise, the linear noise, and the bounded random noise) while the performance of the CNRI algorithm is seriously degraded by the noises, which reveals that the IINRI algorithm is an efficient and powerful approach to solve dynamic Sylvester equation under noise perturbations.

The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as $f-x$ denconvolution and $f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tradeoff between random noise suppression and seismic signal preservation.

To improve the observability of low-light images, a low-light image enhancement algorithm based on nonsubsampled shearlet transform (NSST) is presented (LIEST). The proposed algorithm can synchronously achieve contrast improvement, noise suppression, and the enhancement of specific directional details. An enhancement framework of low-light noisy images is first derived, and then, according to the framework, a low-light noisy image is decomposed into low-pass subband coefficients and bandpass direction subband coefficients by NSST. Then, in the NSST domain, an illumination map is estimated based on a bright channel of the low-pass subband coefficients, and noise is simultaneously suppressed by shrinking the bandpass direction subband coefficients. Finally, based on the estimated illumination map, the low-pass subband coefficients, and the shrunken bandpass direction subband coefficients, inverse NSST is implemented to achieve low-light image enhancement. Experiments demonstrate that the LIEST exhibits superior performance in improving contrast, suppressing noise, and highlighting specific details as compared to seven similar algorithms.

This paper proposes some practical methodology and design aids for the linear motion servocontrol systems such as the electromagnetic launchers. Mason’s gain formula was first used in the state space to solve for the transfer function of the complex and multiloop system rapidly and conveniently. The classical feedback control referring to the separation principle, involving a tracking controller and an observer-based compensator, may work well but has limited bandwidth under the constraint of the nominal inertia, and this fact is demonstrated rigorously in this paper and improved by a novel compensation scheme. According to the time-domain performance and Nyquist view, a delicate parameter design of control parameters is made for the tracking controller and the robust observer. The algorithm is verified by use of a rapid control prototype real-time simulation platform. The comparison of the experimental results shows that the proposed control scheme has good properties of trajectory tracking, antidisturbance, and noise suppression.

In most of resolver-to-digital converters, it is necessary to detect the sinusoidal and cosinusoidal envelopes of rotor angle position from the amplitude-modulated signals the resolver outputs. Conventional synchronous envelope detection methods usually adopt a microprocessor to generate excitation signal and sample resolver signals, which is easy to ensure the synchronization of sampling. However, these methods may result in too large consumption of microprocessor resources, especially when the excitation frequency is very high. In order to reduce the burden of the microprocessor, external excitation can be used instead, and the synchronization pulse for sampling trigger can usually be generated by comparing the externally generated carrier with a reference through comparison circuit. However, the unexpected factors in hardware may cause deviations of the synchronization pulse instants and thus affect the accuracy of the detected envelopes. In order to solve this problem, a novel strategy of synchronous envelope detection by using hardware circuits is proposed in this paper. First, error propagation of the unexpected factors to the synchronization pulse is minimized by optimizing the reference voltage for the comparison circuit. Second, the effects of the synchronization pulse instant deviations on the accuracy of the detected envelopes are minimized by optimizing the sampling phase of the resolver signals. Theoretical analysis indicates that high-accuracy synchronous envelope detection can be realized since the minimum effects of unexpected factors. Experimental results support the conclusion of theoretical analysis, and prove the effectiveness and feasibility of the proposed strategy.

Magnetic anomaly detection (MAD) has been widely used for detecting some hidden ferromagnetic objects. Orthonormal basis function (OBFs) detector is one of the most popular methods of MAD. The OBFs detector works effectively under white Gaussian noise. However, the practical geomagnetic noise is colored noise with a power spectral density of $1 \mathord {\left /{ {\vphantom {1~f^{\alpha }}} }\right. } f^{\alpha }$ ( $f$ is frequency and $\alpha $ is noise exponent), and the signal-to-noise ratio (SNR) is usually very low. In order to improve magnetic anomaly detection performance in the case of colored noise and low SNR, a novel detection method by using full connected neural network (FCN) is proposed in the paper. Firstly, the detector based on FCN is designed and two kinds of features that include the signal’s statistical property and the magnetic moment’s characteristics of the target are extracted and used as the input of neural network; Then, the optimal network structure with proper number of layers and nodes is obtained; Finally, the detection performance of the detector under different SNRs and orientations of target’s magnetic moment is evaluated. Simulation results show that the proposed method has better performance and achieves an incremental detection probability of about 5% to 40% under colored Gaussian noise with different noise exponent than traditional method. In the end, experiments under real geomagnetic noise also verify the effectiveness of the proposed method.

A new structure for suppressing the relative intensity noise (RIN) from an optical source is presented. It is based on an integrated dual-polarization dual-parallel modulator and a balanced detector. It only requires a single fiber connected between the transmitter and the receiver whereas two fibers are required in the conventional dual-output modulator based RIN suppression structure. Experimental results demonstrate RIN suppression of >10 dB over 1-16 GHz frequency range with and without RF signal modulation.

State of Charge (SOC) is a key parameter for battery management and vehicle energy management. Recently used SOC estimation methods for lithium-ion battery for vehicles have problems of too simple a base model for the battery and large sampling noise in both the voltage and current signals. To improve the accuracy of SOC estimation and consider that the extended Kalman filter algorithm needs linear approximation of the system equation, the unscented Kalman filter (UKF) algorithm was used to reduce the influence of sampling noise, and an improved algorithm with better filtering effect and SOC estimation accuracy was proposed. Based on the SOC estimation and battery model, the peak power prediction method for the battery is proposed and used in the power distribution strategy for Series HEV. Considering the frequent changes in load current and sampling noise, an experiment was designed to verify the effectiveness and robustness of the algorithm. The experimental results show that the UKF algorithm and the improved UKF algorithm can achieve 6% and 1.5% estimation error. The power distribution strategy based on battery SOC estimation and peak power prediction is tested and validated.

Cognitive radio (CR) is promising for smart grids (SGs) in terms of overcoming the problem of spectrum inefficiency and interference mitigation. With the intensive research into full-duplex communication technologies, the licensed spectrum available for CR-based SGs (CR-SGs) may be more complex and difficult to use, as more primary traffic exists on the licensed spectrum. As a result, CR-SGs may experience more severe interference from primary users (PUs), and the corresponding transmission opportunities may be impaired. In this paper, based on distinctive polarization features of samples from different PUs, the signal polarization of each PU is extracted from the hybrid samples by studying the polarization similarity of the samples. Based on the signal polarization derived for each PU, the transmitting opportunities for CR-SGs can be derived. Both the interference from CR-SGs to PUs and the throughput of CR-SGs are examined. Numerical results show that the spectrum opportunities for CR-SGs can be guaranteed and that the interference inflicted on PUs is at a satisfactory level.

This letter proposes a novel double regularization unmixing-based method for hyperspectral image (HSI) superresolution. The proposed cost function contains two data-fidelity terms, the endmember regularization term and the abundance regularization term. Since the double regularization unmixing terms are able to exploit the spatial structure information of endmember and abundance, respectively, the nonnegative factorization (spectral unmixing) error is minimized. As a result, the performance of the proposed HSI superresolution method can be enhanced in terms of noise suppression and the special structure information preservation of reconstruction images. Finally, the associated optimization problem is effectively solved by an alternating direction optimization algorithm. Simulation results illustrate that the proposed method has a better performance than the state-of-the-art methods in terms of both visual effectiveness and quality indices.

In this paper, a nonsmooth Kalman filtering method is proposed for noise suppression of micropositioning stages with piezoelectric actuators described by the so-called sandwich model with hysteresis. According to the characteristics of the system, a nonsmooth stochastic state-space equation is constructed. In this model, an autoswitcher is introduced to adapt the nonsmooth operation conditions. Then, the convergence of the novel Kalman filter is discussed. Subsequently, the comparison in simulation between the proposed filtering scheme with the unscented Kalman filter and particle filter is presented. Finally, the experimental results on a micropositioning stage with piezoelectric actuator are illustrated.

We investigate feed forward noise suppression for ASE-seeded WDM systems in relation to its limitations. In particular, limitation factors such as background ASE of reflective light sources and filtering effect are thoroughly investigated. We also measure the penalty induced by feed forward parameter variation from the designed value. Although the BER performance is improved by noise suppression, the statistical noise distribution change reduces the effectiveness of noise suppression.

An improved rotated sub-space (RSS) algorithm is proposed for the application of array technology in partial discharge (PD) location in transformer. The algorithm is based on classic RSS algorithm and high-order cumulants, and it is studied through numerical simulation and experimental tests. Based on an L-shaped and a cross-shaped array, the simulation of single and multiple PD source direction is carried out to compare the classic RSS algorithm with improved RSS algorithm. The results confirm the processing capacity in wideband focus, aperture expansion, enhance of directing, and noise suppression of improved RSS algorithm. Moreover, both algorithms are used for PD location experiments, showing that the error of improved RSS algorithm is $\sim 3^{\circ }$ . Improved RSS algorithm solves the problem of wideband focusing in PD location, enhances the performance of the array aperture and directing, achieves the effect of the array consists of much more elements with a virtual expansion, simplifying the follow-up circuit.

To reduce the number of elements and the cost of follow-up circuit of a $16 \times 16$ ultrasonic array sensor, two ultrasonic array sensors are designed. One is L shaped with 13 elements and the other is cross shaped with 25 elements. Based on these two ultrasonic array sensors, this paper compares the directional performance of a two-sided correlation transformation algorithm, which is a broadband focusing algorithm, an array expanded algorithm based on fourth-order cumulants, and a combination of these two algorithms, namely, synthesis algorithm. The broadband focusing algorithm can adjust to the broadband ultrasonic signal emitted by partial discharge (PD) source. After virtual expansion, the L-shaped and cross-shaped ultrasonic arrays can achieve the effects of an array with 97 and 193 elements, respectively. The adaptive capacity of broadband ultrasonic signal emitted by the PD source, and the precision of direction to PD are compared for the three algorithms. Directing simulation and experiments with single or multiple PD sources are carried out. The results show that the directing precision is highest with the synthesis algorithm, the error is

Multi-scale decomposition has been an invaluable tool for the processing of physiological signals. Much focus in multi-scale decomposition for processing such signals have been based on scale-space theory and wavelet transforms. In this study, we take a different perspective on multi-scale decomposition by investigating the feasibility of utilizing a Bayesian-based method for multi-scale signal decomposition called Bayesian Residual Transform (BRT) for the purpose of physiological signal processing. In BRT, a signal is modeled as the summation of residual signals, each characterizing information from the signal at different scales. A deep cascading framework is introduced as a realization of the BRT. Signal-to-noise ratio (SNR) analysis using electrocardiography (ECG) signals was used to illustrate the feasibility of using the BRT for suppressing noise in physiological signals. Results in this study show that it is feasible to utilize the BRT for processing physiological signals for tasks such as noise suppression., 10 pages

This paper presents a new algorithm for the onboard calibration of three-axis magnetometers under noisy environment. The ordinary least squares (OLS) estimator is inconsistent for ellipsoid fitting problem. Proper corrections of the OLS estimator are derived, which can provide a consistent solution to the magnetometer's calibration parameters without noise preknown. Simulation and experimental results are presented and discussed, supporting the application of the algorithm in the noisy platform.

In this letter, an ultra-wideband common mode filter is proposed for high-speed differential signal transmission. By etching a slot in the inner patch of a double slit complementary split ring resonator (S-DBCSRR), the noise rejection bandwidth can be expanded. The equivalent circuit model and surface current distribution are given to explain the working principle of the filter. From the simulated and measured results, it is found that the fractional bandwidth of the presented filter is 92% with a noise suppression level of 20 dB, and the differential signal can propagate with little degradation.

Magnetoencephalography (MEG) is a promising technology, which could be used in a variety of biomedical applications. However, MEG electromagnetic measurement is usually degraded by noise. Noise suppression in MEG measurement is particularly challenging because it is difficult to remove the noise and preserve the information components in the MEG data. In this study, a novel noise suppression method, based on robust principal component analysis (RPCA) technique, is presented and applied to the estimation of bio-electromagnetic field in source space for the first time. The proposed method gives a constrained optimization of MEG electromagnetic domain transformations such that the matrix of transformed MEG measurement can be decomposed as the sum of a sparse matrix of noise and a low-rank matrix of denoised data. Applying the proposed method to a number of simulations showed significant improvement of the result accuracy.

We propose an improved pilot-aided (PA) optical carrier phase recovery (CPR) method, using average operation for noise suppression in PA coherent systems. Performance of this method is investigated and compared with other CPR methods through simulations for 28 Gbaud QPSK, 16-, and 64-QAM systems. Results show that PA-CPR with averaging greatly outperforms PA-CPR without averaging, and achieves better or similar performance compared with other PAor non-PA-CPR methods. In addition, we propose a method using maximum likelihood (ML) phase estimation following PA-CPR with averaging to further improve the laser phase noise tolerance. Specifically, after optimizing the bandwidth of the pilot extraction filter, the maximum linewidth symbol duration products (Δf·T) for 1-dB optical signal to noise ratio penalty at BER of 1 × 10-3 are improved from 1 × 10-4, 3.5 × 10-5, and 1 × 10-5 (by PA-CPR without averaging) to 6.6 × 10-4, 2 × 10-4, and 3.8 × 10-5 (by PA-CPR with averaging), for 28 Gbaud QPSK, 16-and 64-QAM systems, respectively. When ML is following PA-CPR with averaging, Δf·T values for 1-dB OSNR penalty are further improved to 9 × 10-4, 4.8 × 10-4, and 6.5 × 10-5 for QPSK, 16-and 64-QAM systems, respectively.

Based on the concept of localization, a novel array design etching electromagnetic band-gap (EBG) structures on both the power plane and ground plane in the region of noise source and noise-sensitive devices is first proposed to mitigate simultaneous switching noise (SSN). Then, a super-array design is proposed for better suppression. It has shown good performance in eliminating noise. The -55-dB suppression bandwidth can be broadened from 244 MHz to 20 GHz.

Grounded guard traces are increasingly used to reduce the coupling-induced crosstalk, but the incurred ringing noise will strongly limit the performance for the microstrip structures. This paper describes the generation mechanism of the ringing noise and derives an analytical formula of the noise magnitude. Besides, an enhanced microstrip guard trace design is proposed to eliminate the ringing noise by covering the original microstrip structure with a superstrate of higher permittivity. A design space versus the superstrate thickness and the dielectric constant are constructed and in which, the guard trace needs be grounded at the two ends only without causing any ringing noise. Finally, the time-domain simulations and experiments are performed to verify the proposed concept.

An adaptive spectral technique for ground clutter and noise suppression in weather radar echoes is presented. This technique detects weak echoes that are masked by the residuals from ground clutter. The technique is demonstrated on two clear air cases observed with Doppler weather radar. After adaptive suppression of ground clutter and its residue, features appear over the Oklahoma City urban area where otherwise none could be seen. These are interpreted as birds' corridors between two lakes and along a river.

WOS: 000302729800012, The authors describe the noise characterization of a mid-wavelength-infrared (MWIR) photodiode based on indium arsenide and gallium antimonide (InAs/GaSb) superlattice (SL), addressing the influence of different passivation layers applied to the surface of the device. The MWIR InAs/GaSb SL design structure is based on p-i-n configuration grown by the molecular beam epitaxy on a (001) n-GaSb substrate. The SiO2-passivated SL photodiodes demonstrated a Schottky-limited noise up to a bias voltage of -0.1 V where the measured peak responsivity is 1.37 A/W with a cut-off wavelength of 4.9 mu m and the specific detectivity as high as 1.23 x 10(12) cm. Hz(1/2)/W, demonstrating the high quality of the fabricated MWIR SL photodiodes. The noise measurements exhibited a frequency-dependent plateau (i.e., 1/f noise) for unpassivated and Si3N4-passivated samples, whereas 1/f-type noise suppression (i.e., frequency-independent plateau) with a noise current reduction at about 30 Hz of more than one order of magnitude was observed for the SiO2-passivated ones.

Magnetocardiography (MCG), the magnetic equivalent to electrocardiography (ECG), is one of the most promising major applications of SQUID systems. Recent advances in SQUID-system technology such as improved noise suppression techniques, better field sensitivity (in particular for HTS SQUIDs), real time options, vector magnetometers and novel signal analysis approaches have appreciably reduced the technical constraints that hindered until recently the implementation of MCG into practical clinical use. Although MCG features several obvious advantages compared with ECG, it remains difficult to present convincing clinically attractive results to medical doctors that would initiate a commercial breakthrough for SQUID systems. Recent clinical MCG investigations have, however, revealed several areas of cardiology where MCG could play a significant role. This presentation will summarize the technical achievements and next steps necessary for system innovation, as well as the crucial clinical trials for the future commercial and clinical success of cardiac SQUID-systems.

A method is proposed for suppressing Gaussian noise by extracting local singularities. The method is based on truncating Riemann series decomposition, whose components naturally characterize different orders of Holder regularity. The approach yields a single-step filtering technique whose performance is comparable to three-step wavelet decomposition and thresholding techniques.

The partial discharge (PD) measurement is a sensitive method for testing and monitoring the insulation condition of HV apparatus. Digital PD measurements have become state of the art. Computer-based PD diagnostic systems nowadays can be used to increase the reliability of modern gas-insulated system (GIS). Also on-line monitoring of HV equipment is becoming increasingly important to enable prevention of a sudden breakdown of PD affected components. On-line monitoring must be done on-site, but an important problem of on-site PD measurement is the superposition of PD data and interference. Furthermore, reliable recognition of the type or nature of the insulation defect is necessary for any risk assessment. Traditionally, this is achieved with reference to expert knowledge. An alternative approach is presented in this paper, which treats the fundamentals of automated computer-based PD diagnosis, taking into account that successful noise suppression is essential for a successful on-site diagnosis.

Ferrite films with a chemical composition Ni0.2Zn0.3Fe2O4 of 3 mum thickness were deposited at 90degC from an aqueous solution directly on the inner ground and/or the power source layers of the following two different types of four-layered printed wiring boards (PWBs), in order to suppress the electromagnetic noise emission due to high frequency currents generated in the inner layers. PWB (a), which is the most commonly used structure, is composed of two outer signal layers, an inner ground layer, and an inner power layer. PWB (b) is composed of two outer signal layers, which contain the power supply lines as well, and two inner ground layers. Radiated emission from the ferrite film-embedded PWB (a) was successfully suppressed to be 2 to 5 dB lower than that without the ferrite film. This tendency became more prominent with increasing frequency. A similar noise suppression effect was also observed for the ferrite film-embedded PWB (b), up to 4 dB. This suggests that we can suppress radiated emission also in the case where the ground and the power planes are not placed symmetrically in the inner layers. The frequency dependence of radiated noise suppression effect by the ferrite film corresponded to the frequency dependence of transmission attenuation power ratio (P loss/P in) , i.e. the suppression effect upon conducted noise due to magnetic loss characteristics of the film. This indicates that the suppression of radiated emission from the four-layered PWBs was due to the attenuation of GHz-range noise current in the ground and/or the power layers.

We investigate the effects of intraband crosstalk on the intensity noise suppression of spectrum-sliced incoherent light sources using semiconductor optical amplifiers (SOAs). Both Q-factor and electrical spectrum measurements clearly show that intraband crosstalk deteriorates the SOA-based noise suppression by breaking the correlation between optical frequency components of light.

Cancellation of common-mode intensity noise in microwave-photonic links is found to be limited by the applied intensity modulation. A model is developed and verified experimentally for the dependence of the maximum noise suppression on modulation index, for two-tone and multichannel modulation. For example, for a root-mean-square modulation index of 0.3, suppression is limited to 5 dB.

In 1926, the Institute of Radio Engineers (IRE) awarded its Medal of Honor to Greenleaf Whittier Pickard. The award was in recognition of his contributions to research on crystal detectors, antennas, wave propagation, and noise suppression. During his long career, he was a leader in the creation of the radio engineering profession and a prolific author of research papers published in the Proceedings of the IEEE. He served as the second president of the IRE during 1913.

A novel microwave-photonic link is demonstrated using a unique AlGaAs-GaAs electrooptic mode-converter-based polarization modulator in a balanced output configuration. This enables suppression of common-mode intensity noise and optical-amplifier-induced beat noise using a polarization-selective balanced optical receiver. Unlike the conventional approach using a dual-output Mach-Zehnder modulator, the complementary output signals are combined naturally as orthogonal polarizations into one transmission fiber.

Using an intracavity Pound-Drever-Hall technique, simultaneous optical frequency comb stabilization within /spl plusmn/3-MHz range and super-mode phase noise suppression were demonstrated for a 10-GHz harmonically mode-locked semiconductor ring laser. Together with an additional phase-lock loop, timing jitter integrated from 10 Hz to 10 MHz (5 GHz) was 63.5 fs (161 fs).

Balanced photodetectors with top-illuminated charge-compensated unitraveling-carrier photodiodes designed for high-power applications are demonstrated. A 20-/spl mu/m-diameter photodiode balanced pair achieved a bandwidth of 10 GHz, and a large-signal saturation current of 25 mA for each photodiode. A 10-/spl mu/m-diameter photodiode balanced pair achieved a bandwidth of 26 GHz, and a large-signal saturation current of 15 mA for each photodiode. Broad-band common mode rejection ratio and noise suppression were also characterized.

A new family of data dependent nonlinear filters (DDNL) is presented. Coefficients are computed locally and the absolute distance between signal samples is used to determine a rank order dependent weighting function. The new filters have very good performance regarding noise suppression and impulse rejection and at the same time provide better edge preservation than the averager and other linear smoothers. >

Presents corrections to, “Noise suppression for dual-energy CT through entropy minimization,” (Petrongolo, M. and Zhu, L., IEEE Trans. Med. Imag., vol. 34, no. 11, pp. 2286–2297, Nov. 2015).