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A generalization of the problem of writing on dirty paper is considered in which one transmitter sends a common message to multiple receivers. Each receiver experiences on its link an additive interference (in addition to the additive noise), which is known noncausally to the transmitter but not to any of the receivers. Applications range from wireless multiple-antenna multicasting to robust dirty paper coding. We develop results for memoryless channels in Gaussian and binary special cases. In most cases, we observe that the availability of side information at the transmitter increases capacity relative to systems without such side information, and that the lack of side information at the receivers decreases capacity relative to systems with such side information. For the noiseless binary case, we establish the capacity when there are two receivers. When there are many receivers, we show that the transmitter side information provides a vanishingly small benefit. When the interference is large and independent across the users, we show that time sharing is optimal. For the Gaussian case, we present a coding scheme and establish its optimality in the high signal-to-interference-plus-noise limit when there are two receivers. When the interference power is large and independent across all the receivers, we show that time-sharing is again optimal. Connections to the problem of robust dirty paper coding are also discussed. [ABSTRACT FROM AUTHOR]

Costa's celebrated "writing on dirty paper" (WDP) shows that the power-constrained channel Y = X + S + Z, with Gaussian Z, has the same capacity as the standard AWGN channel Y = X + Z, provided that the "interference" S (no matter how strong it is) is known at the transmitter. While this ability for perfect interference cancelation is very appealing, it relies heavily on the Gaussianity of the (unknown) noise Z. We construct an example of "bad" noise for writing on dirty paper, namely, "difference set noise". If the interference S is strong, then difference-set noise limits the WDP capacity to at most 2 bits. At the same time, like in the AWGN case, the zero-interference capacity grows without bound with the input constraint. Thus almost 100% of the available capacity is lost in WDP in the presence of difference-set noise. This high capacity loss is due to the "entropy amplification property" (EAP) of noise with an aperiodic probability distribution. Using the EAP and the duality between WDP and Wyner--Ziv source coding, we also give an example of dramatic rate-loss in quantizing encrypted source. [ABSTRACT FROM AUTHOR]

We compare the capacity of dirty-paper coding (DPC) to that of time-division multiple access (TDMA) for a multiple-antenna (multiple- input multiple-output (MIMO)) Gaussian broadcast channel (BC). We find that the sum-rate capacity (achievable using DPC) of the multiple-antenna BC Is at most min(M, K) times the largest single-user capacity (i.e., the TDMA sum-rate) in the system, where M is the number of transmit antennas and K is the number of receivers. This result is Independent of the number of receive antennas and the channel gain matrix, and is valid at all signal-to-noise ratios (SNRs). We investigate the tightness of this bound In a time-varying channel (assuming perfect channel knowledge at receivers and transmitters) where the channel experiences uncorrelated Rayleigh fading and In some situations we find that the dirty paper gain is upper-bounded by the ratio of transmit-to-receive antennas. We also show that min(M, K) upper-bounds the sum-rate gain of successive decoding over TDMA for the uplink channel, where M is the number of receive antennas at the base station and K is the number of transmitters. [ABSTRACT FROM AUTHOR]

We derive the capacity region of the two-user dirty-paper Gaussian multiaccess channel (MAC) with conferencing encoders. In this MAC, prior to each transmission block, the transmitters can hold a conference in which they can communicate with each other over error-free bit pipes of given capacities. The received signal suffers not only from additive Gaussian noise but also from additive interference, which is known noncausally to the transmitters but not to the receiver. The additive interference is modeled as Gaussian or uniform over a sphere. We show that the interference can be perfectly mitigated, i.e., that the capacity region without interference can also be achieved in its presence. This holds irrespective of whether the transmitters learn the interference before or after the conference. It follows as a corollary that also for the MAC with degraded message sets, the interference can be perfectly mitigated if it is known noncausally to the transmitters. To derive our results, we generalize Costa's single-user writing-on-dirty-paper achievability result to channels with dependent interference and not-necessarily Gaussian noise. [ABSTRACT FROM AUTHOR]

Superchannel transmission is a candidate to realize Tb/s-class high-speed optical communications. In order to achieve higher spectrum efficiency, the channel spacing shall be as narrow as possible. However, densely allocated channels can cause non-negligible inter-channel interference (ICI) especially when the channel spacing is close to or below the Nyquist bandwidth. In this paper, we consider joint decoding to cancel the ICI in dense superchannel transmission. To further improve the spectrum efficiency, we propose the use of Han–Kobayashi superposition coding. In addition, for the case when neighboring subchannel transmitters can share data, we introduce dirty-paper coding for pre-cancelation of the ICI. We analytically evaluate the potential gains of these methods when ICI is present for sub-Nyquist channel spacing. [ABSTRACT FROM PUBLISHER]

Consider several source nodes communicating across a wireless network to a destination node with the help of several layers of relay nodes. Recent work by Avestimehr has approximated the capacity of this network up to an additive gap. The communication scheme achieving this capacity approximation is based on compress-and-forward, resulting in noise accumulation as the messages traverse the network. As a consequence, the approximation gap increases linearly with the network depth. This paper develops a computation alignment strategy that can approach the capacity of a class of layered, time-varying wireless relay networks up to an approximation gap that is independent of the network depth. This strategy is based on the compute-and-forward framework, which enables relays to decode deterministic functions of the transmitted messages. Alone, compute-and-forward is insufficient to approach the capacity as it incurs a penalty for approximating the wireless channel with complex-valued coefficients by a channel with integer coefficients. Here, this penalty is circumvented by carefully matching channel realizations across time slots to create integer-valued effective channels that are well suited to compute-and-forward. Unlike prior constant gap results, the approximation gap obtained in this paper also depends closely on the fading statistics, which are assumed to be i.i.d. Rayleigh. [ABSTRACT FROM PUBLISHER]

This paper presents an implementation of an ~daptive beamformer in a hands-free unit. The proposec~ ~daptive beamformer suppresses stationary noise an~ nterference sound. The adaptive beamformer and acoustic `cho canceller were implemented in a compact hands-free mit with a low-cost DSP. Experimental results demonstrate he noise reduction performance of the hands-free unit'. [ABSTRACT FROM AUTHOR]

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a small number of noisy linear measurements is an important problem in compressed sensing. In this paper, the high-dimensional setting is considered. It is shown that if the measurement rate and per-sample signal-to-noise ratio (SNR) are finite constants independent of the length of the vector, then the optimal sparsity pattern estimate will have a constant fraction of errors. Lower bounds on the measurement rate needed to attain a desired fraction of errors are given in terms of the SNR and various key parameters of the unknown vector. The tightness of the bounds in a scaling sense, as a function of the SNR and the fraction of errors, is established by comparison with existing achievable bounds. Near optimality is shown for a wide variety of practically motivated signal models. [ABSTRACT FROM PUBLISHER]

In the first part of this paper, a free-form surface matching method called variance-minimization matching (VMM) was proposed to address uneven/open point distributions and measuring noise. The convergence property and sensitivity to measuring defects were theoretically studied. In the second part of this paper, a series of experiments are presented to verify the feasibility of the proposed method in free-form surface matching. The experiments are divided into four sets: a measuring defects experiment, a noise experiment, a convergence experiment, and an artificial experiment. In the first set of experiments, the existing methods are prone to becoming trapped in a local optimum affected by uneven/open point distributions, which shows that measured points incline toward dense areas. However, in VMM, there is little inclination regardless of the increase in the number of measuring defects. In the second set of experiments, sensitivity to varying noise is tested. The results show that VMM helps prevent unstable sliding in the presence of Gaussian noise. In the third set of experiments, we compare convergence speed and convergence stability under different initial positions. It is verified that VMM exhibits the quadratic convergence. Finally, a set of artificial experiments is implemented, revealing that the proposed method is appropriate for use in automated manufacturing processes such as geometric inspection and allowance distribution. Note to Practitioners—Measuring defects usually occur when using a scanning device to obtain the measured points of a workpiece. Weakening the effect of measuring defects on matching results is critical to promoting manufacturing automation. This paper proposes a new method called variance-minimization matching (VMM) that considers measuring defects. In the first part of this paper, the modeling and theoretical analysis of VMM were introduced. In the second part of this paper, simulated experiments are performed to verify the feasibility of VMM in addressing uneven/open point distributions, measuring noise, and large initial positions. Next, artificial experiments employing VMM in geometric inspection and allowance distribution are presented. The proposed method also applies to other automated manufacturing processes, such as workpiece localization, deformation analysis, and complex parts repair. [ABSTRACT FROM AUTHOR]

Resistive random access memory (ReRAM) is often considered as a strong candidate for storing the weights in non-von Neumann neuromorphic computing systems. This paper studies how nonideal memory characteristics, which include programing error, read fluctuation, and retention, affect the inference accuracy of the analog ReRAM neural networks by incorporating memory characteristics extracted from 1-Mb ReRAM into a simulated inference-only neural network. This paper also shows that the different layer in the network can tolerate different amount of such imperfects. We learned four key points: 1) the conductance range of memory with less relative fluctuation is preferred for designing the weight-conductance mapping; 2) the control of programing error is essential for high inference accuracy; 3) retention-induced conductance drift can be fatal to the neuromorphic system. A compensation scheme is proposed in this paper which can effectively recover the inference accuracy; and 4) for multilayer networks, avoiding weight errors in the front layers can help to maintain the inference accuracy by reducing calculation error which may otherwise accumulate and pass down the networks. The concepts and approaches of this paper can also be applied to evaluate other types of nonvolatile memories for artificial neural networks. [ABSTRACT FROM AUTHOR]

It is well known that for high voltage transmission lines in the range of 245 kV–525 kV, particularly in regions of high ground resistivity, lightning back flashover problems could be a major concern. Line arresters have been used successfully to address the problem. Underbuilt ground wires have been proposed as a viable alternative. The present paper shows that the full potential of additional ground wires could be achieved by optimizing their position on the tower. Such optimization often results in a preferred position at the level, or higher than the attachment height of the lower phase conductors; therefore the designation Embedded Ground Conductor. An essential requirement is that such positioning does not infringe in any way on the dielectric integrity of the electrical clearances and, under the influence of induced charge, does not become a source of positive streamers that may lead to additional radio or audible noise. It is found that a streamer- inhibited conductor, when properly placed, fully satisfies the above requirements. It is determined that for 245 kV, 345 kV and 420 kV lines, a streamer-inhibited embedded conductor could improve the back flashover rate by a factor of 10 or more [ABSTRACT FROM AUTHOR]

Machine learning has been widely used in medical image analysis under an assumption that the training and test data are under the same feature distributions. However, medical images from difference devices or the same device with different parameter settings are often contaminated with different amount and types of noises, which violate the above assumption. Therefore, the models trained using data from one device or setting often fail to work for that from another. Moreover, it is very expensive and tedious to label data and re-train models for all different devices or settings. To overcome this noise adaptation issue, it is necessary to leverage on the models trained with data from one device or setting for new data. In this paper, we reformulate this noise adaptation task as an image-to-image translation task such that the noise patterns from the test data are modified to be similar to those from the training data while the contents of the data are unchanged. In this paper, we propose a novel Noise Adaptation Generative Adversarial Network (NAGAN), which contains a generator and two discriminators. The generator aims to map the data from source domain to target domain. Among the two discriminators, one discriminator enforces the generated images to have the same noise patterns as those from the target domain, and the second discriminator enforces the content to be preserved in the generated images. We apply the proposed NAGAN on both optical coherence tomography (OCT) images and ultrasound images. Results show that the method is able to translate the noise style. In addition, we also evaluate our proposed method with segmentation task in OCT and classification task in ultrasound. The experimental results show that the proposed NAGAN improves the analysis outcome. [ABSTRACT FROM AUTHOR]

Polarization and depolarization current measurements are promising on-site, off-line, non-destructive, non-invasive diagnostic tool for condition assessment of paper-oil insulation of transformers. Interpretation of the results of polarization and depolarization current is complex and hence not practiced as a routine diagnostic method for condition monitoring of transformers. This paper discusses various factors affecting polarization and depolarization current measurements with a deeper insight into the theoretical and practical aspects of this technique. Some of the concerns of PDC measurement are explained with support from simulation and experimental studies on paper oil insulation of a laboratory model transformer. Few case studies are discussed to highlight the difficulties in interpretation of results. [ABSTRACT FROM AUTHOR]

A problem of image denoising, when images are corrupted by a non-stationary noise, is considered in this paper. Since, in practice, no a priori information on noise is available, noise statistics should be pre-estimated prior to image denoising. In this paper, deep convolutional neural network (CNN) based method for estimation of a map of local, patch-wise, standard deviations of noise (so-called sigma-map) is proposed. It achieves the state-of-the-art performance in accuracy of estimation of sigma-map for the case of non-stationary noise, as well as estimation of a noise variance for the case of an additive white Gaussian noise. Extensive experiments on image denoising using estimated sigma-maps demonstrate that our method outperforms recent CNN-based blind image denoising methods by up to 6 dB in PSNR, as well as other state-of-the-art methods based on sigma-map estimation by up to 0.5 dB, providing, at the same time, better usage flexibility. A comparison with the ideal case, when denoising is applied using ground-truth sigma-map, shows that a difference of corresponding PSNR values for the most of noise levels is within 0.1-0.2 dB, and does not exceed 0.6 dB. [ABSTRACT FROM AUTHOR]

In this article, an algorithm is introduced which not only focuses on enhancing low light images but also denoises images to produce more visually pleasing outputs. It is observed that various methods ignore noise during enhancement processes. This causes, enhancement of noise which leads to significant information lost. The paper projected a novel framework for low light images that performs enhancement and denoising jointly. The proposed paper uses $L_{2}$ - $L_{P}$ image decomposition to obtain reflectance and illumination concurrently. The illumination layer is corrected using the proposed weighting distribution and generates enhanced output. Further to this, noise suppressed bilateral filter is employed here for denoising process and gamma function is applied in subsequent step for additional contrast improvement. Experimental trials illustrate that the proposed method yields result with good contrast and brightness when it is compared to various enhancement techniques. [ABSTRACT FROM AUTHOR]

This correspondence paper studies how correlation of the wiretap channel affects the secrecy rate of the artificial noise (AN) scheme. To this end, we first develop a simple but tight lower bound on the ergodic secrecy rate of the AN scheme on correlated multiple-input single-output wiretap channels. Then, utilizing the derived bound, we investigate the influence of correlation on the secrecy rate, which explicitly manifests how the channel correlation affects the optimal power allocation. Ultimately, this paper answers two fundamental questions: when is the AN signal beneficial? and how much power should be allocated for the AN signal to achieve a higher ergodic secrecy rate? in terms of channel correlation. [ABSTRACT FROM AUTHOR]

This paper presents a study of the noise behavior of MOSFET devices belonging to a 110-nm CMOS technology in view of applications to the design of low-noise, low-power analog circuits in X-ray detection at free electron lasers (FELs) and the next generation of synchrotrons. The goal of this paper is to provide a valuable and cost-effective option, with respect to more scaled and expensive technology nodes, to designers of the detection systems of FELs and next-generation synchrotron sources. The white component of the noise voltage spectrum and the 1/ $f$ noise contribution is experimentally characterized by noise measurements in a wide frequency range. Data extracted from the characterization have been studied as a function of the bias condition and the gate dimensions (length and width). A comparison of the main parameters with less and more scaled CMOS generations is also provided. [ABSTRACT FROM AUTHOR]

For heterojunction bipolar transistors (HBTs), the noise correlation influences the noise parameters and is therefore relevant for transistor modeling, especially at sufficiently high frequencies. In order to predict the noise correlation, it is necessary to determine the noise transit time in the noise transport model. In this paper, the noise correlation and noise transit time are predicted from the high current model without measured noise data. The four noise parameters are calculated based on the predicted noise transit time and the Y-parameter noise calculation methodology. The extracted noise transit time is validated by the measured result over bias and temperature, which is determined by fitting NFmin of the silicon-germanium (SiGe) HBT between Y-parameter noise calculation methodology and tuner-based noise measurement. It was found that the noise transit time is independent of frequency but dependent on bias and temperature. Also, the results indicate that the calculated four noise parameters based on the extracted noise transit time have a good agreement with the tuner-based noise measurement. [ABSTRACT FROM AUTHOR]

Filter capacitors are among the most serious noise sources in HVDC converter stations, and there are still no satisfactory noise reduction measures. This paper proposes the use of the mass tuning (MT) method to adjust the vibration frequency response characteristics and to realize vibration control and noise reduction. First, the theoretical model of the MT structure is developed based on continuum mechanics. Based on the theoretical model, it is revealed that inserting an MT component into the capacitor element package will cause a decrease in the resonant frequencies and antiresonant frequencies. Furthermore, the movement degree of the resonant frequencies increases as the position of the MT component becomes closer to the package end, while the movement degree of the antiresonant frequency is the largest when the MT component is located in the middle of the package model. The vibrations and the noise levels of an example MT capacitor are then compared with the results of an original capacitor. The comparison results show that the MT scheme designed in this paper can achieve a noise reduction effect of 12–15 dB(A). The proposed reduction method is proven to be both effective and efficient. [ABSTRACT FROM AUTHOR]

Condition monitoring and diagnosis have become an essential part of power transformer asset management. A variety of online and offline measurements have been performed in utilities for evaluating different aspects of transformers’ conditions. However, properly processing measurement data and explicitly correlating these data to transformer condition is not a trivial task. This paper proposes an intelligent framework for condition monitoring and assessment of power transformer. Within this framework, various signal processing and pattern recognition techniques are applied for automatically denoising sensor acquired signals, extracting representative characteristics from raw data, and identifying types of faults in transformers. This paper provides case studies to demonstrate the effectiveness of the proposed framework and techniques for power transformer asset management. The hardware and software platform for implementing the proposed intelligent framework will also be presented in this paper. [ABSTRACT FROM PUBLISHER]

Over the years, there have been a number of cases where users experienced higher noise levels of transformers on-site versus what was measured in the factory acceptance tests. In some of these cases, measurements have shown that a power transformer can have significantly different noise characteristics (both noise level and frequency spectrum) on-site compared to what was measured in the factory. This difference can range from a few decibels to more than 10 dB in some cases. Some of these differences are found to be caused by local environmental factors that influenced the on-site measurements. Others were caused by operating conditions on-site that are different from those of the factory tests. This paper presents results of investigations and measurements performed, over the past several years, on the main contributors to this difference. For each of these contributors, data are presented that demonstrate the impact of this contributor as well as quantify typical magnitudes of this impact. It is hoped that the material presented in this paper will assist users in: 1) understanding some of the pitfalls of on-site measurement of transformer noise; 2) how operating conditions contribute to the noise performance of transformers on-site; and 3) how significantly higher on-site noise levels can, in some cases, indicate an issue with the transformer or any of its components/accessories. It is also hoped that the material in this paper will contribute to industry standards/application guides on noise determination and abatement. [ABSTRACT FROM PUBLISHER]

This paper presents detailed mathematical models of DS-CDMA systems based on binary and chaotic spreading sequences. These systems have been well investigated in the case of flat fading and noise presence in the channel. However, a comprehensive analysis of the systems operating in a wide-band channel does not exist. Therefore, in this paper, the systems are analyzed in detail, and expressions for the probability of error are derived in closed form assuming a wide-band transmission channel. Using both the signal representation in pure discrete time domain and the theory of discrete time stochastic processes, which have not been practically used before, the probability of error expressions are derived in closed form for single- and multi-user binary and chaos based DS-CDMA systems. The improvements in the probability of bit error due to multipath channel nature are quantified in dependence of the random delay and the number of users in the system. The wide-band channel is based on the Saleh-Valenzuela model proposed for the modern wireless networks. [ABSTRACT FROM PUBLISHER]

In this corresponence paper, we address the spectrum sensing problem under the non-Gaussian noise scenario characterized by the symmetric alpha stable ($\text{S}\alpha \text{S}$) model. In this paper, a novel spectrum sensing method is proposed using the maximum generalized correntropy, aimed at improving the spectrum sensing performance in low generalized signal-to-noise ratio conditions. To further enhance the robustness of the proposed method, multiple receive antennas are applied to carry out cooperative spectrum sensing. Besides, a modified conjugate gradient algorithm is used to optimize the sparse vector for cooperative spectrum sensing. Finally, simulation results are presented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Quantifying the level of nonlinear distortions and time-varying effects in frequency response function measurements is a first step toward the selection of an appropriate parametric model structure. In this paper, we tackle this problem in the presence of missing data, which is an important issue in large-scale low-cost wireless sensor networks. The proposed method is based on one experiment with a special class of periodic excitation signals. [ABSTRACT FROM AUTHOR]

Acoustic noise modeling, analysis, and reduction are critical in the design of switched reluctance machines for noise-sensitive applications. Although the analytical method can provide a fast simulation of the acoustic noise, it is generally less accurate compared to the numerical method. In order to improve the simulation accuracy, a novel analytical method is presented in this paper. The proposed method considers the excitation of the high-axial-order vibration modes to simulate the vibration behavior and the acoustic noise in the three-dimensional (3D) domain. Methods of obtaining the input data for the 3D analytical simulations are presented. The acoustic noise results obtained from the numerical simulations and experiments from an 8/6 switched reluctance motor drive are used to validate the analytical method presented in this paper. [ABSTRACT FROM AUTHOR]

Depth maps acquired with ToF cameras have a limited accuracy due to the high noise level and to the multi-path interference. Deep networks can be used for refining ToF depth, but their training requires real world acquisitions with ground truth, which is complex and expensive to collect. A possible workaround is to train networks on synthetic data, but the domain shift between the real and synthetic data reduces the performances. In this paper, we propose three approaches to perform unsupervised domain adaptation of a depth denoising network from synthetic to real data. These approaches are respectively acting at the input, at the feature and at the output level of the network. The first approach uses domain translation networks to transform labeled synthetic ToF data into a representation closer to real data, that is then used to train the denoiser. The second approach tries to align the network internal features related to synthetic and real data. The third approach uses an adversarial loss, implemented with a discriminator trained to recognize the ground truth statistic, to train the denoiser on unlabeled real data. Experimental results show that the considered approaches are able to outperform other state-of-the-art techniques and achieve superior denoising performances. [ABSTRACT FROM AUTHOR]

Through-silicon-via (TSV)-to-TSV coupling is a new phenomenon in 3-D ICs, and it becomes a significant source of signal integrity problems. The existing studies on its extraction and analysis, however, become inaccurate when handling more than two TSVs on full-chip scale. In this paper, we investigate the multiple TSV-to-TSV coupling issue and propose a model that can be efficiently used for full-chip extraction. Then, we perform an analysis on the impact of TSV parasitics on coupling and delay. Unlike the common belief that only the closest neighboring TSVs affect the victim, this paper shows that nonneighboring aggressors also cause nonnegligible coupling noise. Based on these observations, we propose an effective method of reducing the overall coupling level. [ABSTRACT FROM PUBLISHER]