Additive Noise Research Articles

On the Capacity of the Peak-Limited and Band-Limited Channel

We investigate the peak-power limited (PPL) Additive White Gaussian Noise (AWGN) channels in which the signal is band-limited, and its instantaneous power cannot exceed the power P. This model is relevant to many communication systems; however, its capacity is still unknown. We use a new geometry-based approach which evaluates the maximal entropy of the transmitted signal by assessing the volume of the body, in the space of Nyquist-rate samples, comprising all the points the transmitted signal can reach. This leads to lower bounds on capacity which are tight at high Signal to Noise Ratios (SNRs). We find lower bounds on capacity, expressed as power efficiency, that were higher than the known ones by a factor of 3.3 and 8.6 in the low-pass and the band-pass cases, respectively. The gap to the upper bounds is reduced to a power ratio of 1.5. The new bounds are numerically evaluated for FDMA-style signals with limited duration and also are derived in the general case as a conjecture. The penalty in power efficiency due to the peak power constraint is roughly 6 dB at high SNRs. Further research is needed to develop effective modulation and coding for this channel.

A noise-robust acoustic method for recognizing foraging activities of grazing cattle

Farmers must continuously improve their livestock production systems to remain competitive in the growing dairy market. Precision livestock farming technologies provide individualized monitoring of animals on commercial farms, optimizing livestock production. Continuous acoustic monitoring is a widely accepted sensing technique used to estimate the daily rumination and grazing time budget of free-ranging cattle. However, typical environmental and natural noises on pastures noticeably affect the performance limiting the practical application of current acoustic methods. In this study, we present the operating principle and generalization capability of an acoustic method called Noise-Robust Foraging Activity Recognizer (NRFAR). The proposed method determines foraging activity bouts by analyzing fixed-length segments of identified jaw movement events produced during grazing and rumination. The additive noise robustness of the NRFAR was evaluated for several signal-to-noise ratios using stationary Gaussian white noise and four different nonstationary natural noise sources. In noiseless conditions, NRFAR reached an average balanced accuracy of 86.4%, outperforming two previous acoustic methods by more than 7.5%. Furthermore, NRFAR performed better than previous acoustic methods in 77 of 80 evaluated noisy scenarios (53 cases with p<0.05). NRFAR has been shown to be effective in harsh free-ranging environments and could be used as a reliable solution to improve pasture management and monitor the health and welfare of dairy cows. The instrumentation and computational algorithms presented in this publication are protected by a pending patent application: AR P20220100910. Web demo available at: https://sinc.unl.edu.ar/web-demo/nrfar.

Directional Spectrum Estimation for Sea States Generated by the Single Summation Method

The influence of directional spreading of waves is significant for wave-induced loads, wave breaking and nonlinearity of the waves. For physical model testing performed at test facilities such as the Ocean and Coastal Engineering Laboratory at Aalborg University, it is crucial to validate if the test conditions match the target sea states by measurement and analysis of the generated directional wave field. Most of the existing methods assumes a double summation sea state to be present which is valid in the prototype. However, waves in the laboratory are usually generated by single summation. The current paper presents a method to analyse short-crested waves generated by the single summation method. Compared to similar methods oblique reflections are considered instead of only in-line reflections. The results show that the method successfully decomposes the incident and reflected wave fields in the time domain. Thus, for example the incident wave height distribution may be obtained. The sensitivity of the new method to additional reflective directions, noise, calibration errors and positional errors of the wave gauges was found small.

Asymptotic normality and finite-sample robustness of the Fourier spot volatility estimator in the presence of microstructure noise

We study the efficiency and robustness of the Fourier spot volatility estimator by Malliavin and Mancino [2002] when high-frequency prices are contaminated by microstructure noise. Firstly, we show that the estimator is consistent and asymptotically efficient in the presence of additive noise, establishing a Central Limit Theorem (CLT) with the optimal rate of convergence n 1 / 8 . Additionally, we complete the asymptotic theory proved by Mancino and Recchioni [2015] in the absence of noise, obtaining a CLT with the optimal rate of convergence n 1 / 4 . Feasible CLTs with the optimal convergence rate are also obtained, by proving the consistency of the Fourier estimators of the spot volatility of volatility and the quarticity in the presence of noise. Secondly, we introduce a feasible method for selecting the cutting frequencies of the estimator in the presence of noise, based on the optimization of the integrated asymptotic variance. Finally, we provide support to the accuracy and robustness of the method by means of a numerical study and an empirical exercise, which is conducted using tick-by-tick prices of three US stocks with different liquidity.

DeepTensor: Low-Rank Tensor Decomposition With Deep Network Priors.

DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor factors (e.g., a matrix as the outer product of two vectors), where each low-rank tensor is generated by a deep network (DN) that is trained in a self-supervised manner to minimize the mean-square approximation error. Our key observation is that the implicit regularization inherent in DNs enables them to capture nonlinear signal structures (e.g., manifolds) that are out of the reach of classical linear methods like the singular value decomposition (SVD) and principal components analysis (PCA). Furthermore, in contrast to the SVD and PCA, whose performance deteriorates when the tensor's entries deviate from additive white Gaussian noise, we demonstrate that the performance of DeepTensor is robust to a wide range of distributions. We validate that DeepTensor is a robust and computationally efficient drop-in replacement for the SVD, PCA, nonnegative matrix factorization (NMF), and similar decompositions by exploring a range of real-world applications, including hyperspectral image denoising, 3D MRI tomography, and image classification. In particular, DeepTensor offers a 6 dB signal-to-noise ratio improvement over standard denoising methods for signal corrupted by Poisson noise and learns to decompose 3D tensors 60 times faster than a single DN equipped with 3D convolutions.

A Clinical User Study Investigating the Benefits of Adaptive Volumetric Illumination Sampling.

Accurate and fast understanding of the patient's anatomy is crucial in surgical decision making and particularly important in visceral surgery. Sophisticated visualization techniques such as 3D Volume Rendering can aid the surgeon and potentially lead to a benefit for the patient. Recently, we proposed a novel volume rendering technique called Adaptive Volumetric Illumination Sampling (AVIS) that can generate realistic lighting in real-time, even for high resolution images and volumes but without introducing additional image noise. In order to evaluate this new technique, we conducted a randomized, three-period crossover study comparing AVIS to conventional Direct Volume Rendering (DVR) and Path Tracing (PT). CT datasets from 12 patients were evaluated by 10 visceral surgeons who were either senior physicians or experienced specialists. The time needed for answering clinically relevant questions as well as the correctness of the answers were analyzed for each visualization technique. In addition to that, the perceived workload during these tasks was assessed for each technique, respectively. The results of the study indicate that AVIS has an advantage in terms of both time efficiency and most aspects of the perceived workload, while the average correctness of the given answers was very similar for all three methods. In contrast to that, Path Tracing seems to show particularly high values for mental demand and frustration. We plan to repeat a similar study with a larger participant group to consolidate the results.

TGPO-WRHNN: Two-stage Grad-CAM-guided PMRS Optimization and weighted-residual hypergraph neural network for pneumonia detection

Recent studies based on chest X-ray images have shown that pneumonia can be effectively detected using deep convolutional neural network methods. However, these methods tend to introduce additional noise and extract only local feature information, making it difficult to express the relationship between data objects. This study proposes a Two-stage Grad-CAM-guided pre-trained model and removal scheme (PMRS) Optimization and weighted-residual hypergraph neural network model (TGPO-WRHNN). First, our model extracts high-dimensional features using the TGPO module to capture both global and local information from an image. Second, we propose a new distance-based hypergraph construction method (DBHC) to amplify the difference between distances and better distinguish the relation between nearby and distant neighbors. Finally, we introduce a weighted-residual hypergraph convolution module (WRHC) to ensure the model maintains excellent performance, even at deeper levels. Our model was tested on a dataset of chest X-ray images of pediatric patients aged 1 to 5 years at the Guangzhou Women and Children’s Medical Centre by 10-fold cross-validation. The results showed that the method achieved a maximum accuracy of 98.97%, precision of 98.86%, recall of 98.43%, F1 score of 98.64%, and AUC of 99.78%. Compared to other existing models, our model demonstrated improvements of 0.87%, 0.86%, 0.16%, and 0.38% in terms of accuracy, precision, F1 score, and AUC, respectively.

Ensemble responses of auditory midbrain neurons in the cat to speech stimuli at different signal-to-noise ratios

Originally reserved for those who are profoundly deaf, cochlear implantation is now common for people with partial hearing loss, particularly when combined with a hearing aid. This combined intervention enhances speech comprehension and sound quality when compared to electrical stimulation alone, particularly in noisy environments, but the physiological basis for the benefits is not well understood. Our long-term aim is to elucidate the underlying physiological mechanisms of this improvement, and as a first step in this process, we have investigated in normal hearing cats, the degree to which the patterns of neural activity evoked in the inferior colliculus (IC) by speech sounds in various levels of noise allows discrimination between those sounds. Neuronal responses were recorded simultaneously from 32 sites across the tonotopic axis of the IC in anaesthetised normal hearing cats (n=7). Speech sounds were presented at 20, 40 and 60dB SPL in quiet and with increasing levels of additive noise (signal-to-noise ratios (SNRs) –20, –15, –10, –5, 0, +5, +10, +15, +20dB). Neural discrimination was assessed using a Euclidean measure of distance between neural responses, resulting in a function reflecting speech sound differentiation across various SNRs. Responses of IC neurons reliably encoded the speech stimuli when presented in quiet, with optimal performance when an analysis bin-width of 5-10ms was used. Discrimination thresholds did not depend on stimulus level and were best for shorter analysis binwidths. This study sheds light on how the auditory midbrain represents speech sounds and provides baseline data with which responses to electro-acoustic speech sounds in partially deafened animals can be compared.

Ensemble transfer learning meets explainable AI: A deep learning approach for leaf disease detection

Global food security is threatened by plant diseases and manual detection methods are often labor-intensive and time-consuming. Deep learning offers a promising solution by enabling early and accurate detection of leaf diseases. This study presents a novel deep-learning model designed to address the challenges of real-world leaf disease identification. To enhance the model's robustness, we incorporated six datasets (LD, LD1, LD2, LD3, LD4, LD5) which include image augmentation techniques, like flipped versions (LD1) and controlled noise (LD2, LD3). Additionally, we introduced new datasets with additional noise types (LD4) and real-world scenarios (LD5). To further improve accuracy, we employed an ensemble approach, combining MobileNetV3_Small and EfficientNetV2B3 with weighted voting. Our model achieved exceptional performance, surpassing 94 % accuracy on imbalanced data (LD) and exceeding 99 % on balanced, high-quality data (LD1). Even in noisy environments (LD2, LD3, LD4, LD5), our model consistently outperformed other approaches, maintaining an accuracy rate above 90 %. To ensure transparency and interpretability, we utilized Explainable AI (LIME) to visualize the model's decision-making process. These results demonstrate the potential of our model as a reliable and accurate tool for leaf disease detection in practical agricultural settings.

CASSAD: Chroma-Augmented Semi-Supervised Anomaly Detection for Conveyor Belt Idlers

Idlers are essential to conveyor systems, as well as supporting and guiding belts to ensure production efficiency. Proper idler maintenance prevents failures, reduces downtime, cuts costs, and improves reliability. Most studies on idler fault detection rely on supervised methods, which depend on large labelled datasets for training. However, acquiring such labelled data is often challenging in industrial environments due to the rarity of faults and the labour-intensive nature of the labelling process. To address this, we propose the chroma-augmented semi-supervised anomaly detection (CASSAD) method, designed to perform effectively with limited labelled data. At the core of CASSAD is the one-class SVM (OC-SVM), a model specifically developed for anomaly detection in cases where labelled anomalies are scarce. We also compare CASSAD’s performance with other common models like the local outlier factor (LOF) and isolation forest (iForest), evaluating each with the area under the curve (AUC) to assess their ability to distinguish between normal and anomalous data. CASSAD introduces chroma features, such as chroma energy normalised statistics (CENS), the constant-Q transform (CQT), and the chroma short-time Fourier transform (STFT), enhanced through filtering to capture rich harmonic information from idler sounds. To reduce feature complexity, we utilize the mean and standard deviation (std) across chroma features. The dataset is further augmented using additive white Gaussian noise (AWGN). Testing on an industrial dataset of idler sounds, CASSAD achieved an AUC of 96% and an accuracy of 91%, surpassing a baseline autoencoder and other traditional models. These results demonstrate the model’s robustness in detecting anomalies with minimal dependence on labelled data, offering a practical solution for industries with limited labelled datasets.

Natural soundscapes enhance mood recovery amid anthropogenic noise pollution.

In urbanised landscapes, the scarcity of green spaces and increased exposure to anthropogenic noise have adverse effects on health and wellbeing. While reduced speed limits have historically been implemented to address traffic safety, their potential impact on residents' wellbeing, especially in relation to engagement with natural soundscapes, remains understudied. Our study investigates the influence of i) natural soundscapes, including bird song, and ii) the addition of traffic noise to natural soundscapes at two speeds (20 mi/h and 40 mi/h) on mood. We found that natural soundscapes were strongly linked with the lowest levels of anxiety and stress, with an increase in stress levels associated with mixed natural soundscapes with the addition of 20 mi/h traffic noise and the highest levels with 40 mi/h traffic noise. Higher levels of hedonic tone, indicative of positive mood, was noted with natural soundscapes, but diminished when combined with 40 mi/h traffic noise. Our results show that anthropogenic soundscapes including traffic sounds can mask the positive impact of natural soundscapes including birdsong on stress and anxiety. However, reducing traffic speeds in cities could be a positive intervention for enhancing access to nature. Technological solutions, such as the widespread adoption of hybrid and electric vehicles, and urban planning strategies like integrating green spaces into transit routes, offer potential opportunities to mitigate the impact of noise pollution and benefit humans in urban environments.

Improving Gaussian channel simulation using nonunity-gain heralded quantum teleportation

Gaussian channel simulation is an essential paradigm in understanding the evolution of bosonic quantum states. It allows us to investigate how such states are influenced by the environment and how they transmit quantum information. This makes it an essential tool for understanding the properties of Gaussian quantum communication. Quantum teleportation provides an avenue to effectively simulate Gaussian channels, such as amplifier channels, loss channels, and classically additive noise channels. However, implementations of these channels, particularly quantum amplifier channels and channels capable of performing Gaussian noise suppression, are limited by experimental imperfections and nonideal entanglement resources. In this work, we overcome these difficulties using a heralded quantum teleportation scheme that is empowered by a measurement-based noiseless linear amplifier. The noiseless linear amplification enables us to simulate a range of Gaussian channels that were previously inaccessible. In particular, we demonstrate the simulation of nonphysical Gaussian channels otherwise inaccessible using conventional means. We report Gaussian noise suppression, effectively converting an imperfect quantum channel into a near-identity channel. The performance of Gaussian noise suppression is quantified by calculating the transmitted entanglement. Published by the American Physical Society 2024

Global dynamics for the stochastic nonlinear beam equations on the four-dimensional torus

Abstract We study global-in-time dynamics of the stochastic nonlinear beam equations (SNLB) with an additive space-time white noise, posed on the four-dimensional torus. The roughness of the noise leads us to introducing a time-dependent renormalization, after which we show that SNLB is pathwise locally well-posed in all subcritical and most of the critical regimes. For the (renormalized) defocusing cubic SNLB, we establish pathwise global well-posedness below the energy space, by adapting a hybrid argument of Gubinelli-Koch-Oh-Tolomeo (2022) that combines the I-method with a Gronwall-type argument. Lastly, we show almost sure global well-posedness and invariance of the Gibbs measure for the stochastic damped nonlinear beam equations in the defocusing case.

Generation of Entanglement and Perfect One‐Way EPR Steering via Kerr Nonlinearity in Cavity Magnonics System

AbstractA scheme is proposed for generating entanglement and achieving perfect one‐way EPR (Einstein–Podolsky–Rosen) steering in a cross‐shaped cavity magnonics system via the Kerr effect arising from magnetocrystalline anisotropy. The scheme involves two microwave cavities with distinct quality factors that simultaneously couple to a magnon mode of a macroscopic yttrium‐iron‐garnet sphere via the magnetic‐dipole interaction. It is demonstrated that both the bipartite and tripartite entanglement among the three modes will be generated due to Kerr nonlinearity, and the perfect one‐way EPR steering will also be achieved between the cavity with higher quality factor and the magnon mode. Notably, this scheme differs from conventional protocols that produce asymmetric EPR steering by introducing additional unbalanced losses or noises; instead, it can generate and manipulate one‐way EPR steering solely by adjusting the detuning between the magnon mode and the microwave drive field. It is also analyzed that the robustness of the entanglement and EPR steering against the dissipation of magnon and environmental temperature. The present scheme may provide a promising platform and an efficient quantum resource for quantum information processing.

Noise Reduction of Amperometric Electrochemical Sensor using Current Compensation on Transimpedance Amplifier for Dissolved Oxygen Measurement

In this paper, a study of noise reduction of an electrochemical sensor is presented. This study is initiated based on the fact that the signal from the electrochemical sensor has quite high noise. The current signal from the sensor is very weak and noisy due to high impedance and complex process on the sensor and measured media such as a solution of dissolved oxygen. The current is usually converted using a transimpedance amplifier with high voltage-to-current ratio by a feedback resistance, which usually also produces large noise due to high feedback resistance value. This will contribute to an additional noise to the natural noise of the sensor and measured media, causing more noisy result. A modified transimpedance amplifier is proposed to reduce the noise from the sensor and converter to increase signal-to-noise ratio. The modification employs a current compensation, so that the current noise is suppressed at the converter input. The noise and signal measurements at the output of the compensated transimpedance amplifier show noise reduction compared to the standard transimpedance amplifier and measurement error reduction of the measured dissolved oxygen down to 0.89%.

Random transformations to improve mitigation of query-based black-box attacks

This paper proposes methods to upstage the best-known defences against query-based black-box attacks. These benchmark defences incorporate gaussian noise into input data during inference to achieve state-of-the-art performance in protecting image classification models against the most advanced query-based black-box attacks. Even so there is a need to improve upon them; for example, the widely benchmarked Random noise defense (RND) method has demonstrated limited robustness – achieving only 53.5% and 18.1% with a ResNet-50 model on the CIFAR-10 and ImageNet datasets, respectively – against the square attack, which is commonly regarded as the state-of-the-art black-box attack. Therefore, in this work, we propose two alternatives to gaussian noise addition at inference time: random crop-resize and random rotation of the input images. Although these transformations are generally used for data augmentation while training to improve model invariance and generalisation, their protective potential against query-based black-box attacks at inference time is unexplored. Therefore, for the first time, we report that for such well-trained models either of the two transformations can also blunt powerful query-based black-box attacks when used at inference time on three popular datasets. The results show that the proposed randomised transformations outperform RND in terms of robust accuracy against a strong adversary that uses a high budget of 100,000 queries based on expectation over transformation (EOT) of 10, by 0.9% on the CIFAR-10 dataset, 9.4% on the ImageNet dataset and 1.6% on the Tiny ImageNet dataset. Crucially, in two even tougher attack settings, that is, high-confidence adversarial examples and EOT-50 adversary, these transformations are even more effective as the margin of improvement over the benchmarks increases further.

An investigation of errors in ellipse-fitting for cold-atom interferometers

Ellipse fitting is a technique which is used to extract differential phase in cold-atom interferometers, particularly in situations where common-mode noise needs to be suppressed. We use numerical simulation to investigate errors in the ellipse fitting process; specifically, errors due to the presence of additive noise, linear drift in ellipse offset and amplitude, as well as an error that can arise from fringe normalisation. Errors are found to manifest in two ways: bias in the ellipse phase measurement and incomplete suppression of common mode phase noise. We quantify these errors for three different ellipse fitting algorithms and discuss the applicability of these results to future cold atom sensors.

Terahertz Imaging Detects Oral Cariogenic Microbial Domains Characteristics.

Dental caries, associated with plaque biofilm, is highly prevalent and significantly burdens public health. Streptococcus mutans is the main cariogenic bacteria that adheres to the tooth surface and forms an abundant extracellular polysaccharide matrix (EPS) as a cariogenic biofilm scaffold. S. mutans RNase III-encoding gene (rnc) and a putative chromosome segregation protein-encoding gene (smc) are potentially associated with EPS production. In addition, complex interactions between S. mutans and other oral microorganisms synergistically or antagonistically affect the cariogenicity. Commensal streptococci suppress the growth of cariogenic pathogens, whereas Candida albicans mediates the formation of cariogenic biofilm through aggregation and dual-species biofilm formation with S. mutans. However, label-free detection of cariogenic microbial interactions with the EPS matrix is still challenging during laboratory investigations. Herein, we hypothesized that the S. mutans rnc-smc operon affects EPS production and aimed to observe streptococci, S. mutans, and S. mutans-C. albicans using terahertz scanning near-field optical microscopy (THz s-SNOM). The light in the 0.1- to 0.3-THz frequency range interacted with the sample through a nano-probe tip by a point-by-point scanning process. Additional noise reduction of the original image was achieved by a dual kernel Gaussian filter. The monospecies of streptococci, S. mutans smc/rnc mutants, and the dual-species of S. mutans-C. albicans were scanned by THz s-SNOM. This technique provided terahertz near-field scanning images of S. mutans smc/rnc mutants, streptococci, and dual-species of S. mutans-C. albicans. Additional analysis of the original images potentially revealed the structures of the strains, such as cell diameters and cell wall thickness. In conclusion, the results suggested that the S. mutans rnc-smc operon regulates EPS production. Furthermore, this novel label-free detection of a THz near-field scanning technique had the potential to observe the morphologies of bacterial cells and EPS matrix.

A cost-effective load side management solution based on power line carrier communication i-PLCC.

Conventional power systems are almost at the verge of their existence and are quickly being replaced by modern, especially smarter alternatives. Rapid and wide deployment of Load side management, which is a smarter and modern way to ensure efficient and economical use of power, is a classic example. However, there are several challenges which need to be addressed before it can be realized as a commercially viable solution. Suitable communication method, called "Home Area Networks", is a mandatory requirement which should be cost effective, robust and with ability to simultaneously handle large number of devices. Due to various inherent issues, communication protocols commonly used now-a-days, are not fully capable to address the requirements of HANs. Power Line Carrier Communications (PLCC) can be a suitable and efficient alternative of quick realization of HANs. In this paper, various aspects of PLCC have been analyzed for use in HANs. Modulation methods, materials, physical parameters and effect of noise have been analyzed. The paper evaluates performance and effectiveness of FSK modulation with aluminum transmission medium for PLC within a residential environment, focusing on the tradeoffs between distance, transmission medium diameter, and type of transmission medium, and cable gauge, which can degrade the performance of PLCC. A novel simulation model has been presented incorporating RLCG (Resistance, Inductance, Capacitance, and Conductance) tools and additive white Gaussian noise (AWGN). Performance parameter have been comprehensively analyzed of implementation using aluminum as a communication medium. This paper discusses cost effectiveness of PLCC by replacing copper by aluminum as the transmission medium. The study reveals that aluminum cables must be twice the size of copper cable in order to achieve same communication distances. Higher carrier frequencies in FSK modulation increase noise susceptibility, consequently reducing achievable communication distances. Cables with smaller diameters results in lower transmission ranges. These results highlight the trade-offs involved in implementing FSK for in-home PLCC (iPLCC). The study highlights how crucial it is to take these trade-offs into account while developing and refining FSK-based iPLCC systems for use in smart homes.

Gaussian unsteerable channels and computable quantifications of Gaussian steering

Gaussian unsteerable channels and computable quantifications of Gaussian steering