Noise Distribution Research Articles

Risk of Bias When Using Early Failure Criteria in Randomized Clinical Trials With Stereoacuity Outcomes.

The purpose of this study was to explore the effects of early failure criteria for participants in randomized clinical trials (RCTs) on overall trial conclusions. We simulated 10,000 hypothetical RCTs with 2 treatments, 1 linear improvement and 1 with increasing rate of improvement and 6 follow-up visits. Each RCT had 400 participants, with the same baseline stereoacuity distribution. We incorporated random test-retest noise for every visit, and scores were rounded to the nearest observable score. Early failure was defined as worsening of two or more levels. We compared mean outcome stereoacuity between treatment groups, with and without the failure rule, using the two-sample t-test and the proportion of erroneous RCTs (significantly different mean outcome values, where truth is known to be no different). Sensitivity analyses were performed to explore the influence of sample size, baseline distribution of stereoacuity, overall magnitude of mean improvement, magnitude of change for the failure rule, and distribution of noise. A greater proportion of 10,000 simulated RCTs had an erroneous mean difference in outcome with the early failure rule than without (5.49%, 95% confidence interval [CI] = 5.05% to 5.94% vs. 0, 0%, 95% CI = 0% to 0.000001%, difference 5.49%, P < 0.0001). Sensitivity analysis revealed that increased sample size and wider distribution of noise had the greatest influence on increasing proportions of erroneous RCT conclusions. Study designs incorporating participant-level early failure rules increase the risk of erroneous RCT conclusions and should be avoided. We provide data informing the design of future clinical trials. Earlier failure rules at the participant level should be avoided.

GNSS and accelerometer data fusion by variational Bayesian adaptive multi-rate Kalman filtering for dynamic displacement estimation of super high-rise buildings

The integration of Global Navigation Satellite Systems (GNSS) into Structural Health Monitoring (SHM) systems of super high-rise buildings is becoming increasingly prevalent, with a notable trend of combining GNSS with accelerometers to leverage the complementary nature of both technologies for estimating dynamic displacements. However, the multipath errors between the GNSS reference and monitoring stations give rise to the time-varying noises into the process of solving position, resulting in considerable fluctuations in the accuracy of displacement monitoring in complex environments, which impedes the development of GNSS-accelerometer integration techniques in the field of SHM. To address the issue, a variational Bayesian adaptive multi-rate algorithm (VBMRKF) is proposed to rapidly respond the time-varying noise for the GNSS system, thereby facilitating the fusion of GNSS and accelerometer data with greater accuracy. In this algorithm, a convergence criterion is established for approximation of posterior distribution in multi-rate process based on Evidence Lower Bound (ELBO) theory, which reduces the effects of noise accumulation on the accuracy of the approximation processes for the posterior distribution. Furthermore, a noise-sensitive factor is also heuristically proposed, which enables the algorithm to quickly adapt to fluctuations of noise characteristics during multi-rate processes. The numerical simulation results reveal that the algorithm has a higher estimation accuracy for dynamic displacements than the conventional algorithm, and it has a superior adaptability to systems with different frequencies. In instances where the initial settings of noise parameters are highly unreasonable, traditional algorithms even demonstrated a lack of convergence. Conversely, the normalized root mean square errors (NRMSE) of the displacement estimation results obtained by VBMRKF are consistently below 16 %, which also substantiates the robustness of this algorithm. In the presence of time-varying noise, the algorithm demonstrates the capacity to rapidly converge upon the distribution of the noise. Compared to the multi-rate Kalman filtering (MRKF) algorithm, the proposed algorithm achieves the highest reduction of 27.02 % in NRMSE, and compared to around 13.97 % NRMSE of the recent adaptive algorithm, the proposed algorithm also demonstrates significantly superior performance, reducing NRMSE by at least 5.13 %. In addition, regardless of whether the signal is time-varying or not, the algorithm is able to quickly approximate the distribution of noise and produces acceptable displacement estimation results with unreasonable initial settings of the noise parameters, demonstrating its robustness. Finally, the measured accelerometer and GNSS data are employed to successfully determine the two-dimensional dynamic displacement of the 632-metre-high Shanghai Tower at a distance of only 2.1 km from the eye of Typhoon Muifa (202212). A further analysis of the results demonstrates that the estimated displacements are more comprehensive in both time and frequency domains.

Radial Distribution of Electron Quasi-thermal Noise in the Inner Heliosphere

The electron population in the solar wind plasma can be described with three different components: a core, a halo, and a magnetic field aligned strahl. The electron quasi-thermal noise (QTN) is investigated by using an electron population model consisting of a core with a Maxwellian distribution and a halo with a kappa distribution, based on the empirical equations for electron density and temperature and the index for the kappa halo. The power spectra of the electron QTN are calculated at different heliocentric radial distances from 10 to 200 R s . The dependence of the QTN spectrum and effective Debye length on model parameters, including the ratio of the halo to the core for the density and temperature, the kappa index, and the antenna length, is further discussed. The results show that the electron QTN spectrum consists of a plateau in the low-frequency band f < f pt , a peak at the total plasma frequency f pt , and a rapidly decreasing part in the high-frequency band f > f pt . The QTN peak and plateau level continuously decrease as the radial distance increases, with the peak’s shape changing due to the variation of the kappa index. Although the model parameters are variable, the QTN plateau level presents less than an order of change with these parameters changing greatly, and only a monotonic change of the plateau is shown when the parameters are close to the practical situation. The results can provide a reference for future deep-space exploration in the inner heliosphere, and also for the design of detectors.

Posterior Sampling for Random Noise Attenuation via Score-based Generative Models

Random noise attenuation is an ill-posed inverse problem with multiple solutions,especially in complicated field noise situations. We present a method to sample stochastic solutions from the posterior distribution of seismic data for a given noisy input. Posterior sampling can be performed by Langevin dynamics with a conditional score function, which can be described as a trained score network (in score-based generative models) plus an analytical expression related to the noise distribution. Each solution from the posterior distribution is reasonable and of high quality. The numerous solutions we obtain may contain underground structural information of interest. We also achieve interactive posterior sampling by automatically estimating a noise level or manually setting it according to the noise level map of the field noise. Experiments on synthetic and field data verify the superiority of our posterior sampling approach.

Sub-Hz fundamental, sub-kHz integral linewidth self-injection locked 780 nm hybrid integrated laser

Today’s precision experiments for timekeeping, inertial sensing, and fundamental science place strict requirements on the spectral distribution of laser frequency noise. Rubidium-based experiments utilize table-top 780 nm laser systems for high-performance clocks, gravity sensors, and quantum gates. Wafer-scale integration of these lasers is critical for enabling systems-on-chip. Despite progress towards chip-scale 780 nm ultra-narrow linewidth lasers, achieving sub-Hz fundamental linewidth and sub-kHz integral linewidth has remained elusive. Here we report a hybrid integrated 780 nm self-injection locked laser with 0.74 Hz fundamental and 864 Hz integral linewidths and thermorefractive-noise-limited 100 Hz2/Hz at 10 kHz. These linewidths are over an order of magnitude lower than previous photonic-integrated 780 nm implementations. The laser consists of a Fabry-Pérot diode edge-coupled to an on-chip splitter and a tunable 90 million Q resonator realized in the CMOS foundry-compatible silicon nitride platform. We achieve 2 mW output power, 36 dB side mode suppression ratio, and a 2.5 GHz mode-hop-free tuning range. To demonstrate the potential for quantum atomic applications, we analyze the laser noise influence on sensitivity limits for atomic clocks, quantum gates, and atom interferometer gravimeters. This technology can be translated to other atomic wavelengths, enabling compact, ultra-low noise lasers for quantum sensing, computing, and metrology.

Adaptive range gating based on variational Bayesian inference for space debris ranging with spaceborne single-photon LiDAR.

To enhance the accuracy of space debris localization, spaceborne single-photon LiDAR (SSPL) presents a promising technique for accurate target ranging. Extended Kalman filtering (EKF) plays a crucial role in range gating under high dynamic and nonlinear motion conditions of space debris, ensuring accurate state estimation and prior distance data. However, unknown and time-varying statistics of process and measurement noise significantly degrade state estimation accuracy, posing risks of filter divergence and reduced photon reception, ultimately rendering range gating ineffective. To address this challenge, we propose an adaptive range gating method based on variational Bayesian adaptive extended Kalman filtering (ARG-VBAEKF). This method leverages variational Bayesian (VB) posterior approximation to estimate the joint distribution of state and noise. Simulation results demonstrate that ARG-VBAEKF achieves accurate state and noise estimation, thereby effectively enhancing range gating performance in SSPL-based space debris ranging.

Анализ зашумленных спектров электронного парамагнитного резонанса

The paper compares various methods for analyzing the signal of noisy EPR spectra, including those that allow increasing the signal-to-noise ratio. The EPR spectra were modeled (by computer calculation) or adjusted (in the case of an experiment) using the Tsallis function, which made it possible to smoothly adjust the shape of the line. It is shown that the method of minimizing the error function at low noise determines the parameters of the spectrum with high accuracy, but at high noise is inferior to the maximum likelihood method. It was found that the procedure of "smoothing" the spectra increases the signal-to-noise ratio, but practically does not improve the accuracy of determining the signal parameters. Measurements and analysis of the intrinsic noise of the Varian E-4 X-band EPR spectrometer, necessary for the application of the maximum likelihood method, revealed the Gaussian distribution for experimental noise.

Pollution Source Detection With Low‐Cost Low‐Accuracy Sensors Through Coupling Forward Data Assimilation and Inverse Optimization

AbstractData uncertainty affects the accuracy of pollution source detection (PSD), particularly in the background of low‐cost water quality sensing and low‐accuracy data challenge. This study aims to develop a novel PSD method to use low‐accuracy sensor data, namely, the method of coupled forward data Assimilation and inverse Optimization in PSD (A&amp;O‐PSD). This approach primarily employs filtering strategies to handle observation errors and extract hidden trend information during forward water quality data assimilation, and then optimal estimation of pollution source information through inverse optimization with enhanced trend information matching, avoiding the non‐Gaussian distribution challenge of pollution source information. Both real‐world pollution events and semi‐synthetic cases were used to evaluate the methodology and compare its performance with the traditional optimization approach (T‐PSD). The results indicated that T‐PSD is significantly affected by observational and parameter noise, engendering noticeable biases in PSD under the low‐accuracy sensor conditions. In contrast, the A&amp;O‐PSD could accomplish the estimation task of PSD in real‐world pollution events, with improved robustness against noise interference. Furthermore, A&amp;O‐PSD achieved an accuracy improvement of over 10% compared to T‐PSD in estimating pollution source locations within the typical noise distribution range of most low‐accuracy sensors currently available, making it possible to use low‐accuracy data that would otherwise be unusable in T‐PSD. Overall, the A&amp;O‐PSD method, combined with low‐cost low‐accuracy water quality sensing, offers an effective solution for watershed environmental management.

Self-supervised learning for denoising of multidimensional MRI data.

To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image. Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability. The proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images. The proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps.

Retrofit Measures for Aircraft Noise Reduction: Simulation Benchmark and Impact Assessment

DLR, German Aerospace Center, has developed a low-noise retrofit to reduce noise generation of a conventional midrange transportation aircraft, i.e., measures to the airframe and engine exhaust nozzle. Selected measures have been installed onboard of DLR’s flying testbed Advanced Technology Research Aircraft and been subject to a flyover noise campaign. The aircraft in its original configuration and the modified aircraft have been tested and measured in flyover campaigns in 2016 and 2019, respectively. Initial simulation models have been derived from the results and previous component simulations and wind tunnel tests to account for the new reduction measures within DLR’s system noise prediction tool Parametric Aircraft Noise Analysis Module (PANAM). This paper presents the developed measures as selected for the flight test campaigns. An overview of the conducted flight tests is provided. The overall noise simulation process, including aircraft and engine design, flight simulation, and noise prediction, is presented. Simulation results obtained from this process are then compared to the measured levels from the flight campaigns. Estimated simulation uncertainties from PANAM can now be directly compared to differences between measured and simulated levels. Finally, a parameter study is conducted in order to assess the reduction potential of the novel retrofit measures along different simulated flight procedures. Spatial noise distribution in sound exposure level contours is assessed.

دراسة مستويات التلوث الضوضائي في مدينة طرابلس

Noise pollution is known to have significant implications for public health and overall well-being. In this study, noise pollution levels were analyzed in selected areas of Tripoli, Libya. The investigation focused on seven distinct locations: Martyr’s square, Algeria square, University hospital, Al-Jalaa hospital, Al-Dehmani park, Al-Tholathaa park, and the Second Ring Road. Noise levels were assessed during working days and weekends across three different time periods: morning, afternoon, and evening. The primary objective was to conduct a thorough evaluation and analysis of environmental noise pollution in Tripoli. This was achieved by employing sound level measurements throughout the seven areas. Additionally, dB mapping has been utilized to visualize the spatial distribution and intensity of noise at the surveyed locations. The initial observations of the study revealed fluctuations in noise levels across the surveyed locations. These variations suggest potential correlations with temporal factors and human activities. This study provides valuable insights into the noise levels in Tripoli, thus creating a comprehensive database for further studies. The identification of elevated noise levels in specific areas and during particular periods underscores the necessity for targeted environmental control measures, such as the implementation of noise barriers. Therefore, a thorough investigation regarding noise barriers should be conducted. In general, the outcome of this study revealed that noise barriers appear to be necessary alongside the Second Ring Road, along with the planting of tall and dense trees, which play an important role in reducing noise in the surrounding areas. Keywords: Noise pollution; environmental noise; noise mapping, Tripoli city.

Diffusion network with spatial channel attention infusion and frequency spatial attention for brain tumor segmentation.

Accurate segmentation of gliomas is crucial for diagnosis, treatment planning, and evaluating therapeutic efficacy. Physicians typically analyze and delineate tumor regions in brain magnetic resonance imaging (MRI) images based on personal experience, which is often time-consuming and subject to individual interpretation. Despite advancements in deep learning technology for image segmentation, current techniques still face challenges in clearly defining tumor boundary contours and enhancing segmentationaccuracy. To address these issues, this paper proposes a conditional diffusion network (SF-Diff) with a spatial channel attention infusion (SCAI) module and a frequency spatial attention (FSA) mechanism to achieve accurate segmentation of the whole tumor (WT) region in braintumors. SF-Diff initially extracts multiscale information from multimodal MRI images and subsequently employs a diffusion model to restore boundaries and details, thereby enabling accurate brain tumor segmentation (BraTS). Specifically, a SCAI module is developed to capture multiscale information within and between encoder layers. A dual-channel upsampling block (DUB) is designed to assist in detail recovery during upsampling. A FSA mechanism is introduced to better match the conditional features with the diffusion probability distribution information. Furthermore, a cross-model loss function has been implemented to supervise the feature extraction of the conditional model and the noise distribution of the diffusionmodel. The dataset used in this paper is publicly available and includes 369 patient cases from the Multimodal BraTS Challenge 2020 (BraTS2020). The conducted experiments on BraTS2020 demonstrate that SF-Diff performs better than other state-of-the-art models. The method achieved a Dice score of 91.87%, a Hausdorff 95 of 5.47 mm, an IoU of 84.96%, a sensitivity of 92.29%, and a specificity of 99.95% onBraTS2020. The proposed SF-Diff performs well in identifying the WT region of the brain tumors compared to other state-of-the-art models, especially in terms of boundary contours and non-contiguous lesion regions, which is clinically significant. In the future, we will further develop this method for brain tumor three-class segmentation task.

Relaxivity and In Vivo Human Performance of Brand Name Versus Generic Ferumoxytol.

Ferumoxytol is a superparamagnetic iron-oxide product that is increasingly used off-label for contrast-enhanced magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA). With the recent regulatory approval of generic ferumoxytol, there may be an opportunity to reduce cost, so long as generic ferumoxytol has similar imaging performance to brand name ferumoxytol. This study aims to compare the relaxation-concentration dependence and MRI performance of brand name ferumoxytol with generic ferumoxytol through phantom and in vivo experiments. The secondary purpose was to determine the optimal flip angle and optimal weight-based dosing. Phantom experiments were performed using both brand name (AMAG Pharmaceuticals) and generic (Sandoz Pharmaceuticals) ferumoxytol products. Each ferumoxytol product was diluted in saline, and separately in adult bovine whole blood, at 5 iron concentrations ranging from 0.3 to 2.1 mM. Vials were placed in an MR-compatible water bath at 37°C and imaged at both 1.5 T and 3.0 T. Longitudinal and transverse relaxation rate constants (R1, R2, R2*) were measured for each ferumoxytol concentration, and relaxation-concentration curves were estimated. An in vivo dose accumulation study with flip angle optimization was also implemented using a cross-over design, in healthy volunteers. Cumulative doses of 1, 3, 5, and 7 mg/kg diluted ferumoxytol were administered prior to MRA of the chest on a 3.0 T clinical MRI system. For each incremental dose, the flip angle was varied from 40° to 10° in -10° increments over 5 breath-holds followed by a repeated 40° flip angle acquisition. Regions of interest were drawn in the aortic arch, paraspinous muscles, and a noisy area outside of the patient, free from obvious artifact. Signal-to-noise ratio (SNR) was calculated as the quotient of the average signal in the aortic arch and the standard deviation of the noise, corrected for a Rician noise distribution. Contrast-to-noise ratio was calculated as the difference in SNR between the aorta and paraspinous muscles. Absolute SNR and contrast-to-noise ratio values were compared between products for different flip angles and doses. There were no statistically significant or clinically relevant differences in relaxation-concentration curves between AMAG and Sandoz products in phantom experiments. Six healthy volunteers (38.8 ± 11.5 years, 3 female, 3 male) were successfully recruited and completed both imaging visits. No clinically relevant differences in image quality were observed between ferumoxytol products. The optimal flip angle range and dose for both products was 20°-30° and 5 mg/kg, respectively. Brand name and generic ferumoxytol products can be used interchangeably for MRA.

Low-complexity end-to-end deep learning framework for 100G-PON

End-to-end learning allows communication systems to achieve optimal performance compared with conventional blockwise structure design. By modeling the channel with neural networks and training the transmitter and receiver on this differentiable channel, the whole system can be jointly optimized. However, in existing schemes, channel modeling methods, such as the generative adversarial network and long short-term memory network, have complex architectures and cannot track channel changes, leading to less effective end-to-end learning. Meanwhile, the complexity of neural networks deployed at the transmitter and receiver is too high for practical applications. In this work, we propose an efficient and low-complexity end-to-end deep learning framework and experimentally validate it on a 100G passive optical network. It uses a noise adaptation network to model channel response and noise distribution and employs offline pretraining and online tracking training to improve the efficiency and accuracy of channel modeling. For the transmitter, it consists of a pattern-dependent look-up table (PDLUT) based on a neural network (NN-PDLUT) with a single convolutional layer. Further, the receiver is also an NN with a single convolutional layer; thus, the end-to-end signal processing is extremely simple. The experimental results show that end-to-end learning improves the receiver sensitivity by 0.85 and 1.59 dB compared with receiver-only equalization based on Volterra nonlinear equalization (VNLE) and joint equalization based on a PDLUT and a feed-forward equalizer, respectively. Moreover, the number of multiply–accumulate operations consumed by the transmitter and receiver in the end-to-end learning scheme is reduced by 75.7% compared with VNLE-based receiver-only equalization.

Enhancing search pipelines for short gravitational-wave transients with Gaussian mixture modeling

We present an enhanced method for the application of Gaussian mixture modeling (GMM) to the coherent WaveBurst (cWB) algorithm in the search for short-duration gravitational wave (GW) transients. The supervised machine learning method of GMM allows for the multidimensional distributions of noise and signal to be modeled over a set of representative attributes, which aids in the classification of GW signals against noise transients (glitches) in the data. We demonstrate that updating the approach to model construction eliminates bias previously seen in the GMM analysis, increasing the robustness and sensitivity of the analysis over a wider range of burst source populations. The enhanced methodology is applied to the generic burst all-sky short search in the LIGO-Virgo full third observing run (O3), marking the first application of GMM to the 3 detector Livingston-Hanford-Virgo network. For both 2- and 3- detector networks, we observe comparable sensitivities to an array of generic signal morphologies, with significant sensitivity improvements to waveforms in the low quality factor parameter space at false alarm rates of 1 per 100 years. This proves that GMM can effectively mitigate blip glitches, which are one of the most problematic sources of noise for unmodeled GW searches. The cWB-GMM search recovers similar numbers of compact binary coalescence (CBC) events as other cWB postproduction methods, and concludes on no new gravitational wave detection after known CBC events are removed. Published by the American Physical Society 2024

Retraction Note: Adaptive neuro fuzzy estimation of the most influential speckle noise distributions in color images for denoising performance prediction

Retraction Note: Adaptive neuro fuzzy estimation of the most influential speckle noise distributions in color images for denoising performance prediction

Synthesis of a four-point model of an airplane

The modeling of echo signals from distributed radar objects taking into account the noise of their angular coordinates is considered. Relationships are presented that allow the transition from a multipoint model of an arbitrary object containing tens, hundreds and even thousands of emitting points to a model composed of four emitting points located at the vertices of a square. As an example, we synthesized an airplane model containing only four points. It is obtained on the basis of a multipoint model composed of 56 points. Using numerical modeling methods, it has been shown that angular noise generated by multi-point and four-point aircraft models have identical correlation functions and similar parameters of the probability density distribution of angular coordinate noise. The obtained result is also confirmed by semi-natural modeling methods using a matrix simulator.

The method of threat assessment and interference distribution within the framework of cognitive electronic warfare

Problem statement. Electromagnetic spectrum control (EMC) plays an important role in conflict radar. The EMS operational area has unique characteristics that can be used to gain superiority over the enemy. The use of advanced developments in cognitive electronic warfare (CRAB) makes it possible to achieve a dominant position in the use of EMS. The combination of artificial intelligence and machine learning technologies with advanced electronic warfare (EW) technologies has made it possible to take an important step in ensuring maneuverable operations in a duel situation aircraft (LA) – radar station (radar). The presented approach demonstrates the increased efficiency of the CRE due to the use of the threat assessment and interference distribution method (SDR). Goal. The purpose of the article is to show how modern technologies provide a noise distribution strategy that allows aircraft to survive in a dueling situation, despite its complexity. Results. It is shown that the ESD method uses various radar jamming modes, such as: interaction between types and sources of jamming, relative frequency and bandwidth, uncertainty of the threatening environment, and also models the evaluation of radar effectiveness from search to guidance. Practical significance. The article confirms that the conflict radar scenario, which takes into account threat assessments for aircraft and determines the distribution of interference for radar, allows us to determine the optimal interference strategy.

Effect of sound absorption on noise reduction in the automotive industry

Industrial noise is one of the most common physical factors that cause annoyance and damage workers' health in the long term. Precautions should be taken to reduce noise and improve acoustic performance in industrial working environments. This paper aims to analyze the acoustic performance of the automotive industry and contribute to the global outcomes of sustainability and the development of strategies to improve the quality of the working environment through improvement scenarios. For this purpose, the automotive industry in Türkiye was examined as a case study. In-situ acoustic measurements were made in the seat manufacturing unit of an automotive factory and the current situation was transferred to the simulation program. The effects of acoustic improvements on A-weighted sound pressure level and reverberation time at mid-frequencies (500, 1000, 2000 Hz) were investigated through three different scenarios. In the investigations, noise distributions were carried out through noise mapping. The A-weighted sound pressure levels in the automotive industry were reduced by approximately 15 dB. As a result of the study, suggestions for noise control precautions and their effects on the automotive industry seat manufacturing unit are presented.

How to get high-rise residential design to respond road traffic noise

Rapid urbanization causes acute contradictions of road traffic noise on residents' health. Although many works have been done in this field, specific investigations of how to get a design of residential area to correspond road traffic noise is still limited, especially in a high population dense area. This paper, therefore, focused on high-rise residential blocks, researching relations between design variables and distribution of road traffic noise. In the study, a typical high population dense city, Shenzhen was employed as study case. Based on noise map simulation, design element, block layout, design of individual buildings was proved important in traffic noise impact. The results discovered that the noise reduction effect is relatively significant when the building setback is within 30 - 40 meters of setback curb. To minimize traffic noise impact, flexible layout of board point perimeter type might play an important role. Lowering building heights with increasing the number of building rows, two-story podiums around high-rise buildings, and the addition of balcony railing plate could result in significant noise reductions. Focusing on design elements, this work developed an overall evaluation to improve acoustic environment of high-rise residentials based on noise map simulation, and provided significant contributions in responding road traffic noise.