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The standard method for computing noise contours around civil airports is SAE-AIR-1845 (FAA Integrated Noise Model, European ECAC-CEAC Doc. 29). It is subject to the inaccuracies implicit in the model as well as those caused by er...
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The standard method for computing noise contours around civil airports is SAE-AIR-1845 (FAA Integrated Noise Model, European ECAC-CEAC Doc. 29). It is subject to the inaccuracies implicit in the model as well as those caused by erroneous or imprecise input data. Regarding the latter, the existing errors and/or uncertainties, may be amplified in the output results, to a greater or lower extent, in some cases offering unreliable predictions. In order to study this phenomenon, the Institute of Sound and Vibration Research (ISVR -Southampton) carried out a theoretical sensitivity analysis based on the segmentation technique, regarding the input parameters of the SAE-AIR-1845, and obtaining the input variables of the model, the variation of which implied greater changes in the output variables. The results were validated by using the FAA Integrated Noise Model Version 6.0 software, based on the aforementioned document. It has been revealed that the model has a greater sensitivity to factors that modify the flight path, and a lower sensitivity to the other parameters. Thus, an error greater than 10% in the variable "gross weight" offers an additional error of between 3 and 7 dB. However, parameters such as the ID of the flaps hardly modify the results obtained for the least favourable case by 1 dB. As a result of this research, the sensitivity of the model was quantified for each of the input parameters (taken alone and also in interaction with other parameters), and criteria for the minimisation of global error, resulting from uncertainties in the input parameters, were stated.
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This paper presents the methodology adopted for the development of a noise prediction model for Indian traffic conditions on a national highway using the L_(10) approach. This approach is consistent with that used in CRTN (Departm...
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This paper presents the methodology adopted for the development of a noise prediction model for Indian traffic conditions on a national highway using the L_(10) approach. This approach is consistent with that used in CRTN (Department of Transport/ Welsh Office, Calculation of Road Traffic Noise) method and the study was conducted in accordance with the norms laid down therein. This approach was found to be easier as compared to the FHWA's Traffic Noise Model (TNM) in view of non-availability of Reference Energy Mean Emission Level (REMEL) data specific to India. The prediction equations developed here have been used to predict noise levels on the same highway at different locations and distances from the nearside carriageway edge. The results have shown satisfactory agreement between measured and predicted values.
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A model of highway traffic noise if formulated based on vehicle types. The data were collected from local highways in Thailand with free-flow traffic conditions. First, data on vehicle noise was collected from in- dividual vehicle...
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A model of highway traffic noise if formulated based on vehicle types. The data were collected from local highways in Thailand with free-flow traffic conditions. First, data on vehicle noise was collected from in- dividual vehicles using sound level meters placed at a reference distance. Simultaneously, measurements were made of vehicles' spot speeds. Secondly, are data for building the highway traffic noise model. This consists of traffic noise levels, traffic volumes by vehicle classification, average spot speeds by vehicle type, and the geometric dimension of highway sections.
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Compact modeling of the most important high-frequency (HF) noise sources of the MOSFET is presented in this paper, along with challenges in noise measurement and deembedding of future CMOS technologies. Several channel thermal noi...
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Compact modeling of the most important high-frequency (HF) noise sources of the MOSFET is presented in this paper, along with challenges in noise measurement and deembedding of future CMOS technologies. Several channel thermal noise models are reviewed and their ability to predict the channel noise of extremely small devices is discussed. The impact of technology scaling on noise performance of MOSFETs is also investigated by means of analytical expressions. It is shown that the gate tunneling current has a significant impact on MOSFETs noise parameters, especially at lower frequencies. Limitations of some commonly used noise models in predicting the HF noise parameters of modern MOSFETs are addressed and methods to alleviate some of the limitations are discussed.
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Noise prediction models and noise maps are used to estimate the exposure to road traffic noise, but their availability and the quality of the noise estimates is sometimes limited. This paper explores the application of land use re...
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Noise prediction models and noise maps are used to estimate the exposure to road traffic noise, but their availability and the quality of the noise estimates is sometimes limited. This paper explores the application of land use regression (LUR) modelling to assess the long-term intraurban spatial variability of road traffic noise in three European cities. Short-term measurements of road traffic noise taken in Basel, Switzerland (n=60), Girona, Spain (n=40), and Grenoble, France (n=41), were used to develop two LUR models: (a) a "GIS-only" model, which considered only predictor variables derived with Geographic Information Systems; and (b) a "Best" model, which in addition considered the variables collected while visiting the measurement sites. Both noise measurements and noise estimates from LUR models were compared with noise estimates from standard noise models developed for each city by the local authorities. Model performance (adjusted R-2) was 0.66-0.87 for "GIS-only" models, and 0.70-0.89 for "Best" models. Shortterm noise measurements showed a high correlation (r=0.62-0.78) with noise estimates from the standard noise models. LUR noise estimates did not show any systematic differences in the spatial patterns when compared with those from standard noise models. LUR modelling with accurate GIS source data can be a promising tool for noise exposure assessment with applications in epidemiological studies.
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Environmental noise has been a major environmental nuisance in metropolitan cities. To achieve the goal of sustainable community, noise reduction is an important approach. Without systematic noise mapping, the spatio-temporal dist...
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Environmental noise has been a major environmental nuisance in metropolitan cities. To achieve the goal of sustainable community, noise reduction is an important approach. Without systematic noise mapping, the spatio-temporal distribution of noise variations is hard to capture. This study proposes a new methodology framework to combine statistical models and acoustic propagation for dynamic updates of 2D and 3D traffic noise maps by using a limited number of noise sensors in Taipei City based on multisource data including noise monitoring, vehicle detectors, meteorological data, road characteristics, and socio-demographic data. The hourly mean dif-ference between the predicted and measured noise level is within the range of-6.25 dBA to-4.46 dBA in the 2D noise model. For the 3D noise model, the hourly mean prediction error is within the range of 0.02 dBA to 1.93 dBA. Based on the WHO benchmark for excessive road traffic noise, we found at least 30% of inhabitants in Taipei City are exposed to levels exceeding 53 dBA Lden, and >25% are exposed to noise levels exceeding 45 dBA Lnight. The noise maps not only can help identify vulnerable communities to adopt proper approaches for noise reduction but also can remind the residents to take action to improve their quality of life.
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This paper aims to propose a novel Road Traffic Noise Model (RTNM), capable of dynamically assessing road traffic noise levels from reliable data (hourly traffic volumes and speed), supporting or replacing noise sensor networks, a...
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This paper aims to propose a novel Road Traffic Noise Model (RTNM), capable of dynamically assessing road traffic noise levels from reliable data (hourly traffic volumes and speed), supporting or replacing noise sensor networks, and addressing noise pollution concerns. RTNM is composed of two parts: ⅰ) the Vehicle Noise Specific Power model (for the assessment of the sound power level - L_w of passenger cars, considering speed and motorization information) coupled with the CNOSSOS model (for the estimation of heavy-duty vehicles' L_w); and ⅱ) a sound propagation model (to evaluate noise levels at the receiver point). Inputs for the RTNM are retrieved from a radar installed in Aveiro city, Portugal, while the model estimations are validated by comparing them with levels recorded by a noise sensor installed close to the radar. It was found that the resulting noise estimations are robust, with associated mean absolute percentage errors not exceeding 5.8%.
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RF circuit design in deep-submicrometer CMOS technologies relies heavily on accurate modeling of thermal noise. Based on Nyquist's law, predictive modeling of thermal noise in MOSFETs was possible for a long time, provided that pa...
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RF circuit design in deep-submicrometer CMOS technologies relies heavily on accurate modeling of thermal noise. Based on Nyquist's law, predictive modeling of thermal noise in MOSFETs was possible for a long time, provided that parasitic resistances and short-channel effects were properly accounted for. In sub-100-nm technologies, however, microscopic excess noise starts to play a significant role and its incorporation in thermal noise models is unavoidable. Here, we will review several crucial ingredients for accurate RF noise modeling, with emphasis on sub-100-nm technologies. In particular, a detailed derivation and discussion are presented of our microscopic excess noise model. It is shown to qualitatively explain the observed noise (across bias and geometry) in a wide range of commercially available sub-100-nm foundry processes. Besides, the impact of excess noise on the minimum noise figure is discussed.
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Federal Highway Administration's Traffic Noise Model (TNM) versions 2.1 and 2.5 have been analysed to determine their functionality and accuracy in the Indian context. Each of the resulting changes between predicted noise levels a...
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Federal Highway Administration's Traffic Noise Model (TNM) versions 2.1 and 2.5 have been analysed to determine their functionality and accuracy in the Indian context. Each of the resulting changes between predicted noise levels and observed noise levels are analysed, and the agreement diagram is developed between observed noise levels with predicted noise levels. During the measurements, traffic data were collected which were used to create a model of each measurement site. The FHWA model is found accurate within ±10%, and it can be used for flat road traffic noise prediction on Indian roads. The applicable speed of vehicles is between 30 km/h and 60 km/h, and traffic noise levels have been predicted 10 m from the roadside and at a height of 1.2 m from the surface of ground.
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In this paper, we present a physics-based compact model for low frequency noise in high electron mobility transistors (HEMTs). The model is derived considering the physical mechanisms of carrier number fluctuation and mobility flu...
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In this paper, we present a physics-based compact model for low frequency noise in high electron mobility transistors (HEMTs). The model is derived considering the physical mechanisms of carrier number fluctuation and mobility fluctuation in the channel. The model is tunable and hence applicable to a wide range of HEMT devices of different geometries and construction. The model is in excellent agreement with experimental data and TCAD simulations.
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