In this paper, we study some properties of the class of discrete time transformation noise which is generally non-Gaussian and correlated and is generated by passing another noise process through an invertible memoryless nonlinearity. In general, any discrete time noise process can be considered as transformation noise. The dependency structure of the transformation noise is found to be describ...

With the semiconductor industry evolving into the deep sub-micron (DSM) era, crosstalk noise becomes a critical issue that needs to be handled efficiently and accurately. Modern designs like System-on-Chips have millions of noise-prone wires that need to be analyzed. Analysis using circuit-level simulation is not feasible. Efficient static noise analysis, which statically estimate noise based o...

Autoregressive (AR) modeling is widely used in signal processing. The coefficients of an AR model can be easily obtained with a least mean square (LMS) prediction error filter. However, it is known that this filter gives a biased solution when the input signal is corrupted by white Gaussian noise. Treichler suggested the -LMS algorithm to remedy this problem and proved that the mean weight vect...

This research is motivated by the need for physical models for the jet noise source to be used in practical noise prediction schemes for propulsion-airframe integration concepts. The emphasis is on jets with high-subsonic velocities typical of modern turbofan engines. The basis for the source model is an amplitude-modulated traveling wave the wavepacket. Its parameters are determined by minimiz...

This paper presents a methodology to determine all-digital phase-locked loop (ADPLL) circuit variables based on required design specifications, including output phase noise, fractional spur and locking time. An analytical model is developed to characterize the effects of different noise sources on ADPLL output phase noise and fractional spur. Applying the proposed noise model, circuit variables...

This paper provides a new framework for analyzing white noise disturbances in linear systems: rather than the usual stochastic approach, noise signals are described as elements in sets and their effect is analyzed from a worst-case perspective. The paper studies how these sets must be chosen in order to have adequate properties for system response in the worst-case, statistics consistent with t...

Noise is one of the environmental pollutant that is encountered in daily life. Noise pollution has become a major concern of communities living in the vicinity of major highway corridors. In view of the rapid development it is essential to study highway noise with respect to various causative factors. In the present paper a road traffic noise prediction model for Indian conditions is developed ...

Latent representation learned from multi-layered neural networks via hierarchical feature abstraction enables recent success of deep learning. Under the deep learning framework, generalization performance highly depends on the learned latent representation which is obtained from an appropriate training scenario with a taskspecific objective on a designed network model. In this work, we propose ...

We present three models of audiovisual speech perception at varying signal-to-noise ratios (SNR). The first model is Massaro’s Fuzzy Logical Model of Perception (FLMP) applied at each SNR. The second model imposes the constraint that the visual response probabilities are the same regardless of the SNR. Both models describe the data well. Root Mean Squared Error (RMSE) corrected for the numbers ...

Noise can have negative impact on health. Hearing damage, annoyance, sleep disturbance, high blood pressure, poor cardiovascular health is all linked to community noise. Children, people with existing physical and mental illness and elderly people are most susceptible to community noise. High level of noise from sources such as busy traffic can adversely affect the health of the people living n...