Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.

Noise-Robust Texture Description Using Local Contrast Patterns via Global Measures

Abstract: This letter presents a noise-robust descriptor by exploring a set of local contrast patterns (LCPs) via global measures for texture classification. To handle image noise, the directed and undirected difference masks are designed to calculate three types of local intensity contrasts: directed, undirected, and maximum difference responses. To describe pixel-wise features, these responses are separately quantized and encoded into specific patterns based on different global measures. These resulting patterns (i.e., LCPs) are jointly encoded to form our final texture representation. Experiments are conducted on the well-known Outex and CUReT databases in the presence of high levels of noise. Compared to many state-of-the-art methods, the proposed descriptor achieves superior texture classification performance while enjoying a compact feature representation.

Adaptive LADRC-Based Disturbance Rejection Method for Electromechanical Servo System

Abstract: Electromechanical actuator (EMA) exhibits advanced performance in industry, but its dynamic servo responses are constrained by parametric perturbations, load torque variations, and measurement noise. A strong disturbance rejection ability is necessary for EMAs. However, this usually makes them more sensitive to the measurement noise, reducing the steady-state precision. In this article, an adaptive linear active disturbance rejection control (LADRC) controller is proposed to achieve strong antidisturbance performance and reduce noise sensitivity for EMAs. A novel parallel structure is proposed to improve dynamic responses, which replaces the traditional cascade structure of position and speed loops. Aiming to improve the antidisturbance performance, a linear full-order-extended state observer is integrated with the parallel controller, called the LADRC controller. To reduce the difficulty of parameter tuning, the number of tuning parameters of LADRC is reduced to two by a pole placement design. And these two parameters of LADRC can be adjusted adaptively by the hyperbolic tangent function. Finally, the simulation and experimental results are provided to verify the effectiveness of the proposed strategy for EMAs.

The influence of amplifier, interface and biological noise on signal quality in high-resolution EEG recordings

Abstract: First, the intrinsic random noise sources of a biopotential measurement in general are reviewed. For the special case of an electroencephalographic (EEG) measurement we have extended the commonly used amplifier noise model by biological generated background noise. As the strongest of all noise sources involved will dominate the resulting signal to noise ratio (S/N), we have investigated under which conditions this will be the case. We illustrate experimentally that up to 100 Hz S/N practically depends only on cortical generated background noise, while at a few hundred Hz or more amplifier and thermal noise of interelectrode resistance are the major sources.

The ghost in the machine: observing the effects of kernel operation on parallel application performance

Abstract: The performance of a parallel application on a scalable HPC system is determined by user-level execution of the application code and system-level (OS kernel) operations. To understand the influences of system-level factors on application performance, the measurement of OS kernel activities is key. We describe a technology to observe kernel actions and make this information available to application-level performance measurement tools. The benefits of merged application and OS performance information and its use in parallel performance analysis are demonstrated, both for profiling and tracing methodologies. In particular, we focus on the problem of kernel noise assessment as a stress test of the approach. We show new results for characterizing noise and introduce new techniques for evaluating noise interference and its effects on application execution. Our kernel measurement and noise analysis technologies are being developed as part of Linux OS environments for scalable parallel systems.