Investigation and Mitigation of Noise Contributions in a Compact Heterodyne Interferometer

We present a noise estimation and subtraction algorithm capable of increasing the sensitivity of heterodyne laser interferometers by one order of magnitude. The heterodyne interferometer is specially designed for dynamic measurements of a test mass in the application of sub-Hz inertial sensing. A noise floor of 3.31×10−11m/Hz at 100 mHz is achieved after applying our noise subtraction algorithm to a benchtop prototype interferometer that showed a noise level of 2.76×10−10m/Hz at 100 mHz when tested in vacuum at levels of 3×10−5 Torr. Based on the previous results, we investigated noise estimation and subtraction techniques of non-linear optical pathlength noise, laser frequency noise, and temperature fluctuations in heterodyne laser interferometers. For each noise source, we identified its contribution and removed it from the measurement by linear fitting or a spectral analysis algorithm. The noise correction algorithm we present in this article can be generally applied to heterodyne laser interferometers.

Ferry M/V Kramer noise mitigation

The Motor Vessel (M/V) Edward V. Kramer is an aluminum vessel that operates as a small passenger ferry, which is owned and operated by the Department of Homeland Security (DHS) and used to transport DHS personnel and materials to Plum Island, NY. It was placed in service in 2018 and right from the start the sound levels inside the Main Deck compartment were found to be excessive. The original vessel specification included a noise limit of 75 dBA in the Main Deck Passenger Lounge and measured levels were as high as 87 dBA. A ship survey of sound and vibration was performed. Noise predictions to determine the controlling sound paths was also performed based on engine sound and vibration source levels. Recommendations for mitigation were presented and carried out by another shipyard. Mitigation included vibration isolation of the main engines and sound attenuation improvements to the Main Deck Passenger Lounge. After completion of the modifications, another survey was performed in 2021 and results show a reduction by as much as 11 dB in the Main Deck Passenger lounge. Noise estimation methods and details on the noise control treatments are given in the paper.

Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency.