Realizing an AD+ Model as a Derived Model of a Premouse

Ph.DDOCTOR OF PHILOSOPH

A Hollow Coaxial Cable Fabry-Perot Resonator for Liquid Dielectric Constant Measurement

We report, for the first time, a low-cost and robust homemade hollow coaxial cable Fabry-Pérot resonator (HCC-FPR) for measuring liquid dielectric constant. In the HCC design, the traditional dielectric insulating layer is replaced by air. A metal disk is welded onto the end of the HCC serving as a highly reflective reflector, and an open cavity is engineered on the HCC. After the open cavity is filled with the liquid analyte (e.g., water), the air-liquid interface acts as a highly reflective reflector due to large impedance mismatch. As a result, an HCC-FPR is formed by the two highly reflective reflectors, i.e., the air-liquid interface and the metal disk. We measured the room temperature dielectric constant for ethanol/water mixtures with different concentrations using this homemade HCC-FPR. Monitoring the evaporation of ethanol in ethanol/water mixtures was also conducted to demonstrate the ability of the sensor for continuously monitoring the change in dielectric constant. The results revealed that the HCC-FPR could be a promising evaporation rate detection platform with high performance. Due to its great advantages, such as high robustness, simple configuration, and ease of fabrication, the novel HCC-FPR based liquid dielectric constant sensor is believed to be of high interest in various fields

One-Dimensional Sensor Learns to Sense Three-Dimensional Space

A sensor system with ultra-high sensitivity, high resolution, rapid response time, and a high signal-to-noise ratio can produce raw data that is exceedingly rich in information, including signals that have the appearances of noise . The noise feature directly correlates to measurands in orthogonal dimensions, and are simply manifestations of the off-diagonal elements of 2nd-order tensors that describe the spatial anisotropy of matter in physical structures and spaces. The use of machine learning techniques to extract useful meanings from the rich information afforded by ultra-sensitive one-dimensional sensors may offer the potential for probing mundane events for novel embedded phenomena. Inspired by our very recent invention of ultra-sensitive optical-based inclinometers, this work aims to answer a transformative question for the first time: can a single-dimension point sensor with ultra-high sensitivity, fidelity, and signal-to-noise ratio identify an arbitrary mechanical impact event in three-dimensional space? This work is expected to inspire researchers in the fields of sensing and measurement to promote the development of a new generation of powerful sensors or sensor networks with expanded functionalities and enhanced intelligence, which may provide rich n-dimensional information, and subsequently, data-driven insights into significant problems

A Uniform Strain Transfer Scheme for Accurate Distributed Optical Fiber Strain Measurements in Civil Structures

We report a screw-like package design for an embeddable distributed optical fiber strain sensor for civil engineering applications. The screw-like structure is the exterior support for an optical fiber sensor. The bare optical fiber is embedded and secured in a longitudinal groove of the screw-like package using a rigid adhesive. Our packaging scheme prevents damage to the bare optical fiber and ensures that the packaged sensor is accurately and optimally sensing strain fields in civil structures. Moreover, our screw-like design has an equal area in a cross-section perpendicular to and along the screw axis, so strain field distributions are metered faithfully along the length of the embedded optical fiber. Our novel screw-like package optical fiber sensor, interfaced to a Rayleigh scattering-based optical frequency domain reflectometer system enables undistorted, accurate, robust, and spatially-distributed strain measurements in bridges, tunnels, pipelines, buildings, etc. along structural dimensions extending from centimeters to kilometers. Document type: Articl

An Embeddable Strain Sensor with 30 Nano-Strain Resolution based on Optical Interferometry

A cost-effective, robust and embeddable optical interferometric strain sensor with nanoscale strain resolution is presented in this paper. The sensor consists of an optical fiber, a quartz rod with one end coated with a thin gold layer, and two metal shells employed to transfer the strain and orient and protect the optical fiber and the quartz rod. The optical fiber endface, combining with the gold-coated surface, forms an extrinsic Fabry—Perot interferometer. The sensor was firstly calibrated, and the result showed that our prototype sensor could provide a measurement resolution of 30 nano-strain (nε) and a sensitivity of 10.01 µε/ µm over a range of 1000 µε. After calibration of the sensor, the shrinkage strain of a cubic brick of mortar in real time during the drying process was monitored. The strain sensor was compared with a commercial linear variable displacement transducer, and the comparison results in four weeks demonstrated that our sensor had much higher measurement resolution and gained more detailed and useful information. Due to the advantages of the extremely simple, robust and cost-effective configuration, it is believed that the sensor is significantly beneficial to practical applications, especially for structural health monitoring

Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs

Organizations are increasingly recognizing the value of data collaboration for data analytics purposes. Yet, stringent data protection laws prohibit the direct exchange of raw data. To facilitate data collaboration, federated Learning (FL) emerges as a viable solution, which enables multiple clients to collaboratively train a machine learning (ML) model under the supervision of a central server while ensuring the confidentiality of their raw data. However, existing studies have unveiled two main risks: (i) the potential for the server to infer sensitive information from the client's uploaded updates (i.e., model gradients), compromising client input privacy, and (ii) the risk of malicious clients uploading malformed updates to poison the FL model, compromising input integrity. Recent works utilize secure aggregation with zero-knowledge proofs (ZKP) to guarantee input privacy and integrity in FL. Nevertheless, they suffer from extremely low efficiency and, thus, are impractical for real deployment. In this paper, we propose a novel and highly efficient solution RiseFL for secure and verifiable data collaboration, ensuring input privacy and integrity simultaneously.Firstly, we devise a probabilistic integrity check method that significantly reduces the cost of ZKP generation and verification. Secondly, we design a hybrid commitment scheme to satisfy Byzantine robustness with improved performance. Thirdly, we theoretically prove the security guarantee of the proposed solution. Extensive experiments on synthetic and real-world datasets suggest that our solution is effective and is highly efficient in both client computation and communication. For instance, RiseFL is up to 28x, 53x and 164x faster than three state-of-the-art baselines ACORN, RoFL and EIFFeL for the client computation