A low phase noise microwave frequency synthesizer based on parameters optimized NLTL for Cs fountain clock

We report on the development and phase noise performance of a 9.1926 GHz microwave frequency synthesizer to be used as the local oscillator for a Cs fountain clock. It is based on frequency multiplication and synthesis from an ultralow phase noise 5 MHz Oven Controlled Crystal Oscillator (OCXO) and 100 MHz Voltage Controlled Crystal Oscillator (VCXO).The key component of the frequency multiplication is a non-linear transmission-line (NLTL) used as a frequency comb generator. The phase noise of the synthesizer is improved by carefully optimizing the input power, the input and output impedances of the NLTL. The absolute phase noises of the 9.1926 GHz output signal are measured to be -64 dBc/Hz, -83 dBc/Hz, -92 dBc/Hz, -117 dBc/Hz and -119 dBc/Hz at 1 Hz, 10Hz, 100Hz, 1 kHz and 10 kHz offset frequencies, respectively. The residual phase noise of the synthesizer is measured to be -82 dBc/Hz at 1 Hz offset frequency. The measurement result shows that the absolute phase noise at the frequency range of 1 - 100 Hz is mainly limited by the phase noise of the OCXO. The contribution of the absolute phase noise to the fountain clock short-term frequency stability is calculated to be 7.0x10^(-14). The residual frequency stability of the synthesizer is measured to be1.5x10^(-14), which is consistent with the calculated frequency stability due to the residual phase noise of the synthesizer. Meanwhile we designed and realized an interferometric microwave switch in the synthesizer to eliminate the frequency shifts induced by the microwave leakage. The extinction ratio of the switch is measured to be more than 50 dB. In the scheme, we use only commercially available components to build the microwave frequency synthesizer with excellent phase noise performance for high-performance Cs fountain clocks

Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices

Along with the benefits of Internet of Things (IoT) come potential privacy risks, since billions of the connected devices are granted permission to track information about their users and communicate it to other parties over the Internet. Of particular interest to the adversary is the user identity which constantly plays an important role in launching attacks. While the exposure of a certain type of physical biometrics or device identity is extensively studied, the compound effect of leakage from both sides remains unknown in multi-modal sensing environments. In this work, we explore the feasibility of the compound identity leakage across cyber-physical spaces and unveil that co-located smart device IDs (e.g., smartphone MAC addresses) and physical biometrics (e.g., facial/vocal samples) are side channels to each other. It is demonstrated that our method is robust to various observation noise in the wild and an attacker can comprehensively profile victims in multi-dimension with nearly zero analysis effort. Two real-world experiments on different biometrics and device IDs show that the presented approach can compromise more than 70\% of device IDs and harvests multiple biometric clusters with ~94% purity at the same time

Synthesis of tributyl citrate using SO42-/Zr-MCM-41 as catalyst

Zirconium-containing mesoporous molecular sieve SO42-/Zr-MCM-41 was synthesized for catalyst in synthesis of tributyl citrate. The structure was characterized by XRD, N2 Ad/De isotherms and FT-IR. The results indicated that the solid acids show good catalytic performance and are reusable. Under optimum conditions and using SO42-/Zr-MCM-41 as catalyst, the conversion of citric acid was 95%. After easy separation of the products from the solid acid catalyst, it could be reused three times and gave a conversion of citric acid not less than 92%. The structure of tributyl citrate was characterized by FT-IR and 1H-NMR.KEY WORDS: Mesoporous molecular sieve, Tributyl citrate, Synthesis Bull. Chem. Soc. Ethiop. 2011, 25(1), 147-150

Autonomous Learning of Speaker Identity and WiFi Geofence From Noisy Sensor Data

A fundamental building block towards intelligent environments is the ability to understand who is present in a certain area. A ubiquitous way of detecting this is to exploit unique vocal characteristics as people interact with one another in common spaces. However, manually enrolling users into a biometric database is time-consuming and not robust to vocal deviations over time. Instead, consider audio features sampled during a meeting, yielding a noisy set of possible voiceprints. With a number of meetings and knowledge of participation, e.g., sniffed wireless Media Access Control (MAC) addresses, can we learn to associate a specific identity with a particular voiceprint? To address this problem, this paper advocates an Internet of Things (IoT) solution and proposes to use co-located WiFi as supervisory weak labels to automatically bootstrap the labelling process. In particular, a novel cross-modality labelling algorithm is proposed that jointly optimises the clustering and association process, which solves the inherent mismatching issues arising from heterogeneous sensor data. At the same time, we further propose to reuse the labelled data to iteratively update wireless geofence models and curate device specific thresholds. Extensive experimental results from two different scenarios demonstrate that our proposed method is able to achieve 2-fold improvement in labelling compared with conventional methods and can achieve reliable speaker recognition in the wild

Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering

Neural rendering methods have significantly advanced photo-realistic 3D scene rendering in various academic and industrial applications. The recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed combining the benefits of both primitive-based representations and volumetric representations. However, it often leads to heavily redundant Gaussians that try to fit every training view, neglecting the underlying scene geometry. Consequently, the resulting model becomes less robust to significant view changes, texture-less area and lighting effects. We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians, and predicts their attributes on-the-fly based on viewing direction and distance within the view frustum. Anchor growing and pruning strategies are developed based on the importance of neural Gaussians to reliably improve the scene coverage. We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering. We also demonstrates an enhanced capability to accommodate scenes with varying levels-of-detail and view-dependent observations, without sacrificing the rendering speed.Comment: Project page: https://city-super.github.io/scaffold-gs

Second generation Dirac cones and inversion symmetry breaking induced gaps in graphene/hexagonal boron nitride

Graphene/h-BN has emerged as a model van der Waals heterostructure, and the band structure engineering by the superlattice potential has led to various novel quantum phenomena including the self-similar Hofstadter butterfly states. Although newly generated second generation Dirac cones (SDCs) are believed to be crucial for understanding such intriguing phenomena, so far fundamental knowledge of SDCs in such heterostructure, e.g. locations and dispersion of SDCs, the effect of inversion symmetry breaking on the gap opening, still remains highly debated due to the lack of direct experimental results. Here we report first direct experimental results on the dispersion of SDCs in 0$^\circ$ aligned graphene/h-BN heterostructure using angle-resolved photoemission spectroscopy. Our data reveal unambiguously SDCs at the corners of the superlattice Brillouin zone, and at only one of the two superlattice valleys. Moreover, gaps of $\approx$ 100 meV and $\approx$ 160 meV are observed at the SDCs and the original graphene Dirac cone respectively. Our work highlights the important role of a strong inversion symmetry breaking perturbation potential in the physics of graphene/h-BN, and fills critical knowledge gaps in the band structure engineering of Dirac fermions by a superlattice potential.Comment: Nature Physics 2016, In press, Supplementary Information include

Dephasing of Strong-Field-Driven Floquet States Revealed by Time- and Spectrum-Resolved Quantum-Path Interferometry

Floquet engineering, while a powerful tool for ultrafast quantum-state manipulation, faces challenges under strong-field conditions, as recent high harmonic generation studies unveil exceptionally short dephasing times. In this study, using time- and spectrum-resolved quantum-path interferometry, we investigate the dephasing mechanisms of terahertz-driven excitons. Our results reveal a dramatic increase in exciton dephasing rate beyond a threshold field strength, indicating exciton dissociation as the primary dephasing mechanism. Importantly, we demonstrate long dephasing times of strong-field-dressed excitons, supporting coherent strong-field manipulation of quantum materials