ADAPTIVE CABLE BANDWIDTH OPTIMIZATION WITH DEEP CONVOLUTION NEURAL NETWORK

Techniques are described herein to determine the optimal modulation of a channel in real time. Cable bandwidth usage is improved using a state-of-art deep convolutional neural network

Ultrastable embedded surface plasmon confocal interferometry

As disease diagnosis becomes more sophisticated, there is a requirement to measure small numbers of molecules attached to, for instance, an antibody. This requires a sensor capable not only of high sensitivity but also the ability to make measurements over a highly localized region. In previous publications, we have shown how a modified confocal microscope allows one to make localized surface plasmon (SP) measurements on a scale far smaller than the surface plasmon propagation distance. The present implementation presents a new ultrastable interferometer system, which greatly improves the noise performance. Hitherto, we have used the central part of the back focal plane to form a reference beam with the reradiated surface plasmons. In the current system, we block the central part and use the spatial light modulator to deflect s-polarized light into the pinhole to form an interference signal with the surface plasmons, thus creating an ultrastable interferometer formed with two beams incident at very similar angles. We demonstrate the superior noise performance of the system in hostile environments and examine further adaptations of the system to further enhance noise performance

Novelty detection based on extensions of GMMs for industrial gas turbines

The paper applies the application of Gaussian mixture models (GMMs) for operational pattern discrimination and novelty/fault detection for an industrial gas turbine (IGT). Variational Bayesian GMM (VBGMM) is used to automatically cluster operational data into steady-state and transient responses, where extraction of steady-state data is an important pre-processing scenario for fault detection. Important features are extracted from steady-state data, which are then fingerprinted to show any anomalies of patterns which may be due to machine faults. Field data measurements from vibration sensors are used to show that the extensions of GMMs provide a useful tool for machine condition monitoring, fault detection and diagnostics in the field. Through the use of experimental trials on IGTs, it is shown that GMM is particularly useful for the detection of emerging faults especially where there is a lack of knowledge of machine fault patterns

Detection of emerging faults on industrial gas turbines using extended Gaussian mixture models

The paper extends traditional Gaussian Mixture Model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial or complex systems. A Variational Bayesian (VB) method allows a GMM to cluster with its Mixture Components (MCs) to facilitate the extraction of steady-state operational behaviour — this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques which can initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an Outlier Component (GMMOC) is discussed and applied for direct fault detection. To demonstrate the efficacy of the proposed techniques, real-time measurements from operational Industrial Gas Turbines (IGTs) show that the resulting VBGMM facilitates the selection of the number of required MCs to cluster the data, and thereby provide essential input for operational signature recognition. Moreover, GMMOC is shown to facilitate the early detection of emerging faults. An advantage of the VBGMM over traditional pre-defined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMMOC method is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results based on measurements taken from IGTs operating in the field are therefore also included which show that the techniques provide an integrated pre-processing, benchmarking and novelty/fault detection methodology

Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric Views

Automatic perception of human behaviors during social interactions is crucial for AR/VR applications, and an essential component is estimation of plausible 3D human pose and shape of our social partners from the egocentric view. One of the biggest challenges of this task is severe body truncation due to close social distances in egocentric scenarios, which brings large pose ambiguities for unseen body parts. To tackle this challenge, we propose a novel scene-conditioned diffusion method to model the body pose distribution. Conditioned on the 3D scene geometry, the diffusion model generates bodies in plausible human-scene interactions, with the sampling guided by a physics-based collision score to further resolve human-scene inter-penetrations. The classifier-free training enables flexible sampling with different conditions and enhanced diversity. A visibility-aware graph convolution model guided by per-joint visibility serves as the diffusion denoiser to incorporate inter-joint dependencies and per-body-part control. Extensive evaluations show that our method generates bodies in plausible interactions with 3D scenes, achieving both superior accuracy for visible joints and diversity for invisible body parts. The code is available at https://sanweiliti.github.io/egohmr/egohmr.html.Comment: Camera ready version for ICCV 2023, appendix include

Rapid prototyping of waveguide and horn antennas

In this paper we review how fused deposition modelling (FDM) can be deployed for the rapid prototyping of microwave waveguide componentry and antennas. Additive manufacture of such objects allows new, novel and complex structures to be fabricated with lower impact on the environment relative to current manufacturing processes, plus the fast turnaround of design to manufacture and test. Additionally while the resulting physical antenna properties may not be perfect compared to the design or what can be machined, their RF/microwave performance can be quite forgiving thereby allowing the antenna design engineer to fully exploit the rapid prototyping concept

Molecular Analysis of Spring Viraemia of Carp Virus in China: A Fatal Aquatic Viral Disease that Might Spread in East Asian

Spring viraemia of carp (SVC) is a fatal viral disease for cyprinid fish, which is caused by spring viraemia of carp virus (SVCV). To date, no SVC outbreak has been reported in China. Between 1998 and 2002, outbreaks of SVC were reported in ornamental and wild fish in Europe and America, imported from multiple sources including China. Based on phylogenetic analysis, the viral strain isolated from America was shown to be originated from Asia. These outbreaks not only resulted in huge economic losses, but also raise an interesting question as to whether SVCV really exists in China and if so, is it responsible for SVC outbreaks? From 2002 to 2006, we screened 6700 samples from ornamental fish farms using the cell culture method of the Office International des Epizooties (OIE), and further verified the presence of SVCV by ELISA and real-time quantitative RT-PCR. Two infected samples were found and the complete genome of SVCV was sequenced from one of the isolates, termed SVCV-C1. Several unique hallmarks of SVCV-C1 were identified, including six amino acid (KSLANA) insertion in the viral RNA-dependent RNA polymerase (L) protein and ten nucleotide insertion in the region between glycoprotein (G) and L genes in European SVCV strains. Phylogenetic tree analysis of the full-length G protein of selected SVCV isolates from the United Kingdom and United States revealed that G proteins could be classified into Ia and Id sub genogroups. The Ia sub genogroup can be further divided into newly defined sub genogroups Ia-A and Ia-B. The isolates derived from the United States and China including the SVCV-C1 belongs to in the Ia-A sub genogroup. The SVCV-C1 G protein shares more than 99% homology with the G proteins of the SVCV strains from England and the United States, making it difficult to compare their pathogenicity. Comparison of the predicted three-dimensional structure based on the published G protein sequences from five SVCV strains revealed that the main differences were in the loops of the pleckstrin homology domains. Since SVCV is highly pathogenic, we speculate that SVC may therefore pose a serious threat to farmed cyprinid fish in China

Additively manufactured profiled conical horn antenna with dielectric loading

The world's first additively manufactured dielectric loaded profiled conical horn antenna is presented in this letter. With a smooth profiled flare and two loaded dielectric core materials, this horn offers symmetrical patterns, wideband gain, low sidelobe level, and low cross polarization. Additive manufacturing, including electroplating, has been employed to address the fabrication challenges. The measurement results show that the fabrication process produces a horn antenna with reduced mass and volume (<;200 g with three-dimensional-printed flange) and high antenna performance with realized gain 16-20 dBi, sidelobe level -22 to -19 dB across the frequency range from 9 to 15 GHz