Antenna Array Pattern Synthesis via Coordinate Descent Method

This paper presents an array pattern synthesis algorithm for arbitrary arrays based on coordinate descent method (CDM). With this algorithm, the complex element weights are found to minimize a weighted L2 norm of the difference between desired and achieved pattern. Compared with traditional optimization techniques, CDM is easy to implement and efficient to reach the optimum solutions. Main advantage is the flexibility. CDM is suitable for linear and planar array with arbitrary array elements on arbitrary positions. With this method, we can configure arbitrary beam pattern, which gives it the ability to solve variety of beam forming problem, e.g. focused beam, shaped beam, nulls at arbitrary direction and with arbitrary beam width. CDM is applicable for phase-only and amplitude-only arrays as well, and furthermore, it is a suitable method to treat the problem of array with element failures

Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation

Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods. However, the objective of unsupervised learning is likely to be unreliable in challenging scenes. In this work, we present a framework to use more reliable supervision from transformations. It simply twists the general unsupervised learning pipeline by running another forward pass with transformed data from augmentation, along with using transformed predictions of original data as the self-supervision signal. Besides, we further introduce a lightweight network with multiple frames by a highly-shared flow decoder. Our method consistently gets a leap of performance on several benchmarks with the best accuracy among deep unsupervised methods. Also, our method achieves competitive results to recent fully supervised methods while with much fewer parameters.Comment: Accepted to CVPR 2020, https://github.com/lliuz/ARFlo

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Inactivation of Salmonella Enteritidis in liquid eggs using bacteriophage cocktails

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.Land and Food Systems, Faculty ofGraduat

Underwater inertial error rectification with limited acoustic observations

Abstract Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable. The acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration capability. However, the traditional Acoustic Positioning Systems (APS) are expensive and incapable of positioning with limited acoustic observations. Two novel underwater inertial error rectification algorithms with limited acoustic observations are proposed. The first one is the single acoustic-beacon Range-only Matching Aided Navigation (RMAN) method, which is inspired by matching navigation without reference maps and presented for the first time. The second is the improved single acoustic-beacon Virtual Long Baseline (VLBL) method, which considers the impact of indicated relative position increments on virtual beacon reconstruction. Both RMAN and improved VLBL are further developed when multi acoustic-beacons are available, named mAB-RMAN and mAB-VLBL. The comprehensive simulations and field investigations were conducted. The results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline, specifically, the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90% and 98% when using single and double acoustic-beacons, respectively. These proposed techniques will provide new perspectives for underwater positioning, navigation, and timing