Enabling Parallel Wireless Communication in Mobile Robot Teams

Wireless inter-robot communication enables robot teams to cooperatively solve complex problems that cannot be addressed by a single robot. Applications for cooperative robot teams include search and rescue, exploration and surveillance. Communication is one of the most important components in future autonomous robot systems and is essential for core functions such as inter-robot coordination, neighbour discovery and cooperative control algorithms. In environments where communication infrastructure does not exist, decentralised multi-hop networks can be constructed using only the radios on-board each robot. These are known as wireless mesh networks (WMNs). However existing WMNs have limited capacity to support even small robot teams. There is a need for WMNs where links act like dedicated point-to-point connections such as in wired networks. Addressing this problem requires a fundamentally new approach to WMN construction and this thesis is the first comprehensive study in the multi-robot literature to address these challenges. In this thesis, we propose a new class of communication systems called zero mutual interference (ZMI) networks that are able to emulate the point-to-point properties of a wired network over a WMN implementation. We instantiate the ZMI network using a multi-radio multi-channel architecture that autonomously adapts its topology and channel allocations such that all network edges communicate at the full capacity of the radio hardware. We implement the ZMI network on a 100-radio testbed with up to 20-individual nodes and verify its theoretical properties. Mobile robot experiments also demonstrate these properties are practically achievable. The results are an encouraging indication that the ZMI network approach can facilitate the communication demands of large cooperative robot teams deployed in practical problems such as data pipe-lining, decentralised optimisation, decentralised data fusion and sensor networks

Lumina Foundation for Education: Can A Champion for College Attainment Up Its Game?

Established in 2000, Lumina Foundation for Education's mission is defined by a specific goal: to increase the proportion of Americans with college degrees, certificates and credentials to 60 percent by 2025. Overall, NCRP's review revealed a highly focused, effective foundation with savvy policy advocacy strategies, staff that are well respected and initiatives that are progressing ahead of schedule. However, in addition to investing in policy, Lumina should invest more in the community organizations whose support and input are critical to achieving success

Cloud computing adoption framework:A security framework for business clouds

This paper presents a Cloud Computing Adoption Framework (CCAF) security suitable for business clouds. CCAF multi-layered security is based on the development and integration of three major security technologies: firewall, identity management and encryption based on the development of Enterprise File Sync and Share technologies. This paper presents our motivation, related work and our views on security framework. Core technologies have been explained in details and experiments were designed to demonstrate the robustness of the CCAF multi-layered security. In penetration testing, CCAF multi-layered security could detect and block 99.95% viruses and trojans and could maintain 85% and above of blocking for 100 hours of continuous attacks. Detection and blocking took less than 0.012 second per trojan and viruses. A full CCAF multi-layered security protection could block all SQL injection providing real protection to data. CCAF multi-layered security had 100% rate of not reporting false alarm. All F-measures for CCAF test results were 99.75% and above. How CCAF multi-layered security can blend with policy, real services and blend with business activities have been illustrated. Research contributions have been justified and CCAF multi-layered security can offer added value for volume, velocity and veracity for Big Data services operated in the Cloud

Inward FDI in Taiwan and its policy context

Taiwan has long maintained an explicit policy of attracting inward foreign direct investment (IFDI) as part of its growth strategy, although inflows have been subject to various restrictions. The primary objective of Taiwan's stance toward FDI was initially to attract export-oriented investment based upon the competitiveness of its highly educated and productive labor force. More recently, this objective has been modified to focus on attracting FDI into increasingly technology-intensive areas and to encourage or promote domestic technological spillovers. In recent years, although Taiwan's IFDI stock has more than tripled, from US$20 billion in 2000 to US$ 64 billion in 2010, it remains a relatively small recipient compared with its neighboring economies in the Asia-Pacific region. Annual IFDI flows to Taiwan have been in single-digit US$billion during 2000-2010, with a peak of US$ 7 billion in 2007 followed by a steady decline during 2008-2010. In 2010, Taiwan received inward FDI of US$ 2 billion only. It is, however, generally seen that the release of prohibition against mainland China investors on June 30, 2009, and the newly signed landmark cross-strait Economic Cooperation Framework Agreement (ECFA) on June 30, 2010 will reinforce Taiwan's robust investment climate and stimulate IFDI

Qubit-oscillator concatenated codes: decoding formalism & code comparison

Concatenating bosonic error-correcting codes with qubit codes can substantially boost the error-correcting power of the original qubit codes. It is not clear how to concatenate optimally, given there are several bosonic codes and concatenation schemes to choose from, including the recently discovered GKP-stabilizer codes [arXiv:1903.12615] that allow protection of a logical bosonic mode from fluctuations of the mode's conjugate variables. We develop efficient maximum-likelihood decoders for and analyze the performance of three different concatenations of codes taken from the following set: qubit stabilizer codes, analog/Gaussian stabilizer codes, GKP codes, and GKP-stabilizer codes. We benchmark decoder performance against additive Gaussian white noise, corroborating our numerics with analytical calculations. We observe that the concatenation involving GKP-stabilizer codes outperforms the more conventional concatenation of a qubit stabilizer code with a GKP code in some cases. We also propose a GKP-stabilizer code that suppresses fluctuations in both conjugate variables and that can be initialized using only controlled-SUM and Hadamard gates, and formulate qudit versions of GKP-stabilizer codes.Comment: 17 pages, 5 figure

Secretory Leukoprotease Inhibitor: A Native Antimicrobial Protein in the Innate Immune Response in a Rat Model of S. aureus Keratitis

Purpose. To describe the presence of secretory leukocyte protease inhibitor (SLPI), a cationic peptide with antimicrobial and antiprotease activity in the innate immune reaction in a rat model of Staphylococcus aureus keratitis. Methods. Forty female Lewis rats were divided into 2 groups: the infectious keratitis and the epithelial defect groups. Eyes were processed for immunohistochemical studies for SLPI, interleukin-1, interleukin-6, tumor necrosis factor-alpha, and matrix metalloproteinase-8. Results. Immunohistochemical studies confirmed high levels of SLPI, IL-1, IL-6, TNF-α, and MMP-8 expression in eyes with S. aureus keratitis and with epithelial defects, in contrast to undetectable SLPI expression in the normal control corneas. Conclusions. To our knowledge, this paper is the first to demonstrate the presence of SLPI with increased amounts of proinflammatory cytokines in inflamed and infected corneas

COVID-19 and Ophthalmology: A New Chapter in an Old Story

Editorial, No Abstrac

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture