1,883 research outputs found
Disqualification of Opinion-Drafting Attorneys under Federal Circuit Standards for Willful Infringement
Article published in the Michigan State University School of Law Student Scholarship Collection
Development and integration of Honeywell’s One-Wireless network
The purpose of this project has been to develop upon the Honeywell One-Wireless network in the Murdoch University Pilot Plant and integrate it into the Distributed Control System. This will give future students exposure to developing process control schemes around industrial wireless technology in a small plant setting. Industrial Wireless is still on the cutting edge of technology and it will challenge the status quo in Industry with its many advantages. A brief review of Industrial wireless technology has been included in this thesis report to provide the reader a background to the communications technology. Also included is Honeywell’s One-Wireless Network solution which was used in this project. There, where significant challenges in getting the network operational, and as a result a systematic troubleshooting process was followed. Once the network was operational additional wireless instruments where added to expand the network and set up in the system. From here the One-Wireless network was integrated into the Distributive Control System which operates the pilot plant, this was done using Modbus TCP/IP. To determine the effectiveness of the network a post Radio Frequency assessment was carried out to determine the impact of the network and ensure that it was following best practices. Relevant documentation on the network was developed as a handover for future students to build upon the work carried out
Studying Innovation in Businesses: New Research Possibilities
The rapid pace of globalization and technological change has created demand for more and better analysis to answer key policy questions about the role of businesses in innovation. This demand was codified into law in the America COMPETES Act. However, existing business datasets are not adequate to create an empirically based foundation for policy decisions. This paper argues that the existing IRS data infrastructure could be used in a number of ways to respond to the national imperative. It describes the legal framework within which such a response could take place, and outlines the organizational features that would be required to establish an IRS/researcher partnership. It concludes with a discussion of the role for the research policy community.Business microdata, innovation, confidentiality, researcher access, tax policy
X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets
In this paper we propose cross-modal convolutional neural networks (X-CNNs),
a novel biologically inspired type of CNN architectures, treating gradient
descent-specialised CNNs as individual units of processing in a larger-scale
network topology, while allowing for unconstrained information flow and/or
weight sharing between analogous hidden layers of the network---thus
generalising the already well-established concept of neural network ensembles
(where information typically may flow only between the output layers of the
individual networks). The constituent networks are individually designed to
learn the output function on their own subset of the input data, after which
cross-connections between them are introduced after each pooling operation to
periodically allow for information exchange between them. This injection of
knowledge into a model (by prior partition of the input data through domain
knowledge or unsupervised methods) is expected to yield greatest returns in
sparse data environments, which are typically less suitable for training CNNs.
For evaluation purposes, we have compared a standard four-layer CNN as well as
a sophisticated FitNet4 architecture against their cross-modal variants on the
CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data
being removed, and find that at lower levels of data availability, the X-CNNs
significantly outperform their baselines (typically providing a 2--6% benefit,
depending on the dataset size and whether data augmentation is used), while
still maintaining an edge on all of the full dataset tests.Comment: To appear in the 7th IEEE Symposium Series on Computational
Intelligence (IEEE SSCI 2016), 8 pages, 6 figures. Minor revisions, in
response to reviewers' comment
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedented
leaps in diverse tasks spanning from speech and object recognition to context
awareness and health monitoring. As a result, an increasing number of
AI-enabled applications are being developed targeting ubiquitous and mobile
devices. While deep neural networks (DNNs) are getting bigger and more complex,
they also impose a heavy computational and energy burden on the host devices,
which has led to the integration of various specialized processors in commodity
devices. Given the broad range of competing DNN architectures and the
heterogeneity of the target hardware, there is an emerging need to understand
the compatibility between DNN-platform pairs and the expected performance
benefits on each platform. This work attempts to demystify this landscape by
systematically evaluating a collection of state-of-the-art DNNs on a wide
variety of commodity devices. In this respect, we identify potential
bottlenecks in each architecture and provide important guidelines that can
assist the community in the co-design of more efficient DNNs and accelerators.Comment: Accepted at MobiSys 2019: 3rd International Workshop on Embedded and
Mobile Deep Learning (EMDL), 201
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