1,720,964 research outputs found
Performance enhancements in next generation wireless networks using network coding : a case study in WiMAX
Get PDFThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 125-130).In this thesis, we design and implement a network-coding-enhanced network architecture for next generation wireless networks. The architecture applies intra-session random linear network coding as a packet erasure code below the IP layer. Using WiMAX as a case study, a series of point-to-point single-interface experiments are conducted to compare the performance of the architecture to that of HARQ and ARQ mechanisms. The performance measures are packet loss percentage, throughput and file transfer delay. The experiments use the Global Environment for Network Innovations (GENI) WiMAX platforms. UDP traffic considered; Iperf and UDP based File Transfer Protocol (UFTP) are used as measurement applications. The proposed architecture substantially decreases packet loss percentage from around 11-32% to nearly 0%. Compared to HARQ and ARQ mechanisms, the architecture can offer up to 5.9 times gain in throughput and 5.5 times reduction in end-to-end fi le transfer delay.by Surat Teerapittayanon.M.Eng
Recommended from our members
Intelligence Distribution Network
No full textThe applications of deep neural networks (DNN) have grown in number in recent years, but there is a need to better support their deployment in the field. This dissertation introduces Intelligence Distribution Network (IDN), a platform specifically designed to support this increasing demand for DNN inference computations. IDN is a fault-tolerant decentralized peer-to-peer (p2p) network that delivers fast, low-cost, scalable execution of DNN models. IDN does this by optimizing DNN inference for this setting, and then distributing the computation among spare computing resources nearby.
IDN reduces inference time using BranchyNet, a technique to modify the standard DNN structure with ''exit branches'' at certain locations throughout the network, allowing some samples to exit the computation early and decreasing the inference time.
IDN scales inference to the entire IDN p2p network using Distributed Deep Neural Network (DDNN), a method that allows DNN inference to be distributed among a computing hierarchy consisting of devices, edges, and clouds.
IDN achieves fault tolerance and high availability for DNN inference with ParallelNet. ParallelNet can generate multiple DNN model of various sizes, each fitting a device of varying compute capacity. Each device can execute its model independently in parallel. ParallelNet allows DNN inference to be run on more devices independently, improving the fault tolerance and availability of DNN inference.
IDN incentivizes the creation of a large number of high quality DNN models that can be shared among users using DaiMoN, a Decentralized Artificial Intelligence Model Network. DaiMoN improves upon today's limited, redundant, and siloed DNN model sharing structures by incentivizing peers to collaborate and share DNN models to improve the accuracy of a given problem in a decentralized manner.
Finally, to manage how IDN is used, the ComputeSwap protocol incentivizes peers to participate in the network via an optimistic debt-repayment structure that probabilistically results in repayment based on credits earned for executing inference and debts acquired by using the network. This encourages peers to service inference computations that are needed by other peers by maintaining a balance to not accumulate too much debt or give too little credit.Engineering and Applied Sciences - Computer Scienc
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Recommended from our members
Embedded Binarized Neural Networks
No full textWe study embedded Binarized Neural Networks (eBNNs) with the aim of allowing current binarized neural networks (BNNs) in the literature to perform feedforward inference efficiently on small embedded devices. We focus on minimizing the required memory footprint, given that these devices often have memory as small as tens of kilobytes (KB). Beyond minimizing the memory required to store weights, as in a BNN, we show that it is essential to minimize the memory used for temporaries which hold intermediate results between layers in feedforward inference. To accomplish this, eBNN reorders the computation of inference while preserving the original BNN structure, and uses just a single floating-point temporary for the entire neural network. All intermediate results from a layer are stored as binary values, as opposed to floating-points used in current BNN implementations, leading to a 32x reduction in required temporary space. We provide empirical evidence that our proposed eBNN approach allows efficient inference (10s of ms) on devices with severely limited memory (10s of KB). For example, eBNN achieves 95\% accuracy on the MNIST dataset running on an Intel Curie with only 15 KB of usable memory with an inference runtime of under 50 ms per sample. To ease the development of applications in embedded contexts, we make our source code available that allows users to train and discover eBNN models for a learning task at hand, which fit within the memory constraint of the target device.Engineering and Applied SciencesAccepted Manuscrip
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Recommended from our members
Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices
No full textWe propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices. When supported by a scalable distributed computing hierarchy, a DDNN can scale up in neural network size and scale out in geographical span. Due to its distributed nature, DDNNs enhance sensor fusion, system fault tolerance and data privacy for DNN applications. In implementing a DDNN, we map sections of a DNN onto a distributed computing hierarchy. By jointly training these sections, we minimize communication and resource usage for devices and maximize usefulness of extracted features which are utilized in the cloud. The resulting system has built-in support for automatic sensor fusion and fault tolerance. As a proof of concept, we show a DDNN can exploit geographical diversity of sensors to improve object recognition accuracy and reduce communication cost. In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.Engineering and Applied SciencesAccepted Manuscrip
- …
