Performance enhancements in next generation wireless networks using network coding : a case study in WiMAX

Thesis (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

StitchNet: Composing Neural Networks from Pre-Trained Fragments

We propose StitchNet, a novel neural network creation paradigm that stitches together fragments (one or more consecutive network layers) from multiple pre-trained neural networks. StitchNet allows the creation of high-performing neural networks without the large compute and data requirements needed under traditional model creation processes via backpropagation training. We leverage Centered Kernel Alignment (CKA) as a compatibility measure to efficiently guide the selection of these fragments in composing a network for a given task tailored to specific accuracy needs and computing resource constraints. We then show that these fragments can be stitched together to create neural networks with accuracy comparable to that of traditionally trained networks at a fraction of computing resource and data requirements. Finally, we explore a novel on-the-fly personalized model creation and inference application enabled by this new paradigm. The code is available at https://github.com/steerapi/stitchnet

Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction

Purpose: We implemented the blip-up, blip-down circular echo planar imaging (BUDA-cEPI) sequence with readout and phase partial Fourier to reduced off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is computationally intensive. Thus, we developed an ML-based reconstruction technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction. Methods: BUDA-cEPI RUN-UP - a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also incorporated into the reconstruction to effectively take advantage of the smooth phase prior, and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS. Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of off-resonance, partial Fourier, and residual aliasing artifacts. However, the reconstruction time is approximately 225 seconds per slice, which may not be practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5% normalized root mean square error detected, while the reconstruction time is approximately 3 seconds. Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ~88x when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application.Comment: Number: Figures: 8 Tables: 3 References: 7

Rapid prototyping of 3D DNA-origami shapes with caDNAno

DNA nanotechnology exploits the programmable specificity afforded by base-pairing to produce self-assembling macromolecular objects of custom shape. For building megadalton-scale DNA nanostructures, a long ‘scaffold’ strand can be employed to template the assembly of hundreds of oligonucleotide ‘staple’ strands into a planar antiparallel array of cross-linked helices. We recently adapted this ‘scaffolded DNA origami’ method to producing 3D shapes formed as pleated layers of double helices constrained to a honeycomb lattice. However, completing the required design steps can be cumbersome and time-consuming. Here we present caDNAno, an open-source software package with a graphical user interface that aids in the design of DNA sequences for folding 3D honeycomb-pleated shapes A series of rectangular-block motifs were designed, assembled, and analyzed to identify a well-behaved motif that could serve as a building block for future studies. The use of caDNAno significantly reduces the effort required to design 3D DNA-origami structures. The software is available at http://cadnano.org/, along with example designs and video tutorials demonstrating their construction. The source code is released under the MIT license

PNNU: parallel nearest-neighbor units for learned dictionaries

We present a novel parallel approach, parallel nearest neigh- bor unit (PNNU), for finding the nearest member in a learned dictionary of high-dimensional features. This is a computation fundamental to machine learning and data analytics algorithms such as sparse coding for feature extraction. PNNU achieves high performance by using three techniques: (1) PNNU employs a novel fast table look up scheme to identify a small number of atoms as candidates from which the nearest neighbor of a query data vector can be found; (2) PNNU reduces computation cost by working with candidate atoms of reduced dimensionality; and (3) PNNU performs computations in parallel over multiple cores with low inter-core communication overheads. Based on e cient computation via techniques (1) and (2), technique (3) attains further speed up via parallel processing. We have implemented PNNU on multi-core ma- chines. We demonstrate its superior performance on three application tasks in signal processing and computer vision. For an action recognition task, PNNU achieves 41x overall performance gains on a 16-core compute server against a conventional serial implementation of nearest neighbor computation. Our PNNU software is available online as open source.Funded by Naval Postgraduate SchoolIntel CorporationAgreement no. N00244-15-0050 (NPS