Now showing items 1-20 of 57

    • Achieving High Throughput Ground-to-UAV Transport via Parallel Links 

      Lin, Chit-Kwan; Kung, H. T.; Lin, Tsung-Han; Tarsa, Stephen John; Vlah, Dario (IEEE, 2011)
      Wireless data transfer under high mobility, as found in unmanned aerial vehicle (UAV) applications, is a challenge due to varying channel quality and extended link outages. We present FlowCode, an easily deployable link-layer ...
    • Blind Signal Classification via Sparse Coding 

      Gwon, Youngjune Lee; Dastangoo, Siamak; Kung, H. T.; Fossa, Carl (2016)
      We propose a novel RF signal classification method based on sparse coding, an unsupervised learning method popular in computer vision. In particular, we employ a convolutional sparse coder that can extract high-level ...
    • BranchyNet: Fast inference via early exiting from deep neural networks 

      Teerapittayanon, Surat; McDanel, Bradley; Kung, H. T. (IEEE, 2017)
      Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer. However, the improved performance of additional layers in a deep ...
    • A Chip Architecture for Compressive Sensing Based Detection of IC Trojans 

      Tsai, Yi-Min; Huang, Kang-Yen; Kung, H. T.; Vlah, Dario; Gwon, Youngjune Lee; Chen, Liang-Gee (Institute of Electrical and Electronics Engineers, 2012)
      We present a chip architecture for a compressive sensing based method that can be used in conjunction with the JTAG standard to detect IC Trojans. The proposed architecture compresses chip output resulting from a large ...
    • CloudSense: Continuous Fine-Grain Cloud Monitoring with Compressive Sensing 

      Kung, H. T.; Lin, Chit-Kwan; Vlah, Dario (Institute of Electrical and Electronics Engineers, 2011)
      Continuous fine-grain status monitoring of a cloud data center enables rapid response to anomalies, but handling the resulting torrent of data poses a significant challenge. As a solution, we propose CloudSense, a new ...
    • Coding-Based System Primitives for Airborne Cloud Computing 

      Lin, Chit-Kwan (2013-02-25)
      The recent proliferation of sensors in inhospitable environments such as disaster or battle zones has not been matched by in situ data processing capabilities due to a lack of computing infrastructure in the field. We ...
    • Collaborative Compressive Spectrum Sensing in a UAV Environment 

      Chen, Kevin; Kung, H. T.; Vlah, Dario; Hague, Daniel; Muccio, Michael; Poland, Brendon (Institute of Electrical and Electronics Engineers, 2011)
      Spectrum sensing is of fundamental importance to many wireless applications including cognitive radio channel assignment and radiolocation. However, conventional spectrum sensing can be prohibitively expensive in computation ...
    • Competing Mobile Network Game: Embracing antijamming and jamming strategies with reinforcement learning 

      Gwon, Youngjune Lee; Dastangoo, Siamak; Fossa, Carl; Kung, H. T. (IEEE, 2013)
      We introduce Competing Mobile Network Game (CMNG), a stochastic game played by cognitive radio networks that compete for dominating an open spectrum access. Differentiated from existing approaches, we incorporate both ...
    • Compressed Statistical Testing and Application to Radar 

      Chen, Hsieh-Chung; Kung, H. T.; Wicks, Michael C. (2012-12-06)
      We present compressed statistical testing (CST) with an illustrative application to radar target detection. We characterize an optimality condition for a compressed domain test to yield the same result as the corresponding ...
    • Compressive Sensing Based Channel Feedback Protocols for Spatially-Correlated Massive Antenna Arrays 

      Kuo, Ping-Heng; Kung, H. T.; Ting, Pang-an (Institute of Electrical and Electronics Engineers, 2012)
      Incorporating wireless transceivers with numerous antennas (such as Massive-MIMO) is a prospective way to increase the link capacity or enhance the energy efficiency of future communication systems. However, the benefits ...
    • Compressive Sensing Medium Access Control for Wireless LANs 

      Lin, Tsung-Han; Kung, H. T. (Institute of Electrical and Electronics Engineers, 2012)
      We propose a medium access control (MAC) protocol for wireless local area networks (LANs) that leverages the theory of compressive sensing. The proposed compressive sensing MAC (CS-MAC) exploits the sparse property that, ...
    • Compressive Sensing with Directly Recoverable Optimal Basis and Applications in Spectrum Sensing 

      Gwon, Youngjune Lee; Kung, H. T.; Vlah, Dario (2011)
      We describe a method of integrating Karhunen-Loeve Transform (KLT) into compressive sensing, which can as a result leverage KLT’s optimality in revealing the sparsity of a signal. We present two complementary results: (1) ...
    • Compressive Sensing with Optimal Sparsifying Basis and Applications in Spectrum Sensing 

      Gwon, Youngjune Lee; Kung, H. T.; Vlah, Dario (Institute of Electrical and Electronics Engineers, 2012)
      We describe a method of integrating Karhunen-Loève Transform (KLT) into compressive sensing, which can as a result improve the compression ratio without affecting the accuracy of decoding. We present two complementary ...
    • Computing Sparse Representations in O(N log N) Time 

      Lin, Tsung-Han; Kung, H. T. (Signal Processing with Adaptive Sparse Structured Representations (SPARS 2013), 2013)
    • Concurrent Channel Access and Estimation for Scalable Multiuser MIMO Networking 

      Lin, Tsung-Han; Kung, H. T. (2012-07-26)
      This paper presents the design of MIMO/CON (“MIMO with concurrent channel access and estimation”), a PHY/MAC cross-layer design delivering throughput scalable to many users for multiuser MIMO wireless networking. By allowing ...
    • Deep Sparse-coded Network (DSN) 

      Gwon, Youngjune Lee; Cha, Miriam; Kung, H. T. (2015)
      We introduce Deep Sparse-coded Network (DSN), a deep architecture based on sparse coding and dictionary learning. Key advantage of our approach is two-fold. By interlacing max pooling with sparse coding layer, we achieve ...
    • Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices 

      Teerapittayanon, Surat; McDanel, Bradley; Kung, H.; Teerapittayanon (IEEE, 2017-06)
      We 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) ...
    • DISTROY: Detecting Integrated Circuit Trojans with Compressive Measurements 

      Gwon, Youngjune Lee; Kung, H. T.; Vlah, Dario (2012-12-05)
      Detecting Trojans in an integrated circuit (IC) is an important but hard problem. A Trojan is malicious hardware it can be extremely small in size and dormant until triggered by some unknown circuit state. To allow wake-up, ...
    • Embedded Binarized Neural Networks 

      McDanel, Bradley; Teerapittayanon, Surat; Kung, H. (Junction Publishing, 2017-02-20)
      We 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 ...
    • FlowCode: Multi-site data exchange over wireless ad-hoc networks using network coding 

      Kung, H. T.; Lin, Chit-Kwan; Lin, Tsung-Han; Tarsa, Stephen John; Vlah, Dario (IEEE, 2009)
      We present FlowCode, a system that exploits network coding at the granularity of traffic flows to facilitate fault-tolerant data exchange in wireless mesh networks. Applications include multi-site data replication in ad-hoc ...