Now showing items 1-13 of 13

    • 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 ...
    • 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 ...
    • 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 ...
    • 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 ...
    • 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 ...
    • 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, ...
    • Inferring Origin Flow Patterns in Wi-Fi with Deep Learning 

      Gwon, Youngjune Lee; Kung, H. T. (USENIX, 2014)
      We present a novel application of deep learning in networking. The envisioned system can learn the original flow characteristics such as a burst size and inter-burst gaps conceived at the source from packet sampling done ...
    • Language Recognition via Sparse Coding 

      Gwon, Youngjune Lee; Campbell, William M.; Sturim, Douglas E.; Kung, H. T. (2017-09-29)
      Spoken language recognition requires a series of signal processing steps and learning algorithms to model distinguishing characteristics of different languages. In this paper, we present a sparse discriminative feature ...
    • Optimizing Media Access Strategy for Competing Cognitive Radio Networks 

      Gwon, Youngjune Lee; Dastangoo, Siamak; Kung, H. T. (IEEE, 2013)
      This paper describes an adaptation of cognitive radio technology for tactical wireless networking. We introduce Competing Cognitive Radio Network (CCRN) featuring both communicator and jamming cognitive radio nodes that ...
    • Scaling network-based spectrum analyzer with constant communication cost 

      Gwon, Youngjune Lee; Kung, H. T. (Institute of Electrical and Electronics Engineers, 2013)
      e propose a spectrum analyzer that leverages many networked commodity sensor nodes, each of which sam- ples its portion in a wideband spectrum. The sensors operate in parallel and transmit their measurements over a wireless ...
    • Sparse Robust Recovery and Learning 

      Gwon, Youngjune Lee (2015-05-18)
      Sparse linear models pose dual views toward data that are embodied in compressive sensing and sparse coding. Despite mathematical equivalence, compressive sensing and sparse coding are two different classes of application ...
    • Statistical screening for IC Trojan detection 

      Gwon, Youngjune Lee; Kung, H. T.; Vlah, Dario; Huang, Keng-Yen; Tsai, Yi-Min (Institute of Electrical and Electronics Engineers, 2012)
      We present statistical screening of test vectors for detecting a Trojan, malicious circuitry hidden inside an integrated circuit (IC). When applied a test vector, a Trojan-embedded chip draws extra leakage current that is ...