J.H. McClellan

Georgia Institute of Technology, Atlanta, Georgia, United States

Are you J.H. McClellan?

Claim your profile

Publications (217)289.61 Total impact

  • Kyle Krueger, Waymond R. Scott, James H. McClellan
    [Show abstract] [Hide abstract]
    ABSTRACT: Dictionary matching techniques have been an e ective way to detect the location and orientation of buried targets using electromagnetic induction (EMI) sensors. Two problems with dictionary detection are that they require a large amount of computer storage to enumerate nine dimensions, and fine discretization of the parameter space must be used to reduce modeling error. The proposed method shrinks the dictionary size by five orders of magnitude, and reduces modeling error by directly solving for the 3×3 tensor model of the target. A robust lowrank matrix approximation algorithm has been implemented which can also account for directional insensitivities in the measurements.
    Proc SPIE 06/2013;
  • Chenchi Luo, Lingchen Zhu, J.H. Mcclellan
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a general structure for FIR filters with adjustable magnitude and phase responses controlled by a few parameters. The Farrow structure which uses one parameter to control the fractional delay of an FIR filter can be viewed as special case. A filter bank structure consisting of different types of linear phase differentiators forms the basis of the structure. The filter bank outputs are combined with coefficients derived from a polynomial expansion of the desired frequency response. The magnitude and phase responses are controlled by synthesizing the polynomial coefficients from the small set of control parameters. A new optimal polynomial approximation strategy is also proposed to better approximate the family of target frequency responses.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
  • Chenchi Luo, Lingchen Zhu, J.H. Mcclellan
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a novel digital blind calibration method for time interleaved analog to digital converters (TIADCs). A simple cost function based on the cross-correlation of channel statistics is used to derive a steepest descent algorithm for the compensation of timing mismatch errors. Instead of calibrating the timing mismatches independently for each channel, only one adaptation channel needs to be calibrated within a closed loop. The calibration of the rest of the channels can be coordinated according to a scaling relationship established during an initialization stage. As a result, both the computational complexity and convergence speed of the proposed algorithm can be improved significantly with little loss in calibration performance.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
  • G.A. Krudysz, J.H. Mcclellan
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we describe an on-line learning platform that enables students to develop conceptual knowledge, organize related concepts around supporting resources, and test their conceptual knowledge and its organization through numerous practice problems. We present these ideas in the context of our ongoing web-based educational platform ITS which we have deployed since 2010 for an introductory Signal Processing course at Georgia Tech. Specifically, we discuss the addition of two new features: the “Concept Browser” and the “Resource Selector”. These tools provide a user interface that explicitly fosters a concept oriented learning environment.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
  • K.R. Krueger, J.H. Mcclellan, W.R. Scott
    [Show abstract] [Hide abstract]
    ABSTRACT: Sensor array measurements can be inverted to image a region containing targets. The resulting amplitude image is usually interpreted as target strength versus location, but often the imaged amplitude is a function of more parameters than just the location. Sparse target regions can be imaged with dictionary based modeling which relies on enumeration of each parameter with a dense grid. With many parameters, the dictionary becomes too large, which leads to computational complexity issues. This paper shows how additional parameters, such as target orientation and symmetry, can be represented by a tensor matrix instead of a simple amplitude. Furthermore, the tensor can be treated as a continuous variable just like amplitude, which enables extraction of multiple parameters, while reducing the storage requirements of the dictionary, and reducing off-grid modeling error.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
  • Chenchi Luo, J.H. Mcclellan
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a new perspective on the relationship between the sampling and aliasing. Unlike the uniform sampling case, where the aliases are simply periodic replicas of the original spectrum, random sampling theory shows that the randomization of sampling intervals shapes the aliases into a noise floor in the sampled spectrum. New insights into both the Fourier random sampling problem and Compressive Sensing theory can be obtained using the theoretical framework of random sampling. This paper extends the theory of continuous time random sampling to deal with random discrete intervals generated from a clock. A key result is established to relate the discrete probability distribution of the sampling intervals to the power spectrum of the aliasing noise. Based on the proposed theory, a generic discrete random sampling hardware architecture is also proposed for sampling and reconstructing a class of spectrally sparse signals at an average rate significantly below the Nyquist rate of the signal.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
  • Kyle Krueger, James H. McClellan, Scott, Waymond R., Jr
    [Show abstract] [Hide abstract]
    ABSTRACT: Compressive sensing (CS) techniques have shown promise for subsurface imaging applications using wideband sensors such as stepped-frequency ground-penetrating radars (GPR). Excellent images can be computed using the CS techniques. However, the problem size is severely limited for 3-dimensional imaging problems which seem to require an explicit representation matrix that involves six dimensions. This paper shows how the underlying propagation model leads to a block-Toeplitz structure in two of the dimensions which can be exploited to reduce both the storage and computational complexity. The reduction by three orders of magnitude in computational resources for the CS problem will make 3-dimensional imaging applications feasible.
    Proc SPIE 05/2012;
  • Source
    A.C. Gurbuz, Volkan Cevher, J.H. McClellan
    [Show abstract] [Hide abstract]
    ABSTRACT: Bearing estimation algorithms obtain only a small number of direction of arrivals (DOAs) within the entire angle domain, when the sources are spatially sparse. Hence, we propose a method to specifically exploit this spatial sparsity property. The method uses a very small number of measurements in the form of random projections of the sensor data along with one full waveform recording at one of the sensors. A basis pursuit strategy is used to formulate the problem by representing the measurements in an overcomplete dictionary. Sparsity is enforced by $\ell_1$-norm minimization which leads to a convex optimization problem that can be efficiently solved with a linear program. This formulation is very effective for decreasing communication loads in multi sensor systems. The algorithm provides increased bearing resolution and is applicable for both narrowband and wideband signals. Sensors positions must be known, but the array shape can be arbitrary. Simulations and field data results are provided to demonstrate the performance and advantages of the proposed method.
    IEEE Transactions on Aerospace and Electronic Systems 01/2012; · 1.30 Impact Factor
  • K. Krueger, J.H. McClellan, W.R. Scott
    [Show abstract] [Hide abstract]
    ABSTRACT: Compressive sensing (CS) techniques have shown promise for sparse imaging applications such as ground penetrating radar (GPR). However, CS involves the enumeration of a dictionary which implies huge storage requirements when the problem is large and multidimensional. This paper shows that the underlying propagation model can have invariance properties that simplify the dictionary. Specifically, translational invariance in the GPR case leads to a block-Toeplitz structure that can be exploited to reduce both the storage, by a factor of N in each block-Toeplitz dimension, and the computational complexity. Exploiting this reduction in storage for the 3-dimensional GPR imaging problem makes the CS solution feasible for underground object detection.
    Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012 IEEE 7th; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: Capacitive touch screens are ubiquitous in today's electronic devices. Improved touch screen responsiveness and resolution can be achieved at the expense of the touch screen controller analog hardware complexity and power consumption. This paper proposes an alternative compressive sensing based approach to exploit the sparsity of simultaneous touches with respect to the number of sensor nodes to achieve similar levels of responsiveness. It is possible to reduce the analog data acquisition complexity at the cost of extra digital computations with less total power consumption. Using compressive sensing, in order to resolve the positions of the sparse touches, the number of measurements required is related to the number of touches rather than the number of nodes. Detailed measurement circuits and methodologies are presented along with the corresponding reconstruction algorithm.
    Emerging and Selected Topics in Circuits and Systems, IEEE Journal on. 01/2012; 2(3):639-648.
  • Chenchi Luo, J. McClellan, M. Borkar, A. Redfern
    [Show abstract] [Hide abstract]
    ABSTRACT: Improved capacitive touch screen responsiveness can be achieved at the expense of the touch screen controller analog hardware complexity and power consumption. This paper proposes a compressive sensing based approach to exploit the sparsity of simultaneous touches (e.g., 10 or less per person) with respect to the number of sensor nodes (e.g., 100s) to achieve similar levels of responsiveness with lower levels of analog complexity and power consumption. This is done by showing that the number of measurements required for touch detection is related to the number of touches rather than the number of nodes.
    Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on; 01/2012 · 4.63 Impact Factor
  • Chenchi Luo, J. McClellan, M. Borkar, A. Redfern
    [Show abstract] [Hide abstract]
    ABSTRACT: Loudspeakers in portable consumer electronic devices are frequently small in size. Due to the low sensitivity of their drive units, they are pushed to their power handling and mechanical limits by powerful amplifiers in an attempt to reach high volumes. To protect against excessive diaphragm excursions, a model based algorithm is proposed which regulates the voltage input signal to the loudspeaker while minimizing unnecessary system interventions.
    Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on; 01/2012 · 4.63 Impact Factor
  • Source
    Ali Cafer Gurbuz, James H. McClellan, Waymond R. Scott Jr
    [Show abstract] [Hide abstract]
    ABSTRACT: Feature detection in sensing problems usually involves two processing stages. First, the raw data collected by a sensor, such as a Ground Penetrating Radar (GPR), is inverted to form an image of the subsurface area. Second, the image is searched for features like lines using an algorithm such as the Hough Transform (HT), which converts the problem of finding spatially spread patterns in the image space to detecting sparse peaks in the HT parameter space. This paper exploits the sparsity of features to combine the two stages into one direct processing step using Compressive Sensing (CS). The CS framework finds the HT parameters directly from the raw sensor measurements without having to construct an image of the sensed media. In addition to skipping the image formation step, CS processing can be done with a minimal number of raw sensor measurements, which decreases the data acquisition cost. The utility of this CS-based method is demonstrated for finding buried linear structures in both simulated and experimental GPR data.
    Digital Signal Processing. 01/2012; 22:66-73.
  • Mu-Hsin Wei, W.R. Scott, J.H. McClellan
    [Show abstract] [Hide abstract]
    ABSTRACT: The EMI response of a target can be accurately modeled by a sum of relaxations. However, it is difficult to obtain the model parameters from measurements when the number of relaxations is unknown. We have previously proposed estimation methods for the model parameters from single measurements. In this paper, we exploit the invariance property of the relaxation frequencies and propose to obtain more accurate estimates using multiple measurements that are often available. This is accomplished by casting the modeling problem into a jointly-sparse vector recovery problem. The proposed method is shown to deliver robust estimation using synthetic, laboratory data, and field data.
    Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International; 01/2012
  • Source
    Chenchi Luo, J.H. McClellan
    [Show abstract] [Hide abstract]
    ABSTRACT: Digital filters with adjustable bandwidth(s) are generally desirable in many applications like audio processing and telecommunication. This paper proposes a generalized Far row structure for adjustable bandwidth linear-phase FIR filters designed under a minimax design criterion. The band width of the proposed filter structure can be continuously adjusted with an updating routine that only involves a few multiplications and additions. Moreover, the generalized structure can be designed to effectively reduce the dynamic range of basis filter coefficients, which is desirable when making a fixed-point implementation on FPGAs.
    Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on; 06/2011 · 4.63 Impact Factor
  • Mu-Hsin Wei, J.H. McClellan, W.R. Scott
    [Show abstract] [Hide abstract]
    ABSTRACT: The electromagnetic induction response of a target can be accurately modeled by a sum of real exponentials. However, in practice, it is difficult to obtain the model parameters from measurements. We previously proposed a constrained linear method that can robustly estimate the model parameters when they are nonnegative. In this letter, we present a modified ℓ<sub>p</sub>-regularized least squares algorithm, for 0 ≤ p ≤ 1, that eliminates the nonnegative constraint. An empirical method for choosing the regularization parameter is also studied. Using tests on synthetic data and laboratory measurements, the proposed method is shown to provide robust estimates of the model parameters in practice.
    IEEE Geoscience and Remote Sensing Letters 04/2011; · 1.82 Impact Factor
  • Chenchi Luo, J.H. McClellan, P.T. Bhatti
    [Show abstract] [Hide abstract]
    ABSTRACT: Filterbank implementations and simulations can be used successfully in introductory signal processing lab courses by avoiding some of the analytical complexities and focusing on real-world applications. One excellent area is human hearing where the cochlea is well modelled by a filterbank. A lab project that simulates a cochlear implant (CI) combines elements of signal processing with biomedical engineering. Another intriguing application is the decoding of dual-tone multiple-frequency (DTMF) signals used in telephones. Designing a filterbank with multiple bandpass channels to extract signal of interest motivates students to learn filter design, and also provides them with a sense of accomplishment once the whole system is is working. In addition, these filterbank labs can be supported with graphical user interfaces (GUIs) that illustrate how the important components of the system must work together. A comprehensive GUI for the CI simulation is presented along with a GUI tool for filter design. The CI GUI shows the expected signal behavior in the channels after filtering and after detection, as well as having sound input-output, so that students can listen to the key signals.
    Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE), 2011 IEEE; 02/2011
  • Mu-Hsin Wei, James H. McClellan, Waymond R. Scott
    IEEE Geosci. Remote Sensing Lett. 01/2011; 8:233-237.
  • Mu-Hsin Wei, Waymond R. Scott, James H. McClellan
    [Show abstract] [Hide abstract]
    ABSTRACT: Several landmine detection techniques using electromagnetic induction (EMI) sensors have been proposed in the past decade. In this paper, we propose a class of detection techniques based on the discrete spectrum of relaxation frequencies (DSRF). Two DSRF detection methods are demonstrated: one using the support vector machine and one using the k-nearest neighbor method. A soil model is also proposed to identify EMI response from the magnetic properties of the soil. A detection framework is suggested to incorporate the soil model and the classifier. The robustness of landmine detection using the DSRF is demonstrated. Approved for public release; distribution is unlimited.
    2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, Vancouver, BC, Canada, July 24-29, 2011; 01/2011
  • Source
    Chenchi Luo, James H. McClellan
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a compressive sampling scheme based on random temporal sampling using a successive approximation register (SAR) ADC architecture. Variable wordlength data samples at random sampling times would be produced by the SAR converter, so a modified reconstruction algorithm is proposed to recover signals that are sparse or compressible in a known basis. The modified reconstruction algorithm addresses the variable wordlength resolution issue of SAR ADC samples introduced by the random sampling scheme. We demonstrate that the proposed sampling and reconstruction scheme performs significantly better compared with uniform sampling on the same SAR ADC architecture.
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic; 01/2011 · 4.63 Impact Factor

Publication Stats

2k Citations
289.61 Total Impact Points

Institutions

  • 1989–2012
    • Georgia Institute of Technology
      • • School of Electrical & Computer Engineering
      • • Center for Signal & Image Processing
      Atlanta, Georgia, United States
  • 2009
    • Rice University
      • Department of Electrical and Computer Engineering
      Houston, TX, United States
    • Ankara University
      • Department of Electronic Engineering
      Ankara, Ankara, Turkey
  • 2007
    • University of Maryland, College Park
      • Center for Automation Research (CfAR)
      College Park, MD, United States
  • 2006
    • Samsung Thales
      Sŏul, Seoul, South Korea
  • 2000–2004
    • Clark Atlanta University
      • Department of Engineering
      Atlanta, Georgia, United States
  • 1999–2004
    • Georgia Tech Research Institute
      Atlanta, Georgia, United States
  • 1998
    • Massachusetts Institute of Technology
      Cambridge, Massachusetts, United States
  • 1995–1998
    • Rose Hulman Institute of Technology
      • Department of Electrical and Computer Engineering
      Terre Haute, IN, United States
    • Atlanta Technical College
      Atlanta, Georgia, United States
    • Washington State University
      • School of Electrical Engineering and Computer Science
      Pullman, WA, United States
  • 1996–1997
    • Arizona State University
      • School of Electrical, Computer and Energy Engineering
      Mesa, AZ, United States
  • 1994
    • University of California, Berkeley
      • Department of Electrical Engineering and Computer Sciences
      Berkeley, MO, United States
  • 1993
    • SRI International
      Menlo Park, California, United States