John R. Buck

Bio: John R. Buck is an academic researcher from University of Massachusetts Dartmouth. The author has contributed to research in topics: Covariance matrix & Beamforming. The author has an hindex of 21, co-authored 113 publications receiving 3469 citations. Previous affiliations of John R. Buck include Dartmouth College & Massachusetts Institute of Technology.

Information entropy of humpback whale songs.

Abstract: Many theories of nonhuman animal communication posit a first‐order Markov model in which the next signal depends only on the current one Such a model precludes a hierarchical structure to the communication signal Information theory and signal processing provide quantitative techniques to estimate the underlying complexity of an arbitrary signal or symbol sequence These techniques are applied to humpback whale songs and demonstrate that any first‐order Markov model fails to attain the underlying bound of complexity in these songs Humpback songs are symbolized into alphabet sequences using spectrograms and self‐organizing neural nets [Walker, unpublished] The entropy of the song sequence is measured with a first‐order parametric Markov model, and with a nonparametric sliding window method [Kontoyiannis et al, IEEE Trans Info Theory 44, 1319–1327 (1998)] Preliminary analyses suggest that the entropy of the first‐order Markov model is significantly higher than that of the nonparametric model, meaning that any first‐order Markov source cannot reasonably model humpback songs Furthermore, it is found that the symbolized song statistics are locally but not globally stationary, implying that these songs possess a hierarchical structure [Work supported by NSF Ocean Sciences]

The signals and systems concept inventory

Abstract: The signal processing community needs quantitative standardized tools to assess student learning in order to improve teaching methods and satisfy accreditation requirements. The Signals and Systems Concept Inventory (SSCI) is a 25-question multiple-choice exam designed to measure students' understanding of fundamental concepts taught in standard signals and systems curricula. When administered as a pre- and postcourse assessment, the SSCI measures the gain in conceptual understanding as a result of instruction. This paper summarizes the three-year development of this new assessment instrument and presents results obtained from testing with a pool of over 900 students from seven schools. Initial findings from the SSCI study show that students in traditional lecture courses master approximately 20% of the concepts they do not know prior to the start of the course. Other results highlight the most common student misconceptions and quantify the correlation between signals and systems and prerequisite courses.

Iterative and sequential algorithms for multisensor signal enhancement

Abstract: In problems of enhancing a desired signal in the presence of noise, multiple sensor measurements will typically have components from both the signal and the noise sources. When the systems that couple the signal and the noise to the sensors are unknown, the problem becomes one of joint signal estimation and system identification. The authors specifically consider the two-sensor signal enhancement problem in which the desired signal is modeled as a Gaussian autoregressive (AR) process, the noise is modeled as a white Gaussian process, and the coupling systems are modeled as linear time-invariant finite impulse response (FIR) filters. The main approach consists of modeling the observed signals as outputs of a stochastic dynamic linear system, and the authors apply the estimate-maximize (EM) algorithm for jointly estimating the desired signal, the coupling systems, and the unknown signal and noise spectral parameters. The resulting algorithm can be viewed as the time-domain version of the frequency-domain approach of Feder et al. (1989), where instead of the noncausal frequency-domain Wiener filter, the Kalman smoother is used. This approach leads naturally to a sequential/adaptive algorithm by replacing the Kalman smoother with the Kalman filter, and in place of successive iterations on each data block, the algorithm proceeds sequentially through the data with exponential weighting applied to allow adaption to nonstationary changes in the structure of the data. A computationally efficient implementation of the algorithm is developed. An expression for the log-likelihood gradient based on the Kalman smoother/filter output is also developed and used to incorporate efficient gradient-based algorithms in the estimation process. >

Active learning increases student performance in science, engineering, and mathematics

Abstract: creased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes—although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.

The Design and Implementation of FFTW3

Abstract: FFTW is an implementation of the discrete Fourier transform (DFT) that adapts to the hardware in order to maximize performance. This paper shows that such an approach can yield an implementation that is competitive with hand-optimized libraries, and describes the software structure that makes our current FFTW3 version flexible and adaptive. We further discuss a new algorithm for real-data DFTs of prime size, a new way of implementing DFTs by means of machine-specific single-instruction, multiple-data (SIMD) instructions, and how a special-purpose compiler can derive optimized implementations of the discrete cosine and sine transforms automatically from a DFT algorithm.

Table Of Integrals Series And Products

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The expectation-maximization algorithm

Abstract: A common task in signal processing is the estimation of the parameters of a probability distribution function Perhaps the most frequently encountered estimation problem is the estimation of the mean of a signal in noise In many parameter estimation problems the situation is more complicated because direct access to the data necessary to estimate the parameters is impossible, or some of the data are missing Such difficulties arise when an outcome is a result of an accumulation of simpler outcomes, or when outcomes are clumped together, for example, in a binning or histogram operation There may also be data dropouts or clustering in such a way that the number of underlying data points is unknown (censoring and/or truncation) The EM (expectation-maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation The EM algorithm is presented at a level suitable for signal processing practitioners who have had some exposure to estimation theory

Full duplex radios

Abstract: This paper presents the design and implementation of the first in-band full duplex WiFi radios that can simultaneously transmit and receive on the same channel using standard WiFi 802.11ac PHYs and achieves close to the theoretical doubling of throughput in all practical deployment scenarios. Our design uses a single antenna for simultaneous TX/RX (i.e., the same resources as a standard half duplex system). We also propose novel analog and digital cancellation techniques that cancel the self interference to the receiver noise floor, and therefore ensure that there is no degradation to the received signal. We prototype our design by building our own analog circuit boards and integrating them with a fully WiFi-PHY compatible software radio implementation. We show experimentally that our design works robustly in noisy indoor environments, and provides close to the expected theoretical doubling of throughput in practice.