A. A. (Louis) Beex

A. A. (Louis) Beex

Virginia Polytechnic Institute and State University

H-index: 24

North America-United States

Professor Information

University

Virginia Polytechnic Institute and State University

Position

- ECE - Wireless@VT - DSPRL

Citations(all)

1984

Citations(since 2016)

306

Cited By

1763

hIndex(all)

24

hIndex(since 2016)

7

i10Index(all)

42

i10Index(since 2016)

6

Email

University Profile Page

Virginia Polytechnic Institute and State University

Research & Interests List

stochastic/adaptive/array signal processing

signal modeling

Co-Authors

H-index: 54
Louis Scharf

Louis Scharf

Colorado State University

H-index: 54
Martha Ann Bell

Martha Ann Bell

Virginia Polytechnic Institute and State University

H-index: 43
Daniel Graupe

Daniel Graupe

University of Illinois at Chicago

H-index: 35
Jerry Park

Jerry Park

Virginia Polytechnic Institute and State University

H-index: 34
Christopher Beattie

Christopher Beattie

Virginia Polytechnic Institute and State University

H-index: 27
D M Wilkes

D M Wilkes

Vanderbilt University

H-index: 19
Carl Dietrich

Carl Dietrich

Virginia Polytechnic Institute and State University

H-index: 18
William "Chris" Headley

William "Chris" Headley

Virginia Polytechnic Institute and State University

H-index: 16
Behnam Bahrak

Behnam Bahrak

University of Tehran

Professor FAQs

What is A. A. (Louis) Beex's h-index at Virginia Polytechnic Institute and State University?

The h-index of A. A. (Louis) Beex has been 7 since 2016 and 24 in total.

What are A. A. (Louis) Beex's research interests?

The research interests of A. A. (Louis) Beex are: stochastic/adaptive/array signal processing, signal modeling

What is A. A. (Louis) Beex's total number of citations?

A. A. (Louis) Beex has 1,984 citations in total.

What are the co-authors of A. A. (Louis) Beex?

The co-authors of A. A. (Louis) Beex are Louis Scharf, Martha Ann Bell, Daniel Graupe, Jerry Park, Christopher Beattie, D M Wilkes, Carl Dietrich, William "Chris" Headley, Behnam Bahrak.

Top articles of A. A. (Louis) Beex

Non-asymptotic bounds for discrete prolate spheroidal wave functions analogous with prolate spheroidal wave function bounds

Prolate spheroidal wave functions and discrete prolate spheroidal wave functions are two distinct sets of functions, in spite of the names suggesting the latter being the discrete version of the former. Both sets of functions are eigenfunctions of 2nd order differential equations whose coefficients behave in a very similar way over the concentration interval in terms of monotonicity and non-negativity. As a result of this similarity, we show that certain bounds that are well established for PSWFs apply also to DPSWFs but only for an appropriate set of conditions. Furthermore, the results for DPSWF apply to the entire domain unlike for PSWFs where the results are limited to the concentration band.

Authors

Karim A Said,AA Louis Beex

Journal

Applied and Computational Harmonic Analysis

Publish By

Academic Press

Publish Date

2023/3/1

On the correlation concentration of discrete prolate spheroidal sequences

For theessential set of discrete prolate spheroidal sequences (DPSS) corresponding to a given discrete-time-bandwidth product, we show that the auto and cross-correlations between the member sequences of the set also exhibit high concentration over a correlation length twice the concentration length of the member sequences. We start by deriving one upper bound and two lower bounds for the energy of the correlation sequence of index-limited DPSSs from which we derive similar bounds for the energy of DPSS correlation sequences. Then, we obtain an upper bound for the tail energy of DPSS correlation sequences. Finally, we obtain a lower bound for the concentration ratio of DPSS correlation sequences. All derived bounds require knowledge of the following parameters for each DPSS sequence in the correlation pair: corresponding eigen-value, Fourier transform evaluated at two special frequency points …

Authors

Karim A Said,AA Louis Beex

Journal

IEEE Transactions on Signal Processing

Publish By

IEEE

Publish Date

2021/12/13

Multi-antenna pre-processing for improved rfml in congested spectral environments

In this work, a novel signal detection approach for dense co-channel environments is developed that leverages the intelligent combination of traditional signal preprocessing and deep radio frequency machine learning. Specifically, a novel multi-antenna preprocessing stage is developed to ease the signal processing burden of the deep learning algorithm. Easing this burden enables deep learning to be focused on specifically solving the sensing problem which helps minimize its footprint, improves its convergence during training, and reduces the required size of training datasets. Performance results of the proposed approach demonstrate that this intelligent combination of traditional and deep learning approaches leads to a detector that minimizes the impact of interference sources and nuisance signals and compensates for challenging propagation environments.

Authors

MR Williamson,William C Headley,William H Clark,Joey McCollum,Thomas Krauss,L Lusk,D Jenkins,T Villemez,MO Moore,Daniel J Jakubisin,A Poetter,Alan J Michaels,AA Beex,Joseph D Gaeddert

Publish By

IEEE

Publish Date

2021/12/13

MSE Analysis of Bi-scale LMS Used for Narrowband Interference Cancellation

Adaptive LMS (Least Mean Square) equalizers are widely used in digital communication systems for their simplicity of implementation. Conventional adaptive filtering theory suggests that the upper bound on performance of such an equalizer is determined by the performance of a Wiener filter of the same structure. However, in the presence of a narrowband interferer the performance of the (normalized) LMS equalizer can be better than that of its Wiener counterpart. The Bi-scale NLMS (BLMS) algorithm enhances this NLMS (Normalized LMS) characteristic by simultaneously using two instantiations of NLMS that run at very different time scales. In this paper, the derivation of a predictive model for the MSE (Mean Square Error) performance of the BLMS equalizer as narrowband interference canceler is shown. The predictive model can be used to adjust canceler parameters on the fly without the delay needed for time …

Authors

Tamoghna Roy,Takeshi Ikuma,AA Louis Beex

Publish By

IEEE

Publish Date

2020/11/18

Signaling Basis with High Inter-Symbol Interference Rejection Pilots in Doubly Dispersive Channels

In pilot aided transmissions over doubly dispersive channels, channel estimation is impaired due to contamination of pilot symbols by leakage from neighboring data symbols in rectangular window based block transmissions; no overlap in time between pulses corresponding to neighboring symbols. Block transmissions can be viewed as a basis consisting of time-frequency shifts of a prototype window shape, commonly known as Gabor bases, where the window shape controls the time-frequency confinement; better confinement is obtained when non-rectangular window shapes are used, however at the loss of orthogonality. In this work, a non-Gabor basis comprised of discrete prolate spheroidal sequences is considered for signaling due to the resilience of its high order sequences to time-frequency spread; beyond a certain sequence order, the higher the order the better the resilience. By using such high order …

Authors

Karim Said,AA Louis Beex

Publish By

IEEE

Publish Date

2018/12/9

Power Measurement Based Code Classification for Programmable Logic Circuits

Traditional cyber security and monitoring systems rely on prior knowledge about possible attacks, which renders them ineffective against novel schemes (zero-day attacks). Moreover, intruders are targeting different types of devices (e.g. Programmable Logic Circuits/Controllers (PLC)) for which traditional security systems are unavailable. A solution to this problem is provided by analysis of the power consumption behavior of a system and relating power consumption characteristics to behavior internal to the device. In this work, a detection system is developed which is capable of discriminating between different codes executed by the target device, a PLC, based on passive measurement at the external power supply point; this is unlike previous methods, which relied on a specific sensor located directly over the chip. Results show that for a PLC, which is executing four different codes, the classification system can …

Authors

Tamoghna Roy,AA Louis Beex

Publish By

IEEE

Publish Date

2018/12/6

Iterative Channel and Symbol Estimation for OFDM and for SIMO Diversity

This paper proposes a new approach of Iterative Channel and Symbol Estimation (ICSE) with applications in Orthogonal Frequency Division Multiplexing (OFDM) and Single Input Multiple Output (SIMO) diversity systems. Our method extends the traditional approaches of Pilot Symbol Assisted Method (PSAM) and Decision Directed Method (DDM) for symbol detection. This paper describes the key mathematical methods for (i) estimating the log likelihood ratio of the unknown information bearing symbol given an uncertain estimate of the channel (derived using sparsely-transmitted pilot symbols), and (ii) subsequently re-estimating the channel coefficients using this log likelihood ratio. Formulations in this paper, thus, establish the underlying methods that enable the ICSE. The main contributions of this paper are to (i) provide a specific method of estimating the channel coefficients using uncertain symbol/bit estimates …

Authors

Yash M Vasavada,AA Beex,Jeffrey H Reed

Publish By

IEEE

Publish Date

2018/7/16

Classification of ADHD and non-ADHD subjects using a universal background model

ADHD affects a major portion of our children, predominantly boys. Upon diagnosis treatment can be offered that is usually quite effective. Diagnosis is generally based on subjective observation and interview. As a result, an objective test for the detection or presence of ADHD is considered very desirable.Based on EEG, across multiple channels, using autoregressive model parameters as features, ADHD detection is approached here in analogy with the imposter problem known from speaker verification. Gaussian mixture models are used to define ADHD and universal background models so that a likelihood ratio detector can be designed. The efficacy of this approach is reflected in the traditional detector performance measures of the area-under-the-curve and equal-error-probability. The results – based on a limited database of males, approximately 6 years of age – indicate that high probability of detection and low …

Authors

Juan Lopez Marcano,Martha Ann Bell,AA Louis Beex

Journal

Biomedical signal processing and control

Publish By

Elsevier

Publish Date

2018/1/1

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