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- Siddhartha Chatterjee, Leonardo R. Bachega, +11 authors Peng Wu
- IBM Journal of Research and Development
- 2005

of a high-performance SIMD floating-point unit for Blue Gene/L S. Chatterjee L. R. Bachega P. Bergner K. A. Dockser J. A. Gunnels M. Gupta F. G. Gustavson C. A. Lapkowski G. K. Liu M. Mendell R. Nair C. D. Wait T. J. C. Ward P. Wu We describe the design of a dual-issue single-instruction, multipledata-like (SIMD-like) extension of the IBM PowerPCt 440… (More)

- James Theiler, Guangzhi Cao, Leonardo R. Bachega, Charles A. Bouman
- IEEE Journal of Selected Topics in Signal…
- 2011

A variety of problems in remote sensing require that a covariance matrix be accurately estimated, often from a limited number of data samples. We investigate the utility of several variants of a recently introduced covariance estimator-the sparse matrix transform (SMT), a shrinkage-enhanced SMT, and a graph-constrained SMT-in the context of several of these… (More)

- George Almási, Leonardo R. Bachega, +21 authors Edi Shmueli
- IPDPS
- 2003

The BlueGene/L supercomputer will use system-on-achip integration and a highly scalable cellular architecture to deliver 360 Teraflops of peak computing power. With 65,536 compute nodes, BlueGene/L represents a new level of scalability for parallel systems. As such, it is natural for many scalability challenges to arise. In this paper, we discuss challenges… (More)

- Guangzhi Cao, Leonardo R. Bachega, Charles A. Bouman
- IEEE Transactions on Image Processing
- 2011

Covariance estimation for high dimensional signals is a classically difficult problem in statistical signal analysis and machine learning. In this paper, we propose a maximum likelihood (ML) approach to covariance estimation, which employs a novel non-linear sparsity constraint. More specifically, the covariance is constrained to have an eigen decomposition… (More)

- Leonardo R. Bachega, Guangzhi Cao, Charles A. Bouman
- 2010 IEEE International Conference on Acoustics…
- 2010

Recently, the Sparse Matrix Transform (SMT) has been proposed as a tool for estimating the eigen-decomposition of high dimensional data vectors [1]. The SMT approach has two major advantages: First it can improve the accuracy of the eigendecomposition, particularlywhen the number of observations, n, is less the the vector dimension, p. Second, the resulting… (More)

- Leonardo R. Bachega, Siddhartha Chatterjee, +8 authors T. J. Christopher Ward
- Proceedings. 13th International Conference on…
- 2004

We describe the design, implementation, and evaluation of a dual-issue SIMD-like extension of the PowerPC 440 floating-point unit (FPU) core. This extended FPU is targeted at both IBM's massively parallel Blue-Gene/L machine as well as more pervasive embedded platforms. It has several novel features, such as a computational crossbar and cross-load/store… (More)

This paper addresses two issues related to the detection of hyperspectral anomalies. The first issue is the evaluation of anomaly detector performance even when labeled data is not available. The second issue is the estimation of the covariance structure of the data in local detection methods, such as the RX detector, when the number of available training… (More)

- José Fernando R. Bachega, Luís Fernando S. M. Timmers, Lucas Assirati, Leonardo R. Bachega, Martin J. Field, Troy Wymore
- Journal of Computational Chemistry
- 2013

Hybrid quantum chemical/molecular mechanical (QCMM) potentials are very powerful tools for molecular simulation. They are especially useful for studying processes in condensed phase systems, such as chemical reactions that involve a relatively localized change in electronic structure and where the surrounding environment contributes to these changes but can… (More)

We study a problem of detecting deterministic signals buried in correlated clutter using wireless sensor networks. We are specifically interested in developing a distributed algorithm over the network to detect the presence of a deterministic signal while keeping low communication delay and energy associated with the distributed computation. In this paper,… (More)