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3,224
Optimal Bi-Level Quantization of i.i.d. Sensor Observations for Binary Hypothesis Testing
- IEEE Trans. Inform. Theory
, 2002
"... We consider the problem of binary hypothesis testing using binary decisions from independent and identically distributed (i.i.d). sensors. Identical likelihood-ratio quantizers with threshold are used at the sensors to obtain sensor decisions. Under this condition, the optimal fusion rule is known t ..."
Abstract
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Cited by 15 (2 self)
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We consider the problem of binary hypothesis testing using binary decisions from independent and identically distributed (i.i.d). sensors. Identical likelihood-ratio quantizers with threshold are used at the sensors to obtain sensor decisions. Under this condition, the optimal fusion rule is known
Least squares quantization in pcm.
- Bell Telephone Laboratories Paper
, 1982
"... Abstract-It has long been realized that in pulse-code modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as t ..."
Abstract
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Cited by 1362 (0 self)
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conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy. The optimization criterion used is that the average quantization noise power be a minimum. It is shown that the result obtained here goes over into the Panter and Dite result
Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding
- IEEE TRANS. ON INFORMATION THEORY
, 1999
"... We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing information-embedding rate, mini ..."
Abstract
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Cited by 496 (14 self)
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, minimizing distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we
RATE CONTROL IN BI-LEVEL IMAGE CODING
, 2000
"... As we continue to pursue the illusive goal of a paperless society, we are increasingly digitizing our documents, and despite increases in computer speeds and capacities, the requirement for data compression is still paramount. The JBIG2 standard is the latest international bi-level image compression ..."
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As we continue to pursue the illusive goal of a paperless society, we are increasingly digitizing our documents, and despite increases in computer speeds and capacities, the requirement for data compression is still paramount. The JBIG2 standard is the latest international bi-level image
Grassmannian beamforming for multiple-input multiple-output wireless systems
- IEEE TRANS. INFORM. THEORY
, 2003
"... Transmit beamforming and receive combining are simple methods for exploiting the significant diversity that is available in multiple-input and multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal beamformi ..."
Abstract
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Cited by 329 (38 self)
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Transmit beamforming and receive combining are simple methods for exploiting the significant diversity that is available in multiple-input and multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal
Model building using bi-level optimization
- J GLOB OPTIM
, 2010
"... In many problems from different disciplines such as engineering, physics, medicine, and biology, a series of experimental data is used in order to generate a model that can describe a system with minimum noise. The procedure for building a model provides a description of the behavior of the system ..."
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under study and can be used to give a prediction for the future. Herein a novel hierarchical bi-level implementation of the cross validation method is presented. In this bi-level schema, the leader optimization problem builds (training) the model and the follower checks (testing) the developed model
Likelihood Constrained Bi-Level Optimization of Classification Performance
"... ¾ We wish to classify a high-dimensional observation as belonging to one of two classes. Toward that end, we present a bi-level optimization procedure for maximizing, conditional on observed training data and subject to maximum likelihood constraints, a measure of the-dimensional class-conditional p ..."
Abstract
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Cited by 1 (1 self)
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¾ We wish to classify a high-dimensional observation as belonging to one of two classes. Toward that end, we present a bi-level optimization procedure for maximizing, conditional on observed training data and subject to maximum likelihood constraints, a measure of the-dimensional class
A Bi-Level Optimization Approach For Technology Selection
, 2002
"... Technology selection is a crucial step in the process of aircraft design. If the performance and economic requirements are not fulfilled for any combination of the design variables, new technologies need to be infused in the design. Typically, the designer has a pool of technology options. The techn ..."
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on identifying an optimal point in an intermediate variable space, which later on serves as the target point for technology selection. The new approach, called `Bi-level approach' provides additional insights and expedites technology selection, thus rendering efficiency to the preliminary design process
In Defense of Nearest-Neighbor Based Image Classification
"... State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric Nearest-Neighbor (NN) based image classifiers require no training time and have other favorable properties. However, the large performance gap between th ..."
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Cited by 266 (2 self)
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in the space of the local image descriptors (and not in the space of images). NBNN computes direct ‘Imageto-Class’ distances without descriptor quantization. We further show that under the Naive-Bayes assumption, the theoretically optimal image classifier can be accurately approximated by NBNN. Although NBNN
Results 1 - 10
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3,224
