Ramiro Jordan

Geometric attacks on image watermarking systems

Synchronization errors can lead to significant performance loss in image watermarking methods, as the geometric attacks in the Stirmark benchmark software show. The authors describe the most common types of geometric attacks and survey proposed solutions.

Wireless communications and networking: an overview

This paper presents an overview of wireless local-area networks (LANs) and wireless personal area networks (PANS), with emphasis on the two most popular standards: IEEE 802.11, and Bluetooth. While there are many such surveys in the current literature and online, we attempt here to present wireless LANs and PANS in a unified fashion as a viable alternative to wired LANs, while stressing the remaining challenges and limitations.

Angle QIM: a novel watermark embedding scheme robust against amplitude scaling distortions

Quantization index modulation (QIM) watermarking has received a great deal of attention ever since the rediscovery of Costas result on codes with host-interference rejecting properties. While such embedding schemes exhibit considerable improvement in watermark capacity over their earlier predecessors, (e.g. spread-spectrum), their fragility to even the simplest attacks soon became apparent. Among such attacks, amplitude scaling has received special attention. We introduce a quantization scheme, named angle QIM (AQIM), that is provably insensitive to amplitude scaling attacks. Instead of embedding information by quantizing the amplitude of pixel values, AQIM works by quantizing the angle formed by the host-signal vector with the origin of a hyperspherical coordinate system. Hence, AQIMs invariance to amplitude scaling can be shown by construction. Experimental results are presented for the bit error rate performance of AQIM under additive white Gaussian noise attacks.

The effect of the random jitter attack on the bit error rate performance of spatial domain image watermarking

In this paper we study the effects of synchronization errors on the bit error rate performance of spatial domain image watermarking. For that, we introduce the concept of the random jitter attack. This attack is characterized by the displacement of each pixel position by a random amount given by an arbitrary distribution, followed by interpolation over the modified sampling grid. An analogy is made between this attack and the errors introduced by timing jitter during sampling. Under such an analogy, a channel model is proposed to describe the effects of the jitter attack on the watermarked image. This model suggests that the effects of the jitter attack can be analyzed as the addition of signal dependent noise to the watermarked image. We derive expressions that relate the strength of the jitter attack to the bit error rate obtained for watermark decoding. These expressions are compared to experimental results obtained by performing the random jitter attack over a spatial domain spread spectrum watermark.

Superresolution parallel magnetic resonance imaging: Application to functional and spectroscopic imaging

Standard parallel magnetic resonance imaging (MRI) techniques suffer from residual aliasing artifacts when the coil sensitivities vary within the image voxel. In this work, a parallel MRI approach known as Superresolution SENSE (SURE-SENSE) is presented in which acceleration is performed by acquiring only the central region of k-space instead of increasing the sampling distance over the complete k-space matrix and reconstruction is explicitly based on intra-voxel coil sensitivity variation. In SURE-SENSE, parallel MRI reconstruction is formulated as a superresolution imaging problem where a collection of low resolution images acquired with multiple receiver coils are combined into a single image with higher spatial resolution using coil sensitivities acquired with high spatial resolution. The effective acceleration of conventional gradient encoding is given by the gain in spatial resolution, which is dictated by the degree of variation of the different coil sensitivity profiles within the low resolution image voxel. Since SURE-SENSE is an ill-posed inverse problem, Tikhonov regularization is employed to control noise amplification. Unlike standard SENSE, for which acceleration is constrained to the phase-encoding dimension/s, SURE-SENSE allows acceleration along all encoding directions--for example, two-dimensional acceleration of a 2D echo-planar acquisition. SURE-SENSE is particularly suitable for low spatial resolution imaging modalities such as spectroscopic imaging and functional imaging with high temporal resolution. Application to echo-planar functional and spectroscopic imaging in human brain is presented using two-dimensional acceleration with a 32-channel receiver coil.

Interactive DSP course development/teaching environment

The authors have developed an interactive environment for the creation and maintenance of dynamic, active multimedia-based teaching mechanisms. The environment is designed to be user-friendly and to facilitate the creation of educational material. This tool has already been used to create courses in multidimensional signal processing (MDSP) by researchers working together while geographically separated.

High-power microwave-induced TM/sub 01/ plasma ring

Open-shutter photography was used to capture the air breakdown pattern induced by a TM/sub 01/ mode radiated by a high-power backward wave oscillator. The resultant plasma ring was formed in air adjacent to a conical horn antenna fitted with a membrane to keep the experiment under vacuum. This image was digitized and further processed using Khoros 2.0 software to obtain the dimensions of the plasma ring. This information was used in an air breakdown analysis to estimate the radiated power, and agrees within 10% with the power measured using field mapping with an open-ended WR-90 waveguide.

Eigenvector-based spectral enhancement of nuclear magnetic resonance profiles of small volumes from human brain tissue

Nuclear Magnetic Resonance (NMR) spectroscopy is a low-energy technique which suffers from poor inherent signal-to-noise ratio (SNR). In a clinical setting, it is often desirable to study small regions of tissue in patients to aid in the detection and diagnosis of disease states. Analysis of the smaller regions, however, degrades the SNR further and renders conventional spectral estimation techniques such as the discrete Fourier transform useless. We demonstrate the utility of two complex eigenvector-based algorithms, Multiple Signal Classification (MUSIC) and Minimum Norm, in the detection of resonances within small sample volumes. The results indicate that these methods are clearly superior to Fourier transform-based techniques currently available on clinical NMR scanners.

Waterfilling Estimation for AWGN MIMO Channel Modeled as a Random Matrix

Waterfilling solutions provide optimal power distribution in multiple-input multiple-output (MIMO)system design. However, the optimal distribution is usually obtained through costly computational processes, such as the determination of the system eigenvalues. For communication channels in a fast paced environment, the costs are even higher due to the necessity of tracking channel changes. In addition, the computational costs increase with the number of inputs and outputs, i.e. the size of the MIMO channel matrix. A solution for reducing the computational burden is to utilize pre-determined waterfilling based on the channel’s statistics. No updates are required unless the channel statistical characteristics change. This work studies waterfilling estimations based on random matrix theory. The results can be applied when the channel coefficients follow a Rayleigh distribution and the noise is additive, white, and Gaussian.

Game theoretic mechanism design applied to machine learning classification

The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. Numerous approaches have been developed ranging from neural network models striving to replicate neurophysiology to more abstract mathematical manipulations which identify numerical similarities. Nevertheless a common theme amongst the varied approaches is that learning techniques incorporate a strategic component to try and yield the best possible decision or classification. The mathematics of game theory formally analyzes strategic interactions between competing players and is consequently quite appropriate to apply to the field of machine learning with potential descriptive as well as functional insights. Furthermore, game theoretic mechanism design seeks to develop a framework to achieve a desired outcome, and as such is applicable for defining a paradigm capable of performing classification. In this work we present a game theoretic chip-fire classifier which as an iterated game is able to perform pattern classification.