While large language models excel in a variety of natural language processing (NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they must either rely on off-the-shelf automatic speech recognition (ASR) systems for transcription, or be equipped with an in-built speech modality. This work focuses on the former scenario, where LLM’s accuracy on SLU tasks is constrained by the accuracy of a fixed ASR system on the spoken input. Specifically, we tackle speech-intent classification task, where a high word-error-rate can limit the LLM’s ability to understand the spoken intent. Instead of chasing a high accuracy by designing complex or specialized architectures regardless of deployment costs, we seek to answer how far we can go without substantially changing the underlying ASR and LLM, which can potentially be shared by multiple unrelated tasks. To this end, we propose prompting the LLM with an n-best list of ASR hypotheses instead of only the error-prone 1-best hypothesis. We explore prompt-engineering to explain the concept of n-best lists to the LLM; followed by the finetuning of Low-Rank Adapters [1] on the downstream tasks. Our approach using n-best lists proves to be effective on a device-directed speech detection task as well as on a keyword spotting task, where systems using n-best list prompts outperform those using 1-best ASR hypothesis; thus paving the way for an efficient method to exploit ASR uncertainty via LLMs for speech-based applications.

Device-directed speech detection (DDSD) is the binary classification task of distinguishing between queries directed at a voice assistant versus side conversation or background speech. State-of-the-art DDSD systems use verbal cues, e.g acoustic, text and/or automatic speech recognition system (ASR) features, to classify speech as device-directed or otherwise, and often have to contend with one or more of these modalities being unavailable when deployed in real-world settings. In this paper, we investigate fusion schemes for DDSD systems that can be made more robust to missing modalities. Concurrently, we study the use of non-verbal cues, specifically prosody features, in addition to verbal cues for DDSD. We present different approaches to combine scores and embeddings from prosody with the corresponding verbal cues, finding that prosody improves DDSD performance by upto 8.5% in terms of false acceptance rate (FA) at a given fixed operating point via non-linear intermediate fusion, while our use of modality dropout techniques improves the performance of these models by 7.4% in terms of FA when evaluated with missing modalities during inference time.

Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the learning capacity of such streaming models (i.e., by adding more parameters) to improve the predictive power may not be viable for real-world tasks. In this work, we propose a new loss, Streaming Anchor Loss (SAL), to better utilize the given learning capacity by encouraging the model to learn more from essential frames. More specifically, our SAL and its focal variations dynamically modulate the frame-wise cross entropy loss based on the importance of the corresponding frames so that a higher loss penalty is assigned for frames within the temporal proximity of semantically critical events. Therefore, our loss ensures that the model training focuses on predicting the relatively rare but task-relevant frames. Experimental results with standard lightweight convolutional and recurrent streaming networks on three different speech based detection tasks demonstrate that SAL enables the model to learn the overall task more effectively with improved accuracy and latency, without any additional data, model parameters, or architectural changes.

Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e.g. a smartphone) is critical for achieving naturalistic, engaging, and privacy-centric interactions with the VA. To this end, we present a novel approach to predict the user intention (whether the user is speaking to the device or not) directly from acoustic and textual information encoded at subword tokens which are obtained via an end-to-end (E2E) ASR model. Modeling directly the subword tokens, compared to modeling of the phonemes and/or full words, has at least two advantages: (i) it provides a unique vocabulary representation, where each token has a semantic meaning, in contrast to the phoneme-level representations, (ii) each subword token has a reusable "sub"-word acoustic pattern (that can be used to construct multiple full words), resulting in a largely reduced vocabulary space than of the full words. To learn the subword representations for the audio-to-intent classification, we extract: (i) acoustic information from an E2E-ASR model, which provides frame-level CTC posterior probabilities for the subword tokens, and (ii) textual information from a pretrained continuous bag-of-words model capturing the semantic meaning of the subword tokens. The key to our approach is that it combines acoustic subword-level posteriors with text information using the notion of positional-encoding to account for multiple ASR hypotheses simultaneously. We show that the proposed approach learns robust representations for audio-to-intent classification and correctly mitigates 93.3% of unintended user audio from invoking the VA at 99% true positive rate.

Before the recent success of deep learning methods for automated medical image analysis, practitioners used handcrafted radiomic features to quantitatively describe local patches of medical images. However, extracting discriminative radiomic features relies on accurate pathology localization, which is difficult to acquire in real-world settings. Despite advances in disease classification and localization from chest X-rays, many approaches fail to incorporate clinically-informed domainspecific radiomic features. For these reasons, we propose a Radiomics-Guided Transformer (RGT) that fuses global image information with local radiomics-guided auxiliary information to provide accurate cardiopulmonary pathology localization and classification without any bounding box annotations. RGT consists of an image Transformer branch, a radiomics Transformer branch, and fusion layers that aggregate image and radiomics information. Using the learned self-attention of its image branch, RGT extracts a bounding box for which to compute radiomic features, which are further processed by the radiomics branch; learned image and radiomic features are then fused and mutually interact via cross-attention layers. Thus, RGT utilizes a novel end-to-end feedback loop that can bootstrap accurate pathology localization only using image-level disease labels. Experiments on the NIH ChestXRay dataset demonstrate that RGT outperforms prior works in weakly supervised disease localization (by an average margin of 3.6% over various intersection-over-union thresholds) and classification (by 1.1% in average area under the receiver operating characteristic curve). We publicly release our codes and pre-trained models at https://github.com/VITAGroup/chext.

We address the problem of detecting speech directed to a device that does not contain a specific wake-word.Specifically, we focus on audio coming from a touch-based invocation.Mitigating virtual assistants (VAs) activation due to accidental button presses is critical for user experience.While the majority of approaches to false trigger mitigation (FTM) are designed to detect the presence of a target keyword, inferring user intent in absence of keyword is difficult.This also poses a challenge when creating the training/evaluation data for such systems due to inherent ambiguity in the user's data.To this end, we propose a novel FTM approach that uses weakly-labeled training data obtained with a newly introduced data sampling strategy.While this sampling strategy reduces data annotation efforts, the data labels are noisy as the data are not annotated manually.We use these data to train an acoustics-only model for the FTM task by regularizing its loss function via knowledge distillation from an ASR-based (LatticeRNN) model.This improves the model decisions, resulting in 66% gain in accuracy, as measured by equal-error-rate (EER), over the base acoustics-only model.We also show that the ensemble of the LatticeRNN and acousticdistilled models brings further accuracy improvement of 20%.

Voice trigger detection is an important task, which enables activating a voice assistant when a target user speaks a keyword phrase.A detector is typically trained on speech data independent of speaker information and used for the voice trigger detection task.However, such a speaker independent voice trigger detector typically suffers from performance degradation on speech from underrepresented groups, such as accented speakers.In this work, we propose a novel voice trigger detector that can use a small number of utterances from a target speaker to improve detection accuracy.Our proposed model employs an encoder-decoder architecture.While the encoder performs speaker independent voice trigger detection, similar to the conventional detector, the decoder is trained with metric learning and predicts a personalized embedding for each utterance.A personalized voice trigger score is then obtained as a similarity score between the embeddings of enrollment utterances and a test utterance.The personalized embedding allows adapting to target speaker's speech when computing the voice trigger score, hence improving voice trigger detection accuracy.Experimental results show that the proposed approach achieves a 38% relative reduction in a false rejection rate (FRR) compared to a baseline speaker independent voice trigger model.

Accurate classification and localization of abnormalities in chest X-rays play an important role in clinical diagnosis and treatment planning. Building a highly accurate predictive model for these tasks usually requires a large number of manually annotated labels and pixel regions (bounding boxes) of abnormalities. However, it is expensive to acquire such annotations, especially the bounding boxes. Recently, contrastive learning has shown strong promise in leveraging unlabeled natural images to produce highly generalizable and discriminative features. However, extending its power to the medical image domain is under-explored and highly non-trivial, since medical images are much less amendable to data augmentations. In contrast, their prior knowledge, as well as radiomic features, is often crucial. To bridge this gap, we propose an end-to-end semi-supervised knowledge-augmented contrastive learning framework, that simultaneously performs disease classification and localization tasks. The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation. Specifically, we first apply an image encoder to classify the chest X-rays and to generate the image features. We next leverage Grad-CAM to highlight the crucial (abnormal) regions for chest X-rays (even when unannotated), from which we extract radiomic features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray. In this way, our framework constitutes a feedback loop for image and radiomic features to mutually reinforce each other. Their contrasting yields knowledge-augmented representations that are both robust and interpretable. Extensive experiments on the NIH Chest X-ray dataset demonstrate that our approach outperforms existing baselines in both classification and localization tasks.

In this paper we demonstrate speech synthesis using different electroencephalography (EEG) feature sets recently introduced in [1]. We make use of a recurrent neural network (RNN) regression model to predict acoustic features directly from EEG features. We demonstrate our results using EEG features recorded in parallel with spoken speech as well as using EEG recorded in parallel with listening utterances. We provide EEG based speech synthesis results for four subjects in this paper and our results demonstrate the feasibility of synthesizing speech directly from EEG features.

Deep Neural Network-based speech recognition systems are widely used in most speech processing applications. To achieve better model robustness and accuracy, these networks are constructed with millions of parameters, making them storage and compute-intensive. In this paper, we propose Bin & Quant (B&Q), a compression technique using which we were able to reduce the Deep Speech 2 speech recognition model size by 7 times for a negligible loss in accuracy. We have shown that our algorithm is generally beneficial based on its effectiveness across two other speech recognition models and the VGG16 model. In this paper, we have empirically shown that Recurrent Neural Networks (RNNs) are more sensitive to model parameter perturbation than Convolutional Neural Networks (CNNs), followed by fully connected (FC) networks. Using our B&Q technique, we have shown that we can establish parameter sharing across layers instead of just within a particular layer.

We design a Blind Network-Assisted Diversity Multiple Access (BNDMA) method for resolving packet collisions in synchronous packet-switched networks. As opposed to typical BNDMA schemes, the method operates in non-blocking mode. Idle transmitters at the start of a collision resolution interval may join the set of active transmitters and contact the receiver before the end of the interval. A naive queuing analysis of the proposed scheme is exponentially complex in the size of the network. We carry out a computationally efficient analysis of the network throughput and queuing delay. We show that the suggested scheme reduces the queuing delay of the buffered packets at the transmitters without sacrificing the maximum throughput achieved by standard BNDMA. Further insights are derived from the numerical experiments.

The performance of automatic speech recognition systems(ASR) degrades in the presence of noisy speech. This paper demonstrates that using electroencephalography (EEG) can help automatic speech recognition systems overcome performance loss in the presence of noise. The paper also shows that distillation training of automatic speech recognition systems using EEG features will increase their performance. Finally, we demonstrate the ability to recognize words from EEG with no speech signal on a limited English vocabulary with high accuracy.

Understanding the neural processing of natural speech processing is an important first step for designing Brain-Computer Interface (BCI) based speech enhancement and speech recognition systems. Complex neural signals like electroencephalography (EEG) are time-varying and has a non-linear relationship with continuous speech. Linear models can decode stimulus features reliably, but the correlation between the reconstructed signal and continuous EEG remain low despite attempts at optimization. In the current application, we demonstrate the utility of a Recurrent Neural Networks (RNN) model to relate various stimuli features such as the envelope, spectrogram to the continuous EEG in a cocktail party scenario. We use a Long Short-Term Memory (LSTM) neural network architecture that has self-connecting loops which help in preserving past information to predict future value. Given that predictability plays a critical role in speech comprehension, we posit that such a neural network architecture yield better results. In attended condition, for native participants, the LSTM models yield 30% and 22% mean correlation improvement and for non-native participants, 43% and 37% improvement over linear models for envelope and spectrogram respectively with EEG. Finally, we have trained a single model to predict the native language of a participant using EEG and it yielded 95% accuracy.

The fast temporal changes of a millimeter wave channel necessitate frequent estimation of the channel. Power reduction techniques for the channel estimation process for ultra-wideband 5G systems are highly desirable. High speed analog-to-digital converters for the wideband data conversion and high speed baseband processing of the Nyquist rate digital samples are the main contributors to high power consumption. This work utilizes the subsequence properties of Zadoff Chu sequences and presents a training based channel estimation algorithm that can operate at a fraction of the symbol rate and thus save power. The algorithm also provides a framework for tradeoff between channel estimation performance and computational complexity. This can allow a receiver to go into power saving mode during high signal to noise ratio channel estimation. Our analysis and simulation results show that our sub-Nyquist based approach achieves maximum likelihood performance at full rate sequence for a single path channel model.

We propose a relax-and-round approach combined with a greedy search strategy for performing complex lattice basis reduction. Taking an optimization perspective, we introduce a relaxed version of the problem that, while still nonconvex, has an easily identifiable family of solutions. We construct a subset of such solutions by performing a greedy search and applying a projection operator (element-wise rounding) to enforce the original constraint. We show that, for lattice basis reduction, such a family of solutions to the relaxed problem is the set of unitary matrices multiplied by a real, positive constant and propose a search strategy based on modifying the complex eigenvalues. We apply our algorithm to lattice-reduction aided multipleinput multiple-output (MIMO) detection and show a considerable performance gain compared to state of the art algorithms. We perform a complexity analysis to show that the proposed algorithm has polynomial complexity.

We present a blind collision resolution algorithm in slow fading channels based on retransmission diversity. The algorithm neither assumes packet nor symbol synchronization of the different users and it does not demand estimates of the arrival times of the colliding signals. The proposed scheme works independently of the relative alignment of the packets, and so it can also resolve synchronous collisions. The decoding complexity does not scale with the packet size and thus does not burden the receiver. Having forgone synchronization, the penalty paid is a longer queueing delay of data at the transmitters. Still the algorithm achieves high throughputs similar to synchronous network division multiple access (NDMA) protocols.

Artificial intelligence is often touted as the ultimate automation technology capable of outperforming humans. It is also feared by some because of its potential to eliminate certain jobs. In this paper, we describe scenarios in which man-machine symbiosis, or properly designed combinations of man and machine, can actually outperform man and machine. We also present a statistical solution to a constrained version of a generic problem in man-machine symbiosis. Specifically, we solve the problem of optimal selection, ordering and presentation of data to a human to solve a class of problems that artificial intelligence can fail to solve on its own, such as fraud detection. The man-machine symbiosis solution we present overcomes human cognitive biases which stand in the way of their rational decision making.

A generalized sequential probability ratio test (GSPRT) is a classical algorithm for binary sequential hypothesis testing. Though it is well-studied in the literature, there has been no optimal design of this test due to the difficulty of choosing its thresholds. In this paper we formulate the binary sequential hypothesis testing as an optimization problem. The latter is non-convex, and finding a global minimizer of the objective is combinatorially complex in the number of stages of the sequential test. On the other hand, greedily minimizing the objective has linear complexity but achieves sub-optimal results. We propose a generalization of the greedy approach that allows the designer to trade off complexity for closeness of the thresholds to their optimal values. Simulation results show that the proposed method gives arbitrarily close solution to optimal by increasing the window span of future sample distributions that are utilized to set a current test threshold. The window span is a hyper-parameter that is optimized for the target application.

We consider the problem of selecting and ordering a subset of N' out of N observations to be presented to a human being in the context of a binary hypothesis testing problem. We restrict our attention to i.i.d. Gaussian observations. We propose an extension of the approximate subset sum algorithm, and show that it can be used to solve the problem with polynomial complexity. Furthermore, we show that the solution yields near optimal detection performance when compared to the case where all N observations are optimally processed.

Imaging of physiological functions using magnetic resonance imaging is limited due its slow data acquisition speed. Previously various techniques based on data sharing in the spatiotemporal k-space or sparse recovery methods have been proposed to increase imaging speeds in dynamic MRI. This paper presents a novel formulation for fast dynamic MRI which combines the generic linear dynamical system based spatiotemporal model with sparse recovery techniques. Specifically, the formulation uses a known prior time-evolution model for the physiological function implicitly and enforces the model errors (innovations) to be sparse. The preliminary results of dynamic MRI recovery experiments on an in-vivo myocardial perfusion dataset show that the proposed approach preserves details like edges and fine structures in recovered images better than previous k-space data-sharing and sparse recovery techniques individually.

Direction of arrival (DoA) estimation of high-resolution beams is critical for cell search and maintaining communication in millimeter wave (mmWave) cellular systems. All-digital solutions for DoA estimation, though desirable for their flexibility and performance, are impractical because of their use of high-speed, power hungry Analog-Digital Converters (ADCs) at each antenna element. In this paper we take a novel approach to formulate a fully digital DoA estimation solution. Our method utilizes mmWave propagation characteristics to properly design a pilot signal that enables the reduction of the ADC speed and the number of antenna by a significant amount while achieving a desired performance goal. Our proposed method applies subspace-based methods like MUSIC on the heavily subsampled received signals, and performs the DoA estimation on a small subset of the dominant multi-paths in the channel one at a time, thus maintaining the integrity of the DoA estimation algorithm while significantly reducing computational complexity.

We present a novel QRS complex detection scheme from ECG with motion artifact. The algorithm relies on subspace learning and template matching. QRS complex detection during exercise is a challenging problem because multiple artifacts affect the ECG measurement. Motion artifact is considered to be the main disturbance added to the measurement during exercise. To deal with the problem, we train a dictionary to represent motion artifact using information from a tri-axis accelerometer, and then remove the artifact contribution from noisy ECG measurements. We select the GCC-PHAT filter for efficient QRS detection on the denoised ECG measurements. We show that the proposed algorithm has appreciably higher motion artifact reduction capability and lower computational complexity than competing algorithms. It is therefore a preferred alternative for implementation in mobile health monitoring systems.

In this paper we propose a novel detection algorithm that delivers impressive savings in the sensing time of the capacitive touchscreen systems using sequential energy detection methods. We also show that these savings can translate into a significant boost to the operational battery life of today's mobile devices. We provide numerical analysis of sequential energy detection scheme, and the average sample number of measurements required to decide the presence of a touch input is derived. The analysis and simulation results confirm that the proposed scheme can save at least 70% or more computational resources for performing touch detection under realistic noise condition as compared to the conventional fixed sample size detection scheme.

In this paper, we consider the problem of optimally ordering information to a human subject to maximize detection performance in a binary hypothesis testing problem. We begin by proposing a modification of the traditional Bayesian solution to hypothesis testing problems to incorporate the effect of human cognitive biases. Next, we consider the problem of selecting a subset of information to maximize detection performance in truncated hypothesis testing problems. We then use the solution to that problem to determine the real time ordering of information to enhance human binary hypothesis testing. We verify through simulations that the proposed ordering methods with and without cognitive biases minimize the probability of miss and the probability of false alarm.

High spatial resolution in functional magnetic resonance imaging improves its sensitivity to brain activation signals by reducing partial volume effects. However, the long acquisition times required for high spatial resolution limit the temporal resolution in fMRI studies. Consequently, the low temporal sampling bandwidth leads to increase in physiological noise and poor modeling of the functional activation dynamics. Thus, fast techniques capable of recovering fMRI time-series from under-sampled data are desirable to improve the sensitivity and specificity of fMRI for functional brain mapping. This paper presents an under-sampled fMRI recovery using low-rank plus sparse matrix decomposition signal model. This model is suited for blocked or slow event-related fMRI studies, where the low-rank matrix captures the temporally static T*2-weighted image patterns and, the sparse matrix captures the pseudo-periodic brain activation signal. The preliminary results of under-sampled recovery on in-vivo fMRI data show recovery of BOLD activation in human superior colliculus with contrast-to-noise ratio ≥ 4.4 (85% of reference) up to acceleration factors of 3.

Chronic effects of electrode implantation in the brain tissue alter the neural channel signal-to-noise ratio (SNR) over time. Variability of signal quality over time poses a difficult challenge in long-term decoding of neural signals for Brain Computer Interface (BCI). Specifically, all channels observed during a neural recording session may not be observed during the next recording session. This paper describes a novel approach that effectively overcomes these challenges by identifying reliable channels and features in any given trial, estimating unobservable or unreliable features and adapting the neural signal classifier with no user input in real time. The proposed decoder predicts one of eight arm directions with an accuracy, unmatched in the literature, of above 90% in two monkeys over 4-6 weeks, achieving robustness against time and also varying environmental conditions. Application of these decoders reduces neural prosthetic training time and user frustration thus improving the usability of BCI.

In this paper sequential detection problems are treated in the context of cognitive biases. We present a general bias model and we design a generalized sequential probability ratio test (GSPRT) to mitigate the bias impact following a composite hypothesis testing approach. We also derive an optimal ordering of the incoming observations for fast detection defined in terms of the average sample number (ASN) of observations. We verify through numerical analysis that the designed detector fulfills the time and accuracy requirements. Results show that its performance emulates that of a Bayesian detector optimized for fast sequential detection in absence of biases.

One of the challenges in using intra-cortical recordings like Local Field Potentials for Brain Computer Interface (BCI) is their inherent day-to-day variability and non-stationarity caused by subject motivation and learning. Practical Brain Computer Interfaces need to overcome these variations, as models trained on characteristic features from one day fail to represent new characteristics of another. This paper proposes a novel adaptive model that adjusts to signal variation by appending new features to the existing model and without knowledge of actual hand kinetics in an unsupervised way. With this adapting model we investigated the effects of learning and model adaptation on BCI performance. Using this new model we dramatically improve on all previously published long term decoding and show that target direction is accurately decoded in 95% of the trials over two weeks and in 85% of the trials in varying environments. Since the model needs no separate re-calibration, it can reduce user frustration and improve BCI experience.

Channel idle time distribution based secondary transmission strategies have been studied intensively in the literature. Under various performance metrics, the ultimate performance of secondary devices are eventually dictated by the presumed channel idle time distribution. Such distributions can take any arbitrary form in practice. In this work, we study idle time distributions in wireless local area networks (WLAN) using large amount of the channel idle time data collected in real-world WLAN networks. We demonstrate with experimental data that the channel idle time distribution can be closely modeled by hyper-exponential distribution. Furthermore, one can treat the primary packet arrival process as a semi-Markov modulated Poisson process. Several secondary transmission strategies are discussed under this model. It is shown that using only one hyper-exponential distribution, the secondary user can achieve a desirable performance when the primary packet arrival process is stationary. However, experimental data suggests that in practice, this process is not stationary and the secondary user can experience a large performance loss with stationary transmission strategy. We propose a novel transmission strategy that achieves suboptimal secondary user performance when the idle time distribution is not stationary. The performances of secondary transmission strategies are demonstrated using experimental data.

In this work, we study non-parametric hypothesis testing problem with density function constraints. The empirical likelihood ratio test has been widely used in testing problems with moment (in)equality constraints. However, some detection problems cannot be described using moment (in)equalities. We propose a density function constraint along with an empirical likelihood ratio test. This detector is applicable to a wide variety of robust parametric/non-parametric detection problems. Since the density function constraints provide a more exact description of the null hypothesis, the test outperforms many other alternatives such as the empirical likelihood ratio test with moment constraints and robust Kolmogorov-Smirnov test, especially when the alternative hypothesis has a special structure.

A critical requirement for dynamic magnetic resonance imaging (MRI) is to reduce image acquisition times while maintaining high spatial resolutions to capture the underlying process with high-information rates. This paper presents a sparse signal recovery based fast MRI method which uses: 1) dictionary learning for sparse representation of signals for encoding of data redundancy in physiological functions and, 2) a tagging radio-frequency pulses based novel MR signal encoding formulation to uniformly sample the k-space, even at high acceleration factors. The preliminary results of dynamic MR image recovery experiments using tagging based MR signal acquisition method on an in-vivo myocardial perfusion dataset outperforms the equivalent dynamic MRI method implemented with variable density k-space under-sampling.

This paper presents a simple but high performance architecture for multi-channel analog-to-digital conversion. Based on compressive sensing, only one SAR ADC is needed to convert multi-channel sparse inputs, leading to significant analog power saving and hardware saving. Moreover, it helps avoid problems occurring in conventional multi-channel ADCs such as timing skew, offset mismatch, and gain mismatch. A 12-bit SAR ADC converting 4-channel sparse signals simultaneously is designed in 130nm CMOS process. The design reaches a SNDR of 66.3dB and consumes an average power of 58μW at the sampling frequency of 1MHz. The L1minimization method is chosen to reconstruct the input signals. The single-tone and multi-tone inputs can be reconstructed with a minimum precision of 68dB and 55dB THD, respectively.

In this paper we study the inferential use of goodness of fit tests in a non-parametric setting. The utility of such tests will be demonstrated for the test case of spectrum sensing applications in cognitive radios. For the first time, we provide a comprehensive framework for decision fusion of a ensemble of goodness-of-fit testing procedures through an Ensemble Goodness-of-Fit test. Also, we introduce a generalized family of functionals and kernels called Φ-divergences which allow us to formulate goodness-of-fit tests that are parameterized by a single parameter s. The performance of these tests is simulated under gaussian and non-gaussian noise in a MIMO setting. We show that under uncertainty or non-gaussianity in the noise, the performance of non-parametric tests in general, and phi-divergence based goodness-of-fit tests in particular, is significantly superior to that of the energy detector with reduced implementation complexity. Especially important is the property that the false alarm rates of our proposed tests is maintained at a fixed level over a wide variation in the channel noise distributions.

Segmentation of liver from volumetric images forms the basis for surgical planning required for living donor transplantations and tumor resections surgeries. This paper introduces a novel idea of using sparse representations of liver shapes in a learned structured dictionary to produce an accurate preliminary segmentation, which is further evolved using a joint image and shape based level-set framework to obtain the final segmented volume. Structured dictionary for liver shapes can be learned from an available training dataset. The proposed approach requires only 3 orthogonal segmented masks as user-input, which is less than half the number required by current state-of-the-art interaction-based methods. The increased accuracy of the preliminary segmentation translates into faster convergence of the evolution step and highly accurate final segmentations with mean average symmetric surface distances (ASSD) [1] of (1.03±0.3)mm when tested on a challenging dataset containing 62 volumes. Our approach segments a volume on an average of 5 mins and, is 25% (approx.) faster than comparably performing techniques.

Channel idle time distribution (CITD) based secondary transmission strategies have been studied intensively in the literature. The performance of secondary devices are limited by the presumed CITD. But none of them provides a reasonable characterization of the CITD in reality. In this paper, we carefully examine the feature of the CITD using large amount of channel idle time data collected in various WLAN networks. It is demonstrated that the CITD can be well approximated by hyperexponential densities. Based on the estimated density, we propose a novel multiple-shot transmission strategy which takes advantage of the hyperexponential feature of the CITD. This strategy achieves at least twice the increase for the data we examine in average secondary access time compared to conventional transmission strategies.

We describe a turbo coding scheme with nonsystematic recursive convolutional codes for channels that exhibit insertions and deletions along with substitution errors. The scheme uses the MAP decoding algorithm for this channel and a special codeword arrangement to simplify the synchronization process. The decoder uses a special trellis structure to extract synchronization extrinsic information that is exchanged between the two constituent decoders. The proposed scheme is shown to provide improvement over earlier channel coding approaches for this channel model.

Restricted visualization of the surgical field is one of the most critical challenges for minimally invasive surgery (MIS). Current intraoperative visualization systems are promising. However, they can hardly meet the requirements of high resolution and real time 3D visualization of the surgical scene to support the recognition of anatomic structures for safe MIS procedures. In this paper, we present a new approach for real time 3D visualization of organ deformations based on optical imaging patches with limited field-of-view and a single preoperative scan of magnetic resonance imaging (MRI) or computed tomography (CT). The idea for reconstruction is motivated by our empirical observation that the spherical harmonic coefficients corresponding to distorted surfaces of a given organ lie in lower dimensional subspaces in a structured dictionary that can be learned from a set of representative training surfaces. We provide both theoretical and practical designs for achieving these goals. Specifically, we discuss details about the selection of limited optical views and the registration of partial optical images with a single preoperative MRI/CT scan. The design proposed in this paper is evaluated with both finite element modeling data and ex vivo experiments. The ex vivo test is conducted on fresh porcine kidneys using 3D MRI scans with 1.2 mm resolution and a portable laser scanner with an accuracy of 0.13 mm. Results show that the proposed method achieves a sub-3 mm spatial resolution in terms of Hausdorff distance when using only one preoperative MRI scan and the optical patch from the single-sided view of the kidney. The reconstruction frame rate is between 10 frames/s and 39 frames/s depending on the complexity of the test model.

The Common Spatial Pattern (CSP) method is a powerful technique for feature extraction from multichannel neural activity and widely used in brain computer interface (BCI) applications. By linearly combining signals from all channels, it maximizes variance for one condition while minimizing for the other. However, the method overfits the data in presence of dense recordings and limited amount of training data. To overcome this problem we construct a sparse CSP (sCSP) method such that only subset of channels contributes to feature extraction. The sparsity is achieved by a greedy search based generalized eigenvalue decomposition approach with low computational complexity. Our contributions in this study are extension of the greedy search based solution to have multiple sparse filters and its application in a BCI problem. We show that sCSP outperforms traditional CSP in the classification challenge of the multichannel ECoG data set of BCI competition 2005. Furthermore, it achieves nearly similar performance as infeasible exhaustive search and better than that of obtained by LI norm based sparse solution.

Temporal Dynamic Spectrum Access (DSA) in Cognitive Radio Networks exploits time gaps between the primary users transmissions. In this work, the fundamental performance limit on the throughput of cognitive radio networks that coexist with a primary network that uses a Carrier Sensing Multiple Accesses (CSMA) medium access protocol is studied. Secondary transmission strategies that do not explicitly account for the CSMA protocol of the primary network usually lead to a channel capture. The problem cannot be addressed by monitoring interference or probability of collision. Here we formulate the optimal transmission strategy problem that avoids channel capture. We show the proposed strategy achieves a performance close to the optimal strategy that do not account for channel capture, while completely eliminates channel capture effect.

This paper presents a novel approach for segmented rapid magnetic resonance (MRI) imaging using structured sparse representations. In recent years, MR imaging speed has been improved through techniques relying on reduced data acquisition either to allow less-perceivable artifacts or to exploit the data-redundancy. But, these techniques require design of randomized sampling patterns which prove to be sub-optimal from the point of attaining high acceleration gains. The proposed approach treats the imaging problem as a segmented imaging problem constrained to structured dictionaries learned from a small training dataset. The solution to which, is further relaxed to allow recovery of segmented component images from the under-sampled Fourier space. The under-sampling corresponds to traversing few commonly used sampling trajectories only (such as: spirals and radials), i.e., no special sampling pattern design is needed. This highly reduces the number of trajectory acquisitions needed per image formation and, reconstruction in learned dictionaries leads to recovery of automatic segmented images. Experimental results on a real-patient cardiac cine-MR data-set recover high quality segmented images at an acceleration factor of R=13.5 using only 2-spiral sampling trajectories.

In this paper we propose a novel suite of transmit opportunity detection methods that effectively exploit the gray space present in high traffic packet networks. Our method adopts a holistic view of the primary network and proactively decides the maximum safe transmit power that does not affect any of the nodes in the primary network. This is achieved via an implicit feedback mechanism from the Primary system to the Secondary system. This is the first time that such a PU-SU feedback link has been shown to naturally arise in the framework of Dynamic Spectrum Access. A novel use of primary packet interarrival duration is developed to rapidly and robustly perform change detection in the primary network's Quality of Service. We develop and evaluate the performance of a battery of tests for such a network state change detection and provide guidelines for transitioning from one test to another in a sequential manner. We thoroughly validate the efficacy of the methods through exhaustive field testing on real world large scale IEEE 802.11 WLAN deployments.

Traditional cognitive radio protocols rely on identifying intervals of time when a channel is available to secondary users. To date, the detection of such opportunities has relied on single threshold based binary hypothesis testing schemes. In this paper, we describe the shortcomings of traditional single threshold transmit opportunity methods. We then propose a hysteresis based detection scheme that uses two different thresholds for detecting that a channel that was unavailable has become available or that an available channel is no longer available. We provide numerical simulation and experimental results to show that the proposed double threshold approach reduces interference to the primary network and enhances the throughput of the secondary network.

We study the sequential energy detection problem in the context of spectrum sensing for cognitive radio networks. We formulate a novel Sequential Energy Detector and provide a comprehensive study of its performance. The sensitivity of the Sequential Test to primary signal variance estimation is addressed for the first time ever in this paper. Specifically, we develop an Iterative Hybrid Bayesian method to robustly estimate the primary signal variance. Through extensive simulations it is demonstrated that our Sequential version of the energy detector delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection and False Alarm. Our simulations also demonstrate the enhanced robustness gained via the use of the new Variance Estimator which converges in only 10 iterations on average and delivers a performance within 10% of that with perfect knowledge of the actual primary signal variance.

Recent increase in the number of Autism cases has triggered an alarm in our society. Lack of effective diagnostics, interventions and associated cost makes early intervention and long term treatment difficult. In this paper, we describe novel methods to assist management by automatically detecting stereotypical behavioral patterns using accelerometer data. We use the Iterative Subspace Identification (ISI) algorithm to learn subspaces in which the sensor data lives. It extracts orthogonal subspaces which are used to generate dictionaries for clustering and for signal representation. It is also applied to detecting segments from acoustics data. We further improve the algorithm by detecting novel events which were not known to the system during the training. Using these methods, we achieved an average of 83% and 90% of classification rates for flapping and rocking behaviors and 93% for novel behavioral patterns studied in this paper.

This paper presents a new algorithm for real time tracking of the exterior and interior surfaces of organs using sparse sampling of the exterior surfaces. The tracking is based on identifying subspaces in which the coefficients of spherical harmonic representations of the surfaces live. It uses pre operative CT/MRI scans during training, and needlescopic images acquired during tracking. We study different strategies for sampling the exterior organ surface using the needlescopic images and also apply the method to 3D frame interpolation. Specially, we provide (1)the first demonstration of real time interior organ surface reconstruction using sparse sampling of exterior surfaces with error rate as low as 0.095%, and (2) algorithms' application in 3D cardiac frame interpolation with error rate of only 1.15%while reducing radiation rate by 90%.

We study the distributed sequential energy detection problem in the context of spectrum sensing for cognitive radio networks. We formulate a novel Doubly Sequential Energy Detector (DSED) and provide a comprehensive study of its performance. Specifically, we present the first method that sequentially combines the decisions of the Cognitive Radio nodes wherein each node is running an independent Sequential Energy Detector (SED). Through extensive simulations it is demonstrated that (i)our novel sequential version of the energy detector delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection (PD) and False Alarm (PFA), and (ii) the Doubly Sequential Procedure at the Base Station further boosts the SED performance while improving the robustness for shadowed Cognitive Radio nodes. For example, for a PD > 0.95, our simulations demonstrate that the DSED has a PFA <; 0.20 while utilizing upto 8 times fewer samples than the equivalent energy detector upto a Signal to Noise Ratio of -10 dB, below which its performance gracefully degrades.

Traditional Cognitive Radio Networks aim to utilize the radio spectrum white space. We present a radically different approach which exploits the excess Signal-to-Noise ratios at which the primary network nodes operate. This paper introduces ProTOMAC (Proactive Transmit Opportunity Detection at MAC Layer), the first spectrum access paradigm that leverages the gray space present at the MAC layer via a novel conceptual extension of the Interference Temperature (IT) approach. The ProTOMAC architecture is based on detecting changes in the primary network packet statistics. Specifically, we develop novel techniques based on iteratively updated information theoretic divergence measures along with rapid and accurate kernel density estimators. These enable the secondary network to operate subject to an interference constraint that ensures a given primary network QOS. ProTOMAC presents a generalized scheme for coexistence of dissimilar Packet Based Networks. We provide an efficient implementation of ProTOMAC using commercial off-the-shelf equipment. The coexistence efficacy of ProTOMAC is validated on an IEEE 802.11g WLAN via establishing a 500 kbps secondary link with a range of 3 meters with an early interference detection time of 240-600 ms. For the first time, identification and exploitation of hitherto unutilized transmission opportunities in Packet Based Networks has been made possible.

We describe the operation of common convolutional decoding algorithms in the presence of insertions, deletions, as well as substitutions in the received message. We first propose a trellis description that can handle the existence of insertions and deletions. Then, we use this trellis diagram to develop the Viterbi algorithm and the Log-MAP algorithm in the presence of synchronization errors. The proposed techniques are presented in the most general form where standard convolutional codes are used and no change to the encoder is required. We establish the effectiveness of the proposed algorithms using standard convolutional codes at different rates.

We present the implementation of a directional beacon-based positioning algorithm using radio frequency signals. This algorithm allows each mobile node to compute its position with respect to a set of reference nodes which are equipped with a rotating directional antenna. The use of directional beacon-based algorithm for position location eliminates the need for strict synchronization between the reference nodes and the mobile node. In contrast to positioning algorithms that rely on signal propagation time and bandwidth, the proposed algorithm depends on the beam-width and rotational speed of the directional antenna. We will show that these parameters can be optimized with a low cost solution that provides good positioning accuracy. The system implementation is based on the GNU radio software platform and the universal software radio peripheral as the hardware component. We present an enhanced maximum likelihood method for estimating the received signal amplitude profile. To deal with obstructed line-of-sight scenarios, we do not rely purely on the received signal strength and instead formulate a least squares problem to estimate the line-of-sight component in a multipath environment. These advanced signal processing techniques yield a more accurate estimate of the bearing of the mobile node with respect to each of the reference nodes. We also show that the proposed positioning algorithm is tolerant to errors in timing and synchronization. We demonstrate the ability to obtain mobile node position estimates with sub-meter accuracy by transmitting a narrowband signal of 1 kHz bandwidth in the 2.4-2.5 GHz band. The experimental results show a mean position error of 0.759 m, in a field measuring 55m by 43m, using eight 90° rotations of the antenna.

This paper presents a new cooperative spectrum sensing scheme, which allocates two separate short periods for sensing and cooperation to achieve high capability of detecting the Primary User (PU) with low interference. A mathematical model is built to derive a closed formulation of the detection probability of the CR users for guiding a real system design. Extensive simulations results demonstrate that the proposed method is superior to non-cooperative sensing and relay-based method [3] in terms of high sensing agility and less interference to the PU. Especially, comparing with [3], the proposed design increases the detection speed by 48% and reduces the interference to PU by 43%. This new sensing protocol owns the features of low complexity and independence of special hardware support, making it easy and flexible for real system implementation.

Microarray technologies had been used to measure DNA copy number data. The copy number represents the relative fluorescent intensity level between control and test DNA samples. Variation of this number may lead to many genetic diseases such as cancer. Unfortunately, the observed copy numbers are corrupted by noise due to experimental errors and probes accuracy, making the variations hard to detect. Different techniques had been proposed to denoise the data and to extract the important feature such as the breakpoints from the variant regions. In this paper, we present a robust procedure for the analysis of DNA copy number data based on maximum likelihood principle using global information of the entire data record. We show that Dynamic programming can be used to compute the DNA copy number estimates and reduce the computational complexity. Furthermore, we employ the Minimum Description Length rule to estimate the number of unknown parameters. Using simulated and real data, we show that the proposed method outperforms other popular commercial software and published algorithms.

We demonstrate that the remarkable advantages of compressed sensing remain in force when the information operator is constrained to obey the physical rules of a multistatic SAR measurement. The design guidelines of the SAR information operator for lscr2reconstructions is compared to those provided for generic lscr1reconstructions. We report little or no degradation in compression performance when using an information operator obeying SAR sampling constraints. Simulations for a Shepp-Logan image show an image is faithfully reconstructed when the number of measurements is about a third of the number of image pixels, using a minimum total-variation technique. We observed high sensitivity in performance and algorithm convergence to small perturbations in the measurement vectors.

Localizing micro cracks in critical components is crucial in the field of continuous structural health monitoring. In this paper, we utilize several signal processing and machine learning techniques such as hierarchical clustering and support vector machines (SVM) to process multisensor acoustic emission (AE) data generated by the inception and propagation of cracks. We present preliminary laboratory results that explore the pairwise event correlation of AE waveforms generated in the process of controlled crack propagation, and use these characteristics for clustering AE. By averaging the AE events within each cluster obtained from hierarchical clustering, we compute super-acoustics with higher signal to noise ratio (SNR) and use them in the second step of our analysis for calculating the time of arrival information (TOA) for crack localization. We utilize a SVM classifier to recognize the so called P-waves in the presence of noise by using features extracted from the frequency domain for accurate earliest arrival detection. Preliminary results show that our method has the potential to be a component of a structural health monitoring system based on acoustic emissions for instance for bridges.

Deformable organ tracking has been a challenge in various medical applications. This paper proposes an algorithm for 3D organ tracking based on spherical harmonics (SH) and subspace clustering. The potential deformation subspaces are identified from training data, based on which an extremely low density sampling strategy and a low cost deformation construction method are designed. Both theoretical analysis and simulations verified that the presented tracking algorithm minimizes the number of sampling locations, storage and computation complexity, while maintaining high accuracy. The designed approach can be applied to in vivo 3D organ tracking and visualization during surgical intervention.

Recomputation of the previously evaluated similarity results between biological sequences becomes inevitable when researchers realize errors in their sequenced data or when the researchers have to compare nearly similar sequences, e.g., in a family of proteins. We present an efficient scheme for updating local sequence alignments with an affine gap model. In principle, using the previous matching result between two amino acid sequences, we perform a forward-backward alignment to generate heuristic searching bands which are bounded by a set of suboptimal paths. Given a correctly updated sequence, we initially predict a new score of the alignment path for each contour to select the best candidates among them. Then, we run the Smith-Waterman algorithm in this confined space. Furthermore, our heuristic alignment for an updated sequence shows that it can be further accelerated by using reusable dynamic programming (rDP), our prior work. In this study, we successfully validate "relative node tolerance bound" (RNTB) in the pruned searching space. Furthermore, we improve the computational performance by quantifying the successful RNTB tolerance probability and switch to rDP on perturbation-resilient columns only. In our searching space derived by a threshold value of 90 percent of the optimal alignment score, we find that 98.3 percent of contours contain correctly updated paths. We also find that our method consumes only 25.36 percent of the runtime cost of sparse dynamic programming (sDP) method, and to only 2.55 percent of that of a normal dynamic programming with the Smith-Waterman algorithm.

Genetic diseases are characterized by the presence of genetic variations. These variations can be described in the form of copy number. Microarray-based comparative genomic hybridization is a high-resolution technique used to measure copy number variations. However, the observed copy numbers are corrupted by noise, making variations breakpoints hard to detect. In this paper, we provide a framework for the analysis of copy number. The first part of the framework uses an extended version of nonlinear diffusion filter as pre-processing technique to denoise the observed data base. The extension accounts for the nonuniform physical distance between probes. The second part uses estimates the relative frequency of local and global genomic variations across multiple samples to identify statistically and biologically significant variations. For evaluation, we provide copy number variations results using simulated and real data samples. We also validate the predicted copy number variation segments of copy number gain and copy number loss using the experimental molecular tests quantitative polymerase chain reaction and show that our proposed approach is superior to popular commercial software.

We introduce and investigate the concept of random sampling to the problem of image reconstruction in a multistatic SAR system. We first develop an appropriate figure of merit for the invertibility of the measurement data. We then show numerical simulations of a particular random measurement process under various parameters and compare the performance of this random sampling to that of the sampling given by a deterministic elliptical transform. We expose new connections between SAR processing and random matrix theory, and develop a fundamental result relating image quality to SAR system parameters for this mode.

We propose a new approach to underdetermined Blind Source Separation (BSS) using sparse decomposition over monochannel dictionary atoms and compare it to multichannel dictionary approaches. We show that the new approach is easily extended to any single channel decomposition method and allows for faster computation of algorithms such as the Bounded Error Subset Selection (BESS) because of the reduced dimension of the search space. Experimental results on Matching Pursuit (MP) and BESS algorithms show that our method can give better Signal to Interference Ratio performance than pursuit methods based on multichannel dictionary atoms.

The IEEE 802.22 Standard promises to be the first practical implementation of the cognitive radio technology. It is based on dynamic spectrum sensing and opportunistic access of the bands that are not currently in use by TV transmitters. FFT based pilot sensing of the TV signal is recommended in the standard. The sensing is carried out by averaging over a fixed number of multiple dwells (6-10). We propose a sequential version of the scheme where the number of dwells required is dynamically varying according to the fidelity of the received signal. We show via simulation that the proposed sequential sensing strategy yields a throughput gain and reduces system overhead.

In this paper, we propose a new framework for assessing the biological significance of the outputs of any biclustering algorithm. The framework relies on the p-value computed by a Fisher's exact test on a 2x2 contingency table derived from gene ontology (GO) enrichment level and chromatin immunoprecipitation (ChIP) data enrichment level. We illustrate the framework using our published robust biclustering algorithm (RoBA), the Cheng and Church (CC) algorithm, and a well-defined set of yeast cell cycle gene expression data and chip-chip data. Our evaluation also shows that the biclusters identified by RoBA are biologically more homogeneous than the ones identified by the Cheng and Church (CC) algorithm.

In this paper we study the performance of IEEE 802.11e enhanced distributed channel access (EDCA) priority schemes under finite load and error-prone channel. We introduce a multi-dimensional Markov Chain model that includes all the mandatory differentiation mechanisms of the standard: QoS parameters, CWMIN, CWMAXarbitration inter-frame space (AIFS), and the virtual collision handler. The model faithfully represents the functionality of the EDCA access mechanisms, including lesser known details of the standard such as the management of the backoff counter which is technically different from the one used in the legacy DCF. We study the priority schemes under both finite load and saturation conditions. Our analysis also takes into consideration channel conditions.

This paper discusses the design and analysis of post coded OFDM (PC-OFDM) systems. Coded or precoded OFDM systems are generally employed to overcome the symbol recovery problem in uncoded OFDM systems. We show that PCOFDM systems are a special case of precoded OFDM systems that offer advantageous complexity-performance trade-offs. In particular, PC-OFDM systems introduce frequency diversity by manipulating the OFDM symbols in the time domain so that the computational complexity of the system can be significantly reduced. We discuss the design principles of PC-OFDM transmitter that uses upsampling operation and the spreading codes to introduce frequency diversity. We obtain the spreading code construction criterion for minimum error performance and give examples of spreading codes for PC-OFDM systems. We also describe the design of low-complexity receiver for PC-OFDM systems. In particular, our proposed partial spreading scheme results in a low complexity decoupled detector. The probability of error analysis of the receiver leads us to postulate different design criteria. We investigate different choices for detection algorithms suitable for PC-OFDM receiver and compare their performance through simulations over Rayleigh and IEEE UWB channels.

The goal of the sensor network localization problem is to determine positions of all sensor nodes in a network given certain pairwise noisy distance measurements and some anchor node positions. This paper describes a distributed localization algorithm based on second-order cone programming relaxation. We show that the sensor nodes can estimate their positions based on local information. Unlike previous approaches, we also consider the effect of inaccurate anchor positions. In the presence of anchor position errors, the localization is performed in three steps. First, the sensor nodes estimate their positions using information from their neighbors. In the second step, the anchors refine their positions using relative distance information exchanged with their neighbors and finally, the sensors refine their position estimates. We demonstrate the convergence of the algorithm numerically. Simulation study, for both uniform and irregular network topologies, illustrates the robustness of the algorithm to anchor position and distance estimation errors, and the performance gains achievable in terms of localization accuracy, problem size reduction and computational efficiency.

We derive a family of sequences which obey a characteristic-phase constraint as it applies to finite sequences. We show these sequences are special cases of the well-known Welti sequences. We construct a transmit waveform which allows extremely efficient decomposition, whose outputs are simultaneously interpretable as adjacent correlation-filter outputs, and as lossless input signal representations. We discuss implementation of receive and transmit functions.

We describe a system for assisting patients in a home setting who suffer from cognitive impairments due to traumatic brain injury. The system integrates fixed wireless home sensors and wearable wireless sensors. We focus on the task of classifying activities of daily living. We locate and track the subjects with the help of home sensors and capture the details of an executed activity with a 2-axis wearable wireless accelerometer sensor attached to the right wrist. We extract time domain and frequency domain features for each task and classify them with Gaussian mixture models followed by a majority voter. The majority voter provides low false positive rates while continuously tracking the tasks. The experimental results from 2 subjects in recognizing 4 distinct daily activity tasks are promising.

The goal of the sensor network localization problem is to determine positions of all the sensor nodes in a network given certain pairwise noisy distance measurements and inaccurate anchor node positions. A two-step distributed localization approach based on second-order cone programming (SOCP) relaxation is presented. In the first step, the sensor nodes determine their positions based on local information and in the second step, the anchor nodes refine their positions using information from the neighboring nodes. Our numerical study shows that the sensor and anchor positions cannot be estimated in a single step; the sensors must be estimated first for the results to converge. The second step enables anchors which are in the convex hull of their neighbors to refine their positions. Extensive simulation results with inaccurate anchor positions and noisy distance measurements are presented. These illustrate the robustness of the algorithm and the performance gains achievable in terms of problem size reduction, computational efficiency and localization accuracy.

In this paper, we propose group-biomarkers as an alternative to the traditional single biomarkers used to date for the detection of ovarian cancer. Group-biomarkers are a set of genes that are used simultaneously for the diagnosis of early-stage and/or recurrent cancer. We describe a procedure for identifying such group-biomarkers from a data set of gene expression levels corresponding to normal and diseased ovarian tissue as well as tissue from other organs. The procedure starts with a list of potential single biomarkers. It then uses an order preserving biclustering step to identify other genes that are co-regulated with the candidate single biomarkers across the normal and diseased ovarian tissue and tissue from other organ. We present a statistical analysis that demonstrates that group-biomarkers have a much better detection performance than single biomarkers as exhibited by receiver operating characteristics curves.

In this paper, we propose an optimal discrete rate switch-based multiuser diversity (DSMUDiv) scheduling scheme that reduces the feedback load while preserving most of the performance of opportunistic scheduling. In order to reduce the feedback rate, quantized values indicating the modulation level are feedback instead of the full values of the SNRs (quantized SNRs). We examine the DSMUDiv scheme using two scheduling criteria depending on the distribution of the mobile users in the cell: (i) absolute signal-to-noise ratio (SNR)-based scheduling in the case of independent and identical distributed (i.i.d.) users, and (ii) normalized SNR-based scheduling in the case of i.-non-i.d. users. The paper includes the derivation of closed-form expressions of the feedback load, spectral efficiency and probability of access. Monte Carlo simulation is used to evaluate the spectral efficiency in the case of normalized SNR-based scheduling. We compare our scheduling scheme under slow Rayleigh fading assumption with the optimal (full feedback load) selective diversity scheduling scheme

Abstract In this paper, we investigate the performance of a blind adaptive modulation scheme that does not require any channel knowledge and just uses binary feedback, thereby decreasing feedback load. Retransmission of erroneous packet is not considered. In particular, we present an analytical framework for the performance evaluation of a simple wireless system in terms of receiver and transmitter structure. The system requires no knowledge of the channel and relies on a binary feedback. Slow and fast Rayleigh fading channel conditions are considered. The paper includes the derivation of the closed‐form expressions of the spectral efficiency. In some cases, closed‐form expression for packet error rate (PER) are derived. Our results show relatively high PER but some applications can still operate in a satisfactory fashion in these conditions, such as voice communication. Using coded modulation with high coding gain and increasing the number of blocks per time slot decrease the PER even more. An advantage of this system is that it uses a low complexity receiver, which sends binary feedback. Copyright © 2006 John Wiley & Sons, Ltd.

Multiple-input multiple-output (MIMO) is the new technology that has the potential of increasing the transmission speeds and extending the range in wireless LANs. While MIMO links has been extensively researched in the PHY layer research community, very few studies has been done on the MAC level. In this paper we perform cross-layer analysis to study the impact of spatial multiplexing using MMSE and ZF on the MAC layer. We discuss an analytical model that takes into account the packet error rate (PER) as a loss factor in calculating the MAC throughput. The model is built on the legacy IEEE 802.11 DCF access mechanism. Expressions for throughput and average service time of packets are provided. The analytical expressions are solved using MATLAB and the results are validated by simulations.

Sequence alignment or decoding in molecular biology is mostly done via computationally expensive dynamic programming (DP) based approaches. Unfortunately, as sequencing errors are discovered frequently, researchers must repeat all previous similarity analysis for the erroneous sequence. This can take hours or days. In this work, we derive relative tolerance bounds on node distances from a root node that guarantee that partial shortest path distances remain optimal. We then propose an algorithm that uses these bounds to skip all unperturbed parts of a sequence when recomputing an alignment. We also discuss techniques to reduce the memory requirements of the algorithm by focusing on the highly conserved segments of the sequence. Experimental results establish that our proposed alignment procedure can update alignment decisions of modified sequence with 4.6% to 18% of the number of computations required by the normal Needleman-Wunsch algorithm, depending on sequence length. Higher computational savings are achieved with longer sequences.

Orthogonal frequency division multiplexing (OFDM) provides a viable solution to communicate over frequency selective fading channels by converting them to an equivalent collection of flat fading channels. In doing so, OFDM systems fail to reap the benefits of diversity available in multipath fading channels. To ameliorate this shortcoming of OFDM, explicit diversity in the form of redundant or non-redundant coding is needed. Our main goal in this paper is to explore different options for low complexity encoder design and compare their performance and complexity. Specifically, in the class of non-redundant codes we discuss the use of signal space diversity codes in OFDM systems. For redundant codes, we introduce a novel low complexity postcoded-OFDM system where coding is employed after performing the IFFT (inverse fast Fourier transform) in the transmitter to reduce system complexity. Simulation results show that postcoded-OFDM with redundant coding outperforms other choices of encoder design considered in this paper

The goal of bounded error subset selection (BESS) is to find the sparsest representation of an Ntimes1 vector b using vectors from a dictionary A of size NtimesM, such that the approximation is within a distance delta from b. Here delta is a user defined approximation threshold. Specifically, the goal is to find the sparsest vector x such that parAx - bpar les delta. The BESS is a reformulation of the classical subset selection problem. We describe two enumeration approaches with bounded complexities that find the optimal solution to the BESS problem. In particular, the paper describes the first exhaustive enumeration solution to subset selection type problems with polynomial complexity. Furthermore, it also describes a lower complexity stack decoding approach that finds a solution to the BESS problem with a complexity that is proportional to that of orthogonal matching pursuit. The approaches described here have a markedly better rate-distortion behavior than any of the other known solutions to the subset selection and BESS problems

Uncovering genetic pathways is equivalent to finding clusters of genes with expression levels that evolve coherently under subsets of conditions. This can be done by applying a biclustering procedure to gene expression data. Given a microarray data set with M genes and N conditions, we define a bicluster with coherent evolution as a subset of genes with expression levels that are non-decreasing as a function of a particular ordered subset of conditions. We propose a new biclustering procedure that identifies all biclusters with a specified number of K conditions in parallel with O(MK) complexity. Unlike almost all prior biclustering techniques, the proposed approach is guaranteed to find all biclusters with a specified minimum numbers of genes and conditions in the data set. All of the biclusters it identifies have no imperfection, i.e., the evolutions of the genes in each bicluster will be coherent across all conditions in the bicluster. Furthermore, the complexity of the proposed approach is lower than that of prior approaches. We also discuss the biological significance of the biclusters computed by the algorithm for a set of yeast gene microarray data

Orthogonal frequency division multiplexing (OFDM) is emerging as a key modulation scheme for future high speed packets based wireless transmission systems. In a wireless multiuser network system, when an opportunistic scheduling is employed in the OFDM system with adaptive modulation, it may encounter implementation problems due to the need for a large amount of channel information. In this paper we propose a decentralized scheduling scheme to exploit multiuser diversity (MUDiv) in OFDM systems. Users access the channel using a simple variation of the ALOHA random access protocol, termed channel-aware/collision-limited ALOHA. Based on the channel quality, users randomly transmit a request packet with a probability p. When a collision occurs, collided users will retransmit a request packet with probability p. We present an analytical analysis of the average system throughput (average spectral efficiency). We compare our proposed scheduling scheme with the proposed scheduling scheme , the optimal selective diversity scheduling scheme (centralized scheduling), and the round-robin scheduling scheme.

A new image data-hiding technique is proposed. The proposed approach modifies blocks of the image after projecting them onto certain directions. By quantizing the projected blocks to even and odd values, one can represent the hidden information properly. The proposed algorithm performs the modification progressively to ensure successful data extraction without the need for the original image at the receiver side. Two techniques are also presented for correcting scaling and rotation attacks. The first approach is an exhaustive search in nature, which is based on a training sequence that is inserted as part of the hidden information. The second approach uses wavelet maxima as image semantics for rotation and scaling estimation. Both algorithms have proved to be effective in correcting rotation and scaling distortion.

In this paper, we present a novel data-embedding system with high embedding capacity. The embedding algorithm is based on the quantized projection embedding method with some enhancement to achieve high embedding rates. In particular, our system uses a random permutation of the columns of a Hadamard matrix as projection vectors and a fixed perceptual mask based on the JPEG default quantization table for the quantization step design. As a result, the data-embedding system achieves 1/167 (1 bit out of 167 raw image bits) to 1/84 hiding ratios with a BER of around 0.1% in the presence of JPEG compression attacks, while maintaining visual distortion at a minimum.

In IEEE 802.11 WLANs, stations can operate at different data rates. For example, in 802.11b, wireless stations transmit at four date rates 11, 5.5, 2 and 1 Mbps. A station changes its modulation type and transmits at a lower rate when it faces signal fading and interference. In this paper we introduce a model to capture the capability of the standard to operate at different data rates under both, finite load and saturation conditions. Many improvements are also made in order to make the model more consistent with the standard than existing models. The proposed analysis applies to both the basic access and the RTS/CTS access mechanisms. The results we obtained from the model also show the performance anomaly of the standard when it operates at different data rates that were previously observed in the literature, our experiments and simulation. We use the model to study a scheme we proposed earlier to avoid the degradation in the performance by changing the frame sizes of the stations according to their speeds. Throughput performance evaluation is provided and an analytical expression for average service time is given. Experiments were performed to validate the model

In this paper, we propose an optimal discrete rate switch-based multiuser diversity system (DSMUDiv) that reduces the feedback load while preserving the essential of the scheme's performance in some cases. We examine the DSMUDiv scheme using two scheduling criteria depending on the distribution of the mobile users in the cell: (i) absolute signal-to-noise ratio (SNR)-based scheduling in the case of independent and identical distributed (i.i.d.) users, and (ii) normalized SNR-based scheduling in the case of non-i.i.d. users. The paper includes the derivation of closed-form expressions of the feedback load in the case of absolute and normalized SNR-based scheduling and the spectral efficiency in the case of absolute SNR-based scheduling. Computer simulation is used to evaluate the spectral efficiency in the case of normalized SNR-based scheduling. Under slow Rayleigh fading assumption, we compare our scheme with the optimal (full feedback load) selective diversity scheme

We propose an enhanced directional beacon based position location procedure for UWB systems. Unlike previous work that relies on strongest return and therefore prone to errors in obstructed line of sight (OLOS) environments, our procedure identifies the LOS return by detecting the earliest arrival. To overcome synchronization problems, we propose a correlation based window algorithm to detect the earliest arrival and hence the LOS component across a 360 deg rotation of directional beacon. We performed a detailed and exact analysis of the algorithm that has not yet reported in the literature. The analysis quantifies the dependence of the algorithm on the variance of noise and the beam pattern. The analysis also motivated us to mitigate the effect of noise. We, therefore, propose an improved algorithm and implement it in a computationally efficient way. To improve position estimation we perform a three point search around the estimated time index n and selects the point that minimizes l2norm of a performance measure vector. From simulation results we claim position location accuracy of around 30 cm with a pragmatic 4-element antenna array using 2.4 GHz UWB pulses and 4-bit A/D converter at 28 dB SNR

This paper addresses ranging in indoor quasi-static sensor environments and presents a time-of-arrival (TOA) based ranging algorithm. A statistical model of the multipath channel in the form of the signal return and noise characterization is derived, and utilized to distinguish signal components from noise. The algorithm then uses multiple signal receptions at each base station, to differentiate between line-of-sight (LOS) and non-LOS components, and to accurately estimate the position of the LOS component in the received multipath signal. The location is estimated through a mathematical programming problem formulation. Using a synthesized bandwidth of 2 GHz, a 4-bit analog-to-digital converter (ADC) and with 5-10 dB signal-to-noise ratio (SNR), range estimation with sub-meter accuracy is achieved. Furthermore, the associated range estimation error does not increase with increase in the transmitter-receiver range

A reduced complexity version of the bounded error subset selection (BESS) algorithm is proposed. By relaxing the integer constraint in the original BESS algorithm, we show that the BESS problem can be reformulated as an ordinary linear program instead of an integer program with exponential worst-case complexity. We retain the sparseness of the representation in the modified BESS by weighting the dictionary with the minimum 2-norm solution of the subset selection problem corresponding to the BESS problem at hand. The proposed algorithm is compared to the basis pursuit, orthogonal matching pursuit, and the best orthogonal basis algorithms. It is shown that the proposed algorithm has a better packing property and an improved rate-distortion behavior.

We use local discriminant bases and linear discriminant analysis to classify EEG of left and right hand movement execution and imagination. The local discriminant bases adaptively segment and extract features from real and imagined movement EEG (2003 BCI competition) using cosine packets and Kullback-Leibler, Euclidean and Hellinger class separability (CS) criteria. We also tried principal component analysis (PCA) as another feature reduction method. In our case, CS ordered coefficients resulted in lower classification error than PCA using a smaller number of coefficients. We observed that the most discriminative components were located on the post movement beta and alpha synchronization. Pre-movement features were also selected by the algorithm. We believe that these segments correspond to the mental state and strategy of the subject during the movement execution/imagination. The main advantage of the algorithm is that it adaptively finds these physiological states in an ongoing EEG. This may eliminate the inter- and intra-subject variability. The average error rate of the classification was 12.7% for movement execution and 14.2% for movement imagination. Accordingly, the algorithm would be the 3rd best in the 2003 BCI (brain-computer interface) competition.

Insect damaged wheat kernels (IDK) are characterized by a small hole bored into the kernel by insect larvae. This damage decreases flour quality as insect proteins interfere with the bread-making biochemistry and insect fragments are very unsightly. A prototype system was set up to detect IDK by dropping them onto a steel plate and processing the acoustic signal generated when kernels impact the plate. The acoustic signal was processed by three different methods: (1) modeling of the signal in the time domain; (2) computing time domain signal variances in short time windows; and (3), analysis of the frequency spectra magnitudes. Linear discriminant analysis was used to select a subset of features and perform classification. 98% of un-damaged kernels and 84.4% of IDK were correctly classified.

Uncovering genetic pathways is equivalent to finding clusters of genes with expression levels that evolve coherently under subsets of conditions. This can be done by applying a biclustering procedure to gene expression data. We propose a new biclustering procedure that derives biclusters from candidate subsets of conditions. These candidate subsets of conditions are identified by comparing pairs of gene expression data. To reduce complexity, the procedure discards early in the candidate subset of conditions formation stage any subset that is predicted to have less than a desired minimum number of conditions. When the biclusters are required to have more than a minimum number of genes, we show that further reduction in complexity can be achieved with no loss of performance by comparing each gene with only a subset of all genes. The proposed approach finds all genes expression levels that evolve coherently under each of the candidate subsets of conditions using a fast approximate pattern matching technique. This approximate pattern matching procedure can find a pattern in a list even if instances of the pattern in the list have random insertions of characters between consecutive characters in the pattern. As compared to prior techniques, the approach finds all maximum size biclusters with a number of conditions greater than a specified minimum. It has a run time equivalent to the fastest of these techniques, even though the fastest biclustering techniques are not guaranteed to find all biclusters.

A novel lattice decoder called the cube decoder (CD) is proposed in this paper. The cube decoder finds the nearest lattice point to the received signal vector inside a hypercube centered at the received vector. The dimensions of the hypercube depends on the modulating lattice. This is achieved by reformulating the detection problem as a bounded-error subset selection (BESS) and solving a binary integer program. In this paper, it is assumed that the channel is known to the receiver. The proposed decoder uses the lattice reduction technique to reduce the interference introduced by the channel. Simulation shows that the CD gives near-optimal performance. Unlike the sphere decoder (SD), the complexity of the CD shows weak dependence on SNR.

To date, the systems approach to science, which emphasizes the connections among phenomena studied at different scales and by different disciplines, is causing dramatic changes in how scientific results are communicated. These changes drive a shift on how to propagate data with certain properties so that it can be used intelligently by others. In this work we elaborate on three major factors governing the propagation module of data provenance. The proposed propagation module provides an efficient solution for many critical problems in the management and provenance of scientific data. Unlike previous work, our work aims at realizing data provenance from models to storage. The natural representation of data as objects and its utility for capturing provenance has led us to consider a new storage architecture based on the object-based storage (OSD) technology. An outline of this framework is discussed.

A new framework for data embedding in audio is proposed. The basic idea of the algorithm is to change the length of the intervals between salient points of the audio signal to embed data. The intervals are quantized and the data is embedded in the quantization indices. In our particular implementation, we use the wavelet extrema of the signal envelope as the salient points. We propose novel ideas for practical implementation that can be used by other data embedding schemes as well. The algorithm is robust to common audio processing operations, e.g., mp3 lossy compression, low pass filtering, sampling rate conversion, and time-scale modification (TSM). The perceptual quality of the audio signal after embedding the data depends on the technique used for TSM.

We present a novel technique for detecting the presence of a gradual transition in video sequences and automatically identifying its type. Our scheme focuses on analyzing the characteristics of the underlying special edit effects and estimates actual transitions by polynomial data interpolation. In particular, a B-spline interpolation curve fitting technique is used. We make use of "goodness" of fitting to determine the presence of gradual transitions. Our approach is able to recover the original transition behavior of an edit effect even if it is distorted by various post-processing stages. Our gradual transition detectors have been extensively tested on various genres of real video sequences to evaluate the performance of the proposed algorithms.

We propose a turbo decoding framework in the presence of insertions and deletions as well as substitutions in the received sequence. The decoding algorithm is based on the Viterbi algorithm for this special channel. The synchronization information is exchanged between decoders in the proposed structure and extrinsic information is used, as is commonly done in turbo decoders. Simulation results indicate that the proposed turbo structure does indeed improve the performance of the Viterbi algorithm, particularly at high code rates.

In this paper, we consider the problem of providing highly accurate time delay or ranging estimates using multiple receptions of ranging signals in multi-band communication systems. We describe a novel ranging approach that reduces the variance of the time delay estimate by M/sup 3/ in white Gaussian noise. The new technique synthesizes the return corresponding to a virtual large bandwidth signal by appropriately merging the returns of M low bandwidth ranging signals from M different sub-bands. These low bandwidth signals can be generated by decomposing a high time resolution large bandwidth signal using a full-tree wavelet decomposition filter bank. The corresponding perfect reconstruction synthesis filter bank is used at the receiver to combine the received signals. Alternatively, we show that the procedure can rely on the preamble sequences used in the proposed IEEE 802.15.3a wireless PAN standard and other communication systems. We study the effect of fading and multipath channels on the performance of the proposed scheme. Simulation results are provided to compare the performance of this scheme with traditional ones.

We reformulate the problem of finding the sparsest representation of a given signal using an overcomplete dictionary as a bounded error subset selection problem. Specifically, the reconstructed signal is allowed to differ from the original signal by a bounded error. We argue that this bounded error formulation is natural in many applications, such as coding. Our novel formulation guarantees the sparsest solution to the bounded error subset selection problem by minimizing the number of nonzero coefficients in the solution vector. We show that this solution can be computed by finding the minimum cost flow path of an equivalent network. Integer programming is adopted to find the solution.

An algorithm was developed to separate pistachio nuts with closed shells from those with open shells. It was observed that upon impact on a steel plate, nuts with closed shells emit different sounds than nuts with open shells. Two feature vectors extracted from the sound signals were Mel cepstrum coefficients and eigenvalues obtained from the principle component analysis of the autocorrelation matrix of the signals. Classification of a sound signal was done by linearly combining feature vectors from both Mel cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable. During the training phase, sounds of the nuts with closed shells and open shells were used to obtain a representative vector of each class. The accuracy of closed shell nuts was more than 99% on the test set.

Video on demand services require video multicasting protocols to provide efficient and reliable performances over various client request rates. While reactive protocols perform very well when video demand is low, well-known reactive protocols can not effectively handle these videos in situations of high demand. In this work we have developed an efficient video multicasting protocol, the feedback based multicasting protocol, that requires lower bandwidth to multicast a video at any access rate. The proposed protocol uses an online stream merging scheme, requiring lower merging costs than other previous schemes. We have provided both analysis and simulation to show the performance gain over previous protocols. From an analytical method, we found the expected service bandwidth of the feedback based multi-casting protocol (FMP) is less than that of previous reactive protocols. Furthermore, The FMP shows similar bandwidth requirement to one of the best existing proactive protocols at high client request rates.

Due to the spectrum gap between subcarriers in an UWB-OFDM system, an N-tone sigma-delta modulator can be used to introduce appropriately placed nulls into the noise spectrum of such systems. The resulting performance is evaluated in terms of in-band quantization noise and excess resolution gained. Moreover, we relate the performance of our system to that of a traditional oversampled sigma-delta modulator operating in a lowpass system. A higher-order N-tone sigma-delta modulator having better noise shaping ability is also introduced for use in UWB-OFDM systems.

We study the theory and implementation of pulsed orthogonal frequency division multiplexing (pulsed-OFDM) modulation. Pulsed-OFDM is an enhancement to the leading proposal to the IEEE 802.15.3a wireless personal area networks standardization effort, known as multi-band OFDM. In particular, we show in this paper that the pulsed-OFDM system has better performance than the non-pulsed system in indoor multipath channels and considerably lower complexity and power consumption. We begin by studying the, spectral characteristics of pulsed OFDM and the added degrees of diversity that it provides. Next, we discuss the design of receivers for such a system. We show that the diversity branches can be captured and demodulated by one or more fast Fourier transform (FFT). We then focus on a system for the IEEE 802.15.3a standard and derive a particularly low complexity implementation for that system. The implementation is based on carefully designed punctured convolutional codes. It also exploits the normal inefficiencies in an FFT architecture to implement the parallel FFT operations required to demodulate a full diversity pulsed OFDM with lower complexity and smaller area than the single FFT used by the non-pulsed system. We conclude by presenting realistic simulation results for the measured indoor propagation channels provided by the IEEE 802.15.3a standard.

The sphere decoding (SD) algorithm has been widely recognized as an important algorithm to solve the maximum likelihood detection (MLD) problem, given that symbols can only be selected from a set with a finite alphabet. The complexity of the sphere decoding algorithm is much lower than the directly implemented MLD method, which needs to search through all possible candidates before making a decision. However, in high-dimensional and low signal-to-noise ratio (SNR) cases, the complexity of sphere decoding is still prohibitively high for practical applications. In this paper, a simplified SD algorithm, which combines the K-best algorithm and SD algorithm, is proposed. With carefully selected parameters, the new SD algorithm, called SD-KB algorithm, can achieve very low complexity with acceptable performance degradation compared with the traditional SD algorithm. The low complexity of the new SD-KB algorithm makes it applicable to the simultaneously operating piconets (SOP) problem of the multi-band orthogonal frequency division multiplex (MB-OFDM) scheme for the high- speed wireless personal area network (WPAN). We show in particular that the proposed algorithm provides over 4 dB gain in bit error rate (BER) performance over the baseline MB-OFDM scheme when several piconets interfere with each other. The SD-KB algorithm can provide pseudo-MLD solutions, which have significant performance gain over the baseline method, especially when the signal-to-interference ratio (SIR) is low. The cost of performance improvement is higher complexity. However, the new SD algorithm has predictable computation complexity even in the worst scenario.

Surviving geometric attacks in image watermarking is considered to be of great importance. In this paper, the watermark is used in an authentication context. Two solutions are being proposed for such a problem. Both geometric and invariant moments are used in the proposed techniques. An invariant watermark is designed and tested against attacks performed by StirMark using the invariant moments. On the other hand, an image normalization technique is also proposed which creates a normalized environment for watermark embedding and detection. The proposed algorithms have the advantage of being robust, computationally efficient, and no overhead needs to be transmitted to the decoder side. The proposed techniques have proven to be highly robust to all geometric manipulations, filtering, compression and slight cropping which are performed as part of StirMark attacks as well as noise addition, both Gaussian and salt & pepper.

This paper first addresses the problem of known signal detection over both Rayleigh and Nakagami fading channels. More specifically, it studies the effect of different diversity schemes as a remedy to improve the probability of detection. Maximum ratio combining, selection combining, and switch and stay diversity schemes over Rayleigh fading channels are investigated. Closed form expressions for the average probability of detection for no diversity and diversity cases are obtained. The second part of this paper deals with both generalized likelihood ratio and uniformly most powerful tests for unknown-signal detection over Rayleigh fading channels. For this purpose, a simple decision criterion assuming only the knowledge of the noise power spectral density and the signal bandwidth is provided.

Broadcasting protocols can improve the efficiency of video on demand services by distributing videos that are likely to be simultaneously viewed by many clients. We have developed an efficient video broadcasting protocol called the scheduled broadcasting protocol. This protocol reduces the complexity of managing incoming streams as well as the bandwidth required to broadcast. To assess the benefit of the new protocol, we performed various simulations to compare its performance with that of previous broadcasting protocols. The simulation results show that our protocol's required bandwidth is very close to the theoretical minimum for all client waiting times. Furthermore, comparison with previous broadcasting protocols shows how our broadcasting protocol can dramatically reduce both the number of simultaneous streams during the video duration and that of required modules for watching a video.

Sleep apnea syndrome (SAS) is one of the most common breathing related sleep disorders. Sleep apnea (SA) may be of particular concern in chronic heart failure patients due to its high levels of cardiovascular morbidity and mortality. Our aim was to assess the diagnostic potential of SA using spectral analysis of nocturnal heart rate, and to introduce new simple time domain heart rate variability (HRV) measures for screening SA. Data subjects supplied by the Physionet database (http://www.Physionet.org/challenge/2000/) were analyzed. The preliminary results established the effectiveness of the spectral algorithm as a potential detection tool for SA with p<0.0001, and demonstrated the usefulness of the new proposed time domain parameters.

A new method for generating and detecting the UWB-OFDM signal using a modified sigma-delta modulator is proposed. Unlike narrowband OFDM, the UWB-OFDM spectrum can have gaps between subcarriers. The modified sigma-delta modulator, dubbed N-Tone sigma-delta, introduces N zeros at the frequencies in the quantization noise spectrum. These zeros match the locations of frequencies used by the OFDM system and the quantization noise spectrum fills the gaps in the spectrum of the UWB-OFDM signal. In fact this new structure could be used in other UWB systems anytime we have gaps in the spectrum of the transmitted signal. We describe both the transmitter and receiver structures for UWB-OFDM. We also study the spectrum of the underlying system.

We present a new video broadcasting protocol called the scalable preloading broadcast protocol. This protocol provides cost effective access times to viewers by using a scalable preloading method. The new protocol reduces the video stream management complexity as well as the required broadcasting bandwidth. To demonstrate the benefit of new protocol, we perform various simulations to compare its performance with that of previous broadcasting protocols. With simulation results, we show that our protocol's required bandwidth is very close to the theoretical minimum throughout all client waiting times. Furthermore, by comparison with previous broadcasting protocols, we also show how our broadcasting protocol can dramatically reduce the video stream decoding complexity.

The integration of multimedia services into wireless communication networks is a major source of future technological advances. One of the main challenging issues in this endeavor is the resource optimization strategy. This paper addresses this issue from the perspective of minimizing the total power consumption of a mobile terminal while maintaining a guaranteed quality-of-service (QoS). For many years, the management strategy has dealt primarily with bandwidth allocation, network capacity, and QoS. However, due to the integration of multimedia services, the increasing energy consumption of a mobile unit is also becoming a dominant factor in the design of communication systems. In this paper, we describe two technologies that can make a wireless multimedia communication system more energy-efficient while ensuring QoS. These technologies consist of an energy-efficient communication protocol for the uplink channel and a low-complexity multirate transmission scheme. We also provide a video transmission example using the H.263 standard in the proposed system to demonstrate the importance of our total power optimization strategy. The simulation results show that a savings of 10-32% is achieved in the total energy consumption of the mobile unit.

We introduce modifications to common convolutional decoders to accommodate channels with Insertions and deletions. For the Viterbi decoder, new states are added to the trellis diagram to represent the new situation and the expansion algorithm is modified accordingly. For sequential decoding, we provide modifications to the stack algorithm and a new metric is introduced. Also, we developed a systematic way for convolutional code construction using Simulated Annealing so as to maximize the distance between codewords In the presence of insertions and deletions. The proposed techniques are shown to be superior to previous approaches for this problem, and have no additional code overhead.

We describe a candidate high-bit rate communication system for short range networking in high performance computing clusters. The system uses a hybrid ultra-wideband orthogonal frequency division-multiplexing scheme. The transmitted signals are sparse pulse trains modulated by a frequency selected from a properly designed set of frequencies. The train itself consists of frequency modulated ultra-wide pulses. The system achieves good detection by integrating several pulses, and high throughput by transmitting frequencies in parallel. Unlike traditional orthogonal frequency division-multiplexing systems, a given tone is transmitted only during parts of the transmission interval. We provide an analysis of the system in a multipath environment and illustrate its performance in a realistic setting.

In real-time multicast communication scalability, reliability, feedback implosion, and packet loss recovery are of paramount importance. In this work we have developed an efficient feedback-free, entirely receiver-driven, and reliable visual information delivery hybrid scheme (SIGMA-EC) for multicast applications over unreliable communication networks, that requires no per-receiver status at the sender. Our scheme's encoding and decoding processing time is an order of magnitude faster than conventional FEC-based loss recovery schemes. Among other attractive features, the reconstructed image quality gets progressively better. Furthermore, we obtain all these advantages using a remarkably simple structure, both conceptually as well as computationally.

In this work, we propose a new channel model that is suited for systems with irregular sampling, e.g. selective data embedding in digital media. The channel model accounts for the possible occurrence of false alarms, i.e. extra data bits, in the received sequence. We propose modifications to the common decoding schemes of the convolutional codes namely, the Viterbi and the sequential decoding to compensate for these false alarms. The simulation results establish the effectiveness of the proposed algorithms in detecting false alarms with high rates.

In this paper we will address the problem of constructing a nonparameteric decision boundary for watermark detection. Most current watermarking algorithms have a parametric decision boundary that can be undone if the pirate has unlimited access to the detector. In this work we propose a fractal decision boundary which can not be estimated. That boundary is obtained by processing the decision boundary corresponding to the underlying watermarking algorithm. The performance of the new technique is essentially similar to any watermarking algorithm from which it is derived.

Providing quality-of-service (QoS) guarantees over wireless packet networks requires a thorough understanding and quantification of the interactions among the traffic source, the wireless channel characteristics, the underlying link-layer error control mechanisms, and the various packet dropping policies adopted by the upper network layers. We explore the idea of cooperation among some of these different aspects to assist the packet level error recovery. First, we revise UDP to tolerate a certain amount of channel error and to allow the delivery of partially corrupted packets. Secondly, we propose a novel technique to resolve the problem of missed bits without the need to identify their exact locations. It is based on modifying the stack algorithm for convolutional decoding. Simulation results establish the effectiveness of the proposed approach for detecting and correcting missed bits, which have direct impact on packet loss and on the overall system performance.

We describe a generalized attack on image watermarking schemes when the decoder is publicly available. The attack applies to most common watermarking schemes. In particular, we describe in detail the implementation of the attack against correlator-based watermark detectors using the least mean square (LMS) algorithm. We establish the effectiveness of the attack in removing the watermark with least distortion. Also, we describe a counterattack to avoid this problem by using nonparametric decision boundaries at the detector.

We construct a full data rate space-time (ST) block code over M=2 transmit antennas and T=2 symbol periods, and we prove that it achieves a transmit diversity of 2 over all constellations carved from Z[i]/sup 4/. Further, we optimize the coding gain of the proposed code and then compare it to the Alamouti code. It is shown that the new code outperforms the Alamouti (see IEEE J Select. Areas Commun., vol.16, p.1451-58, 1998) code at low and high signal-to-noise ratio (SNR) when the number of receive antennas N>1. The performance improvement is further enhanced when N or the size of the constellation increases. We relate the problem of ST diversity gain to algebraic number theory, and the coding gain optimization to the theory of simultaneous Diophantine approximation in the geometry of numbers. We find that the coding gain optimization is equivalent to finding irrational numbers "the furthest," from any simultaneous rational approximations.

In using acoustic emissions (AE) for mechanical diagnostics, one major problem is the differentiation of events due to crack growth in a component from noise of various origins. This work presents two algorithms for automatic clustering and separation of AE events based on multiple features extracted from experimental data. The first algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA), and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM), which outputs the noise and AE signals into separate neurons. The algorithm is verified with two sets of data, and a correct classification ratio of over 95% is achieved. The second algorithm characterizes the AE signal subspace based on the principal eigenvectors of the covariance matrix of an ensemble of the AE signals. The latter algorithm has a correct classification ratio over 90%.

A new algorithm for audio watermarking is proposed. The basic idea of the algorithm is to change the length of the intervals between salient points of the audio signal to embed data. We propose several novel ideas for practical implementations that can be used by other watermarking schemes as well. The algorithm is robust to common audio processing operations e.g. MP3 lossy compression, low pass filtering, and time-scale modification. The watermarked signal has very high perceptual quality and is indistinguishable from the original signal.

Computer security has become the main stream issue to protect the sensitive information in open networks. The complete secure system is hard to design these days due to vulnerabilities in firewalls, file and application servers, email servers, and Web servers. The solution to problems of computer security and privacy can be achieved by the cryptography mechanism. A popular way to implement security protocols uses a combination of several cryptographic schemes to overcome various limitations of security primitives. The design of scalable security protocols that are interrelated with a single cryptosystem for multimedia is a challenging cryptographic technology in the cryptologic research community. We propose the scalable cryptographic scheme for security services. The proposed eigen-based security primitives solves a wide range of secrecy, non-repudiation, authenticity, and integrity problems in many security aware multimedia applications. The new cryptography technology may play an important role in the trusted network interpretation environments.

The advent of electronic publishing creates strong interest in converting existing printed documents into electronic formats. During this process, image reproduction problems can occur due to the formation of moire patterns in the screened halftone areas. A wavelet packet based color halftone segmentation algorithm is first designed to locate possible halftone regions based on a decision function. A color halftone descreening technique based on the color sigma filter is proposed which does not assume any a priori knowledge about the halftoning process, making it applicable to any color halftone image. Combined with color halftone segmentation techniques, a complete document processing algorithm for color documents is proposed. Experimental results are offered to illustrate the performance of our algorithm.

Wireless networks and packet networks such as the Internet are prone to long burst errors and transient failures. In contrast to the traditional schemes that first introduce some amount of redundancy among descriptions before transmitting multimedia data to combat channel impairments, we investigate a new scheme to tackle this problem. The proposed scheme is to first capture the most important visual features of a given image extracted from Sigma filtering preprocessing, into independent equal length packets. At the receiver side, we adopt a smart interpolation-based technique. The reconstruction quality at the receiver depends only on the number of packets received, but is independent of the place from where they were cropped. The preliminary results on standard test images show that our reconstructed image is very pleasant to human eyes, and achieves reasonable PSNR values, while at the same time using a lower bit rate than other traditional reported schemes based on multiple description coding.

We propose a novel algorithm for high-quality data embedding in audio. The algorithm is based on changing the relative length of the middle segment between two successive maximum and minimum peaks to embed data. Spline interpolation is used to change the lengths. To ensure smooth monotonic behavior between peaks, a hybrid orthogonal and nonorthogonal wavelet decomposition is used prior to data embedding. The possible data embedding rates are between 20 and 30 bps. However, for practical purposes, we use repetition codes, and the effective embedding data rate is around 5 bps. The algorithm is invariant after time-scale modification, time shift, and time cropping. It gives high-quality output and is robust to mp3 compression.

This work considers a fundamental problem of image transmission over error-prone channels. The impairments that we target are long burst errors and transient channel failures, as would occur in the wireless network because of an obstruction in the transmission path, or severe traffic congestion in a packet network such as the Internet. These impairments cause many packets to be dropped. In contrast to the traditional schemes that first introduce some amount of redundancy among descriptions, and then use this correlation for combating channel impairments, we investigate a new scheme to tackle this problem. The proposed scheme is to capture the most important visual features of a given image, extracted by sigma filtering preprocessing, into independent equal length packets. At the receiver side, we adopt a smart interpolation-based technique. The reconstruction quality at the receiver depends only on the number of packets received, but is independent of the place from where they were cropped. The preliminary results on standard test images show that our reconstructed image is very pleasant to human eyes, and achieves reasonable PSNR values, while at the same time using a lower bit rate than other reported traditional schemes based on multiple description coding.

We treat the problem of recovering the correct message if extra bits (false alarms) are added to the body of the message at random locations. This situation is common for some watermarking systems with selective embedding which is typical when the human visual (or audio) system is incorporated to enhance the quality of the watermarking system. We propose an efficient decoding scheme for convolutional codes using some modifications of the Viterbi algorithm. Extra states are added to represent the possible false alarms and a new expansion algorithm of the modified trellis diagram is proposed. The simulation results show the high efficiency of our algorithm in detecting random false alarms with high rates.

We present a novel m-ary tree hierarchical search strategy for stationary radar target localization in the presence of white Gaussian noise. This is done in the context of a discretized version of the problem of optimal beamforming, or radar transmit and receive pattern design. We assume that the target is equally likely to be in one of M discrete cells and that we have L observations at our disposal. We recursively group the search cells into m groups until the size of each group reduces to one cell, thus creating a m-ary search tree of depth log/sub m/(M). We, then, allocate the available L observations among the tree levels in a manner that maximizes the probability of correctly locating the target. We compare the performance of the novel search strategy with that of previous techniques and demonstrate its superior performance.

New media distribution channels have created a strong need for digital media personalization that helps both users and producers get the most of media products. We concentrate on the customized music delivery issue. We propose an approach for constructing a sequence of tracks that satisfies user requirements and at the same time optimally exploits music catalogs. An optimal solution consists of examining all possible tracks enumeration in the database. This is clearly a combinatorial NP-hard problem. We use vector space concepts to formulate the constrained sequence retrieval problem as an integer program. Our experiments demonstrate the power of our approximation to the original problem in reducing the search space and producing valid solutions.

Acoustic emission-based techniques are promising for nondestructive inspection of mechanical systems. For reliable automatic fault monitoring, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. In this paper we propose the application of the Kohonen network for this purpose. The principal components of the short-time Fourier transforms of the data were applied input of the network. The clustering results confirm the capability of the Kohonen network for reliable source identification of acoustic emission signals, assuming enough care has been taken in implementing the training algorithm of the network.

In this paper, we present an unsupervised color image segmentation algorithm. By first processing a color image via the proposed color sigma filter, pixels within the same semantic region become more concentrated around their centroid in the perceptual color coordinate system. A k-mean algorithm is then designed to automatically differentiate the image into non-overlapping semantic objects. Because of the periodicity in the hue component, we apply two manifolds to completely cover the hue vector, and fuse distinguished regions from both manifolds to obtain the final image segmentation. The computational complexity of our algorithm is O(N), where N is the total number of pixels, and no priori information is assumed. We provide examples to illustrate the performance of our procedure.

In this paper we investigate the use of multi-hypothesis sequential detection techniques to enhance effort allocation in hierarchical radar target localization in the presence of white Gaussian noise. This is done in the context of a discretized version of the problem of optimal beam-forming, or radar transmit and receive pattern design. We assume that the target is equally likely to be in one of M discrete cells and that we have L observations at our disposal. We recursively group the search cells into m groups until the size of each group reduces to one cell, thus creating an m-ary search tree of depth log/sub m/(M). We then allocate the available L observations among the tree levels in a manner that maximizes the probability of correctly locating the target. The main contribution of this paper is the use of multi-hypothesis sequential detection along with on-line effort allocation to enhance the performance of a constrained off-line allocation strategy. This approach is shown to have superior performance compared with the previously proposed unconstrained off-line allocation strategy.

We present a novel technique for watermarking digital images based on its moments. The watermark is composed of the mean of several functions of the second and third order moments designed to be invariant to scaling and orthogonal transformations this has the advantage of making the watermark image dependent. The watermarked image is a linear combination of the original image and a weighted nonlinear transformation of the original. The weight is computed such that the mean of the watermarked image invariants is a predefined number. Watermark detection is as simple as computing the moment invariants of the received image. Our approach has a guaranteed visual transparency up to a contrast modification. The proposed algorithm has proved to be highly robust to all geometric manipulations, filtering, compression and small cropping which are performed as part of StirMark attacks as well as noise addition, both Gaussian and salt and pepper.

In this paper, we describe a system for classifying rock sole fish. The contour of the fish and existence of a stripe pattern constitute two important features to distinguish between different subspecies of rock sole. The system uses a multiscale sigma filter and active contour algorithm to extract the contour of the groundfish. The extracted contour is then transformed into a one dimensional signature to serve as the first feature to differentiate the northern rock sole from the southern rock sole. Next, our system uses a pattern enhancing algorithm and transforms the extracted pattern to the spatial frequency domain to serve as the second feature. A matched subspace filter is then applied to recognize each feature. The proposed recognition system integrates the classification result from both channels. We illustrate the performance of the procedure on sample images of northern and southern rock soles.

We present a novel algorithm for high capacity data embedding. The proposed algorithm gives high hiding ratios up to 1/13 (1 embedded bit out of 13 raw image pixels) subject to JPEG compression with quality factor equals 75. With such high capacity, one can easily embed some important regions of an image inside the image itself with very small perceptual distortion. In the proposed algorithm, the DCT coefficients of 8/spl times/8 blocks are first rearranged using Hilbert curves. Data is embedded using projections on a random orthogonal set. The algorithm recovers the embedded data without any reference to the original image, with very low BER (around 0.1%). Finally, the proposed algorithm shows robustness to JPEG compression.

A system for retrieving a sequence of music excerpts or songs based on users and producers requirements is proposed in this paper. Our system provides a flexible way to retrieve music pieces based on its contents as well as user-defined constraints. The proposed system allows online users to extract a sequence of songs whose first and last tracks are known and at the same time the in-between songs have minimum inter-track differences and satisfy predefined requirements. We model the problem as a constrained minimum cost flow problem which leads to a binary integer linear program (BILP) that can be solved in a reasonable amount of time. 1

We apply a sequential experiment design procedure to the problem of signal selection for radar target classification. Radar waveforms are designed to discriminate between targets possessing a doubly spread reflectivity function that are observed in clutter. The waveforms minimize decision time by maximizing the discrimination information in the echo signal. Each waveform selected maximizes the Kullback-Leibler (1951) information number that measures the dissimilarity between the observed target and the alternative targets. We discuss in details two scenarios. In the first scenario, the target environment is assumed fixed during illumination. In this case, the optimal waveform selection strategy leads to a fixed library of waveforms. During actual classification, the sequence in which the waveforms are selected from the library is determined from the noise to clutter power in the range-Doppler support of the targets. In the second scenario, the target environment changes between pulse transmissions. In this case, the maximum discrimination information is obtained by a repeated transmission of a single waveform designed from the reflectivity function of the targets. We show that our choice of signals can produce significant gains in detection performance.

In this paper, we describe a new tree-based CDMA receiver that can optimally trade complexity for detection performance. It yields the detector with the best detection performance for a given desired complexity level. Alternatively, it yields the lowest complexity receiver for any given desired detection performance. We describe a technique for designing receivers with linear complexity (including the optimal linear detector and decorrelator). We then explain how we can increase performance at the expense of a minimal increase in complexity. We show that as complexity increases to the level of that of the optimal receiver, our design approach automatically produces the optimal receiver. We also explain how our approach can be used with a minimum-mean-square-error design criterion and coded CDMA transmission. Finally, we illustrate with several examples the superiority of the receivers designed with our approach and discuss their advantages.

Summary form only given. Data embedding algorithms embed binary streams in host multimedia signals. The embedded data can add features to the host multimedia signal or provide copyright protection. We review requirements for transparent data embedding techniques in audio signals. We describe and contrast current approaches to data embedding in audio. In particular, we emphasize the advantages and limitations of the various approaches. We also describe possible signal processing and protocol level attacks on audio watermarking algorithms. We conclude with a discussion of future research directions.

In this paper we present a method for the deterministic blind identification of single-input multiple-output systems with unknown model order. The technique, that is applicable to both the FIR and IIR cases, requires only an upper bound of the model order. It is based on the special kernel structure of block Toeplitz matrices. When the model order is overestimated, this special structure entails the true solution to be embedded in the overestimated solution in a unique shift-chain form. This special shift-chain structure is then utilized to extract the true solution.

In this paper we discuss and compare different hierarchical search strategies for stationary radar target localization in the presence of white Gaussian noise. This is done in the context of a discretized version of the problem of optimal beam-forming, or radar transmit and receive pattern design. We assume that the target is equally likely to be in one of M discrete cells. We compare the performance of search strategies using the proposed approaches for beam-form design in target localization problems.

Shot boundary detection is an important for retrieval of visual media in multimedia systems. Video temporal segmentation algorithms use thresholding techniques to identify both abrupt shot changes and gradual shot transitions. Thresholding methods frequently provide false positives due to large variations in gradual transition regions. The principal component decomposition and progressive nonlinear filter (PNF) have been developed to suppress spurious temporal information in gradual transition regions while preserving abrupt shot changes. The spurious shot boundary information can be further minimized by a multistage shot boundary detection approach.

Ultrasound detection of capillary blood flow is difficult as capillary blood flow provides a weak backscattered signal with a small Doppler shift. To aid in the detection of such signals, a new algorithm is introduced exploiting two powerful ideas. First, the receiver is developed by creating subspaces corresponding to expected flow and clutter signals. It then exploits the fact that some of the basis vectors in the flow and clutter subspaces are very nearly perpendicular. By projecting the input data onto the perpendicular components of the flow subspace a very sensitive detector can be realized. Secondly, the detector coherently sums inputs from several angles. In vitro ultrasound data is collected and applied to the detector. The algorithm utilizing multiple angles is shown to be sensitive detecting flow in the experimental data down to 5 mm/sec in a 6 mm diameter channel.

Image segmentation is essential for image recognition. To achieve image segmentation, it is often desirable to process the image into piecewise smooth regions while preserving or even enhancing important edges. In this paper, we first design a multiscale sigma filter to achieve the above objective. To locate the object more accurately, we then follow the sigma filter processing step and a multiscale active contour method with much simpler implementation than the originally proposed active contour algorithm.

In this work we describe a simple transform-coefficient sorting algorithm that enhances the performance of image compression techniques. We use multiresolution grids to localize significant pixels and send out pixel values using successive approximation. In the wavelet domain our method performs slightly better than SPIHT (in average 0.1 dB of PSNR). In the DCT domain our method outperforms the SPIHT-based method and the significant tree quantization method by 1 dB. Our approach breaks the dominant role played by the zero tree structure in image coding, and provides a low complexity solution to image compression.

In this work we propose a novel perceptual coding strategy to encode images at low bit rates. Our approach encodes the most important visual features of an image extracted by using a sigma filter. Preliminary results show that the image decoded by our scheme contains less visual artifacts than the SPIHT decoded image at low bit rates, and is very pleasant to human eyes. Unlike other low bit rate progressive coding methods, in which the low-bit-rate decoded image quality is good only at low resolution, our scheme provides good perceptual quality regardless of resolution sizes. Our method provides a promising way to make image browsing faster while maintaining good image perceptual quality.

In this paper we present a new watermarking technique for digital images. Our approach modifies blocks of the image after projecting them onto certain directions. By quantizing the projected blocks to even and odd values we can represent the hidden information properly. The proposed algorithm does the modification progressively to ensure successful data extraction without any prior information being sent to the receiver side. In order to increase the robustness of our watermark to scaling and rotation attacks we also present a solution to recover the original size and orientation based on a training sequence which is inserted as part of the watermark.

Article Fraud detection and self embedding Share on Authors: Tse-Hua Lan Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MinnesotaView Profile , Ahmed H. Tewfik Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota Dept. of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MinnesotaView Profile Authors Info & Claims MULTIMEDIA '99: Proceedings of the seventh ACM international conference on Multimedia (Part 2)October 1999 Pages 33–36https://doi.org/10.1145/319878.319887Online:01 October 1999Publication History 8citation660DownloadsMetricsTotal Citations8Total Downloads660Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In this paper, we introduce a new video summarization procedure that produces a dynamic (video) abstract of the original video sequence.Our technique compactly summarizes a video data by preserving its original temporal characteristics (visual activity) and semantically essential information.It relies on an adaptive nonlinear sampling.The local sampling rate is directly proportional to the amount of visual' activity in localized sub-shot units of the video.The resulting video abstract is highly compact.To get very short, yet semantically meaningful summaries, we propose an event-oriented abstraction scheme, in which two semantic events; emotionaldialogue and violentfeatured action, are characterized and abstracted into the video summary before all other events.If the length of the summary permits, other non key events are then added.

We describe a novel approach for detecting perfect and imperfect periodicities in polyphonic music. The approach relies on beat and rhythm information extracted from the raw data after low-pass filtering. The beat and rhythm information is analyzed with a binary tree or trellis tree parsing depending on the length of the pauses in the underlying signal. This analysis yields accurate periodicity patterns at macro and micro scales. We illustrate the effectiveness of our approach using music segments from various cultures and explain its use in music classification and content-based retrieval.

In this paper, we solve the problem of inter and intra-frame frequency leakage in harmonic based audio coding approaches. Specifically, we first use an FFT to estimate the harmonic parameters. Because of the limited frequency resolution of the windowed FFT, there is an uncertainty in the measured frequency within one frequency bin. To minimize this uncertainty, we use these estimates as seeds to a minimum least squares problem and solve for the optimal harmonic parameters. The resulting residual should be essentially free of harmonics and consist of only attacks and noise. We are preserving the attacks and coding them separately. The resulting residual noise is modeled and coded using wavelet transforms and noise modeling. We also present a solution to the problem of inter-frame frequency fading and highlight the benefit of using a rectangular coordinate representation of the harmonic data. The improved coder that we propose can be viewed as an object based approach to audio coding and leads to perceptually pleasant progressive transmission and representation schemes for multimedia applications.

We present a new video summarization procedure that produces a dynamic (video) abstract of the original video sequence. Our approach relies on an adaptive nonlinear sampling of the video. The local sampling rate is directly proportional to the amount of visual activity in localized sub-shot units of the video. The resulting video abstract is highly compact. At playtime, linear interpolation is used to provide the viewer with a summary of the video that accurately preserves the relative length and amount of activity in each sub-shot unit.

In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented.

In this paper, we propose a novel adaptive arithmetic coding method that uses dual symbol sets: a primary symbol set that contains all the symbols that are likely to occur in the near future and a secondary symbol set that contains all other symbols. The simplest implementation of our method assumes that symbols that have appeared in the previously are highly likely to appear in the near future. It therefore fills the primary set with symbols that have occurred in the previously. Symbols move dynamically between the two symbol sets to adapt to the local statistics of the symbol source. The proposed method works well for sources, such as images, that are characterized by large alphabets and alphabet distributions that are skewed and highly nonstationary. We analyze the performance of the proposed method and compare it to other arithmetic coding methods, both theoretically and experimentally. We show experimentally that in certain contexts, e.g., with a wavelet-based image coding scheme that has appeared in the literature, the compression performance of the proposed method is better than that of the conventional arithmetic coding method and the zero-frequency escape arithmetic coding method.

We present a progressive refinement approach to M-ary detection problems. The approach leads on average to a logarithmic reduction in the complexity of the detector. It relies on designing binary decision trees that trade complexity with probability of error. We also discuss simplified solutions that can be used in several cases of interest in wireless communications such as CDMA multiuser detection and blind equalization.

We present a novel motion-based video indexing scheme for fast content-based browsing and retrieval in a video database. The proposed technique constructs a dictionary of prototype objects to support query by motion. The first step in our approach extracts moving objects by analyzing layered images constructed from the coarse data in a 3-D wavelet decomposition of the video sequence. These images capture motion information only. Moving objects are modeled as collections of interconnected rigid polygonal shapes in the motion sequences that we derive from the wavelet representation. The motion signatures of the object are computed from the rotational and translational motions associated to the elemental polygons that form the objects. These signatures are finally stored as potential query terms.

In this paper we present a technique for the blind identification of single-input multiple-output (SIMO) pole-zero (PZ) systems using only second order statistics of the system output data. The system input is treated as an unknown deterministic sequence, and hence, restrictive i.i.d. Assumptions on the input sequence are not required. We estimate the poles and zeros of the channels in two steps: (1) estimate the product of all permutations of a numerator and a denominator polynomial from two different channels, and (2) extract individual numerator and denominator polynomials for each channel from the above estimate. Our technique performs well even with short records of data.

Wavelet denoising techniques are used to remove additive Gaussian noise by thresholding the wavelet coefficients. Like other transform based filters, wavelet shrinkage methods provide blur or visual artifacts that are exhibited in the neighborhood of image edges. The motive for implementing the hybrid wavelet transform filter (HWTF) is to provide a discriminate smoothing operator for noise removal. The undesirable smoothing effects can be minimized by performing two stages of wavelet de-noising and gray scale transform methods. The experimental results show that the proposed nonlinear wavelet filtering for noise reduction is an efficient technique to improve image quality.

We study how to use the H.263 video communications standard efficiently to save the total consumed energy of a mobile unit in cellular networks. Particularly, we study the computational power dissipation of various operation modes available in the H.263 coding standard. We show how to achieve low power consumption in a mobile unit by judiciously selecting the operating mode of H.263 in response to the mobile environment changes (e.g., slow fading and path loss), while maintaining a good video quality level. Our preliminary results show that a mobile with the proposed method consumes 10% less energy than a mobile that comes with the most efficient power-rate mode (with a search window size of 8) of H.263 video coding in one-hour mobile communication simulation. It even saves around 32% energy over a mobile operating with an advanced coding method (using four negotiating options).

We present a novel technique to characterize and index violent scenes in general TV drama and movies. Our goal is to identify violent signatures and localize violent events within a movie to support "high-level" video indexing. In particular, we exploit multiple "audio-visual" signatures to create a perceptual relation for conceptually meaningful violent scene identification. Potential applications are automatic blocking of violence in movies watched by children, hiding violence using data hiding or information filtering and genre classification of digital video database.

A new low complexity approach to motion estimation for video indexing is proposed. The fast polynomial regression transform (FPRT) is utilized to provide the efficient storage layout model and effective video database for manipulating the multimedia information system. The video content information of video image is compressed by FPRT. Global translational motion vectors and location of dynamic regions are computed from the compressed data. Our experiments show that this approach is very efficient for fast motion based video indexing.

We study the problem of jointly optimizing source/channel/modulation coding to achieve a given quality of service while minimizing the total power consumption of mobiles. This paper emphasizes the design of a robust transmission framework in the context of a total power minimization scheme. We design a simple but efficient transmission scheme which is called multistage coded modulation that combines source/channel (S/C) coding and multirate modulation (MM). We then integrate into this scheme a complexity-scalable video coder to achieve the best trade-offs in multimedia wireless communications between total power, quality of service, and capacity. Preliminary results show superior video frame quality (around 3 dB better than the simple S/C coding scheme) of the proposed system in the severe channel condition, with total power consumption kept to a minimum.

We present a watermarking procedure to embed copyright protection into digital video. Our watermarking procedure is scene-based and video dependent. It directly exploits spatial masking, frequency masking, and temporal properties to embed an invisible and robust watermark. The watermark consists of static and dynamic temporal components that are generated from a temporal wavelet transform of the video scenes. The resulting wavelet coefficient frames are modified by a perceptually shaped pseudorandom sequence representing the author. The noise-like watermark is statistically undetectable to thwart unauthorized removal. Furthermore, the author representation resolves the deadlock problem. The multiresolution watermark may be detected on single frames without knowledge of the location of the frames in the video scene. We demonstrate the robustness of the watermarking procedure to several video degradations and distortions.

We review developments in transparent data embedding and watermarking for audio, image, and video. Data-embedding and watermarking algorithms embed text, binary streams, audio, image, or video in a host audio, image, or video signal. The embedded data are perceptually inaudible or invisible to maintain the quality of the source data. The embedded data can add features to the host multimedia signal, e.g., multilingual soundtracks in a movie, or provide copyright protection. We discuss the reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream. We also discuss the issues and problems associated with copy and copyright protection and assess the viability of current watermarking algorithms as a means for protecting copyrighted data.

We propose a new time-scale modification method for high quality audio signals. Our approach strives to preserve pitch and timbre. In our method, the signal is represented as the sum of sinusoidal components and a residual (edges and noise). The decomposition is computed via a combined harmonic and wavelet representation. Time-scaling is performed on the harmonic components and residual components separately. The harmonic portion is time-scaled by demodulating each harmonic component to DC, interpolating and decimating the DC signal, and remodulating each component back to its original frequency. The residual portion is time-scaled by preserving edges and relative distances between the edges while time-scaling the stationary (noise) components between the edges.

We present a new approach to video sequence segmentation into individual shots. Unlike previous approaches, our technique segments the video sequence by combining two streams of information extracted from the visual track with audio track segmentation information. The visual streams of information are computed from the coarse data in a 3-D wavelet decomposition of the video track. They consist of (i) information derived from temporal edges detected along the time evolution of the intensity of each pixel in temporally sub-sampled spatially filtered coarse frames, and (ii) information derived from the coarse spatio-temporal evolution of intra-frame edges in the spatially filtered coarse frames. Our approach is particularly matched to progressively transmitted video.

Techniques for automatic monitoring of faults in machinery are being considered as a means to safely simplify or dispense with expensive periodic fault inspection procedures. This paper presents results from an ongoing investigation into the feasibility of using acoustic emissions (AEs) for automatic detection of microcrack formation/growth in machine components.

We present a new approach for automatic video scene segmentation and content based indexing. Our approach detects video shots and builds a collection of key frames and representative frames. Scene segmentation and video indexing are based on a temporally windowed principal component analysis of a subsampled version of the video sequence. Two discriminants are derived from the principal components on a frame by frame basis. The discriminants are used for scene change detection and key frame extraction and classification into relevant clips. The system creates an adjacency matrix to build the scene transition graph. The scene transition graph allows easy access to video image-based information.

We present a new video shot classification and clustering technique to support content-based indexing, browsing and retrieval in video databases. The proposed method is based on the analysis of both the audio and visual data tracks. The visual stream is analyzed using a 3-D wavelet transform and segmented into shot units which are matched and clustered by visual content. Simultaneously, speaker changes are detected by tracking voiced phonemes in the audio signal. The clues obtained from the video and speech data are combined to classify and group the isolated video shots. This integrated approach also allows effective indexing of the audio-visual objects in multimedia databases.

We apply a sequential experiment design procedure to the problem of signal selection for radar target classification. Radar waveforms are designed to discriminate between targets possessing a doubly-spread reflectivity function that are observed in clutter. The waveforms minimize decision time by maximizing the discrimination information in the echo signal. Each waveform selected maximizes the Kullback-Leibler (1951) information number that measures the dissimilarity between the observed target and alternative targets. We discuss two scenarios. In the first scenario, the target environment is assumed fixed during illumination. In this case the optimal waveform selection strategy leads to a fixed library of waveforms. During actual classification, the sequence in which the waveforms are selected from the library is determined from the noise to clutter power in the range-Doppler support of the targets. In the second scenario, the target environment changes between pulse transmissions. In this case, the maximum discrimination information is obtained by a repeated transmission of a single waveform designed from the reflectivity function of the targets. We show that our choice of signals can produce significant gains in detection performance.

We propose a technique to search through large image collections. Each database image is stored as a combination of potential query terms (i.e., objects, texture regions, etc.) and non-query terms. The query terms are represented by affine-invariant B-spline moments and wavelet transform subbands. The dual representation supports a two-stage image retrieval system. A user-posed query is first mapped to a dictionary of prototype object contours represented by B-spline moments. The B-spline mapping reduces the query search space to a subset of the original database. Furthermore, it provides an estimate of the affine transformation between the query and the prototypes. The second stage consists of a set of embedded VQ dictionaries of multiresolution subbands of image objects. The estimated affine transformation is employed as a correction factor for the multiresolution VQ mapping. A simple bit string matching algorithm compares the resulting query VQ codewords with the codewords of the database images for retrieval.

We introduce a watermarking procedure to embed copyright protection into digital video. Our video dependent watermarking procedure directly exploits the masking and temporal properties to embed an invisible and robust watermark. The watermark consists of static and dynamic temporal components that are generated from a temporal wavelet transform of the video scenes. To generate the watermark, the resulting wavelet coefficient frames are modified by a perceptually shaped pseudo-random sequence representing the author. The noise-like watermark is statistically undetectable to thwart unauthorized removal. Furthermore, the author representation resolves the deadlock problem. The multiresolution watermark may be detected on single frames without knowledge of the location of the frames in the video scene. We demonstrate the robustness of the watermarking procedure to several video distortions.

We introduce a scheme for hiding high bit-rate supplementary data, such as secondary video, into a digital video stream by directly modifying the pixels in the video frames. The technique requires no separate channel or bit interleaving to transmit the extra information. The data is invisibly embedded using a perception-based projection and quantization algorithm. The data hiding algorithm supports user-defined levels of accessibility and security. We illustrate our algorithm using examples of real-time video-in-video and speech-in-video. We also demonstrate the robustness of the data hiding procedure to video degradation and distortion, e.g., those that result from additive noise and compression.

We propose an image coding algorithm which employs data hiding techniques to "fold" an image into itself. Data hiding is a process of encoding extra information into a host image by making small modifications to its pixels. In our approach, an image is split into two parts of equal size: a host image and a residual image. The residual image is compressed into a bit stream and then embedded into the host image. The host image, which is 50% of the size of the original image, is coded using standard compression techniques. The embedded data does not increase the bit rate of the coded image. As a result, one may code only 50% pixels of the original image and still have perfect reconstruction. Experimental results indicate that the algorithm has a lot of potential in image coding.

In this paper, we introduce a novel approach for adaptive minimization of the total energy consumption in multimedia wireless communications subject to achieving a given quality of service. Our approach exploits trade-offs between the effects of energy consumed in processing (source and channel coding) and energy consumed in transmission under different noise and channel conditions, on the received quality of the multimedia. We present several simulation results involving image transmission that illustrate the energy consumption savings that can be achieved using our proposed approach.

We concentrate on the problem of converting audio samples into MIDI data, and describe a technique for processing music signals that attempts to extract note onsets and pitches, for a small class of music signals. Segmentation of the signals is based upon common onsets of tones and their proximity to edges in the signal. Pitch detection is performed by decomposing the short-time spectra of the segments as linear combinations of spectra in a dictionary of recorded sound segments.

We present a watermarking procedure to embed copyright protection into video sequences. To address issues associated with video motion and redundancy, individual watermarks are created for objects within the video. Each watermark is created by shaping an author and video dependent pseudo-random sequence according to the perceptual masking characteristics of the video. As a result, the watermark adapts to each video to ensure invisibility and robustness. Furthermore, the noise-like watermark is statistically undetectable. The watermark also resolves multiple ownership claims. We demonstrate the robustness of the watermarking procedure to video degradations, e.g., those that result from noise, MPEG compression, cropping, and printing and scanning.

In this paper, we describe novel encoding and decoding methods for multiscale difference equation (MSDE) signal models. Recently, MSDE signal models have been introduced to exploit self-similarities in signals. The encoding process for MSDE models requires an adaptive signal representation from the dictionary of translations and dilations of the signal to be modeled. Here, we propose a new iterative technique that tries to select an 'active' set under the modeling constraints so that the portion of the signal representation due to the 'inactive' set has small or zero norm. Using methods similar to 'line searches and backtracking' used for global non-linear optimization, this method yields the global minima for exact representation. The decoding process involves an eigenvector analysis of a particular matrix. We provide a fast algorithm to obtain the required eigenvector in O(N/sup 2/ log N) operations. We also provide a perturbation analysis of MSDE models that gives a bound on the reconstruction error due to errors in the MSDE coefficients.

We describe a novel high quality audio coding method using adaptive signal representation, based on sinusoidal and wavelet analysis of signals. First, we perform a harmonic analysis of the signal to remove strong periodic structures or tones from the signal. Then we carry out wavelet analysis that are useful in tracking the transients of the signal. These transients are then removed from the wavelet coefficients. The remaining coefficients have broadband noise-like structure. Since this method separates out tones (sinusoids), transients, and broadband noise, we may use tonal, noise, and temporal masking information to individually encode the tones and the wavelet coefficients. Our experiments suggest that this method yields a nominal bit rate of 1 bit/sample for high quality audio compression.

This paper describes a new wavelet domain RLS algorithm. The algorithm exploits the special sparse structure of the wavelet transform of wide classes of correlation matrices and that of the Cholesky factors of these matrices. Specifically, the algorithm updates a sparse QR factorization of the wavelet domain input data matrix. It then uses these factors to obtain the least-squares (LS) filter coefficients. The computational complexity of the proposed method is O(M log(M)) flops even when the input signal is not a time-series. Its convergence performance is similar to the traditional RLS algorithm. The new algorithm provides a method for trading error performance for lower computational complexity. Simulation results validate the performance of the algorithm.

A number of over-complete dictionaries such as wavelets, wave packets, cosine packets etc. have been proposed. Signal decomposition on such over-complete dictionaries is not unique. This non-uniqueness provides us with the opportunity to adapt the signal representation to the signal. The adaptation is based on sparsity, resolution and stability of the signal representation. The computational complexity of the adaptation algorithm is of primary concern. We propose a new approach for identifying the sparsest representation of a given signal in terms of a given over-complete dictionary. We assume that the data vector can be exactly represented in terms of a known number of vectors.

We develop a new coding technique for images and text documents that integrates content-based retrieval support directly into the compressed files. Furthermore, it minimizes a weighted sum of the expected compressed file size and expected query response time. Files are coded into three parts: a header consisting of all query terms which occur in the file, locations of the query terms, and remainder of the file. The file header is constructed by concatenating the codewords of a multiresolution representation of each query term which appear in that file. The coding algorithm completely specifies the relative position and length of all query terms codewords. Our approach leads to a progressive refinement retrieval by successively reducing the number of searched files as more bits are read. It also supports progressive transmission, modification of compressed data, and query term location dependence. 1. INTRODUCTION Information systems need to efficiently store and provide content-based a...

Efficient compression of binary textual images is very important for applications such as document archiving and retrieval, digital libraries and facsimile. The basic property of a textual image is the repetitions of small character images and curves inside the document. Exploiting the redundancy of these repetitions is the key step in most of the coding algorithms. We use a similar compression method in the subband domain. Four different subband decomposition schemes are described and their performance on a textual image compression algorithm is examined. Experimentally, it is found that the described methods accomplish high compression ratios and they are suitable for fast database access and keyword search.

Two species of crabs are Chionoecetes bairdi and C. opilio and their hybrids live in the Bering Sea. The two species differ in generic, morphological and morphometric characteristics. Inter breeding of C. bairdi and C. opilio results in a hybrid form with intermediate morphological and morphometric characteristics. These species were determined by analyzing the empirical covariance matrices associated with the two species and the hybrid and by taking into account the statistical reliability of the estimated principal components. A modified eigen image classifier is implemented based on the assumption that the data is drawn from multivariate Gaussian distributions with different means and covariance matrices. The clustering technique is introduced to minimize the misclassification rate of the eigen image classifier.

We describe a technique to encode images for content-based access and retrieval. Potential query terms are first extracted from an image and represented in terms of multiresolution subbands. A vector quantizer structure then maps the subbands of each image object onto a set of embedded dictionaries. An algorithm is used to exploit the occurrence and query probabilities of the objects for efficient coding and retrieval. Furthermore, a new browsing tool based on multiresolution prototypes is proposed. A prototype object is associated with each dictionary entry. Prototype objects may be substituted for subband data for high quality image browsing during retrieval.

We propose a watermarking scheme to hide copyright information in an image. The scheme employs visual masking to guarantee that the embedded watermark is invisible and to maximize the robustness of the hidden data. The watermark is constructed for arbitrary image blocks by filtering a pseudo-noise sequence (author id) with a filter that approximates the frequency masking characteristics of the visual system. The noise-like watermark is statistically invisible to deter unauthorized removal. Experimental results show that the watermark is robust to several distortions including white and colored noises, JPEG coding at different qualities, and cropping.

We construct a theory of binary wavelet decompositions of finite binary images. The new binary wavelet transform uses simple module-2 operations. It shares many of the important characteristics of the real wavelet transform. In particular, it yields an output similar to the thresholded output of a real wavelet transform operating on the underlying binary image. We begin by introducing a new binary field transform to use as an alternative to the discrete Fourier transform over GF(2). The corresponding concept of sequence spectra over GF(2) is defined. Using this transform, a theory of binary wavelets is developed in terms of two-band perfect reconstruction filter banks in GF(2). By generalizing the corresponding real field constraints of bandwidth, vanishing moments, and spectral content in the filters, we construct a perfect reconstruction wavelet decomposition. We also demonstrate the potential use of the binary wavelet decomposition in lossless image coding.

We present a novel nonlinear filtering approach for detecting weak signals in heavy noise from short data records. Such detection problems arise in many applications including communications, radar, sonar, medical imaging, seismology, industrial measurements, etc. The performance of a matched filter detector of a weak signal in heavy noise is directly proportional to the observation time. We discuss an alternative detection approach that relies on a nonlinear filtering of the input signal using a bistable system. We show that by adaptively selecting the parameters of the system, it is possible to increase the ratio of the square of the amplitude of a sinusoid to that of the noise intensity around the frequency of the sinusoid (stochastic resonance). The sinusoid can then be reliably detected at the output of the nonlinear system using a suitable matched filter even when the data record is short.

We propose a novel approach for low bit rate perceptually transparent image compression. It exploits both frequency and spatial visual masking effects and uses a combination of Fourier and wavelet transforms to encode different bands. Frequency domain masking is computed by using a fine to coarse analysis step. Spatial domain masking is computed either by using Girod's (1989) model or a coarse to fine analysis step that accurately computes local contrast. A discrete cosine transform is used in conjunction with frequency domain masking to encode the low frequency bands. The medium and high frequency bands are encoded using spatial domain masking and a wavelet transform. The encoding of these bands is based on a recursive selection of the important edges in each band. It uses cross-band prediction to minimize the bit rate. Experiments show the approach can achieve a very high quality to nearly transparent compression at bit rates of 0.2 to 0.4 bits/pixel.

This paper presents novel data acquisition schemes for improving the quality of magnetic resonance images (MRI). These approaches determine the number of times that k-space (frequency domain) samples have to be acquired and averaged to form an MRI image, by taking into account the characteristics of the human visual system and the location in k-space of the data samples. They lead to dramatic reductions in imaging time (by a factor of 8 to 10) with no reduction in image quality. Alternatively, they produce higher quality imagery using the same amount of imaging time as conventional approaches.

We study the B-mode/Doppler duplex imaging problem. We begin by looking at the problem of determining the true velocity vector only. We develop a model for the power spectrum of a signal reflected by a line of point scatterers with a Poisson distribution. We show that with circularly symmetric apertures it is possible to use the expression of that power spectrum to determine the true velocity vector from a single excitation and two measurements. We also describe an illumination configuration that guarantees that the velocity estimation process is range-invariant. We conclude the paper by studying the problem of simultaneously estimating the range and true velocity of a flow. In particular, we show that this problem is completely characterized by a generalized range-2-D Doppler ambiguity function that depends on the excitation signal and the transducer geometry.

The wavelet transform provides a multiresolution representation of images. Edges, which are visually important, produce large coefficients across several scales in the wavelet transform domain. By tracking and predicting these edge coefficients across scales in the wavelet transform domain, we can greatly improve the compressed image quality with little degradation in compression ratio. This paper proposes a novel model-based edge tracking and prediction in the wavelet domain. It separates textures from edges and codes them differently. Edges are coded via an edge tracking and prediction, while textures are coded with either ordinary wavelet based image coding techniques or a "wavelet-like" filter bank which is similar to the tuning channels in the human vision system. The coding noise is then coded with a noise modelling. Visual masking models are also used to ensure the compressed image has little or almost no perceptual distortion.

This paper develops a new multiuser detector for antenna arrays. The receiver uses an adaptive multiuser receiver in a multiple sensor environment. Using this receiver, two schemes are proposed for use with antenna arrays. The first scheme is a simple extension of the multiuser detection scheme to a multisensor environment, and has a performance better than the single channel decorrelator detector. The second scheme adaptively implements an array-decorrelator detector, which exploits both spatial and code diversity. It is different from the first scheme in that it uses the array response vector estimates of each user to reduce the correlation between users's transmitted signal. It therefore, avoids the reduction in performance when signature correlations become significant. 1 Introduction Code Division Multiple Access (CDMA) has received considerable interest in recent years.A considerable amount of work has been devoted to alleviating the so called "near/far" problem, which occurs wh...

A novel signal modeling technique in which an arbitrary sequence of data points is represented as samples of the solution to a multiscale difference equation is proposed. Such a model completely characterizes a number of higher derivatives of the signal as well as the signal itself. It provides a recursive signal interpolation mechanism as a function of scale. It also leads to multigrid type signal filtering, detection and estimation algorithms. The existence and uniqueness of L/sub 1/ and L/sub 2/ solutions to the multiscale difference equation are first investigated. The paper generalizes the results obtained for the two scale difference equation. Next, we provide conditions for the existence of unique solution to such an equation. Finally, techniques for modeling an arbitrary set of data samples as samples of the solution to a multiscale difference equation are presented. Specifically, we describe the encoding and decoding steps in these techniques. We also prevent audio signal modeling examples to illustrate multiscale difference equation models.>

The convergence rate of the least mean squares (LMS) algorithm is poor whenever the adaptive filter input auto-correlation matrix is ill-conditioned. We propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks the subspaces corresponding to clusters of eigenvalues of the auto-correlation matrix of the input to the adaptive filter, which have the same order of magnitude. The algorithm updates the projection of the tap weights of the adaptive filter onto each subspace using LMS algorithms with different step sizes. The technique also permits adaptation only in those subspaces, which contain strong signal components leading to a lower excess mean squared error (MSE) as compared to traditional algorithms.>

It is shown that the M-band wavelet domain correlation matrix of a noise process consists of linear structures as long as the spatial correlation function of the noise process decays asymptotically to zero as the distance between samples of the noise tends to infinity and the analyzing M-band wavelet has a large (/spl ges/5) number of vanishing moments. On the other hand, the M-band wavelet domain correlation matrix corresponding to plane waves consists of "plateau" like structures. Next, a procedure that discriminates between the signal information carried by plane waves and the noise information based on the structure of the M-band wavelet domain correlation matrix of the array outputs is proposed.>

Solves the problem of adaptively selecting a set of N radar waveforms to obtain the optimal L/sub 2/ estimate of a target range-Doppler reflectivity function. The authors assume that the targets correspond to one of two classes. They show that the waveform selection strategy involves an initial class identification phase followed by a pure imaging phase. They provide a recipe for minimizing the number of waveforms needed for the class identification phase. They show that this leads to an optimal adaptive target imaging strategy.>

Constructs a theory of wavelet decompositions of binary images. The construction defines binary valued wavelets and scaling functions and their associated spectral properties. The authors begin by introducing a new binary field transform and the corresponding concept of sequence spectra over GF(2). Using this transform, a theory of binary wavelets is then developed in terms of 2-band perfect reconstruction filter banks. By generalizing the corresponding real field constraints of bandwidth, vanishing moments, and spectral content in the filters, a perfect reconstruction wavelet decomposition is created. An example to illustrate the potential use for compression applications is included.>

A procedure for detecting step edges in noisy signals that does not involve any prefiltering of the data is proposed. It locates step edges in 1-D noisy signals as follows. First, it computes the eigenvectors corresponding to the three smallest eigenvalues of a matrix formed with the discrete Fourier transform of the given data. Next, it estimates the edge locations by finding the local minima in the sum of the spectra of the computed eigenvectors. The technique computes a point edge map for 2-D images by analyzing each row, column, and 45 degrees and 135 degrees diagonal in the image. The computational complexity of the proposed procedure is determined.

A class of data transformation matrices is introduced for the estimation of angles of arrivals of multiple wideband sources. The proposed transformation matrices are unitary and minimize the average of the squared norm of the focusing error over the angles of interest without prior knowledge of source locations. They can also be used with the multigroup sources and lead to lower resolution thresholds than those obtained with other techniques. A comparison of the statistical performance of the proposed focusing procedure with that of the spatial resampling method is provided.>

A novel audio synthesis/coding approach based on an optimization of the wavelet transform of the audio signal is presented. In each frame of the audio signal, the authors identify the wavelet which yields an approximation to the audio segment that requires the minimum number of bits. The wavelet approximation is constrained to have no perceptual distortion or a distortion that is less than some fixed maximum level. Two wavelet coefficient representations are considered. In the first representation, the quantization step is fixed and the magnitude of the transform coefficients is optimized. In the second approach the wavelet coefficients are adaptively quantized. The authors show that the second representation leads to simpler optimization problems. They suggest several approximations which reduce the complexity of the optimization problem. Experiments indicate that high quality speech and audio reconstruction is possible in the range of 8-9 kb/s.>

It is shown that a large set of (not necessarily stationary) correlation matrices may be transformed into a matrix that consists of essentially banded subblocks. The transformation is accomplished by premultiplication and postmultiplication with an orthogonal matrix whose elements are derived from the impulse response of a suitably designed cascade of alias-free multirate analysis filter banks. It is further proved that the Cholesky factor of the transformed matrix also consists of essentially banded subblocks. These two observations are combined to show that the linear positive definite systems of equations that arise in statistical signal processing can be solved in O(max(N log/sup 2/ (N), N/sup 2/)) operations while matrix-vector multiplication steps may be implemented in O(N log (N)) operations. An error analysis of the proposed linear positive definite system solver is also provided.>

The authors generalize the discrete orthogonal two-band (or dyadic) wavelet decomposition to the M-band case. Specifically, it is shown that any finite energy signal can be explained in terms of the dilates and translates of M-1 M-band wavelets. The advantage of such decompositions is that they are much more compact than two-band wavelet decompositions. This compactness is important both in coding applications and for the development of fast signal processing algorithms. A complete characterization of discrete orthogonal M-band wavelets is given, including a recipe for constructing such wavelets.>

It is shown that the discrete wavelet coefficients of fractional Brownian motion at different scales are correlated and that their auto- and cross-correlation functions decay hyperbolically fast at a rate much faster than that of the autocorrelation of the fractional Brownian motion itself. The rate of decay of the correlation function in the wavelet domain is primarily determined by the number of vanishing moments of the analyzing wavelet.>

Two techniques for finding the discrete orthogonal wavelet of support less than or equal to some given integer that leads to the best approximation to a given finite support signal up to a desired scale are presented. The techniques are based on optimizing certain cost functions. The first technique consists of minimizing an upper bound that is derived on the L/sub 2/ norm of error in approximating the signal up to the desired scale. It is shown that a solution to the problem of minimizing that bound does exist and it is explained how the constrained minimization over the parameters that define discrete finite support orthogonal wavelets can be turned into an unconstrained one. The second technique is based on maximizing an approximation to the norm of the projection of the signal on the space spanned by translates and dilates of the analyzing discrete orthogonal wavelet up to the desired scale. Both techniques can be implemented much faster than the optimization of the L/sub 2/ norm of either the approximation to the given signal up to the desired scale or that of the error in that approximation.>

A maximum likelihood (ML) estimator for the parameters of Gaussian versions of the fractionally differenced white noise process is developed. A closed-form expression for the likelihood equation is given, and Cramer-Rao bounds are computed for finite size sample data sets. It is shown how the theory can be extended to the case where the fractionally differenced white noise process is observed in the presence of white noise. The results obtained with this ML approach are satisfactory, with a mean square error which is very close to the theoretically computed Cramer-Rao bound.>

It is argued that a good wavelet for representing a given signal is one that leads to a compact representation of the signal up to a given scale. Unfortunately, minimizing the distance between a given signal and its approximation up to a given scale using any wavelet is a difficult optimization problem. Instead, a tight upper bound on that distance is developed. The bound is an explicit function of the discrete set of coefficients defining the wavelet. By minimizing this upper bound one then obtains a good wavelet for representing the given signal.>

Two new iterative multigrid algorithms for image reconstruction from Fourier magnitude data are proposed. The first algorithm is based on the error reduction algorithm and the second is based on a quasi-Newton's method. Both algorithms take advantage of a multigrid scheme to speed up the convergence rate of the iterations. Furthermore, they recover more high frequency components of the image than their non-multigrid counterpart. Experimental study has shown the great potential of both algorithms.>

Efficient recursive smoothing algorithms are developed for isotropic random fields that can be obtained by passing white noise through rational filters. The estimation problem is shown to be equivalent to a countably infinite set of 1-D separable two-point boundary value smoothing problems. The 1-D smoothing problems are solved using a Markovianization approach followed by a standard 1-D smoothing algorithm. The desired field estimate is then obtained as properly weighted sum of the 1-D smoothed estimates. The 1-D two-point boundary value problems are also shown to have the same asymptotic properties and yield a stable spectral factorization of the power spectrum of the isotropic random fields.>

A novel procedure for estimating the number and frequencies of complex exponential signals in the presence of an unknown colored background noise is developed. Under the assumption that the background noise has a rational power spectrum (or a power spectrum that can be closely approximated by a rational one), it is shown that the number and frequencies of the complex exponentials can be estimated by rooting a polynomial that is formed from elements at the vectors spanning the null-space of a Hankel matrix. The entries of this matrix are formed from the correlation sequence of the noisy observations corresponding to the complex exponentials. A method for separating the roots of the polynomial that are due to the complex expotentials from those that are due to the noise is also presented. It is based on properties of the Szego polynomials associated with the correlation sequence of the noisy observations corresponding to the complex exponentials.>

A novel linear maximum-entropy method (MEM) spectral-estimation algorithm for 2-D isotropic random fields is presented. This procedure differs from pervious 2-D MEM algorithms by the fact that maximum advantage is taken of the symmetries implied by isotropy. It is shown that the isotropic MEM problem has a linear solution and that it is equivalent to the problem of constructing the optimal linear filter for estimating the underlying isotropic field at a point on the boundary of a disk of radius R, given noisy measurements of the field inside the disk. A fast algorithm for computing the estimation filter is then used to obtain the MEM spectral estimate.>

Sampling theorems are developed for isotropic random fields and their associated Fourier coefficient processes. A wave-number-limited isotropic random field z(r) is considered whose spectral density function is zero outside a disk of radius B centered at the origin of the wavenumber plane. z(r) can be reconstructed in the mean-square sense from its observation on the countable number of circles with radius r/sub i/=i pi /B, i in N, or of radius r/sub i/=a/sub i,n//B, i in N, where a/sub 1,n/ denotes the ith zero of the nth-order Bessel function J/sub n/(x), and n is arbitrary.>