Today, convolutional neural network (CNN) pruning techniques often rely on manually crafted importance criteria and pruning structures. Due to their heuristic nature, these methods may lack generality, and their performance is not guaranteed. In this paper, we propose a theoretical framework to address this challenge by leveraging the concept of $\gamma$γ-weak submodularity, based on a new efficient importance function. By deriving an upper bound on the absolute error in the layer subsequent to the pruned layer, we formulate the importance function as a $\gamma$γ-weakly submodular function. This formulation enables the development of an easy-to-implement, low-complexity, and data-free oblivious algorithm for selecting filters to be removed from a convolutional layer. Extensive experiments show that our method outperforms state-of-the-art benchmark networks across various datasets, with a computational cost comparable to the simplest pruning techniques, such as $l_{2}$l2-norm pruning. Notably, the proposed method achieves an accuracy of 76.52%, compared to 75.15% for the overall best baseline, with a 25.5% reduction in network parameters. According to our proposed resource-efficiency metric for pruning methods, the ACLI approach demonstrates orders-of-magnitude higher efficiency than the other baselines, while maintaining competitive accuracy.

RGB-Thermal (RGB-T) tracking enhances visual tracking robustness by combining RGB and thermal infrared (TIR) modalities, addressing limitations of RGB-only trackers under challenging conditions such as low light and appearance variations. However, most existing RGB-T trackers rely on complex fusion modules or modality-specific architectures, sacrificing efficiency for performance. In this paper, we propose a novel Multi-level Self-Distillation (MSD) framework that adapts a one-stream RGB tracker to the RGB-T setting without modifying the network architecture or adding any extra parameters. RGB and TIR inputs are jointly processed through a shared backbone, and training is guided by a combination of self-supervised and supervised objectives to enhance cross-modal feature representation. The self-supervised component includes a contrastive loss that aligns semantically consistent regions across template-search pairs, as well as a modality-gap alignment loss that reduces discrepancies between RGB and TIR features. These internal signals complement task-driven supervision, including an intermediate focal loss that strengthens early localization by enhancing shallow and mid-level features, modality-specific losses that preserve distinctive cues under partial modality degradation, and a fused tracking loss that drives final bounding box prediction. Comprehensive evaluations on LasHeR, RGBT234, and GTOT benchmarks demonstrate that MSD achieves state-of-the-art tracking accuracy while maintaining the computational efficiency of the original RGB tracker. Our work establishes a new paradigm in multi-modal tracking by demonstrating that optimized training strategies can outperform complex architectural modifications, offering significant practical advantages for real-world deployment.

Convolutional neural network (CNN) pruning has traditionally relied on heuristically designed importance criteria, often leading to limited generalizability and inconsistent performance. In this article, we propose a novel framework centered around filter replacement (FR), introducing pruning as a process of replacing selected filters with zero filters. Through a rigorous analysis, we derive an upper bound on the absolute error in the output of the subsequent layer and use this bound to define an efficient importance function. This importance function exhibits $\gamma $ -weakly submodular properties, enabling the development of a simple, low-complexity, and data-free oblivious algorithm for selecting filters to prune. In addition, we extend the FR framework to include nonzero filter alternatives, leveraging a best-approximation technique to construct optimal replacements for the pruned filters. Extensive experiments on benchmark networks and datasets validate the effectiveness of our method. The proposed approach achieves state-of-the-art results, with a complexity comparable to basic techniques such as $l_{2}$ -norm pruning. Notably, our pruning method achieves 76.52% accuracy (ACC) in ResNet-50 on the ImageNet dataset, surpassing the baseline of 75.15%, while reducing network parameters by 25.5%. Our proposed resource efficiency (RE) metric assesses that the layer interdependence-aware pruning (LIAP) method is up to $10^{11}$ times more efficient than existing techniques, setting a new standard for resource-aware CNN pruning.

RGB-T object tracking leverages visible and thermal infrared modalities to enhance robustness in challenging environments. While deep learning-based RGB-T trackers predominantly use feature-level fusion, pixel-level fusion remains underexplored. This paper introduces the Hierarchical Feature Difference Auto-Encoder (HFDAE), a novel pixel-level fusion approach that refines the RGB modality before tracker input. HFDAE adaptively enhances RGB content using hierarchical TIR features, dynamically emphasizing object saliency. HFDAE consists of three key components: (1) a shallow RGB autoencoder that preserves structural and color information, (2) a TIR encoder with variable-depth decoders generating hierarchical TIR representations, and (3) a fusion module that integrates salient thermal features into the RGB image. Salient features are extracted by computing differences between hierarchical and base TIR images, which are then added to the base RGB image to generate the final fused output. Unlike conventional pixel-level fusion methods, HFDAE is optimized directly for tracking, learning fusion strategies without predefined modality assumptions. Extensive experiments on benchmark datasets demonstrate HFDAE’s superior tracking accuracy and robustness across diverse scenarios. The proposed approach improves the base tracker’s precision rate by approximately 10%. Code is available at https://github.com/mohamed-e-awad/HFDAE.

This work proposes a novel multi-scale attention-enhanced dual-task network, MSA-DTNet, to simultaneously address two critical tasks, segmentation and registration. MSA-DTNet is designed for high-resolution 3D MRI data to incorporate multi-scale convolutions to capture both local and global features and attention mechanisms to enhance the model’s focus on key anatomical structures. By jointly optimizing segmentation and registration tasks, our network improves anatomical consistency and overall performance in medical image processing. The segmentation decoder produces high-quality segmentation maps, while the registration decoder outputs a displacement field for aligning images with a reference. A novel hybrid loss function is also proposed to optimize the model during training. The experiments on the brain MRI dataset demonstrate that MSA-DTNet outperforms existing state-of-the-art networks in terms of dice score (DSC), intersection over union (IoU), precision and recall in segmentation, and DSC and mean squared error (MSE) in registration tasks. Our model also achieves significant performance improvements, even with limited labeled data, by leveraging semi-supervised learning.

In this paper, we propose a robust channel estimation technique for reconfigurable intelligent surface (RIS)-aided generalized frequency division multiplexing (GFDM) systems operating in high mobility scenarios. Leveraging the channel’s inherent sparsity in the delay-Doppler domain, the method employs a sparse Bayesian learning (SBL) framework constructed with a hierarchical Laplace prior. The expectation-maximization (EM) algorithm is then used to iteratively update the prior model parameters. Furthermore, to reduce computational complexity in the RIS phase optimization, only the strongest delay-Doppler channel path that maximizes the effective channel gain is selected. Simulation results confirm that the proposed approach achieves reliable performance with reduced pilot overhead.

Diffusion models have provided the state-of-the-art performances for different computer vision tasks, including the task of underwater image enhancement. One of the challenges in the task of underwater image enhancement is that various spatial regions of the image require different restoration techniques. In order to address this, we propose a novel diffusion-based underwater image enhancement network, in which by employing the two ideas of uncertainty-aware learning and feature recalibration based on the color tones dominated in the underwater environments, it is able to provide superior performances. Specifically, the former idea strives to process various spatial regions of the underwater image based on their restoration uncertainty, while the latter technique recalibrates the features generated by the diffusion model by taking the various color tones in the underwater environments into consideration. The results of different experimentations show the superiority of the proposed diffusion-based model over the other state-of-the-art underwater image enhancement networks.

Recent advancements in the classification of Alzheimer's disease have leveraged the automatic feature generation capability of convolutional neural networks (CNNs) using neuroimaging biomarkers. However, most of the existing CNN-based methods often disregard the local features of the brain data, which leads to a loss of subtle fine-grained features in the brain imaging data. Moreover, the existing CNN architectures, which mainly rely on global features, do not pay much attention to the discriminability of the extracted features for the task of classification of Alzheimer's disease. Moreover, the existing architectures often end up using a large number of parameters to enhance the richness of the extracted features. This paper proposes a novel lightweight deep CNN, which extracts local and global contextual features from the sagittal slices of structural MRI data and uses both of these two types of features for the classification of the disease. The main idea used in designing the proposed network is to process separately the local and global features by using modules that pay a special attention to extract local and global contextual features. The fused local and global contextual features are then used for the classification of Alzheimer's disease. The proposed network is tested for the binary and multiclass classifications of the disease using the MR images taken from the ADNI database. The proposed network is shown to provide a performance that is significantly higher than that provided by other existing state-of-the-art networks, yet using a number of parameters that is a small fraction of that used by the other schemes.

The brain-computer interface (BCI) establishes a connection between a device and the human brain, with electroencephalography (EEG) signal is being used as the most common means for such a communication. We use EEG signal data that has a very limited number of samples for the motor imagery (MI) classification task. This paper proposes a novel densely connected residual graph convolutional network (DenseResGCN) and uses it in developing a few-shot learning method called FewShotEEG method. Our proposed method is capable of classifying the limited EEG signal data into four MI classes. The proposed method outperforms the state-of-the-arts few-shot methods in terms of the accuracy.

In this paper, we propose a novel convolutional neural network called MAGNet that employs multi-scale and global attention mechanisms for the task of medical image segmentation. This network is shown effectively to handle the segmentation task of an image of a given modality provided the network is suitably trained using a training set of the same modality. Experiments are performed to train the proposed network using three different training sets of images (CT, colonoscopy, and non-mydriatic 3CCD images), each acquired from a different imaging technique, resulting in three different trained models. The three trained models are tested on the respective test sets. Each model is shown to significantly outperform the state-of-the-art networks in terms of intersection over union, dice coefficient, and accuracy.

Obtaining robust representational features is essential for achieving high performance in image retrieval. One effective strategy to boost the representational capacity of deep features is through feature aggregation, a technique that combines features from various feature maps of a deep network. In this paper, we propose a novel feature aggregation method that integrates feature maps from different levels of abstraction within a deep network into a single feature vector. The proposed method employs the particle swarm optimization algorithm to adaptively assign optimal weights to each group of feature maps. Extensive experiments are conducted to validate the effectiveness of our proposed feature aggregation method. It is shown that the proposed method significantly outperforms existing state-of-the-art feature aggregation methods on various benchmark datasets.

Image retrieval aims to find similar images to a given query by matching features extracted directly from the images of a database. Deep convolutional neural networks provide an excellent framework for obtaining highly representative feature vectors from images to improve an image retrieval method. Deep residual networks perform better than existing deep networks, as they can incorporate useful information into the feature vectors through residual learning by designing appropriate operations in the employed residual block. One type of such information is spatial information obtained at different scales and levels of abstraction. In this paper, a novel residual block is proposed to generate a rich set of features for the task of image retrieval. The development of the residual block consists of three modules: a hierarchical spatial feature extraction module focusing on spatial information at different abstraction levels, a multi-scale feature extraction module that generates features at three different scales, and a feature fusion module. The results of experiments on various datasets and an ablation study show that the proposed residual block noticeably improves the representational capacity of the network, which, in turn, significantly enhances the retrieval performance of the deep image retrieval network.

The representational quality of the generated feature vectors for images is essential for image retrieval models to achieve high performance. Spatial information is crucial in obtaining highly representative feature vectors for image retrieval, and deep convolutional neural networks provide an excellent framework to generate such features. Through convolutional operations, deep convolutional neural networks include spatial information in the feature maps. However, most available architectures cannot include adequate spatial details in the feature maps required for high-performance image retrieval. Deep residual networks are deep networks capable of including useful information through residual learning. This paper proposes a novel residual block to generate feature maps by focusing on spatial information. The proposed residual block comprises three modules: a spatial feature extraction module, a hierarchical feature extraction module, and a feature fusion module. The first module includes spatial information in the feature maps at different levels of abstraction, while the second module includes spatial information using conventional convolution hierarchy. The third model fuses the outputs of the first two modules to provide a very rich set of feature maps. The present study tests a deep network employing the proposed residual block. The results indicate that the proposed network performs comparably or is superior to state-of-the-art methods on standard benchmarks, thus showing the effectiveness of the proposed residual block in improving the representational capacity.

Feature attention is a technique used in deep neural networks to provide a discriminative processing of the various regions in an image based on their significance for enhancing the image restoration performance. In this paper, we develop a novel image restoration network, in which the feature maps extracted by the network are recalibrated using a pixel-wise feature attention and the recalibration process is guided by the structural and textural information of the image resulting from the Otsu’s method for its segmentation. It is shown that using this segmentation guidance strategy for recalibrating feature maps is indeed helpful in enhancing the quality of the restored images. The proposed image restoration network outperforms the state-of-the-art light-weight image restoration networks on benchmark datasets.

Alzheimer’s disease (AD) is a progressive brain disorder affecting millions of people worldwide. An accurate diagnosis of AD plays a significant role in identifying the progression of the disease at its prodromal stage, i.e., mild cognitive impairment (MCI). In this paper, we propose a lightweight deep model to classify the patients into diagnostic groups, AD vs. normal control (NC) or progressive MCI (pMCI)vs. stable MCI (sMCI), with high accuracy, using MRI data. The proposed model uses separable and attention-based convolution operations. The separable convolution can reduce the complexity of the model by splitting a kernel into two separate kernels that do depth-wise and pointwise convolution operations, respectively. Moreover, integrating an attention-based convolution, which concatenates the convolutional and attentional feature maps, can capture the most relevant features for improved classification with fewer filters. From the experimental results on the Alzheimer’s disease neuroimaging initiative (ADNI) database, compared to the state-of-the-art methods, it is observed that the proposed method shows significant improvement in the classification performance in terms of accuracy, specificity, sensitivity, and AUC. In addition, the proposed method drastically reduces the number of parameters without affecting the performance.

ThiNet is a recent method for pruning convolutional neural networks. This method uses a norm of a subset of the components of the output resulting from the convolutional layer succeeding the layer from which the filters are to be removed for pruning the network. The ThiNet algorithm is very time-consuming, in view of the fact that the filters for removal are selected one by one iteratively. In this paper, we propose a modified version of ThiNet, in which the same information on the output of the same convolutional layer as used by ThiNet is employed to select all the filters together in a single step, for pruning the network. The proposed modified algorithm is shown to have a time-complexity that is only a small fraction of that of ThiNet or any other state-of-the-art algorithm and that the pruned network has almost the same reduction in its accuracy as that of the network pruned by ThiNet.

Morphological operations are nonlinear mathematical operations that are capable of performing signal processing tasks based on the structures and textures of the signals. With this motivation of the capability of morphological operations, in this paper, a novel residual block that can generate morphological features of images and fuse them with the conventional hierarchical features has been proposed. The proposed residual block is then used to design a light-weight deep neural network architecture in a residual framework for the task of image super resolution. It is shown that a fusion of morphological features of images with the conventional hierarchical features can improve the super resolution capability of a deep convolutional network. Experiments are performed to demonstrate the effectiveness of the proposed idea of using morphological operations and the superiority of the network designed based on this idea in super resolving low quality images.

The design of ultralight-weight super-resolution convolutional neural networks capable of providing images with high visual quality is crucial in many real-world applications with limited power and storage capacity, such as mobile devices and portable cameras. In this paper, a new ultralight-weight super-resolution network, based on the idea of using multiresolution level feature interpolation in a residual framework, is developed. In the proposed network, the multiple resolution level interpolated features generated are fused and the resulting feature maps are added to the residual features obtained from a shallow convolutional neural network. The proposed network is applied to various benchmark datasets and is shown to outperform the state-of-the-art ultralight-weight image super-resolution networks existing in the literature.

In this paper, a novel multimodal convolutional neural network (CNN) fusion architecture is proposed for heart sound signal classification. Instead of using features from just one domain, general frequency features as well as Mel domain features are extracted from the raw heart sound. The multimodal CNN fusion architecture is individually trained based on the feature maps resulting from various feature extraction methods. These feature maps are then merged for optimizing the diversified extracted features. The proposed method provides an opportunity to explore the optimal selection of features for heart sound classification. Extensive experimentations are carried out, showing that an outstanding accuracy of 98.5% is achieved by the multimodal CNN architecture, which outperforms the other state-of-the-art approaches.

Brain computer interface is a developing technology that can provide enhanced quality of life to individuals suffering from various disabilities. In this work, a new binary electroencephalography (EEG) signal decoding algorithm is proposed using a modified common spatial pattern and capsule network. The proposed method is realized by extracting the spectral-temporal common spatial pattern features from the EEG signals while preserving the time resolution of the signal. The resulting features are fed into the capsule network for automatic feature extraction and classification. The capsule network is known to be superior to convolutional neural networks in requiring less training data, which makes it a promising candidate for EEG signals classification. The performance of the proposed method is evaluated and compared to that of the other methods by conducting several experiments. The results demonstrate that the proposed method provides recognition accuracy higher than that provided by other methods.

In this paper, a new regularization term in the form of L1-norm based fractional gradient vector flow (LF-GGVF) is presented for the task of image denoising. A fractional order variational method is formulated, which is then utilized for estimating the proposed LF-GGVF. Overlapping group sparsity along with LF-GGVF is used as priors in image denoising optimization framework. The Riemann-Liouville derivative is used for approximating the fractional order derivatives present in the optimization framework. Its role in the framework helps in boosting the denoising performance. The numerical optimization is performed in an alternating manner using the well-known alternating direction method of multipliers (ADMM) and split Bregman techniques. The resulting system of linear equations is then solved using an efficient numerical scheme. A variety of simulated data that includes test images contaminated by additive white Gaussian noise are used for experimental validation. The results of numerical solutions obtained from experimental work demonstrate that the performance of the proposed approach in terms of noise suppression and edge preservation is better when compared with that of several other methods.

To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.

The JPEG compression scheme introduces blocking artifacts when the images are decompressed. JPEG image deblocking schemes based on deep neural networks map a JPEG decompressed image to its corresponding deblocked image. Employing a residual block that is capable of generating a rich set of high frequency residual features in a deep JPEG image deblocking network can improve its representational capability, and therefore, enhance the network performance. In this paper, we propose two residual blocks that generate rich high frequency residual features. The first residual block generates features from the high frequency component of its input signal in addition to generating conventional hierarchical residual features using convolutional operations. The second one is a fractal residual block that is developed by replacing the conventional convolutions in the first block by the block itself. The two proposed residual blocks are, respectively, used in recursive (non-fractal) and non-recursive (fractal) neural networks for the task of JPEG deblocking. The results of the experiments performed on the two proposed deblocking networks show their performance superiority over the respective state-of-the-art deblocking networks.

Residual blocks use skip connections in order to facilitate the flow of information in the network and thus, provide a good network performance. As different objects in a generic image appear at different scales, employing a multi-scale feature generation module in a residual block for image super resolution can further improve the network performance. In this paper, a new residual block that generates features at multiple scales is proposed for the task of image super resolution. In order to enhance the representational capability of the network while keeping its complexity low, the proposed residual block uses two different feature generation techniques, namely, multi-scale granular channel feature generation and uni-scale holistic channel feature generation, and fuses their output feature maps. It is shown that the network using the proposed residual block outperforms the state-of-the-art lightweight super resolution networks on four benchmark datasets with various scaling factors.

Deep neural networks use a nonlinear end-to-end mapping in order to transform a low resolution image to the high resolution one. Residual blocks facilitate the flow of the information in deep neural networks and enhance the network performance. In this paper, a new residual block that enhances the representational capability of a super resolution network is proposed. The proposed residual block combines the features generated in various receptive fields using different hierarchical levels of convolution operations or convolution operations in conjunction with the space-to-depth and depth-to-space operations in order to provide a rich set of residual features. The experimental results demonstrate the superiority of the super resolution network using the proposed residual block over the state-of-the-art light-weight super resolution networks in terms of objective and subjective metrics.

Deep convolutional networks provide very high quality super resolution images through a learning process by a nonlinear end-to-end mapping between low and high resolution images. Many of the state-of-the-art super resolution networks employ residual blocks in their network architectures, where in each residual block the high frequency residual signals are added to the feature maps input to the block. In this paper, a new residual block is proposed for the problem of image super resolution. The proposed residual block consists of three modules, namely, feature transformation module, nonlinear edge extraction module and feature fusion module. The feature transformation module produces high frequency residual signals and the nonlinear edge extraction module extracts the edges of the features input to the block. These generated high frequency features are then fused using the feature fusion module in order to produce a very rich set of high frequency residual features. The performance of the super resolution network using the proposed residual block is compared with that of the state-of-the-art light-weight super resolution schemes on four benchmark datasets. It is shown that the proposed super resolution scheme outperforms the state-of-the-art light-weight super resolution networks, when both the performance and number of parameters of the network are simultaneously taken into consideration.

Deep image super resolution networks use a nonlinear end-to-end mapping between the low and high resolution versions of an image and therefore, provide a good performance. As the different parts of a single image appear in different scales, developing a deep learning based image super resolution scheme that is capable of generating features at different scales and levels is essential. In this paper, a new residual block is proposed with a view of generating a rich set of features extracted at different scales and levels. The development of the proposed block is carried out using two distinct strategies, the first one focussing on generating features directly in two different scales, whereas the second one aims at generating multi-scale features indirectly by extracting them from two different hierarchical levels of abstraction. It is shown through experimental results that the proposed scheme of designing the residual block results in a network that provides a superior performance with reduced number of parameters than that provided by the light-weight networks using other types of residual blocks.

Restoration of a blurred and subsampled image is an ill-posed problem. In this paper, a two-stage convolutional network is proposed to carry out the processes of upsampling and deblurring to restore the original image. The main idea in the proposed scheme is that the deblurring process is attempted on a high PSNR image obtained after removing the ringing effect that is necessarily caused by the upsampling process. The evaluation of the proposed scheme is carried out using a benchmark dataset in terms of PSNR. The scheme is shown to outperform the state-of-the-art schemes, namely, the sparse coding network, the non-local means filters and the centralized sparse representation.

Image compression is a field in image processing that tries to remove redundant information in an image. Losing the information in lossy compression techniques such as JPEG results to artifacts in the decompressed image that necessitates the image restoration. In this work, a new image restoration scheme based on deep neural nets and maxout activation functions for the application of image deblocking is proposed. Experimental results are presented to demonstrate the superiority of the proposed method both in terms of subjective and objective metrics.

In this paper, a new scheme for single image super resolution using convolutional neural networks and subband reconstruction theory is proposed. In the design of the network, which is referred to as SRSubBandNet, each subband of the residual signal between the high and low resolution images is reconstructed from all the previous subbands. Skip connections between the first, middle and the last SRBs are utilized to address the gradient vanishing problem in the proposed network. SRSubBandNet provides competitive results in terms of both subjective and objective qualities when applied to various benchmark datasets.

Busy life as well as the prevalence of infotainment is increasingly making people more occupied even during tasks that require serious attention. One such task is driving and at the same time getting involved in activities that may distract drivers cognitively from watching the road and cause fatal accidents. This paper presents a method that is capable of monitoring different types of distractions, such as talking and texting on cell phone, casual eating, and operating cabin equipment while driving, so that a driver can be assisted to remain cautious on the road. The proposed method automatically detects and tracks fiducial body parts of a driver from video captured by a camera mounted on the front windshield inside a vehicle. Relative distances between the tracking trajectories are used as features that represent actions of the driver. Then, the well-known kernel support vector machine is applied for recognizing a particular distraction from the features extracted from body parts. The proposed feature is also compared with previously employed features for tracking-based human action recognition schemes to substantiate its better result in terms of mean accuracy and robustness for distraction recognition. The effectiveness of the proposed method of distraction recognition is also analyzed with respect to tracking errors.

Structural information, in particular, the edges present in an image are the most important part that get noticed by human eyes. Therefore, it is important to denoise this information effectively for better visualization. Recently, research work has been carried out to characterize the structural information into plain and edge patches and denoise them separately. However, the information about the geometrical orientation of the edges are not considered leading to sub-optimal denoising results. This has motivated us to introduce in this paper an adaptive steerable total variation regularizer (ASTV) based on geometric moments. The proposed ASTV regularizer is capable of denoising the edges based on their geometrical orientation, thus boosting the denoising performance. Further, earlier works exploited the sparsity of the natural images in DCT and wavelet domains which help in improving the denoising performance. Based on this observation, we introduce the sparsity of an image in orthogonal moment domain, in particular, the Tchebichef moment. Then, we propose a new sparse regularizer, which is a combination of the Tchebichef moment and ASTVbased regularizers. The overall denoising framework is optimized using split Bregman-based multivariable minimization technique. Experimental results demonstrate the competitiveness of the proposed method with the existing ones in terms of both the objective and subjective image qualities.

Data security is a main concern in everyday data transmissions over the Internet. A possible solution to guarantee secure and legitimate transaction is via hiding a piece of tractable information into the multimedia signal, that is, watermarking. In this paper, we propose a new color image watermarking scheme and its corresponding detector in the sparse domain. The watermark detector aims at verifying the ownership and circumventing any unauthorized duplication of the digital data. Most of the existing color image watermarking schemes disregard the inter-channel dependencies. In view of this, we take into account the interchannel dependencies between RGB channels and interscale dependencies of the sparse coefficients of color images by employing the hidden Markov model. An efficient detector is designed by establishing a binary hypothesis test through which the existence of the hidden watermark is examined. Experiments are conducted to evaluate the performance of the proposed watermark detector for color images. The results show that the proposed detector provides detection rates higher than those provided by the other detectors, even in the presence of attacks. It is also shown that the proposed detector exhibits better performance in terms of the robustness of the embedded watermark.

In this paper, a novel noncircularity-based localization method for mixed near-field (NF) and far-field (FF) sources is proposed with a symmetric uniform linear array (ULA) in the presence of unknown mutual coupling (UMC). Based on the principle of rank reduction (RARE), the multiple parameters of the sources including direction of arrival (DOA), range and mutual coupling coefficient (MCC) are decoupled, so that only several one-dimensional (1-D) spectral searches are required for their estimation. Meanwhile, the proposed method can also distinguish the types of sources without any extra processing. Simulation results are provided to demonstrate the effectiveness of the proposed method for the classification and localization of mixed sources under UMC.

In this paper, first, a new fusion technique, referred to as hybrid fusion (HBF) technique, based on feature-level fusion and the best unimodal system for multimodal biometric system recognition, is proposed. Secondly, a new weighting technique, referred to as mean-extrema based confidence weighting (MEBCW) technique, based on the scores obtained from feature-level fusion and the best unimodal system, is proposed. Finally, a weighted hybrid fusion, referred to as weighted hybrid fusion (WHBF) technique, is developed by incorporating MEBCW in HBF, in order to improve the overall recognition rate of a multimodal biometric system. The performance of the proposed method, in terms of equal error rate and genuine acceptance rates @5.3% and @7.2% false acceptance rates, is evaluated on a multi-biometric system. The experimental results show that the performance of a multi-biometric systems using the proposed fusions is superior to that of the uni-biometric systems or to that of the system using existing level of fusions.

The vector-based hidden Markov model (HMM) is a powerful statistical model for characterizing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross-orientation dependencies of the wavelet coefficients. In this paper we propose a scheme for designing a blind multibit watermark decoder incorporating the vector-based HMM in wavelet domain. The decoder is designed based on the maximum likelihood criterion. A closed-form expression is derived for the bit error rate and validated experimentally with Monte Carlo simulations. The performance of the proposed watermark detector is evaluated using a set of standard test images and shown to outperform the decoders designed based on the Cauchy or generalized Gaussian distributions without or with attacks. It is also shown that the proposed decoder is more robust against various kinds of attacks compared with the state-of-the-art methods.

The performance of a multi-biometric system can be improved using an efficient normalization technique under the simple sum-rule-based score-level fusion. It can also be further improved using normalization techniques along with a weighting method under the weighted sum-rule-based score-level fusion. In this paper, at first, we present two anchored score normalization techniques based on the genuine and impostor scores. Specifically, the proposed normalization techniques utilize the information of the overlap region between the genuine and impostor scores and their neighbors. Second, we propose a weighting technique that is based on the confidence of the matching scores by considering the mean-to-maximum of genuine scores and mean-to-minimum of impostor scores. A multi-biometric system having three biometric traits, fingerprint, palmprint, and earprint, is utilized to evaluate the performance of the proposed techniques. The performance of the multi-biometric system is evaluated in terms of the equal error rate and genuine acceptance rate @0.5% false acceptance rate. The receiver operating characteristics are also plotted in terms of the genuine acceptance rate as a function of the false acceptance rate.

In this paper, a novel pilot-aided algorithm is proposed for the detection of integer frequency offset (IFO) in orthogonal frequency division multiplexing (OFDM) systems. By transforming the IFO into two new integer parameters, the proposed method can largely reduce the number of trial values for the true IFO. The two new integer parameters are detected using two different pilot sequences, a periodic pilot sequence and an aperiodic pilot sequence. It is shown that the new scheme can significantly reduce the computational complexity while achieving almost the same performance as compared to previous methods.

Multimedia data piracy in the Internet is a growing problem, since it provides easy and fast data transmission. Watermarking is regarded as a solution to restrain unauthorized duplication or distribution data. Image watermarking research mostly focuses on grayscale images with an extension to color images. However, most of these techniques ignore dependencies between color channels. In view of this, in this work, a multichannel color image watermarking technique and its corresponding detector in the wavelet domain is proposed. The inter-channel dependencies between RGB channels and inter-scale dependencies of the wavelet coefficients of color image are taken into account by employing the vector-based hidden Markov model. We conduct experiment on a set of color images to assess the performance of the proposed watermark detector. The results show that the performance of the proposed detector is superior to that of the other detectors in terms of the imperceptibility of the embedded watermark and the detection rate. It is also shown that the proposed detector has better performance in presence or absence of different kinds of attacks in comparison to the other existing methods.

Statistical modeling of high dimensional, correlated, and complex imaging data obtained from various longitudinal neuroimaging studies has become an inevitable part of automatic disease diagnosing tasks. In this paper, we propose a parametric brain image modeling based on the statistical properties of non-subsampled shearlet transform (NSST) coefficients. The NSST detail coefficients of multimodal neuroimaging data exhibit highly non-Gaussian property, i.e., the probability density function (PDF) of the NSST coefficients are sharply peaked around zero with heavy tails. As a consequence, the marginal statistics of the detail subband coefficients are modeled by student's t location-scale PDF, which has heavier tails (more prone to outliers) than the Gaussian distribution for smaller values of the shape parameter. The Jensen-Shannon divergence (JSD) goodness-of-fit shows that the detail NSST subbands of neuroimaging data in the longitudinal Alzheimer's disease neuroimaging initiative database are well approximated by student's t location-scale distribution compared to that by the traditional generalized Gaussian distribution.

This paper investigates the spectral efficiency of multiuser multiple-input multiple-output systems with a large number of antennas at the base station that serves single-antenna users in one cell. It is assumed that the base station estimates the channel with the help of uplink training and then employs the zero-forcing technique to detect the data signals transmitted by the various users. An optimal power control scheme over pilot and data power based on large-scale fading is proposed to maximize the sum spectral efficiency for a given total energy budget in a coherence interval. Simulation results show that the spectral efficiency of the proposed method is superior to that of other existing methods. It Is also shown that, In order to maximize the sum spectral efficiency, more power should be allocated to the data signal power at high signal-to-nolse ratios and less power at low signal-to-noise ratios.

This paper studies the problem of maximizing the spectral efficiency in a massive multi-user MIMO downlink (DL) system where the base station employs a large number of antennas and serves single-antenna users in one cell, assuming time- division duplex transmission. In order to maximize the spectral efficiency in a DL transmission, a new method for power allocation among users is proposed based on the water-filling approach. To this end, a lower bound on the achievable rate is derived for each user in the presence of maximum- ratio transmission precoding and employed in defining the spectral efficiency. Since maximizing the spectral efficiency is an NP-hard problem, an effective algorithm is also proposed to find Karush-Kuhn-Tucker (local maximum) points. The performance of the proposed power allocation method is verified by conducting simulations and shown to be superior to other existing methods in terms of higher spectral efficiency.

In this paper, we present a multiclass data classifier, denoted by optimal conformal transformation kernel (OCTK), based on learning a specific kernel model, the CTK, and utilize it in two types of image recognition tasks, namely, face recognition and object categorization. We show that the learned CTK can lead to a desirable spatial geometry change in mapping data from the input space to the feature space, so that the local spatial geometry of the heterogeneous regions is magnified to favor a more refined distinguishing, while that of the homogeneous regions is compressed to neglect or suppress the intraclass variations. This nature of the learned CTK is of great benefit in image recognition, since in image recognition we always have to face a challenge that the images to be classified are with a large intraclass diversity and interclass similarity. Experiments on face recognition and object categorization show that the proposed OCTK classifier achieves the best or second best recognition result compared with that of the state-of-the-art classifiers, no matter what kind of feature or feature representation is used. In computational efficiency, the OCTK classifier can perform significantly faster than the linear support vector machine classifier (linear LIBSVM) can.

Single-microphone speech dereverberation is a challenging problem of de-convolving the reverberation produced by the room impulse response from the speech signal, when only one observation of the reverberant signal (one microphone) is available. By using linear prediction (LP)-residuals and spectral subtraction as two promising tools for dereverberation, a new technique is proposed. The first stage of the proposed technique consists of pre-whitening followed by a delayed long-term LP filtering whose kurtosis or skewness of LP-residuals is maximized to control the weight updates of the inverse filter. A nonlinear spectral subtraction scheme is the second stage of the proposed technique. It is shown that that the proposed algorithms outperform the existing major single-microphone methods in terms of a number of qualitative and quantitative measures.

Dissimilarities in equal error rates (EERs) of multiple matchers heavily influence the performance of multi-biometric systems. A normalization technique aims at improving the recognition rate of such a system. In view of this, in this paper, an anchored normalization technique, referred to as improved anchored min-max (IAMM) technique for a multimodal biometric system, is developed. In the proposed technique, the anchor value is computed from the raw matching score sets corresponding to each of the modalities used in the system. This anchor value does not require a priori knowledge of the equal error rates and genuine/impostor score distributions of the individual matchers used in the system. It takes into account the average and variations of the score values that occur more than once in each score set. The performance of IAMM, in terms of EER and genuine acceptance rates @10% and @20% false acceptance rates, is evaluated on a multi-biometric system. The experimental results show that the performance of a multi-biometric system using the proposed normalization technique is superior to that of the uni-biometric systems or to that of the system using the existing normalization techniques.

The success of sparse representation, in face recognition and visual tracking, has attracted much attention in computer vision in spite of its computational complexity. These sparse representation-based methods assume that the coding residual follows either Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding residuals in real scenarios. In order to deal with such issues in visual tracking, a novel generative tracker is proposed in a Bayesian inference framework by exploiting both the robust sparse coding and the principle component analysis (PCA) algorithm. In contrast to the existing algorithms, the proposed method introduces weighted least squares into the PCA reconstruction avoiding the much complex l1-regularization. Further, it is proposed to generate an occlusion map based on weights, and is used to avoid updating the occlusion information during incremental subspace learning. The performance evaluation on the challenging image sequences demonstrates that the proposed method performs favorably when compared with the several state-of-the-art methods.

Feature extraction from each scale of an image pyramid to construct a feature pyramid is considered as a computational bottleneck for many object detectors. In this paper, we present a novel technique for the approximation of feature pyramids in the 2D discrete cosine transform (2DDCT) domain. The proposed method is based on a feature resampling technique in the 2DDCT domain, and exploits the effect of resampling an image on the feature responses. Experimental results show that the proposed scheme provides feature approximation accuracy higher than that of the spatial domain counterpart using gradient magnitude or gradient histograms. Further, when the proposed method is employed for pedestrian detection, it provides a logaverage miss-rate lower than that provided by the state-of-the-art techniques on INRIA, ETH, and TUD datasets and performs favorably on Caltech dataset, while performing in real-time.

Speckle noise reduction is a prerequisite task in images captured by ultrasonography systems due to their inherent noisy nature. In this work, we propose a new despeckling method in the contourlet domain using the Cauchy prior. The multiplicative speckle noise is first transferred to an additive one using a logarithmic transform. The logarithmically-transformed contourlet coefficients of the image and noise are assumed to be the Cauchy and Maxwell distributions, respectively. In order to estimate the noise-free contourlet coefficients, an efficient closed-form Bayesian maximum a posteriori estimator is developed. Simulations are carried out to evaluate the performance of the proposed despeckling method by using the synthetically-speckled and real ultrasound images. It is shown that the proposed method outperforms several existing techniques in terms of the signal-to-noise ratio and is able to preserve the diagnostically signific ant details of the ultrasound images.

In a distributed video coding problem, use of a correct model for the correlation noise plays a significant role in improving the decoding performance and consequently in providing higher coding efficiency. In this work, we first study the predictive and additive correlation noise models at the DCT coefficient band level for transform-domain distributed video coding. Then, bounds on compression rates for encoding the quantized DCT coefficient band are obtained for both the correlation noise models. We then investigate how the distribution of the DCT coefficient bands in each WZ frame affects the compression rate bound in each correlation noise model. It is shown that for the DCT coefficient bands being non-uniformly distributed, the compression rate bound in the additive correlation noise model lower than that in the predictive one. Moreover, it is shown that selecting a wrong correlation model leads to compression rate loss in the decoder. The simulation results are provided to validate the theoretical investigation.

The performance of multiple matchers heavily influence the recognition accuracy of a multi-biometric system under the simple sum-rule-based score-level fusion. In this paper, a weighting technique, referred to as score reliability based weighting (SRBW) technique, is developed to estimate weights for the matchers in order to improve the recognition rate of multi-biometric systems at the score level. In the proposed technique, the reliabilities are computed directly from the raw matching scores obtained from the individual matchers. The proposed weighting technique does not require a priori knowledge of the rankings of matching scores, or the equal error rates, or the genuine/impostor score distributions of the individual matchers used in the system. The experimental results show that the performance of a multi-biometric system using the proposed weighting technique is superior to that of the uni-biometric systems or to that of the multi-biometric systems using the existing weighting techniques in terms of equal error rate and genuine acceptance rate at 1% false acceptance rate.

In this paper, we investigate new methods for preamble-aided coarse timing estimation in orthogonal frequency division multiplexing systems. Two novel timing metrics using high-order statistics-based correlation and differential normalization functions are first proposed. The performance of the new timing metrics is then evaluated using different criteria, including class separability, robustness to the carrier frequency offset, and computational complexity. It is shown that the new timing metrics can considerably increase the class separability due to their more distinct values at correct and wrong timing instants, and thus give a significantly better detection performance as compared with the existing timing metrics. Furthermore, a new method for coarse estimation of the start of the frame is proposed, which remarkably reduces the probability of intersymbol interference (ISI). The improved performances of the new schemes in multipath fading channels are shown by the probabilities of false alarm, missed detection, and ISI obtained through computer simulations.

In recent years, many works on digital image watermarking have been proposed all aiming at protection of the copyright of an image document or authentication of data. This paper proposes a novel watermark decoder in the contourlet domain . It is known that the contourlet coefficients of an image are highly non-Gaussian and a proper distribution to model the statistics of the contourlet coefficients is a heavy-tailed PDF. It has been shown in the literature that the normal inverse Gaussian (NIG) distribution can suitably fit the empirical distribution. In view of this, statistical methods for watermark extraction are proposed by exploiting the NIG as a prior for the contourlet coefficients of images. The proposed watermark extraction approach is developed using the maximum likelihood method based on the NIG distribution. Closed-form expressions are obtained for extracting the watermark bits in both clean and noisy environments. Experiments are performed to verify the robustness of the proposed decoder. The results show that the proposed decoder is superior to other decoders in terms of providing a lower bit error rate. It is also shown that the proposed decoder is highly robust against various kinds of attacks such as noise, rotation, cropping, filtering, and compression.

There are a number of image denoising methods in the wavelet domain using statistical models. It is known that the performance of such methods can be significantly improved by taking into account the statistical dependencies between the wavelet coefficients. It is shown that the vector-based hidden Markov model (VB-HMM) is capable of capturing both the subband marginal distribution and the inter-scale, intra-scale and cross orientation dependencies of the wavelet coefficients. In view of this, we propose a new maximum a posteriori estimator using the VB-HMM as a prior for the wavelet coefficients of images. This is realized by deriving an efficient closed-form expression for the shrinkage function. Experimental results are performed to evaluate the performance of the proposed denoising method. The results demonstrate that the proposed method outperforms some of the state-of-the-art techniques in terms of both the peak signal to noise ratio and perceptual quality.

In this paper, we propose a real-valued ESPRIT algorithm for two-dimensional direction of arrival (2D DOA) estimation of noncircular signals using arbitrarily spaced acoustic vector sensor array. By utilizing the noncircularity of the signals, the proposed algorithm provides a better estimation performance while having only slightly larger computational complexity as compared to the traditional complex ESPRIT algorithm. Furthermore, the proposed algorithm gives automatically paired azimuth and elevation angle estimates without requiring extra pair matching. Simulation results are presented to demonstrate the estimation performance of the proposed algorithm as compared to the traditional ESPRIT algorithm as well as Cramer-Rao bound (CRB).

The success of sparse representation in face recognition has motivated the development of sparse representation-based appearance models for visual tracking. These sparse representation-based trackers show state-of-the-art performance, but at the cost of computationally expensive l1-norm minimization. As the computational cost prevents the tracker from being used in real-time systems such as real-time surveillance and military operations, it has become a very important issue. With the aim of reducing the computational complexity of l1-norm minimization, a structural local DCT sparse appearance model is proposed in a particle filter framework. Application of DCT on local patches helps to reduce the dimensions of the dictionary as well as candidate samples by using low-pass filtered DCT coefficients. This in turn helps to remove the information relating to occlusion and background clutter thereby reducing the ambiguity created while computing the confidences of the target samples. The proposed method is evaluated on the challenging image sequences available in the literature and its performance compared with three recent state-of-the-art methods. It is shown that the proposed method provides superior/similar performance for most of the sequences with reduced computational complexity in l1-norm minimization.

Speckle reduction has been a prerequisite for many SAR image processing tasks. This work presents a new approach for despeckling of SAR images in the contourlet domain using the alpha-stable distribution. It is shown that the alpha-stable distribution provides a good fit for the contourlet coefficients of an image, since it can capture the large peak and heavy tails of the distribution of the empirical data. This model is then exploited in a Bayesian maximum a posteriori estimator to restore the noise-free contourlet coefficients. The performance of the proposed despeckling method is evaluated using synthetically-speckled and real SAR images. Simulations are carried out using synthetically speckled images to investigate the performance of the proposed method, and compare it with that of some of the existing methods. The experimental results show that the proposed method can provide better preservation of the edges and can yield better visual quality as compared to some of the existing methods.

Digital watermarking has been widely used in the copyright protected images in multimedia. This paper addresses the blind watermark detection problem in contourlet domain. It is known that the contourlet coefficients of images have non-Gaussian property and can be well modelled by non-Gaussian distributions such as the normal inverse Gaussian (NIG). In view of this, we exploit this model to derive closed-form expressions for the test statistics and design an optimum blind watermark detector in the contourlet domain. Through conducting several experiments, the performance of the proposed detector is evaluated in terms of the probabilities of detection and false alarm and compared to that of the other existing detectors. It is shown that the proposed detector using the NIG distribution is superior to other detectors in terms of providing higher rate of detection. It is also shown that the proposed NIG-based detector is more robust than other detectors against attacks, such as JPEG compression and Gaussian noise.

A new contourlet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise. This method takes into account the statistical dependencies among the contourlet coefficients of different scales. In view of this, a non-Gaussian multivariate distribution is proposed to capture the across-scale dependencies of the contourlet coefficients. This model is then exploited in a Bayesian maximum a posteriori estimator to restore the clean coefficients by deriving an efficient closed-form shrinkage function. Experimental results are performed to evaluate the performance of the proposed denoising method using typical noise-free images contaminated by simulated noise. The results show that the proposed method outperforms some of the state-of-the-art methods in terms of both the subjective and objective criteria.

MOTIVATION: Insertion/deletion (indel) and amino acid substitution are two common events that lead to the evolution of and variations in protein sequences. Further, many of the human diseases and functional divergence between homologous proteins are more related to indel mutations, even though they occur less often than the substitution mutations do. A reliable identification of indels and their flanking regions is a major challenge in research related to protein evolution, structures and functions. RESULTS: In this article, we propose a novel scheme to predict indel flanking regions in a protein sequence for a given protein fold, based on a variable-order Markov model. The proposed indel flanking region (IndelFR) predictors are designed based on prediction by partial match (PPM) and probabilistic suffix tree (PST), which are referred to as the PPM IndelFR and PST IndelFR predictors, respectively. The overall performance evaluation results show that the proposed predictors are able to predict IndelFRs in the protein sequences with a high accuracy and F1 measure. In addition, the results show that if one is interested only in predicting IndelFRs in protein sequences, it would be preferable to use the proposed predictors instead of HMMER 3.0 in view of the substantially superior performance of the former.

BACKGROUND: The alignment of multiple protein sequences is one of the most commonly performed tasks in bioinformatics. In spite of considerable research and efforts that have been recently deployed for improving the performance of multiple sequence alignment (MSA) algorithms, finding a highly accurate alignment between multiple protein sequences is still a challenging problem. RESULTS: We propose a novel and efficient algorithm called, MSAIndelFR, for multiple sequence alignment using the information on the predicted locations of IndelFRs and the computed average log-loss values obtained from IndelFR predictors, each of which is designed for a different protein fold. We demonstrate that the introduction of a new variable gap penalty function based on the predicted locations of the IndelFRs and the computed average log-loss values into the proposed algorithm substantially improves the protein alignment accuracy. This is illustrated by evaluating the performance of the algorithm in aligning sequences belonging to the protein folds for which the IndelFR predictors already exist and by using the reference alignments of the four popular benchmarks, BAliBASE 3.0, OXBENCH, PREFAB 4.0, and SABRE (SABmark 1.65). CONCLUSIONS: We have proposed a novel and efficient algorithm, the MSAIndelFR algorithm, for multiple protein sequence alignment incorporating a new variable gap penalty function. It is shown that the performance of the proposed algorithm is superior to that of the most-widely used alignment algorithms, Clustal W2, Clustal Omega, Kalign2, MSAProbs, MAFFT, MUSCLE, ProbCons and Probalign, in terms of both the sum-of-pairs and total column metrics.

An improved fast iterative shrinkage thresholding algorithm (IFISTA) for image deblurring is proposed. The IFISTA algorithm uses a positive definite weighting matrix in the gradient function of the minimization problem of the known fast iterative shrinkage thresholding (FISTA) image restoration algorithm. A convergence analysis of the IFISTA algorithm shows that due to the weighting matrix, the IFISTA algorithm has an improved convergence rate and improved restoration capability of the unknown image over that of the FISTA algorithm. The weighting matrix is predetermined and fixed, and hence, like the FISTA algorithm, the IFISTA algorithm requires only one matrix vector product operation in each iteration. As a result, the computational burden per iteration of the IFISTA algorithm remains the same as in the FISTA algorithm. Numerical examples are presented that demonstrate the improved performance of the IFISTA algorithm over that of the FISTA and iterative shrinkage thresholding (ISTA) algorithms in terms of the convergence speed and the peak signal-to-noise ratio.

The past decade has witnessed significant progress in object detection and tracking in videos. In this paper, we present a model for collaboration between a pre-trained object detector and multiple single object trackers in the particle filter tracking framework. For each frame, we construct an association between the trackers and the detections, and when a tracker is successfully associated to a detection, we treat this detection as the key-sample for this tracker. We present a dual motion model that incorporates the associated detections with the object dynamics. Then, a likelihood function provides different weights for the propagated and the newly created particles, reducing the effect of false positives and missed detections in the tracking process. In addition, we use generative and discriminative appearance models to maximize the appearance variation among the targets. The performance of the proposed algorithm compares favorably with that of the state-of-the-art approaches on three public sequences.

The wavelet coefficients of images show heavy-tailed marginal statistics as well as strong inter- and intra-subbands and across orientations dependencies. The vector-based hidden Markov model (HMM) has been shown to be an effective statistical model for wavelet coefficients, which is capable of capturing both the subband marginal distribution and the inter-scale and intra-scale dependencies of the wavelet coefficients. In this paper, we propose a locally-optimum watermark detector using the HMM model for image wavelet coefficients. The performance of the proposed detector is studied through simulation and is shown to be superior to that of other detectors in terms of the imperceptibility of the embedded watermark and detection rate.

A space time code based partial rank affine projection (PRAP) algorithm is proposed. The proposed algorithm uses an input signal where the input signal matrix Xkbecomes an orthogonal matrix. For this input signal, matrix (XkTXk) becomes a diagonal matrix whose inverse can be easily computed. Thus, the proposed algorithm saves a significant amount of computations. Due to this feature the proposed PRAP algorithm is shown to offer a faster convergence speed and a smaller computational burden per iteration than the NLMS algorithm does.

In this paper, we address the problem of estimating the two-dimensional (2-D) directions of arrival (DOA) of multiple signals, by means of a sparse L-shaped array. The array consists of one uniform linear array (ULA) and one sparse linear array (SLA). The shift-invariance property of the ULA is used to estimate the elevation angles with low computational burden. The source waveforms are then obtained by the estimated elevational angles, which together with each sensor of the SLA, considered as a linear regression model, will be used to estimate the azimuth angle by the modified total least squares (MTLS) technique. The new algorithm yields correct parameter pairs without requiring the computationally expensive pairing operation, and therefore, it has at least two advantages over the previous L-shaped array based algorithms: less computational load and better performance due to using the SLA. Simulation results show that our method provides accurate and consistent 2-D DOA estimation results which could not be achieved by other methods with comparable computational complexity.

It is known that the contourlet coefficients of images have non-Gaussian property and heavy tails. In view of this, an appropriate distribution to model the statistics of the contourlet coefficients would be the one having large peaks, and tails heavier than that of a Gaussian PDF, i.e., a heavy-tailed PDF. This paper proposes a new image modeling in the contourlet domain, where the magnitudes of the coefficients are modeled by a symmetric alpha-stable distribution which is best suited for modeling transform coefficients with a high non-Gaussian property and heavy tails. It is shown that the alpha-stable family of distributions provides a more accurate model to the contourlet subband coefficients than the formerly used distributions, namely, the generalized Gaussian and Laplacian distributions, both in terms of the subjective measure of the Kolmogorov-Smirnov distance and the objective measure of comparing the log-scale histograms.

Compressive sampling matching pursuit (CoSaMP) is an efficient compressive sensing algorithm holding rigorous estimation error bounds and low computational complexity, when it deals with an additive noise signal model in the observation domain. However, in some applications, e.g., speech enhancement (SE), noise is added to a signal in the time domain, where the conventional CoSaMP cannot be directly applied. In this paper, we establish the theory of CoSaMP to address the time-domain noise, referred to as Tdn-CoSaMP, which extends the canonical theory of CoSaMP. In particular, we prove the existence of a new upper bound of Tdn-CoSaMP, which is found to be larger than that of the conventional CoSaMP by appending two additional terms: a multiplier$1+\sqrt{{N\over s}}$, where$N$is the dimension of the signal, and an${\ell_1}$norm of the noise${1\over\sqrt{s}}\Vert {\mbi{e}}\Vert_1$scaled by the sparse level$s$of the signal. We also apply Tdn-CoSaMP to the SE task based on the sequential denoising of overlapped frames in the discrete cosine transform (DCT) domain. The proposed system, CoSaMP-based speech enhancement (CoSaMPSE), has been evaluated in terms of both objective and subjective criteria on various types of noise. Positive results have been achieved for denoising stationary and nonstationary white Gaussian noise (WGN) and are comparable to other SE methods. Moreover, due to its low computational complexity, CoSaMPSE is possible to be combined with optimally modified log-spectrum amplitude estimation (OMLSA) and able to achieve complementary denoising effects in various noisy conditions.

In the past decade, several schemes for digital image watermarking have been proposed to protect the copyright of an image document or to provide proof of ownership in some identifiable fashion. This paper proposes a novel multiplicative watermarking scheme in the contourlet domain. The effectiveness of a watermark detector depends highly on the modeling of the transform-domain coefficients. In view of this, we first investigate the modeling of the contourlet coefficients by the alpha-stable distributions. It is shown that the univariate alpha-stable distribution fits the empirical data more accurately than the formerly used distributions, such as the generalized Gaussian and Laplacian, do. We also show that the bivariate alpha-stable distribution can capture the across scale dependencies of the contourlet coefficients. Motivated by the modeling results, a blind watermark detector in the contourlet domain is designed by using the univariate and bivariate alpha-stable distributions. It is shown that the detectors based on both of these distributions provide higher detection rates than that based on the generalized Gaussian distribution does. However, a watermark detector designed based on the alpha-stable distribution with a value of its parameter α other than 1 or 2 is computationally expensive because of the lack of a closed-form expression for the distribution in this case. Therefore, a watermark detector is designed based on the bivariate Cauchy member of the alpha-stable family for which α = 1 . The resulting design yields a significantly reduced-complexity detector and provides a performance that is much superior to that of the GG detector and very close to that of the detector corresponding to the best-fit alpha-stable distribution. The robustness of the proposed bivariate Cauchy detector against various kinds of attacks, such as noise, filtering, and compression, is studied and shown to be superior to that of the generalized Gaussian detector.

In this paper, a novel involutory parametric transform is proposed by exploiting the reciprocal-orthogonal parametric transform. In addition, a recursive algorithm is proposed for its simple construction and fast computation. The transform has a very large number of independent parameters that are useful for many applications. Specifically, we show by implementing the double random phase encoding technique that the independent parameters of the proposed transform can successfully be used as an additional secret key for image encryption.

There are two main ways by which antenna arrays can significantly improve the performance of the direction-of-arrival (DOA) estimation. In the first method, one can extend the array aperture by designing a sparse antennas array. The second method makes use of the temporal information of the received signals, such as the signal waveform. A few articles have dealt with DOA estimation by combining the above two approaches. In this paper, we present a DOA estimation and tracking method by employing the known waveform of the signal and the parallel recursive least square (RLS) technique. When the waveform of the signal is known, the output of each sensor in the array can be considered as a linear regression model of which the coefficients contain the whole information for estimating the DOA. Therefore, the RLS filter is used to estimate and track these coefficients and then the relationship among the coefficients of all the sensors is exploited to obtain the final DOA value without ambiguity. Finally, computer simulation of the proposed method with comparison to the previous methods is conducted.

Fitting biophysical models to real noisy data jointly with extracting fundamental biophysical parameters has recently stimulated tremendous studies in computational neuroscience. Hodgkin-Huxley (HH) neuronal model has been considered as the most detailed biophysical model for representing the dynamical behavior of the spiking neurons. In this paper, we derive, for the first time, the dual extended Kalman filtering (DEKF) approach for the HH neuronal model to track the dynamics and estimate the parameters of a single neuron from noisy recorded membrane voltage. As unscented Kalman filter (UKF) has been already applied to the HH model, a quantitative comparison between these methods is accomplished in our simulation for different signal to observation noise ratios. Our simulations demonstrate the high accuracy of DEKF in the prediction and estimation of hidden states and unknown parameters of the HH neuronal model. Faster implementation of DEKF (than UKF) makes it particularly useful in dynamic clamp technique.

In the authors previous work, a compressive sensing (CS)‐based method has been proposed to address speech enhancement (SE) in adverse environments (CS‐SPEN) based on an assumption of sparse noise. However, this assumption may not be satisfied in practical noisy environments. In this study, the authors study this issue by relaxing this assumption to consider a general non‐sparse noise case, such that the proposed method naturally extends the previous one. In particular, they solve the theoretic difficulty of CS‐SPEN on the treatment of non‐sparse noise by using a relaxed upper bound for the constraint governing data consistency and a relaxed estimation error bound. Their main result is mathematically proved. In addition, the effectiveness of the proposed method is demonstrated by computational simulations, showing certain improvements to the previous method for both stationary and non‐stationary white Gaussian noises across various segmental signal‐noise‐ratios (SNRs). In these cases, the proposed method is shown to have comparable results to the state‐of‐the‐art SE alogrithms and some advantages over them at low SNRs. CS‐SPEN without the sparse noise assumption works evenly with CS‐SPEN with the sparse noise assumption for car internal and F16 cockpit noises.

Regulatory interactions among genes and gene products are dynamic processes and hence modeling these processes is of great interest. Since genes work in a cascade of networks, reconstruction of gene regulatory network (GRN) is a crucial process for a thorough understanding of the underlying biological interactions. We present here an approach based on pairwise correlations and lasso to infer the GRN, taking into account the variable time delays between various genes. The proposed method is applied to both synthetic and real data sets, and the results on synthetic data show that the proposed approach outperforms the current methods. Further, the results using real data are more consistent with the existing knowledge concerning the possible gene interactions.

The number of projections is a critical factor in tomographic imaging. The larger the number, the better the quality of the reconstructed image; however, it increases the radiation dose delivered to the patient. Therefore, it is important to keep the number of projections as small as possible. Traditionally, the projections are taken by moving the x-ray source around the patient at uniform angular steps. Taking projections at nonuniform steps may result in better images as compared with that obtained using uniform projections. This paper describes two different approaches that adjust the step size to adaptively select the angle of projections. The first one is based on the spectral richness of the acquired projections and the second relies on the amount of new information added by successive projections. The superior performance of the two proposed methods over the uniform projection scheme is demonstrated through simulation results using both phantom and real images.

A novel iterative adaptive filtering approach is proposed to remove the Transcranial Magnetic Stimulation (TMS) induced artifact from multi-channel recordings of neural responses to sensory stimuli. For each specific channel, the average of all trials is considered as the input to the adaptive filter whose coefficients are calculated by minimizing the mean square error between the voltage trace of that trial and the filter output. The residues of all trials serve as an initial estimate of the neural response. Once this estimate is calculated, the input of the adaptive filter is modified by subtracting the mean residue. It is shown that the modified input provides a better estimate of the mean TMS artifact, which serves the input of the adaptive filer, in the next iteration. Therefore, new filter coefficients are estimated in the next iteration, for each single trial, and the procedure continues till no considerable changes in the residues occur. We report a quantitative verification of the accuracy of our method by generating a controlled simulation. Furthermore, applying the algorithm to experimental data confirms the accuracy of our approach and its usefulness for extracting neurophysiological responses occurring in temporal proximity to TMS pulses.

An accurate direction-of-arrival (DOA) estimation algorithm with sparse sensor array is proposed. By dividing the nonuniform linear sparse array (NLSA) into two uniform linear sparse arrays (ULSA), the subarray response vectors yield a property of rotational invariance in the estimation of rough DOA without ambiguity using the so-called generalized ESPRIT. According to the estimated rough DOA, the accurate DOA of each source is then obtained by the proposed alternating null-steering technique (ANST). Simulation results that demonstrate the performance of the algorithm are provided.

Signal sparsity is the fundamental requirement of compressive sensing (CS) techniques. In our previous work, a CS-based speech enhancement algorithm has been proposed. However, several issues concerning speech sparsity have not yet been thoroughly studied. In this paper, we focus on studying the following issues: (1) the sparsity of clean speech and audio signals; (2) the sparsity of various noise signals; (3) analysis of the capacity of two sparse transforms i.e., wavelet and discrete cosine transform (DCT), to explore speech sparsity. In this respect, several measures are proposed to analytically compare the wavelet transform with DCT. We found that (1) signal compressibility is an important factor for the CS-based method. (2) DCT explores the best compressibility for noisy signals and achieves the best enhancement performance; (2) The CS-based speech enhancement methods are more efficient in reducing the noise with worse compressibility.

The regulation of gene expression is a dynamic process, hence it is of vital interest to identify and characterize changes in gene expression over time. We present here a general statistical method for detecting changes in microarray expression over time within a single biological group and is based on repeated measures (RM) ANOVA. In this method, unlike the classical F-statistic, statistical significance is determined taking into account the time dependency of the microarray data. A correction factor for this RM F-statistic is introduced leading to a higher sensitivity as well as high specificity. We investigate the two approaches that exist in the literature for calculating the p-values using resampling techniques of gene-wise p-values and pooled p-values. It is shown that the pooled p-values method compared to the method of the gene-wise p-values is more powerful, and computationally less expensive, and hence is applied along with the introduced correction factor to various synthetic data sets and a real data set. These results show that the proposed technique outperforms the current methods. The real data set results are consistent with the existing knowledge concerning the presence of the genes. The algorithms presented are implemented in R and are freely available upon request.

In this paper, the problem of joint space-time parameter estimation for underwater wireless communication channels in a multipath environment is addressed. We consider the receive antenna array to be configured with multiple vector sensors, each of which consists of a pair of orthogonal velocity sensors. A quadrilinear model for the channel is formulated, and a quadrilinear decomposition method developed for joint angle and delay estimation (JADE). In addition, a computationally simple subspace-based algorithm is proposed for the problem under consideration. The basic idea behind this algorithm is to use the angle information embedded in the velocity vector sensors to start two polynomial rooting procedures for the angle and delay in succession. Simulation results are finally presented to verify the efficacy of the proposed algorithms.

In this paper, an efficient algorithm for fast computation of the conjugate symmetric sequency-ordered complex Hadamard transform (CS-SCHT) of any length that is a power of two is proposed using the Kronecker product. Since the CS-SCHT matrix is factored into a product of sparse matrices, the resulting structure for the algorithm is very attractive for implementation and similar to that of the well-known Walsh-Hadamard transform, except for some multiplications by -1 or (-√(-1)). It is shown that the proposed N-point complex-valued CS-SCHT algorithm requires Nlog2(N) complex additions/subtractions and (N/2-1) multiplications by (-√(-1)).

In this paper, a one-parameter eight-point orthogonal transform suitable for image compression is proposed. An algorithm for its fast computation is developed and an efficient structure for a simple implementation valid for all possible values of its independent parameter is proposed. It is shown that an appropriate selection of the values of the parameter results in a number of new multiplication-free transforms having a good compromise between the computational complexity and performance. Applying the proposed transform to image compression, we show that it outperforms the existing transforms having complexities similar to that of the proposed one.

This paper presents a new method of estimating the direction-of-arrival (DOA) for multiple signals using minimum redundancy linear sparse subarrays (MRLSS). The proposed method makes use of the array structure to obtain the extended correlation matrix that is constructed by Kronecker Steering Vectors (KSVs) of which each contains the ambiguous and unambiguous angle with a one-to-one relationship. Our method enjoys two advantages in comparison to the existing methods. First, the cyclic ambiguity can be resolved by the one-to-one mapping of unambiguous angle without requiring additional algorithms such as MUSIC and MODE. Second, the proposed method can deal with different unambiguous angles with the same ambiguous angles, which could not have been possible by using the traditional schemes due to the fact that our method obtains the ambiguous and unambiguous angles simultaneously.

This paper addresses the problem of the direction-of-arrival (DOA) estimation using fewer receivers than sensors. Inspired by the Compressed Sensing (CS) theory developed in recent years, we present a new preprocessing scheme for a large array using a small size receiver. Unlike the traditional ℓ2-norm-based algorithms by judicious selection of the preprocessing matrix, the proposed scheme uses a random weight generator as a measurement of the compressed sensing to form the output data for each time interval. The formulated CS problem for DOA estimation is then solved based on the convex programming via ℓ1-norm approximation such as Dantzig Selector. We consider two different scenarios in the CS domain, i.e., the angle domain and the angle-frequency domain. It is shown that the number of receivers can be reduced significantly for a given number of sensors by using the proposed CS-based DOA estimation approach.

This letter investigates the problem of joint estimation of the direction of departure (DOD) and the direction of arrival (DOA) for multi-input multi-output (MIMO) array systems. A new bistatic MIMO array system, configured with multiple transmit sensors and multiple velocity receive sensors is introduced, and a new joint DOD and DOA estimation algorithm is proposed. The key idea behind the proposed algorithm is to use the DOA information embedded in the velocity sensors to start 1-D MUSIC searches for the DOD and DOA in succession. The proposed successive MUSIC algorithm is suitable for irregular array geometry, imposes less constraint on the receive sensor spacing, and requires no parameter pairing nor two-dimensional searching.

Discrete transforms have been widely used in various signal processing applications. Specifically, transform-based data encryption techniques have become attractive for many recent communication systems. In this paper, we propose a fast and efficient image encryption method based on the reciprocal-orthogonal parametric (ROP) transform. By exploiting the properties of the ROP transform, we show that its independent parameters can successfully be used as an additional secret key for encryption. Experiments results carried clearly show the efficiency of the proposed encryption method.

Most of the existing multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) channel estimation methods do not take into account the effect of the pulse-shaping filter in the transmitter nor of the matched filter in the receiver, thus leading to an estimation solution for the composite channel including the pulse-shaping and matched filters, instead of the pure wireless channel. This solution is not sufficiently accurate because of the extra length of the composite channel induced by the two filters especially in the scenario with a small pure channel length. In this study, the authors present a novel methodology for the estimation of the pure multipath channels of pulse-shaped MIMO-OFDM systems. By utilising the knowledge of pulse-shaping and matched filters, the authors develop two channel estimation approaches, namely, a semi-blind approach for the sampling duration-based channels, in which the multipath occurs at the sampling instant and a training-based least-square technique for the upsampling duration-based channels where the multipath may occur in a fraction of sampling duration. A number of computer simulation-based experiments are conducted, and these simulation results confirm the efficacy of the proposed approaches.

A novel framework of the motion-compensated 3-D wavelet transform (MC3DWT) for video denoising is presented in this paper. The motion-compensated temporal wavelet transform is first performed on a sliding window of video frames consisting of previously denoised frames and the current noisy frame. The 2-D spatial wavelet transform is then performed on the temporal subband frames, thus realizing a 3-D wavelet transform. Any of established wavelet-based still image denoising algorithms can then be applied to the high-pass 3-D subbands. The operation of the inverse 2-D spatial wavelet transform followed by the inverse temporal wavelet transform reconstructs the video frames in the buffer. The denoised current frame may be used as an output for real-time processing; meanwhile, the past frames can be updated, one of which may be used as a delayed output for post-processing or for real-time processing that allows some amount of delay. The proposed MC3DWT framework integrates both the spatial filtering and recursive temporal filtering into the 3-D wavelet domain and effectively exploits both the spatial and temporal redundancies. Experimental results have demonstrated a superior visual and quantitative performance of the proposed scheme for various levels of noise and motion.

A new bivariate maximum a posteriori estimator is proposed for the magnitude components of the dual-tree complex wavelet transform (DT-CWT) coefficients in order to reduce additive white Gaussian noise in a video. The estimator considers the fact that the magnitude components of the DT-CWT coefficients of the Gaussian distributed noise fit the generalized Gamma distribution very well. For spatial filtering, the joint distribution function of the magnitude components of the DT-CWT coefficients of the two neighboring frames of a video is considered to be locally-adaptive bivariate Gaussian having a non-negative mean. The correlation coefficient of this distribution function acts as an indirect measure of the motion of the DT-CWT coefficients between two neighboring frames. A recursive time averaging of the spatially filtered magnitude components is adopted for further noise reduction. Experimental results on test video sequences show that the proposed estimator provides an average peak signal-to-noise ratio that is higher than that provided by the others.

In this paper, a semi-blind algorithm is proposed for the detection of most significant tap (MST) in the sparse channel estimation of OFDM systems. Based on an analysis of the second-order statistics of the signal received through a noise-free sparse channel, a direct relationship between the positions of the most significant taps (MST) of the sparse channel and the lags of the nonzero correlation functions is revealled, leading to an efficient semi-blind MST detection algorithm. By using the acquired MST position, a sparse least square channel estimate is then obtained. A number of computer simulation-based experiments are carried out to confirm the effectiveness of the proposed semi-blind MST detection algorithm and the associated sparse LS channel estimation method.

The second-order statistics (SOS) of the received signal are very often used in blind and semi-blind channel estimation. In this paper, an analysis of signal perturbation in SOS of the received signal is first conducted, revealling that, even in the noise-free case, some SOS-based blind and semi-blind algorithms are subject to a signal perturbation error. Based on the analysis, a very efficient transmit scheme that can completely cancel the signal perturbation error at the receiver in the noise-free case is proposed. Computer simulations show that by employing the proposed signal perturbation cancellation approach, the mean square error (MSE) of the SOS estimate can be sufficiently suppressed in the noisy case.

Ultrasound imaging is widely used for diagnostic purposes among the clinicians. A major problem concerning the ultrasound images is their inherent corruption by the multiplicative speckle noise that hampers the quality of the diagnosis, and reduces the efficiency of the algorithms for automatic image processing. In this paper, we propose a new spatially adaptive wavelet-based method in order to reduce the speckle noise from ultrasound images. A spatially adaptive threshold is introduced for denoising the coefficients of log-transformed ultrasound images. The threshold is obtained from a Bayesian maximum a posteriori estimator that is developed using a symmetric normal inverse Gaussian probability density function (PDF) as a prior for modelling the coefficients of the log-transformed reflectivity. A simple and fast method is provided to estimate the parameters of the prior PDF from the neighbouring coefficients. Extensive simulations are carried out using synthetically speckled and ultrasound images. It is shown that the proposed method outperforms several existing techniques in terms of the signal-to-noise ratio, edge preservation index and structural similarity index and visual quality, and in addition, is able to maintain the diagnostically significant details of ultrasound images.

Traditional statistical detectors of the discrete wavelet transform (DWT)-based image watermarking use probability density functions (PDFs) that show inadequate matching with the empirical PDF of image coefficients in view of the fact that they use a fixed number of parameters. Hence, the decision values obtained from the estimated thresholds of these detectors provide substandard detection performance. In this paper, a new detector is proposed for the DWT-based additive image watermarking, wherein a PDF based on the Gauss-Hermite expansion is used, in view of the fact that this PDF provides a better statistical match to the empirical PDF by utilizing an appropriate number of parameters estimated from higher-order moments of the image coefficients. The decision threshold and the receiver operating characteristics are derived for the proposed detector. Experimental results on test images demonstrate that the proposed watermark detector performs better than other standard detectors such as the Gaussian and generalized Gaussian (GG), in terms of the probabilities of detection and false alarm as well as the efficacy. It is also shown that detection performance of the proposed detector is more robust than the competitive GG detector in the case of compression, additive white Gaussian noise, filtering, or geometric attack.

In this paper, the symmetric normal inverse gaussian (SNIG) probability density function (PDF) is proposed as a highly suitable prior for modelling the DCT coefficients of natural images. A new method, based on minimizing the Kullback-Leibler divergence between the proposed prior and the empirical PDF extracted from image data, is proposed to estimate the SNIG parameters. The efficacy of the proposed parameter estimation technique is tested using Monte-Carlo simulations. It is shown that the SNIG PDF is a more effective prior as compared to the generalized Gaussian (GG), α-stable, and Laplacian PDFs for modelling the full-frame DCT coefficients of natural images. For the block-DCT coefficients, the SNIG PDF is shown to be better than the GG and Laplacian PDFs, and comparable to the α-stable one, while incurring much less complexity for parameter estimation.

In this paper, we define a blind-block reciprocal- transpose matrix operator (BBRT). One of the interesting proprieties of the BBRT matrix is that it is very easy to obtain its inverse. We then propose a new blind-block reciprocal parametric transform and show that its matrix operator is a BBRT matrix. The transform has a large number of independent parameters that are useful for many applications and can specifically be used as an additional secret key for encryption and watermarking. It is shown that the proposed transform reduces to some of the existing transforms and also to new classes of parametric transforms having some desirable proprieties.

Discrete Tchebichef transform (DTT), derived from a discrete class of the popular Chebyshev polynomials, is a novel orthogonal transform that has high energy compaction and de-correlation properties. Therefore, in this paper, DTT is examined and treated for transform coding applications. A framework is laid to derive an approximation- free integer representation of DTT to meet the current application requirements. A fast algorithm is further proposed for multiplier-free computation of DTT. The image compression performance of the 4- point DTT is found to be superior to that of the 4-point discrete cosine transform (DCT) and integer cosine transform (ICT), the integer approximation of DCT. It is shown that the fast DTT is easily derived, has low complexity, does not involve approximations and can be carried out within the same dynamic range. Hence, DTT can be used for image and data compression applications. Since the image compression performance and computational simplicity of DTT are found to be significantly better than that of ICT, the use of DTT in place of ICT for transform coding in the H.264/AVC looks promising.

A new detector using modified Gauss-Hermite (GH) probability density function (PDF) is proposed for the wavelet-domain image watermarking scheme. It is shown that the proposed PDF matches the empirical one of image wavelet coefficients better than other conventional PDFs such as the generalized Gaussian and Bessel K-form. This is because of the fact that the modified GH PDF utilizes an arbitrary number of higher order moments of the wavelet coefficients instead of considering only the first few for the parameter estimation process. The proposed PDF is then used for designing the statistical detector for a wavelet-based image watermarking algorithm. Experimental results on a standard image database show that the proposed detector provides a higher detection probability and lower false alarm than that provided by the others.

Most of the existing MIMO-OFDM channel estimation methods do not take into account the effect of the pulse- shaping Alter in the transmitter nor of the matched Alter in the receiver, thus leading to an estimation solution for the composite channel including the pulse-shaping and matched Alters, instead of for the pure wireless channel. This solution is neither directly applicable to practical communication systems nor sufficiently accurate due to the extra length of the composite channel induced by the two Alters. In this paper, a semi-blind channel estimation method is proposed for pulse-shaped MIMO-OFDM systems. By utilizing the knowledge of pulse-shaping and matched Alters, a time domain semi-blind estimation method is developed for the pure multi-path channel. In order to reduce the computational burden of the time-domain algorithm, a frequency-domain alternative is derived. A number of computer simulation-based experimentations are conducted, and these simulations confirm the effectiveness of the proposed semi-blind method.

In this paper, a perturbation analysis of two subspace-based semi-blind MIMO channel estimation approaches is conducted. Our analysis shows that, in the noise-free case, the whitening-rotation (WR)-based algorithm is subject to a signal perturbation error, while the nulling-based algorithm is a signal perturbation free scheme with an ideal nulling constraint imposed on the channel matrix. This explains why the WR-based method is efficient only in the low SNR case, and concludes that the nulling-based approach is better for moderate to high SNRs. A novel closed-form mean square error (MSE) expression is also derived for the nulling-based blind estimation method, in which an appealing scheme for the determination of the weighting factor is presented. The nulling-based method with the proposed weighting scheme is validated via computer simulations, showing a very high estimation accuracy of our semi-blind solution in terms of the MSE of the channel estimate.

In this paper, the lattice structure of a class of filter banks that possesses both the linear-phase property and the mirror-image symmetry, referred to as MIS-LPPRFB, has been investigated. By combining the MIS and the LP constraints and proposing a new simplified scheme, a reduced lattice structure with fewer parameters has been developed. It is shown that the introduction of the MIS has imposed certain constraints on the invertible matrices in the lattice structure of the conventional LPPRFBs, reducing the number of free parameters by nearly one-half. Using the new lattice structure, the optimization design problem of MIS-LPPRFBs is also addressed with an objective of achieving a high performance filter bank for its use in image compression coding.

The second-order statistics of a time-domain signal are very often used in blind and semi-blind channel estimation. Considering that the received signal in MIMO-OFDM systems might be corrupted in the time-domain due to some adverse factors such as frequency offset and large peak-to-average power ratio (PAPR), an IFFT processor is required in the receiver to achieve a good-quality time-domain signal. This additional IFFT incurs extra computational complexity and probably a long time delay as well in real-time communication systems. In this paper, we propose a new algorithm for the computation of the time-domain correlation matrix directly from the received frequency- domain signal. The proposed frequency-domain correlation matrix estimation method is then used to develop a new semi-blind MIMO-OFDM channel estimation approach. A number of computer simulation based experiments are conducted, confirming the effectiveness of the proposed method.

In this paper, a novel signal perturbation free transmit scheme is proposed for MIMO channel estimation. A perturbation analysis of the WR-based method is first conducted, showing that the method is subject to a signal perturbation error and therefore, its performance is very poor under the moderate to high signal-to-noise ratios (SNRs). A new transmit structure is then proposed to cancel the signal perturbation error at the receiver in order to improve the performance of the WR-based method in the high SNR case. Computer simulations show that the WR-based method with the proposed signal perturbation free transmit scheme significantly outperforms the original WR-based method as well as the training-based LS method in terms of the MSE of the channel estimate.

The probability density functions (PDFs) of the wavelet coefficients play a key role in many wavelet-based image processing algorithms, such as denoising. The conventional PDFs usually have a limited number of parameters that are calculated from the first few moments only. Consequently, such PDFs cannot be made to fit very well with the empirical PDF of the wavelet coefficients of an image. As a result, the shrinkage function utilizing any of these density functions provides a substandard denoising performance. In order for the probabilistic model of the image wavelet coefficients to be able to incorporate an appropriate number of parameters that are dependent on the higher order moments, a PDF using a series expansion in terms of the Hermite polynomials that are orthogonal with respect to the standard Gaussian weight function, is introduced. A modification in the series function is introduced so that only a finite number of terms can be used to model the image wavelet coefficients, ensuring at the same time the resulting PDF to be non-negative. It is shown that the proposed PDF matches the empirical one better than some of the standard ones, such as the generalized Gaussian or Bessel K-form PDF. A Bayesian image denoising technique is then proposed, wherein the new PDF is exploited to statistically model the subband as well as the local neighboring image wavelet coefficients. Experimental results on several test images demonstrate that the proposed denoising method, both in the subband-adaptive and locally adaptive conditions, provides a performance better than that of most of the methods that use PDFs with limited number of parameters.

This paper studies an important problem, namely, the joint multipath routing and rate control for multidescription coded (MD-coded) video streaming in wireless ad hoc networks. In addition to selecting a pair of paths to optimize the expected end-to-end video quality, we also explore an optimal packet skipping strategy for the rate control in order to minimize the impact of the skipped packets on the quality of the video. The R-D hint information, consisting of the size of the packets in bits and the importance of the packets for reconstructing the video, is used to characterize the packets in an R-D sense. Since searching for paths to minimize the expected end-to-end video distortion by simultaneously considering the skipped packets prior to the transmission and those dropped/delayed during the transmission is a highly complex problem and is expected to be NP-hard, we develop a heuristic greedy-relaxation-based routing solution that enables the system to efficiently select near-optimal paths. Extensive simulation studies have been conducted to compare the performance of the proposed algorithm with that of several existing algorithms, showing the superior performance of the proposed one. Such a joint rate control and multipath routing approach provides an important methodology for high-quality real-time video streaming applications over ad hoc wireless networks.

A major problem in medical ultrasonography is the inherent corruption of ultrasound images with speckle noise that severely hampers the diagnosis and automatic image processing tasks. In this paper, an efficient wavelet-based method is proposed for despeckling medical ultrasound images. A closed-form Bayesian wavelet-based maximum a posteriori denoiser is developed in a homomorphic framework, based on modelling the wavelet coefficients of the log-transform of the reflectivity with a symmetric normal inverse Gaussian (SNIG) prior. A simple method is presented for obtaining the parameters of the SNIG prior using local neighbors. Thus, the proposed method is spatially adaptive. Experiments are carried out using synthetically speckled and real ultrasound images, and the results show that the proposed method performs better than several other existing methods in terms of the signal-to-noise ratio and visual quality.

As developments in wireless networks continue, there is an increasing expectation with regard to supporting high- quality real-time video streaming service in such networks. The recent advances in multi-description (MD) multi-path transport has made it a promising technology for content-rich wireless multimedia communications. This paper presents a rate-distortion (R-D) optimized packet scheduling algorithm (OPT- MD) for streaming MD-coded video along multiple wireless paths. Our algorithm relies on R-D hint information that is used to characterize a packet in a R-D sense. The information consists of the size of the packet in bits and the importance of the packet for reconstructing the video. Each of the video description adaptively selects certain important packets for transmission according to the quality of the transmission path by simultaneously considering bandwidth, bit error rate, and delay so that the overall end-to-end video distortion in terms of the mean square error (MSE) is minimized. Extensive simulation results demonstrate that OPT-MD can improve the quality of video streaming significantly as compared to a conventional scheduling approach that does not consider the relative importance of the video packets and the channel conditions (RANDOM-MD). The gains in performance reach up to 5 dB and 4 dB for streaming MD-coded format QCIFFORMANandTABLEvideo sequences, respectively, in the scenario of adaptation to a simulated time- varying network channel. Our efforts in this work provides an important methodology for high-quality real-time video streaming applications over wireless networks.

Statistical estimation techniques for the wavelet-based image denoising use suitable probability density functions (PDFs) as prior functions for the image coefficients. Due to the intrascale dependency of the local neighboring image wavelet coefficients, the prior functions are assumed to be stationary. In this paper, it is shown that the stationary Gram-Charlier (GC) PDF models the image coefficients better than the traditional ones, such as the stationary Gaussian and stationary generalized Gaussian PDFs. A Bayesian wavelet-based maximum a posteriori estimator is then developed by using the proposed GC prior function. Experimental results on standard images show that the proposed estimator provides a denoising performance, which is better than that of several existing denoising methods in terms of signal-to-noise ratio and visual quality.

Medical ultrasound images are widely used for diagnostic purposes. A major problem regarding these images is in their inherent corruption by speckle noise in a multiplicative fashion. The presence of speckle noise severely hampers the interpretation and analysis of medical ultrasound images. This paper presents a fast and reliable wavelet-based method for reducing the speckle in medical ultrasound images. A wavelet-based Bayesian maximum a posteriori denoiser is developed in a homomorphic framework. The wavelet coefficients of the log-transformed signal are modelled by a conditional Gaussian distribution, whereas those of the log-transformed speckle with a Maxwell distribution. The signal variances are obtained by using the local neighbors thus, making the method spatially adaptive. Simulations are performed using synthetically speckled and real ultrasound images. The results show that the proposed method can perform better than some of the existing methods in terms of the signal-to-noise ratio. Furthermore, the proposed method is fast, and preserves diagnostically important details.

In this paper, a semi-blind channel estimation method is presented for MIMO-OFDM systems. The new method uses the linear prediction for obtaining a blind constraint on the MIMO-OFDM channel matrix as well as the least-squares approximation for the training signal. The proposed method can be regarded as an extension of an existing semi-blind MIMO channel estimation algorithm. Yet the extension is nontrivial, since the formulation of the MIMO-OFDM signal and the related blind constraint cannot easily be obtained from the MIMO counterpart. The proposed algorithm is simulated using Monte-Carlo method and compared with the LS method in terms of the mean square error (MSE) of the estimation. Simulation results show that the proposed method consistently outperforms the LS method when the same training signal is used.

In this paper, several new cosine-modulated filter banks (CMFBs) are developed by using multiple prototype filters in conjunction with a proper modulation scheme. First, the conventional CMFB is formulated as a cascade of a modulation matrix and a polyphase matrix with a bidiagonal structure. Under this framework, it is then revealed that the proposed new CMFBs are a special class of paraunitary filter banks (PUFBs), whose polyphase matrices are of different sparse patterns. It is shown that when M prototype filters are used, a full polyphase matrix representing a general PUFB can be obtained, thus providing a bridge connecting CMFBs and PUFBs. It is also shown that with more free parameters involved in the CMFBs, one can achieve a tradeoff between the performance of the filter bank and its design/implementation complexity

The speckle noise complicates the human and automatic interpretation of synthetic aperture radar (SAR) images. Thus, the reduction of speckle is critical in various SAR image processing tasks. In this paper, we introduce a new spatially adaptive wavelet-based Bayesian method for despeckling the SAR images. The wavelet coefficients of the logarithmically transformed reflectance and speckle noise are modeled using the zero-location Cauchy and zero-mean Gaussian distributions, respectively. These prior distributions are then exploited to develop a Bayesian minimum mean absolute error estimator as well as a maximum a posteriori estimator. A new context-based technique with a reduced complexity is proposed for incorporating the spatial dependency of the wavelet coefficients with the Bayesian estimation processes. Experiments are carried out using typical noise-free images corrupted with simulated speckle noise as well as real SAR images, and the results show that the proposed method performs favorably in comparison to some of the existing methods in terms of the peak signal-to-noise ratio, speckle statistics and structural similarity index, and in its ability to suppress the speckle in the homogeneous regions

In order to take advantage of the byte-type data parallelism in the existing single-instruction multiple-data (SIMD) technique, this paper introduces the concept of 8-bit partial sums, obtained by a 4-bit right-shift operation on the sum of the 16 luminance values in a column of a 16 x 16 block of a video frame. Since these partial sums are of only eight bits, eight of them can be processed concurrently in a single 64-bit SIMD register. A method of employing these partial sums in order to speed up a given block motion-estimation algorithm is then proposed. The notion of the 8-bit partial sums is extended to the four-level case. It is shown that there are 15 possible methods of utilizing these multilevel 8-bit partial sums to accelerate a block motion-estimation algorithm without any loss of accuracy of the algorithm. Each of these 15 methods is used in the full-search algorithm to determine the one that provides the lowest computational complexity. This method is adopted as the chosen scheme to accelerate various block motion-estimation algorithms. Extensive simulations are carried out on eight video sequences showing that substantial speed-up can be achieved when the chosen scheme is incorporated with the various motion-estimation algorithms. The simulation results also demonstrate that the implementation on SIMD architectures can further accelerate the execution of the proposed scheme by more than 93% percent.

The paper proposes a joint probability density function to model the video wavelet coefficients of any two neighboring frames and then applies this statistical model for denoising. The parameter of the density function that measures the correlation between the wavelet coefficients of the two frames is used as an index for the motion. The joint density function is employed for spatial filtering of the noisy wavelet coefficients by developing a bivariate maximum a posteriori estimator. A recursive time averaging of the spatially filtered wavelet coefficients is adopted for further noise reduction. Simulation results on test video sequences show an improved performance both in terms of the peak signal-to-noise ratio and the perceptual quality compared to that of the other denoising algorithms

This paper presents a new algorithm for wavelet based image compression by exploiting zero tree concept in the wavelet decomposed image. The algorithm has a big edge over previously developed wavelet based image compression algorithms in that it utilizes inter and intra band correlation simultaneously, something that previous algorithms failed to exploit. Besides the improvement in coding efficiency, the algorithm also uses significantly lower memory for computation and coding thereby reducing the complexity of the algorithm. The striking feature of the algorithm is pass independent coding that makes it suitable for application to error protection schemes and makes it less vulnerable to data loss due to noisy communication channel. The algorithm codes all the color bands independently thus enabling differential coding for the color information. The paper starts with the discussion of the concept underlying the algorithm and then sees the algorithm in a broader light. Comparisons have been made to SPIHT, the well known zero tree coder for wavelet based image compression in terms of coding efficiency, memory requirements and error resiliency.

In this paper, we propose a novel, temporally-adaptive maximum a posteriori (MAP) estimation algorithm for the reduction of additive video noise in the wavelet domain. We have exploited the fact that the spatial and temporal redundancies, which exist in a video sequence in the time domain, also persist in the wavelet domain. This allows the video motion to be captured in the wavelet domain. A new statistical model for video sequences is proposed, where the subband coefficients in individual frames as well as the wavelet co-efficient difference occurring between two consecutive frames are modeled using the generalized Laplacian distribution. Based on this model, a MAP estimator is developed that estimates the noise-free wavelet coefficients in the current frame, conditioned on the noisy coefficients in the current frame and the filtered coefficients in the past frame. The proposed algorithm has been tested using several different test sequences and corrupting noise powers and the experimental results show that the proposed scheme outperforms several state-of-the-art spatio-temporal filters in time and wavelet domains in terms of quantitative performance as well as visual quality.

It is very important to perceptually shape the watermark signal before embedding it into a host image according to the characteristics of the HVS (human vision system) since watermark invisibility is a necessary requirement for a successful watermarking application. Two popular HVS models have been proposed to deal with this problem: DCT-based Watson model and DWT-based PWM (pixel-wise masking) model, which correspond to the DCT-based and DWT-based watermarking techniques, respectively. Even though there is a common belief that the PWM model is better than the Watson model, there have been no studies that compare these two approaches. This paper is devoted to such a comparison. Our results show that the believed superiority of the PWM model relative to the Watson model is not correct and the Watson model indeed outperforms the PWM model. We argue that more accurate masking strategies in the wavelet domain are needed for DWT-based watermarking applications.

In this paper, we introduce a new spatially adaptive homomorphic Bayesian wavelet-based method for despeckling synthetic aperture radar (SAR) images. The wavelet coefficients of the logarithmically transformed reflectance image and the speckle noise image are modeled using a symmetric normal inverse Gaussian prior and an additive white Gaussian noise distribution, respectively. These models are then exploited to develop a Bayesian maximum a posteriori estimator. A method is proposed for estimating the parameters of the assumed prior. The noise-free variance of a wavelet coefficient is locally estimated, and used in a minimum mean square error estimator to obtain the corresponding noise-free coefficient. Experiments are carried out on two synthetically speckled images and a real SAR image. The results show that the proposed method has a performance that is superior to that of the other existing methods in terms of the peak signal-to-noise ratio, ability to suppress the speckle in the homogeneous regions.

This paper proposes an efficient algorithm to compute the reverse jacket transform algorithm by introducing a two-step decomposition strategy coupled with an appropriate use of the Kronecker product. Comparisons are carried out with the existing algorithms and the results show that a significant reduction in the number of data transfers and address generations as well as the structural complexity can be easily achieved using the proposed algorithm without increasing the arithmetic complexity. It is also shown that the three weights used in the existing reverse jacket transform are not required and just two are sufficient. Further, it is shown that a significant reduction in the number of multiplications can be achieved by using two rather than three weights, without losing the generality of the reverse jacket transform

In this paper, the existing one-dimensional (1-D) radix-2/4 decimation-in-frequency (DIF) fast Fourier transform (FFT) algorithm is generalized to the case of an arbitrary dimension by introducing a mixture of radix-(2 times 2 times ... times 2) and radix-(4 times 4 times ... times 4) index maps. The introduction of these index maps coupled with an appropriate use of the Kronecker product enable us to design an efficient multi-dimensional (M-D) split vector-radix DIF FFT algorithm and characterize its butterfly by simple closed-form expressions allowing easy software or hardware implementation of the algorithm for any dimension. It is shown that the proposed algorithm substantially reduces the complexity compared to the existing M-D FFT algorithms

This paper proposes a novel wavelet domain based spatio-temporal filter for video denoising that exploits the temporal as well as spatial correlations which exist in the subband representation of the video sequence. The temporal redundancy or correlation among the corresponding wavelet coefficients in neighboring frames is minimized by the use of discrete cosine transform. These decorrelated noisy wavelet coefficients are then denoised spatially via a low-complexity wavelet shrinkage method, which utilizes the correlation that exists between subsequent resolution levels. The proposed scheme shows promising results and outperforms state-of-the-art spatio-temporal filters in time as well as wavelet domains, both in terms of PSNR and visual quality.

In this paper, a generalized polyphase (GP) structure based design method for Mth-band FIR filters is presented. A few new transform matrices are proposed as seed matrix for generating a GP structure, which overcomes some limitations of the commonly used Hadamard transform. Under the framework of a general GP structure realization, the relationship between the original filter and the interpolator as well as the constituent filter is revealed from the frequency response perspective. This relationship facilitates the design problem of GP structure based FIR filters, since the original large-tap filter is reduced to a number of short-length constituent filters which can easily be designed according to the derived specifications. In particular, a closed-form frequency specification for the design of Mth-band filters is obtained. Design examples are given to illustrate the effectiveness of the proposed method.

In this paper, a realization scheme that combines the singular-value decomposition (SVD) and the generalized polyphase (GP) structure is proposed for 2D linear-phase FIR filters. With a small number of extra additions, a high-order 2D FIR filter is converted to several lower-order 2D subfilters. These subfilters are then realized using the SVD, yielding a parallel implementation structure for each 2D subfilter which consists of a number of 1D short-tap FIR filters. Due to the energy compaction of the SVD and the frequency-selective property of the GP structure, the number of parallel branches in each 2D subfilter is significantly reduced without introducing a large error. It is also shown that the various symmetries of 2D filters, such as the quadrantal symmetry and the central symmetry, are well preserved in the proposed GP-SVD structure.

In the multiresolution motion estimation (MRME) techniques originally proposed by Zhang and Zafar, four MRME algorithms have been proposed. In one of these algorithms, the motion vectors in the low-pass subband are properly scaled and used as the final motion vectors for all the other subbands, and, in another, the properly scaled motion vectors in the first algorithm are used as predictions and further refined. The former algorithm requires a much lighter computational load and fewer coding bits for the motion vectors than the latter; on the other hand, the latter is able to provide a better MRME performance than the former. In this paper, we propose a new MRME technique that takes advantage of both of the above algorithms. In the proposed algorithm, the sum of absolute difference associated with each of the scaled motion vectors as in the first algorithm is calculated, and the result compared with the sum of the absolute values of the amplitudes of the wavelet coefficients within the motion block to be compensated. The outcome of the comparison decides if these scaled motion vectors are accepted as the final ones. For the coding of motion information, the motion vectors used for the prediction and their patterns of applicability to higher resolution levels, called the indices, are coded.

The performance of various wavelets, including those known to be well suited for the coding of still images, has been evaluated for the multiresolution motion estimation of video sequences. The multiresolution motion estimation scheme proposed by Zhang and Zafar, which has been widely cited in the literature, is used as the simulation scheme in this study. In our study, the prediction mean square error in the wavelet transform coefficient domain is used as the measure for prediction performance. In order to show the overall rate distortion performance, the number of bits needed to encode the motion vectors is also calculated. Simulation results show that the 7/9 biorthogonal wavelet, one of the best wavelets for the coding of still images, is the best wavelet for the task of multiresolution motion estimation among the wavelets evaluated in this study.

A radix-2/16 decimation-in-frequency (DIF) fast Fourier transform (FFT) algorithm and its higher radix version, namely radix-4/16 DIF FFT algorithm, are proposed by suitably mixing the radix-2, radix-4 and radix-16 index maps, and combing some of the twiddle factors. It is shown that the proposed algorithms and the existing radix-2/4 and radix-2/8 FFT algorithms require exactly the same number of arithmetic operations (multiplications+additions). Moreover, by using techniques similar to these, it can be shown that all the possible split-radix FFT algorithms of the type radix-2/sup r//2/sup rs/ for computing a 2/sup m/-point DFT require exactly the same number of arithmetic operations.

We provide a framework for developing a low-complexity adaptive filtering algorithm by incorporating the concept of partial-updating into the technique of finding the gradient vector in the hyperplane based on the L/sub /spl infin//-norm criterion. The resulting algorithm is referred to as the partial-update normalized sign LMS (PU-NSLMS) algorithm. A specific case of the PU-NSLMS algorithm, called the M-Max PU-NSLMS algorithm, based on the concept of having a minimum Euclidean length of the coefficient-update vector, is considered. It is shown that this algorithm is computationally less complex compared to the partial-update normalized least-mean squares (PU-NLMS) algorithm. Results concerning the mean-square analysis of the M-Max PU-NSLMS algorithm are given. The performance of this algorithm is compared with that of the PU-NLMS algorithm in the case of network echo cancellation. It is shown that the convergence rate of the proposed algorithm is comparable to that of the PU-NLMS algorithm, but with a reduced complexity, making it a good choice for applications requiring a long filter tap, especially for real-time implementations.

In this paper, a new architecture of the redundant residue number system (RRNS) based quasi-chaotic coding is proposed for the secure telecommunication systems and networks. In the proposed architecture, a number of modulo operations required by the existing designs are replaced by binary coding operations to simplify the design. Also, a moduli selection method and a residue-to-binary converter with error-correction capability are proposed to further improve the efficiency of the design specifically for FPGA implementation. The proposed architecture is implemented and tested using Matlab and Xilinx FPGA hardware. The results show that compared to the existing design, the proposed design requires only 80% of the hardware resources while maintaining the same speed. The power consumption is also reduced by 25%.

For original paper see Pei and Chen (IEEE Signal Process. Lett., vol.11, no.5, p.459-62, 2004). The present authors report that the same algorithm has been previously proposed in Bouguezel et al. (IEEE Int. Symp. Circuits Syst., vol.3, p.698-701, 2003).

A special class of even-symmetric periodic signals is introduced. The most distinctive feature of these signals is that their real-valued Fourier coefficients can be calculated by forming a weighted average of the signal values using integer-valued coefficients. The signals arise from number-theoretic concepts concerning a class of functions called even arithmetical functions. The integer-valued weighting coefficients, being sums of complex roots of unity, are the Ramanujan sums and may be computed recursively or through closed-form arithmetical relations. The recursive method of computation is based on the cyclotomic polynomials and is described in detail. If the signal values are integers, the computation of the discrete Fourier transform (DFT) coefficients of this class of signals can be performed in an exact quantization-error-free manner by performing arithmetical operations on integers. The theoretical development is supplemented by concrete examples.

In this paper, by using the properties of the higher order statistics (HOS) of speech and noise signals, we develop an improved voice activity detection (VAD) scheme. The proposed scheme employs the logarithm of the kurtosis of the LPC residual of a speech signal and is shown to be more effective and efficient in detecting active speech in medium to low signal-to-noise ratio (SNR) conditions without being unduly affected by the variations in the signal energy. To overcome the inability of the HOS in detecting unvoiced speech, another metric (the low band to full band energy ratio) is introduced. Depending on the estimated mean SNR, the proposed scheme works adaptively in two modes: a simple mode using only the SNR, and an enhanced mode using the HOS, the low band to full band energy ratio and the SNR. This scheme is capable of avoiding unnecessary computations, while maintaining the same performance as that working only in the enhanced mode. Simulations results are presented to demonstrate the effectiveness of the proposed voice activity detection scheme.

A novel technique for despeckling the medical ultrasound images using lossy compression is presented. The logarithm of the input image is first transformed to the multiscale wavelet domain. It is then shown that the subband coefficients of the log-transformed ultrasound image can be successfully modeled using the generalized Laplacian distribution. Based on this modeling, a simple adaptation of the zero-zone and reconstruction levels of the uniform threshold quantizer is proposed in order to achieve simultaneous despeckling and quantization. This adaptation is based on: (1) an estimate of the corrupting speckle noise level in the image; (2) the estimated statistics of the noise-free subband coefficients; and (3) the required compression rate. The Laplacian distribution is considered as a special case of the generalized Laplacian distribution and its efficacy is demonstrated for the problem under consideration. Context-based classification is also applied to the noisy coefficients to enhance the performance of the subband coder. Simulation results using a contrast detail phantom image and several real ultrasound images are presented. To validate the performance of the proposed scheme, comparison with two two-stage schemes, wherein the speckled image is first filtered and then compressed using the state-of-the-art JPEG2000 encoder, is presented. Experimental results show that the proposed scheme works better, both in terms of the signal to noise ratio and the visual quality.

In this paper, we present a method of kernel optimization by maximizing a measure of class separability in the empirical feature space, an Euclidean space in which the training data are embedded in such a way that the geometrical structure of the data in the feature space is preserved. Employing a data-dependent kernel, we derive an effective kernel optimization algorithm that maximizes the class separability of the data in the empirical feature space. It is shown that there exists a close relationship between the class separability measure introduced here and the alignment measure defined recently by Cristianini. Extensive simulations are carried out which show that the optimized kernel is more adaptive to the input data, and leads to a substantial, sometimes significant, improvement in the performance of various data classification algorithms.

In this paper, we propose a low cost motion-compensated interpolation technique to improve the video quality for the low bit-rate video encoded in conjunction with frame dropping. The proposed approach exploits the block-based motion vector field available to the decoder to avoid the complex motion estimation at the receiver. An iterative refinement technique derived using the finite element method is employed to efficiently conceal the interpolation errors caused by unfilled and overlapped pixels in the predicted frames. Consequently, no pixel classification is needed in the proposed technique, thus substantially reducing the computational complexity. Simulation results show that this technique results in reconstructed frames with good visual quality.

This paper proposes a novel spatio-temporal filter for video denoising that operates entirely in the wavelet domain and is based on temporal decorrelation. For effective noise reduction, the spatial and the temporal redundancies, which exist in the wavelet domain representation of a video signal, are exploited. Using simple and closed form expressions, the temporal information in the wavelet domain is first decorrelated in order to minimize the redundancy. The decorrelated noise-free coefficients are then modeled using a generalized Gaussian prior. For spatial filtering of the noisy wavelet coefficients, a new, low-complexity wavelet shrinkage method, which utilizes the correlation that exists between subsequent resolution levels, is proposed. Experimental results show that the proposed scheme outperforms state-of-the-art spatio-temporal filters in time and wavelet domains, both in terms of PSNR and visual quality.

A discrete-time fractional-order differentiator is modeled as a finite-impulse response (FIR) system. The system yields fractional-order derivatives of Riemann-Liouville type for a uniformly sampled polynomial signal. The computation of the output signal is based on the additive combination of the weighted outputs of N cascaded first-order digital differentiators. For differentiators of fractional order with a terminal value equal to zero, the weights are time-varying. The weights are obtained in a closed form involving the Stirling numbers of the first kind. The system tends to a time-invariant integer-order differentiator when the order of the derivative tends to an integer value. It yields exact fractional- or integer-order derivatives of a sampled polynomial signal of a certain order.

Codebook initialization usually has a significant effect on the performance of vector quantization algorithms. This letter presents a new scheme of codebook initialization in which the competitive learning and code vector splitting are incorporated together to produce a good initial codebook. Based mainly on the geometrical measurements of the learning tracks of the code vectors, the competitive splitting mechanism shows an ability to appropriately allocate code vectors according to the spatial distribution of the input data and, therefore, tends to give a better initial codebook. Comparisons with other initialization techniques demonstrate the effectiveness of the new scheme.

It is shown that, by choosing appropriate values for the parameter a of the generating kernel in constructing P.J. Burt and E.H. Adelson's pyramid for motion estimation (see IEEE Trans. Commun., vol.31, p.337-45, 1983), one can eliminate the operation of the floating point multiplications needed in such a construction and reduce the computational load to the same order as in the case of the mean pyramid. When a is chosen to be 3/8, it is demonstrated, through simulation studies, that the corresponding Burt and Adelson's pyramid does not degrade the performance of motion estimation, as compared to that using kernels giving the best performance, and this pyramid also provides perceptually better motion-compensated images than those provided by the mean pyramid.

This paper presents a new self-creating model of a neural network in which a branching mechanism is incorporated with competitive learning. Unlike other self-creating models, the proposed scheme, called branching competitive learning (BCL), adopts a special node-splitting criterion, which is based mainly on the geometrical measurements of the movement of the synaptic vectors in the weight space. Compared with other self-creating and nonself-creating competitive learning models, the BCL network is more efficient to capture the spatial distribution of the input data and, therefore, tends to give better clustering or quantization results. We demonstrate the ability of the BCL model to appropriately estimate the cluster number in a data distribution, show its adaptability to nonstationary data inputs and, moreover, present a scheme leading to a multiresolution data clustering. Extensive experiments on vector quantization of image compression are given to illustrate the effectiveness of the BCL algorithm.

Recently, the authors developed a new synthesis technique for the design of higher-order Bode-type variable-amplitude (VA) wave-digital (WD) equalizers. The salient feature of the resulting VA WD equalizers is that they permit the continuous variation of the WD equalizer transfer function from a shaping transfer function to its inverse by changing the value of a single variable digital multiplier only. The proposed design technique was based on the WD realization of the corresponding positive-real analog prototype shaping impedance function, and on the realization of the equalizer transfer function as the reflectance of the shaping impedance function with respect to the constituent variable digital multiplier. This paper is concerned with an investigation of the bounded-input bounded-output (BIBO) stability of general-order VA WD equalizers. It is shown that the resulting conditions are both necessary and sufficient for the BIBO stability of the VA WD equalizers for the entire range of values for the variable digital multiplier. These conditions can be checked in a straightforward fashion in terms of the characteristics of the shaping transfer function alone. An application example is given to illustrate the main results.

Fast block motion estimation algorithms are needed for real-time implementations of video coding standards due to the high computational complexity of the full-search algorithm for block motion estimation. In this paper, an algorithm using 8-bit partial sums of 16 luminance values for a fast block motion estimation is proposed. The technique of using the partial sums is employed to reduce the computational complexity of not only the full-search algorithm but also some of the fast block motion estimation algorithms while maintaining their accuracy. Furthermore, it is shown that the byte-type data-parallelism on an SIMD architecture can be utilized to access and process these partial sums concurrently to accelerate the process of motion estimation. Simulation results are presented to demonstrate that the use of the partial sums can accelerate the execution of the full-search, three-step search, and four-step search algorithms on an SIMD architecture significantly.

Recently, the authors developed a new synthesis technique for the design of higher-order Bode-type variable-amplitude (VA) wave-digital (WD) equalizers. The salient feature of the resulting VA WD equalizers is that they permit the continuous variation of the WD equalizer transfer function from a shaping transfer function to its inverse by changing the value of a single variable digital multiplier only. The proposed design technique was based on the WD realization of the corresponding positive-real analog prototype shaping impedance function, and on the realization of the equalizer transfer function as the reflectance of the shaping impedance function with respect to the constituent variable digital multiplier. This paper is concerned with an investigation of the bounded-input bounded-output (BIBO) stability of general-order VA WD equalizers. It is shown that the resulting conditions are both necessary and sufficient for the BIBO stability of the VA WD equalizers for the entire range of values for the variable digital multiplier. These conditions can be checked in a straightforward fashion in terms of the characteristics of the shaping transfer function alone. An application example is given to illustrate the main results.

In this paper, a pyramidal motion estimation technique that makes use of the motion correlation within a pyramidal level is proposed. Instead of scaling the motion vectors from the adjacent lower pyramidal level as motion predictions as is done in the conventional technique, in the proposed technique, the motion vectors from the neighboring motion blocks are taken into consideration as possible candidates. Each of these candidate motion vectors is used for prediction and refined. The motion vector that has the least matching distortion is chosen as the final motion vector. Compared to the conventional pyramidal motion estimation technique, the proposed method effectively overcomes the problem of propagation of false motion vectors. Simulation studies show that a substantial performance improvement is achieved, both in terms of the prediction mean square error and the number of coding bits for the motion vectors.

Subband techniques have been recently developed for adaptive filters, since some of the applications such as acoustic echo cancellation and wideband active noise control need adaptive filters with hundreds of taps, which result in high computational complexity and low convergence rate. By the use of subband adaptive algorithms, the computational complexity may be reduced along with convergence rate; however, a delay is introduced in the signal path. To remove the delay, the delayless subband adaptive filter architecture with both the open loop and the closed loop structures have been introduced. This paper presents a new open loop delayless subband adaptive filter structure with critical sampling, where the performance concerning the mean square error of the subband adaptive algorithm, caused due to the aliasing existing in the subband structure, is superior to the results obtained up to now for open loop delayless structures.

In this paper, an hybrid filter is presented for restoration of color images in a mixed noise environment, where both impulsive and correlation noise may be present. The proposed hybrid filter is composed of two stages, the first stage to remove the impulsive noise and the second to remove the correlated noise. The median filters and their variants are the most popular filter types used for impulsive noise suppression. However, median filters and their variants tend to remove fine image details and destroy fine texture in the mixed noise environment or when the signal to noise ratio is low. In order to overcome the limitations of the median filters, an adaptive simplified-model Kalman filter (ASMKF) is propsed for the suppression of impulsive noise in the first stage of the hybrid filter. In the second stage of the proposed hybrid filter, to remove correlated noise, discrete Wavelet Transform (DWT) filter is applied on the impulsive-noise-free image obtained from the first stage. The efficacy of the proposed hybrid filter is illustrated through implementation results obtained on the restoration of a color image.

In this paper, a non-causal median filtering method is proposed to predict the motion vectors across the wavelet subbands of a video frame for multiresolution motion estimation. This median filtering method effectively overcomes the problem of propagation of false motion vectors that exists in the conventional multiresolution motion estimation schemes. A significant feature of the proposed technique is that it imposes no demand for additional bandwidth. Simulation studies show that this median filtering-based multiresolution motion; estimation technique effectively improves the motion prediction performance. It is further shown that this performance improvement is achieved with little increase in the computational complexity.

A fast neuro-beamformer is presented in this paper. The new algorithm is based on the radial-basis function network. To meet real-time requirement, we customize the basis function for fast computation, and apply a recursive least square learning rule to speed up the network training. By comparing the effects of center location and distribution, we can achieve a minimum network for recalling. The network recalling does not require the knowledge of direction-of-arrival, and thus the method is a blind method.

The cross-correlation neural network proposed in Diamantaras and Kung (1994) is an efficient iterative method for singular value decomposition. In this paper, we propose an iterative blind cyclostationary beamforming algorithm, which is inspired by the cross-correlation neural model. It can be used to extract signals with cyclostationarity. The new algorithm is a gradient decent-based method. It is fast, simple, and easy to implement. Simulation shows that it can provide good performance as long as the learning rate is suitably selected.

This paper introduces a novel technique to obtain a schedule for a cyclic data-flow graph (DFG) onto a multiprocessor system. The optimality criteria considered in this scheduling technique are the maximum throughput, minimum input–output (I/O) delay, and minimum hardware resources. In this technique, an all-pair longest path algorithm is used to evaluate the relative firing times of the nodes of the given DFG. The proposed technique for finding these times is quite simple to implement and it has lower time complexity than all the previously proposed techniques. The technique is tested on various benchmark problems to demonstrate its optimal performance. All the optimality criteria are achieved on all the tested benchmarks. However, finding a minimum hardware resource schedule is an NP complete problem, and thus cannot be theoretically ensured. A formal proof of achieving both the throughput and the I/O delay optimality simultaneously is given, and an efficient technique to ensure this is also presented. This technique is quite simple and can be used to ensure delay optimality in any scheduling technique.

We investigate three new methods to predict motion vectors (MVs) across subbands for multi-resolution motion estimation (MRME): linear prediction, median filtering (MF), and multi-candidate techniques. Compared to the conventional MRME techniques, the proposed linear-prediction-based and the MF-based techniques effectively overcome the problem of propagation of false MVs. A significant feature of these two techniques is that they impose no demand for additional bandwidth, and simulation studies show that they not only improve prediction performance, but also reduce the number of bits needed to encode the motion information. It is further shown that the improvement in the performance thus achieved involves little increase in computational complexity.

Adaptive filtering techniques in subbands have been recently developed for a number of applications including acoustic echo cancellation and wideband active noise control. In such applications, hundreds of taps are required resulting in high computational complexity and low convergence rate when using LMS-based algorithms. For fullband systems, new algorithms which try to overcome these drawbacks have been investigated. A class of these algorithms employing variants of the filtered gradient adaptive (FGA) algorithm has been successfully developed. We apply these techniques to a recently proposed subband adaptive filter structure in order to improve the convergence rate and the computational load. Computer simulations show the benefits obtained with these proposed algorithms.

A median filtering-based hierarchical motion vector estimation scheme making use of a pyramidal data structure is proposed. Compared to the conventional hierarchical motion vector estimation schemes, the proposed scheme overcomes the problem of propagation of false motion vectors across resolutions. Simulation studies show that the proposed scheme not only improves the prediction accuracy with respect to the prediction mean square error, but also results in a smoother motion field, which can be encoded with less number of coding bits. It is shown that an improvement in the rate distortion performance is achieved with little increase in the computational complexity. It is also shown that Burt and Adelson's (1983) pyramidal data structure provides the best performance among a number of the generating kernels considered in our study.

Various approaches based on Kalman filtering exist in the literature for image restoration. The reduced order model Kalman filter (ROMKF) has comparable performance with less computational complexity. For further reduction in computational complexity, we suggest a simplified model Kalman filter (SMKF) for image restoration. Furthermore, a hybrid approach based on discrete wavelet transform (DWT) and SMKF is proposed for image restoration with better SNRs especially when the observed image signal-to-noise ratio is low. In the first step, the approach uses the DWT with few resolution levels and a moderate threshold value for denoising the image. The denoised image will provide a better data to SMKF in the second step. The proposed approach is implemented on a visual image to evaluate its performance in comparison with the SMKF and DWT approaches.

A new approach in the enhancement/suppression of narrowband signals, based on the combination of the maximum output SNR and the least-squares optimizing criteria, has been presented previously. The resulting statistically-optimal null filters (SONFs) have also been presented as recursive filters. Because of their time-varying nature, optimality under the MSE criterion and the similarity to the RLS method, it is anticipated that the SONFs may be related to the well known Kalman filter. In this paper, we show the SONFs to be an alternate implementation of the Kalman filter. For the first order case we show this analytically. Due to mathematical intractability, for the higher order case we set up the mathematical formulations and use simulations for verification.

Neural networks with real valued inputs have been proposed in the literature for adaptive equalization and have been used to improve performance of communication channel equalizers. However, neural networks with complex valued inputs and fast convergence are lacking for adaptive equalization. Therefore, in this paper, complex extended Kalman filter (CEKF)-based neural network with complex valued inputs for adaptive equalization of a communication channel is suggested. Performance comparison of the CEKF and complex backpropagation (CBP) neural networks is made through simulation results.

Based on the mechanisms underlying the performance of fractal and DWT, one method using fractal-based selfquantization coding approach to code different subband coefficients of DWT is presented. Within this method finer coefficients are fractal encoded according to the successive coarser ones. Self-similarities inherent between parent and their children at the same spatial location of the adjacent scales of similar orientation are exploited to predict variation of information across wavelet scales. On the other hand, with respect to the HVS model, we assign different error thresholds to different scales and different shape of range blocks to different orientations of the same scale, by which the perceptually lossless high compression ratio can be achieved and the matching processing can be quickened dramatically.

A high-speed parallel residue-to-binary converter is proposed for the moduli set S/sup k/={2/sup m/-1, 2/sup 2(0)m/+1, 2/sup 2(l)m/+1, L, 2.

The conventional variable-size block motion compensation technique, even though superior to the fixed-size block motion-compensation technique, cannot fully utilize the motion information of a frame for its partitioning. This paper presents a quadtree structured region-wise motion-compensation technique that utilizes more effectively the motion content of a frame in terms of the shape, size, and location of the partitioned regions. The proposed technique is based on a new coding scheme of the quadtree structure, where a two-bit code is used. The partitioning of a given frame is carried out through the processes of merging and combining. The merging process partitions the frame into a number of regions by carrying out all possible 4-to-1, 3-to-1, and 2-to-1 merges along the quadtree hierarchy according to some specified criteria, while the combining process combines some of the partitioned regions that have the same motion vector into a single region. The motion vectors of the resulting partitioned regions are coded by a new coding scheme that uses the temporal correlation of the motion fields. Test results of applying the proposed method on a number of MPEG video sequences are included. These results indicate that the proposed method can provide a significantly improved rate-distortion performance.

This paper addresses the problem of designing optimal stack filters by employing an Lp norm of the error between the desired signal and the estimated one. It is shown that the Lp norm can be expressed as a linear function of the decision errors at the binary levels of the filter. Thus, an Lp-optimal stack filter can be determined as the solution of a linear program. The conventional design of using the mean absolute error (MAE), therefore, becomes a special ease of the general Lp norm-based design developed here. Other special cases of the proposed approach, of particular interest in signal processing, are the problems of optimal mean square error (p=2) and minimax (p-->infinity) stack filtering. Since an Linfinity optimization is a combinatorial problem, with its complexity increasing faster than exponentially with the filter size, the proposed Lp norm approach to stack filter design offers an additional benefit of a sound mathematical framework to obtain a practical engineering approximation to the solution of the minimax optimization problem. The conventional MAE design of an important subclass of stack filters, the weighted order statistic filters, is also extended to the Lp norm-based design. By considering a typical application of restoring images corrupted with impulsive noise, several design examples are presented, to illustrate the performance of the Lp-optimal stack filters with different values of p. Simulation results show that the Lp-optimal stack filters with p=or>2 provide a better performance in terms of their capability in removing impulsive noise, compared to that achieved by using the conventional minimum MAE stack filters.

This paper addresses the problem of designing weighted order statistic (WOS) filters by employing an objective function given as the L/sub p/ norm of the error between the desired signal and the estimated one. The conventional design of WOS filters uses a mean absolute error (MAE) objective function, and as such, it is a special case of the general, L/sub p/ norm based design, developed here. It is shown that in stack filtering, the L/sub p/ norm can be expressed as a linear combination of the decision errors incurred by the Boolean operators at each level of the stack filter architecture. Based on this formulation of the L/sub p/ norm, both nonadaptive and adaptive algorithms for the design of L/sub p/ WOS filters are developed. A design example is considered, to illustrate the performance of the designed L/sub p/ WOS filters with different values of p. The simulation results show that the L/sub p/ WOS filters with p/spl ges/2 are capable of removing more impulsive noise compared with the conventional MAE WOS filters.

The variable size block motion compensation (VSBMC) technique is known to be more effective than the fixed-size block motion compensation technique (FSBMC) for video coding. However, the existing VSBMC techniques do not fully utilize the motion information of a frame for its partitioning. This paper presents a new adaptive partitioning scheme that utilizes more effectively the motion content of a frame in terms of the shape and size of the blocks. A new tree structure is proposed that allows not only the conventional four-to-one merge, but also three-to-one and two-to-one merges. This new VSBMC method results in a significantly improved rate-distortion performance. Test results of applying the proposed method on some MPEG video sequences are included.

The conventional fixed-size block motion compensation technique has two major drawbacks. One is the blocking effect that is visually annoying along with a residual image that is difficult to compress. The other is an ineffective and inefficient representation of the motion information because of the non-adaptive frame partitioning. This paper presents a new method that can adaptively divide a frame according to its motion contents and also effectively reduce the blocking effect by applying a windowing technique. Simulation results show that the proposed method can significantly improve the visual quality of the prediction image and increase the coding efficiency.

In this paper, we propose an alternate non-parametric statistically optimal method of null filtering. One of the important features of this method lies in its ability to process signals of short record lengths. The optimality criteria for maximum output SNR and the minimum mean-square error are combined to generate the new approach. The method is first designed for the coherent case (where the desired signal shape is a priori known) and later extended to include the non-coherent case based on orthogonal signal expansion. To deal with a non-orthogonal signal expansion, we propose a sliding Gram-Schmidt orthogonalization. An application to separate two closely spaced damped sinusoids is considered. Simulation results are presented comparing the proposed methods with the conventional one based on Constrained Notch Filtering.

In this paper, we investigate the application of multidimensional IIR digital filters for video signal processing. The problems of sampling structure conversion and deinterlacing are addressed. A multistage filter structure is then proposed to perform the general conversions among different types of sampling structures. Some simple IIR digital filters are proposed and simulated particularly for the above applications. Some simulation results are also given.

This paper is concerned with the compile-time (static) scheduling of data flow graphs (DFGs) onto multiprocessor systems. It mainly concentrates on producing a rate-optimal time schedule that achieves the minimum iteration period, known as the iteration period bound. A combinatorial theory is developed to produce a rate-optimal time schedule for a fully specified DFG. The DFG is first converted to a critical graph by making all its circuits critical. Next, it is transformed into an acyclic graph through a sequence of circuit contractions. An algorithm is then proposed which achieves the time scheduling of the given DFG by first scheduling the acyclic graph, followed by a scheduling of the critical circuits in an order which is reverse to that of their contraction.

A Hebbian-type learning algorithm for the total least-squares parameter estimation is presented. An asymptotic analysis is carried out to show that the algorithm allows the weight vector of a linear neuron unit to converge to the eigenvector associated with the smallest eigenvalue of the correlation matrix of the input signal. When the algorithm is applied to solve parameter estimation problems, the converged weights directly yield the total least-squares solution. It is shown that the implementations of the proposed algorithm have the simplicity of those of the LMS algorithm, but its noise rejection capability is much superior to those of the least-squares-based algorithms. The applicability and performance of the algorithm are demonstrated through computer simulations of adaptive FIR and IIR parameter estimation problems.>

In this paper, we investigate IIR digital filters for multiresolution image coding. Some relationships between the frequency response of a filter and the quality of the processed images are discussed for decimation and interpolation. A special class of half-band IIR filters (including 1-D filters and 2-D diamond filters) is then proposed. Some simulation results are given for image decomposition/reconstruction and high compression image coding.>

This paper considers the open problem concerning the BIBO stability of mutually inverse 2-D digital filters in the presence of nonessential singularities of the second kind on T/sup 2/. Necessary and sufficient conditions are obtained for the BIBO stability of mutually inverse pairs of 2-D digital filters having such singularities on T/sup 2/. Several illustrative examples are considered.>

A new algorithm for solving the graph bisectioning problem based on tabu search is proposed. The authors run the tabu search algorithm and the Kernighan-Lin algorithm on the same set of random graphs with 50 to 500 nodes and compare their performances. They demonstrate that for all of the graphs their tabu search algorithm provides lower bisection cost than the Kernighan-Lin algorithm; and for all of the graphs with more than 200 nodes, their tabu search algorithm takes less time than the Kernighan-Lin algorithm.>

Least-squares (LS) estimation with a standard feedback neural network (SFBNN) which is based on an electrical model is investigated. In the energy function of a SFBNN, a non-quadratic term is included which is often neglected while solving an optimization problem. It is shown that the non-quadratic term affects the solution of a continuous optimization problem. Properties of the non-quadratic term and the relation between the estimation error and several parameters of the SFBNN are discussed. A technique, called extended space iterative search (ESIS), is introduced to reduce the estimation error. Simulation results are presented to confirm the analysis result and the effectiveness of the proposed technique.>

A method of identification of a mixed-phase system (driven by a zero mean nonGaussian white noise) from its output, by an autoregressive moving-average (ARMA) model is proposed. This is achieved by relating the bispectrum phase and magnitude to the system group delay functions. The method uses only the bispectrum information and it is applicable for MA, AR, or ARMA systems with either mixed-phase poles, mixed-phase zeros, or both. The identification does not involve any solution of a system of equations and is free from the limitations found in many of the existing methods. Its evaluation for different ARMA terms indicates that its performance is good in terms of root-mean-square (which accounts both for bias and variance), since the normalized sum of the sample mean-square error is about 1%.>

A method of extracting the system phase from the bispectrum phase, based on a group delay approach, is proposed. The system phase estimation is achieved by relating the bispectral phase to the system group delay function. The proposed method has certain advantages over some of the existing methods. It is applicable for moving average (MA) or autoregressive (AR) or ARMA systems. Even with a few triple correlation lags, its performance is either superior to or at par with those obtained by Lii and Rosenblatt's method or Brillinger's method. The method has been evaluated for ARMA systems.>

A novel signal controlled constrained notch filter (SC-CNF) structure with global feedback is presented. The SC-CNF is combined with an adaptive linear enhancer to retrieve a multitone signal corrupted by a strong nonstationary FM interference. Using a time-warping technique, the SC-CNF, by its nature time-varying, is transformed into a CNF which is time variant. Such transformation is implemented by nonequally spaced sampling (NESS) of the input mixture. A closed-loop system is proposed to improve the strategy of NESS. By exploiting the self-synchronization process, nearly complete rejection of FM interference is achieved. The condition for the occurrence of self-synchronization is obtained. The self-synchronized SC-CNF provides significant improvement (up to 30 dB) of the signal-to-interference ratio of the entire system output.>

Problems in which the arguments of objective functions are real numbers are considered. Based on the concept of the Hopfield network, a neural network that solves the least-square estimation problem is derived. With this network, the objective function can converge to any inner point of a hypercube, giving a real-valued solution with very great speed. Because of the convex nature of the chosen energy function, the problem of convergence to a local minimum does not arise. Also introduced is a space iterative search technique for finding the optimum solution that can exist at any point within the space. Finally, simulation results are given for solving problems of linear systems and parameter estimations

Two new measures of network performance, namely, the incremental distance sequence and the incremental diameter sequence, are introduced for application in network topology design. These sequences can be defined for both vertex deletions and edge deletions. A complete characterization of the vertex-deleted incremental distance sequence is presented. Proof of this characterization is constructive in nature. A condition for the feasibility of an edge-deleted incremental distance sequence and a procedure for realizing such a sequence are given. Interrelationships between the elements of incremental distance sequences and incremental diameter sequences are studied. Using these results, it is shown that a graph that has a specified diameter and a specified maximum increase in diameters for deletions of vertex sets of given cardinalities can be designed.>

The authors present a fast convergence algorithm for adaptive FIR (finite impulse response) filters. The algorithm controls the step size on the basis of the moving average of the misadjustment level for a faster convergence. Several operations in addition to the LMS operations are used to estimate the misadjustment and average it to provide a stable step size. The algorithm is applied to adaptive noise cancellation to observe the performance. Computer simulation results for noise cancellation show that the convergence characteristics of the algorithm are improved by as much as 90% over those of the lease-mean-squares (LMS) algorithm and even those of the VS (variable step) algorithm for a multitone signal. For a real speech signal, improvement of the proposed algorithm is degraded, but still more than 50% of the LMS convergence time is saved in the simulated case.>

A study is made of a cross-coupled phase-locked loop (CCPLL) used for separation of close sinusoids with slowly varying frequencies. It is assumed that the sinusoids have equal amplitudes, and it is shown that the CCPLL system can effectively separate two sine signals with an observation interval Tor=15 dB. The result has application in fields such as ranging, symbol synchronization, and carrier recovery for coherent demodulation.>

Abstract Relationships among graph invariants such as the number of vertices, diameter, connectivity, maximum and minimum degrees, and regularity are being studied recently, motivated by their usefulness in the design of fault‐tolerant and low‐cost communication and interconnection networks. A graph is called a (d,c,r) graph if it has diameter d, connectivity c, and regularity r. The minimum number of vertices in (d, 1,3), (d,2,3), (d,3,3), and (d,c,c) graphs have been reported in the literature. In this paper, the minimum number of vertices in a (d,c,r) graph with r > c is determined, thereby exhausting all the possible choices of values for d, c, and r. Our proof is constructive and hence we get a collection of optimal (d,c,r) graphs.

The authors present two O(n/sup 2/) planarization algorithms, PLANARIZE and MAXIMAL-PLANARIZE. These algorithms are based on A. Lempel, S. Even, and I. Cederbaum's (1967) planarity testing algorithm and its implementation using PQ-trees. Algorithm PLANARIZE is for the construction of a spanning planar subgraph of an n-vertex nonplanar graph. The algorithm proceeds by embedding one vertex at a time and, at each step, adds the maximum number of edges possible without creating nonplanarity of the resultant graph. Given a biconnected spanning planar subgraph G/sub p/ of a nonplanar graph G, the MAXIMAL-PLANARIZE algorithm constructs a maximal planar subgraph of G which contains G/sub p/. This latter algorithm can also be used to planarize maximally a biconnected planar graph.>

In this paper software and hardware design of a tightly-coupled multimicroprocessor system with distributed common memory and private memory modules, to compute the values of auto- and cross-correlation functions, to recover signals buried in noise and to compute cross- and auto-power spectral density at 128 points simultaneously on the time delay axis or frequency axis, are described. This multimicroprocessor system has two 16-bit micro-processor boards, a single common bus, a centralized common-memory (CMO) and necessary arbitration logic circuits. To each microprocessor board, a distributed common memory, a private memory, I/O and common-memory-access control circuits are added. The addressing scheme is designed such that any common-memory cell has the same address for all the microprocessors. These software and hardware mechanisms suit the present signal processing application well. The computed results are displayed on an oscilloscope or X-Y recorder.

A switched-capacitor (SC) filter circuit realizing low-pass (LP), high-pass (HP), and bandpass (BP) transfer functions is described. This circuit uses only two operational amplifiers (OAs) and is bottom-plate stray-insensitive.

In medium pulse-repetition frequency (PRF) radars, ambiguities may arise in both range and Doppler measurements. Efficient techniques have been established [1,2] to resolve the range ambiguity of a single isolated target using multiple PRF's. In this paper, we describe a simple algorithm to resolve the Doppler ambiguity using the discrete Fourier transform (DFT) output of two PRF's. A condition on the relative values of the two PRF's is derived to account for the errors due to the finite bandwidth of DFT filters. A third PRF is used to identify the declarations of a particular target in different PRF's. Range ambiguities are then resolved in a straightforward manner. A fourth PRF is made use of to extract blind-speed targets. The proposed method is computationally efficient and can be used even when ambiguous returns from several targets are received.

In this paper a simplified procedure for the computation of the threshold of stability for N-dimensional discrete systems is presented and illustrated with a couple of numerical examples.

In this article, we propose some alternative approaches for testing the stability of two-dimensional digital filters without non-essential singularities of the second kind. Some numerical examples are given.

Rader [3] described an algorithm to estimate the period of undersampled periodic signal. We extend this method to estimate ambiguous Doppler frequency in low and medium pulse-repetition frequency (PRF) radars. Multiple PRF's are employed to resolve the ambiguity. The proposed method results in a constant false alarm rate detector. The performance of the algorithm in the presence of noise is presented. It is shown that in association with another detector, the proposed method resolves the Doppler ambiguity with good accuracy even at low signal-to-noise ratios. Finally, the method is a time-domain approach and does not require any spectral analyser to estimate the frequency.

A new formulation for the simulation of modulated signals based on a generalized structure is presented. In this approach a modulated signal is characterized in terms of a time-varying system where differential equation is simulated. Algorithms presented in the paper may be also used to track signal parameters such as the envelope and the carrier. The concept is illustrated by several algorithms of AM and FM simulators.

The presence of various types of symmetries in the frequency responses of a two-dimensional filter function reflects as constraints on its coefficients. The exploitation of these constraints leads to considerable reduction in the design and implementation complexities of two-dimensional filters. In this paper, some new constraints resulting from 4-fold rotational symmetry in frequency responses of two-dimensional filters are derived. The class of McClellan transformations that could be used in the generation of 2-D FIR filters possessing 4-fold rotational symmetry in their frequency responses is also obtained.

A variety of digital filter network structures is available. Different network structures posses different quantization properties. The fixed-point roundoff noise and coefficient sensitivity properties of the Swamy-Thyagarajan Wave Digital Filter, the cascade canonic, the Sedlmeyer-Fettweis Wave Digital Filter and the Generalized-Immittance Converter structures are examined in the present paper. The relative power spectral density and the statistical wordlength of each structure are computed.

A method of designing two-dimensional recursive digital filters is to employ wave digital filter techniques on two-variable analog networks possessing the desired frequency response characteristics. In this paper a study is initiated on the structures of singly terminated and doubly terminated 2-variable lossless ladder networks which are often the starting points in the design of wave digital filters. It is shown that transfer functions of singly terminated ladder networks possess second kind singularities. A class of doubly terminated cascade ladder networks whose transfer functions are free of second kind singularities is identified and their properties studied. The necessary and sufficient condition that a given rational function has to satisfy so that it can be realized as the voltage transfer function of a network belonging to the above class is obtained. A procedure to design such networks from a given transfer function is also described.

In this paper, the effect of multiplier quantization on stable-unstable transitions in N-dimensional filters is studied using a qualitative model. This model is used to determine a priori, for a worst case design, the minimum word length of the multipliers sufficient to retain stability.

The evaluation of the quantization error in two-dimensional (2-D) digital filters involves the following computationJ = \Sigma\min{m=0}\max{\infin} \Sigma\min{n=0}\max{\infin} y^{2}(m,n)In this paper a theorem concerning l2-stability is given and a general method for the evaluation of J based on a Laurent expansion of the integrand is presented. An illustrative example is given.

In this paper the concept of very strict Hurwitz polynomials which find applications in the design of stable two-dimensional digital filters, is extended to three and higher dimensions and the properties of such polynomials are discussed. A testing procedure to check whether a given multi-variable polynomial is a very strict Hurwitz polynomial or not is developed. Application of this concept in the design of n-dimensional digital filters without non-essential singularities of the second kind is then considered. It is shown that the concept of minimum reactive and susceptive, strict positive real functions in several variables is quite useful in the generation and testing of these filters. Such a positive real function is defined and its properties outlined. The application of transformation method to generate 3-dimensional filters from one - and two - dimensional filters is discussed.

A class of two variable Hurwitz polynomials called very strict Hurwitz polynomials (VSHPs) are defined and their properties are studied. Their application in the generation of two variable functions without non-essential singularities of the second kind are indicated. Necessary and sufficient conditions on general and reactance one-variable to two-variable transformations so that they yield two variable transfer functions without singularities of the second kind are obtained.

Necessary and sufficient conditions are obtained for the realization of a class of two-variable analog transfer functions. The transfer function is realized as a resistively-terminated two-port consisting of a cascade of P1and P2-variable lossless two-ports each having all of its transmission zeros at Pi=0 or pi=∞ (i=1,2), It is shown how these analog realizations may be used to realize two-dimensional wave digital filters.

A new formulation of the approximation procedure to generate a circularly symmetric two dimensional infinite impulse response transfer function is described. In this scheme the desired response is approximated first on the axes in the 2-D plane by a one dimensional function and then the remaining coefficients of the 2-D function are determined using an optimization technique. Other techniques to reduce the number of coefficients further are also discussed.

A simple active equalizer network employing two operational amplifiers (OA's), five resistors, and a pair of capacitors is presented. The same network can be used for both amplitude and delay equalizations. Experimental results show that the circuit can be conveniently used for amplitude equalization in voice communications, and for delay equalization in private line data circuits.