Deep learning has been widely applied to seismic signal classification, predominantly through supervised learning, typically relying on large labeled datasets. However, since the process of labeling large volumes of seismic data by domain experts is time-consuming and prone to human error, labeled seismic datasets are scarce. To address the problem of limited labeled data availability, a novel approach for seismic event classification is proposed employing self-supervised learning techniques. Initially, a generative-based self-supervised learning model, specifically an auto-encoder, is designed to extract informative features from the Short Time Fourier Transform of seismic recordings. These features are classified into four categories: earthquakes, micro-earthquakes, rockfalls, and anthropogenic noise. Classification is performed using (a) unsupervised K-means clustering on unlabeled data and (b) semi-supervised approaches, where only 5 to 33.3% of the data are labeled. The proposed semi-supervised method achieves high performance on a publicly available Résif dataset with recall of 0.90 for earthquakes, 0.65 for micro-earthquakes, 0.91 for rockfalls, and 0.84 for noise signals when trained with 20% of the labeled data. Additionally, we introduce a novel method to improve data labeling efficiency by using Self-Organizing Maps to cluster features from large datasets into multiple nodes. Our results demonstrate that the experts can more effectively and confidently label a small number of nodes instead of labeling all the events in the large dataset, thereby reducing the experts’ workload to just 4.6% of the original effort and our study reveals that this approach provides an excellent trade-off between expert labeling effort and classification accuracy, making it a highly effective solution for seismic event labeling. To evaluate the generalization capability of our proposed self-supervised learning model, we tested it on two unseen seismic datasets: the globally distributed Stanford Earthquake Dataset and the regionally focused Pacific Northwest Curated Seismic Dataset. On Stanford Earthquake Dataset, the pre-trained model effectively extracted discriminative earthquake and noise features, achieving high clustering accuracies. The Pacific Northwest Curated Seismic Dataset further challenges generalization with heterogeneous and previously unseen event types such as explosions, and thunder. Despite this diversity, the pre-trained model still preserved meaningful feature separability and captured inter-class relationships among acoustically similar events. Overall, these findings highlight the model’s ability to generalize effectively across both global and regional seismic datasets, underscoring its potential for wide deployment in seismological monitoring and event characterization without extensive retraining.

In the ongoing energy transition, characterized by increased reliance on distributed renewable sources and smart grid technologies, the need for advanced and trustworthy artificial intelligence (AI) in energy management systems is crucial. Non-intrusive load monitoring (NILM), a method for inferring individual appliance energy consumption from aggregate smart meter data, has gained prominence for enhancing energy efficiency. However, advanced deep neural network models used in NILM, while effective, raise transparency and trust concerns due to their complexity. This paper introduces a novel explainability-informed NILM training framework, specifically designed for low-frequency NILM. Our approach aligns with principles for trustworthy AI, focusing on human agency and oversight, technical robustness, and transparency, incorporating explainability directly into the training phase of a NILM model. We propose a novel iterative, explainability-informed NILM training algorithm that uses attribution priors to guide model optimization, including implementation and evaluation of the framework across multiple state-of-the-art NILM architectures, namely, convolutional, recurrent, and dilated causal layers. We introduce a novel Robustness-Trust metric to measure joint improvement in predictive and explainability performance, utilizing explainability metrics of faithfulness, robustness and effective complexity while analyzing model predictive performance against NILM-specific regression and classification metrics. Results broadly show that robust models achieve better explainability, while explainability-enhanced models can lead to improved model robustness. Together, our results demonstrate significant improvements in robustness and transparency of NILM systems across various appliances, model architectures, measurement scales, types of buildings, and energy usage patterns. This work paves the way for more transparent and trustworthy deployments in AI-driven energy systems.

The deployment of deep neural networks (DNNs) on resource-constrained edge devices necessitates efficient, low-complexity algorithms. Knowledge distillation (KD) addresses this through a student-teacher paradigm, transferring knowledge from complex teacher models to simpler student models. Current KD methods often optimize student performance without adequately addressing the reliability and interpretability of transferred knowledge, thus presenting challenges in maintaining both robustness and decision transparency. This paper introduces an Interpretability and Reliability-driven Knowledge Distillation (IR-KD) framework that enhances teacher model interpretability through perception-aligned gradients while leveraging hidden information from weak labels to optimize knowledge transfer. Our approach ensures compressed models remain computationally efficient while improving interpretability, which is essential for trustworthy edge AI deployment. We demonstrate improved predictive performance and model interpretability in non-intrusive load monitoring (NILM) applications as a case study. Quantitative explainability metrics confirm that perception-aligned gradients provide more faithful explanations, validating our approach’s effectiveness in developing reliable and transparent edge AI systems.

Deep learning has found extensive applications in classifying seismic signals in recent years. However, as a black box algorithm, deep learning is still rarely exploited in real-world applications, such as landslide monitoring. This is particularly a concern for geoscientists who prefer to classify seismic signals based on their physical properties, through feature engineering. To build trust in deep learning model outputs, we propose a CNN multi-classifier architecture to classify seismic signals into four classes (earthquake, micro-quake, rockfall and noise), and explain its outputs based on Layer-wise Relevance Propagation. We demonstrate that the provided explanations can lead to a more interpretable model by relating network outputs to geophysical phenomena and showing that distinguishing features extracted by the network are aligned with those identified by geoscientists as pertinent to classes of interest.

The labelling of large seismic datasets is a challenging problem. Currently the methods most favoured by geoscientists are based on well known geophysical properties with STA/LTA ratio pickers remaining highly trusted to generate results which can be quickly attributed due to their ability to pick relatively high Signal to Noise Ratio (SNR) events with high speed and accuracy. We aim to improve on the ability of deep learning methods by the unsupervised clustering of events which can help to visually identify results as belonging to a certain cluster with high confidence without the need for event by event processing. From our previous work we use a Siamese model trained with known labels from an open source dataset we show performance as a classifier and then expand on the method by showing clustering of events, where an expert can have high confidence that certain events are correctly identified, or require further evaluation.

The frequency and intensity of devastating landslides have been increasing worldwide. Timely prediction of slope failure can save lives and protect property. Slope movement is a result of several meteorological and hydrogeophysical variables, such as temperature and moisture content, but this complex relationship is still not well understood. To predict and characterise a slope failure, multiple measurands are usually collected. Since these numerous variables in the predictor set may cause significant increase in complexity, it becomes necessary to use methods that determine the relative importance of measurands that contribute directly to slope failure. To this end, we investigate three methods of visualisation of the feature space and dimensionality reduction, namely Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE) and Linear Discriminant Analysis (LDA), to analyse a range of surface and subsurface measurements from multiple sensors focusing on five stages of slope movement and then make failure predictions using XGBoost regression by setting as predictors two most important components from the extracted features. The results clearly show that LDA better clusters the data points and distinguishes the five different stages of slope movement, including two failures during the period of study encompassing eight years.

Induced microseismic monitoring has gained increased interest recently, to support various subsurface activities, including geothermal exploration and oil and gas production. To accurately detect and locate origins of microseismisity, deep learning-based methods have become popular due to their high accuracy when trained on large well-labelled datasets. However, though a huge amount of publicly available seismic measurements is available, laballed data to train models is very scarce, since labelling is time consuming and requires very specialist knowledge. Building on our prior work on active learning for time-series data, we propose an active learning method that cleverly picks only a small number of samples to query and stops when the proposed stopping criterion is met. We demonstrate that the proposed approach can save up to 83% of labelling effort even when transferred to a well with different sensing equipment from those used to build the training set.

Advanced machine learning algorithms require large datasets, along with good-quality labels to reach state-of-the-art performance. Although measurements themselves can often be easily available, the labelling process is usually a bottleneck. To address this, active learning approaches exploit the fact that different samples provide varying levels of information to the algorithm. However, these approaches often rely on several unrealistic assumptions — an oracle is assumed to provide error-free labels, all at the same cost and effort. We propose novel active learning-based methods for classification of time series measurements, typically obtained from sensors continuously measuring highly fluctuating environmental conditions including electricity consumption, and demonstrate their effectiveness for home energy management applications, where data labelling is a challenge. A new acquisition function is proposed, which accounts for both model and labelling uncertainty and class balancing. A stopping criterion is designed to stop the active learning process after an optimal point is achieved, to reduce labelling effort. We assess the effect of labelling errors on classification performance and propose two ways of mitigating their effects: (i) a re-labelling mechanism based on similarity of provided labels; (ii) a revised loss function based on confidence levels provided by experts. We validate our contributions for energy disaggregation task in a real-world scenario with three application domain experts. Our results show that the proposed methodology significantly improves performance of algorithms transferred to unseen domains with reduced number of labelled samples — from 61% reduction for dishwasher to 93% reduction for kettle.

Smart meters allow the grid to interface with individual buildings and extract detailed consumption information using nonintrusive load monitoring (NILM) algorithms applied to the acquired data. Deep neural networks, which represent the state of the art for NILM, are affected by scalability issues since they require high computational and memory resources, and by reduced performance when training and target domains mismatched. This article proposes a knowledge distillation approach for NILM, in particular for multilabel appliance classification, to reduce model complexity and improve generalization on unseen data domains. The approach uses weak supervision to reduce labeling effort, which is useful in practical scenarios. Experiments, conducted on U.K.-DALE and REFIT datasets, demonstrated that a low-complexity network can be obtained for deployment on edge devices while maintaining high performance on unseen data domains. The proposed approach outperformed benchmark methods in unseen target domains achieving a$F_{1}$-score 0.14 higher than a benchmark model 78 times more complex.

Knowledge distillation (KD) is a machine learning technique widely used in recent years for the task of domain adaptation and complexity reduction. It relies on a Student-Teacher mechanism to transfer the knowledge of a large and complex Teacher network into a smaller Student model. Given the inherent complexity of large Deep Neural Network (DNN) models, and the need for deployment on edge devices with limited resources, complexity reduction techniques have become a hot topic in the Non-intrusive Load Monitoring (NILM) community. Recent literature in NILM has devoted increased effort to domain adaptation and architecture reduction via KD. However, the mechanism behind the transfer of knowledge from the Teacher to the Student is not clearly understood. In this work, we aim to address the aforementioned issue by placing the KD NILM approach in a framework of explainable AI (XAI). We identify the main inconsistency in the transfer of explainable knowledge, and exploit this information to propose a method for improvement of KD through explainability guided learning. We evaluate our approach on a variety of appliances and domain adaptation scenarios and demonstrate that solving inconsistencies in the transfer of explainable knowledge can lead to improvement in predictive performance.

Automatic seismic signal classification methods are extensively investigated to reduce or replace manual interpretation, with great potential in previous research. Discriminative seismic wave propagation physical characteristics, such as velocities and accelerations, are rarely considered for classification. A multitask learning scheme is proposed that utilises the seismic wave equation and three-dimensional (3D) P-wave velocityVpmodel for signal representation learning. The classifier uses the obtained latent feature maps on a convolutional neural network architecture for classification of rockfall, slide quake, earthquake, and natural/anthropogenic noise events, recorded at an ongoing landslide. Our experimental results show that our approach outperforms state-of-the-art methods.

Passive seismics help us understand subsurface processes, e.g. landslides, mining, geothermal systems etc. and help predict and mitigate their effects. Continuous monitoring results in long seismic records that may contain various sources, which need to be classified. Manual detection and labeling of recorded seismic events is not only time consuming but can also be inconsistent when done manually, even in the case where it is done by the same expert. Therefore, an automated approach for classification of continuous microseismic recordings based on a Convolutional Neural Network (CNN) is proposed, with a multiclassifier architecture that classifies earthquakes, rockfalls and low signal to noise ratio quakes. Furthermore, we propose three CNN architectures that take as input time series data, Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) maps. The suitability of these three networks is rigorously assessed over five months of continuous seismometer recordings from the active Super-Sauze landslide in France. We observe that all three architectures have excellent and very similar performance. Furthermore, we evaluate transferability to a geographically distinct seismically active site in Larissa, Greece. We demonstrate that the proposed network is able to detect all 86 catalogued earthquake events, having only been trained on the Super-Sauze dataset and shows good agreement with manually detected events. This is promising as it could replace painstaking manual labelling of events in large recordings.

As the need for using energy-efficient machinery escalates, energy consumption estimation plays an important role in decision support and planning in the agri-sector. Within the present research study, energy consumption in dairy farms was examined. A deep learning-based load disaggregation approach was used to develop data-driven models to quantify individual energy consumption of milk production-related devices of dairy farms, from a single aggregate measurement. According to the experiments conducted on three dairy farms in Germany, load disaggregation from a single aggregate meter is a viable, cheaper alternative to submetering multiple pieces of equipment to accurately quantify electricity consumption at scale in dairy farms in order to provide the decision support needed to inform measures for tackling climate change.

Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes, such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behavior of rock. Understanding the evolution of landslide processes is of paramount importance in predicting or even avoiding an imminent failure. Microseismic monitoring recordings are often continuous, noisy, and consist of signals emitted by various sources. Manually detecting and distinguishing the signals emitted by an unstable slope is challenging. Research on automated end-to-end denoising, detection, and classification of microseismic events, as an early warning system, is still in its infancy. To this effect, our work is focused on jointly evaluating and developing suitable approaches for signal denoising, accurate event detection, nonsite-specific feature construction, feature selection, and event classification. We propose an automated end-to-end system that can process big data sets of continuous seismic recordings fast and demonstrate applicability and robustness to a wide range of events (distant and local earthquakes, slidequakes, anthropogenic noise, etc.). Algorithmic contributions lie in novel signal processing and analysis methods with fewer tunable parameters than the state of the art, evaluated on two field data sets and benchmarked against the state of the art.

This paper designs a Distributed Arithmetic Coding (DAC) decoder using the depth-first search method. In addition, a method is proposed to control the decoder complexity. Simulation results compare the DFD with the traditional Breadth-First Decoder (BFD) showing that under the same complexity constraints, the DFD outperforms the BFD when the code length is not too long and the quality of side information is not too poor.

Classification of seismic events detected from seismic recordings has been gaining popularity for improved interpretation of subsurface processes, e.g., volcanic systems, earthquake activity, induced seismicity and slope stability, in particular landslides. However, due to the variability of signal representation for different classes in the temporal and spectral space, a large feature space is used to discriminate classes. The consequence is additional complexity on the classifier and overfitting. So far, there has been little attempt to address dimensionality reduction via feature selection. In this paper, we propose an iterative, alternating graph feature and classifier learning method for micro-seismic signals via graph Laplacian regularization and normalized graph Laplacian regularization. Using recorded micro-seismic events from an active landslide, we demonstrate improved classification accuracy with a relatively small feature space compared to the state-of-the-art.

It was demonstrated that, as a nonlinear implementation of Slepian-Wolf Coding, Distributed Arithmetic Coding (DAC) outperforms traditional Low-Density Parity-Check (LPDC) codes for short code length and biased sources. This fact triggers research efforts into theoretical analysis of DAC. In our previous work, we proposed two analytical tools, Codebook Cardinality Spectrum (CCS) and Hamming Distance Spectrum, to analyze DAC for independent and identically-distributed (i.i.d.) binary sources with uniform distribution. This article extends our work on CCS from uniform i.i.d. binary sources to biased i.i.d. binary sources. We begin with the final CCS and then deduce each level of CCS backwards by recursion. The main finding of this article is that the final CCS of biased i.i.d. binary sources is not uniformly distributed over [0, 1). This article derives the final CCS of biased i.i.d. binary sources and proposes a numerical algorithm for calculating CCS effectively in practice. All theoretical analyses are well verified by experimental results.

Recent advances in image acquisition and analysis have resulted in disruptive innovation in physical rehabilitation systems facilitating cost-effective, portable, video-based gait assessment. While these inexpensive motion capture systems, suitable for home rehabilitation, do not generally provide accurate kinematics measurements on their own, image processing algorithms ensure gait analysis that is accurate enough for rehabilitation programs. This paper proposes high-accuracy classification of gait phases and muscle actions, using readings from low-cost motion capture systems. First, 12 gait parameters, drawn from the medical literature, are defined to characterize gait patterns. These proposed parameters are then used as input to our proposedmulti-channel time-series classificationand gait phase reconstruction methods. Proposed methods fully utilize temporal information of gait parameters, thus improving the final classification accuracy. The validation, conducted using 126 experiments, with 6 healthy volunteers and 9 stroke survivors with manually-labelled gait phases, achieves state-of-art classification accuracy of gait phase with lower computational complexity compared to previous solutions.1

Energy disaggregation of appliances using non-intrusive load monitoring (NILM) represents a set of signal and information processing methods used for appliance-level information extraction out of a meter's total or aggregate load. Large-scale deployments of smart meters worldwide and the availability of large amounts of data, motivates the shift from traditional source separation and Hidden Markov Model-based NILM towards data-driven NILM methods. Furthermore, we address the potential for scalable NILM roll-out by tackling disaggregation complexity as well as disaggregation on houses which have not been 'seen' before by the network, e.g., during training. In this paper, we focus on low rate NILM (with active power meter measurements sampled between 1-60 seconds) and present two different neural network architectures, one, based on convolutional neural network, and another based on gated recurrent unit, both of which classify the state and estimate the average power consumption of targeted appliances. Our proposed designs are driven by the need to have a well-trained generalised network which would be able to produce accurate results on a house that is not present in the training set, i.e., transferability. Performance results of the designed networks show excellent generalization ability and improvement compared to the state of the art.

Appliance fault in buildings resulting in abnormal energy consumption is known as an anomaly. Traditionally, anomaly detection is performed either at aggregate, i.e., meter-level, or at appliance level. Meter-level anomaly detection does not identify the anomaly-causing appliance, while appliance-level detection requires submetering each appliance in the building. Non-Intrusive Load Monitoring (NILM) has been proposed as an alternative to submetering to detect when appliances are running as well as estimate the appliance energy consumption. So far, applications have revolved around meaningful energy feedback. In this paper, we assess whether NILM can indeed be used for anomaly detection, as an alternative to submetering. We propose a supervised anomaly detection approach, AEM, and evaluate the effectiveness of NILM for anomaly detection. The proposed approach first learns an appliance's normal operation and then monitors its energy consumption for anomaly detection. We resort to real data, aggregate and subme-tered data from the two-year long REFIT dataset. We explain why anomaly detection performs worse with NILM data as compared to submetered data, highlighting the need for new, anomaly-aware NILM approaches.

With growing interest in data-driven classification, deep learning is now prevalent due to its ability to learn feature mapping functions solely from data. For very small training sets, however, deep learning, even with traditional regularization techniques, often overfits, resulting in sub-par classification performance. In this paper, we propose a novel binary classifier deep learning method, based on an iterative quadratic programming (QP) formulation with a graph Laplacian regularizer (GLR), combining the merits of model-based and data-driven approaches. Specifically, the proposed network employs a convolutional neural network (CNN) to learn deep features, which are used to define edge weights for a graph to pose a convex QP problem. Further, we design a novel loss function to penalize samples at the class boundary during semi-supervised learning. Results demonstrate that, given a small-size training dataset, our network outperforms several state-of-the-art classifiers, including CNN, model-based GLR and dynamic graph CNN classifiers.

Machine-type communications and large-scale information processing architectures are among key (r)evolutionary enhancements of emerging fifth-generation (5G) mobile cellular networks. Massive data acquisition and processing will make 5G network an ideal platform for large-scale system monitoring and control with applications in future smart infrastructures. In this work, we investigate a capability of such a 5G network architecture to provide the state estimate of an underlying linear system from the input obtained via large-scale deployment of measurement devices. Assuming that the measurements are communicated via densely deployed cloud radio access network (C-RAN), we formulate and solve the problem of estimating the system state from the set of signals collected at C-RAN base stations. Our solution, based on the Gaussian Belief-Propagation (GBP) framework, allows for large-scale and distributed deployment within the emerging 5G information processing architectures. The presented numerical study demonstrates the accuracy, convergence behavior and scalability of the proposed GBP-based solution to the large-scale state estimation problem.

In digital signal processing, a shift-invariant filter can be represented as a polynomial expansion of a shift operation, that is, the Z-transform representation. When extended to graph signal processing (GSP), this would mean that a shift-invariant graph filter can be represented as a polynomial of the shift matrix of the graph. Prior work shows that this holds under the shift-enabled condition that the characteristic and minimum polynomials of the shift matrix are identical. While the shift-enabled condition is often ignored in the literature, this letter shows that this condition is essential for the following reasons. First, we prove that this condition is not just sufficient but also necessary for any shift-invariant filter to be representable by the shift matrix. Moreover, we provide a counterexample showing that given a filter that commutes with a non-shift-enabled graph, it is generally impossible to convert the graph into a shift-enabled graph with a shift matrix still commuting with the original filter. The result provides a deeper understanding of shift-invariant filters when applied in GSP and shows that further investigation of shift-enabled graphs is needed to make them applicable to practical scenarios.

With growing ageing population, acquiring joint measurements with sufficient accuracy for reliable gait assessment is essential. Additionally, the quality of gait analysis relies heavily on accurate feature selection and classification. Sensor-driven and one-camera optical motion capture systems are becoming increasingly popular in the scientific literature due to their portability and cost-efficacy. In this paper, we propose 12 gait parameters to characterise gait patterns and a novel gait-phase classifier, resulting in comparable classification performance with a state-of-the-art multi-sensor optical motion system. Furthermore, a novel multi-channel time series segmentation method is proposed that maximizes the temporal information of gait parameters improving the final classification success rate after gait event reconstruction. The validation, conducted over 126 experiments on 6 healthy volunteers and 9 stroke patients with handlabelled ground truth gait phases, demonstrates high gait classification accuracy.

Localization of a viewer's region of interest (ROI) on eye gaze signal trajectories acquired by eye trackers is a widely used approach in scene analysis, image compression, and quality of experience assessment. In this paper, we propose a novel clustering approach for ROI estimation from potentially noisy raw eye gaze data, based on signal processing on graphs. The clustering approach adapts graph signal processing (GSP)-based classification by first cleverly selecting a starting data sample, and then classifying the remaining samples. Furthermore, Graph Fourier Transform is used to adjust GSP parameters on-the-fly to maximise accuracy. Experimental results show competitive clustering accuracy of our proposed scheme compared to Density-based spatial clustering of applications with noise (DB-SCAN), Distance-Threshold Identification (I-DT), and Mean-Shift on publicly available Shape Dataset and the potential of estimating ROI accurately on true eye tracker data1.

Low-cost depth sensors, such as Microsoft Kinect, have potential for noncontact health monitoring that is robust to ambient lighting conditions. However, captured depth images typically suffer from high acquisition noise, and hence, processing them to estimate biometrics is difficult. In this paper, we propose to capture depth video of a human subject using Kinect 2.0 to estimate his/her heart rate and rhythm; as blood is pumped from the heart to circulate through the head, tiny oscillatory head motion due to Newtonian mechanics can be detected for periodicity analysis. Specifically, we first restore a captured depth video via a joint bit-depth enhancement/denoising procedure, using a graph-signal smoothness prior for regularization. Second, we track an automatically detected head region throughout the depth video to deduce 3D motion vectors. The detected vectors are fed back to the depth restoration module in a loop to ensure that the motion information in two modules is consistent, improving performance of both restoration and motion tracking. Third, the computed 3D motion vectors are projected onto its principal component for 1D signal analysis, composed of trend removal, bandpass filtering, and wavelet-based motion denoising. Finally, the heart rate is estimated via Welch power spectrum analysis, and the heart rhythm is computed via peak detection. Experimental results show accurate estimation of the heart rate and rhythm using our proposed algorithm as compared to rate and rhythm estimated by a portable oximeter.

Obstructive sleep apnea, characterized by repetitive obstruction in the upper airway during sleep, is a common sleep disorder that could significantly compromise sleep quality and quality of life in general. The obstructive respiratory events can be detected by attended in-laboratory or unattended ambulatory sleep studies. Such studies require many attachments to a patient's body to track respiratory and physiological changes, which can be uncomfortable and compromise the patient's sleep quality. In this paper, we propose to record depth video and audio of a patient using a Microsoft Kinect camera during his/her sleep, and extract relevant features to correlate with obstructive respiratory events scored manually by a scientific officer based on data collected by Philips system Alice6 LDxS that is commonly used in sleep clinics. Specifically, we first propose an alternating-frame H.264 video encoding scheme and bit recovery scheme at the decoder. Next, we perform depth video temporal denoising using a motion vector graph smoothness prior. Then, we build a dual-ellipse model and track a patient's chest and abdominal movements in the denoised videos. Finally, we extract features from both depth video and audio for classifier training and respiratory event detection. Experimental results show 1) that our depth video compression scheme outperforms a competitor that records only the 8 most significant bits, 2) our graph-based temporal denoising scheme reduces the flickering effect without over-smoothing, and 3) our trained classifiers can deduce respiratory events scored manually based on data collected by system Alice6 LDxS with high accuracy.

Distributed arithmetic coding (DAC) is an effective technique for implementing Slepian-Wolf coding (SWC). It has been shown that a DAC code partitions source space into unequal-size codebooks, so that the overall performance of DAC codes depends on the cardinality and structure of these codebooks. The problem of DAC codebook cardinality has been solved by the so-called codebook cardinality spectrum (CCS). This paper extends the previous work on CCS by studying the problem of DAC codebook structure. We define Hamming distance spectrum (HDS) to describe DAC codebook structure and propose a mathematical method to calculate the HDS of DAC codes. The theoretical analyses are verified by experimental results.

Distributed arithmetic coding (DAC) is a variant of AC that can realize Slepian-Wolf coding in a nonlinear way. In our previous work, we defined codebook cardinality spectrum (CCS) and Hamming distance spectrum (HDS) for DAC. In this paper, we make use of CCS and HDS to analyze tailed DAC, which is a form of DAC that, as traditional AC, maps the last few symbols of each source block onto non-overlapped intervals. First, we derive the exact HDS formula for tailless DAC, a form of DAC that maps all the symbols of each source block onto overlapped intervals, and show that the HDS formula previously given is in fact approximation. Then, the HDS formula is extended to tailed DAC. Using CCS, we also deduce the average codebook cardinality, which is closely related to decoding complexity, and rate loss of tailed DAC. The effects of tail length are extensively analyzed. It is revealed that by increasing tail length to a value not close to the bitstream length, closely spaced codewords within the same codebook can be removed at the cost of a higher decoding complexity and a larger rate loss. Finally, theoretical analyses are verified by experiments.

Depth sensors like Microsoft Kinect can acquire partial geometric information in a 3D scene via captured depth images, with potential application to non-contact health monitoring. However, captured depth videos typically suffer from low bit-depth representation and acquisition noise corruption, and hence using them to deduce health metrics that require tracking subtle 3D structural details is difficult. In this paper, we propose to capture depth video using Kinect 2.0 to estimate the heart rate of a human subject; as blood is pumped to circulate through the head, tiny oscillatory head motion can be detected for periodicity analysis. Specifically, we first perform a joint bit-depth enhancement / denoising procedure to improve the quality of the captured depth images, using a graph-signal smoothness prior for regularization. We then track an automatically detected nose region throughout the depth video to deduce 3D motion vectors. The deduced 3D vectors are then analyzed via principal component analysis to estimate heart rate. Experimental results show improved tracking accuracy using our proposed joint bit-depth enhancement / denoising procedure, and estimated heart rates are close to ground truth.

We study multicast of multi-view content in the video plus depth format to heterogeneous clients. We design a joint source-channel coding scheme based on view and rate embedded source coding and rateless channel coding. It comprises an optimization framework for joint view selection and source-channel rate allocation, and includes a fast method for separate optimization of the source and channel coding components, at a negligible performance loss wrt the joint solution. We demonstrate performance gains over a state-of-the-art method based on H.264/SVC, in the case of two client classes.

Optical motion capture systems have been widely adopted for human motion analysis in stroke rehabilitation because of real-time processing and high-accuracy features. However, these systems require a large laboratory space and multiple cameras and thus can be expensive and not transportable. In this paper, we propose a portable, cheap, single-camera motion analysis system to implement upper limb movement analysis. The proposed system consists of video acquisition, camera calibration, marker tracking, autonomous joint angle calculation, visualization, validation and classification. The validation with a state-of-the-art optical motion analysis system using Bland-Altman plot, a typical clinical measure, indicates that the proposed system can accurately capture elbow movement, trunk-tilt, and shoulder movement for diagnosis. Furthermore, the volunteers are explicitly classified into healthy and stroke groups via a support vector machine trained on statistics of the trunk-tilt and shoulder movement. Experimental results show that the proposed system can accurately capture the upper limb movement patterns, automatically classify stroke survivors using ordinal scale classification of upper limb impairment, and offer a convenient and inexpensive solution for upper limb movement analysis.

In an age where there is a strong dependency on electrical appliances for domestic routines, this paper proposes an algorithm for identifying domestic activities from non-intrusive smart meter aggregate data. We distinguish two types of activities: Type I activities are those that can be recognized using only smart meter data and Type II activities are recognized by combining smart meter data with basic environmental sensing (temperature and humidity). For both types of activities, we start by disaggregating the total power usage down to individual electrical appliances. Then, we build an indicative activity model to reason four domestic activities using the Dempster-Shafer theory of evidence. To validate our algorithms, we use real energy and environmental data collected in an actual UK household over a period of three months, benchmarked on a time-stamped log of activities. The results show that it is possible to detect four tested domestic daily activities with high accuracy based on the aggregate energy usage.

Quality of sleep greatly affects a person's physiological well-being. Traditional sleep monitoring systems are expensive in cost and intrusive enough that they disturb the natural sleep of clinical patients. In our previous work, we proposed a non-intrusive sleep monitoring system to first record depth video in real-time, then offline analyze recorded depth data to track a patient's chest and abdomen movements over time. Detection of abnormal breathing is then interpreted as episodes of apnoea or hypopnoea. Leveraging on recent advances in graph signal processing (GSP), in this paper we propose two new additions to further improve our sleep monitoring system. First, temporal denoising is performed using a block motion vector smoothness prior expressed in the graph-signal domain, so that unwanted temporal flickering can be removed. Second, a graph-based event classification scheme is proposed, so that detection of apnoea / hypopnoea can be performed accurately and robustly. Experimental results show first that graph-based temporal denoising scheme outperforms an implementation of temporal median filter in terms of flicker removal. Second, we show that our graph-based event classification scheme is noticeably more robust to errors in training data than two conventional implementations of support vector machine (SVM).

Random Network Coding (RNC) has recently been investigated as a promising solution for reliable multimedia delivery over wireless networks. RNC possess the potential for flexible and adaptive matching of packet-level error resilience to both video content importance and variable wireless channel conditions. As the demand for massive multimedia delivery over fourth generation wireless cellular standards such as Long-Term Evolution (LTE)/LTE-Advanced (LTE-A) increases, novel video-aware transmission techniques are needed. In this paper, we investigate RNC as one such promising technique, building upon our recent work on RNC integration within the LTE/LTE-A Radio Access Network at the Multiple Access Control (MAC) layer (MAC-RNC). The paper argues that the proposed MAC-RNC solution provides fundamentally new set of opportunities for dynamic collaborative transmission, content awareness, resource allocation and unequal error protection (UEP) necessary for efficient wireless multimedia delivery in LTE/LTE-A.

Human gait analysis for stroke rehabilitation therapy using video processing tools has become popular in recent years. This paper proposes a single-camera system for capturing gait patterns using a Kalman-Structural-Similarity-based algorithm which tracks multiple markers simultaneously. This algorithm is initialized by obtaining the user-selected blocks in the first frame of each video, and the tracker is implemented by using Structural-Similarity image quality assessment algorithm to detect each marker frame by frame within a search area determined by a discrete Kalman filter. Experimental results show the trajectories of the markers fixed on the joints of a human body. The obtained numerical results are used to generate gait information (e.g., knee joint angle) that is later used for diagnostics. The proposed method aims to explore an alternative and portable way to implement human gait analysis with significantly less cost compared to a state-of-the-art 3D motion capture system.

Multiple description coding (MDC) has been proposed as a possible solution to real-time video delivery over relay-assisted wireless networks to exploit path diversity. In this paper, we study layered multiple description video over relay-assisted mobile networks, such as LTE-A, and develop a framework that routes packets to different relays based on source-channel-relay parameters. The proposed system comprises, besides layered MDC, (i) application-layer forward error correction via Random linear codes (RLC) that are very suitable in the relaying scenarios, due to their rateless nature, (ii) unequal error protection (UEP), using the recently proposed expanding window technique, that relies on probabilistic scheduling of layered packets. We generate layered multiple descriptions using data slicing and data partitioning features of H.264/AVC and conduct simulations by modelling a two-relay LTE-A setup with the finite state Markov chain model. Simulation results show benefits of relaying with optimized routing and UEP.

We derive an optimization framework for joint view and rate scalable coding of multi-view video content represented in the texture plus depth format. The optimization enables the sender to select the subset of coded views and their encoding rates such that the aggregate distortion over a continuum of synthesized views is minimized. We construct the view and rate embedded bitstream such that it delivers optimal performance simultaneously over a discrete set of transmission rates. In conjunction, we develop a user interaction model that characterizes the view selection actions of the client as a Markov chain over a discrete state-space. We exploit the model within the context of our optimization to compute user-action-driven coding strategies that aim at enhancing the client's performance in terms of latency and video quality. Our optimization outperforms the state-of-the-art H.264 SVC codec as well as a multi-view wavelet-based coder equipped with a uniform rate allocation strategy, across all scenarios studied in our experiments. Equally important, we can achieve an arbitrarily fine granularity of encoding bit rates, while providing a novel functionality of view embedded encoding, unlike the other encoding methods that we examined. Finally, we observe that the interactivity-aware coding delivers superior performance over conventional allocation techniques that do not anticipate the client's view selection actions in their operation.

The error free communication of video data over multi-hop wireless networks is a challenging research problem. Multiple description coding has been proposed as a possible solution to leverage path diversity for error robustness. Forward error correction is an additional protection that can be provided to each description. Random linear codes have had renewed interest fostered by the multi-hop and multi-interface radio receivers. In this study, the descriptions are created using the encoding features of slicing and data partitioning for H.264/AVC video. The unequally protected video is protected with Expanding window-Random linear codes against channel errors. Fading channel error model is used to simulate real-world wireless channels. We also propose an adaptive scheme for video transmission over multiple paths. Such scheme may adapt to the varying channel conditions as is frequently the case in wireless transmission. The results show that the proposed scheme can be used for emerging wireless standards.

In order to estimate the amount of energy that will be generated by a wind farm and provide efficient power distribution planning, it is necessary to deliver information of wind speed at all wind turbines. This paper proposes a scheme for compressing wind speed measurements exploiting both temporal and spatial correlation between the turbine readings via distributed source coding. The proposed scheme relies on a correlation model based on true measurements. A compression scheme proposed is of low encoding complexity and uses a particle-filtering based belief propagation decoder that adaptively estimates the nonstationary noise of the correlation model. Simulation results using realistic models show significant performance improvements compared to the scheme that does not dynamically refine correlation.

Random Network Coding (RNC) has recently been investigated as a promising solution for reliable multimedia delivery over wireless networks. Based on Random Linear Codes (RLC) and their systematic, sparse and Unequal Error Protection (UEP) extensions, RNC possess the potential for flexible and adaptive matching of packet-level error resilience to both video content importance and variable wireless channel conditions. As the demand for massive multimedia delivery over 4G wireless cellular standards such as LTE/LTE-A increases, novel video-aware transmission techniques are needed. In this paper, we investigate RNC as one such promising technique. In contrast to the normal use of RNC as an application layer technique (AL-RNC), this paper focuses on RNC integration within the MAC layer (MAC-RNC) of the LTE/LTE-A Radio Access Network (RAN). The paper argues that the proposed MAC-RNC solution could successfully replace the current MAC layer HARQ error resilience mechanism while providing new opportunities for content awareness, resource allocation and UEP which are fundamental for efficient wireless multimedia delivery.

The authors propose two practical power- and bandwidth-efficient systems based on amplify-and-forward and decode-and-forward schemes to address the problem of information exchange via a relay. The key idea is to channel encode each source's message by using a high-performance non-binary turbo code based on partial unit memory codes to enhance the bit-error-rate performance, then reduce the energy consumption and increase spectrum efficiency by using network coding (NC) to combine individual nodes’ messages at the relay before forwarding to the destination. Two simple and low complexity physical layer NC schemes are proposed based on combinations of received source messages at the relay. The authors also present the theoretical limits and numerical analysis of the proposed schemes. Simulation results under additive white Gaussian noise confirm that the proposed schemes achieve significant bandwidth savings and fewer transmissions over the benchmark systems which do not resort to NC. Theoretical limits for capacity and signal-to-noise ratio behaviour for the proposed schemes are derived. This study also proposes a cooperative strategy that is useful when insufficient combined messages are received at a node to recover the desired source messages, thus enabling the system to retrieve all packets with significantly fewer retransmission request messages.

In this paper, we provide the performance analysis of unequal error protection (UEP) random linear coding (RLC) strategies designed for transmission of source messages containing packets of different importance over lossy packet erasure links. By introducing the probabilistic encoding framework, we first derive the general performance limits for the packet-level UEP coding strategies that encode the packets of each importance class of the source message independently (non-overlapping windowing strategy) or jointly (expanding windowing strategy). Then, we demonstrate that the general performance limits of both strategies are achievable by the probabilistic encoding over non-overlapping and expanding windows based on RLC and the Gaussian Elimination (GE) decoding. Throughout the paper, we present a number of examples that investigate the performance and optimization of code design parameters of the expanding window RLC strategy and compare it with the non-overlapping RLC strategy selected as a reference.

Distributed video coding (DVC) is rapidly gaining popularity as a low cost, robust video coding solution, that reduces video encoding complexity. DVC is built on distributed source coding (DSC) principles where correlation between sources to be compressed is exploited at the decoder side. In the case of DVC, a current frame available only at the encoder is estimated at the decoder with side information generated from other frames available at the decoder. One of the main challenges in DVC design is that correlation among the source and side information needs to be estimated online and as accurately as possible. Since correlation dynamically changes with the scene, in order to exploit the robustness of DSC code designs, we integrate particle filtering (PF) with standard belief propagation (BP) decoding for inference on one joint factor graph to estimate correlation among source and side information. Correlation estimation is performed online as it is carried out jointly with decoding of the graph-based DSC code. Moreover, we demonstrate our joint bit-plane decoding with adaptive correlation estimation schemes within state-of-the-art DVC systems, which are transform-domain based with a feedback channel for rate adaptation. Experimental results show that our proposed system gives a significant performance improvement compared to the benchmark state-of-the-art DISCOVER codec (including correlation estimation) and the case without dynamic PF tracking, due to improved knowledge of timely correlation statistics via the combination of joint bit-plane decoding and particle-based BP (PBP) tracking.

We propose an adaptive distributed compression solution using particle filtering that tracks correlation, as well as performing disparity estimation, at the decoder side. The proposed algorithm is tested on the stereo solar images captured by the twin satellites system of NASA's Solar TErrestrial RElations Observatory (STEREO) project. Our experimental results show improved compression performance w.r.t. to a benchmark compression scheme, accurate correlation estimation by our proposed particle-based belief propagation algorithm, and significant peak signal-to-noise ratio improvement over traditional separate bit-plane decoding without dynamic correlation and disparity estimation.

Application Layer Forward Error Correction (AL)-FEC is increasingly being employed in the emerging wireless multimedia applications, where the multimedia data is sent along with repair data that can be used at the receiver to recover any losses. Raptor codes and Random Linear Codes (RLC) have emerged as promising rateless coding solutions. The DVB-H standard has adopted Raptor codes for IP datacasting, whereas for real-time communications, Reed-Solomon (RS) codes are used as link-layer FEC. This study compares the performance of Raptor codes and RLC for sending the H.264/AVC compressed video traffic over the DVB-H network. The simulations are performed using error traces depicting physical-layer Transport Stream (TS) packet losses in DVB-H. The study highlights the possibility of using RLC for the AL-FEC by selectively configuring codes for optimum performance.

In this paper, we propose an adaptive Distributed Video Coding (DVC) scheme that dynamically estimates correlation statistics of the scene in a video sequence to enhance belief-propagation (BP) Slepian-Wolf (SW) decoding. In order to exploit the robustness of distributed source coding (DSC) designs, we integrate particle filtering with standard BP decoding in one factor graph to estimate online correlation among source and side information. Our proposed system boasts improved performance over classical DVC without correlation estimation, due to improved knowledge of correlation statistics via the combination of bit-plane coding and particle-based BP tracking in each frame, as shown by our results.

This paper describes a complete low-complexity imaging system based on a single MEMS scanning mirror and a single photodetector, together with customized image enhancement algorithms based on sparse signal representation. Due to very low complexity of our developped optical set-up for image acquisition, resulting images suffer visible artifacts. We propose an iterative denoising-deblurring algorithm for image enhancement, which offers significant improvement over wavelet denoising with soft-thresholding. Several image enhancement algorithms are compared using the blind image quality indices (BIQI) as well as visual experience.

Processing of solar image data has become increasingly important for accurate space weather prediction and expanding our understanding about the Sun and Universe. To enable proper analysis, image denoising and contrast enhancement are essential for removal of all artifacts introduced within the acquisition process. Hence, this paper focuses on these two tasks applied on solar images corrupted with pixel dependent Poisson and zero-mean additive Gaussian noise. The denoising frameworks are build upon on two state-of-the-art techniques, K-SVD and BM3D (for natural images) where contrast enhancement of noisy solar images is performed jointly with noise removal using sparse coding adaptive dictionary learning. Results are given for two conventional sets of solar images.

Rateless codes can be advantageously used to provide Application Layer Forward Error Correction (AL-FEC) with a distinct advantage that an infinite number of packets can be generated on the fly from the source packets. For heterogeneous users and/or variable channel conditions, significant adaptation features and improvement in the video quality can be achieved by partitioning the video data into different priority classes. Such partitioned data can be unequally protected using appropriate FEC scheme based on its contribution to video reconstruction. Expanding Window Random Linear Codes (EW RLC) are a simple unequal error protection fountain coding scheme which can adapt to the prioritized data transmission. In this paper, EW RLC are proposed for broadcasting the H.264/Advanced video coding partitioned with the data partitioning feature. The results show viability of the EW RLC for multimedia broadcast applications to suit different data rates and channel conditions.

The paper presents a scalable compressive sampling (CS) scheme for video acquisition. The proposed solution enables progressive reconstruction of video frames with novel measurement matrices. Simulation results show significant performance improvements over the traditional CS technique for the base layer and slightly better performance for the final enhancement layer.

Distributed compression or Distributed Source Coding (DSC) refers to separate compression and joint decompression of multiple correlated sources. Though theoretical foundations were set almost forty years ago, driven by applications such as wireless video surveillance and wireless multimedia communications, DSC has become and still is a very active research area with interest from both academia and industry. While the first decade of this century has seen massive progress in code designs and achievable bounds, recent work is focussed on applications and solving practical DSC limitations such as large codeword size, accurate correlation estimation, etc. The goal of this paper is to provide an overview of the exciting trends and novel applications such as security, remote sensing, wireless data gathering of correlated data, biomedical imaging and infotainment, that DSC makes possible.

Acquiring and processing astronomical images is becoming increasingly important for accurate space weather prediction and expanding our understanding about the Sun and the Universe. These images are often rich in content, large in size and dynamic range. Efficient, low-complexity compression solutions are essential to reduce onboard storage, processing, and communication resources. Distributed compression is a promising technique for onboard coding of solar images by exploiting correlation between successively acquired images. In this paper we propose an adaptive distributed compression solution using particle filtering that tracks correlation, as well as performing disparity estimation, at the decoder side. The proposed algorithm is tested on the stereo solar images captured by the twin satellites system of NASA's STEREO project. Our experimental results show the significant PSNR improvement over traditional separate bit-plane decoding without dynamic correlation and disparity estimation.

Application layer forward error correction is becoming a popular addition to protocols for real-time video delivery over IP-based wireless networks. Since each part of video data is not equally important for video reconstruction, it is beneficial to divide video data based on its importance. Such partitioned data could then be provided with different degree of protection, with the important data having more protection against channel erasures. Data partitioning (DP) is one such low-cost feature in H.264/AVC enabling partitioning of video data based on its importance. In this paper, we propose an Unequal error protection (UEP) scheme to protect the DP H.264/AVC coded video data with Raptor and Random linear codes (RLC). The simulations have been performed using error traces which depict physical-layer Transport Stream (TS) packet losses in DVBH. The results highlight that for broadcasting applications with varying channel conditions, better results can be obtained with dynamic probability of selection of different importance layers.

Application-layer forward error correcting (AL-FEC) codes, providing error protection across blocks of packetized data, are becoming increasingly important in emerging networking applications. In this paper, we investigate AL-FEC solutions based on rate- compatible low-density parity-check (RC LDPC) codes, as they may offer excellent performance with low system complexity. Recent studies on RC LDPC codes are focused on physical layer incremental redundancy hybrid automatic-repeat-request (IR-HARQ) systems and typical wireless channel models. The focus of our study is RC LDPC code design as an AL-FEC solution in packet-level IR-HARQ systems assuming erasure channels between the source and the destination(s). We propose novel design methods for RC LDPC codes over erasure channels motivated by the dependance of finite-length LDPC code performance on the size of the smallest stopping set of its code graph. Simulation experiments demonstrate that the proposed RC LDPC design significantly outperforms the state-of-the-art RC LDPC codes in the error-floor region, and in particular, in the domain of poor channel conditions.

H.264 Scalable Video Coding (SVC) is an extension to the Advanced Video Coding (AVC) H.264 standard which provides efficient scalability functionalities on top of the high coding efficiency of H.264/AVC. SVC allows for temporal, spatial, and quality scalability of the output video stream, encoding the video information into an H.264/AVC base layer and a series of enhancement layers which incrementally improve the quality, increase screen resolution and/or frame rate. SVC is particularly suited for mobile TV reception, since the received video quality is adaptable to variable reception conditions and heterogeneous receiver capabilities. However, mobile TV Digital Video Broadcasting (DVB) standards such as DVB-H and DVB-SH were designed prior to the introduction of SVC, and therefore the underlying transmission protocols are not optimized for scalable video delivery. In this overview paper, we review recently proposed solutions for SVC stream adaptation on the underlying DVB-H/SH protocols, and point out novel technical solutions that are currently under consideration for the next generation mobile broadcasting standard DVB-NGH.

In this paper, we investigate the design of decentralized encoding procedure for distributed random linear coding (RLC) in resource-constrained wireless networks. We propose a novel distributed RLC scheme called “Fireworks”, analyze its performance and support it by simulation results. The presented results demonstrate design flexibility of the proposed scheme, where the design choices influence the trade-off between the coding efficiency and encoding communication costs.

This paper focuses on recent research on unequal error protection random linear coding (UEP RLC) for applications in network coded (NC) multimedia communications. We define a class of UEP RLC called expanding window random linear coding (EW-RLC) and provide exact decoding probability analysis for different importance classes of the source data assuming the Gaussian Elimination (GE) decoder applied at the receiver. Using this analysis, we provide a detailed investigation of the EW-RLC design for the distortion optimized scalable H.264/SVC coded video transmission over packet networks with packet erasures over a range of heterogeneous receivers with varying receiver reception overhead capabilities.

Scale Invariant Feature Transform (SIFT) is a very powerful technique for image registration. While SIFT descriptors accurately extract invariant image characteristics around keypoints, the commonly used matching approach for registration is overly simplified, because it completely ignores the geometric information among descriptors. In this paper, we formulate keypoint matching as a global optimization problem and provide a suboptimum solution using belief propagation. Experimental results show significant improvement over previous approaches.

This paper addresses the problem of wireless delivery of a captured scene from two cameras, which do not communicate with each other, to a central point for joint decoding. We exploit correlation among two camera views using distributed source coding and use complete complementary (CC) data spreading to combat multiple access interference and noise even when the transmitters are de-synchronized. Our distributed source coding scheme is based on uniform scalar quantization in the DCT domain and non-asymmetric Slepian-Wolf coding via turbo codes. The non-asymmetric Slepian-Wolf scheme enables efficient trade-off between the transmission rates of the two cameras. Simulation results indicate that the proposed system outperforms significantly two independently JPEG-encoded streams at low transmission rates.

This paper describes an optical system for low-complexity optical image acquisition based on a single scanning MEMS mirror and a single photodetector. The overall aim of the research is to investigate techniques for image acquisition at electromagnetic wavelengths where the cost and/or technical maturity of detector arrays pose a limitation. In contrast to similar systems built using a digital micromirror device (DMD), the present configuration has advantages of lower cost and potential applicability across a wide spectrum, ranging from visible to Terahertz frequencies. In the present arrangement, light at visible wavelengths from the object passes through a telescope and falls onto a small, scanning MEMS micromirror. The entire image of the object is projected onto the mirror surface and reflected towards a single photodetector with a pinhole at its entrance. Similarly to conventional scanning, by finely changing the tilt-angle of the mirror, the detector sees different areas of the projected image, thereby building up an image pixel-by-pixel. Resolution is increased by allowing for an overlap between neighbouring scanned areas. Iterative bilinear interpolation and wavelet denoising are employed to enhance image quality.

Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms. The image is split into non-overlapping blocks of fixed size, which are independently compressively sampled exploiting sparsity of natural scenes in the Discrete Cosine Transform (DCT) domain. At the decoder, each block is recovered using useful information extracted from the recovery of a neighboring block. In the case of video, a previous frame is used to help recovery of consecutive frames. The iterative algorithm for signal recovery with side information that extends the standard orthogonal matching pursuit (OMP) algorithm is employed. Simulation results are given for Magnetic Resonance Imaging (MRI) and video sequences to illustrate advantages of the proposed solution compared to the case when side information is not used.

In this paper, we address the problem of distributed Raptor code design over information packets located across the network nodes. We propose a novel approach to this problem that consists of generating, encoding and dispersing Raptor packets across the network. Unlike recent node-centric proposals, where network nodes are responsible for collecting information packets and performing Raptor encoding, in the proposed packet-centric approach this task is assigned to Raptor packets. In a two-step encoding procedure that corresponds to precoding and LT-coding step of standard Raptor encoding, Raptor packets randomly traverse the network, collect and encode sufficient number of information packets following exactly a given degree distribution, and finish their paths in a random network node. The efficiency of the distributed Raptor coding scheme is confirmed by simulation results, where their performance is demonstrated to approach closely the performance of standard (centralized) Raptor codes.

The paper combines distributed source coding and compressive sampling for efficient spectrum estimation. Two or more cognitive radios sample the spectrum compressively and independently compress their observations using multiterminal source coding. A central hub collects compressed streams from these radios before performing joint multiterminal source decoding followed by iterative signal reconstruction. Simulation results are provided for two radios performing practical multiterminal source coding with uniform scalar quantization and systematic turbo codes for Slepian-Wolf coding and error protection.

We propose a new technique, compress-spread forward (CSF), for high-performance wireless streaming from two base stations in parallel. CSF uses multiterminal source coding for efficient source compression and complete complementary sequences for error-free multiple access and synchronization. Our practical design shows significant performance gains due to spatial diversity and distributed source coding.

Recent information-theoretic results show the optimality of dirty-paper coding (DPC) in achieving the full capacity region of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). This paper presents a DPC based code design for BCs. We consider the case in which there is an individual rate/signal-to-interference-plus-noise ratio (SINR) constraint for each user. For a fixed transmitter power, we choose the linear transmit precoding matrix such that the SINRs at users are uniformly maximized, thus ensuring the best bit-error rate performance. We start with Cover's simplest two-user Gaussian BC and present a coding scheme that operates 1.44 dB from the boundary of the capacity region at the rate of one bit per real sample (b/s) for each user. We then extend the coding strategy to a two-user MIMO Gaussian BC with two transmit antennas at the base-station and develop the first limit-approaching code design using nested turbo codes for DPC. At the rate of 1 b/s for each user, our design operates 1.48 dB from the capacity region boundary. We also consider the performance of our scheme over a slow fading BC. For two transmit antennas, simulation results indicate a performance loss of only 1.4 dB, 1.64 dB and 1.99 dB from the theoretical limit in terms of the total transmission power for the two, three and four user case, respectively.

Following recent works on the rate region of the quadratic Gaussian two-terminal source coding problem and limit-approaching code designs, this paper examines multiterminal source coding of two correlated, i.e., stereo, video sequences to save the sum rate over independent coding of both sequences. Two multiterminal video coding schemes are proposed. In the first scheme, the left sequence of the stereo pair is coded by H.264/AVC and used at the joint decoder to facilitate Wyner-Ziv coding of the right video sequence. The first I-frame of the right sequence is successively coded by H.264/AVC Intracoding and Wyner-Ziv coding. An efficient stereo matching algorithm based on loopy belief propagation is then adopted at the decoder to produce pixel-level disparity maps between the corresponding frames of the two decoded video sequences on the fly. Based on the disparity maps, side information for both motion vectors and motion-compensated residual frames of the right sequence are generated at the decoder before Wyner-Ziv encoding. In the second scheme, source splitting is employed on top of classic and Wyner-Ziv coding for compression of both I-frames to allow flexible rate allocation between the two sequences. Experiments with both schemes on stereo video sequences using H.264/AVC, LDPC codes for Slepian-Wolf coding of the motion vectors, and scalar quantization in conjunction with LDPC codes for Wyner-Ziv coding of the residual coefficients give a slightly lower sum rate than separate H.264/AVC coding of both sequences at the same video quality.

This paper examines near-capacity dirty-paper code designs based on source–channel coding. We first point out that the performance loss in signal-to-noise ratio (SNR) in our code designs can be broken into the sum of the packing loss from channel coding and a modulo loss, which is a function of the granular loss from source coding and the target dirty-paper coding rate (or SNR). We then examine practical designs by combining trellis-coded quantization (TCQ) with both systematic and nonsystematic irregular repeat–accumulate (IRA) codes. Like previous approaches, we exploit the extrinsic information transfer (EXIT) chart technique for capacity-approaching IRA code design; but unlike previous approaches, we emphasize the role of strong source coding to achieve as much granular gain as possible using TCQ. Instead of systematic doping, we employ two relatively shifted TCQ codebooks, where the shift is optimized (via tuning the EXIT charts) to facilitate the IRA code design. Our designs synergistically combine TCQ with IRA codes so that they work together as well as they do individually. By bringing together TCQ (the best quantizer from the source coding community) and EXIT chart-based IRA code designs (the best from the channel coding community), we are able to approach the theoretical limit of dirty-paper coding. For example, at 0.25 bit per symbol (b/s), our best code design (with 2048-state TCQ) performs only 0.630 dB away from the Shannon capacity.

This paper considers the problem of communicating correlated information from multiple source nodes over a network of noiseless channels to multiple destination nodes, where each destination node wants to recover all sources. The problem involves a joint consideration of distributed compression and network information relaying. Although the optimal rate region has been theoretically characterized, it was not clear how to design practical communication schemes with low complexity. This work provides a partial solution to this problem by proposing a low-complexity scheme for the special case with two sources whose correlation is characterized by a binary symmetric channel. Our scheme is based on a careful combination of linear syndrome-based Slepian-Wolf coding and random linear mixing (network coding). It is in general suboptimal; however, its low complexity and robustness to network dynamics make it suitable for practical implementation.

Fountain codes were introduced as an efficient and universal forward error correction (FEC) solution for data multicast over lossy packet networks. They have recently been proposed for large scale multimedia content delivery in practical multimedia distribution systems. However, standard fountain codes, such as LT or Raptor codes, are not designed to meet unequal error protection (UEP) requirements typical in real-time scalable video multicast applications. In this paper, we propose recently introduced UEP expanding window fountain (EWF) codes as a flexible and efficient solution for real-time scalable video multicast. We demonstrate that the design flexibility and UEP performance make EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be “tuned” at the server side to meet the different reception criteria of heterogeneous receivers. The performance analysis using both analytical results and simulation experiments of H.264 scalable video coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution.

Data streaming from multiple base stations to a client is recognized as a robust technique for multimedia streaming. However the resulting transmission in parallel over wireless channels poses serious challenges, especially multiple access interference, multipath fading, noise effects and synchronization. Spread spectrum techniques seem the obvious choice to mitigate these effects, but at the cost of increased bandwidth requirements. This paper proposes a solution that exploits complete complementary spectrum spreading and data compression techniques jointly to resolve the communication challenges whilst ensuring efficient use of spectrum and acceptable bit error rate. Our proposed spreading scheme reduces the required transmission bandwidth by exploiting correlation among information present at multiple base stations. Results obtained show 1.75 Mchip/sec (or 25%) reduction in transmission rate, with only up to 6 dB loss in frequency-selective channel compared to a straightforward solution based solely on complete complementary spectrum spreading.

Latest advances in sensor and communication technologies and computational intelligence will allow vehicles to observe their surroundings, process the collected information and communicate it with other vehicles, e.g., vehicles can sense adverse road conditions, experience trafic congestion, or detect accidents, and then adjust vehicle control. We investigate the problem of effective intelligence gathering in the perspectives of both communications and data fusion. The target system should have high processing and transmission accuracy for the gathered information as well as low communication load requirement. Assuming a binary source, we determine the theoretical bound for the error probability of gathered information and present a way of communicating the observation from individual vehicles to the destination. Compared to the flooding method, our model reduces the communication load by approximately N/log2N times, with N being the total number of the vehicles, without decreasing the accuracy of the gathered information.

This paper addresses a data-hiding problem where a source image needs to be hidden into another host image. We consider both cases when the decoder has access to the host image (non-blind data-hiding), and when the decoder does not (blind data-hiding). Our proposed solution combines complete complementary (CC) spreading sequences and nested scalar quantization (NSQ). To enhance security, the source image is encrypted prior to embedding. Our simulation results, for both non-blind and blind data-hiding, with AWGN attacks show competitive results. Indeed, for blind data-hiding, our design outperforms the traditional NSQ system by 8 dB.

Digital Fountain (DF) codes have recently been suggested as an efficient forward error correction (FEC) solution for video multicast to heterogeneous receiver classes over lossy packet networks. However, to adapt DF codes to low-delay constraints and varying importance of scalable multimedia content, unequal error protection (UEP) DF schemes are needed. Thus, in this paper, Expanding Window Fountain (EWF) codes are proposed as a FEC solution for scalable video multicast. We demonstrate that the design flexibility and UEP performancemake EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be “tuned” at the server side to meet the different reception conditions of heterogeneous receivers. Performance analysis of H.264 Scalable Video Coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution.

Cover and El Gamal derived the tightest bounds on the capacity of the relay channel using random coding and suggested two coding strategies, namely, decode-forward (DF) and compress-forward (CF), to provide the best known lower bound of the achievable rate. Practical code designs proposed recently mainly exploit DF to approach the lower bound. Following the latest development in practical distributed source-channel coding, this paper studies CF coding with BPSK modulation for the relay channel. In CF scheme, Wyner-Ziv coding is applied at the relay to exploit the joint statistics between signals at the relay and the destination. We employ Slepian-Wolf coded nested scalar quantization (SWCNSQ) in practical Wyner-Ziv coding at the relay and compute the achievable rates of this scheme with BPSK modulation for the half-duplex Gaussian relay channel. We present a code design based on LDPC codes for error protection at the source and NSQ and IRA codes for CF at the relay. Simulation results show that our design comes within 1.48-1.92 dB of the SWCNSQ limit.

Driven by applications in data-hiding, MIMO broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source- channel coding for algebraic binning. We then address practical code design based on nested lattice codes and propose nested turbo codes using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to TCQ, turbo-like TCQ offers structural similarity between the source and channel coding components, leading to more efficient nesting with TTCM and better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. Optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions and exhibits a gap of 0.94, 1.42 and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 bits/sample, respectively.

Multiterminal (MT) source coding refers to separate lossy encoding and joint decoding of multiple correlated sources. Recently, the rate region of bothdirectandindirectMT source coding in the quadratic Gaussian setup with two encoders was determined. We are thus motivated to design practical MT source codes that can potentially achieve the entire rate region. In this paper, we present two practical MT coding schemes under the framework of Slepian–Wolf coded quantization (SWCQ) for both direct and indirect MT problems. The first,asymmetricSWCQ scheme relies on quantization and Wyner–Ziv coding, and it is implemented via source splitting to achieve any point on the sum–rate bound. In the second, conceptually simpler scheme,symmetricSWCQ, the two quantized sources are compressed using symmetric Slepian–Wolf coding via a channel code partitioning technique that is capable of achieving any point on the Slepian–Wolf sum–rate bound. Our practical designs employ trellis-coded quantization and turbo/low-density parity-check (LDPC) codes for both asymmetric and symmetric Slepian–Wolf coding. Simulation results show a gap of only 0.139–0.194 bit per sample away from the sum–rate bound for both direct and indirect MT coding problems.

We propose a system for real-time multimedia multicast over heterogeneous wireless-wireline networks. The encoded source is transmitted from the base station over the wireless radio link to numerous Internet servers. Each server performs distributed source coding (as quantization followed by Slepian-Wolf coding) by exploiting mutual correlation among packets received at different servers. The resulting packets are forwarded to the clients for joint decoding. We provide an algorithm for optimal nonuniform scalar quantizers design at the server side that minimizes the required rate under the decoder bit error rate constraint. For scalable multimedia codes, we develop joint source-channel coding scheme which combines error-protection at the base station and distributed source coding at the servers. Our experimental results show significant performance improvements over conventional solutions due to spatial diversity and distributed source coding gains.

We consider a practical system design for a wireless video conference application, where we exploit the broadcast nature of wireless radio links using network coding. With network coding, the number of necessary downlink transmissions from the intermediate node to the two users is reduced, and thus the throughput is increased. We develop two systems, one based on amplify-and-forward and another on decode-and-forward technique, and compare them to traditional communication systems that do not use network coding. Our practical designs exploit the latest in 3-D wavelet-based scalable video coding and channel coding. Simulation results confirm the advantages of the proposed video communication schemes over conventional ones.

Following recent works on the rate region of the quadratic Gaussian two-terminal source coding problem and limit-approaching code designs, this paper examines multiterminal source coding of two correlated video sequences to save the sum rate over independent coding. Specifically, the first video sequence is coded by H.264 and used at the joint decoder to facilitate Wyner-Ziv coding of the second video sequence. The first I-frame of the right sequence is successively coded by H.264 and Slepian-Wolf coding. An efficient stereo matching algorithm based on loopy belief propagation is then adopted at the decoder to produce pixel-level disparity maps between the corresponding frames of the two decoded video sequences on the fly. Based on the disparity maps, side information for both motion vectors and motion-compensated residual frames of the second sequence are generated at the decoder before Wyner-Ziv encoding. Experimental results on stereo video sequences using H.264, LDPC codes for Slepian-Wolf coding of the motion vectors and scalar quantization in conjunction with LDPC codes for Wyner-Ziv coding of the residual coefficients show savings in terms of the sum-rate when compared to separate H.264 coding at the same video quality.

Extending recent works on distributed source coding, this paper considers distributed source-channel coding and targets at the important application of scalable video transmission over wireless networks. The idea is to use a single channel code for both video compression (via Slepian-Wolf coding) and packet loss protection. First, we provide a theoretical code design framework for distributed joint source-channel coding over erasure channels and then apply it to the targeted video application. The resulting video coder is based on a cross-layer design where video compression and protection are performed jointly. We choose Raptor codes - the best approximation to a digital fountain - and address in detail both encoder and decoder designs. Using the received packets together with a correlated video available at the decoder as side information, we devise a new iterative soft-decision decoder for joint Raptor decoding. Simulation results show that, compared to one separate design using Slepian-Wolf compression plus erasure protection and another based on FGS coding plus erasure protection, the proposed joint design provides better video quality at the same number of transmitted packets. Our work represents the first in capitalizing the latest in distributed source coding and near-capacity channel coding for robust video transmission over erasure channels.

Driven by the applications in data hiding, multiple-input multiple-output broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source channel coding for algebraic binning. We then address a practical code design based on nested lattice codes, and propose nested turbo codes by using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to the TCQ, the turbo-like TCQ offers a structural similarity between the source and channel coding components, leading to a more efficient nesting with TTCM and a better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of the turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. The optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions, and exhibits a gap of 0.94, 1.42, and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 b/s, respectively.

The increasing popularity of video streaming applications that distribute data to a large number of clients motivates the design of reliable multimedia delivery systems capable of adapting to diverse transmission conditions. Receiver-driven layered multicast (RLM) efficiently addresses the issue of heterogeneity in clients' available bandwidths and packet loss rates by shifting rate control to the receiver side. We propose a system for RLM over the Internet and 3G wireless networks based on layered Wyner-Ziv video coding and digital fountain codes. Layered Wyner-Ziv video coding improves robustness to packet loss compared to current scalable video coders, such as MPEG-4 FGS coder, while generating a scalable output bit stream. Digital fountain codes are near-capacity erasure protection codes that are ideally suited for multicast applications due to their rateless property. By combining an error-resilient Wyner-Ziv video coder and rateless fountain codes, our system allows reliable video multicast to an arbitrary number of heterogeneous receivers without the requirement of feedback channels. Simulation results show performance improvements over a previous scheme that exploits multiple description and layered coding.

Abstract We propose a 4G system for transmission of video from a server at the base station to numerous wireless clients. We employ the latest technology in scalable video compression (3‐D wavelet video coding) and in channel coding (punctured turbo codes); for the physical layer, we resort to the multicarrier chip‐interleaved system with two‐layer interleaving, which achieves high spectral efficiency and is very suitable for downlink applications. We develop fast algorithms for a cross‐layer resource allocation that minimize the expected distortion of the reconstructed video averaged over all clients. The algorithms find a near‐optimal power, bandwidth, and subcarrier allocation at the physical layer and a source‐channel symbol allocation at the application layer. Our experimental results demonstrate that such a cross‐layer optimization framework leads to higher quality performance of the overall system. Copyright © 2007 John Wiley & Sons, Ltd.

Real-time multimedia multicast over wireless networks is an exciting application that has generated a lot of interest recently. Its main challenge lies in the stringent bandwidth and time-delay requirements of real-time multimedia and severe impairments of the wireless channels. We develop a system for multimedia multicast over wireless-wireline networks, that leverages the knowledge on network information theory, multimedia processing, error control, and networking. In particular, the encoded multimedia data are broadcast to multiple Internet servers over a wireless channel. Each server merely compresses the signal it has received using distributed source coding. The receiver collects bitstreams from the servers before performing joint decoding. Due to spatial diversity gain and distributed source coding, our system significantly outperforms conventional solutions.

Recent information-theoretical results show the optimality of dirty-paper coding (DPC) in achieving the capacity of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). This paper presents the first practical limit approaching DPC-based design for the MIMO BC. We start with Cover's simplest two-user Gaussian BC and present a code design that operates 1.44 dB away from the capacity region boundary at a transmission rate of 1.0 bit per sample (b/s). Then we consider the non-degraded two-user MIMO fading BC with two transmit antennas. For this setup, the performance loss of our code design is 3.7 dB and 2.45 dB from the the sum-rate capacity when the transmission rate for each user is 1.0 b/s and 2.0 b/s, respectively.

It has been shown recently that under certain conditions there exist dualities between different multiterminal (MT) source and channel coding problems. Following these results, we study lossless MT source coding and deterministic MT channel coding problems and point out different dualities between them. In particular, we show that there exists a functional duality between a Slepian-Wolf (SW) coding problem and a deterministic broadcast channel (DBC) coding problem and between a lossless multiple-description (MD) coding problem and a deterministic multiple-access channel (DMAC) coding problem. In analogy to the duality established between DBC and DMAC coding problems, we further propose a similar duality between SW and lossless MD coding problems; in this way, we form a closed "duality loop" of four MT coding problems, which imposes the existence of a single common rate point in the achievable rate regions of all four dual problems. We also consider duality in zero-error MT coding and shed light on practical code design with an example. Finally, extension to the case with only one lossless/deterministic component in the source/channel coding problem is provided.

A Slepian-Wolf coding scheme for compressing two uniform memoryless binary sources using a single channel code that can achieve arbitrary rate allocation among encoders was outlined in the work of Pradhan and Ramchandran. Inspired by this work, we address the problem of practical code design for general multiterminal lossless networks where multiple memoryless correlated binary sources are separately compressed and sent; each decoder receives a set of compressed sources and attempts to jointly reconstruct them. First, we propose a near-lossless practical code design for the Slepian-Wolf system with multiple sources. For two uniform sources, if the code approaches the capacity of the channel that models the correlation between the sources, then the system will approach the theoretical limit. Thus, the great advantage of this design method is its possibility to approach the theoretical limits with a single channel code for any rate allocation among the encoders. Based on Slepian-Wolf code constructions, we continue with providing practical designs for the general lossless multiterminal network which consists of an arbitrary number of encoders and decoders. Using irregular repeat-accumulate and turbo codes in our designs, we obtain the best results reported so far and almost reach the theoretical bounds.

Summary form only given. In this paper, we consider the case of a noisy channel in Wyner-Ziv coding (WZC) and address distributed joint source-channel coding (JSCC), while targeting at video transmission over packet erasure channels. Our idea is to use a single Raptor code, for both SWC and erasure protection. Raptor codes are the latest addition to a family of low-complexity rateless fountain codes which consist of a high-rate precode and an LT code. We use IRA codes as the precode of our Raptor code, as IRA codes are well suited for distributed JSCC. For the decoder design, due to the presence of side information, we develop a new iterative soft-decision Raptor decoder for joint decoding that combines the received packets and the side information.

Multiterminal (MT) source coding refers to separate lossy encoding and joint decoding of multiple correlated sources. This paper presents two practical MT coding schemes under the same general framework of Slepian-Wolf coded quantization (SWCQ) for both direct and indirect quadratic Gaussian MT source coding problems with two encoders. The first asymmetric SWCQ scheme relies on quantization and Wyner-Ziv coding, and is implemented via source-splitting to achieve any point on the inner sum-rate bound for both direct and indirect MT coding problems. In the second symmetric SWCQ scheme, the two quantization outputs are compressed using multilevel symmetric Slepian-Wolf coding. This scheme is conceptually simpler and can potentially achieve most of the points on the inner sum-rate bound. Our practical designs employ trellis coded quantization, LDPC code based asymmetric Slepian-Wolf code, and arithmetic code and turbo code based symmetric Slepian-Wolf code. Simulation results show a gap of only 0.24-0.29 bit per sample away from the inner sum-rate bound for both direct and indirect MT coding problems.

Cover and El Gamal derived the tightest bounds on the capacity of the relay channel using random coding and proposed two coding strategies, namely decode-and-forward (DF) and compress-and-forward (CF), to provide the best known lower bounds of the achievable rate region. Depending on transmission parameters, either DF or CF could be superior. Several practical code designs based on DF have appeared recently. We present the first practical CF design for the half-duplex Gaussian relay channel based on Wyner-Ziv coding of the received source signal at the relay. Assuming ideal source and channel coding, our design achieves the lower bound of CF. It thus realizes the performance gain of CF over DF promised by the theory when the relay is close to the destination. Our practical implementation based on LDPC codes for error protection at the source and nested scalar quantization and IRA (irregular repeat-accumulate) codes for Wyner-Ziv coding at the relay comes as close as 0.76 dB to the theoretical limit of CF.

Based on recent works on source-channel coding for Wyner-Ziv coding, we consider the case with noisy channel in Wyner-Ziv coding and address distributed joint source-channel coding, while targeting at the important application of video transmission over packet erasure channels. The idea is to use a single channel code for both Slepian-Wolf coding (or source coding with side information at the decoder) and erasure protection. We choose Raptor codes - the best approximation to a digital fountain - for the targeted application and study both encoder and decoder designs under the new setting of distributed joint source-channel coding. Our work represents the first in capitalizing the latest in distributed source coding (e.g., Wyner-Ziv video coding) and near-capacity channel coding (e.g., fountain codes) for robust video transmission over erasure channels.

This paper addresses near-capacity dirty-paper code designs based on TCQ and IRA codes, where the former is employed as the most efficient means of vector quantization and the latter for their capacity-approaching performance. By bringing together TCQ - the best quantizer from the source coding community and EXIT chart based IRA code designs - the best from the channel coding community, we are able to approach the theoretical limit of dirty-paper coding. For example, at 0.25 b/s, one of our code designs (with 1024-state TCQ) performs 0.83 dB away from the capacity

Layered multiple description codes allow robust transmission of scalable media data over packet erasure networks, while providing simple rate adaptation and bandwidth savings for shared bottleneck links. We show how to efficiently design layered multiple description codes for multicast and broadcast applications in memoryless packet erasure networks. Our approach offers a significantly better quality tradeoff among clients than the best previous solution.

We consider a joint source-channel coding system that protects an embedded bitstream using a finite family of channel codes with error detection and error correction capability. The performance of this system may be measured by the expected distortion or by the expected number of correctly decoded source bits. Whereas a rate-based optimal solution can be found in linear time, the computation of a distortion-based optimal solution is prohibitive. Under the assumption of the convexity of the operational distortion-rate function of the source coder, we give a lower bound on the expected distortion of a distortion-based optimal solution that depends only on a rate-based optimal solution. Then, we propose a local search (LS) algorithm that starts from a rate-based optimal solution and converges in linear time to a local minimum of the expected distortion. Experimental results for a binary symmetric channel show that our LS algorithm is near optimal, whereas its complexity is much lower than that of the previous best solution.

In this correspondence, we present iterative algorithms that numerically compute the capacity-power and rate-distortion functions for coding with two-sided state information. Numerical examples are provided to demonstrate efficiency of our algorithms.

A Slepian-Wolf coding scheme that can achieve arbitrary rate allocation among two encoders was outlined in the work of Pradhan and Ramchandran. Inspired by this work, we start with a detailed solution for general (asymmetric or symmetric) Slepian-Wolf coding based on partitioning a single systematic channel code, and continue with practical code designs using advanced channel codes. By using systematic IRA and turbo codes, we devise a powerful scheme that is capable of approaching any point on the Slepian-Wolf bound. We further study an extension of the technique to multiple sources, and show that for a particular correlation model among the sources, a single practical channel code can be designed for coding all the sources in symmetric and asymmetric scenarios. If the code approaches the capacity of the channel that models the correlation between the sources, then the system will approach the Slepian-Wolf limit. Using systematic IRA and punctured turbo codes for coding two binary sources, each being independent identically distributed, with correlation modeled by a binary symmetric channel, we obtain results which are 0.04 bits away from the theoretical limit in both symmetric and asymmetric Slepian-Wolf settings.

Priority encoding transmission is an efficient forward error correction system for the robust transmission of scalable image and video data over packet erasure networks. For a memoryless packet erasure channel we first study the sensitivity of an optimal protection solution to a change in the packet erasure rate and in the number of packets. We then propose a practical error protection algorithm for multicasting and broadcasting applications. Instead of computing an optimal solution for each client independently, we show that comparable results can be obtained much faster by refining protections already computed for other clients. We also consider the situation where clients share a bottleneck link and develop layered multiple description codes that provide a better quality trade-off among all clients than previous solutions.

Asymmetric code design for remote multiterminal source coding in the quadratic Gaussian case is presented in this paper. For remote multiterminal source coding of X, to achieve the minimum sum rate of the two independent encoders subject to a fidelity criterion d, theoretical bounds were derived independently. The main idea is to quantize the first observation Y/sub 1/ and apply Wyner-Ziv coding on Y/sub 2/ by using the quantized version of Y/sub 1/ as side information in an efficient asymmetric coding scheme. The practical code design gives results that are very close to the sum-rate bound.

This paper considers a general multiterminal (MT) system, which consists of L encoders and P decoders. Let X/sub 1/,..., X/sub L/ be memoryless, uniform, correlated random binary vectors of length n, and let x/sub 1/,..., x/sub L/ denote their realizations. Let further /spl Sigma/ = {1,...,L}. The i-th encoder compresses X/sub i/ independently from other encoders. The j-th decoder receives the bitstreams from a set of encoders /spl Sigma//sub j//spl sube/ /spl Sigma/ and jointly decodes them. It should reconstruct the received source messages with arbitrarily small probability of error. To construct a practical coding scheme for this network, we exploit the fact that such a network can be split into P subnetworks, each being regarded as a Slepian-Wolf (SW) coding system with multiple sources. This SW subnetwork consists of a decoder which receives encodings of all X/sub k/'s such that k/spl isin//spl Sigma//sub sw//spl sube//spl Sigma/ and attempts to reconstruct them perfectly. Based on (V. Stankovic et al. 2004), we first provide a code design for this setting, and then extend it to the general case.

This paper addresses practical code design problems for multiterminal communication networks. The basic element of a multiterminal network code is the binning scheme. We aim to develop a unified practical code design paradigm for related problems in multiterminal networks based on source-channel coding for algebraic binning. First, a framework based on Slepian-Wolf coded quantization is highlighted for Wyner-Ziv coding and multiterminal source coding (e.g., the CEO problem). Then, a nested turbo scheme is proposed for Costa coding by exploiting the duality between Costa coding and Wyner-Ziv coding.

Reliable real-time transmission of packetized embedded multimedia data over noisy channels requires the design of fast error control algorithms. For packet erasure channels, efficient forward error correction is obtained by using systematic Reed-Solomon (RS) codes across packets. For fading channels, state-of-the-art performance is given by a product channel code where each column code is an RS code and each row code is a concatenation of an outer cyclic redundancy check code and an inner rate-compatible punctured convolutional code. For each of these two systems, we propose a low-memory linear-time iterative improvement algorithm to compute an error protection solution. Experimental results for the two-dimensional and three-dimensional set partitioning in hierarchical trees coders showed that our algorithms provide close to optimal average peak signal-to-noise ratio performance, and that their running time is significantly lower than that of all previously proposed solutions.

We study joint source-channel coding systems for the transmission of images over varying channels without feedback. We consider the situation where the channel statistics are unknown to the transmitter and focus on systems that enable good performance over a wide range of channel conditions. We first propose a linear-time channel code rate selection algorithm for a hybrid transmission system that combines packetization of an embedded wavelet bitstream into independently decodable packets and forward error correction with a concatenated cyclic redundancy check/rate-compatible punctured convolutional (RCPC) channel coder. We then consider an extension of this hybrid system with additional Reed-Solomon (RS) coding across the packets and give a linear-time algorithm for the efficient selection of both the RS and RCPC code rates. Experimental results for a wireline/wireless link modeled as the combination of a packet erasure channel and a Rayleigh flat-fading channel showed that our schemes significantly outperformed the best previous forward error correction systems in many situations where the actual channel parameter values deviated from the ones used in the optimization of the source-channel rate allocation.

We propose a real-time optimization algorithm that selects an appropriate channel code for hybrid systems that combine packetization of an embedded wavelet bitstream into independently decodable packets and forward error correction using a family of channel codes with error detection and error correction capability. Such systems are very powerful for the transmission of audio, images, and video over fading and erasure channels with varying statistics. We also give an implementation that uses an optimal packetization technique and a concatenated cyclic redundancy check/rate-compatible punctured convolutional coder. Experimental results show that the peak signal-to-noise ratio of the average mean square error of our system is up to 1.74 dB higher than that of the previous best hybrid system for a Rayleigh fading channel and a transmission rate of 0.25 bits per pixel. Finally, we compare the hybrid approach to a state-of-the-art approach that uses a product code to protect the information bitstream.

Joint source-channel coding systems using scalable source codes and forward error correction allow reliable transmission of multimedia data over noisy channels. The performance of such systems highly depends on the source-channel bit allocation strategy. Rate-based error protection schemes, which maximize the expected source rate are very attractive for real-time applications because the optimization can be done very quickly and is independent of the source. In real-world communication, channel conditions are varying in time. Thus, it is important to frequently update the error protection. For two state-of-the-art joint source-channel coding systems, we show that a channel mismatch can lead to a poor performance. We study theoretically and experimentally the dependency of a rate-based optimal protection on the channel statistics and provide an efficient strategy for adjusting the error protection when a channel mismatch occurs.

Sherwood and Zeger (1997) proposed a source-channel coding system where the source code is an embedded bitstream and the channel code is a product code such that each row code is a concatenation of a cyclic redundancy check (CRC) and rate-compatible punctured convolutional codes (RCPC) and the column codes are Reed-Solomon (RS) codes. We improve this system for wireless applications by efficiently reorganizing the source code into a set of independently decodable packets, which makes it more robust in varying channels. We also give a linear-time algorithm for finding an optimal equal error protection for the resulting system. Experimental results show that the performance of our system significantly outperforms that of the current state-of-the-art in fading channels with varying statistics.

For optimal progressive transmission of an embedded image code over a noisy channel, we consider an unequal error protection strategy that minimizes the average of the expected distortion over a set of intermediate rates. In contrast to previous work, we find a near-optimal solution in real-time. For a binary symmetric channel, two state-of-the-art source coders (SPIHT and JPEG200), and a rate-compatible punctured turbo coder as a channel coder, we compare our solution to the strategy that optimizes the end-to-end performance.

We consider unequal error protection strategies for the efficient progressive transmission of embedded image codes over noisy channels. In progressive transmission, the reconstruction quality is important not only at the target transmission rate but also at the intermediate rates. An adequate error protection strategy may, thus, consist of optimizing the average performance over the set of intermediate rates. The performance can be the expected number of correctly decoded source bits or the expected distortion. For the rate-based performance, we prove some interesting properties of an optimal solution and give an optimal linear-time algorithm to compute it. For the distortion-based performance, we propose an efficient linear-time local search algorithm. For a binary symmetric channel, two state-of-the-art source coders (SPIHT and JPEG2000), we compare the progressive ability of our proposed solutions to that of the strategies that optimize the end-to-end performance of the system. Experimental results showed that the proposed solutions had a slightly worse performance at the target transmission rate and a better performance at most of the intermediate rates, especially at the lowest ones.

Embedded image codes are very sensitive to channel noise because a single bit error can lead to an irreversible loss of synchronization between the encoder and the decoder. P.G. Sherwood and K. Zeger (see IEEE Signal Processing Lett., vol.4, p.191-8, 1997) introduced a powerful system that protects an embedded wavelet image code with a concatenation of a cyclic redundancy check coder for error detection and a rate-compatible punctured convolutional coder for error correction. For such systems, V. Chande and N. Farvardin (see IEEE J. Select. Areas Commun., vol.18, p.850-60, 2000) proposed an unequal error protection strategy that maximizes the expected number of correctly received source bits subject to a target transmission rate. Noting that an optimal strategy protects successive source blocks with the same channel code, we give an algorithm that accelerates the computation of the optimal strategy of Chande and Farvardin by finding an explicit formula for the number of occurrences of the same channel code. Experimental results with two competitive channel coders and a binary symmetric channel showed that the speed-up factor over the approach of Chande and Farvardin ranged from 2.82 to 44.76 for transmission rates between 0.25 and 2 bits per pixel.

We consider a joint source-channel coding system that protects an embedded wavelet bitstream against noise using a finite family of channel codes with error detection and error correction capability. The performance of this system may be measured by the expected distortion or by the expected number of correctly received source bits subject to a target total transmission rate. Whereas a rate-based optimal solution can be found in linear time, the computation of a distortion-based optimal solution is prohibitive. Under the assumption of the convexity of the operational distortion-rate function of the source coder, we give a lower bound on the expected distortion of a distortion-based optimal solution that depends only on a rate-based optimal solution. Then we show that a distortion-based optimal solution provides a stronger error protection than a rate-based optimal solution and exploit this result to reduce the time complexity of the distortion-based optimization. Finally, we propose a fast iterative improvement algorithm that starts from a rate-based optimal solution and converges to a local minimum of the expected distortion. Experimental results for a binary symmetric channel with the SPIHT coder and JPEG 2000 show that our lower bound is close to optimal. Moreover, the solution given by our local search algorithm has about the same quality as a distortion-based optimal solution, whereas its complexity is much lower than that of the previous best solution.

Unequal loss protection with systematic Reed-Solomon codes allows reliable transmission of embedded multimedia over packet erasure channels. The design of a fast algorithm with low memory requirements for the computation of an unequal loss protection solution is essential in real-time systems. Because the determination of an optimal solution is time-consuming, fast suboptimal solutions have been used. In this paper, we present a fast iterative improvement algorithm with negligible memory requirements. Experimental results for the JPEG2000, 2D, and 3D set partitioning in hierarchical trees (SPIHT) coders showed that our algorithm provided close to optimal peak signal-to-noise ratio (PSNR) performance, while its time complexity was significantly lower than that of all previously proposed algorithms.

State-of-the-art systems for the transmission of images over wireless channels generate an embedded bitstream and protect it with a product code where the row code is a concatenation of an outer cyclic redundancy check (CRC) code and an inner rate-compatible punctured convolutional (RCPC) code, and the column code is a Reed-Solomon (RS) code. In previous works, the product code was optimized by searching for the best RS protection for each RCPC code rate. We present a local search algorithm that jointly optimizes the RS and the RCPC codes. Experimental results show that our algorithm provides an approximately optimal solution, while its time complexity is much lower than that of the previous works.

Fractal image codes are very sensitive to bit errors because the decoding of a block is dependent not only on the code information associated to this block but also on the code information associated to other blocks. We analyze the sensitivity of a fractal code to transmission errors in a binary symmetric channel. We provide two rate-distortion unequal error protection techniques that allocate the code bits to protection classes in a nearly optimal way. We give an implementation for BCH and RCPC channel codes and show that rate-compatible punctured convolutional (RCPC) codes are preferable. For a binary symmetric channel bit error probability of 0.1 and a total code rate of 0.5 bpp, the loss in reconstruction quality with our best implementation was about 3.38 dB for the 512 /spl times/ 512 Lenna image, yielding a PSNR of 27.12 dB.