Remote Patient Monitoring systems (RPMs) are becoming increasingly important as the aging populationstruggles to manage chronic illnesses and secure consistent healthcare access. While these systems excel in tracking physical metrics, integrating emotion recognition can bridge the often-overlooked connection between emotional and physical well-being. By identifying emotional responses such as discomfort, distress, or contentment, RPM systems can provide immediate feedback for care adjustments and reveal long-term trends. Subtle shifts in emotional patterns may act as early indicators of mental health conditions linked to chronic diseases or transient emotional stress. Expanding RPMs to include emotion awareness makes care more adaptive and holistic. This review explores how emotion recognition can enhance RPM systems by addressing both physical and emotional health. It examines methods that leverage physiological and behavioral responses to capture emotional states, assessing the advantages, limitations, and applicability of these modalities in RPM settings. By incorporating emotion-aware tools, RPMs have the potential to deliver more comprehensive, responsive, and personalized care, revolutionizing healthcare delivery for diverse patient groups.

Federated Learning (FL) is a privacy-preserving algorithm that enables multiple clients to collaboratively train a global model without sharing their local data. This learning algorithm is particularly valuable in privacy-sensitive applications such as Human Activity Recognition (HAR), where users are reluctant to share their personal data. However, a conventional FL system suffers from data heterogeneity and communication overhead. To address these issues, we propose an efficient FL algorithm for image-based HAR using optimal server representations (OSR). OSR efficiently selects a representative set of privacy-preserved images for transmission to the server and improves the global model quality by training on privacy-preserved data. Our comprehensive experiments carried out on three public datasets, namely Stanford40, PPMI, and VOC2012, demonstrate the superiority of OSR in terms of performance and bandwidth usage compared to state-of-the-art approaches.

Recent Diffusion Transformers (DiTs) have shown impressive capabilities in generating single-modality content, including images, videos, and audio. However, the potential of DiTs to enable superb multimodal content creation remains underexplored. To bridge this gap, we introduce AV-DiT, a novel and efficient audio-visual diffusion transformer designed to generate high-quality, realistic videos with synchronized audio tracks. To minimize model complexity and computational costs, our AV-DiT utilizes a modality-shared DiT backbone pre-trained on image-only data, with only newly inserted adapters being trainable. This shared backbone facilitates the generation of both audio and video. Specifically, the video branch incorporates a trainable temporal attention layer into a pre-trained DiT block for capturing the temporal consistency for video generation. In addition, a small number of trainable parameters adapt the image-based DiT block to learn the acoustic characteristics for audio generation. An extra shared self-attention block reused from the DiT block, equipped with lightweight parameters, facilitates feature interaction between audio and visual modalities for alignment. Extensive experiments on the datasets demonstrate that our AV-DiT achieves state-of-the-art performance in joint audio-visual generation with significantly fewer tunable parameters. Furthermore, our results highlight that a single shared image generative backbone with modality-specific adaptations is sufficient for constructing a joint audio-video generator.

In this work, we investigate how to transfer learned knowledge from pre-trained image models for the audio-visual domain without relying on a full finetuning paradigm. To achieve this objective, we propose a novel parameter-efficient scheme called Mixture-of-Modality-Adaptations (MoMA) for audio-visual action recognition, which consists of the dual-path spatial-temporal adaptation for visual modality, the acoustic-aware adaptation for audio modality, and the audio-visual multimodal adaptation for interacting different modalities. Through freezing the original parameters of pre-trained image backbones and introducing lightweight parameter-efficient adapters, our proposed MoMA efficiently adapts the image models to learn audio-visual representation without employing any audio-specific encoders and full finetuning. The experimental results on the action recognition benchmarks indicate that our MoMA achieves competitive or even better performance than existing methods while involving significantly fewer tunable parameters.

Electroencephalography (EEG) based emotion recognition is gaining substantial interest because of its strong association with the area of brain-computer interface. Even though several works exist in the literature, it is still challenging to find discriminative features that can generalize well to different EEG datasets. In this work, we focus on developing a deep learning model that makes use of the spatial and temporal representations of the EEG signal to generate EEG embeddings for emotion recognition. The proposed model uses a self-attention mechanism along with a feature fusion approach to improve the discrimination power of the learned EEG embeddings. Comprehensive experiments are conducted on the DEAP dataset, which demonstrates the superiority of the proposed work, where the attained accuracies for the arousal and valence classification are 91.17% and 90.73% , respectively.

High-frequency components are the most crucial parts of the visual signals for the task of image super resolution. The deep image super resolution networks that are able to process the high-frequency components efficiently can provide high performances. In view of this, in this paper, we develop a new residual block for image super resolution, in which the feature attention process is carried out by focusing on various high-frequency components of the feature tensors. Specifically, we design a novel multi-dimensional filter design technique for the task of image super resolution, and employ it for obtaining a finite impulse response (FIR) high-pass filter bank to be embedded in a deep super resolution network for the feature attention process. Moreover, we utilize two other feature attention processes in the proposed residual block, namely, multi-scale transformerbased and convolutional learnable feature attention mechanisms, to generate rich sets of feature maps for a deep super resolution network. The results of different experiments demonstrate the effectiveness of the various modules of the proposed residual block in enhancing the super resolution performance

Stress is an inevitable part of modern life. While stress can negatively impact a person's life and health, positive and under-controlled stress can also enable people to generate creative solutions to problems encountered in their daily lives. Although it is hard to eliminate stress, we can learn to monitor and control its physical and psychological effects. It is essential to provide feasible and immediate solutions for more mental health counselling and support programs to help people relieve stress and improve their mental health. Popular wearable devices, such as smartwatches with several sensing capabilities, including physiological signal monitoring, can alleviate the problem. This work investigates the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict people's stress status and identify possible factors impacting stress classification accuracy. We use data collected from wrist-worn devices to examine the binary classification discriminating stress from non-stress. For efficient classification, five machine learning-based classifiers were examined. We explore the classification performance on four available EDA databases under different feature selections. According to the results, Support Vector Machine (SVM) outperforms the other machine learning approaches with an accuracy of 92.9 for stress prediction. Additionally, when the subject classification included gender information, the performance analysis showed significant differences between males and females. We further examine a multimodal approach for stress classifications. The results indicate that wearable devices with EDA sensors have a great potential to provide helpful insight for improved mental health monitoring.

This paper presents a human activity recognition (HAR) system with wearable devices. While various approaches have been suggested for HAR, most of them focus on either 1) the inertial sensors to capture the physical movement or 2) subject-dependent evaluations that are less practical to real world cases. To this end, our work integrates sensing in-puts from physiological sensors to compensate the limitation of inertial sensors in capturing the human activities with less physical movements. Physiological sensors can capture physiological responses reflecting human behaviors in executing daily activities. To simulate a realistic application, three different evaluation scenarios are considered, namely All-access, Cross-subject and Cross-activity. Lastly, we propose a Hierarchical Deep Learning (HDL) model, which improves the accuracy and stability of HAR, compared to conventional models. Our proposed HDL with fusion of inertial and physiological sensing inputs achieves 97.16%, 92.23%, 90.18% average accuracy in All-access, Cross-subject, Cross-activity scenarios, which confirms the effectiveness of our approach.

The recent pandemic has brought tremendous changes to everyone’s life, causing stress about losing loved ones, losing jobs, and having changes in sleep or eating habits. This study investigates the feasibility of utilizing Electrodermal Activity (EDA) collected from wearable devices to detect people’s stress. EDA can quantify the changes in sympathetic dynamics by measuring sweat produced by our sweat glands. Currently, the adoption of EDA sensors to commercially off-the-shelf smart-watches is still in the infancy stage, and only a few brands have the EDA sensors implemented into their smartwatch. To facilitate our feasibility study, we need the datasets that contain the EDA signals collected from wearable devices. This paper uses two publicly available datasets containing the EDA signals collected from research-grade wearable devices. We cast the stress detection problem as a binary classification problem and trained the classifiers with three popular machine learning methods: K-Nearest Neighbor, Logistic Regression, and Random Forests. According to experimental results, Random Forests achieves an accuracy of 85.7% to classify stress from non-stress status. The results verified that wearable devices with EDA sensors have the potential to predict stress status.

This paper investigates the employment of photoplethysmography (PPG) for user authentication systems. Time-stable and user-specific features are developed by stretching the signal, designing a convolutional neural network and performing a variation-stable approach with three score fusions. Two evaluation scenarios are explored, namely single-session and two-sessions. In the earlier, the training and testing are done solely on one session data to find the user-specific features, while the second scenario is performed on data from two different sessions to test the time permanence of the features. The verification system was tested on four databases achieving an accuracy of 100% for single-session and 87.3% for two-sessions cases. The simulation results confirm the effectiveness of proposed variation-stable fusion which can be extended to other biometrics. The code is available in [1].

Post-traumatic stress disorder is a major public health concern with a lifetime prevalence rate of 6.1-9.2% in North America. PTSD is known to alter the autonomic nervous system leading to chronic sympathetic arousal including heightened anxiety and hypervigilance. Pupillometry offers a quick and accessible measure of autonomic nervous system imbalances characteristic of PTSD. This study investigates the utility of pupillometry as a biomarker to detect PTSD in a sample of 39 adults with (n = 22) and without (n = 17) PTSD. Participants viewed a 25-minute computer protocol consisting of 5-minute rest phase, 10-minute negative emotionally valent images, and 10-minute guided meditation. We relied on a time-frequency analysis to represent the pupillary responses of two different groups (PTSD-affected individuals and healthy-control subjects). These data were then employed with a CNN network to learn a prediction model. Individuals with PTSD demonstrated increased pupil dilation across the entire protocol. The final outcome revealed an accuracy of 81.09% which indicates the feasibility of using this approach to detecting participants with PTSD in an automated way. Findings from this research have important implications for clinical mental health assessment, diagnostics and treatment.

In many real-world scenarios, data from multiple modalities (sources) are collected during a development phase. Such data are referred to as multiview data. While additional information from multiple views often improves the performance, collecting data from such additional views during the testing phase may not be desired due to the high costs associated with measuring such views or, unavailability of such additional views. Therefore, in many applications, despite having a multiview training data set, it is desired to do performance testing using data from only one view. In this paper, we present a multiview feature selection method that leverages the knowledge of all views and use it to guide the feature selection process in an individual view. We realize this via a multiview feature weighting scheme such that the local margins of samples in each view are maximized and similarities of samples to some reference points in different views are preserved. Also, the proposed formulation can be used for cross-view matching when the view-specific feature weights are pre-computed on an auxiliary data set. Promising results have been achieved on nine real-world data sets as well as three biometric recognition applications. On average, the proposed feature selection method has improved the classification error rate by 31 percent of the error rate of the state-of-the-art.

The photoplethysmography (PPG) is a non-invasive physiological signal that captures the changes in blood volume resulted from heart activity. It carries unique person-specific characteristics that can be utilized for biometric systems. Currently, the use of a biometric system is crucial to ensure the security of the user’s identity. Due to the high sensitivity of the PPG signal, it suffers from extreme variations within the same subject when obtained at different time instances. These variations impose a challenge to employ the PPG signal and hinder the algorithm generalization for many applications including verification and identification systems. In this work, we propose a PPG Biometric Generative Adversarial Network (PBGAN) to create synthetic person-specific and time-stable PPG signals for genuine samples. Two types of classification models are employed with the PBGAN where the focus is on verification scenarios. In addition, we expand our previously recorded PPG dataset from 100 to 170 participants where the new size guarantees the generalization capability of the proposed system. This database along with three public ones are employed to evaluate the performances in terms of uniqueness and time stability. Furthermore, we consider three different training strategies to simulate practical scenarios. The best results acquired from our collected database in terms of Equal Error Rate (EER) is 1.3% for the single-session and 11.5% for the two-sessions scenarios which demonstrate the effectiveness of the proposed method in improving the verification system’s performance. Compared to our previous work, we achieve 1.3% and 1.4% EER improvements in two-sessions’ databases with small computational times which reveals the superiority of our proposed approach for real applications.

In this work, we demonstrates the feasibility of employing the biometric photoplethysmography (PPG) signal for human verification applications. The PPG signal has dominance in terms of accessibility and portability which makes its usage in many applications such as user access control very appealing. Therefore, we developed robust time-stable features using signal analysis and deep learning models to increase the robustness and performance of the verification system with the PPG signal. The proposed system focuses on utilizing different stretching mechanisms namely Dynamic Time Warping, zero padding and interpolation with Fourier transform, and fuses them at the data level to be then deployed with different deep learning models. The designed deep models consist of Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) which are considered to build a user specific model for the verification task. We collected a dataset consisting of 100 participants and recorded at two different time sessions using Plux pulse sensor. This dataset along with another two public databases are deployed to evaluate the performance of the proposed verification system in terms of uniqueness and time stability. The final result demonstrates the superiority of our proposed system tested on the built dataset and compared with other two public databases. The best performance achieved from our collected two-sessions database in terms of accuracy is 98% for the single-session and 87.1% for the two-sessions scenarios.

In recent years, there has been a tremendous growth worldwide in healthcare information systems to provide personalized services smartly. A digital health documentary EHR (Electronic health record) is utilized to keep users sensitive medical or personal data records, which allows medical professionals to access a patient's information in an insecure environment. Thus, providing security and privacy to e-health information is of utmost importance as private sensitive or safety critical data of the users is transmitted over a wireless channel. Motivated by this fact, in this work, we have developed a biometric based lightweight user authentication system that provides users personalized services securely, safely and efficiently. In the proposed authentication, a lightweight data access control process has been described so that only legal users can access the data as per their capability. Further, to maintain user privacy, instead of a user's global identifier, his temporary local identifier is used for communication whereas the system is designed in such a way that in case of emergency, if required, the user global identifier can be recovered. Finally, formal and informal security verification results and performance evaluation comparison demonstrates that the proposed authentication scheme is secure enough to be used in a healthcare environment.

In this paper, we propose an approach to learn generic multi-modal mesh surface representations using a novel scheme for fusing texture and geometric data. Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented images (FGAI). This new fused modality enables us to learn feature representations from 3D data in a highly efficient manner by simply employing standard CNNs in a transfer-learning mode. The proposed approach is both computationally and memory efficient, preserves intrinsic geometric information and learns highly discriminative feature representations by effectively fusing shape and texture information at data level. The efficacy of our approach is demonstrated for the tasks of facial action unit detection and expression classification. The extensive experiments conducted on the Bosphorus and BU-4DFE datasets show that our method produces a significant boost in the performance when compared to state-of-the-art solutions.

This study presents a deep neural network architecture that achieves state of the art multi-session verification and identification performance for Transient Evoked Otoacoustic Emission (TEOAE) biometric system. TEOAE is a 20ms long response generated by the ear that is naturally strong against falsification, and replay attacks. It can be measured using a device with a speaker and multiple microphones. Previous TEAOE authentication methods focused on single-session or mixed-session performance. Our method focuses on multi-session authentication performance. We train a neural network model that generates a TEOAE embedding that is separable in Euclidean space by using the triplet loss function. These embeddings are used to create identity templates which are used to authenticate the user. We achieved identification accuracy of 99.3 ± 1.04%, and achieved an EER(Equal Error Rate) of 0.187 ± 0.146% for verification scenarios. Our method has achieved 7.56% performance increase for identification scenarios and 13.3% performance increase for verification scenarios over previous methods when averaged across all tests.

In recent years, brainwaves (EEG) have gained increasing attention in the field of biometric authentication because they feature vital advantages being more secure and impossible to replicate. In this paper, a new approach for the EEG-based biometric recognition system is proposed using steady-state Auditory Evoked Potentials (AEPs). This class of modular brainwaves adds extra features to the system like cancelability and two-step authentication. To investigate the biometric potential of AEPs, brainwaves from 40 subjects were recorded while being stimulated by multiple auditory tones modulated at two frequency bands; 40 Hz (m-40) and 80 Hz (m-80). Each subject participated in two sessions on two different days for time-permanence evaluation. Brain-Computer Interface (BCI) techniques were adopted here for the rapid estimation of the AEPs using canonical correlation analysis. The energy distribution of the AEPs in different frequency bands represented the subject-unique features. For intra-session setup, correct recognition rates up to 96.46% and equal error rates as low as 0% were achieved using the m-80 stimulation over all the 40 subjects. Moreover, results across different sessions showed high recognition rates (94.5 - 96.5%) and low error rates (2 - 4%) over the same number of subjects. These results show that AEPs carry subject discriminating features allowing the possibility of employing AEPs as a biometric trait.

In this paper, the biometric potential of eye movement and eye blinking for human recognition task is investigated. These modalities might be useful for specific biometric applications like driver authentication for law enforcement. For this purpose, a database of 22 subjects was build where eye movements and blinks were recorded using Gazepoint GP3 while users were watching real driving sessions. Eye movement features were extracted from eye fixations and saccades separately. Eye blinking features include the blink pattern, its speed and acceleration patterns, and time delineation features. Evaluation of each modality was investigated first, then, both modalities are combined in a multi-modal setup for performance improvement. Although the employment of eye movement or eye blinking separately as a biometric trait might not be secure enough, the fusion of both traits achieves higher levels of identification which are comparable to that of other conventional biometric traits like fingerprint.

Person re-identification is a well-known technique for recognizing a person, who is continuously observed by nonoverlapping cameras in a wide area surveillance. Body part-based re-identification gives a higher level of importance to certain parts of body instead of giving equal importance to whole body. This is mainly due to the fact that certain body parts can be distinguishable better than the others in presence of variable viewing positions, occlusions, illuminations and resolutions of the scenes. This paper focuses on the evaluation of the discrimination power of three body parts, viz., head, torso, and leg for their potential use in fusion-based re-identification technique. In particular, the transformation of features between two body parts is conducted in a warp function space that consists of positive cost for same pair of targets and negative cost for different pair of targets. The support vector machine-based classifier is used to discriminate these two types of cost and the results are evaluated in terms of correct recognition for a given false alarm rate. Experiments are conducted on two publicly available databases, namely, CAVIAR4ReID and CUHK01. The results conclude that torso is the most important body part in the problem of person re-identification.

Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.

Fingerprints have been extensively used for biometric recognition around the world. However, fingerprints are not secrets, and an adversary can synthesis a fake finger to spoof the biometric system. The mainstream of the current fingerprint spoof detection methods are basically binary classifier trained on some real and fake samples. While they perform well on detecting fake samples created by using the same methods used for training, their performance degrades when encountering fake samples created by a novel spoofing method. In this paper, we approach the problem from a different perspective by incorporating electrocardiogram (ECG). Compared with the conventional biometrics, stealing someone's ECG is far more difficult if not impossible. Considering that ECG is a vital signal and motivated by its inherent liveness, we propose to combine it with a fingerprint liveness detection algorithm. The combination is natural as both ECG and fingerprints can be captured from fingertips. In the proposed framework, the ECG and fingerprint are combined not only for authentication purpose but also for liveness detection. We also examine automatic template updating using ECG and fingerprint. In addition, we propose a stopping criterion that reduces the average waiting time for signal acquisition. We have performed extensive experiments on the LivDet2015 database which is presently the latest available liveness detection database and compare the proposed method with six liveness detection methods as well as 12 participants of LivDet2015 competition. The proposed system has achieved a liveness detection equal error rate (EER) of 4.2% incorporating only 5 s of ECG. By extending the recording time to 30 s, liveness detection EER reduces to 2.6% which is about 4 times better than the best of six comparison methods. This is also about 2 times better than the best results achieved by the participants of the LivDet2015 competition.

This paper proposes an anaesthesia monitoring system that accurately measures the depth of anaesthesia through 40-Hz auditory steady-state response. With accurate and fast depth of anaesthesia measuring, the monitor can reduce the incidence of awareness during surgical operation. The proposed denoising method for extracting 40-Hz auditory steady-state cycles, adaptive multilevel wavelet denoising, enabled the system to extract auditory steady-state response cycles from fewer epochs and over short periods of time which is of crucial importance in monitoring anaesthesia. The noise estimation scheme, adaptive threshold levels, rearranging, and multilevel denoising of frames increase the accuracy and signal to noise ratio of the extracted cycles. The modified fuzzy c-means clustering scheme, proposed to improve clustering performance in noisy data bases where no prior information about the level of noise and signal energy is available, is used for clustering the auditory steady-state cycles. Weighting the features with a novel algorithm and based on their differentiating role in clustering, the modified fuzzy c-means improves fuzziness in cluster partitions and the geometrical structure of the data. An index called depth of anaesthesia index is defined and determined at each cycle based on the clustering information of the cycle and the previous ones. The algorithm is applied to auditory steady-state response signals recorded from 20 human subjects during surgical operations with Propofol-induced general anaesthesia. The accuracy of the depth of anaesthesia index is validated through the subjects' medical markers, clinical parameters, and the recorded bispectral index during the induction phase. Depth of anaesthesia index is verified to be accurate and able to detect fast transitions between different levels of anaesthesia. The computed depth of anaesthesia indices detected the induction of anaesthesia on average 55 s faster than bispectral index and 17 s earlier than loss of eyelash reflex.

This paper presents a novel method for extracting auditory steady state response (ASSR) signals from background electroencephalogram. 40-Hz ASSR signals are sensitive to subject's state of consciousness and can be used as a monitor for the depth of anaesthesia. The suggested method is a multilevel adaptive wavelet denoising scheme that extracts ASSR cycles faster than the currently used averaging schemes and can monitor depth of anesthesia with minimum delay. It estimates the variance of noise and adapts the threshold at each denoising level. The algorithm benefits from the fact that wavelet transform preserves temporality and takes into consideration the correlation of the neighbor wavelet coefficients. Our method extracts ASSR from small number of epochs in a short time moreover, it does not neglect the variations of the signal from one epoch to the other and outperforms averaging. The performance of the proposed scheme is evaluated on the synthetic and on real data recorded during induction of anaesthesia ASSR signals in the paper.

The objective of a continuous authentication system is to continuously monitor the identity of subjects using biometric systems. In this paper, we proposed a novel feature extraction and a unique continuous authentication strategy and technique. We proposed One-Dimensional Multi-Resolution Local Binary Patterns (1DMRLBP), an online feature extraction for one-dimensional signals. We also proposed a continuous authentication system, which uses sequential sampling and 1DMRLBP feature extraction. This system adaptively updates decision thresholds and sample size during run-time. Unlike most other local binary patterns variants, 1DMRLBP accounts for observations' temporal changes and has a mechanism to extract one feature vector that represents multiple observations. 1DMRLBP also accounts for quantization error, tolerates noise, and extracts local and global signal morphology. This paper examined electrocardiogram signals. When 1DMRLBP was applied on the University of Toronto database (UofTDB) 1,012 single session subjects database, an equal error rate (EER) of 7.89% was achieved in comparison to 12.30% from a state-of-the-art work. Also, an EER of 10.10% was resulted when 1DMRLBP was applied to UofTDB 82 multiple sessions database. Experiments showed that using 1DMRLBP improved EER by 15% when compared with a biometric system based on raw time-samples. Finally, when 1DMRLBP was implemented with sequential sampling to achieve a continuous authentication system, 0.39% false rejection rate and 1.57% false acceptance rate were achieved.

Recently Electrocardiogram (ECG) has been proposed as a biometric modality which offers liveliness detection. The fact that ECG is a vital signal makes it challenging to work with as it is affected by physical and psychological changes. In realistic applications, this type of biometrics still needs to be verified in conditions related to the practical use. In real life our body posture changes frequently, therefore in the context of a biometric system our body posture may be different in enrolment and verification which can potentially decrease the performance of the system. In this paper we first investigate the effect of the body posture on the accuracy of ECG biometric systems. Second, a new method is presented that is able to clearly distinguish the ECG signal of different postures of an individual. Finally, we propose a posture-detection verification system in order to mitigate the effect of body posture by first detecting the posture of a subject and then identifying it.

The increasing availability of low-cost hardware along with the rapid growth of wireless devices has enabled the development of Wireless Multimedia Sensor Networks (WMSNs). Multimedia content such as video and audio streaming is transmitted over a WMSN which can easily be deployed with low cost. However, enabling real-time data applications in those networks demands not only Quality of Service (QoS) awareness, but also efficient energy management. Sensor network devices have limited energy resources. The limited energy poses significant threats on the QoS of WMSNs. In this paper, to improve the efficiency of QoS-aware routing, we examine an angle-based QoS and energy-aware dynamic routing scheme designed for WMSNs. The proposed approach uses the inclination angle and the transmission distance between nodes to optimize the selection of the forwarding candidate set and extend network lifetime. Simulation results indicate that considerable lifetime values can be achieved.

Recently, the demand for more robust protection against unauthorized use of mobile devices has been rapidly growing. This paper presents a novel biometric modality Transient Evoked Otoacoustic Emission (TEOAE) for mobile security. Prior works have investigated TEOAE for biometrics in a setting where an individual is to be identified among a pre-enrolled identity gallery. However, this limits the applicability to mobile environment, where attacks in most cases are from imposters unknown to the system before. Therefore, we employ an unsupervised learning approach based on Autoencoder Neural Network to tackle such blind recognition problem. The learning model is trained upon a generic dataset and used to verify an individual in a random population. We also introduce the framework of mobile biometric system considering practical application. Experiments show the merits of the proposed method and system performance is further evaluated by cross-validation with an average EER 2.41% achieved.

Electrocardiogram (ECG) biometrics are becoming increasingly popular. Numerous approaches to ECG processing have been proposed over the past years and the field has drawn significant attention from the biometrics community. However, less attention has been paid to developing a standard for ECG biometric testing for the evaluation of such algorithms. This paper proposes a set of standards for ECG signal recording and presents the UofT ECG Database (UofTDB) in order to evaluate the performance of various ECG biometric methods. Compared to existing databases, the UofTDB has three important characteristics namely, large population size (1012 individuals), varying body postures, physical exercise and acquisition over a long period. A promising equal error rate of under 5% is also reported.

This work evaluates feasibility of using electrocardiogram (ECG) signals from fingertips for biometrics. These non-intrusive easily acquired signals are tested for performance in verification mode using a large publicly available database containing fingertip ECG. Two methodologies are presented for the design of an authentication system for scalable access control wherein we propose use of Non-Negative Sparse Coding followed by Linear Discriminant Analysis. Furthermore, based on the application scenario, two classifier methods are proposed. We compare our system with AC/LDA for performance in large-scale deployment scenarios and scalability. For our methods, promisingly low equal error rates are obtained - in particular, 2.59% for a population size of 1012 users.

Dynamic routing is considered an attractive solution for many network applications such as monitoring and tracking. In dynamic routing the path between the source and the destination can change dynamically following the network conditions. Next node selection criteria can have a great impact on the selection of each path and as a consequence on the network performance. Moreover, each network application may have different Quality of Service (QoS) requirements. In this paper, we examine the impact of the next node selection criteria on the network performance. Three routing protocols with different approaches along with their routing algorithms are presented. To examine the performance of each approach a discrete event simulator is used. It is shown that the selection criteria should be carefully designed following the QoS requirements of the network application. Furthermore, the obtained performance results can be used as an indicator of the appropriateness of a dynamic protocol for a variety of applications.

Scalability and sustainability are two fundamental requirements in Wireless Sensor Networks (WSNs). Inch scale sensor nodes can operate unattended for long periods if they have sufficient energy sources. In this work, a Self-Powered Sensor Network (SPSN) testbed is introduced. SPSN is cost-efficient and has large-scale deployability. It combines cognitive networking principles with efficient routing approaches and energy harvesting techniques. SPSN is used for indoor CO2 monitoring as well as for outdoor gas leak detection. The performance of the system for channel estimation at an outdoor environment is examined. Experimental results show that SPSN can be used for a plethora of other applications as well.

An appealing solution for unattended surveillance and monitoring applications is Self-powered Wireless Sensor Networks (WSNs). One of the main reasons is that the energy which is derived from power harvesting can significantly extend the network lifetime. Consequently, the network can work unattended for long periods. However, WSNs are characterized by multi-hop lossy links and resource constrained nodes while they have to face the coexistence problem with other applications. Opportunistic Routing (OR) is a routing paradigm to improve network performance in lossy wireless networks. At the same time, Cognitive Radio (CR) technology enables unlicensed operation in licensed bands. In this work, a combination of these two research approaches in a novel routing protocol is presented. A Spectrum and Energy Aware Opportunistic Routing (SEA-OR) protocol is proposed and designed for Self-powered WSNs. Moreover, a prioritization scheme which balances the packet advancement, the residual energy and the link reliability is introduced. Preliminary results show an improvement in network lifetime and delivery ratio. The performance of the introduced protocol is also evaluated in prototypes.

Wireless Sensor Networks are increasingly employed as unobtrusive and infrastructureless networks in both indoor and outdoor environments. In order to reach their full potential, a number of key issues, such as localization and topology control, need to be addressed. However, the performance of these protocols is significantly impacted by the assumptions made about the underlying physical layer. Realistic radio propagation models, used as the physical layer models, provide a more accurate evaluation of protocols when performing network simulations. This paper therefore analyzes the performance of a number of propagation models in a real indoor environment. Specifically, the Unit Disk, Lognormal Shadowing, Volcano Indoor Multi-Wall and WINNER II Stochastic channel models are investigated. Field measurements are performed in an office building to empirically determine the channel parameters and evaluate the models based on various error metrics. A network connectivity analysis is also performed using Monte Carlo simulations to demonstrate the impact the choice of the physical layer model has on the network backbone construction. This paper shows that the Volcano Indoor Multi-Wall and WINNER II Stochastic channel models provide better estimate of the actual path losses in an indoor environment. It also shows that the errors introduced can cause connectivity algorithms to significantly under-estimate (sometimes up to a 14 times under-estimation) the power requirements necessary to guarantee a connected network.

Subject monitoring and tracking is one of the most appealing classes of applications for Wireless Sensor Networks (WSNs). Numerous inch scale nodes can sense, collect and distribute crucial information with low deployment cost. For instance, the movement of endangered species in a national park can be monitored with a WSN. However, in such applications privacy issues can jeopardize the successful deployment of the network. An adversary might trace the network traffic over the same paths and eventually locate the source in the network. In this paper, we propose a source-location privacy scheme that employs randomly selected intermediate nodes based on inclination angles. The introduced Angle-based Dynamic Routing Scheme (ADRS) is analysed and is compared with the Phantom Single-path Routing Scheme (PSRS). Simulation results demonstrate that ADRS improves the safety period and the packet latency.

Biometrics is attracting increasing attention in privacy and security concerned issues, such as access control and remote financial transaction. However, advanced forgery and spoofing techniques are threatening the reliability of conventional biometric modalities. This has been motivating our investigation of a novel yet promising modality transient evoked otoacoustic emission (TEOAE), which is an acoustic response generated from cochlea after a click stimulus. Unlike conventional modalities that are easily accessible or captured, TEOAE is naturally immune to replay and falsification attacks as a physiological outcome from human auditory system. In this paper, we resort to wavelet analysis to derive the time-frequency representation of such nonstationary signal, which reveals individual uniqueness and long-term reproducibility. A machine learning technique linear discriminant analysis is subsequently utilized to reduce intrasubject variability and further capture intersubject differentiation features. Considering practical application, we also introduce a complete framework of the biometric system in both verification and identification modes. Comparative experiments on a TEOAE data set of biometric setting show the merits of the proposed method. Performance is further improved with fusion of information from both ears.

This paper addresses the challenges of evaluating electrocardiogram (ECG) biometric recognition systems. While this new biometric modality has attracted significant interest, a majority of the prior art has approached it from the perspective of a typical biometric modality and has neglected the physiological factors that directly affect the behavior of the respective systems. In an effort to bring to the table the idiosyncratic properties of the ECG biometric modality, this paper presents the UofT ECG database and offers a comprehensive analysis of the underlying interindividual variability under a number of conditions, such as body posture, physical activity, and time lapse. The performance of various methodologies is reported under the above-mentioned conditions and a method based on template fusion is proposed to address these shortcomings.

In this demonstration proposal we describe a prototype of a Wireless Sensor Network (WSN) for monitoring the air quality of an arbitrary indoor infrastructure environment. Specifically, the proposed demonstration deals with an application of wireless mesh networks for monitoring the carbon dioxide (CO2) levels of an indoor environment, supporting guaranteed real-time data acquisition and display. In the proposed demonstration we will illustrate a number of advantages of opportunistic routing, including dynamic node deployment and dynamic routing path selection, opportunistic resource utilization, robustness to interference and guaranteed multi-hop QoS (Quality of Service) for an indoor gas concentration monitoring network.

In existing ECG-based biometric recognition systems, the feature extraction and matching are performed in Euclidean spaces. However, there are many scenarios (e.g., biometric template encryption for privacy protection, or low-complexity classification in an identification mode of operation) in which it is useful to binarize the feature vectors. The main contribution of this paper is a Hamming-distance classifier for ECG biometrics based on SPEC-Hashing. The proposed system was evaluated over a database of ECG signals from 52 different subjects that were collected at the Biometrics Security Laboratory of the University of Toronto. The EER of the Hamming-distance classifier was found to be 5.5% for closed-set matching and 14.82% for open set matching.

Under the cognitive networking architecture, this paper presents an opportunistic routing protocol for cognitive radio in Wireless Sensor Networks (WSNs), which can deliver higher performance and efficiency in multihop wireless communications. Cognitive Networking with Opportunistic Routing, (CNOR), opportunistically routes traffic across paths over all available spectrum. A discrete event simulator is applied to evaluate and compare the proposed scheme against three other routing protocols: traditional routing with single channel, traditional routing with multiple channels and opportunistic routing with single channel. It is shown that by integrating opportunistic routing with cognitive radio much better results can be obtained, with respect to energy consumption, throughput and latency.

Survivability is crucial in Wireless Sensor Networks (WSNs) especially when they are used for monitoring and tracking applications with limited available resources. In this paper we are proposing the use of an energy Efficient Cognitive Unicast Routing (ECUR) protocol that tries to keep a balance between the energy consumption and the packet delay in a WSN. The proposed routing protocol has a next node selection criterion to change the routing path dynamically following the network conditions and the channel availability while the energy consumption per node is also considered. Simulation results are presented that show an increase in network lifetime of up to 30% compared with geographic opportunistic routing while the packet delay remains similar.

The common approach for 3D face recognition is to register a probe face to each of the gallery faces and then calculate the sum of the distances between their points. This approach is computationally expensive and sensitive to facial expression variation. In this paper, we introduce the iterative closest normal point method for finding the corresponding points between a generic reference face and every input face. The proposed correspondence finding method samples a set of points for each face, denoted as the closest normal points. These points are effectively aligned across all faces, enabling effective application of discriminant analysis methods for 3D face recognition. As a result, the expression variation problem is addressed by minimizing the within-class variability of the face samples while maximizing the between-class variability. As an important conclusion, we show that the surface normal vectors of the face at the sampled points contain more discriminatory information than the coordinates of the points. We have performed comprehensive experiments on the Face Recognition Grand Challenge database, which is presently the largest available 3D face database. We have achieved verification rates of 99.6 and 99.2 percent at a false acceptance rate of 0.1 percent for the all versus all and ROC III experiments, respectively, which, to the best of our knowledge, have seven and four times less error rates, respectively, compared to the best existing methods on this database.

Discriminant analysis methods are powerful tools for face recognition. However, these methods cannot be used for the single sample per person scenario because the within-subject variability cannot be estimated in this case. In the generic learning solution, this variability is estimated using images of a generic training set, for which more than one sample per person is available. However, because of rather poor estimation of the within-subject variability using a generic set, the performance of discriminant analysis methods is yet to be satisfactory. This problem particularly exists when images are under drastic facial expression variation. In this paper, we show that images with the same expression are located on a common subspace, which here we call it the expression subspace. We show that by projecting an image with an arbitrary expression into the expression subspaces, we can synthesize new expression images. By means of the synthesized images for subjects with one image sample, we can obtain more accurate estimation of the within-subject variability and achieve significant improvement in recognition. We performed comprehensive experiments on two large face databases: the Face Recognition Grand Challenge and the Cohn-Kanade AU-Coded Facial Expression database to support the proposed methodology.

The electrocardiogram (ECG) is a new and promising modality for biometric recognition. This signal is typically collected within welfare monitoring environments along with other vital signals. As opposed to static biometric modalities like the iris or the fingerprints, ECG is time-dependent thereby presenting the opportunity of continuously authenticating subjects in such environments. However, ECG is affected by both physical and psychological activities, which have to be taken into consideration for successful deployment of this biometric. This paper demonstrates the effects of psychology on the performance of ECG biometric systems and proposes a novel methodology for automatic template updating in order to mitigate the risks associated with poor biometric matching.

This paper investigates the potential use of Transient Otoacoustic Emissions (TEOAE) for biometric recognition. Multiresolution decomposition of TEOAE is done by a modified Bivariate EmpiricalMode Decomposition (BEMD) combined with an auditory model. Matching scores are computed by combining ranked correlations across different levels. Recognition rate with recording from left ear is 96.30% and can be improved to 98.15% by utilizing a matching score fusion with information from right ear.

Empirical mode decomposition (EMD) is an adaptive method for nonlinear and nonstationary signal processing. Although the algorithm is easy to implement and widely deployed, its theoretical background and limitations remain uncertain. This paper investigates the performance of EMD in two tone separation problem, especially for the transition region between perfect separation and failure, with emphasis on the effect of the initial phase. Relationships between amplitude ratio, frequency ratio, initial phase and performance are derived.

Opportunistic routing schemes have been studied over the past decade to provide better performance in multihop wireless networks, by taking the advantage of the broadcasting nature of wireless channels. Simulation tools for such study are important and have been investigated to understand the network behavior. In this paper, we use real and simulated channel data to further elaborate the performance of opportunistic networks. In particular, a channel model is built by radio signal strength measurement in an indoor infrastructure, and the output of the channel model is used to feed an opportunistic network simulator. The paper then compares the performance of multihop wireless communications in opportunistic and traditional schemes.

Traditional blind image iterative algorithms are designed for Gaussian noise by using the L2-norm error term. For robustness against the influence of non-Gaussian noise, an efficient projected subgradient algorithm for blind image deconvolution is developed, based on a TV cost function with the L1-norm error term. Because of using the subgradient technique, the proposed subgradient algorithm can minimize the L1-TV cost function directly. By contrast, existing L1norm-based image restoration algorithms only minimize the approximate L1cost function and assume a known blur. Illustrative examples show that under suboptimal regularization parameters, the projected subgradient algorithm is efficient in producing better image estimate than two traditional blind image iterative algorithms in terms of both ISNR and perception.

Emotion modeling and recognition has drawn extensive attention from disciplines such as psychology, cognitive science, and, lately, engineering. Although a significant amount of research has been done on behavioral modalities, less explored characteristics include the physiological signals. This work brings to the table the ECG signal and presents a thorough analysis of its psychological properties. The fact that this signal has been established as a biometric characteristic calls for subject-dependent emotion recognizers that capture the instantaneous variability of the signal from its homeostatic baseline. A solution based on the empirical mode decomposition is proposed for the detection of dynamically evolving emotion patterns on ECG. Classification features are based on the instantaneous frequency (Hilbert-Huang transform) and the local oscillation within every mode. Two experimental setups are presented for the elicitation of active arousal and passive arousal/valence. The results support the expectations for subject specificity, as well as demonstrating the feasibility of determining valence out of the ECG morphology (up to 89 percent for 44 subjects). In addition, this work differentiates for the first time between active and passive arousal, and advocates that there are higher chances of ECG reactivity to emotion when the induction method is active for the subject.

This paper discusses ECG biometric recognition in a distributed system, such as smart cards. In a setting where every card is equipped with an ECG sensor to record heart beats from the fingers, and to subsequently perform identity verification, the interest is in protecting the card holder from a set of unknown/unseen biometric traits. Prior works have examined ECG biometrics in settings where a particular subject was to be identified among a set of enrollees. However, this treatment limits the applicability of this biometric. The Autocorrelation - Linear Discriminant Analysis (AC/LDA) is revisited, to propose a strategic extension of the methodology, in order to account for recognition among unknown individuals (blind verification). The discriminant is trained individually for every smart card, on the samples of the subject to be enrolled, as well as a generic dataset of ECG recordings. This enables the recognizer to protect the template against attacks by biometric samples that have not been used to train the discriminant. In addition, we present a methodology for the selection of the matching threshold, which targets to control false acceptance while being experimentally optimized for a particular smart card.

This work presents a novel methodology for the transformation of facial expressions, to assist face biometrics. It is known that identification using only one image per subject poses a great challenge to recognizers. This is because drastic facial expressions introduce variability, on which the recognizer is not trained. The proposed framework uses only one image per subject to predict intra-class variability, by synthesizing new expressions, which are subsequently used to train the discriminant. The expression of the gallery is transformed using the bivariate empirical mode decomposition (BEMD), which allows for simultaneous analysis of the probe image and a targeted expression mask. We advocate that 2D BEMD is a powerful tool for multi-resolution face analysis. The performance of the proposed framework, tested over a database of 96 individuals, is 90% for an FAR of 1%.

Biometric secret-key binding inherently requires signal processing and error correction schemes due to noisy measurement readings. Two previously proposed strategies, Quantization Index Modulation (QIM) and Wyner-Ziv (WZ) coding, are studied in the context of bio metric key binding. We characterize the tradeoff between key rate leakage and key rate-reconstruction distortion, showing that while WZ coding has a better rate-leakage tradeoff than QIM, the latter has a better rate-reconstruction tradeoff. A new strategy is proposed to combine the merits of these schemes. Known as distortion-enhanced Wyner-Ziv coding (DE-WZ), this scheme is demonstrated to exhibit improved flexibility based on numerical results for a uniform source model and scalar quantization.

Wireless sensor networks (WSN) can be an attractive solution for a plethora of communication applications, such as unattended event monitoring and tracking. One of the looming challenges that threaten the successful deployment of these sensor networks is source-location privacy, especially when a network is deployed to monitor sensitive objects. In order to enhance source location privacy in sensor networks, we propose the use of an opportunistic mesh networking scheme and examine four different approaches. Each approach has different selection criteria for the next relay node. In opportunistic mesh networks, each sensor transmits the packet over a dynamic path to the destination. Every packet from the source can therefore follow a different path toward the destination, making it difficult for an adversary to backtrack hop-by-hop to the origin of the sensor communication.

The effect of an arbitrary indoor infrastructure environment on the performance of a wireless multihop network is investigated. To this end, an accurate channel modeling tool based on 3D ray tracing is used first to evaluate the signal strength in different areas of the environment. Then, a discrete event simulator is applied to examine the performance of the network with two classes of routing protocols: traditional vs. opportunistic. It is shown that for an indoor environment, opportunistic routing performs better based on the obtained results, with respect to throughput, delay and delivery ratio.

Abstract This work investigates the feasibility of ECG‐based identity management in mobile health monitoring applications. A body area network that operates in conjunction with ECG biometric recognition is explored for mobile monitoring of patients, rescuers, pilots, soldiers, or field agents in general. Among the major challenges of this technology is the stability of the signals over the monitoring duration. Time dependency is responsible for ECG destabilization, which becomes a significant issue for reliable monitoring. We propose a novel framework that addresses this inadequacy, by updating a gallery template when feature matching is compromised. In addition, strategies for tackling privacy issues in medical data management are proposed. A protocol level solution is discussed, to deal with the ethical issues of this technology. An automatic way of aggregating and managing personal information is presented, designated to operate on the basis of anonymity. The experimental performance measured over long‐ECG recordings demonstrates promising results. Copyright © 2010 John Wiley & Sons, Ltd.

The generation of changeable and privacy-preserving biometric templates is important for the pervasive deployment of biometric technology in a wide variety of applications. This paper presents a systematic analysis of random transformation-based methods for addressing the changeability and privacy problems in biometrics-based verification systems. The proposed methods transform the original biometric feature vectors using random transformations, and the sorted index numbers (SIN) of the resulting vectors in the transformed domain are stored as the biometric templates. Three types of random transformations, namely, random additive transform, random multiplicative transform, and random projection, are discussed and analyzed. The random transformations, in combination with the SIN approach, constitute repeatable and noninvertible transformations; hence, the generated templates are changeable and provide privacy protection. The effectiveness of the proposed methods is well supported by both detailed analysis and extensive experimentation on a face verification problem.

Biometric hash finds extensive applications in multimedia security systems. Biometric hashing schemes combine biometric features with random numbers for robust and secure human authentication or recognition. A novel biometric hashing scheme which is secure and robust to lighting changes is proposed in this paper. First, the local binary pattern (LBP) based histogram sequence or vector is employed to represent a normalized face image. Secondly, the pseudorandom sequence is generated by using the user specific secret seed (hash key) and it is orthonormalized by using Gram-Schmidt algorithm. Third, the inner product between the histogram vector and pseudorandom sequence is computed. The biometric hash is obtained by thresholding the inner product with the threshold calculated by using Otsu method. Our scheme is tested on the face image data. Hamming distance is used to measure the performance of the scheme. Experimental results show that our scheme is secure and robust to lighting changes.

Medical biometrics offer direct solutions to the liveness and impersonation detection risks which dominate traditional biometric modalities, like the iris, the face or the fingerprint. The electrocardiogram (ECG) is a cardiac signal which falls under this category, and which has lately drawn interest from the biometrics community. This paper presents a novel recognition method based on ECG signals, which enhances the AC/LDA feature extraction algorithm, by incorporating the periodicity transform (PT). It is demonstrated that PT is a powerful tool not only in assessing the matching validity of the signal, but also in handling heart rate changes. The performance of the system over 52 subjects is 92.3%.

The generation of cancelable and privacy preserving biometric templates is important for the pervasive deployment of biometric technology in a wide variety of applications. This paper presents a novel approach for cancelable biometric authentication using random multiplicative transform. The proposed method transforms the original biometric feature vector through element-wise multiplication with a random vector, and the sorted index numbers of the resulting vector in the transformed domain are stored as the biometric template. The changeability and privacy protecting properties of the generated biometric template are analyzed in detail. The effectiveness of the proposed method is well supported by extensive experimentation on a face verification problem.

Biometric encryption (BE) has recently been identified as a promising paradigm to deliver security and privacy, with unique technical merits and encouraging social implications. An integral component in BE is a key binding method, which is the process of securely combining a signal, containing sensitive information to be protected (i.e., the key), with another signal derived from physiological features (i.e., the biometric). A challenge to this approach is the high degree of noise and variability present in physiological signals. As such, fuzzy methods are needed to enable proper operations, with adequate performance results in terms of false acceptance rate and false rejection rate. In this work, the focus will be on a class of fuzzy key binding methods based on dirty paper coding known as quantization index modulation. While the methods presented are applicable to a wide range of biometric modalities, the face biometric is selected for illustrative purposes, in evaluating the QIM-based solutions for BE systems. Performance evaluation of the investigated methods is reported using data from the CMU PIE face database.

This paper presents a novel approach for face based biometric recognition. The proposed method is based on the sorted index numbers (SIN) of appearance based facial features. A new algorithm is introduced to measure the similarity between SIN vectors. Due to the non-invertibility of the transformation from the original features to the SIN vectors, the proposed method can preserve the privacy of the users. The effectiveness of the proposed method is tested on a large generic data set, which contains images from several well known face databases. Experimental results demonstrate that the proposed solution may improve the recognition accuracy in both identification and verification scenarios.

Wireless mesh networks can provide for attractive solutions to telecommunication services, where cost-effective broadband access can be realized, by means of frequency reuse and lower engineering cost in installing. A novel network architecture of opportunistic mesh networks is presented here, based upon the cognitive networking concept which opportunistically utilizes the network resources including both spectrum bandwidth and mesh station availability. The proof-of-concept prototypes are developed to show: (1) the supportance of real-time traffic over multiple wireless hops by the opportunistic resource utilization; (2) the drop-and-play networking model which can vastly save the network planning/deployment costs.

Here is to propose a demonstration of opportunistic wireless mesh networks for broadband wireless Internet access, supporting real-time services, e.g., of voice and video. The enabling technology is of the large-scale cognitive networking, which opportunistically utilizes network resources including both spectrum bandwidth and mesh radio (station) availability. We further demonstrate that: (1) vast saving in the deployment/planning costs of wireless mesh networks can be achieved by establishing fluid networks without predetermined topology; (2) Better (opportunistic) network resource utilization can be achieved to establish reliable communications over multiple wireless hops. The network performance therefore reaches its instantaneous maximum.

Broadcasting allows efficient information sharing and fusion in wireless networks. In this paper, the energy efficiency limits in wireless broadcasting, defined as the minimal achievable broadcast energy consumption per bit (BEB), are studied. Specifically, we consider that a source node broadcasts information bits in a planar disk region A. And n relay nodes, which assist the broadcasting, are also placed in A. The limits are studied for both arbitrary and random wireless networks, under both non-cooperative and cooperative relay transmissions models. Closed form expressions are obtained. The results show that the minimal BEB, in general, decreases polynomially with n, and increases polynomially with the area of A. Cooperative relay transmissions offer at most a constant gain, in terms of the minimal BEB, over noncooperative transmissions, except for the free space propagation. As an example of application, our results are utilized in the study of wireless sensor networks, where the broadcast information fusion strategy is described and analyzed.

A set of semi-fragile watermarks Wfr= {V1, V2,.., Vv} can be used as building blocks for constructing any digital fingerprint. One such family, is obtained by modulating the sign bits alone of selective DCT-AC coefficients, where, each Vjrepresents the positions of a disjoint subset of modulated coefficients. We show that a linear collusion of Kjand '0' its erasure. By design, each block Vjis unperturbed by specific collusion patterns and so by facilitating complementary coverage of each newly added block Vj+1, a compact anti-collusion code (ACC)for tracking linear collusions can be constructed.

The Open Systems Interconnect (OSI) layered ar- chitecture paradigm for computer networks, which led to great successes in wired communications, such as Internet, has been the default paradigm in developing wireless data networks as well. However, in wireless sensor networks, the optimization for lower energy consumption under application-specific Quality of Service (QoS) requirements, necessitates the joint consideration of multiple OSI layers, which is known as the cross layer design. In this paper, we propose the Embedded Wireless Interconnect (EWI), as an architecture platform for energy stringent wireless networks, replacing the OSI paradigm. The concept of EWI is then illustrated by a design example of Low Energy Self- Organizing Protocol (LESOP) for target tracking sensor net- works.

Conventional OFDM systems notoriously suffer from a high peak-to-average power ratio (PAPR), which makes hardware implementation problematic. We propose a general framework for reducing PAPR using a combination of time-domain interpolation and resampling. In essence, this combination allows for both pulse shaping as well as phase changes to be performed. It is demonstrated that the framework offers not only improved flexibility, but also reduced computational complexity compared to existing methods. The former benefit is due to the availability of various user-controllable parameters in the framework, while the latter is a result of performing PAPR reduction directly in the time domain. We also show that the accompanying channel estimation and equalization procedures can be achieved in a practical manner, with only modest modifications. The simulation results illustrate that, even though the complexity requirements are substantially lower, the PAPR reduction capability of this framework is comparable to that of existing PAPR reduction methods.

The dynamic acoustic radiation force produced by two confocal ultrasound beams, can be used for imaging the acoustic properties of tissue. In this paper we investigate how nonlinear propagation in an attenuating medium can affect image quality. Specifically, the effects of the second harmonic on the image formation process are studied, by means of the system point-spread-function (PSF). Incorporation of tissue nonlinearities in the model and extraction of higher-harmonic information, indicates that a more localized radiation force distribution and reduced sidelobe effects can be achieved and this could improve image resolution.

QAM transmission of OFDM signals achieves good spectral efficiency while greatly simplifying the equalization process. However, RF transmitters must be operated in their linear region and highly stable oscillators (low phase-noise) are a necessity or the BER will degrade significantly. If angle-modulation is instead used, then the RF signal has constant envelope and phase-noise has an additive effect, the result being more efficient power transmission and orthogonality is maintained with a noisy oscillator. Angle-modulation has lower spectral efficiency then QAM and angle-demodulators suffer a threshold effect: the receiver output SNR degrades, significantly once below a certain input SNR. To apply angle-modulated OFDM systems in practice this threshold effect and ifs impact on the BER (for a given spectral efficiency) must be analyzed. This paper is the first to present such an analysis for phase-modulation (OFDM-PM) in AWGN channels

We propose an efficient training-based OFDM channel impulse response (CIR) and carrier frequency offset (CFO) estimation algorithm that addresses the problem of phase noise (PHN), assuming that the PHN has a known prior distribution. The optimal joint estimation of CIR, PHN and CFO was described in an earlier work of ours. In this paper, we focus on the case where a training symbol consists of two identical halves in the time domain, and propose a variant to Moose's CFO estimation algorithm that accounts for PHN in CFO estimation. This is followed by an optimal joint CIR and PHN estimation scheme tailored for this "repeating training symbol" setup. It is assumed that the PHN process is Gaussian with known mean and covariance matrix. This encompasses both Wiener PHN and Gaussian PHN. It is shown through simulations that the proposed algorithm performs almost as well as the optimal JCPCE algorithm at much lower complexity. To further reduce the complexity of the proposed scheme, the conjugate gradient (CG) method is used and we show that it can be realized using the Fast Fourier Transform (FFT).

We propose an optimal training-based OFDM channel impulse response (CIR) estimation algorithm that addresses the phase noise (PHN) and carrier frequency offset (CFO) problem. If left unattended, these combined problems severely degrade the accuracy of the channel estimate and ultimately the quality of the wireless link. The solution involves the joint optimization of a complete log-likelihood function over the unknown CIR, PHN and CFO. To reduce the complexity of the proposed algorithm, a simplification based on the conjugate gradient method is introduced, yielding an efficient realization using the fast Fourier transform (FFT) with only minor performance degradation

We study cooperative wireless communications in the physical layer of a Poisson distributed wireless sensor network, where the spatial diversity of multiple relay nodes is utilized to improve the link performance. The tradeoff among network power consumption, spectral efficiency, outage probability, and sensor node density is discussed under the proposed cooperative transmission protocol for sensor networks (CTP-SN). CTP-SN is considered as a typical implementation of the two-phase cooperative transmission paradigm in wireless sensor networks. We derive an asymptotic upper bound for the capacity outage probability of CTP-SN. The bound is shown to be decreasing exponentially, when the sensor node density increases. Via the bound, we demonstrate that the cooperative protocol performs asymptotically much better than the non-cooperative direct transmission

In this paper, the case where multiple degraded (blurred and noisy) acquisitions of the same scene are available, is investigated. An efficient iterative deconvolution system is introduced, where the multisensor images are fused, based on the classification of each image in a predetermined number of classes that represent the components of a finite mixture of normal densities (FMN). The EM algorithm is utilized for the learning of the FMN model. The recursive employment of the classification and fusion processes, followed by an optimized adaptive filtering, converges to a global enhanced version of the original scene in only a few iterations. Experimental results establish the efficiency of the proposed scheme.

We propose to develop wireless sensor networks with mobile sinks (MSSN), under high sensor node density, where multiple sensor nodes need to share one single communication channel in the node-to-sink transmission. Under the guideline of trading network energy consumption for the successfully retrieved packets, optimal and suboptimal transmission scheduling algorithms, which exhibit exponential and linear complexity respectively, are discussed under the desired application. The computer simulations show that the suboptimal algorithms perform nearly as good as the optimal one.

In this letter, the principles of Bayesian changepoint detection are applied to the problem of frame synchronization in wireless systems that use a periodic training signal as part of the packet preamble. An autocorrelation-based metric is used, which allows modeling the synchronization problem as a problem of changepoint detection of a Gaussian process. A MAP estimator for the changepoint is derived that suits the wireless scenario. Examples and comparisons are made using the IEEE 802.11a OFDM standard. It is demonstrated that the proposed method has advantages over some existing synchronization approaches in terms of accuracy, latency, and/or complexity.

A zooming framework suitable for single-sensor digital cameras is introduced and analyzed in this paper. The proposed framework is capable of zooming and enlarging data acquired by single-sensor cameras that employ the Bayer pattern as a color filter array (CFA). The approach allows for operations on noise-free data at the hardware level. Complexity and cost implementation are thus greatly reduced. The proposed zooming framework employs: 1) a spectral model to preserve spectral characteristics of the enlarged CFA image and 2) an adaptive edge-sensing mechanism capable of tracking the underlying structural content of the Bayer data. The framework readably unifies numerous solutions which differ in design characteristics, computational efficiency, and performance. Simulation studies indicate that the new zooming approach produces sharp, visually pleasing outputs and it yields excellent performance, in terms of both subjective and objective image quality measures.

We present a theoretical framework for the linear collusion analysis of watermarked digital video sequences, and derive a new theorem equating a definition of statistical invisibility, collusion-resistance, and two practical watermark design rules. The proposed framework is simple and intuitive; the basic processing unit is the video frame and we consider second-order statistical descriptions of their temporal inter-relationships. Within this analytical setup, we define the linear frame collusion attack, the analytic notion of a statistically invisible video watermark, and show that the latter is an effective counterattack against the former. Finally, to show how the theoretical results detailed in this paper can easily be applied to the construction of collusion-resistant video watermarks, we encapsulate the analysis into two practical video watermark design rules that play a key role in the subsequent development of a novel collusion-resistant video watermarking algorithm discussed in a companion paper.

We develop a novel video watermarking framework based on the collusion-resistant design rules formulated in a companion paper. We propose to employ a spatially-localized image dependent approach to create a watermark whose pairwise frame correlations approximate those of the host video. To characterize the spread of its spatially-localized energy distribution, the notion of a watermark footprint is introduced. Then we explain how a particular type of image dependent footprint structure, comprised of subframes centered around a set of visually significant anchor points, can lead to two advantageous results: pairwise watermark frame correlations that more closely match those of the host video for statistical invisibility, and the ability to apply image watermarks directly to a frame sequence without sacrificing collusion-resistance. In the ensuing overview of the proposed video watermark, two new ideas are put forward: synchronizing the subframe locations using visual content rather than structural markers and exploiting the inherent spatial diversity of the subframe-based watermark to improve detector performance. Simulation results are presented to show that the proposed scheme provides improved resistance to linear frame collusion, while still being embedded and extracted using relatively low complexity frame-based algorithms.

In previous work, it has been found that using a data burst with size varying according to the encountered channel stationarity can improve efficiency and QoS for burst- by-burst wireless communication systems. In this paper, adaptive modulation is incorporated as a further means for improving spectral efficiency. The modulation scheme is switched according to the operating channel conditions, with an adaption metric based on the MSE. When the channel conditions are favorable, a high-throughput modulation mode is used for efficiency. Conversely, a robust lower-throughput mode is selected under adverse channel conditions, for quality. Obtained results show the utility of adaptive modulation in conjunction with a variable-size burst for optimizing spectral efficiency, while maintaining a prescribed level of QoS.

Burst-by-burst equalization strategies assume that data in a burst are received under quasi-stationary channel conditions. Hence, using a fixed-size burst results in conservative transmission efficiency, since worst case scenarios must be accounted for. We investigate the potential of a variable-size burst for efficiency in time-varying environments. First, a channel model explicitly describing environment changes is developed. Analysis of variance is then proposed for tracking the channel stationarity. Based on knowledge of the channel stationarity, the burst size is adapted most advantageously. Obtained results show the feasibility of a variable-size burst to improve efficiency compared to a fixed-size counterpart.

A new system is proposed for gait analysis and recognition applications. The new system is based on a denoising process and a new angular transform that are applied on binary silhouettes. Each human silhouette in a gait sequence is transformed into a low dimensional feature vector consisting of average pixel distances from the center of the silhouette. The sequence of feature vectors corresponding to a gait sequence is used for identification based on a minimum-distance criterion between test and reference sequences. By using the new system on the gait challenge database, improvements in recognition performance are seen in comparison to other methods of similar or higher complexity.

In this paper, the case where a low-resolution image is initially watermarked and the watermark is afterwards detected on a noisy interpolated version of the watermarked image is investigated, using a correlation detector. Polyphase decomposition is utilized at the detector side in order to enable the flexible formation of a fused image, which is appropriate for watermark detection. The optimal fused correlator, obtained by combining information from different image components, is derived through a statistical analysis of the correlation detector properties and employment of Lagrange multipliers. It is shown that it is always preferable to perform detection on a fused image rather than the original image. Experimental results establish the efficiency of the proposed scheme.

We propose a methodology for gait recognition based on dynamic time warping. The gait sequences are initially partitioned into gait cycles and then the test cycles are compared to reference cycles using dynamic time warping. The final distance between a test and a reference sequence is determined using a nonlinear rule. Experimental results are reported showing an improvement in recognition performance in comparison to the baseline algorithm on the "gait challenge" database.

Frame and frequency synchronization schemes are critical in the design of OFDM receivers. In burst communications, such as HIPERLAN/2 and IEEE 802.11a WLANs, synchronization must be achieved at the beginning of the packet using only the given preamble. Thus, for such systems, it's important to design accurate synchronization methods that converge quickly. The most common approach combines an autocorrelation based metric with a max (or min) search. However, such methods typically achieve only coarse synchronization and need to be made more accurate through combinations with other approaches (such as cross-correlation or maximum-likelihood). An autocorrelation based metric is also used, but instead the correct timing is estimated by looking for changes in the gradient of the metric. The proposed method achieves higher accuracy than autocorrelation based max/min searches and has lower latency than many other methods. We provide an approximate closed-form expression for the gradient change that is statistically accurate in both AWGN and frequency-selective channels and offer simulation results illustrating the performance of the proposed method in 802.11a networks.

This paper presents a novel robust watermarking approach called FuseMark based on the principles of image fusion for copy protection or robust tagging applications. We consider the problem of logo watermarking in still images and employ multiresolution data fusion principles for watermark embedding and extraction. A human visual system model based on contrast sensitivity is incorporated to hide a higher energy hidden logo in salient image components. Watermark extraction involves both characterization of attacks and logo estimation using a rake-like receiver. Statistical analysis demonstrates how our extraction approach can be used for watermark detection applications to decrease the problem of false negative detection without increasing the false positive detection rate. Simulation results verify theoretical observations and demonstrate the practical performance of FuseMark.

We propose an iterative blind beamforming strategy for short-burst high-rate DS-CDMA systems. The blind strategy works by creating a set of "training sequences" in the receiver that is used as input to a semi-blind beamforming algorithm, thus producing a corresponding set of beamformers. The objective then becomes to find which beamformer gives the best performance (smallest bit error). Two challenges we face are: (1) to find a semi-blind algorithm that requires very few training symbols (to minimize the search time); (2) to find an appropriate criterion for picking the beamformer that offers the best performance. Different semi-blind algorithms and criteria are tested. The recently proposed SBCMACI (semi-blind CMA with channel identification) (Casella, I.R.S. et al., PIMRC, p.1972-6, 2002) is demonstrated to be ideal because of how few training symbols it needs for convergence. Of the tested criteria, one based on feedback from the decoder (essentially using trellis information) is shown to achieve nearly optimal performance.

In this paper, we consider parsimonious Gauusian and table (heavy-tailed) models, which best capture the self-similarity of aggregate packet traffic in broadband networks. Using the effective bandwidths theory, we extend the recent results on stable self-similar-driven queues with infinite buffer to the finite buffer case that model routers/switches more accurately. Large deviations results are extended from the large buffer regime to the many sources limiting regime. Unfortunately, in the stable case, traditional large-deviations formulae degenerate into not very helpful asymptotic results, unlike the Gaussian case. This has a negative impact in engineering considerations (e.g., connection admission control, buffer management, statistical multiplexing gains), with respect to those results, and leads to alternative solutions, e.g. empirical/numerical and simulation techniques.

Considerable research has been conducted lately on attempting to integrate the dual processes of signal compression and perceptual coding, in order to achieve high signal quality at low bit rates, while ensuring, at the same time, copyright protection of the transmitted digital media. An unconventional data hiding technique leading to compressed forms of source video signals, is introduced in this paper, where both the audio information and the chrominance (I and Q) components of the video frames are hidden in the wavelet transform coefficients of the luminance (Y) component, on a frame-by-frame basis. Furthermore, the proposed scheme allows for a subsequent JPEG compression of each frame for additional bitrate reduction, while maintaining satisfying quality of the reconstructed audio and chrominance information.

We investigate the tail probabilities of a multiplexer driven by /spl alpha/-stable self-similar traffic. We consider a parsimonious 4-parameter traffic model, which best captures the long range dependence and heavy-tails of aggregate packet traffic in broadband networks. Input traffic with these characteristics, induces buffer dynamics which are qualitatively different from those which arise in traditional traffic management. Using the effective bandwidth theory, we extend the recent results on /spl alpha/-stable self similar-driven queues with infinite buffer to the finite buffer case that model routers/switches more accurately. Queuing simulations with real broadband traffic emphasise the improvements in engineering considerations (e.g., connection admission control, buffer management, statistical multiplexing gains), with respect to the existing results so far. Our experiments involve a large set of real broadband network traffic which consists of Ethernet LAN, Internet WAN and MPEG-1 compressed video traces.

This paper presents a novel video watermarking algorithm based on two key ideas: statistical invisibility and content-synchronized placement. We argue that statistical invisibility is essential to protect video watermarks from statistical collusion, and present a natural way to induce this property using a content-dependent spatially localized watermarking framework. We introduce the notion of a watermark footprint, the spatial locations over which its energy is spread. By defining localized footprints with regular structures, e.g., a set of subframes within each frame, current image watermarking techniques can immediately be applied at the subframe-level. We address the issue of reduced spatial redundancy by proposing an attack model based on bilinear interpolation, and embedding the watermark into regions with lower expected distortions. Results are presented to demonstrate the effectiveness of the algorithm.

We propose a new model for network heavy traffic approximation, based on /spl alpha/-stable self-similar processes, namely the skewed linear fractional stable noise. The model demonstrates more flexibility than existing models in fitting different levels of burstiness and correlation in the data. Nonetheless, it is parsimonious in the number of parameters, which have a direct physical meaning. An algorithmic procedure for the estimation of the model parameters is presented, and an asymptotic lower bound of the residual queueing distribution is derived. Extensive simulations are presented, where the new model is fitted to bursty Ethernet data collected at Bellcore (now Telcordia) Laboratories. Furthermore, new measurements of aggregate Web and Webcasting traffic are introduced along with traffic generated by the fitted new model. Queueing simulations of a G/D/1 system confirm our analytical results regarding the tail of the queue distribution.

In natural scenes, still images as well as sequences, backgrounds, and objects' surfaces usually have a textural structure. Therefore, in order to efficiently code images it is crucial to investigate the texture compression problem. In this paper, a perceptually lossless, synthesis-by-analysis texture coding method is presented. The proposed approach is model based; the parameters of the model consist of a binary excitation signal and the parsimonious representation of the reconstruction filter. The estimated parameters, which allow to one synthesize, at the decoder site, a texture that is perceptually indistinguishable from the original one, are then compressed using a lossless strategy, which is based on a fast binary wavelet transformation specifically tailored to binary images. The proposed method leads to very good perceptual results superior to those of existing techniques.

In this paper, we present two novel approaches for the classification of blurry images. It is assumed that the blur is linear and space invariant, but that the exact blurring function is unknown. The proposed fusion-based approaches attempt to perform the simultaneous tasks of blind image restoration and classification. We call such a problem blind image fusion. The techniques are implemented using the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image restoration and the Markov random field (RIRF)-based fusion method for classification by Schistad-Solberg et al. Simulation results on synthetic and real photographic data demonstrate the potential of the approaches. The algorithms are compared with one another and to situations in which blind blur removal is not attempted.

We present and analyze an approach to improve the performance of a broad class of watermarking schemes through attack characterization. Traditional robust watermarking methods use little information concerning the way in which the image is tampered to estimate the embedded watermark. In our novel scheme we propose adding two types of watermarks: reference and robust. The reference watermark is used to assess the way in which the marked image has been modified so that the robust watermark can be optimally extracted to maximize security. Analysis and simulation results are provided to demonstrate the significant performance improvement when the proposed approach is employed in an existing watermarking scheme.

In this paper, we present an automatic, multi-frame, region-feature based motion segmentation technique. Region features are extracted from the first two frames and tracked over several frames. A few motion models are obtained by trajectory clustering on the set of trajectories of the region features' centroids. Final segmentation is obtained by region classification based on these motion models. The proposed technique combines the advantages of feature based methods and gradient based methods. Unlike the traditional two-frame formulation which is widely used in the literature, our method is a multi-frame formulation, which provides a more coherent interpretation of the scene over time and generates more reliable segmentation result.

In this paper, we consider the problem of digital watermarking to ensure the credibility of multimedia. We specifically address the problem of fragile digital watermarking for the tamper proofing of still images. Applications of our problem include authentication for courtroom evidence, insurance claims, and journalistic photography. We present a novel fragile watermarking approach which embeds a watermark in the discrete wavelet domain of the image by quantizing the corresponding coefficients. Tamper detection is possible in localized spatial and frequency regions. Unlike previously proposed techniques, this novel approach provides information on specific frequencies of the image that have been modified. This allows the user to make application-dependent decisions concerning whether an image, which is JPEG compressed for instance, still has credibility. Analysis is provided to evaluate the performance of the technique to varying system parameters. In addition, we compare the performance of the proposed method to existing fragile watermarking techniques to demonstrate the success and potential of the method for practical multimedia tamper proofing and authentication.

We present a novel technique for the digital watermarking of still images based on the concept of multiresolution wavelet fusion. The algorithm is robust to a variety of signal distortions. The original unmarked image is not required for watermark extraction. We provide analysis to describe the behaviour of the method for varying system parameter values. We compare our approach with another transform domain watermarking method. Simulation results show the superior performance of the technique and demonstrate its potential for the robust watermarking of photographic imagery.

In this paper, we propose a noise filtering scheme, which is based on a multichannel homomorphic transformation, for color photographic images corrupted by signal-dependent film grain noise. The proposed method performs the estimation of the noise parameter using higher-order statistics (skewness or kurtosis) of the corrupted image and filtered image statistics. This parameter estimation technique can be used to generate color film grain noise that has applications in motion picture productions. After a theoretical description of the method employed, experimental results are provided.

In this paper we present a novel fragile watermarking scheme for the tamper-proofing of multimedia signals. Unlike previously proposed techniques, the novel approach provides spatial and frequency domain information on how the signal is modified. We call such a technique a telltale tamper-proofing method. Our design embeds a fragile watermark in the discrete wavelet domain of the signal by quantizing the corresponding coefficients with user-specified keys. Tamper detection is possible in the localized spatial and frequency regions of the given signal. We provide analysis, simulations and comparisons with two other tamper-proofing methods to show the potential of the proposed approach in detecting and characterizing the distortion imposed on the signal.

A semiblind approach for equalization and jammers rejection at the base station in wireless communications is presented. It is based on the idea of enlarging the effective length of a known short training sequence by using a few additional information symbols and the finite alphabet property of the communication signals. Simulation results for spatio-temporal processing of 4-QPSK signals transmitted by simultaneous intracell users demonstrate the advantage of the proposed approach in situations where the length of the training sequence is not large enough to allow estimation of the desired signal with previously proposed methods.

We consider the performance of frequency-hopping spread spectrum (FH SS) radio networks in a Poisson field of interfering terminals using the same modulation and power. The problem is relevant to wireless random-access communication systems where little information about transmitters requires stochastic modeling of their positions. Assuming that the signal strength is attenuated over distance r on average as 1/r/sup m/, we show that the interference in the network could be modeled as a mixture of Gaussian and /spl alpha/-stable noise. Based on this modeling, we derive expressions for the probability of error (P/sub e/) for systems with M-ary frequency shift keying (FSK) which use conventional envelope detectors. Because conventional envelope detectors are optimum only in Gaussian noise and are suboptimum in the noise considered, we also investigate noncoherent detectors which offer improved performance. We examine receivers with limiting nonlinearities and detectors which are optimal in Cauchy noise. Numerical calculations and Monte Carlo simulations are provided to confirm the accuracy of the analysis presented. The results obtained are useful in the performance evaluation of multiple-access radio networks in environments varying from urban settings to office buildings with deterministic and stochastic propagation laws such as lognormal shadowing and Rayleigh fading.

In this paper, we propose a new decomposition scheme and an efficient interpolation algorithm for affine transformation of a digital image. We try to reconstruct the affine-transformed image by resampling it with the highest possible quality, lowest complexity and throughput rate. Based on the proposed decomposition, the transform is completed by a sequence of 3-pass translations and a scaling operation where each of them is one-dimensional in nature. This method preserves quality and guarantees simplicity. We place the emphasis on the feasibility of a parallel implementation that can benefit from pipeline technologies. Further, an efficient FFT-based implementation of this new algorithm is suggested. Experimental evidence of the effectiveness and robustness of the proposed method is reported. The problem is relevant to video transmission, image registration, and computer graphics manipulation.

In radar tracking the target motion is best modeled in Cartesian coordinates. Its position is however measured in polar coordinates (range and azimuth). Tracking in Cartesian coordinates with noisy polar measurements requires either converting the measurements to a Cartesian frame of reference and then applying the linear Kalman filter to the converted measurement or using the extended Kalman filter (EKF) in mixed coordinates. The first approach is accurate only for moderate cross-range errors; the second approach is consistent only for small errors. A new efficient tracking algorithm using the multidimensional Gauss-Hermite quadratures to propagate the mean and the covariance of the conditional probability density function is presented. This method is compared with the EKF and the converted measurement Kalman filter (CMKF) and it is shown to be more accurate.

In this paper, we propose a new radar tracking algorithm based on the Gaussian sum filter. We have developed a new systematic and efficient way to approximate a non-Gaussian and measurement-dependent function by a weighted sum of Gaussian density functions. We have derived the formula for updating the weights involved in the bank of Kalman-type filters and also suggested a way to alleviate the growing memory problem inherent in the Gaussian sum filter. Our method is compared with the extended Kalman filter (EKF) and the converted measurement Kalman filter (CMKF) and it is shown to be more accurate in term of position and velocity errors.

We present an approach for still image watermarking in which the watermark embedding process employs multiresolution fusion techniques and incorporates a model of the human visual system (HVS). The original unmarked image is required to extract the watermark. Simulation results demonstrate the high robustness of the algorithm to such image degradations as JPEG compression, additive noise and linear filtering.

We propose a novel method for the robust classification of blurred and noisy images that incorporates ideas from data fusion. The technique is applicable to blind situations in which the exact blurring function is unknown. The approach treats differently deblurred versions of the same image as distinct correlated sensor readings of the same scene. The images are fused during the classification process to provide a more reliable result. We show analytically that the various restorations can be treated as images acquired from different but correlated sensor readings. Experimental results demonstrate the potential of the method for robust classification of imagery.

We present a new, suboptimum, method for the detection of signals in noise for which standard methods of inference are difficult to implement. The method is based on the empirical characteristic function (ECF) and exploits the correlation structure of the ECF evaluated at the finite number of points. We assume discrete time detection so that N i.i.d. data samples are available per symbol. We examine the performance of the proposed method for the mixture of /spl alpha/-stable and Gaussian noise. The detectors based on the ecf show substantial improvements in performance compared to linear detectors and exhibit better performance than the locally optimum (LO) detectors.

Classical linear image restoration techniques assume that the linear shift invariant blur, also known as the point-spread function (PSF), is known prior to restoration. In many practical situations, however, the PSF is unknown and the problem of image restoration involves the simultaneous identification of the true image and PSF from the degraded observation. Such a process is referred to as blind deconvolution. This paper presents a novel blind deconvolution method for image restoration. The method is flexible for incorporating different constraints on the true image. An example of the method is given for situations in which the imaged scene consists of a finite support object against a uniformly grey background. The only information required are the nonnegativity of the true image and the support size of the original object. For situations in which the exact object support is unknown, a novel support-finding algorithm is proposed.

In this paper, the authors present a convergence analysis for the NAS-RIF (nonnegativity and support constraints recursive inverse filtering) algorithm used in blind image restoration. A novel approach is presented to determine sufficient conditions for the global convergence of the technique. The approach is general to many signal processing algorithms and incorporates Lyapunov's direct method used commonly in nonlinear system analysis. The sufficient conditions for convergence are determined to be in the form of constraints on the blurred image pixels which can be tested for prior to the use of the NAS-RIF algorithm. An apparent trade-off between the quality of the restoration and the uniqueness of the solution is found.

This paper considers the performance of FH SS radio networks in a Poisson field of interfering terminals using the same modulation and power. Assuming 1/r/sup m/ attenuation of signal strength over distance, the interference in the network is modeled as a mixture of Gaussian and circularly symmetric /spl alpha/-stable noise. The problem is relevant to mobile communication systems where the mobility of users requires random modeling of transmitter positions in the network. We derive formulas for the probability distributions of the interference in FH SS radio networks. We generalize the results of Sousa (see IEEE Trans. I.T., vol.38, no.6, p.1743-1754, 1992) where attention was limited to Cauchy random variables, a special subclass of stable distributions, and where the effect of a background (Gaussian) noise was neglected. Based on the formulas derived, we calculate the probability of symbol error for radio links in environments varying from urban settings to office buildings. The results obtained allow the prediction of the performance of wireless systems under a wide range of conditions.

A simple method is proposed for blind identification of discrete-time nonlinear models consisting of two linear time invariant (LTI) subsystems separated by a polynomial-type zero memory nonlinearity (ZMNL) of order N (the LTI-ZMNL-LTI model). When the input to the model is a circularly symmetric Gaussian sequence, the linear subsystem of the model can be identified efficiently using slices of the N+1/sup th/ order polyspectrum of the output signal, even when the second linear subsystem is of non-minimum phase (NMP). The ZMNL coefficients need not be known. The order N of the nonlinearity can, in principle, be estimated from the received signal. The methods possess noise suppression characteristics. Computer simulations support the theory.

We consider the performance of frequency-hopping spread spectrum (FH SS) radio networks in a Poisson field of interfering terminals using the same modulation and power. The problem is relevant to mobile communication systems where the mobility of users requires random modeling of transmitter positions in the network. Assuming logarithmic attenuation of the signal strength over distance between the transmitter and receiver, we show that the interference in the network could be modeled as a mixture of Gaussian and circularly symmetric /spl alpha/-stable noise. Based on this model, we derive union bound approximations for the probability of error for FH systems with M-ary frequency shift keying (FSK). We generalize some of the results of Sousa (see IEEE Trans. I.T., vol.38, no.6, p.1743, 1992), where the focus was limited to Cauchy random variables (RVs), a special subclass of stable distributions, and where the effect of a background (Gaussian) noise was neglected. Numerical calculations and Monte Carlo simulations confirm the accuracy of our analysis. The results obtained allow the prediction of wireless system performance in environments varying from urban settings to office buildings.

The polycepstra and prediction equalization algorithm (POPREA) is proposed for blind equalization of nonminimum phase channels. The algorithm equalizes the amplitude and phase of the channel independently by employing linear prediction and tricepstrum principles, respectively. It guarantees convergence to a global solution. The tracking and cancellation of phase due to carrier frequency offset is carried out independently of equalization. It is demonstrated, by means of computer simulations, that the proposed POPREA is able to open the eye pattern of QAM signal constellations faster than existing polyspectra-based equalizers. The complexity of the algorithm is high but comparable to that of polyspectra equalizers.>

The effects of impulsive noise on the performance of optimal and suboptimal binary receivers are examined. The noise mixture consists of a sum of Gaussian noise and impulsive noise. The impulsive noise is modelled using /spl alpha/-stable processes. The optimal receiver structure is derived using Bayes hypothesis testing and the suboptimal receivers consist of linear and nonlinear structures. The obtained results will be useful in the performance evaluation of digital communication links subject to Gaussian and impulsive noises. Many situations exist in which the performance of systems is affected by a mixture of Gaussian and impulsive noises that could benefit from this analysis (atmospheric noise in radio links, ice cracking in underwater sonar etc.).>

We present a motion estimation (ME) approach which is not constrained by the classical optical flow assumptions of constant illumination intensity and rigid motion with two degrees of freedom. This approach accounts for motion consisting of three dimensions of displacements and three dimensional rotations of a ridged surface. We present a model of this six dimensional motion and show how it can be represented with an affine transformation of an image. Next we present a novel approach of applying the image transformation by satisfying its interpolation and resampling needs in a different manner. The interpolation and resampling process is replaced by a frequency domain shaping technique. Finally we present some simulations of the overall motion estimation technique and compare its performance to the displaced frame difference block matching approach.>

Identification of non-parametric linear systems with cyclostationary inputs has received considerable attention in recent years. However, identification of parametric linear models has received very little attention. In this paper, some methods are proposed for identification of moving average (MA) and autoregressive moving average (ARMA) linear models with fractionally spaced data input using only the output sequence. It is shown that q-length MA and MA part of ARMA can be identified using only q points of the cyclic autocorrelation provided it is nonzero at two or more incommensurate cycle frequencies. This can be ensured by using the sum of cycle frequency separated signals or by using signals with a low frequency pilot. Computer simulations are presented to support the methods.>

The impact of nonGaussian impulsive noise combined with Gaussian noise on the performance of the binary transmission is analyzed. The impulsive noise is modeled as an alpha-stable process. The probability of error for optimum, linear and nonlinear receivers is derived. The proposed nonlinear detectors show substantial improvements in performance compared to linear ones. The obtained results will be useful in performance evaluation of digital communication links subject to Gaussian and impulsive noises.>

The sensitivity of the tricepstrum equalization algorithm (TEA) to imperfect carrier phase recovery is discussed. Structures of combined TEA-based equalization and carrier phase tracking are proposed. Performance evaluation of the proposed structures is made by means of computer simulations. The obtained results will be useful to any polyspectra-based equalizer.>

An adaptive blind equalization method is introduced for nonminimum phase communication channels. The method estimates the inverse channel impulse response, by using the complex cepstrum of the fourth-order cumulants (tricepstrum) of the synchronously sampled received signal. As such, the proposed adaptive method depends only on the statistics of the received sequence, and is capable of reconstructing separately both the minimum and maximum phase response of the channel. It is demonstrated, by means of extensive simulations, that the proposed tricepstrum-based equalization scheme performs well and outperforms other existing blind equalizers, at the expense of higher computational complexity.>

An adaptive filtering method is proposed for the identification of a non-Gaussian, white-noise-driven, linear, generally non-minimum-phase system. The non-minimum-phase impulse response of the system is estimated from the updated differential cepstrum parameters of the higher order cumulants (polycepstra) of the system output. The updated cepstrum parameters are obtained by utilizing higher order cumulants and an LMS (least mean squares) type algorithm. It is shown, using Monte-Carlo simulation examples, that the proposed method would perform very well at the expense of more computations. The use and effectiveness of the method in blind linear-equalization applications is demonstrated.>

A novel discrete-time method is proposed for estimating the impulse response of a frequency-selective digitally modulated communication channel. The received signal is first demodulated and sampled and then the fourth-order cumulants of the resulting discrete-time sequence are estimated. The method estimates the channel impulse response from the complex cepstrum of the aforementioned fourth-order cumulants (i.e. tricepstrum). The method depends only on the second- and fourth-order statistics of the transmitted sequence and is capable of reconstructing nonminimum-phase impulse responses. Monte Carlo simulation results demonstrate the effectiveness of the method, its low sensitivity to observation noise, and its improved performance in terms of probability of error or the reconstructed transmitted sequence.>