Abstract Computer vision and sensor technology advancements have enabled significant progress in 3D reconstruction. However, critical challenges remain when extending these methods to underwater environments. This paper addresses the disparities between terrestrial and underwater 3D reconstruction by presenting a novel comparative framework that evaluates identical objects in these distinct settings under real-world conditions. To mitigate underwater-specific challenges such as light absorption, scattering, and reduced image clarity, a new enhancement methodology combining RGB stretching and Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed. Experimental results demonstrate that the enhancement increased processing time by 2.99% compared to the underwater model, while improving reconstructed points by 5.05%, detected features by 3.12%, and reconstructed features by 6.58%. Furthermore, structural metrics such as smoothness, genus connectivity, and average skewness showed significant positive improvements. Furthermore, structural metrics showed significant improvements with genus connectivity increasing by 1.29%, smoothness improving by 0.69%, and average skewness increasing by 0.18%, based on the raw underwater model and underwater enhanced model. By providing a direct cross-environment comparison, this study bridges the gap between terrestrial and underwater 3D reconstruction, offering critical insights into methodological adaptations necessary for robust and reliable modeling in diverse environmental conditions. The proposed framework lays the foundation for advancing underwater 3D reconstruction technology to achieve parity with its terrestrial counterpart, with applications spanning cultural heritage preservation, marine exploration, and environmental monitoring.

Abstract AR applications are rapidly gaining adoption, expanding users’ perceptual capabilities. Their potential for addressing visual occlusions and effectively extending the user’s line of sight significantly enhances their value in various contexts, from professional to personal use. This work presents a novel system designed to project 3D human poses onto AR glasses, enabling users to perceive concealed individuals behind solid objects, addressing a critical limitation of traditional visual perception. To achieve real-time and accurate 3D projection, we employ fiducial markers strategically placed within the environment. The markers are periodically fused with IMU sensor data to accurately estimate the user’s head orientation, a crucial step for correct spatial alignment. Furthermore, we leverage a multi-view 3D human pose estimation method using calibrated cameras and incorporate attention mechanisms. These mechanisms focus the system on relevant features, improving accuracy and minimizing 3D joint error. Our experiments demonstrate that the proposed framework accurately projects 3D skeletal representations onto AR glasses, even when significant occlusions are caused by solid objects or other occupants within the scene. This novel approach offers a method to enhance situational awareness in dynamic environments where visibility is compromised, potentially benefiting various applications, from first response scenarios to security and surveillance.

This work addresses the problem of 3D human pose and shape estimation from monocular video. While significant progress has been made in this domain, most existing approaches focus solely on forward human motion and neglect the reverse process. To overcome this oversight, we propose dual stream networks consisting of decoders that analyze both forward and reverse human motion. The network outputs are fused using residual connectors, which effectively integrate the predictions. Exploiting temporal transformers further enhance ac-curacy. Our method achieves state-of-the-art results on well-established datasets in terms of both 3D error and temporal consistency metrics, demonstrating its robustness and effectiveness. Furthermore, our network operates at over 150 FPS, making it suitable for real-time inference.

The adoption of Decision Support Systems (DSS) by farmers faces significant challenges, including the need for user-friendly interfaces, comprehensive training, reliable support, and affordable pricing. Addressing these challenges is critical to advancing sustainable agriculture and meeting global food production demands. This paper introduces an advanced DSS tailored for aquaponics, leveraging cutting-edge technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain. These technologies are integrated into the DSS to optimize resource management, ensure cybersecurity, and simplify complex processes through an intuitive interface. Real-time data is presented in an accessible format, enabling farmers of all skill levels to adopt the system with ease. The DSS features AI-driven robotic traps with 75% accuracy in real-time insect detection, autonomous robots achieving 94% precision in 3D spot spraying, and nutrient analyzers with over 92% accuracy in monitoring critical levels. A blockchain layer ensures secure data verification, traceability, and distributed AI model verification. Advanced hierarchical clustering algorithms analyze pest and nutrient dynamics, providing actionable insights for farm management. The system emphasizes interoperability and scalability, supporting seamless integration with various aquaponic setups. Field evaluations demonstrate the DSS’s capacity to reduce pesticide use by 50%, enhance crop yields, and lower sample analysis costs by 70%, highlighting its efficiency and sustainability. Data visualization latency remains below 430ms, enabling real-time responsiveness, while predictive models achieve 91% accuracy in forecasting pest population trends. These results solidify the DSS's role as a transformative tool in precision agriculture. By improving productivity, plant health, and the utilization of biopesticides and biofertilizers, this work bridges the gap between theoretical models and real-world applications. It illustrates the DSS's transformative potential for digital agriculture, offering a scalable and effective solution for sustainable farming practices globally. This study provides valuable insights into the broader applications of such systems, marking a significant advancement in agricultural innovation and technology integration.

The proliferation of AI models in everyday devices has highlighted a critical challenge: prediction errors that degrade user experience. While existing solutions focus on error detection, they rarely provide efficient correction mechanisms, especially for resource-constrained devices. We present a novel system enabling users to correct AI misclassifications through few-shot learning, requiring minimal computational resources and storage. Our approach combines server-side foundation model training with on-device prototype-based classification, enabling efficient error correction through prototype updates rather than model retraining. The system consists of two key components: (1) a server-side pipeline that leverages knowledge distillation to transfer robust feature representations from foundation models to device-compatible architectures, and (2) a device-side mechanism that enables ultra-efficient error correction through prototype adaptation. We demonstrate our system's effectiveness on both image classification and object detection tasks, achieving over 50% error correction in one-shot scenarios on Food-101 and Flowers-102 datasets while maintaining minimal forgetting (less than 0.02%) and negligible computational overhead. Our implementation, validated through an Android demonstration app, proves the system's practicality in real-world scenarios.

In modern software systems, early and accurate vulnerability detection is crucial. Traditional Static Analysis Tools (SATs) highlight potential security issues, providing fine-grained information including lines of code and vulnerability categories; however, they are hindered by a large number of false alarms. On the other hand, Artificial Intelligence (AI)-based Vulnerability Prediction (VP) has emerged as a promising alternative for vulnerability identification in software products. Nevertheless, current VP methods face important limitations, such as the granularity level of the predictions, since VP is commonly conducted at the file or function level. In this study, we examine whether the utilization of AI-based vulnerability prediction as a filtering mechanism for static analysis alerts could reduce the number of false alarms, leading to more practical Static Application Security Testing (SAST). The results of the analysis show that this approach improves the practicality of static analysis, reducing false positives, with the impact on the detection accuracy being small.

Task placement optimization in cloud-edge-fog environments is a challenging problem that requires balancing multiple objectives, such as minimizing latency and energy consumption, while adhering to resource constraints. This paper proposes a framework that integrates Deep Reinforcement Learning with Graph Neural Networks to address these challenges. Specifically, we explore the effectiveness of such architectures, including Graph Convolutional Networks and Message Parsing Neural Networks, within a DRL agent for task allocation. The framework is evaluated on synthetic task flow graphs, representing parallel workflows of varying complexities ($\mathbf{1 0}$and$\mathbf{1 0 0}$tasks), and benchmarked against traditional methods such as Genetic Algorithms and a Random Agent. Our results demonstrate that the RL Agent with GCN layers outperforms the MPNN-based RL Agent, GA, and Random Agent in small-scale scenarios while it performs equivalently with the MPNN-based RL Agent in largescale scenarios in which both surpass the heuristic approach and the Random agent.

Blockchain technology advancements have made it feasible to build secure, decentralized networks with numerous use cases in various industries, including banking, education, government, and health. In higher education, there is a significant need for certification and credential verification through digital means, as fake certificates are common in the labor market. The BlockAdemiC system presented in this article falls into solutions for such issues. BlockAdemiC is a blockchain-based educational platform that makes use of cutting-edge features in blockchain technology to ensure the required degree of trust both at the individual user and institutional level. Specifically, BlockAdemiC represents a digital distributed security system for the certification and verification of educational activities, degrees, and skills in higher education and lifelong learning, producing an immutable educational passport. Information distributed using blockchain enhances trust in the exchange of information between institutions, agencies, companies, and alumni and introduces a trustworthy mechanism for content verification and authentication. For self-sovereign identity and identification management, the system utilizes distributed ledger technology via Hyperledger frameworks and offers users authority over their identities and credentials, removing the need for the involvement of central authorities to validate and verify user identities. Smart contracts are additionally supported, which provide the connection for storing student activities that take place on educational platforms on the EOSIO blockchain. The BlockAdemiC was evaluated via a technological feasibility study focusing on the system’s performance. The initial findings show that the system can efficiently process a high load of queries in parallel similar to the real-world situation and can be further considered for large-scale deployment and use.

Abstract Embodied virtual agents (EVAs) are beginning to be researched to improve human–computer interaction. As EVAs become increasingly integrated into various aspects of daily life, understanding how to optimize their design to foster trust and likability among users is paramount. Leveraging insights from social psychology, particularly the concept of homophily, this study investigates the impact of perceived personality traits on user perceptions of EVAs. Specifically, we explore whether aligning the personality traits of EVAs with those of users increases engagement and fosters positive interactions. Drawing on a sample of 382 participants recruited through Amazon Mechanical Turk, we assessed participants' personality traits using the Big Five Inventory—2S, while the perceived extroversion of the agent was manipulated through facial expressions and body posture. Our findings suggest that participants were able to accurately identify the perceived extroversion of the agent (p = .014), and significant results indicate a homophily effect on trust, with participants exhibiting greater trust in agents perceived as having a similar level of extroversion (p < .01). However, no significant effect on likability was detected, suggesting a more nuanced relationship between perceived personality traits and user preferences. These findings highlight the potential of leveraging homophily in designing more engaging EVAs and underscore the importance of considering user–agent compatibility in human–computer interaction.

In the continuously evolving digital landscape, risk assessment represents an essential cornerstone in cybersecurity, crucial for the identification, evaluation and prioritization of potential threats and vulnerabilities within complex digital ecosystems. Vulnerability scoring systems play a pivotal role in this process by providing structured methodologies to quantify and qualify the severity and impact of vulnerabilities. This paper presents a comprehensive comparison of various vulnerability scoring systems approaches, examining their structures, methodologies, and unique attributes. Beginning with a review of the critical role of risk assessment in the dynamic cyber threat landscape, it emphasizes the fundamental significance of vulnerability scoring systems in risk evaluation. The selection criteria for the vulnerability scoring systems that are analyzed are clarified, focusing on justifying choices based on relevance and significance while considering parameters guiding their selection. A detailed analysis of the selected vulnerability scoring systems follows, delving into methodologies, metrics, and distinct characteristics, providing an in-depth understanding of their functionalities. Subsequently, a comparative evaluation of these systems is conducted, highlighting their respective strengths, weaknesses, and overall effectiveness in cybersecurity risk assessment, while benefits and drawbacks of each vulnerability scoring system are examined to offer a comprehensive perspective. The conclusions of this comparative study bring together the results, summarizing essential insights gathered through the analysis of the vulnerability scoring systems. Overall, this paper provides a structured evaluation of the available vulnerability scoring systems, offering valuable insights for professionals in the field of cybersecurity, as well as future research involved in vulnerability management and risk assessment.

The Dark Web, known for its anonymity and illicit activities, presents considerable challenges for Law Enforcement Agencies (LEAs) due to the complexity and volume of data generated within it. Online marketplaces on the Dark Web are notorious for facilitating illegal activities such as drug trafficking, counterfeit goods, and weapons sales while using advanced obfuscation techniques to avoid detection. The unstructured nature of data on these platforms and their constantly evolving operations make manual extraction and analysis exceedingly difficult.This paper addresses the pressing need for structured information extraction from Dark Web marketplaces, with a specific focus on firearm-related listings. Traditional rule-based methods have proven inadequate due to their reliance on HTML tags and pattern recognition, necessitating more adaptive solutions. Thus, the application of Large Language Models (LLMs) and Prompt Engineering to tackle these challenges is explored. By leveraging the capabilities of LLMs, this study aims to transform the extraction process into a more efficient and accurate system. Various generative models and prompt formulations are tested, to determine the most effective approach for extracting detailed information such as product specifications, pricing, and seller details.The proposed pipeline involves feeding crawled marketplace pages into a generative model, which then identifies Product Details Pages (PDPs) and consequently extracts relevant information from them. The use of LLMs marks a significant advancement over traditional methods, enhancing the accuracy and comprehensiveness of data extraction. Additionally, this research highlights the effectiveness of prompt engineering in improving information retrieval.This work underscores the critical need for sophisticated tools to monitor and combat illegal activities on the Dark Web, particularly in the context of firearm trafficking. By refining techniques for automated data extraction and applying cutting-edge LLM and prompt engineering methods, this study aims to support LEAs in their efforts to disrupt and dismantle criminal networks and enhance public safety.

Grippers act as the physical link between robotic manipulators and their surroundings, allowing robots to handle objects and perform a variety of tasks, such as the manipulation of grapes during harvesting. To improve the design of grippers, we propose an automated framework, which starts with an initial design that resembles human hands’ configuration during grape harvesting and optimizes it based on the characteristics of the grape variety to be harvested. In this direction, the proposed framework utilizes parametric models for both the grape and the gripper and integrates them into an automated grasp planning simulation procedure. All the components are combined in a custom graphical user interface (GUI), making the entire process more user-friendly. Results from our experimentation showed that the proposed framework has the potential to significantly improve the efficiency and effectiveness of gripper design, especially for applications involving the handling of objects with unique parameters and requirements.

Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in numerous vision tasks. However, their high processing requirements necessitate efficient hardware acceleration to meet the application's performance targets. In the space of FPGAs, streaming-based dataflow architectures are often adopted by users, as significant performance gains can be achieved through layer-wise pipelining and reduced off-chip memory access by retaining data on-chip. However, modern topologies, such as the UNet, YOLO, and X3D models, utilise long skip connections, requiring significant on-chip storage and thus limiting the performance achieved by such system architectures. The paper addresses the above limitation by introducing weight and activation eviction mechanisms to off-chip memory along the computational pipeline, taking into account the available compute and memory resources. The proposed mechanism is incorporated into an existing toolflow, expanding the design space by utilising off-chip memory as a buffer. This enables the mapping of such modern CNNs to devices with limited on-chip memory, under the streaming architecture design approach. SMOF has demonstrated the capacity to deliver competitive and, in some cases, state-of-the-art performance across a spectrum of computer vision tasks, achieving up to 10.65 × throughput improvement compared to previous works. The tool is available at https://github.com/ICIdsl/smof.git.

Air quality intensifies climate change through global pollution, particularly affecting lower and middle-income countries that lack local ground pollutant monitoring networks. While atmospheric pollutant satellite measurements offer a broad view, their coarse spatial resolution limits detailed air pollution insights, resulting in unmonitored regions and information gaps. Furthermore, satellites generate extensive data that is often challenging to directly correlate with ground air pollution stations. To overcome this, we propose a multimodal self-supervised approach that learns from diverse satellite sources for air pollution monitoring. More specifically, aside from incorporating a combination of multi-spectral and spectral modalities (Sentinel-2 & 5P products), we also leverage tabular land cover data. Their integration into multimodal self-supervised learning is highlighted, employing a novel augmentation scheme that results in more resilient embeddings. The proposed approach integrates a self-supervised redundancy reduction loss in a multi-modal fashion, capturing both inter-modal and intra-modal correspondences. Furthermore, an adaptive loss weighting mechanism is introduced to effectively combine different multi-modal losses. Our approach’s efficacy is showcased in the air pollution prediction task, exhibiting a noteworthy improvement of up to 17% compared to existing methods. Furthermore, in our experiments, the applicability of our approach in other environmental tasks is also exhibited.

The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005°. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics. The ready-to-use data, processing code & examples are available at https://github.com/angelisgiorgos/RegionalAQDatasets

In this study, the critical issue of air pollution and its impact on quality of life is addressed by developing technologies for monitoring air quality and identifying areas of concern. A notable challenge in this domain is the collection of air pollution data, which often lacks differentiation between normal and abnormal quality levels. Recognizing the importance of detecting anomalies in air pollution data, a novel methodology is introduced that not only safeguards human health but also enhances the overall data quality. The proposed approach involves the injection of abnormal events into air pollution datasets by leveraging the temporal distribution characteristics of the data. To assess the validity of these generated anomalies, the Kolmogorov-Smirnoff test is employed. Furthermore, the developed method is evaluated by exploiting SoA deep learning based models for anomalies detection. Finally, a deep attention-based autencoder method is designed, namely AT-MCRAAD that demonstrates superior performance over existing traditional and contemporary algorithms in identifying these anomalous events.

This paper examines the usefulness of optimizing the anomaly score threshold determining the predicted class of the Isolation Forest binary classifier for outlier detection, as well as the benefits of combining classification models with different preprocessing arguments in an ensemble for an overall prediction. The purpose of anomaly detection in this work is the timely forecasting of failures of specific type for predictive maintenance based on sensorial data related to the production process. The implementation was carried out on an anode production equipment in the aluminium industry. The experimental results show that, especially for larger forecasting horizons, the introduced approach significantly increases the classification evaluation performance compared to our previous work, with an improvement in Matthews Correlation Coefficient of up to $\mathbf{0. 0 6 2}$.

Reliable and robust navigation of autonomous mobile robots in indoor environments faces significant challenges due to the absence of GPS, visual degradation, repetitive structures, illumination variations, and low texture. These factors adversely affect localization systems. Current robots often use a uniform navigation approach, regardless of the varying localization uncertainties within different indoor environments. In this paper, we propose a holistic, active vision-based path planning method that produces efficient trajectories, aiming to minimize localization error and enhance navigation performance. Specifically, we utilize a 3D model of an indoor environment to derive an Artificial Potential Field (APF) with its associated localizability scores that encapsulate both visual features’ richness and fiducial markers’ placement. APF is employed to direct a Kinematically Constrained Bi-directional Rapidly Exploring Random Tree (KB-RRT) planner towards the calculation of optimal paths, prioritizing high localization areas. Subsequently, we use an online weight-adaptive MPC-based approach that, apart from robust path planning and obstacle avoidance, guides the robot towards areas with the most robust visual features in order to further refine the localization error. The proposed framework has been extensively tested in both simulation and real-world experiments with a mobile robot in a visually challenging indoor environment.

Vulnerability prediction is an important mechanism for secure software development, as it enables the early identification and mitigation of software vulnerabilities. Vulnerability Prediction Models (VPMs) are Machine Learning (ML) models able to detect potentially vulnerable software components based on information retrieved from their source code. Despite the notable advancements in the field of vulnerability prediction, especially with the utilization of Deep Learning (DL) and text mining techniques, current literature still lacks a highly accurate, reliable, and practical VPM. Recently, the Large Language Models (LLMs), which have demonstrated remarkable capabilities in text understanding and processing, have started being utilized for vulnerability prediction, demonstrating highly promising results. The purpose of the present paper is to explore the utilization of LLMs in the field of vulnerability detection, identify challenges and open issues that still need to be addressed, and potentially propose directions for future research. Our analysis suggests that while LLM-based VPMs have outperformed traditional DL approaches in vulnerability prediction, significant challenges still need to be addressed to be considered sufficiently accurate, reliable, and practical.

Industrial equipment calibration is an essential element for the proper functioning of any production plant. Without frequent and proper calibration, the quality and efficiency of the overall production process are threatened. Despite its significance, it is often performed manually based on qualitative measures that may lead to suboptimal behavior. In this article, we propose a deep reinforcement learning (RL)-based methodology to automate the calibration process and evaluate it in an elevator control use case. Moreover, to overcome data scarcity we develop a simulation environment based on generative modeling that creates synthetic RL episodes. The proposed methodology relies on a minimal set of sensors and actuators, i.e., a webcam, an Arduino board, three stepper motors, and an edge computational unit. Nevertheless, experimental evaluations indicate that it can perform in real-time applications achieving accurate calibration results.

3D Convolutional Neural Networks are gaining increasing attention from researchers and practitioners and have found applications in many domains, such as surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval. However, their widespread adoption is hindered by their high computational and memory requirements, especially when resource-constrained systems are targeted. This paper addresses the problem of mapping X3D, a state-of-the-art model in Human Action Recognition that achieves accuracy of 95.5% in the UCF101 benchmark, onto any FPGA device. The proposed toolflow generates an optimised stream-based hardware system, taking into account the available resources and off-chip memory characteristics of the FPGA device. The generated designs push further the current performance-accuracy pareto front, and enable for the first time the targeting of such complex model architectures for the Human Action Recognition task.

We report on the development of a cutting-edge brain-computer interface (BCI) network that leverages the sensor output of a low cost electroencephalogram (EEG) headband to detect specific eye and head movements, enabling intelligent wheelchair navigation. Using a hybrid CNN-LSTM architecture, our method achieves high accuracy classification of these movements while maintaining low inference time on the complete EEG signal. To validate our approach, we conducted a comprehensive experimental evaluation.

Online discussion boards have become a tool for traffickers to widen their reach in the illegal firearm trade. This provides opportunities for authorities to detect firearm trafficking networks, as well as interdict arms shipments arranged by such networks. The present work offers a fresh solution to the pressing problem of spotting dubious firearm transaction chats on online discussion boards. The suggested methodology integrates state-of-the-art capabilities, such as natural language understanding and unsupervised clustering methods, to support the creation of more efficient cybercrime solutions. The necessity to target and flag questionable discussions about the trading of firearms in Internet forums is the main issue this work addresses. The major claim in this study centers on the use of intent recognition sequence patterns within dialogues and unsupervised clustering of such sequences. This method groups conversations based on the general direction and subject. The research methodology involves several key steps: i) data collection and manual annotation from the subreddit r/GunDeals, chosen due to its proximity to the primary subject of illicit firearm tracking, ii) training an intent classification Transformer-based model to generate intent sequences for each conversation, iii) preprocessing the intent sequences for clustering and encoding them using a polynomial method. iv) implementing clustering techniques while tuning various hyperparameters to optimize the results. The proposed methodology effectively classifies conversations into meaningful clusters, providing actionable insights. This research contributes to the body of knowledge by presenting a novel approach to detecting suspicious firearm trade conversations online. The practical implications are significant, as this work can be leveraged by law enforcement agencies to enhance their Internet scanning capabilities. The ability to efficiently process large volumes of text conversations and flag specific content can aid in addressing illicit firearm trading more effectively.

Heart failure is a life-threatening disease with an increasing prevalence around the globe. Electronic health (eHealth) technologies provide a solution to enhance the monitoring and management of heart failure. In this direction, the CORRAL system utilizes user-friendly web and mobile-based components for heart failure integrated care management by multidisciplinary health professional teams, patients, and informal caregivers. CORRAL enables the co-design of the patient’s personalized care plan providing a detailed medical record, as well as risk prediction and decision-support capabilities. A conversational virtual assistant contributes to the empowerment of users, while it is optimized for data protection and sovereignty. With its patient-centric and holistic approach, CORRAL aims to optimize treatment adherence and self-management and to improve the quality of life of the patient.

For Human Action Recognition tasks (HAR), 3D Convolutional Neural Networks have proven to be highly effective, achieving state-of-the-art results. This study introduces a novel streaming architecture-based toolflow for mapping such models onto FPGAs considering the model's inherent characteristics and the features of the targeted FPGA device. The HARFLOW3D toolflow takes as input a 3D CNN in ONNX format and a description of the FPGA characteristics, generating a design that minimises the latency of the computation. The toolflow is comprised of a number of parts, including (i) a 3D CNN parser, (ii) a performance and resource model, (iii) a scheduling algorithm for executing 3D models on the generated hardware, (iv) a resource-aware optimisation engine tailored for 3D models, (v) an automated mapping to synthesizable code for FPGAs. The ability of the toolflow to support a broad range of models and devices is shown through a number of experiments on various 3D CNN and FPGA system pairs. Furthermore, the toolflow has produced high-performing results for 3D CNN models that have not been mapped to FPGAs before, demonstrating the potential of FPGA-based systems in this space. Overall, HARFLOW3D has demonstrated its ability to deliver competitive latency compared to a range of state-of-the-art hand-tuned approaches, being able to achieve up to 5× better performance compared to some of the existing works. The tool is available at https://github.com/ptoupas/harflow3d.

Surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval are just few of the many applications in which 3D Convolutional Neural Networks are exploited. However, their extensive use is restricted by their high computational and memory requirements, especially when integrated into systems with limited resources. This study proposes a toolflow that optimises the mapping of 3D CNN models for Human Action Recognition onto FPGA devices, taking into account FPGA resources and off-chip memory characteristics. The proposed system employs Synchronous Dataflow (SDF) graphs to model the designs and introduces transformations to expand and explore the design space, resulting in high-throughput designs. A variety of 3D CNN models were evaluated using the proposed toolflow on multiple FPGA devices, demonstrating its potential to deliver competitive performance compared to earlier hand-tuned and model-specific designs.

Autonomous vehicle navigation in complex and unpredictable outdoor environments requires extensive and detailed understanding of the surrounding area and compliance with the traffic rules. In this paper, we attempt to imitate human driver behavior towards autonomous navigation that is suitable for diverse, challenging environments, whether urban, semi-structured or rural-like. Our approach starts with a novel method that we propose for extracting free space area using RGB and LiDAR data, in combination with a rough topometric map for route planning. Local goals are extracted in the final drivable region, and the vehicle draws a local path in the free space that is approximately in line with the overall path via a lattice planner. Our method is evaluated both in the publicly available KITTI urban dataset and a custom-made dataset of a semi-structured environment. In both cases, the results highlight the potential of our approach for further advancements in autonomous navigation and the development of safer and more human-like behaviors in driverless vehicles compared to the existing trajectory prediction state-of-the-art methods that make use of a topometric map.

Herein, we propose a framework for the generation of photorealistic synthetic datasets using High Dynamic Range Imaging (HDRI) that include all kinds of information computer vision algorithms need. Furthermore, by utilizing the proposed framework, we demonstrate cross-domain knowledge transfer in a semantic segmentation scenario. We found that deep neural networks trained with our synthetic images or with a mix of real and synthetic perform equal to or in cases better than those trained solely on real images. To our knowledge, this is the first work that uses HDRI to successfully transfer knowledge from the synthetic domain to the real world.

Demand Response (DR) is becoming a cornerstone element in the current energy sector, particularly for the EU energy markets. For this reason, considerable effort has been spent on standardising demand response data models. As a result, there is an ever-growing number of demand response proposals based on these standards. However, these proposals are usually centralised, and those that rely on cloud solutions use the cloud as a centralised data store assuming that the data is already homogenised when stored, i.e. all the data has the same format and model. Nevertheless, in practise, DR proposals rely on several components that provide data in heterogeneous formats and models. Furthermore, the different DR standards define models for different data formats that hinder data exchange between different DR systems. In this article, a generic tool called CIM is presented, which allows existing DR systems to distribute their components in the cloud, providing a solid security and privacy framework for data exchange. In addition, the CIM implements a semantic interoperability layer that is capable of translating data into a normalised form when exchanged so that it can be transparently consumed by DR components. Experiments advocate the CIM as a solution for DR systems to decentralise their architectures and exchange heterogeneous data even with other DR systems that follow different DR standards.

Fire incidents in residential and industrial areas are often the cause of human casualties and property damage. Although there are existing systems that detect fire and monitor the presence of people in indoor areas, research on their implementation in embedded platforms is limited. This article introduces an ultra-low-power embedded system for fire detection and crowd counting using efficient deep learning methods. For the prediction of fire occurrences, environmental and gas sensor along with multilayer perceptron nodes are used. For crowd counting, a custom lightweight version of YOLOv5 is introduced, using an architecture based on ShuffleNetV2, resulting in a model with low memory requirements, high accuracy predictions, and fast inference on an embedded platform. The accuracy, power consumption, and memory requirements of the proposed system are evaluated using public datasets and datasets acquired by the environmental and image sensors, and its performance is compared to that of existing approaches.

Deep learning (DL) has certainly improved industrial inspection, while significant progress has also been achieved in metrology with impressive results reached through their combination. However, it is not easy to deploy metrology sensors in a factory, as they are expensive, and require special acquisition conditions. In this article, we propose a methodology to replace a high-end sensor with a low-cost one introducing a data-driven soft sensor (SS) model. Concretely, a residual architecture (R $^{2}$ esNet) is proposed for quality inspection, along with an error-correction scheme to lessen noise impact. Our method is validated in printed circuit board (PCB) manufacturing, through the identification of defects related to glue dispensing before the attachment of silicon dies. Finally, a detection system is developed to localize PCB regions of interest, thus offering flexibility during data acquisition. Our methodology is evaluated under operational conditions achieving promising results, whereas PCB inspection takes a fraction of the time needed by other methods.

Traffic categorization is considered of paramount importance in the network security sector, as well as the first stage in network anomaly detection, or in a network-based intrusion detection system (IDS). This paper introduces an artificial intelligence (AI) network traffic classification pipeline, including the employment of state-of-the-art image-based neural network models, namely Vision Transformers (ViT) and Convolutional Neural Networks (CNN), whereas the primary element of this pipeline is the transformation of raw traffic data into grayscale pictures introducing a properly developed IDS-Vision Toolkit as well. This approach extracts characteristics from network traffic data without requiring domain expertise and could be easily adapted to new network protocols and technologies (i.e. 5G). Furthermore, the proposed method was tested on the CIC-IDS-2017 dataset and compared to a well-known feature extraction strategy on the same dataset. Finally, it surpasses all suggested binary classification algorithms for the CIC-IDS-2017 dataset to the best of our knowledge, paving the path for further exploitation in the 5G domain to successfully address related cybersecurity challenges.

Europe, during the last decade, is facing major migration flows, due to several crises out the border of its territory. In this regard, the prediction of migration flows place a significant r ole. The development of the internet and the rise of Big Data pave the path for the development of early warning systems with promising forecasting power. This paper presents a novel approach to migration prediction, using neural network architectures, in combination with Natural Language Processing techniques, that monitor and search for early signals in the national press of countries of origin and destination. This is the first study to use topic modeling to monitor the national press to predict migration. The pipeline proposed in this paper uses the following three major datasets: EUROSTAT, GDELT and ALL-NEWS. The topic classifier, which is proposed, consists of a Latent Dirichlet Allocation(LDA) model trained using the ALL-NEWS dataset. Big Data in the form of national press articles can be of tremendous value to the problem of migration prediction.

5G architectures will utilize the virtualization of the network functions (VNF) and the use of Multi-access edge computing (MEC) to gain multiple benefits such as simpler service orchestration, while simultaneously covering diverse use cases even with strict performance requirements. 5G service orchestration mechanisms will need to allow more efficient and flexible network deployment and operations in a resource-efficient and delay-sensitive manner. A field that is expected to be greatly boosted by these advances, is Cellular Vehicle to Everything communications. 5G will enable cooperative, connected and automated mobility services, which are often are safety critical while also having stringent delay requirements. This paper, proposes a mechanism that predicts the future position of a vehicle moving in both urban and/or highway environments. Based on this knowledge, it decides on the optimal position of VNFs so that the allocation of network resources can be preemptively requested. The objective of this mechanism is to ensure the uninterrupted, continuous connections of the vehicles, resulting in minimal or no service interruption time while ensuring an optimal utilization of Edge Cloud and MEC resources.

Photonic circuits pave the way to ultrafast computing and real-time inference of applications with paramount importance, such as imaging flow cytometry (IFC). However, current implementations exhibit inherent restrictions that consequently diminish the neural networks (NNs) complexity that can be supported.

Successful transition of manufacturing enterprises to Industrie 4.0 (I4.0) is highly dependent on the adoption and integration of new technologies, toward making manufacturing processes even smarter and less wasteful. In this paper, we present the OPTIMAI project architecture for zero-defect manufacturing (ZDM), applicable to a variety of industrial verticals. To realise a standards-based approach, we elaborate on the parallels drawn between the presented architectural framework and two leading reference architectures underpinning the "factories of the future" vision (RAMI 4.0 and IIRA). System specifications for ZDM are hence defined according to the perspectives of the two architectural models, allowing us to examine cutting-edge technologies for ZDM (such as blockchain, AI and AR) as both an I4.0 solution, as well as an Industrial Internet of Things system.

Automation of vineyards cultivation necessitates for mobile robots to retain accurate localization system. The paper introduces a stereo vision-based Graph-Simultaneous Localization and Mapping (Graph-SLAM) pipeline custom-tailored to the specificities of vineyard fields. Graph-SLAM is reinforced with a Loop Closure Detection (LCD) based on semantic segmentation of the vine trees. The Mask R-CNN network is applied to segment the trunk regions of images, on which unique visual features are extracted. These features are used to populate the bag of visual words (BoVW s) retained on the formulated graph. A nearest neighbor search is applied to each query trunk-image to associate each unique feature descriptor with the corresponding node in the graph using a voting procedure. We apply a probabilistic method to select the most suitable loop closing pair and, upon an LCD appearance, the 3D points of the trunks are employed to estimate the loop closure constraint to the graph. The traceable features on trunk segments drastically reduce the number of retained BoVWs, which in turn expedites significantly the loop closure and graph optimization, rendering our method suitable for large scale mapping in vineyards. The pipeline has been evaluated on several data sequences gathered from real vineyards, in different seasons, when the appearance of vine trees vary significantly, and exhibited robust mapping in long distances.

Digital image processing for the early detection of plant pests as insects in vegetable crops is essential for plant's yield and quality. In recent years, deep learning has made strides in the digital image processing, opening up new possibilities for pest monitoring. In this paper, state-of-the-art deep learning models are presented to detect common insect pests in vegetable cultivation named whiteflies and black aphids. Due to the absence of data sources addressing the aforementioned insect pests, adhesive traps for catching the target insects were used for the creation of an annotated image dataset. In total 225 images were collected, and 5904 insect instances were labelled by expert agronomists. This dataset faces many challenges such as the tiny size of objects, occlusions and resemblance. Object detection models were used like YOLOv3, YOLOv5, Faster R-CNN, Mask R-CNN, and RetinaNet as baseline algorithms for benchmark experiments. For achieving accurate results, data augmentation was used. This study has addressed these challenges by applying deep learning models which are able to deal with tiny object detection ascribed to very small insect size. The experiment results exhibit a mean Average Precision (mAP) of 75%. Dataset is available for download at https://zenodo.org/record/7139220

This work presents a comparative analysis of human-crafted and automated feature extraction approaches for the discrimination of hand-based activities among eating, drinking and smoking. In this scheme, accelerometer and gyroscope sensors were utilised to capture activity signals. For this reason, wearable devices that embed the aforementioned sensors were employed to collect activity data from 12 office workers. The two approaches that were developed for feature mapping were evaluated equally on the collected dataset. Both the proposed schemes achieved to classify the hand-based activities. However, based on the experimental process, this study shows that the human-crafted features that extracted valuable information from the time and frequency domain of the raw signal measurements outperformed the automated feature mapping that utilised deep learning advances. The relevant results prove that the human-crafted features can recognise hand-based activities with 0.9109 and, on the other hand, automated features with a 0.907 F1 weighted score over the dataset.

From smart homes to smart cities and Industry 4.0 to Transportation Systems, Internet of Things (IoT) is a domain which promises incredible growth coupled with great impact, in numerous fields. IoT networks are composed of numerous different Things, arranged in diverse topologies with diverse needs. This diversity is partially due to the numerous areas where IoT applications are utilized, which at their entirety can be referred as the IoT ecosystem. The IoT ecosystem suffers from numerous vulnerabilities, due to reasons such as design flows, hardware limitation or simply human error and is subject to various attacks targeting IoT services, platforms and networks. These attacks can have significant consequences such as economic losses, service disruption or data leaks. Cyber-attacks are an unavoidable and must be faced in tandem with the global growth of IoT networks. An approach that can assist in developing robust, intelligent Cyber-security tools for IoT is using Artificial Intelligence. In the following paper, a mechanism is presented that automatically selects appropriate mitigation actions in an optimal way to countermeasure attacks faced by IoT networks. This is achieved by using an novel Artificial Intelligence mechanism based on a Deep Neural Architecture called Pointer Networks to optimize security-related KPIs. Experimental results, show that the proposed method produces equal or better Pareto optimal solutions, performs faster compared to state-of-the-art (SoA) algorithms and scales better.

With smart vehicles interconnected with multiple systems and other entities, whether they are people or IoT devices, the importance of a digital identity for them has emerged. We present in this paper how a Self-Sovereign Identities combined with blockchain can provide a solution to this end, in order to decentralize the identity management and provide them with capabilities to identify the other entities they interact with. Such entities can be the owners of the vehicles, other drivers and workshops that act as service providers. Two use cases are examined along with the interactions between the participants, to demonstrate how a decentralized identity management solution can take care of the necessary authentication and authorization processes. Finally, we test the system and provide the measurements to prove its feasibility in real-life deployments.

During the last decades, the research endeavours on autonomous driving found great resonance in Advanced Driver-Assistance Solutions that equipped the contemporary civilian vehicles and significantly boosted their driver-less mobility. The existing applications are mostly focused on urban scenarios where signs, road lanes and markers are well defined and ordered favouring the motion of the vehicles whilst, less attention has been paid to the semi-structured and rural environments where traffic infrastructure is scarce. The paper at hand introduces a holistic framework for autonomous vehicles navigation in semi-structured environments. Semantic cues fused with geometrical information of LiDAR data are used for road detection and tracking. OpenStreetMaps are employed as a rough route planner, the waypoints of which are rectified via a probability distribution function over the visible area of vehicle’s vicinity. Thus, vehicle’s localization is obtained by Normal Distribution Transform (NDT) SLAM, where the covariance of egomotion estimation is obtained by processing short-term 3D maps, fused with GPS measurements by means of an extended Kalman filter. Local planning and execution of vehicle’s motion is applied on local cost maps formulated by the union of 2D laser readings and the detected road boundaries fitted through Bézier curves. The complete framework has been evaluated with the aid of a real Autonomous Guided Vehicle in a constrained semi-structured urban area, exhibiting robust navigation performance.

Automated guided vehicles operation in human populated factory environments is a challenging task, especially when there is a demand to operate without following fixed paths defined by guide wires, magnetic tape, magnets, or transponders embedded in the floor. The paper at hand introduces a vision-based method enabling safe and autonomous operation of pallet moving vehicles that accommodate pallet detection, pose estimation, docking control and pallet pick up in such industrial environments. A dedicated perception topology relying on monocular vision and laser-based measurements has been applied and installed on-board a novel robotic pallet truck. Pallet detection and pose estimation are performed in two steps. Firstly, a deep neural network is used for the fast isolation of pallets' regions of interest and, secondly, model-based geometrical pattern matching on point cloud data is applied to extract the pallet pose. Robot alignment with candidate pallet is performed with a dedicated visual servoing controller. The developed method has been extensively evaluated both in simulated and real industrial environments with the pallet truck and proved to have real-time performance achieving increased accuracy in navigation, pallet detection and pick-up.

Acoustics has received a great research interest for human activity detection in indoor and outdoor environments. Compared to vision-based approaches, microphones can achieve a high percentage of recognition accuracy in a variety of activities, while not being affected by lighting conditions. Furthermore, audio-based activity detection can be considered an unobtrusive method, as long as the data is not related to speech or other sensitive information and no data is sent on cloud. Selecting the appropriate audio features that can achieve a high recognition accuracy and generalize in multiple domestic environments is a challenging task. In this work, three of the most commonly used spectrogram representations are evaluated, based on their spectral magnitude and the spectral energies. Specifically, using multi-channel audio data, the Short-time Fourier Transform (STFT), the Mel and the Gammatone spectrograms are extracted and trained on a 2D Convolutional Neural Network (CNN). The F1-Scores of each feature representation are computed, while the McNemar tests and the Receiver Operating Characteristic (ROC) curves ensure the statistical independence between the magnitude and the energy representations. Extensive experimental results on a public database for detection of daily activities in a home environment, show that the overall highest recognition accuracy is achieved by the STFT magnitude representations.

As autonomous collaborative robots are more widely used in work environments alongside humans it is of great importance to facilitate the communication between people and robotic systems, in a way that promotes safety and productivity. To this end, we propose an Augmented Reality (AR) based system that allows workers in a human-robot collaborative environment to interact with a robot while also receiving information regarding the robot state and plans that relate to the human’s safety and trust, such as the intended movement of the robotic arm or the navigation plan of the mobile platform. To evaluate the effectiveness of the proposed system we conducted experiments with 13 participants, where two users had to work in the same workspace while being assisted by a mobile manipulator. We measured the task completion time as well as the robot idle time using our AR-based human-robot interaction system and compared them to a conventional setup without the use of augmented reality. Additional, subjective evaluations related to user satisfaction, system usability, perceived safety and trust showed that users assessed the system in a positive way and preferred AR visualization over more traditional interfaces.

Publisher Copyright: © 2021

Abstract Recently, a new management and resource delivery model based on self‐organization and self‐management (SOSM) principles for heterogeneous cloud infrastructures was proposed. The novel architecture was designed and implemented for the provision of cloud resources for delivering high performance computing services. The cost of deployment of such framework on large‐scale cloud infrastructures is prohibitive, thus discrete‐time simulation techniques were adopted in order to evaluate its efficiency and scalability. In this work, extensive simulation experimentation is being reported and discussed, concerning four evaluation criteria, for comparing the traditional centralized resources allocation scheme to the alternative of SOSM. From the results obtained, it can be derived that SOSM can provide improved service delivery, computational efficiency, power consumption, scalability and management of resources for millions of cloud nodes, compared to the centralized resource allocation approach.

The global prevalence of diabetes is escalating. Several digital health strategies have recently risen to assist in optimal diabetes management and reduce clinical, economic, and humanistic implications. This paper describes the first prototype of an Educational Virtual Assistant (EVA) intended to increase people's engagement in diabetes management, through continuous education, interaction, and recommendations. Since the patient holds a pivotal role in his disease progression, the overall objective of EVA is to reduce the risk of critical events, by fostering them to adopt optimal diabetes management strategies and finally improving their daily quality of life. EVA was trained based on standardized educational material and implemented based on natural language processing (NLP) techniques that present an opportunity to use natural language understanding (NLU) through machine learning (ML). The assistant was integrated into a diabetes self-management application and tested within Chrodis Plus JA. Initial usability and acceptability results are presented here. Overall, this pilot study yielded a positive opinion, as well as suggestions for improvement. On-going work includes testing the first prototype with older people with T2D plus health professionals in primary care units in Greece and incorporating a ML-based context-sensitive dialogue manager, which is expected to convey a more flexible dialogue flow.

Obesity is one of the most significant public health problems of the 21st century, having been recognized by the World Health Organization as the epidemic of this century. This problem has been affecting men, women, and children of all races and all ages, particularly in urban areas. OCARIoT is a research and development project financed by the Rede Nacional de Pesquisa (Brazil) and the European Union to conceive a technological solution based on the Internet of Things to face Childhood Obesity. An OCARIoT solution is to use a Decision Support System to encourage children to have healthy habits, with the help of IoT devices. This work presents the development of an algorithm for detecting unhealthy trends and forecast values in time series, which will be a tool to assist the Decision Support System.

Diet tracking via self-reports or manual taking of meal photos might be difficult, time-consuming, and discouraging, especially for children, which limits the potential of long-term dietary assessment. We present the design and development of a proof of concept of an automated and unobtrusive system for diet tracking integrating: a) a social robot programmed to automatically capture photos of food and motivate children, b) a deep learning model based on Google Inception V3, applied for the use case of image-based fruit recognition, c) a RESTful microservice architecture deployed to deliver the model outcomes to a platform aiming at childhood obesity prevention. We illustrate the feasibility and virtue of this approach, towards the development of the next-generation computer-assisted systems for automated diet tracking.

We present a new keypoint detection method that generalizes Harris corners for multispectral images by considering the input as a quaternionic matrix. Standard keypoint detectors run on scalar-valued inputs, neglecting input multimodality and potentially missing highly distinctive features. The proposed detector uses information from all channel inputs by defining a quaternionic autocorrelation matrix that possesses quaternionic eigenvectors and real eigenvalues, for the computation of which channel cross-correlations are also taken into account. We have tested the proposed detector on a variety of multispectral images (color, near-infrared), where we have validated its usefulness.

The contemporary SLAM mapping systems assume a static environment and build a map that is then used for mobile robot navigation disregarding the dynamic changes in this environment. The paper at hand presents a novel solution for the problem of life-long mapping that continually updates a metric map represented as a 2D occupancy grid in large scale indoor environments with movable objects such as people, robots, objects etc. suitable for industrial applications. We formalize each cell's occupancy as a failure analysis problem and contribute temporal persistence modeling (TPM), an algorithm for probabilistic prediction of the time that a cell in an observed location is expected to be “occupied” or “empty” given sparse prior observations from a task specific mobile robot. Our work is evaluated in Gazebo simulation environment against the nominal occupancy of cells and the estimated obstacles persistence. We also show that robot navigation with life-long mapping demands less replans and leads to more efficient navigation in highly dynamic environments.

Inevitably, the explosion of the Internet of Things (IoT) has alerted the modern human life. Apart from the benefits that this new technology offers to the users of the IoT devices, there are also dangers related to Cyber Security. Traditional methodologies that support and strengthen the Cyber Security can not be applied to the low consumption IoT devices. Hence, many attack detection methodologies and systems that respect the constraints of the IoT have been presented recently. In this paper, an anomaly detection mechanism is proposed that focuses on the threat detection, by combining statistical and machine learning methodologies to detect abnormalities in time-series of the network traffic. Moreover, this new framework is a lightweight Cyber Security solution, since its computational logic is included in the edge layer. The results of the experiments that were conducted in a test bed, indicated the high performance of the methodology in terms of Accuracy, Precision, Recall and F-measure.

Abstract The use of Convolutional Neural Networks (CNNs) as a feature learning method for Human Activity Recognition (HAR) is becoming more and more common. Unlike conventional machine learning methods, which require domain-specific expertise, CNNs can extract features automatically. On the other hand, CNNs require a training phase, making them prone to the cold-start problem. In this work, a case study is presented where the use of a pre-trained CNN feature extractor is evaluated under realistic conditions. The case study consists of two main steps: (1) different topologies and parameters are assessed to identify the best candidate models for HAR, thus obtaining a pre-trained CNN model. The pre-trained model (2) is then employed as feature extractor evaluating its use with a large scale real-world dataset. Two CNN applications were considered: Inertial Measurement Unit (IMU) and audio based HAR. For the IMU data, balanced accuracy was 91.98% on the UCI-HAR dataset, and 67.51% on the real-world Extrasensory dataset. For the audio data, the balanced accuracy was 92.30% on the DCASE 2017 dataset, and 35.24% on the Extrasensory dataset.

Abstract Satellite image denoising is a recent trend in image processing, but faces many challenges due to the environmental factors. Previous works have developed many filters for denoising the hyperspectral satellite images. Accordingly, this work utilizes an adaptive filter with the type 2 fuzzy system and the optimization-based kernel interpolation for the satellite image denoising. Here, the image denoising has been done through three steps, namely noise identification, noise correction and image enhancement. Initially, the type 2 fuzzy system identifies the noisy pixels in the satellite image and converts the image into a binary image, which is passed through the adaptive nonlocal mean filter (ANLMF) for the noise correction. Finally, the kernel-based interpolation scheme carries out the image enhancement, which is done through the proposed chronological Jaya optimization algorithm (chronological JOA) that is developed by modifying Jaya optimization algorithm (JOA) with the chronological idea. The performance of the proposed denoising scheme is analyzed by considering the satellite images from two standard databases, namely Indian pines database and NRSC/ISRO satellite database. Also, the comparative analysis is performed with the state-of-the-art denoising methods using the evaluation metrics, peak signal to noise ratio (PSNR), structural similarity index (SSIM) and second derivative-like measure of enhancement (SDME). From the results, it is exposed that the proposed adaptive filter with the chronological JOA has the improved performance with the PSNR of 22.0408 dB, SDME of 244.133 dB and SSIM of 0.872.

The implications of frailty in older adults' health status and autonomy necessitates the understanding and effective management of this widespread condition as a priority for modern societies. Despite its importance, we still stand far from early detection, effective management and prevention of frailty. One of the most important reasons for this is the lack of sensitive instruments able to early identify frailty and pre-frailty conditions. The FrailSafe system provides a novel approach to this complex, medical, social and public health problem. It aspires to identify the most important components of frailty, construct cumulative metrics serving as biomarkers, and apply this knowledge and expertise for self-management and prevention. This paper presents a high-level overview of the FrailSafe system architecture providing details on the monitoring sensors and devices, the software front-ends for the interaction of the users with the system, as well as the back-end part including the data analysis and decision support modules. Data storage, remote processing and security issues are also discussed. The evaluation of the system by older individuals from 3 different countries highlighted the potential of frailty prediction strategies based on information and communication technology (ICT).

E-health home based solutions reduce healthcare costs and allow aging population to continue their daily life independently. Our objective, is to combine simple IoT sensors and machine learning techniques, in order to provide a home based solution that is able to detect behavioral changes of elderly people who live alone. For this purpose, we introduce a non-intrusive, spatio-temporal abnormal behavior detection approach. In this approach, motion and door sensor signals are elaborated to produce contextual metrics, which are filtered from any deviant observations, after performing a silhouette analysis on five outlier detection algorithms. Next, the combination of a classification and a regression based approach is proposed for detecting abnormalities in the metrics, both in the contexts of space and time. IoT sensor data from ten elderly people houses have been collected and seven different machine learning algorithms have been analyzed in order to evaluate the performance of the individual as well as the combined approach.

Childhood obesity is a major public health concern since it is associated with obesity during adulthood and the occurrence of diseases such as diabetes and cancer. In this paper we present a platform for childhood obesity prevention, which is based on interactive games played between a social robot and the child. The interactions between the robot and the child occur within four distinct stages: a) Familiarization with the robot, b) education and knowledge assessment (e.g., for a balanced diet), c) behavioural data collection & empowerment, and d) personalized goal-setting. The platform was evaluated in terms of usability with 10 participants achieving a System Usability Score (SUS) of 93.8. Overall, our results show the feasibility of the adopted approach and the potential of social robots in playing a major role for promoting healthy behaviours and preventing childhood obesity.

The exponentially increasing volumes of health data of different types is a challenge for reaching robust conclusions. The Health Vision platform overcomes this issue by providing advanced and multi-scaling data analysis and visualisation. The platform functions through user defined workflows, each organised into four distinct layers: 1. Raw Data, 2. Data Analytics, 3. Decision Support and 4. Data visualisation. In the Raw Data Layer, data from medical records, healthcare platforms, smart homes, monitoring sensors, and medical devices, clinical features from Electronic Health Records (EHRs), etc. can be considered, while the Data Analytics Layer provides data analysis services for data mining. The Decision Support Layer facilitates the selection of appropriate visualisation methods taking into consideration the specifications of the raw data and the output of the data analysis. Finally, the Data visualisation Layer provides intelligent visualisation methodology in order for the user to interact with the data and gain meaningful results. Visualisation is supported by a reactive workflow mechanism, allowing the composition of analysis and visualisation services in a custom application-specific data flow. We demonstrate and evaluate this novel approach in several use cases, where different types of data are involved, including: Clustering of Sjögren's syndrome patients, visualisation of Parkinson's disease patients, mutation association analysis in Chronic Lymphocytic Leukemia patients, behavioral analysis of asthma patients and clinical analysis based on clinical similarities of older adults.

This work concerns a gait matching problem. An experimental simulation of people walking in an interior area, some of which had wearables with accelerometer and gyroscope, and some of which were recorded by some camera set up in this area, was conducted. From all devices data from single-point vectors were acquired, thus preserving privacy. The aim was to match each person carrying a wearable with the correct camera recording, if this recording existed, or not to match the person with any camera recording, if it was not recorded by a camera for sufficient time. For this matching, deliberately chosen pairs of wearable and camera features were correlated after state-of-the-art-based automatic synchronization, and the correlation matrices resulting from the feature pairs, after rationally transformed to improve results, were fused with the best linear combination. From the experiments, perfect matching results were obtained when the time series to be compared lasted at least 50sec, no matter any other assumption.

The eHealth constitutes the largest wave of change in the sector of healthcare. In this context, Internet of Things is of immense importance since connected data would facilitate treatment with more efficiency and comprehensive knowledge and would "act" as preventive medicine. Monitoring health data and making them ubiquitously accessible to predefined and authorized healthcare personnel are shared through various IoT platforms, which usually lack IoT-protocol-interoperability. In this paper, we present the impact of IoT systems in the eHealth services' evolution and we introduce the VICINITY ecosystem solution. Its high-level architecture is analyzed and several security considerations are offering "thought for food".

The following topics are dealt with: Internet of Things; wireless sensor networks; mobile computing; learning (artificial intelligence); autonomous aerial vehicles; protocols; computer network security; data privacy; data analysis; telecommunication traffic.

In this work we analyze the security requirements and challenges of e-Health Internet of Things (IoT) applications and propose a complete architecture to address them. This architecture combines VICINITY IoT Framework security features together with General Data Protection Regulation (GDPR) compliant mechanisms in order to provide secure e-Health services to elders and middle-aged people. We also demonstrate how an Ambient Assisted Living (AAL) and an mHealth application were designed and implemented, addressing the current security and privacy requirements.

This work presents an initial research on Bath and Metal height features forecasting in electrolysis process based on machine learning multidimensional time series models. It is also examines two clustering architectures in order to find clusters of electrolytic pots with common behavior. The utilized models did not achieve good performance for the Bath height, but in the contrary, the models are able to predict the Metal height within acceptable error margins for the industrial use that is aimed to. An Artificial Neural Network (ANN) and Bidirectional Recurrent Neural Network (BRNN) respectively achieved the best performance for the bath and metal height.

Today, the use of IoT devices, the inter-connectivity of machines and systems, and the development of novel analytic algorithms which are main characteristics of Industry 4.0 are also transferred to other domains connected to the industrial one. The advances on industrial domain have impact on waste management one as these domains are strictly connected. The connection with sensors on industrial partners premises, the big data availability and the significant advances on data analytics, enable the waste management companies to smarten their domain and automate many of their solutions and processes. In this work, we present a data analytics platform for the optimization of waste management procedures. By using this platform, a waste management company is able to monitor and analyze sensors data from industrial partners bins and optimize its planning based on different analytic tools and historical data. Besides the platform description, special focus is given on data analytics algorithms and methods.

Intrusion Detection Systems (IDSs) are considered as one of the fundamental elements in the network security of an organisation since they form the first line of defence against cyber threats, and they are responsible to detect effectively a potential intrusion in the network. Many IDS implementations use flow-based network traffic analysis to detect potential threats. Network security research is an ever-evolving field and IDSs in particular have been the focus of recent years with many innovative methods proposed and developed. In this paper, we propose a deep learning model, more specifically a neural network consisting of multiple stacked Fully-Connected layers, in order to implement a flow-based anomaly detection IDS for multi-class classification. We used the updated CICIDS2017 dataset for training and evaluation purposes. The experimental outcome using MLP for intrusion detection system, showed that the proposed model can achieve promising results on multi-class classification with respect to accuracy, recall (detection rate), and false positive rate (false alarm rate) on this specific dataset.

This work presents a predictive maintenance methodology aiming at forecasting specific types of faults of an industrial equipment for anode production, utilizing process sensor data from operation periods. The challenge of this problem is the early detection of a fault, particularly just before it occurs. For the forecasting, some unsupervised machine learning architectures were tested. Several considerations were made for the pre-processing steps as well. Finally, automatic feature selection methods were introduced, one of which was used to find the most significant features within successive time windows of the evaluated historical data set. The experimental results, which conform to the visual observations, show that a warning time frame around 20 minutes before the incident is feasible for 43% of the incidents of a particular fault type within a critical 1.5-month period, whereas only in about 0.1% of the timestamps more than 75 minutes before such a fault an alarm is raised.

Intelligent systems and applications on manufacturing domain aim to improve decision-making capabilities, ease complex decision problems, offer predictions related to maintenance activities and provide cost savings to companies. In order to support the aforementioned functionalities, the intelligent prediction and decision support systems are based on machine learning and signal processing techniques, AI algorithms, IoT devices, data mining and modeling techniques, rules and fuzzy logic systems, and advance visualizations. In this paper, we introduce an intelligent information management system that aims to provide predictive maintenance and enhance decision support in a leading lift manufacturer. The proposed solution is a decision support system equipped with analytic tools, IoT sensors and visualizations. The system supports the full cycle of polishing procedures of the lift manufacturer, as it starts from predictive maintenance during the polishing machines' operation and ends in the scrap metals' removal after the operation. Both the intelligent information system and the scenario of its usage in the lift manufacturer's shop floor are presented in this work.

Speech Activity Detection (SAD) plays an important role in mobile communications and automatic speech recognition (ASR). Developing efficient SAD systems for real-world applications is a challenging task due to the presence of noise. We propose a new approach to SAD where we treat it as a two-dimensional multilabel image classification problem. To classify the audio segments, we compute their Short-time Fourier Transform spectrograms and classify them with a Convolutional Recurrent Neural Network (CRNN), traditionally used in image recognition. Our CRNN uses a sigmoid activation function, max-pooling in the frequency domain, and a convolutional operation as a moving average filter to remove misclassified spikes. On the development set of Task 1 of the 2019 Fearless Steps Challenge, our system achieved a decision cost function (DCF) of 2.89%, a 66.4% improvement over the baseline. Moreover, it achieved a DCF score of 3.318% on the evaluation dataset of the challenge, ranking first among all submissions.

Communication service providers (CSPs) face competitive pressure to increase infrastructure utilization and reduce operational costs while at the same time maximize bandwidth and maintain quality of service for end users. Content Distribution Networks (CDNs) are widely used to achieve these goals by optimally distributing content to cache servers at the edge, access and core networks. More recently, virtualization and cloud computing are being used to deploy and utilize virtual Content Delivery Networks (vCDNs) to reduce costs and increase elasticity while avoiding performance, quality, reliability and availability limitations that characterize traditional CDNs. In order to study the effectiveness of virtual cache placements and optimal distribution over a real-world large-scale vCDN infrastructure, we model and simulate BT's network using a novel parallel simulation framework. Results show that virtual cache placement impacts on the number of accepted requests, resource consumption (compute and network), and end-user network latency. We also present a trade-off analysis between infrastructure provider utility and service customer utility to quantity the number and distribution of these virtual cache placements and to determine the optimal number of virtual cache servers that should be deployed.

Visual analytics have an important role in the exploration and analysis of large amounts of data in IoT applications. Data visualizations can provide overviews of different aspects of data and user interaction can assist exploration. Recent advances in machine learning and Artificial Intelligence have provided methods that can be used in conjunction with visual analytics to enhance user perception. However, AI methods are often used as "black boxes", making them difficult for end-users to trust. In this paper, a novel visual analytics platform is presented, targeting two goals: a) an architecture for the creation of custom interactive visual analytics dashboards using well-defined components linked to each other, and b) the inclusion of components specifically for making AI methods more explainable. The proposed architecture and components are being used in the context of the AI4IoT pilot within the AI4EU project, which targets air quality monitoring through AI and visualization.

Power grid is a major part of modern Critical Infrastructure (CIN). The rapid evolution of Information and Communication Technologies (ICT) enables traditional power grids to encompass advanced technologies that allow them to monitor their state, increase their reliability, save costs and provide ICT services to end customers, thus converting them into smart grids. However, smart grid is exposed to several security threats, as hackers might try to exploit vulnerabilities of the industrial infrastructure and cause disruption to national electricity system with severe consequences to citizens and commerce. This paper investigates and compares honey-x technologies that could be applied to smart grid in order to distract intruders, obtain attack strategies, protect the real infrastructure and form forensic evidence to be used in court.

Carpooling is a mobility concept that appears to be the answer when it comes to challenges in urban mobility derived by population growth. In carpooling, the same amount of people move with fewer vehicles leading to reduced traffic congestion and consequently to less CO2 emissions, fuel consumption and drivers frustration. However, there has always been scepticism around carpooling due to the inherent mistrust between drivers and passengers. In recent years, some reputation systems have been proposed to reduce the impact of mistrust on carpooling applications. Among them, the work of Salamanis et al. (Salamanis, 2018), in which a reputation assessment mechanism based on clustering users travel preferences, was introduced. In this paper, we provide an extended version of the previous mechanism and we thoroughly evaluate its robustness in relation with different types of malicious attacks and clustering algorithms. In addition, we compare our mechanism with a benchmarking reputation system that utilizes the simple arithmetic mean to calculate reputation values based on users ratings. The evaluation results indicate that the extended reputation assessment mechanism exhibits more robust behavior compared to the benchmarking system in all types of attacks when using the hierarchical clustering algorithm.

Evidence for a causal relationship between air pollution and disease is present in ninety percent of the urban population, having an impact mainly on predisposed individuals. Respiratory health is a condition highly affected by outdoor and indoor air pollution. In more detail, exposure to a variety of environmental air pollutants may exacerbate a group of respiratory diseases comprising by chronic obstructive pulmonary disease, asthma, respiratory infection, and lung cancer. In this paper, we propose a comprehensive methodology to implement an air quality monitoring system towards the prevention of the effects of air pollution on respiratory health. Our approach combines distributed sensors that collect air quality information at regular intervals. Outdoor air quality monitoring stations, indoor air quality devices, and wearables, form the sensor network in the proposed system. The air quality monitoring system includes also an artificial intelligent monitoring module and a clinical decision support module, for personalized guidance, which has been developed and successfully deployed in asthma disease.

In this study, the requirements elicitation approach employed in the context of the KONFIDO project is presented. KONFIDO introduces a technical paradigm for secure and interoperable cross‐border health data exchange by leveraging novel approaches and cutting‐edge technologies, such as homomorphic encryption and blockchains. Being a key part of the overall user requirements engineering methodology, requirements elicitation focused on producing high‐level, end‐user goals following a systematic procedure. First, the main business processes were identified based on the project's pilot scenarios. These business processes were the subject of a threat analysis, which identified the respective assets and a list of security risks/threats. Threats were further elaborated, considering the outcome of relevant projects and applicable best practices/standards. As a result, a set of user goals were identified and analysed in detail. Finally, a meta‐analysis of the produced goals against the employed information sources was applied, highlighting the importance of standards as a guide for defining requirements, as well as the complexity concerning the interdependencies among the elaborated business processes, assets, threats, and user goals. As the deployment of the technical solution may be cloud‐based, implications and challenges imposed by the adoption of cloud computing in this setting are also presented.

Health data exchange is a major challenge due to the sensitive information and the privacy issues entailed. Considering the European context, in which health data must be exchanged between different European Union (EU) Member States, each having a different national regulatory framework as well as different national healthcare structures, the challenge appears even greater. Europe has tried to address this challenge via the epSOS ("Smart Open Services for European Patients") project in 2008, a European large-scale pilot on cross-border sharing of specific health data and services. The adoption of the framework is an ongoing activity, with most Member States planning its implementation by 2020. Yet, this framework is quite generic and leaves a wide space to each EU Member State regarding the definition of roles, processes, workflows and especially the specific integration with the National Infrastructures for eHealth. The aim of this paper is to present the current landscape of the evolving eHealth infrastructure for cross-border health data exchange in Europe, as a result of past and ongoing initiatives, and illustrate challenges, open issues and limitations through a specific case study describing how Italy is approaching its adoption and accommodates the identified barriers. To this end, the paper discusses ethical, regulatory and organizational issues, also focusing on technical aspects, such as interoperability and cybersecurity. Regarding cybersecurity aspects per se, we present the approach of the KONFIDO EU-funded project, which aims to reinforce trust and security in European cross-border health data exchange by leveraging novel approaches and cutting-edge technologies, such as homomorphic encryption, photonic Physical Unclonable Functions (p-PUF), a Security Information and Event Management (SIEM) system, and blockchain-based auditing. In particular, we explain how KONFIDO will test its outcomes through a dedicated pilot based on a realistic scenario, in which Italy is involved in health data exchange with other European countries.

Abstract This paper presents the architecture of a decision support environment for large-scale assessment of compliance against web accessibility recommendations and legislations. The proposed decision support environment aims at integrating, extending and further enhancing existing web accessibility solutions making them customizable to the needs of different stakeholders, transferable to different sectors in web and mobile environments, in order to minimize costs and development time by also increasing scalability and improving their accessibility and usability.

Material aging has a significant effect on the realistic rendering of artwork objects. Small deformations of the surface structure, color or texture variations contribute to the realistic look of artwork objects. These aging effects depend on material composition, object usage, weathering conditions, and a large number of other physical, biological, and chemical parameters. In this work we focus on local deformations due to corrosion/erosion and finally cracks mainly by modeling the behavior of displacements locally. Micro-profilometry provides the quantitative measurements of the surface texture and roughness at micro-metric level, which is used to obtain information about material changes over time in terms of its surface deformation. We present a method for deriving a model for simulating aging based on micro-profilometry measurements taken on material sample plates during an emulated aging process. Subsequently, we use this model for realistic rendering of aged artwork objects.

With the proliferation of high resolution 3D scanners, the quality of recorded 3D models has greatly improved. Nonetheless, while geometric fidelity is important, color information is still required to achieve photo-realistic 3D models. In this regard, texture reconstruction techniques combine color images from several views in order to optimally color the mesh of a 3D model. Nonetheless, a major challenge that is often overlooked by existing approaches is the technical limitations of color acquisition devices that lead to erroneously colored 3D models. In this paper, a novel technique is presented that formulates texture reconstruction as an optimization problem incorporating a color correction term in its objective function. The underlying rationale is to exploit external to the 3D scanner color measurements that can be available from more reliable sensors such as a UV- VIS spectrometer. Such measurements are often available for objects of high aesthetic value such as artworks of cultural heritage objects. Through experimental evaluation of our method on a real painting we demonstrate the superiority of the proposed technique, compared to state-of-the-art texture reconstruction, providing a reliable representation of the artworks appearance both in terms of numerical accuracy and visual observation.

This work presents a predictive maintenance methodology so as to forecast possible equipment stoppages (or faults) of an industrial equipment for anode production along with the fault type in real time, utilizing process sensor data from operation periods. The warning timeframe so as equipment stoppage to be predicted has been set by the process experts as far as possible before the incident occurs. For the forecasting, some widely used machine learning architectures are tested. The visualization of the features patterns and the simulation results show that a warning timeframe around 5-10 minutes before the incident occurs is a feasible goal.

The H2020 European research project Safe-Guarding Home IoT Environments with Personalised Real-time Risk Control (GHOST) aims to develop a cyber-security layer on IoT smart home installations. The proposed system analyses packet-level data flows for building patterns of communications between IoT devices and external entities. To ensure non-repudiation, integrity and authentication of the data captured, they are stored in a Blockchain, a distributed ledger network, as digitally-signed transactions. Since the data can potentially include sensitive user information, it is imperative to promote trust by informing users about the operating principles of the network as well as to request the acceptance of a consent form by them. This paper presents the design and implementation of a Forms of Consent application, a Distributed Application that interacts with a set of Smart Contracts deployed on a private Ethereum network. The application is being developed as part of the GHOST project.

The European research project GHOST challenges the traditional cyber security solutions for the Internet of Things (IoT) sector by exploiting novel technologies, such as blockchain, to provide resilience and integrity of decision making on the communication exchange in a smart home context. When it comes to novel cyber security solutions for extremely heterogeneous environments like IoT and smart homes, the key focus is typically given to the understanding of network activities and elimination of suspicious traffic. The GHOST project adds an extra dimension to this approach by integrating blockchain technology at its core decision mechanism. On a daily basis, each GHOST installation is encountering malicious behaviour and suspicious IoT communications, where easy information sharing with other installations, as well as decentralised decision making, are mandatory features for the efficient protection of the end-user. GHOST's Smart Contracts (SC) are designed to tackle in an easy, yet productive way, the reporting on suspicious IP addresses which the IoT devices in a smart home are trying to communicate with. Two variations of blacklisting smart contracts are presented in this paper, covering a diverse spectrum of possible attack vectors while closely following the Privacy by Design (PbD) principles. A reputation scoring scheme for malicious IPs reporting is integrated in the SC, uncovering the implementation details on the penalisation of existing entries in case of malicious behaviour of reporting devices.

The aim of the proposed work is to introduce a secure and interoperable Demand Response (DR) management platform that will assist Aggregators (or other relevant Stakeholders involved in DR business scenarios) in their decision making mechanisms over their portfolios of prosumers. This novel architecture incorporates multiple strategies and policies provided from energy market stakeholders, establishing a more modular and future-proof DR solution. By employing an innovative multi-agent decision making system and self-learning algorithms to enable aggregation, segmentation and coordination of several diverse clusters, consisting of supply and demand assets, a fully autonomous design will be delivered. This DR framework is further fortified in terms of data security by not only implementing cutting-edge blockchain infrastructure, but also by making use of Smart Contracts and Decentralized Applications (dApps) which will further secure and facilitate Aggregators-to-Prosumers transactions. The blockchain technologies will be combined with well-known open protocols (i.e. OpenADR) towards also supporting interoperability in terms of information exchange.

Automatic subsurface mapping is essential in the construction services, as it is anticipated to become the main operational environment of the future robots to be realized in the respective domain. Towards this direction, the paper at hand, introduces for the first time herein, an integrated framework for subsurface mapping by exploiting a surface operating mobile robot with a Ground Penetrating Radar (GPR). The mobile robot tows the GPR antenna, which is mounted on a specifically designed trailer, and is utilized as the mean to cover the surface area, while at the same time the antenna scans the subsurface by emitting electromagnetic pulses. The gathered data are processed for the construction of a subsurface 3D map. Specifically, image processing techniques, that involve background segmentation, HOG [1] feature extraction, hypothesis verification and matching are applied on the 2D radargram (B-Scan) for the detection of the salient points that correspond to buried utilities. By employing the pulse propagation velocity into the subsurface and the soil utilities, the salient points are expressed in world coordinates and used for the composition of the 3D subsurface map. Our method has been evaluated on a real test site, accompanied by ground-truth annotation data of experts and revealed remarkable performance, exhibiting not only the feasibility of underground mapping but also the capacity to obtain exploitable results for underground robotic applications.

This video features RAMCIP, a new service robot developed to provide proactive and discreet assistance to elderly with Mild Cognitive Impairments (MCI), supporting their daily activities at home. Starting with a thorough analysis of needs and requirements of the target population, the RAMCIP robot was developed as an integrated ensemble of advanced H/W and S/W components, realizing the robot skills of perception, cognition, safe navigation, grasping, manipulation, and human-robot communication, ample to operate in real, rather challenging domestic environments. The RAMCIP use-cases include proactive assistance provision to user's cooking, eating and medication activities, through discreet user monitoring and robot interventions by reminders and robotic manipulations., RAMCIP can bring the medicine, recognize fallen objects and electric appliance that has been forgotten turned on. It also recognizes the user walking in low-light conditions and turns on the light, as well as detects cases of emergency such as a fall. The robot provides also the user with cognitive training games and stimulates the user to contact with relatives through video-calls. Pilot trials of the RAMCIP robot have been performed in real homes of more than ten different users, in Barcelona, Spain; the video at hand exhibits the robot performing the target use cases.

Attacks on the content and quality of service of IoT platforms have economic and physical consequences well beyond the Internet's lack of security. This paper describes a new research project on “Secure and Safe Internet of Things” (SerIoT) to improve both the information and physical security of IoT applications platforms in a holistic and cross-layered manner. The purpose is to be able to create secure operational IoT platfnrms for diverse applieations.

For a proactive and user-centered robotic assistance and communication, an assistive robot must make decisions about the level of assistance to be provided. Therefore, the robot must be aware of the preferences and the capabilities of the elderly. At the same time, relying on a sensing setup which is totally embedded in the assistive robot would increase its usability. In the framework of the RAMCIP project, a novel skills evaluation methodology has been developed to make the robot aware of the user's perceptual, cognitive and motor skills. This paper presents such a methodology and its preliminary evaluation. Based on a task analysis of the activities for which the robot provides assistance, the user's skills are given a score which is updated at different time scales based on the source of information. Highly reliable information is gathered from caregivers at a low rate by means of a graphical interface hosted by the robot. This information refers to standard medical examinations. Based on the modules for motion tracking, object and activity recognition, specific actions of ADL are selected to update motor skills score at a higher rate, which is typically twice per day. The two sources of information are then fused in a Kalman filter. Preliminary results on the illustrative example of arm precision show that the robot's sensing and cognitive capabilities suffice to obtain a state-of-the-art evaluation of the arm precision skill.

Sensor-based quantitative assessment of frailty in older adults usually requires expensive wearables or ambient sensors. However, low cost activity tracking systems can be effective in discriminating frailty characteristics. In this work, a system for assessment of the frailty of older people is presented, based on the analysis of data describing the daily in-house activity. More specifically, room-to-room movements are recorded using a set of Bluetooth beacons, located in fixed positions inside each room. A smartphone is used to locate the timestamps of room transitions, and these are used to calculate the time spent in each room, thus generating the time intervals signal. Then, several features depicting the in-house mobility are extracted from each signal and are evaluated, in terms of correlation with the subject's frailty status. Furthermore, the features are used to train a classifier for frailty assessment. Two approaches are evaluated, with different number of levels of frailty, and the results indicate that this procedure can lead to reliable older people frailty assessments, especially when two levels of frailty are considered.

BACKGROUND: Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. RESULTS: Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. CONCLUSIONS: The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.

The reduction of road congestion requires intuitive urban congestion-control platforms that can facilitate transport stakeholders in decision making. Interactive ITS visual analytics tools can be of significant assistance, through their real-time interactive visualizations, supported by advanced data analysis algorithms. In this paper, an interactive visual analytics platform is introduced that allows the exploration of historical data and the prediction of future traffic through a unified interactive interface. The platform is backed by several data analysis techniques, such as road behavioral visualization and clustering, anomaly detection, and traffic prediction, allowing the exploration of behavioral similarities between roads, the visual detection of unusual events, the testing of hypotheses, and the prediction of traffic flow after hypothetical incidents imposed by the human operator. The accuracy of the prediction algorithms is verified through benchmark comparisons, while the applicability of the proposed toolkit in facilitating decision making is demonstrated in a variety of use case scenarios, using real traffic and incident data sets.

In this paper, a gamified social platform designed for people living with Dementia (PLWD) and their caregivers is presented. This platform constitutes a support tool that strengthens self-care and builds community capacity and engagement at the point of care. Its architecture related to gamification aspects was designed to be flexible and scalable to support personalized services such as user monitoring, social networking, cognitive skills training and improvement of the adherence to treatment guidelines for PLWD and their caregivers. Advanced visual analytics and reporting tools are available for medical professionals and social workers to support the decision-making processes. The outcomes of this approach are delivered through a set of gamification concepts running in parallel to create motivation for achieving the desired behavioural change. After projecting all user group expectations on a social game canvas, the impact evaluation will assess the intended effects of the proposed gamification approach on the welfare on PLWD and their surroundings.

The Decision Support tool of the myAirCoach platform is presented in this work. MyAirCoach is a web-based platform for personalized management of asthma patients. The Decision Support tool is used from medical experts to generate knowledge-based rules for asthma patients. The tool is a web-based application that includes a simple-to-use rule editor for generating medical rules in Arden Syntax, which is an HL7 standard. The logic of the rules in formulated in disjunctive normal form. Using the this tool, medical experts can generate rules and specify the set of rules that are applicable for alerting each patient, thus creating a personalized decision support system for asthma patients.

Complex multimedia data handling is as a hugechallenge for the computer science community. Social networks, manufacturing, retail, national and cyber-security, medicine, computational biology, etc. are related to a variety of heterogeneousimages and videos that create unique methodologicalchallenges. Data complexity arising from these large-scale applicationsseek for high-performance processing to obtain datainsights. Dimensionality reduction techniques reveal a managementexcellence towards multimedia data manipulation. Manydimensionality reduction techniques can be described within theframework of graph embedding, where a neighborhood graph isconstructed from the data, in order to reveal their structure. Existingtechniques usually handle only a single data representation, so-called modality, resulting in a single neighborhood graph. However, data often are multimodal, i.e. the same semantic conceptis described by various diverse representations. In the contextof graph embedding, the multiple modalities can be representedas multiple neighborhood graphs among the data. In this paper, an extension of the graph embedding framework is presented, where a multimodal graph is constructed as a weighted sumof the multiple unimodal graphs. Different from other methods, the weights of this sum are adaptively calculated as the solutionto an optimization problem, where an introduced measure ofgraph consistency, based on a rational assumption regardingthe similarities between objects, is optimized. The experimentalresults of the comparison of the proposed Multimodal GraphEmbedding (MGE) dimensionality reduction method to existingwork prove that this adaptive weighting scheme leads to superiorperformance in a number of experimental settings and datasets.

Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide personalized results, while not considering the accessibility of the results for users with vision or other types of impairments. In this paper, a novel approach is presented which incorporates the accessibility of images for users with various vision impairments, such as color blindness, cataract and glaucoma, in order to rerank the results of an image search engine. The accessibility of individual images is measured through the use of vision simulation filters. Multi-objective optimization techniques utilizing the image accessibility scores are used to handle users with multiple vision impairments, while the impairment profile of a specific user is used to select one from the Pareto-optimal solutions. The proposed approach has been tested with two image datasets, using both simulated and real impaired users, and the results verify its applicability. Although the proposed method has been used for vision accessibility-based reranking, it can also be extended for other types of personalization context.

In this paper we propose an algorithm for multiple object tracking, a heavily researched but still challenging problem of computer vision. We follow the tracking by detection paradigm in an online fashion and formulate tracking as a typical assignment problem between detections and existing tracks that is solved by a modification of the Hungarian algorithm. Contrary to other methods that use a multitude of features based on appearance, optical flow and prior knowledge gained through training, we solely use clusters of point trajectories to link detections and tracks. Point trajectories are robust under partial occlusions and allow the expansion of a track even in the absence of a detection. At the core of our algorithm lies a motion segmentation method that extracts coherent clusters from triangulated point trajectories. Our algorithm achieves competitive results on the 2D MOT 2015 benchmark showcasing its potential.

Geometry contraction algorithms are introduced in this work to implement the diverse respiratory configurations of lung related diseases associated with airways obstructions. In addition, computational fluid dynamics (CFD) techniques along with fluid particle tracing (FPT) methods are utilized to efficiently evaluate the behavior of the airflow during the inhalation period, as well as to clarify the features of the inhaled particles in terms of regional deposition. Useful deductions are drawn regarding personalized medication in obstructed conditions.

As clouds increase in size and as machines of different types are added to the infrastructure in order to\nmaximize performance and power efficiency, heterogeneous clouds are being created. However, exploiting\ndifferent architectures poses significant challenges. To efficiently access heterogeneous resources and, at the\nsame time, to exploit these resources to reduce application development effort, to make optimisations easier\nand to simplify service deployment, requires a re-evaluation of our approach to service delivery. We\npropose a novel cloud management and delivery architecture based on the principles of self-organisation\nand self-management that shifts the deployment and optimisation effort from the consumer to the software\nstack running on the cloud infrastructure. Our goal is to address inefficient use of resources and\nconsequently to deliver savings to the cloud provider and consumer in terms of reduced power consumption\nand improved service delivery, with hyperscale systems particularly in mind. The framework is general but\nalso endeavours to enable cloud services for high performance computing. Infrastructure-as-a-Service\nprovision is the primary use case, however, we posit that genomics, oil and gas exploration, and ray tracing\nare three downstream use cases that will benefit from the proposed architecture.

Knowledge modelling at industrial level consists an importunate activity nowadays due to the ceaseless advances in technologies and standards applied as well as the extensive amount of unrelated real-time and historical data at shop-floor level. A Common Interface Data Exchange Model (CIDEM) is hereby introduced towards unifying continuously produced data from heterogeneous and distributed information sources - on different levels and granularities - into a shared vocabulary that can unobtrusively communicate with industrial standards and protocols (i.e. B2MML, gbXML, MIMOSA). For further enhancing the information model a conceptual definition is employed leading to a semantically enriched model which enables more understandable high level knowledge diffusion. This model has been applied to various industrial applications, one of the most important being industrial safety, through incident recognition.

In this work, a modified version of a Slope Statistic Profile (SSP) method is proposed, capable to detect real-time incidents that occur in two interdependent time series. The estimation of incident time point is based on the combination of their linear trend profiles test statistics, computed on a consecutive overlapping data window. Furthermore, the proposed method uses a self-adaptive sliding data window. The adaptation of the size of the sliding data window is based on real-time classification of the linear trend profiles in constant and equal time intervals, according to two different linear trend scenarios, suitably adjusted to the conditions of the problem we face. The proposed method is used for the robust identification of a malfunction and it is demonstrated to real datasets from a chemical process pilot plant that is situated at the premises of CERTH / CPERI during the evolution of the performed experiments at the process unit.

Border gateway protocol (BGP) is the main protocol used on the Internet today, for the exchange of routing information between different networks. The lack of authentication mechanisms in BGP, render it vulnerable to prefix hijacking attacks, which raise serious security concerns regarding both service availability and data privacy. To address these issues, this study presents BGPGraph, a scheme for detecting and visualising Internet routing anomalies. In particular, BGPGraph introduces a novel BGP anomaly metric that quantifies the degree of anomaly on the BGP activity, and enables the analyst to obtain an overview of the BGP status. The analyst, is afterwards able to focus on significant time windows for further analysis, by using a hierarchical graph visualisation scheme. Furthermore, BGPGraph uses a novel method for the quantification of information visualisation that allows for the evaluation, and optimal selection of parameters, in case of the corresponding visual analytics algorithms. As a result, by utilising the proposed approach, four new BGP anomalies were able to be identified. Experimental demonstration in known BGP events, illustrates the significant analytics potential of the proposed approach in terms of identifying prefix hijacks and performing root cause analysis.

The exploration of the potential correlations of traffic conditions between roads in large urban networks, which is of profound importance for achieving accurate traffic prediction, often implies high computational complexity due to the implicated network topology. Hence, focal methods are required for dealing with the urban network complexity, reducing the performance requirements that are associated to the classical network search techniques (e.g., Breadth First Search). This paper introduces a graph-theory-based technique for managing spatial dependence between roads of the same network. In particular, after representing the traffic network as a graph, the local neighbors of each road are extracted using Breadth First Search graph traversal algorithm and a lower complexity variant of it. A Pearson product-moment correlation-coefficient-based metric is applied on the selected graph nodes for a prescribed number of level sets of neighbors. In order to evaluate the impact of the new method to the traffic prediction accuracy achieved, the most correlated roads are used to build a STARIMA model, taking also into account the possible time delays of traffic conditions between the interrelated roads. The proposed technique is benchmarked using traffic data from two different cities: Berlin, Germany, and Thessaloniki, Greece. Benchmark results not only indicate significant improvement on the computational time required for calculating traffic correlation metric values but also reveal that a different variant works better in different network topologies, after comparison to third-party approaches.

Mobile devices are evolving and becoming increasingly popular over the last few years. This growth, however, has exposed mobile devices to a large number of security threats. Malware installed in smartphones can be used for a variety of malicious purposes, including stealing personal data, sending spam SMSs, and launching Denial of Service (DoS) attacks against core network components. Authentication and access-control-based techniques, employed by network operators fail to provide integral protection against malware threats. In order to solve this issue, the activity of each mobile device in the network must be taken into account, and combined with the activities of all the other devices. The communication activity in the mobile network has a source, a destination, and possibly communication weights (e.g., the number of calls between two mobile devices). This relational nature of the communication activity is naturally represented with graphs. This indicates that graphs can be utilized in order to provide better representations of the entire network activity, and lead to better detection results when compared to methods that consider the activity of each mobile device individually. Towards this end, this paper proposes a novel graph-based descriptor for the detection of anomalies in mobile networks, using billing-related information. The graph-based descriptor represents the total activity in the network. Smaller graphs are afterwards extracted from the graph-based descriptor, each one representing the activity of one mobile device (e.g., Calls or SMSs), while multiple features are calculated for each such graph. These features are subsequently used for the supervised classification on network events, and the identification of anomalous mobile devices. Experimental results and comparison of the proposed anomaly detection method to the existing work, show that the graph-based descriptor has superior performance in a variety of scenarios.

Flow cytometry (FC) is widely used for diagnostic purposes in clinical practice. This analysis typically aims at clustering cellular events according to their biological characteristics, known as gating, and then use the selected clusters in order to conclude about clinical outcomes. As each step of this process is highly subjective, various proposed methods have attempted to automate each step of the procedure separately, but any method has been proposed in order to automate the whole diagnostic process. We constructed a tool that simulates the experts decisions during the whole process in order to conclude if a sample is pathologic or not (`healthy'). We used flow cytometric data from 10 individuals with a diagnosis of chronic lymphocytic leukemia (CLL) from a panel that produces 7 files for each sample. With the help of the present tool we were able to identify whether the analysis of the tested sample confirms the diagnosis of CLL, thus successfully reproducing the experts' decisions at each step of the diagnostic workflow. The validation was conducted by experts against the traditional manual procedure. The proposed methodology is the first attempt to automate the entire process, which is a prerequisite for a fully automated diagnostic system that would ensure objectivity to the clinical diagnostic procedure. The experimental results presented herein show that our proposed new technique has satisfying performance at each level of evaluation.

A novel big data building performance evaluation knowledge processing and mining system utilizing visual analytics is going to be presented in this paper. A large dataset comprised of building information, energy consumption, environmental measurements, human presence and behavior and business processes is going to be exploited for the building performance evaluation. Building performance evaluation is one of the most important factors in engineering that leads to building renovation and construction with low energy consumption and gas emissions in conjunction with comfort, utility and durability. For this purpose, business processes occurring in the building are correlated with the energy consumption and the human flows in the spatiotemporal domain modeling the dynamic behavior of the building. These models lead to the extraction of useful semantic information and the detection of spatiotemporal patterns that are important for the evaluation of the building performance. Furthermore, a number of novel visual analytics techniques allow the end-users to process data in different temporal resolutions and with different temporal filters, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed visual analytics techniques support design and energy management decisions by visualizing the building measurements regarding business and comfort aspects. To do so, the proposed system includes a variety of techniques and components, properly selected to offer quick identification of focal points and evaluation of the building performance. Considering the increasing interest and the green building goals of almost all world governments including EU, the suggested methodology and application could be rendered a very useful tool for the Architecture and Engineering Community working on Building Performance Simulation and Analysis, and all related communities in Architect, Engineering and Construction (AEC) industry.

In this work, a novel, multi-space, real-time and robust human tracking system is going to be presented. The system exploits a multi-camera network monitoring the multi-space dynamic environment under interest, detecting and tracking the humans in it. The system is able to handle the dynamic changes of the environment, as well as partial occlusions utilizing virtual top cameras. Furthermore, the system is able to real-time visualize the detection and tracking results on the architectural map of the dynamic environment, as well as a variety of statistics. The visual spatio-temporal analysis of the tracked data are presented in a consolidated form for the overall monitoring area and analytically for each space separately and for each tracked human. These statistics could be also combined with the energy consumption in the area, as well as with other environmental data providing semantic information such as comfort. The overall system is equipped with a number of visual interactive tools providing real-time spatio-temporal human presence analysis offering to the user the opportunity to capture and isolate the areas/spaces with high human presence, the days and times of high human presence, to correlate this information with the potential energy consumption and indicators such as comfort.

We introduce an open framework for built-in accessibility support at all stages of product development based on the Virtual User Model concept. The goal is to introduce simulation-based and VR testing into the automotive, smart living spaces, workplace, infotainment and personal healthcare applications areas. Our framework also aims to ensure that future products and services are being systematically designed for all people including those with functional limitations. An open simulation platform parted of several tools provides automatic simulation feedback and reporting for guideline/methodologies compliance and quality of service. To achieve this objective, detailed virtual user physical, cognitive, behavioral and psychological models as well as the corresponding simulation models have been integrated to support simulation and testing at all stages of product planning and development.

Using data visualization techniques can be of significant assistance in exploring multimedia databases. Data visualization is typically addressed as a unimodal learning task, where data are described with only one feature set, or modality. However, using multiple data modalities has been proved to increase the performance of learning methods. In this paper a novel approach for exploiting the multiple available modalities for visualization is proposed, motivated by the field of multi-objective optimization. Initially, each modality is considered separately. A graph of the dissimilarities among the data and the corresponding minimum spanning tree are formed. The suitability of a particular data placement is quantified using multiple cost functions, one for each modality. The utilized cost functions are defined in terms of graph aesthetic measures, computed for the unimodal minimum spanning trees. The cost functions are then used as the multiple objectives of a multi-objective optimization problem. Solving the problem results in a set of Pareto optimal placements, which represent different trade-offs among the various objectives. Experimental evaluation shows that the proposed method outperforms current multimodal visualization methods both in discovering more visualizations and in producing ones which are more aesthetically pleasing and easily perceivable.

This paper presents BGPfuse, a scheme for visualizing and exploring BGP (Border Gateway Protocol) path change anomalies. BGPfuse uses a set of BGP features that are capable of quantifying the degree of anomaly of each path change event. Moreover, visual methods are introduced for performing the efficient fusion of these multiple features. The exploitation of the human perception, allows to overcome the static-nature of the existing weight-based fusion approaches. A Parallel Coordinates approach is used to visualize these features, which is further enhanced with filtering capabilities, so as to discriminate between normal and abnormal events. BGPfuse uses multiple linked graph views so as to represent in depth the relationships among the involved Autonomous Systems (ASes), as well as a combined graph view to highlight structural similarities between all the individual feature graphs. The structural similarities as well as the filtering capabilities provided by BGPfuse, enable the analyst to perform visual fusion of the BGP features, so as to detect any suspicious behavior and focus only in the most interesting cases. Experimental demonstration of BGPfuse, shows the analytical potential of the proposed approach by decisively capturing malicious BGP hijacking events.

In this work we present a visual analytics tool introducing a new kind of graph visualization that exploits the nodes' degree to provide a simplified and more abstract, yet accurate, representation of the most important elements of a security data set and their inter-relationships. Our visualization technique is designed to address two shortcomings of existing graph visualization techniques: scalability of visualization and comprehensibility of results. The main goal of our visual analytics tool is to provide security analysts with an effective way to reason interactively about various attack phenomena orchestrated by cyber criminals. We demonstrate the use of our tool on a large corpus of spam emails by visualizing spam campaigns performed by spam botnets. In particular, we focus on the analysis of spam sent in March 2011 to understand the impact of the Rustock takedown on the botnet ecosystem. As spam botnets continue to play a significant role in the worldwide spam problem, we show with this application how security visualization based on abstract graphs can help us gain insights into the strategic behavior of spam botnets, and a better understanding of large-scale spammers operations.

This letter presents a novel probabilistic framework for augmenting the recognition performance of biometric systems with information from continuous soft biometric (SB) traits. In particular, by modelling the systematic error induced by the estimation of the SB traits, a modified efficient recognition probability can be extracted including information related both to the hard and SB traits. The proposed approach is applied without loss of generality in the case of gait recognition, where two state-of-the-art gait recognition systems are considered as hard biometrics and the height and stride length of the individuals are considered as SBs. Experimental validation on two known, large datasets illustrates significant advances in the recognition performance with respect to both identification and authentication rates.

SUMMARY The addition of semantic information into Web services (WS) results in more accurate search and retrieval in service registries. The key issue to facilitate organization of services, taking into account their semantics, is the development of automatic mechanisms that generate appropriate mappings between Web service elements and their semantics‐enabled counterparts. In this paper, we introduce an ontology‐based mechanism for automatic semantic categorization of WS and their structural components. The presented approach, as opposed to similar ones, takes into account the lexicographic, structural, and data type characteristics of WS. Moreover, a software tool that implements the proposed service categorization mechanism is presented, and a benchmark process is executed that reveals outstanding performance of the developed mechanism in comparison with a relevant state‐of‐the‐art approach. Copyright © 2011 John Wiley & Sons, Ltd.

This paper presents work conducted toward the biosignals-based automatic recognition of boredom, induced during video-game playing. For this purpose, common biosignal feature extraction methods were exploited and their capability to identify boredom was assessed. Moreover, for the first time, Legendre and Krawtchouk moments, as well as novel moment variations, were extracted as biosignal features and their potential toward automatic affect recognition was examined using the specific application scenario. The present analysis was conducted with ECG and GSR data collected from 19 different subjects, while boredom was naturally induced during the repetitive playing of a 3D video game. Conventional biosignal features as well as moment-based ones were found to be effective for the automatic recognition of boredom by achieving classification accuracies around 85 percent. Then, the joint use of moments and moment variations with conventional features was found to significantly improve classification accuracy by producing a maximum correct classification ratio of 94.17 percent.

This paper proposes a probabilistic framework for enhancing the recognition performance of 3D object queries based on soft shape descriptors, such as volume, surface and convexity. The goal is to improve the initial ranking results of pure geometry-based algorithms by capitalizing on the discriminative power of objects with close values on the so-called “soft shape descriptor”. The proposed method has been applied to benchmarking 3D object databases providing promising results demonstrating a clear improvement on the precision/recall ratio.

Abstract In this paper we present a new method for improving the performance of the widely used Bounding Volume Hierarchies for collision detection. The major contribution of our work is a culling algorithm that serves as a generalization of the Separating Axis Theorem for non parallel axes, based on the well‐known concept of support planes. We also provide a rigorous definition of support plane mappings and implementation details regarding the application of the proposed method to commonly used bounding volumes. The paper describes the theoretical foundation and an overall evaluation of the proposed algorithm. It demonstrates its high culling efficiency and in its application, significant improvement of timing performance with different types of bounding volumes and support plane mappings for rigid body simulations.

The widespread deployment of biometric systems has raised public concern about security and privacy of personal data. In this paper, we present a novel framework for biometric template security in multimodal biometric authentication systems based on error correcting codes. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder based on distributed source coding principles. It is shown that the proposed method binds the biometric template in a cryptographic key which does not reveal any information about the original biometric data even if it is compromised by an attacker. Furthermore, the advantages of the proposed method in terms of security and impact on matching accuracy are discussed. We assess the performance of the proposed method in the context of HUMABIO, an EU Specific Targeted Research Project, where face and gait biometrics are employed in an unobtrusive application scenario for human authentication. Experimental evaluation on a multimodal biometric database demonstrates the validity of the proposed method.

This letter presents a novel framework for gait recognition augmented with soft biometric information. Geometric gait analysis is based on Radon transforms and on gait energy images. User height and stride length information is extracted and utilized in a probabilistic framework for the detection of soft biometric features of substantial discrimination power. Experimental validation illustrates that the proposed approach for integrating soft biometric features in gait recognition advances significantly the identification and authentication performance.

In the above letter (ibid., vol. 17, no. 4, pp. 367-370, Apr. 10), equation (5) requires modification. The revised equation is presented here.

This paper presents a novel framework for partial matching and retrieval of 3-D models based on a query-by-range-image approach. Initially, salient features are extracted for both the query range image and the 3-D target model. The concept behind the proposed algorithm is that, for a 3-D object and a corresponding query range image, there should be a virtual camera with such intrinsic and extrinsic parameters that would generate an optimum range image, in terms of minimizing an error function that takes into account the salient features of the objects, when compared to other parameter sets or other target 3-D models. In the context of the developed framework, a novel method is also proposed to hierarchically search in the parameter space for the optimum solution. Experimental results illustrate the efficiency of the proposed approach even in the presence of noise or occlusion.

Human authentication using biometric traits has become an increasingly important issue in a large range of applications. In this paper, a novel channel coding approach for biometric authentication based on distributed source coding principles is proposed. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder and error correcting codes are employed for user verification. It is shown that the effective exploitation of the noise channel distribution in the decoding process improves performance. Moreover, the proposed method increases the security of the stored biometric templates. As a case study, the proposed framework is employed for the development of a novel gait recognition system based on the extraction of depth data from human silhouettes and a set of discriminative features. Specifically, gait sequences are represented using the radial and the circular integration transforms and features based on weighted Krawtchouk moments. Analytical models are derived for the effective modeling of the correlation channel statistics based on these features and integrated in the soft decoding process of the channel decoder. The experimental results demonstrate the validity of the proposed method over state-of-the-art techniques for gait recognition.

In this paper, a novel approach for 3-D Shape description and retrieval based on the theory of ellipsoidal harmonics is presented. Four novel descriptors are introduced: the surface ellipsoidal harmonics descriptor, which concerns 3-D objects that are described as polygonal surfaces; the volumetric ellipsoidal harmonics descriptor, which is applicable to volumetric 3-D objects; the generalized ellipsoidal harmonics descriptor that is applied to any local 3-D object descriptors; and, finally, the combined ellipsoidal-spherical harmonics descriptor, which leads to a compact and powerful descriptor that inherits the advantages of both approaches: the rotation invariance properties of the spherical harmonics and the directional information enclosed in ellipsoidal harmonics. Experimental results performed using well-known 3-D object databases prove the retrieval efficiency of the proposed approach.

To overcome the shortcomings posed by audio rendering of web pages for blind users, this paper implements an interaction technique where web pages are parsed so as to automatically generate a virtual reality scene that is augmented with a haptic feedback. All elements of a web page are transformed into a corresponding "hapget" (haptically-enhanced widget), a three dimensional widget exhibiting a behavior that is consistent with their web counterpart and having haptic extension governed by usability guidelines for haptic interaction. A set of implemented hapgets is described and used in some examples. All hapgets introduced an extension to UsiXML, a XML-compliant User Interface Description Language that fosters model-driven engineering of user interfaces. In this way, it possible to render any UsiXML-compatible user interface thanks to the interaction technique described, and not only web pages.

A novel gait authentication scheme based on distributed source coding principles is proposed. Biometric recognition is formulated as a coding problem with noisy side information at the decoder and error correcting codes are employed for user authentication. The effective exploitation of the noise channel statistics in the decoding process improves performance. It is also shown that the proposed method increases the security of the stored biometric templates. Gait recognition is based on the extraction of depth data from human silhouettes and a set of discriminative features. The experimental results demonstrate the validity of the proposed method.

In this paper, a fuzzy expert system (FES) for the detection of the physiological manifestations of extreme hypovigilance is presented. A large number of features that describe the eyelid activity of drivers is examined, and fuzzy logic is used for the fusion of the most prominent features to not only increase the accident prediction accuracy but also provide a reliable system that generates a small number of false warnings. For the development and testing of the system, driving simulator data from 35 drowsy subjects were used. In addition, a secondary control group of 13 alert drivers was used for the estimation of the trained system's false alarm ratio. The results show that a fuzzy combination of eyelid activity parameters may lead to a system with high sensitivity and specificity in predicting sleep onset and related accidents.

This paper presents a novel framework for 3-D object content-based search and retrieval, appropriate for both partial and global matching applications. The framework is based on a graph representation of a 3-D object which is enhanced by local geometric features. The 3-D object is decomposed into meaningful parts and an attributed graph is constructed based on the connectivity of the parts. Every 3-D part is approximated with a suitable superellipsoid and a novel 3-D shape descriptor, called a 3-D distance field descriptor, is computed and associated to the corresponding graph nodes. The matching process used is based on attributed graph matching algorithm appropriate for this application. The proposed method not only provides successful retrieval results in terms of geometric similarity but also is invariant to rotation, translation and scaling of an object as well as to the different poses of articulated objects. Finally, it can be effectively used for partial and global 3-D object retrieval.

The present paper presents a novel approach for gait identification using 3D data and Krawtchouk moments to generate the descriptor feature vectors. The gait sequence is captured by a stereoscopic camera and the resulting 2.5D data are processed to generate a 3D hull of the captured silhouette. The 3D Protrusion Transform is then proposed that generates a silhouette image containing protrusion information. Finally, the descriptor vector of the extended silhouette is calculated using the Krawtchouk moments. Experimental evaluation illustrates that the proposed scheme is highly efficient in identifying gait sequences when compared to state of the art approaches.

In this paper, a method suitable for partial matching between 3D objects is presented. The 3D objects are firstly segmented into meaningful parts extending a method which is based on the medial surface of the objects. Then, geometric features are extracted for each part using the spherical trace transform. The extracted features are combined and their covariance matrix is computed as a descriptor of each part. The contribution of the proposed approach is that a meaningful segmentation of 3D objects based on medial surface is achieved and that partial matching is performed on meaningful parts, in a rotation, translation and scaling invariant manner. The experimental results performed, proved that the proposed approach achieves accurate partial matching results in terms of distinct meaningful parts as well as satisfactory overall accuracy.

This paper proposes an innovative gait identification and authentication method based on the use of novel 2-D and 3-D features. Depth-related data are assigned to the binary image silhouette sequences using two new transforms: the 3-D radial silhouette distribution transform and the 3-D geodesic silhouette distribution transform. Furthermore, the use of a genetic algorithm is presented for fusing information from different feature extractors. Specifically, three new feature extraction techniques are proposed: the two of them are based on the generalized radon transform, namely the radial integration transform and the circular integration transform, and the third is based on the weighted Krawtchouk moments. Extensive experiments carried out on USF ldquoGait Challengerdquo and proprietary HUMABIO gait database demonstrate the validity of the proposed scheme.

This paper presents a novel layered and fast framework for real-time collision detection and haptic interaction in virtual environments based on superquadric virtual object modeling. An efficient algorithm is initially proposed for decomposing the complex objects into subobjects suitable for superquadric modeling, based on visual salience and curvature constraints. The distance between the superquadrics and the mesh is then projected onto the superquadric surface, thus generating a distance map (SQ-Map). Approximate collision detection is then performed by computing the analytical equations and distance maps instead of triangle per triangle intersection tests. Collision response is then calculated directly from the superquadric models and realistic smooth force feedback is obtained using analytical formulae and local smoothing on the distance map. Experimental evaluation demonstrates that SQ-Map reduces significantly the computational cost when compared to accurate collision detection methods and does not require the huge amounts of memory demanded by distance field-based methods. Finally, force feedback is calculated directly from the distance map and the superquadric formulae.

In this paper, a 3D shape-based approach is presented for the efficient search, retrieval, and classification of protein molecules. The method relies primarily on the geometric 3D structure of the proteins, which is produced from the corresponding PDB files and secondarily on their primary and secondary structure. After proper positioning of the 3D structures, in terms of translation and scaling, the Spherical Trace Transform is applied to them so as to produce geometry-based descriptor vectors, which are completely rotation invariant and perfectly describe their 3D shape. Additionally, characteristic attributes of the primary and secondary structure of the protein molecules are extracted, forming attribute-based descriptor vectors. The descriptor vectors are weighted and an integrated descriptor vector is produced. Three classification methods are tested. A part of the FSSP/DALI database, which provides a structural classification of the proteins, is used as the ground truth in order to evaluate the classification accuracy of the proposed method. The experimental results show that the proposed method achieves more than 99 percent classification accuracy while remaining much simpler and faster than the DALI method.

Measuring the similarity between three-dimensional (3-D) objects is a challenging problem, with applications in computer vision, molecular biology, computer graphics, and many other areas. This paper describes a novel method for 3-D model content-based search based on the 3-D Generalized Radon Transform and a querying by-3-D-model approach. A set of descriptor vectors is extracted using the Radial Integration Transform (RIT) and the Spherical Integration Transform (SIT), which represent significant shape characteristics. After the proper alignment of the models, descriptor vectors are produced which are invariant in terms of translation, scaling and rotation. Experiments were performed using three different databases and comparing the proposed method with those most commonly cited in the literature. Experimental results show that the proposed method is adequately satisfactory in terms of both precision versus recall and time needed for retrieval, and that it can be used for 3-D model search and retrieval in a highly efficient manner.

In this paper, the 2D polar wavelet transform is proposed for content based search and retrieval of 3D objects. After the decomposition of a 3D object's volume into a set of planes, the 2D polar wavelet transform is applied to each of them, generating a set of 2D rotation invariant features. These features comprise the input to the spherical trace transform for the final descriptor extraction, which is used for the shape matching. The 2D wavelet transform demonstrated experimentally a high discriminative power, as compared to other existing methods.

In this paper a 3D shape-based approach is presented for the efficient search, retrieval and classification of protein molecules. The method relies on the geometric 3D appearance of the proteins, which is produced from the corresponding PDB files. After proper positioning and alignment of the 3D structures, in terms of translation and scaling, the 3D structures are decomposed into planes. Then, the polar Fourier transform is applied to the planes creating a new domain of concentric spheres. In this new domain a set of functionals is applied so as to produce descriptor vectors, which are completely invariant to rotation and perfectly describe their 3D shape. Experimental results performed using a portion of the FSSP/DALI database shoed that the proposed method achieves more than 98% classification accuracy with less complexity and much simplicity and it is very fast comparing with the DALI method.

The present paper describes an enhanced haptic virtual reality application for geometry education. The proposed application allows the user to create and edit a scene that consists of three-dimensional geometrical objects in order to form and solve complex geometrical problems. The core of the proposed scheme is based on a novel interference detection algorithm, which utilizes implicit surfaces, such as superquadrics, and their analytical description to speed up collision detection between the virtual hand and the virtual environment. Intersection tests are executed utilizing the implicit analytical formulae of the superquadrics. Experimental results demonstrate the high applicability of the proposed application and the huge gain in speed of the proposed collision detection approach when compared to state of the art methods.

This paper presents a novel object-based method for the generation of a stereoscopic image sequence from a monoscopic video, using bidirectional two-dimensional motion estimation for the recovery of rigid motion and structure and a Bayesian framework to handle occlusions. The latter is based on extended Kalman filters and an efficient method for reliably tracking object masks. Experimental results show that the layered object scene representation, combined with the proposed algorithm for reliably tracking object masks throughout the sequence, yields very accurate results.

The present paper proposes a novel method for content-based search in a database of VRML three-dimensional (3-D) models. The proposed technique is based on a querying-by-3-D-model approach. A set of shape-based descriptors are extracted from the reference 3-D model and compared to the corresponding descriptors of the VRML models contained in the database. The descriptors used vary from simple geometric measurements such as the aspect ratio or a binary 3-D shape mask to more complex and sophisticated shape-based criteria such as the edge paths of each 3-D model. Similarity measures are then introduced for the specific descriptors and introduced into a 3-D model-matching algorithm. Experimental results are presented, evaluating the performance of the proposed method.

We propose an innovative environment for presenting products through the Internet using modern visualization techniques and providing high level of interaction with the user. The environment consists of three main modules, namely a) the scene-authoring tool, b) the interactive scene-viewer and c) the assembly-demonstration tool. These modules are bound together with the utilization of a robust ontology-based knowledge management system.

This paper describes a novel method for 3D model content-based search and retrieval based on the 3D radon transform and a querying-by-3D-model approach. Descriptors are extracted using the 3D radon transform and applying a number of functionals on the transform's coefficients. Similarity measures are then created for the extracted descriptors and introduced into a 3D model-matching algorithm. This results in a very fast and accurate matching method. Experimental results are presented evaluating the performance of the proposed method in terms of precision versus recall diagrams.

A novel method for the watermarking of 3D models is proposed, which is robust to geometric distortions such its rotation, translation and uniform scaling. After proper positioning and alignment of the 3D models, a watermark is embedded in the vertices of the model using a robust technique for modifying imperceptibly the location of a subset of the 3D model vertices. The watermark can be used as a link to an identifier for the 3D model and the entire system can be used for data hiding applications. One application is the use of watermark as a link to 3D model descriptors for content-based search and retrieval. Experimental results show the ability of the proposed method to the aforementioned attacks. The proposed method is also robust against vertex reordering attack.

This paper presents a novel pyramidal representation scheme for deformable object modelling, which uses a hierarchical approach to optimize the system's performance by executing the simulation in every level of a pyramid. The simulation results of each level are used to predict the lower level's state. The prediction is used as initial guess for the simulation of the lower level. The above procedure is repeated until the final level is reached. Experimental evaluation demonstrates that the proposed scheme is able to reduce significantly the computational cost, especially when the simulation involves procedures, which need large numerical computation like the conjugate gradient in implicit integration schemes.

This paper presents a novel approach in 3D content-based search and retrieval. First, a set of functional are applied on a 3D model's volume producing a new domain of concentric spheres. In this new domain a new set of functionals is applied, resulting to a completely rotation invariant descriptor vector, which is used for 3D model matching. Experiments were performed using a database and comparing the proposed method with the MPEG-7 3D shape spectrum descriptor. Experimental results show that the proposed method is superior in terms of precision versus recall and can be used for 3D model search and retrieval in a highly efficient manner.

This paper presents the virtual reality applications developed for the feasibility study tests of the EU funded IST project ENORASI. ENORASI aims at developing a highly interactive and extensible haptic VR training system that allows visually impaired people, especially those blind from birth, to study and interact with various virtual objects. A number of custom applications have been developed based on the interface provided by the CyberGrasp haptic device. Eight test categories were identified and corresponding tests were developed for each category. Twenty-six blind persons conducted the tests and the evaluation results have shown the degree of acceptance of the technology and the feasibility of the proposed approach.

In this paper an object-based method to generate additional stereo views from monoscopic image sequences using rigid motion and structure estimation by extended Kalman filters is presented. First, rigid scene objects are segmented and feature points in each object are extracted and tracked throughout the video sequence. Then, motion, structure and focal length are estimated recursively for each object, using extended Kalman filters, as described in Azarbayejani and Pentland (1995). Furthermore, the feature points and depths in the stereo image are computed and interpolated using 2-D Delaunay triangulation. Finally, a stereo image generation algorithm is proposed that uses the camera and structure equations to project the 3-D points in a new virtual stereo view for each frame. The generation of stereoscopic scenes is possible even when multiple moving rigid objects exist in the scene. Experimental results show that a layered stereo object representation yields improved results.

A novel hybrid frequency and spatial domain method is presented for blockiness reduction in low bit rate compressed images. The proposed method consists of two stages acting complementarily. In the first, better estimates of the reconstructed DCT coefficients are obtained based on their observed probability distribution. In the second, an efficient postprocessing scheme, consisting of a region classification algorithm and spatial adaptive filtering, is applied. Experimental results illustrating the performance of the proposed method are presented and evaluated.

A novel frequency-domain technique for image blocking artifact detection and reduction is presented. The algorithm first detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the discrete cosine transform (DCT) coefficients are modeled by a Laplacian probability function. Then, for each block affected by blocking artifacts, its DC and AC coefficients are recalculated for artifact reduction. To achieve this, a closed-form representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the mean squared difference of slope for every frequency separately. This correction of each DCT coefficient depends on the eight neighboring coefficients in the subband-like representation of the DCT transform and is constrained by the quantization upper and lower bound. Experimental results illustrating the performance of the proposed method are presented and evaluated.

A novel frequency domain technique for image blocking artifact detection is presented. The algorithm detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the DCT coefficients are modeled by a Laplacian probability function. Experimental results illustrating the performance of the proposed method are presented and evaluated.

Thepaper proposes a novel method for content-based search in a database of VRML 3D models. The proposed technique is based on a querying-by-3D-model approach. A set of shape-based descriptors are extracted from the reference 3D model and compared to the corresponding descriptors of the VRML models contained in the database. The descriptors used vary from simple geometric measurements such as the aspect ratio or a binary 3D shape mask to more complex and sophisticated shape-based criteria such as the edge paths of each 3D model. Similarity measures are then introduced for the specific descriptors and introduced into a 3D model-matching algorithm. Experimental results are presented, evaluating the performance of the proposed method.

A novel frequency domain technique for image blocking artifact reduction is presented. For each block, its DC and AC coefficients are recalculated for artifact reduction. To achieve this, a closed form representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the mean squared difference of slope (MSDS), for every frequency separately. This correction of each DCT coefficient depends on the eight neighboring coefficients in the subband-like representation of the DCT and is constrained by the upper and lower bound of the quantized DCT coefficients. Experimental results illustrating the performance of the proposed method are presented and evaluated.

Summary form only given, as follows. The transmission of the large store of information contained in 3D medical data sets through limited capacity channels is a critical procedure in many telemedicine applications. Techniques are presented for the compression of visualizations of 3D image data for efficient storage and transmission. Methods are first presented for the transmission of the 3D surface of the objects using contour following methods. Alternately, the visualization at the receiver may be based on a series of depth maps corresponding to some motion of the object, specified by the medical observer. Depth maps may be transmitted by using depth map motion compensated prediction. Alternately, a wire-mesh model of the depth map may be formed and transmitted by encoding the motion of its nodes. All these methods are used for the transmission of the 3D image with visualization carried out at the receiver. Methods are also developed for efficient transmission of the images visualized at the encoder site. These methods allow remote interactive manipulation (rotation, translation, zoom) of the 3D objects, and may be implemented even if the receiver is a relatively simple and inexpensive workstation or a simple monitor. In all these cases, the coding of binocular views of the 3D scene is examined and recommendations are made for the implementation of coders of stereo views of 3D medical data.

The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column sampling. In each case, the efficiency of the linear predictors is enhanced nonlinearly. Directional postprocessing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely efficient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.

This paper presents a novel procedure for the representation and coding of three-dimensional (3-D) surfaces using hierarchical adaptive triangulation. The proposed procedure is based on pyramidal analysis using the quincunx sampling minimum variance interpolation (QMVINT) filters. These are reduced pyramids with quincunx sampling applied to the parametric representation of the surface, chosen so as to minimize the variance of the interpolation error, and thus, when combined with the appropriate encoding of the coefficients, optimize the compression of the mesh information transmitted. At the same time, it produces a hierarchy of meshes based on quincunx sampling where coarse meshes are as similar to their finer versions as possible. This is very much desirable in progressive transmission. Depending on its interpolation error and the available bitrate, each filtered sample is a candidate for becoming a vertex of the mesh. The result is a progressive sequence of meshes consisting of more triangles wherever large variations exist and fewer in uniform regions. Complete correspondence between triangles at each level is identified, resulting in an efficient hierarchical representation of the mesh. The algorithm can be also used for the triangulation of a specific region of interest. Experimental results demonstrate that the proposed scheme provides an improvement in quality (MSE) by a factor of two when compared with other well known adaptive triangulation schemes.

Efficient techniques are introduced in this paper for the identification of the occlusion and visible background and foreground areas in a noisy stereoscopic image pair. Three different Bayes decision methods are tested for this purpose. The first, and uses three hypotheses for the formulation of the Bayes decision rules, adopting the right image as a reference. After performing a dual-Bayes decision test having each time as a different image of the stereo pair as reference, consistency checking is added to these tests to form the second method. Finally, four compound hypotheses are used in the third method, which is the most accurate but also the more detailed and computationally involved of three. Experimental results illustrating the performance of the techniques are presented and evaluated.

This paper extends a known efficient technique for rigid three-dimensional (3-D) motion estimation so as to make it applicable to motion estimation problems occuring in image sequence coding applications. The known technique estimates 3-D motion using previously evaluated 3-D correspondence. However, in image sequence coding applications, 3-D correspondence is unknown and usually only two-dimensional (2-D) motion vectors are initially available. The novel neural network (NN) introduced in this paper uses initially estimated 2-D motion vectors to estimate 3-D rigid motion, and is therefore suitable for image sequence coding applications. Moreover, it is shown that the NN introduced in this paper performs extremely well even in cases where 3-D correspondence is known with accuracy. Experimental results are presented for the evaluation of the proposed scheme.

A methodology is presented for the optimal construction of multichannel reduced pyramids by selecting the interpolation synthesis postfilters so as to minimize the error variance at each level of the pyramid. The general optimization methodology is applied for the optimization of pyramids for the compression of electrocardiographic signals and RGB colored images.

In this paper, a procedure is described for deformable boundary detection of medical tools, called stents, in angiographic images. A stent is a surgical stainless steel coil that is placed in the artery in order to improve blood circulation in regions where a stenosis has appeared. Assuming initially a set of three-dimensional (3-D) models of stents and using perspective projection of various deformations of the 3-D model of the stent, a large set of synthetic two-dimensional (2-D) images of stents is constructed. These synthetic images are then used as a training set for deriving a multivariate Gaussian density estimate based on eigenspace decomposition and formulating a maximum-likelihood estimation framework in order to reach an initial rough estimate for automatic object recognition. The silhouette of the detected stent is then refined by using a 2-D active contour (snake) algorithm integrated with a novel iterative initialization technique, which takes into consideration the geometry of the stent. The algorithm is experimentally evaluated using real angiographic images containing stents.

This paper describes a three-dimensional (3-D) model-based unsupervised procedure for the segmentation of multiview image sequences using multiple sources of information. The 3-D model is initialized by accurate adaptation of a two-dimensional wireframe model to the foreground object of one of the views. The articulation procedure is based on the homogeneity of parameters, such as rigid 3-D motion, color, and depth, estimated for each subobject, which consists of a number of interconnected triangles of the 3-D model. The rigid 3-D motion of each subobject for subsequent frames is estimated using a Kalman filtering algorithm, taking into account the temporal correlation between consecutive frames. Information from all cameras is combined during the formation of the equations for the rigid 3-D motion parameters. The threshold used in the object segmentation procedure is updated at each iteration using the histogram of the subobject parameters. The parameter estimation for each subobject and the 3-D model segmentation procedures are interleaved and repeated iteratively until a satisfactory object segmentation emerges. The performance of the resulting segmentation method is evaluated experimentally.

A rate-distortion framework is used to define a displacement vector field estimation technique for use in video coding. This technique achieves maximum reconstructed image quality under the constraint of a target bit rate for the coding of the vector sequence. The technique may be adapted so as to limit its smoothing effect to homogeneous areas and avoid highly textured areas and edges. Use of this technique is evaluated for two application areas in which the need for high compression of displacement vector fields is particularly acute. The first is motion field coding for very-low-bit-rate image sequence transmission, as in video-phone applications. The second application area is coding for the transmission of dense disparity fields. This is needed for the generation at the receiver of intermediate viewpoints through spatial interpolation. It is also needed in a number of other applications requiring accurate depth knowledge, including three-dimensional medical data transmission and transmission of scenes to be postprocessed using depth-keyed segmentation. Experimental results illustrating the performance of the proposed technique in these application areas are presented and evaluated.

A new method is presented for the analysis of the effects of Lloyd-Max quantization in subband filterbanks and for the optimal design of such filterbanks. A rigorous statistical model of a vector Lloyd-Max quantizer is established first, consisting of a linear time-invariant filter followed by additive noise uncorrelated/with the input. On the basis of this model, an expression for this variance of the error of a subband coder using Lloyd-Max quantizers is explicitly determined. Given analysis filters that statistically separate the subbands, it is shown that this variance is minimized if the synthesis filters are chosen, which mould achieve perfect reconstruction in lossless coding. The globally optimum of such a filterbank, minimizing the coder error variance, is further obtained by proper choice of its analysis filters. An alternative design method is also evaluated and optimized. In this, the errors correlated with the signal are set to zero, leaving a random error residue uncorrelated with the signal. This design method is optimized by choosing the analysis filters so as to minimize the random error variance. The results are evaluated experimentally in the realistic setting of a logarithmically split subband image coding scheme.

In the present work, the nonlinear principal component analysis (NLPCA) method is combined with vector quantization for the coding of images. The NLPCA is realized using the backpropagation neural network (NN), while vector quantization is performed using the learning vector quantizer (LVQ) NN. The effects of quantization in the quality of the reconstructed images are then compensated by using a novel codebook vector optimization procedure.

This paper describes a procedure for model-based coding of all the channels of a multiview image sequence. The scheme is initialized by the adaptation of a wireframe model to the consistent depth information. Robust classification techniques are then used to obtain an articulated description of the foreground of the scene (head, neck, shoulders). The object articulation procedure is based on a novel scheme for the segmentation of the rigid 3D motion fields of the triangle patches of the 3D model object. Spatial neighborhood constraints are used to improve the reliability of the original triangle motion estimation. The motion estimation and motion field segmentation procedures are repeated iteratively until a satisfactory object articulation emerges. Rigid 3D motion estimation is performed next for each resulting sub-object. The performance of the resulting articulation method is evaluated experimentally.

Reduced pyramids, including in particular pyramids without analysis filters are known to produce excellent results when used for lossless signal and image compression. The present paper presents a methodology for the optimal construction of such pyramids by selecting the interpolation synthesis post-filters so as to minimize the error variance at each level of the pyramid. This establishes optimally efficient interpolative pyramidal lossless compression. It also has the added advantage of producing lossy replicas of the original which, at lower resolutions retain as much similarity to the original as possible. The general optimization methodology is developed first, for a general family of reduced pyramids. Subsequently, this is applied to the optimization of pyramids in this family formed using 2D quincunx sampling matrices. Optimal versions of these techniques are determined for 2D images characterized by separable or isotropic correlation functions. The advantages of the developed methods are demonstrated by experimental evaluation.

A rate-distortion framework is used to define a very low bit-rate coding scheme based on quadtree segmentation and optimized selection of motion estimators. This technique achieves maximum reconstructed image quality under the constraint of a target bit rate for the coding of the vector field and segmentation information. First, a complete scheme is proposed for hybrid two-dimensional (2-D) and three-dimensional (3-D) motion estimation and compensation. The quadtree object segmentation is optimized for hybrid motion estimation in the rate-distortion sense. This scheme adapts to the depth of the quadtree and the technique used for motion estimation for each leaf of the tree. A more sophisticated technique, adapted to the requirements of a very low bit-rate coder, is also proposed which also considers the transmission of the prediction error corresponding to the particular choice of the motion estimator. Based on these coding schemes, two versions of a very low bit-rate image sequence coder are developed. Experimental results illustrating the performance of the proposed techniques in very low bit-rate image sequence coding application areas are presented and evaluated.

An object-based coding scheme is proposed for the coding of a stereoscopic image sequence using motion and disparity information. A hierarchical block-based motion estimation approach is used for initialization, while disparity estimation is performed using a pixel-based hierarchical dynamic programming algorithm. A split-and-merge segmentation procedure based on three-dimensional (3-D) motion modeling is then used to determine regions with similar motion parameters. The segmentation part of the algorithm is interleaved with the estimation part in order to optimize the coding performance of the procedure. Furthermore, a technique is examined for propagating the segmentation information with time. A 3-D motion-compensated prediction technique is used for both intensity and depth image sequence coding. Error images and depth maps are encoded using discrete cosine transform (DCT) and Huffman methods. Alternately, an efficient wireframe depth modeling technique may be used to convey depth information to the receiver. Motion and wireframe model parameters are then quantized and transmitted to the decoder along with the segmentation information. As a straightforward application, the use of the depth map information for the generation of intermediate views at the receiver is also discussed. The performance of the proposed compression methods is evaluated experimentally and is compared to other stereoscopic image sequence coding schemes.

A new method is presented for the analysis of the effects of Lloyd-Max quantization in subband filter banks and for the optimal design of such filter banks. A rigorous statistical model of a vector Lloyd-Max quantizer is established first, consisting of a linear time-invariant filter followed by additive noise uncorrelated with the input. On the basis of this model, an expression for this variance of the error of a subband coder using Lloyd-Max quantizers is explicitly determined. Given analysis filters which statistically separate the subbands, it is shown that this variance is minimized if the synthesis filters are chosen, which would achieve perfect reconstruction in lossless coding. The globally optimum filter bank, minimizing the coder error variance, is further obtained by proper choice of its analysis filters. The results are evaluated experimentally in the realistic setting of a logarithmically split subband image coding scheme.

Optimal hierarchical coding is sought, for progressive or scalable image transmission, by minimizing the variance of the error difference between the original image and its lower resolution renditions. The optimal, according to the above criterion, pyramidal image coders are determined for images subject to corruption by quantization or transmission noise. Given arbitrary analysis filters and assuming adequate knowledge of the noise statistics, the optimal synthesis filters are found. The optimal analysis filters are subsequently determined, leading to formulas for globally optimal structures for pyramidal and subband image decompositions. Experimental results illustrate the implementation and performance of the optimal coders.

Methods are proposed for coding of the depth map and disparity fields for stereo or multiview image communication applications. Block-based and wireframe modeling techniques are examined for the coding of isolated depth map information and 2-D and 3-D motion compensation techniques for the coding of depth map sequences are evaluated.

Image compression methods for progressive transmission using optimal hierarchical decomposition, partition priority coding (PPC), and multiple distribution entropy coding (MDEC) are presented. In the proposed coder, a hierarchical subband/wavelet decomposition transforms the original image. The analysis filter banks are selected to maximize the reproduction fidelity in each stage of progressive image transmission. An efficient triple-state differential pulse code modulation (DPCM) method is applied to the smoothed subband coefficients, and the corresponding prediction error is Lloyd-Max quantized. Such a quantizer is also designed to fit the characteristics of the detail transform coefficients in each subband, which are then coded using novel hierarchical PPC (HPPC) and predictive HPPC (PHPPC) algorithms. More specifically, given a suitable partitioning of their absolute range, the quantized detail coefficients are ordered based on both their decomposition level and partition and then are coded along with the corresponding address map. Space filling scanning further reduces the coding cost by providing a highly spatially correlated address map of the coefficients in each PPC partition. Finally, adaptive MDEC is applied to both the DPCM and HPPC/PHPPC outputs by considering a division of the source (quantized coefficients) into multiple subsources and adaptive arithmetic coding based on their corresponding histograms. Experimental results demonstrate the great performance of the proposed compression methods.

Temporal frame interpolation techniques are presented, based on an object-based algorithm for 3-D motion estimation. This algorithm uses a joint estimation-segmentation scheme to minimize the displaced frame difference between a frame and its motion compensated prediction from the previous frame. Depth information is estimated beforehand from each stereo pair. Special attention is paid to the exploitation of occlusion information so as to improve the reconstruction quality of the interpolated frames. Experimental results are used to evaluate the performance of the proposed methods and to compare with more conventional frame interpolation techniques.