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.

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 $\mathbf{0. 0 6 2}$.

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.

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations.

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.

Information and Communication Technology (ICT) is an integral part of Critical Infrastructures (CIs), bringing both significant pros and cons. Focusing our attention on the energy sector, ICT converts the conventional electrical grid into a new paradigm called Smart Grid (SG), providing crucial benefits such as pervasive control, better utilisation of the existing resources, self-healing, etc. However, in parallel, ICT increases the attack surface of this domain, generating new potential cyberthreats. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) architecture which constitutes an overall solution aiming at protecting SG, by enhancing situational awareness, detecting timely cyberattacks, collecting appropriate forensic evidence and providing an anonymous cybersecurity information-sharing mechanism. Operational characteristics and technical specifications details are analysed for each component, while also the communication interfaces among them are described in detail.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.