Smart Water Distribution Systems (WDSs) are physical water networks whose pumps, valves, and tanks are operated through a digital layer of sensors, Programmable Logic Controllers (PLCs), and Supervisory Control and Data Acquisition (SCADA) systems. This connectivity improves operational efficiency but also exposes critical water infrastructure to cyber-attacks. Studying how to operate such systems resiliently requires simulators that reproduce both the hydraulic process and the control network. Existing cyber–physical testbeds, however, are largely passive: they model a single control architecture and record how an attack manifests, without letting an operator's defense logic act while the attack unfolds. We introduce WaCSim (Water and Cyber Simulation), an open-source cyber–physical co-simulation framework that addresses this gap. WaCSim couples the EPANET hydraulic engine with an emulated industrial control network (Mininet and MiniCPS), introducing two critical capabilities. First, it supports decentralized (PLC-to-PLC), centralized (SCADA), and hybrid control within a single framework. In hybrid mode, a PLC receives the same physical measurement via two independent paths, enabling attackers to target them differentially and exposing semantic conflicts that single-architecture tools cannot represent. Second, it establishes the defender as an active, in-loop actor, allowing researchers to embed custom control, detection, and conflict-resolution algorithms directly into the simulation loop to actively alter the hydraulic trajectory as an attack progresses. Through case studies, we demonstrate how WaCSim models cyber-attacks, evaluates defense mechanisms, and supports the development of intelligent control systems. These examples illustrate WaCSim's utility as a research and development platform for advancing the robustness, security, and operational intelligence of smart water networks.

Air pollution interpolation is crucial for civil management: it is used to transform limited sensor data into comprehensive pollution maps. Various methods, including deterministic, geostatistical, and Machine Learning (ML)-based techniques have been utilized for this purpose. Deterministic methods rely on mathematical rules for estimation, whereas geostatistical techniques are based on spatial correlations. ML leverages historical data for predictions. Each method has limitations: deterministic methods do not adequately model environmental complexity, geostatistical methods struggle with small-scale areas, and ML depends heavily on data availability. On the other hand, air pollution simulators based on physicochemical dispersion models capture intricate pollution dispersion patterns, yet running them at high resolution for continuous interpolation is often computationally demanding. Recent advancements in ML offer a complementary pathway by leveraging simulated data to enable rapid, real-time interpolation. This study introduces an air pollution interpolation approach combining simulated air-dispersion patterns through an educated machine that includes machine learning and environmental modeling that considers boundaries, pollution sources, obstacles, wind dynamics, and topography. Specifically, we present a combined ML-based linear regression model designed to infer dense concentration maps from a sensor array using state-of-the-art simulation methods. We dub this the Ridiculously Simple data-driven air pollution Interpolation Method (RSIM). The RSIM method was evaluated on both synthetic and real-life-based simulation models. The synthetic scenarios included an industrial area with point pollution sources and an urban road surrounded by buildings simulating traffic-related pollution. The real-world environment consisted of sensor data and simulations from Antwerp, Belgium. The results indicate that this method outperforms standard techniques for reconstructing dense pollution maps from sparse sensing, and demonstrates significant promise for other real-world applications.

Accurate feed management in marine aquaculture is critical for maximizing fish growth and minimizing environmental impacts. While previous approaches have leveraged acoustic monitoring and neural network-based classification to assess feeding, these methods often lack scalability and precision for industrial deployment. This study introduces a matched-filter audio signal processing technique, informed by domain knowledge, to continuously quantify fish feeding intensity in gilthead seabream (Sparus aurata) aquaculture. By extracting a species-specific bite acoustic template and applying matched filtering with sliding window aggregation, we generate a robust, continuous feeding intensity label from passive acoustic recordings. Machine learning regression models (XGBoost and Random Forest) validate the approach, demonstrating that environmental and biological variables explain 89% of the variation in feeding intensity. The proposed methodology offers a simple, scalable, and cost-effective solution for real-time feed optimization and welfare monitoring in aquaculture systems. By reducing data dimensionality and enhancing sensitivity to subtle behavioral changes, this framework supports the deployment of advanced data-driven monitoring tools and paves the way for practical integration in commercial aquaculture operations.

Dense pollution maps are essential for understanding and reducing air pollution. However, pollution measurements are often sparse. Common methods to address this gap are chemistry transport models (CTMs) and air pollution interpolation models. Nonetheless, many of these models have poor performances and intrinsic systematic biases. Here, a machine education approach, which integrates a CTM and in situ measurements with Artificial Intelligence (AI) techniques, is proposed for generating dense pollution maps with enhanced accuracy. Specifically, the CHIMERE CTM is combined with a Neural Network, eXtreme Gradient Boosting (XGBoost), or Random Forest AI methods. The results show that the educated machine models significantly improved predictions of air pollution levels compared to the standalone CTM. The machine education model combining a neural network with CHIMERE performed best among all models, followed closely by the educated model with XGBoost, while the Random Forest model trailed. Relative to CHIMERE, the proposed models achieved reductions of up to 51.34% and 50.54% in the root mean square error (RMSE) and mean absolute error (MAE), respectively, for NO₂. For PM_2.5, the models demonstrated relative reductions of up to 40.08% and 36.54%, respectively. CHIMERE exhibited an inherent systematic underestimation bias, characterized by mean fractional bias (MFB) values of 0.509 and 0.725 for NO₂ and PM_2.5, respectively. Our models successfully eliminated this bias. Furthermore, promising dense air pollution maps were generated using our models.

Intermittent water supply is a prevalent strategy employed in water distribution systems (WDS) facing deteriorating conditions. However, this approach can have several drawbacks, including insufficient supply, pressure drops, water losses due to leakages, and unequal service levels. Further, these issues are often exacerbated when consumers establish private storage facilities and increase the peak demand, which leads to a feedback loop of worsening conditions. Thus, when a budget is available, restoring the system's functionality as fast as possible is crucial. The current study presents a method to optimize the rehabilitation of intermittent water supply by improving system design through investments and operational control settings. The method was developed for the challenge presented in the battle of the intermittent water supply (BIWS), where network performance is evaluated through nine different objectives over a five year horizon of planning and rehabilitation. The proposed method is based on a greedy optimization approach that was specifically tailored to the challenge of optimizing WDS under extreme hydraulic conditions. To overcome the formidable computational burden in the BIWS challenge, several heuristics are presented for reducing the search space. The results obtained reflect a dramatic improvement in the network performance, with 97.8% of the consumers having continuous supply and water loss reduced from 47% to 23.7% of the total inflow. We also present a generic greedy approach that can be applied to any water network for various decision-making problems.

The Metaverse, a virtual world enabling user interaction with digital environments and assets, increasingly utilizes blockchain and Non-Fungible Tokens (NFTs) to represent unique characters and items. Representing and valuing individual digital characters is crucial in the Metaverse as it establishes ownership, identity, and status within these virtual worlds. This research investigates decision-making within collaborative online games, where players face the challenge of optimizing their character's value through strategic NFT acquisition. This challenge is analogous to the Knapsack Problem, aiming to maximize the value of items selected within a limited capacity, just as players seek to maximize character rarity within budget constraints. We propose a Decision Support System (DSS) employing genetic algorithms to assist players in tackling this complex optimization problem. A simulation framework, based on the "SunflowerLand" Metaverse, demonstrates that players using the DSS achieve significantly higher character rarity, highlighting the potential of this approach to enhance player experience.

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address this challenge, we propose the Wave (from) Polarized Light Learning (WPLL), a learning based remote sensing method for laboratory implementation, capable of inferring surface elevation and slope maps in high resolution. The method uses the polarization properties of the light reflected from the water surface. The WPLL uses a deep neural network (DNN) model that approximates the water surface slopes from the polarized light intensities. Once trained on simple monochromatic wave trains, the WPLL is capable of producing high-resolution reconstruction of the 2D water surface slopes and elevation in a variety of irregular wave fields. The method's robustness is demonstrated by showcasing its high wavenumber/frequency response, its ability to reconstruct wave fields propagating in arbitrary angles relative to the camera optical axis, and its computational efficiency. This developed methodology is a cost-effective near-real time remote sensing tool for laboratory water surface waves measurements, setting the path for upscaling to open sea application for research, monitoring, and short-time forecasting.

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. Here we present this supervised dataset of polarimetric images of the water surface coupled with the water surface elevation measurements made by a linear array of resistance-type wave gauges (WG). The water waves were mechanically generated in a laboratory waves basin, and the polarimetric images were captured under an artificial light source. Meticulous camera and WGs calibration and instruments synchronization supported high spatio-temporal resolution. The data set covers several wavefield conditions, from simple monochromatic wave trains of various steepness, to irregular wavefield of JONSWAP prescribed spectral shape and several wave breaking scenarios. The dataset contains measurements repeated in several camera positions relative to the wave field propagation direction.

Synthetic datasets are widely used in applications like missing data imputation, simulations, training data-driven models, and system robustness analysis. Typically based on historical data, these datasets need to represent specific system behaviors while being diverse enough to challenge the system with a broad range of inputs. This paper introduces a method using discrete Fourier transform to generate synthetic time series with similar statistical moments to any given signal. The method allows control over the similarity level between the original and synthetic signals. Analytical proof shows that this method preserves the first two statistical moments and the autocorrelation function of the input signal. It is compared to ARMA, GAN, and CoSMoS methods using various environmental datasets with different temporal resolutions and domains, demonstrating its generality and flexibility. A Python library implementing this method is available as open-source software.

Hyperspectral imaging considers the measurement of spectral signatures in near and far field settings. In the far field setting, the interactions of material spectral signatures are typically modeled using linear mixing. In the near field setting, material signatures frequently interact in a nonlinear manner (e.g., intimate mixing). An important task in hyperspectral imaging is to estimate the distribution and spectral signatures of materials present in hyperspectral data, i.e., unmixing. Motivated by forensics, this work considers a specific unmixing task, namely, the problem of foreground material signature extraction in an intimate mixing setting where thin layers of foreground material are deposited on other (background) materials. The unmixing task presents a fundamental challenge of unique (identifiable) recovery of material signatures in this and other settings. We propose a novel model for this intimate mixing setting and explore a framework for the task of foreground material signature extraction with identifiability guarantees under this model. We identify solution criteria and data conditions under which a foreground material signature can be extracted up to scaling and elementwise-inverse variations with theoretical guarantees in a noiseless setting. We present algorithms based on two solution criteria (volume minimization and endpoint member identification) to recover foreground material signatures under these conditions. Numerical experiments on real and synthetic data illustrate the efficacy of the proposed algorithms.

Mobile monitoring provides robust measurements of air pollution. However, resource constraints often limit the number of measurements so that assessments cannot be obtained in all locations of interest. In response, surrogate measurement methodologies, such as videos and images, have been suggested. Previous studies of air pollution and images have used static images (e.g., satellite images or Google Street View images). The current study was designed to develop deep learning methodologies to infer on-road pollutant concentrations from videos acquired with dashboard cameras. Fifty hours of on-road measurements of four pollutants (black carbon, particle number concentration, PM_2.5 mass concentration, carbon dioxide) in Bengaluru, India, were analyzed. The analysis of each video frame involved identifying objects and determining motion (by segmentation and optical flow). Based on these visual cues, a regression convolutional neural network (CNN) was used to deduce pollution concentrations. The findings showed that the CNN approach outperformed several other machine learning (ML) techniques and more conventional analyses (e.g., linear regression). The CO₂ prediction model achieved a normalized root-mean-square error of 10-13.7% for the different train-validation division methods. The results here thus contribute to the literature by using video and the relative motion of on-screen objects rather than static images and by implementing a rapid-analysis approach enabling analysis of the video in real time. These methods can be applied to other mobile-monitoring campaigns since the only additional equipment they require is an inexpensive dashboard camera.

Accurate and cost-effective sea state measurements, in terms of spatio-temporal distribution of water surface elevation (water waves), is of great interest for scientific research and various engineering, industrial, and recreational applications. To this end, numerous measurement techniques have been developed over the years. None of these techniques, however, are universally applicable across various ocean and laboratory conditions and none provide near-real-time data. We utilized the latest advances in polarimetric imaging to develop a new remote sensing method based on machine learning methodology and polarimetric reflection measurements for inferring surface waves elevation and slope. The method utilizes a newly available, inexpensive polarimetric camera providing images of the water surface in a high spatio-temporal resolution at several linear polarization angles. Algorithms based on artificial neural networks ( ANN s) are then trained to obtain high-resolution reconstructions of the water surface slope state from those images. The ANN s are trained on laboratory-collected supervised datasets of prescribed mechanically generated monochromatic wave trains and tested on a stochastic wave field of JONSWAP spectral shape. The proposed method, based on inferring the surface slope from polarimetric images, provides a dense estimate of the water surface. The results of this study pave the way for the development of accurate and cost-effective near-real-time remote sensing tools for both laboratory and open sea wave measurements.

The operation of water distribution systems (WDS) is an energy-intensive process, which is subject to constraints such as consumer demands, water quality, and pressure domains. As such, tracing an operation policy in which constraints are met while energy costs are minimized, is a foremost objective for water utilities. Given the inherent uncertainties in WDS operation and the importance of supply continuity, it is essential to find an operational strategy that is robust against a wide range of circumstances. One promising approach for optimization under uncertainty is robust optimization (RO), which assures a robust (feasible) solution to realizations of the uncertain parameters, within predefined bounds. This study presents an RO-based method for optimizing pump scheduling under uncertainties of consumer demands and pumping costs. The method can capture various types of correlations between the uncertain parameters, thus better reflecting the uncertain nature of WDS operation. The developed methodology is demonstrated in two case studies with different levels of complexity. The impacts of uncertainty levels and correlation coefficients are analyzed to demonstrate their implications on operation policy. The results show the advantages of using RO with tradeoffs between costs and constraints satisfaction.

Data analysis has increasingly relied on machine learning in recent years. Since machines implement mathematical algorithms without knowing the physical nature of the problem, they may be accurate but lack the flexibility to move across different domains. This manuscript presents a machine-educating approach where a machine is equipped with a physical model, universal building blocks, and an unlabeled dataset from which it derives its decision criteria. Here, the concept of machine education is deployed to identify thin layers of organic materials using hyperspectral imaging (HSI). The measured spectra formed a nonlinear mixture of the unknown background materials and the target material spectra. The machine was educated to resolve this nonlinear mixing and identify the spectral signature of the target materials. The inputs for educating and testing the machine were a nonlinear mixing model, the spectra of the pure target materials (which are problem invariant), and the unlabeled HSI data. The educated machine is accurate, and its generalization capabilities outperform classical machines. When using the educated machine, the number of falsely identified samples is ~ 100 times lower than the classical machine. The probability for detection with the educated machine is 96% compared to 90% with the classical machine.

Water supply systems (WSS) are significant energy consumers, which makes it crucial to find methods to increase energy efficiency. A novel approach is presented here to locate and quantify the most vulnerable elements in terms of energetic efficiency. The method is based on a linear programming (LP) optimization model and the elimination of different components in the system to analyze their impact on energy costs. Pump reallocation is then suggested as a novel dynamic design paradigm for temporary changes in the system to cope with extreme scenarios. Exposing the critical components of WSS improves maintenance prioritization and inventory and contributes to the planning of future investments in the system. Pump reallocation provides an innovative approach for response to critical conditions, it is suggesting rethinking the design concepts to incorporate not just long-term solutions but also rapid, temporary steps in response to failures.

Catastrophic gas leak events require human First Responder Teams (FRTs) to map hazardous areas (red zones). The initial task of FRT in such events is to assess the risk according to the pollution level and to quickly evacuate civilians to prevent casualties. These teams risk their lives by manually mapping the gas dispersion. This process is currently performed using hand-held gas detectors and requires dense and exhaustive monitoring to achieve reliable maps. However, the conventional mapping process is impaired due to limited human mobility and monitoring capacities. In this context, this paper presents a method for gas sensing using unmanned aerial vehicles. The research focuses on developing a custom path planner—Boundary Red Emission Zone Estimation (BREEZE). BREEZE is an estimation approach that allows efficient red zone delineation by following its boundary. The presented approach improves the gas dispersion mapping process by performing adaptive path planning, monitoring gas dispersion in real time, and analyzing the measurements online. This approach was examined by simulating a cluttered urban site in different environmental conditions. The simulation results show the ability to autonomously perform red zone estimation faster than methods that rely on predetermined paths and with a precision higher than ninety percent.

Air pollution control and mitigation are important factors in wellbeing and sustainability. To this end, air pollution monitoring has a significant role. Today, air pollution monitoring is mainly done by standardized stations. The spread of those stations is sparse and their cost hinders the option of adding more. Thus, arises the need for cheaper and available means to assess air pollution. In this article, a mathematical method to solve the inverse problem of aerosols tomography is proposed. The suggested method applies filtered back-projection method on a pixel-wise blur estimation. Using the method, particles' concentrations in a 3D space is reconstructed from photos taken from different angles. The proposed method is shown to be very effective for assessing air pollution levels by means of multi angle imaging. Specifically, estimating images' blur as an indication for Particulate Matter (PM) ambient levels. The results of the research point towards strong correlation between image blur and pollution level in the medium and the ability to reconstruct the aerosols distribution in space.

Air pollution is one of the prime adverse environmental outcomes of urbanization and industrialization. The first step toward air pollution mitigation is monitoring and identifying its source(s). The deployment of a sensor array always involves a tradeoff between cost and performance. The performance of the network heavily depends on optimal deployment of the sensors. The latter is known as the location–allocation problem. Here, a new approach drawing on information theory is presented, in which air pollution levels at different locations are computed using a Lagrangian atmospheric dispersion model under various meteorological conditions. The sensors are then placed in those locations identified as the most informative. Specifically, entropy is used to quantify the locations’ informativity. This entropy method is compared to two commonly used heuristics for solving the location–allocation problem. In the first, sensors are randomly deployed; in the second, the sensors are placed according to maximal cumulative pollution levels (i.e., hot spots). Two simulated scenarios were evaluated: one containing point sources and buildings and the other containing line sources (i.e., roads). The entropy method resulted in superior sensor deployment in terms of source apportionment and dense pollution field reconstruction from the sparse sensors’ network measurements.

Industrial activities involve the manipulation of harmful chemicals. As there is no way to guarantee fail-safe operation, the means and response methods must be planned in advance to cope with a chemical disaster. In these situations, first responders assess the situation from the atmospheric conditions, but they have scant data on the source of the contamination, which curtails their response toolbox. Hence, a sensor deployment strategy needs to be formulated in real-time based on the meteoro-logical conditions, sensor attributes, and resources. This work examined the tradeoff between sensor locations and their attributes. The findings show that if the sensor locations are optimal, the number is more important than quality, in that the sensors’ dynamic range is a significant factor when quantifying leaks but is less important if the goal is solely to locate the leak source/s. This methodology can be used for sensor location-allocation under real-life conditions and technological constraints.

The timely detection of crop diseases is critical for securing crop productivity, lowering production costs, and minimizing agrochemical use. This study presents a crop disease identification method that is based on Convolutional Neural Networks (CNN) trained on images taken with consumer-grade cameras. Specifically, this study addresses the early detection of wheat yellow rust, stem rust, powdery mildew, potato late blight, and wild barley net blotch. To facilitate this, pictures were taken in situ without modifying the scene, the background, or controlling the illumination. Each image was then split into several patches, thus retaining the original spatial resolution of the image while allowing for data variability. The resulting dataset was highly diverse since the disease manifestation, imaging geometry, and illumination varied from patch to patch. This diverse dataset was used to train various CNN architectures to find the best match. The resulting classification accuracy was 95.4 ± 0.4%. These promising results lay the groundwork for autonomous early detection of plant diseases. Guidelines for implementing this approach in realistic conditions are also discussed.

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

In light of the need for objective mechanism-based diagnostic tools, the current research describes a novel diagnostic support system aimed to differentiate between anxiety and depression disorders in a clinical sample. Eighty-six psychiatric patients with clinical anxiety and/or depression were recruited from a public hospital and assigned to one of the experimental groups: Depression, Anxiety, or Mixed. The control group included 25 participants with no psychiatric diagnosis. Participants performed a battery of six cognitive-behavioral tasks assessing biases of attention, expectancies, memory, interpretation and executive functions. Data were analyzed with a machine-learning (ML) random forest-based algorithm and cross-validation techniques. The model assigned participants to clinical groups based solely on their aggregated cognitive performance. By detecting each group's unique performance pattern and the specific measures contributing to the prediction, the ML algorithm predicted diagnosis classification in two models: (I) anxiety/depression/mixed vs. control (76.81% specificity, 69.66% sensitivity), and (II) anxiety group vs. depression group (80.50% and 66.46% success rates in classifying anxiety and depression, respectively). The findings demonstrate that the cognitive battery can be utilized as a support system for psychiatric diagnosis alongside the clinical interview. This implicit tool, which is not based on self-report, is expected to enable the clinician to achieve increased diagnostic specificity and precision. Further, this tool may increase the confidence of both clinician and patient in the diagnosis by equipping them with an objective assessment tool. Finally, the battery provides a profile of biased cognitions that characterizes the patient, which in turn enables more fine-tuned, individually-tailored therapy.

Successful detection of pain in cows could circumscribe the therapeutic window for treatment before the cow's condition deteriorates further. While severe clinical cases that are characterized as painful have clear behavioral and physiological manifestations, mild pain may go unnoticed in cows due to their stoic nature. This work presents the first step in developing a warning system that will enable the identification of mild pain in dairy cows. In a set of three experiments, a topical application of 10 % capsaicin cream was used to elicit a noxious sensation. Experiment 1 was aimed at establishing capsaicin as a noxious model for bovines (n = 11). Each cow was treated with neutral cream on day one and the noxious cream on day two. Since the duration of capsaicin effects on bovine skin is unknown, Experiment 2 was designed to evaluate capsaicin's impact on bovines 30 min after application (n = 17). Physiological signs were collected in response to the capsaicin cream application and were compared to the application of the neutral cream. In Experiment 3 physiological signs and continuous behavioral data were recorded (n = 22, four cows participated in Experiment 1). Each cow was treated with neutral cream on day one and the noxious cream on days two and three, i.e., repeated exposure to the noxious stimulus. Heart and breathing rates were elevated soon after the noxious treatment but not for the neutral cream. Blood oxygen saturation was inconclusive. Changes in daily activity patterns consecutive to the noxious challenge included a decrease in rumination time and an increase in lying bouts. These results are in line with what would be expected for physiological and behavioral effects of pain in cows. Additional data are required to rule out habituation or sensitization to the procedure. The resulting database was then used to develop a machine-learning algorithm to detect noxious sensations by applying random forest classifiers trained with two different approaches. The learning herd approach, in which a specialist labels a set of observations and uses them to derive a classifier for new observations from the same herd, achieved 82 % ±9% accuracy. The unlearning herd approach, in which a single database is used to train a classifier that can be applied to members of other herds, resulted in an accuracy of 86 %±18 %. The data discussed in this study meet the requirements of an automatic on-site noxious detection system; real-time on-farm measurements, informative of negative high-arousal states.

Estimating the source term in the case of multiple leaks using a sparse sensor array is a challenging task. Here, the effect of sensor array/leak configurations on the reliability of the source term estimation is studied using two new measures. The first describes the overall change in the sensor array response to different source terms. The second represents the effect of the source term on the readout of each sensor in the array. These measures are subjected to several model cases differing in sensor array/leak configurations. Then, the source term is estimated using a self-adaptive multiobjective evolutionary (MOEA) search algorithm combined with a gas dispersion model. The method searches for a set of leaks, each one of which has a typical emission rate and location that results in a minimal difference between the sensors' actual and computed pollution concentration. This objective, which is often used for source term estimation, is traded off against the second objective of maintaining a minimum number of active sources, which follows Occam's razor principle of parsimony. Analysis of the results obtained for these model cases suggests that the measures can be implemented as a design tool using a combination of computer simulation and field experiments before operational deployment.

Yellow rust (YR) wheat disease is one of the major threats to worldwide wheat production, and it often spreads rapidly to new and unexpected geographic locations. To cope with this threat, integrated pathogen management strategies combine disease-resistant plants, sensors monitoring technologies, and fungicides either preventively or curatively, which come with their associated monetary and environmental costs. This work presents a methodology for timely detection of YR that cuts down on hardware and computational requirements. It enables frequent detailed monitoring of the spread of YR, hence providing the opportunity to better target mitigation efforts which is critical for successful integrated disease management. The method is trained to detect YR symptoms using reflectance spectrum (VIS–NIR) and a classification algorithm at different stages of YR development to distinguish them from typical defense responses occurring in resistant wheat. The classification method was trained and tested on four different spectral datasets. The results showed that using a full spectral range, a selection of the top 5% significant spectral features, or five typical multispectral bands for early detection of YR in infected plants yielded a true positive rate of ~ 86%, for infected plants. The same data analysis with digital camera bands provided a true positive rate of 77%. These findings lay the groundwork for the development of high-throughput YR screening in the field implementing multispectral digital camera sensors that can be mounted on autonomous vehicles or a drone as part of an integrated disease management scheme.

Anxiety and depression are distinct—albeit overlapping—psychiatric diseases, currently diagnosed by self-reported-symptoms. This research presents a new diagnostic methodology, which tests rigorously for differences in cognitive biases among subclinical anxious and depressed individuals. 125 participants were divided into four groups based on the levels of their anxiety and depression symptoms. A comprehensive behavioral test battery detected and quantified various cognitive–emotional biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The prediction model for differentiating between symptomatic participants (i.e., high symptoms of depression, anxiety, or both) compared to the non-symptomatic control group revealed a 71.44% prediction accuracy for the former (sensitivity) and 70.78% for the latter (specificity). 68.07% and 74.18% prediction accuracy was obtained for a two-group model with high depression/anxiety, respectively. The analysis also disclosed which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.

A method for producing dense air pollution maps, based on any given air-pollution dispersion model, is presented. The scheme consists of two phases. At the first stage, sources' locations and emission rates, i.e., source term estimation, as a function of the model's parameter space are sought (“backward computation”). Then, the source term is used to generate the dense maps utilizing the same dispersion model (“forward computation”). The algorithm is model-invariant to the dispersion model, and thus is suitable for a wide range of applications according to the required accuracy and available resources. A simulation of an industrial area demonstrated that this method produced more accurate maps than current state-of-the-art techniques. The resulting dense air pollution map is thus a valuable tool for air pollution mitigation, regulation and research.

The hyperspectral unmixing problem arises in remote sensing, chemometrics, and biomedical engineering applications. The spectral signature of a single pixel in a hyperspectral cube can be represented as a non-negative combination of non-negative signatures from various materials contained in the physical region corresponding to the pixel (linear mixing). A less studied problem is associated with foreground extraction in an intimate (nonlinear) mixing model. We introduce a framework for foreground signature extraction based on a proposed patch model. We introduce identifiability conditions for the single and multiple patch cases. Using these conditions, we present an algorithm for the identifiable recovery of foreground signatures. Numerical experiments on real and synthetic data illustrate the efficacy of the proposed approach.

Air pollution control and mitigation are important factors in wellbeing and sustainability. To this end, air pollution monitoring has a significant role. Today, air pollution monitoring is mainly done by standardized stations. The spread of those stations is sparse and their cost hinders the option of adding more. Thus, arises the need for cheaper and available means to assess air pollution. In this article, a method for assessing air pollution levels by means of multi angle imaging is presented. Specifically, the focus is on estimating images’ blur as an indication for PM (Particulate Matter) ambient levels. The suggested method applies back-projection Radon transform. By back projection methodology, particles’ concentration at each voxel in a 3D space is reconstructed from photos taken from a few different angles.

Citizen science is transforming the ways scientific knowledge is created, in that citizens participate in active scientific research, and large scientific databases can be accessed online. However, data availability does not guarantee public use or the relevance of these resources. This paper addresses the ways in which non-expert adults involved in a citizen science initiative, perceive, understand and use its scientific information. Participants responded to an online questionnaire presenting air quality data from ‘Sensing the Air’ citizen science platform, followed by interpretation questions (n = 123). The results showed that 70% of participants were able to interpret the data presented in various visual representations. No differences were found between gender, age or education level. However, respondents with tertiary scientific education obtained higher average scores. Among users who had previous experience with the project, overall scores were higher, and differences based on respondents scientific education were fewer. This may suggest that while scientific education is important in providing skills for data interpretation, it is not the only way to acquire these skills. This study highlights the ability of non-experts to understand and apply scientific data in daily situations and the potential of citizen science to develop scientific skills, competencies and public understanding of science.

Accurate assessment of air pollution exposure is crucial to better public health. Routine monitoring is done by standardized Air Quality Monitoring (AQM) stations, which are spread thinly due to size and cost. Recent technological developments have made Wireless Distributed Environmental Sensor Networks (WDESNs) that consist of low-cost Micro Sensing Units (MSUs) feasible. These MSUs can be spread more densely and provide higher spatial resolution data. The availability of MSUs, however, poses the challenge of selecting optimal sensors’ locations. Previous attempts assumed prior knowledge on pollution levels in the region of interest, and considered MSUs which measured only one pollutant. This paper presents a scheme for finding an optimal deployment of heterogeneous WDESN, which is based only on MSUs characteristics and land use analysis. To this end, a set of optional deployment locations (OLs) is defined. Each OL is characterized by a set of utilities of placing the various MSUs in that location. The optimization process seeks for the set of locations, under budget and resources constraints, that maximizes the overall utility. Using the suggested method leads to an intelligent deployment under a set of given premises. This is demonstrated vs. a real-world deployment scenario, with multiple types of MSUs.

Efficient and safe detection of Bacillus anthracis spores (BAS) is a challenging task especially in bio-terror scenarios where the agent is concealed. We provide a proof-of-concept for the identification of concealed BAS inside mail envelopes using short-wave infrared hyperspectral imaging (SWIR-HSI). The spores and two other benign materials are identified according to their typical absorption spectrum. The identification process is based on the removal of the envelope signal using a new automatic new algorithm. This method may serve as a fast screening tool prior to using classical bioanalytical techniques.

Water is a resource that affects every aspect of life. Intentional (terrorist or wartime events) or accidental water contamination events could have a tremendous impact on public health, behavior and morale. Quick detection of such events can mitigate their effects and reduce the potential damage. Continuous on-line monitoring is the first line of defense for reducing contamination associated damage. One of the available tools for such detection is the UV-absorbance spectrophotometry, where the absorbance spectra are compared against a set of normal and contaminated water fingerprints. However, as there are many factors at play that affect this comparison, it is an elusive and tedious task. Further, the comparison against a set of known fingerprints is futile when the water in the supply system are a mix, with varying proportions, of water from different sources, which differ significantly in their physicochemical characteristics. This study presents a new scheme for early detection of contamination events through UV absorbance under unknown routine conditions. The detection mechanism is based on a new affinity measure, Fitness, and a procedure similar to Gram based amplification, which result in a flexible mechanism to alert if a contamination is present. The method was shown to be most effective when applied to a set of comprehensive experiments, which examined the absorbance of various contaminants in drinking water in lab and real-life configurations. Four datasets, which contained real readings from either laboratory experiments or monitoring station of an operational water supply system were used. To extend the testbed even further, an artificial dataset, simulating a vast array of proportions between specific water sources is also presented. The results show, that for all datasets, high detection rates, while maintaining low levels of false alarms, were obtained by the algorithm.

An algorithm for identifying organic materials using a short-wave infrared hyperspectral imager is presented. The main achievement of this algorithm is to automatically locate a clear reference within the scene, without any prior knowledge regarding the scene, and use it to extract the spectral signature of the target material. This step is required for sub-millimeter organic films since they absorb only a small fraction of the light. Therefore, the obtained spectral signature is a product of the target material and the clean background spectral signature. Detection rate as high as 90% was achieved for three model compounds in a controlled scene with false alarm rate lower than 1% in our model scene, containing a set of synthetic targets (ST). These findings lay the groundwork for further study of real (and probably more complex compared to the ST) cases and materials, required for the development of a detection system that will be capable of locating trace amounts of polluting chemicals to enable improved prevention, regulation, and mitigation acts.

Technological developments, social networking and the emergence of sensory micro-computation platforms have facilitated the recent growth of citizen science-public participation in scientific research. Citizen science provides lay audiences platforms for data collection and classification alongside access to large scientific databases. Although these platforms are intended for non-experts, they are often designed by scientists, who may not fully appreciate their importance. This may result in platforms that are incompatible with users’ needs and thus underused. This article describes the use of Human Computer Interactions (HCI) in a citizen science project for monitoring air-quality in the local environment. Using interviews, focus groups, questionnaires and log data from the project website (n = 138), in a three-phase iterative process, we identified public requirments from an online data presentation platform. The findings suggest participants were interested in real time, local, easy to understand information, which is practical, ready-to-use and presented in the context. These insights were implemented in the design of a new platform, constructed as a simple three-layer information display with representations of air-quality standards and practical recommendations. We examine participants’ use of the platform and discuss motivations and impediments to participation in the future design of citizen science projects for enhancing public engagement in science.

Air pollution management and control are key factors in maintaining sustainable societies. Air quality forecasting may significantly advance these tasks. While short-term forecasting, a few days into the future, is a well-established research domain, there is no method for long-term forecasting (e.g., the pollution level distribution in the upcoming months or years). This paper introduces and defines long-term air pollution forecasting, where long-term refers to estimating pollution levels in the next few months or years. A Discrete-Time-Markov-based model for forecasting ambient nitrogen oxides patterns is presented. The model accurately forecasts overall pollution level distributions, and the expectancy for tomorrow's pollution level given today's level, based on longitudinal historical data. It thus characterizes the temporal behavior of pollution. The model was applied to five distinctive regions in Israel and Australia and was compared against several forecasting methods and was shown to provide better results with a relatively lower total error rate.

Water is a resource that affects every aspect of life. Intentional or accidental contamination events in the water supply system could have a tremendous impact on public health. Quick detection of such events can reduce the expected damage. Continuous onlinemonitoring is the first line of defense for reducing contamination-associated damage. One of the available tools for such detection is ultraviolet (UV)-absorbance spectrophotometry, where the absorbance spectra are compared against a set of normal and contaminated water fingerprints. However, because there are many factors at play that affect this comparison, it is an elusive and tedious task. This study presents a new scheme for early detection of drinking water contamination events through UV absorbance. The detection mechanism is based on a new affinity measure, Fitness, which is flexible enough to identify the source of the drinking water being monitored and alert if contaminants are present. The potential of the method is presented in a set of comprehensive experiments with various contaminants in drinking water extracted directly from a real supply system with mixed sources.

Low-cost air quality sensors offer high-resolution spatiotemporal measurements that can be used for air resources management and exposure estimation. Yet, such sensors require frequent calibration to provide reliable data, since even after a laboratory calibration they might not report correct values when they are deployed in the field, due to interference with other pollutants, as a result of sensitivity to environmental conditions and due to sensor aging and drift. Field calibration has been suggested as a means for overcoming these limitations, with the common strategy involving periodical collocations of the sensors at an air quality monitoring station. However, the cost and complexity involved in relocating numerous sensor nodes back and forth, and the loss of data during the repeated calibration periods make this strategy inefficient. This work examines an alternative approach, a node-to-node (N2N) calibration, where only one sensor in each chain is directly calibrated against the reference measurements and the rest of the sensors are calibrated sequentially one against the other while they are deployed and collocated in pairs. The calibration can be performed multiple times as a routine procedure. This procedure minimizes the total number of sensor relocations, and enables calibration while simultaneously collecting data at the deployment sites. We studied N2N chain calibration and the propagation of the calibration error analytically, computationally and experimentally. The in-situ N2N calibration is shown to be generic and applicable for different pollutants, sensing technologies, sensor platforms, chain lengths, and sensor order within the chain. In particular, we show that chain calibration of three nodes, each calibrated for a week, propagate calibration errors that are similar to those found in direct field calibration. Hence, N2N calibration is shown to be suitable for calibration of distributed sensor networks. Node-to-node calibration is proposed as a general method for field calibration of wireless distributed air-quality sensor networks.

This paper identifies the diverse ways in which participants engage with science, through the same citizen science project. Using multiple data sources, we describe various activities conducted by citizen scientists in an air quality project, and characterize the motivations driving their engagement. Findings reveal several themes, indicative of participants motivations and engagement; worried residents, education and outreach, environmental action, personal interest and opportunistic engagement. The study further illustrates the interconnectivity between science communication and citizen science practices and calls for nurturing this relationship for the mutual advancement of both fields.

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids ofWireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.

Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website.

The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality.

The use of data collected from near-miss reports has added value to safety in high-hazard industries such as aviation and the process industry, but this procedure has not yet taken root in construction management practice. Although the management of near-miss reporting systems and the collection of near-miss event reports have been the focus of recent research efforts, the structural analyses of reports have not yet been investigated. This paper reports on a multi-phase research that studied near misses related to crane work and their safety risk potential. Former research stages concentrated on collecting near-miss as well as accident stories and established a structured database of tower-crane related events. The reported events were further classified into five homogeneous clusters and their potential for escalating into accidents at a given outcome severity level was assessed. The research stage reported in this paper builds on the results of the previous stage results, and implements the analytic hierarchy process (AHP) to evaluate the quantitative outcome severity level values, thus computing each event's total risk potential. The research outcomes reveal inter-relations between technical factors and human factors in the tower-crane domain. Further application is demonstrated, using the risk potential values for construction company safety assessments.

Learning lessons from near-miss reports is a well-known procedure in various high-hazard industries. The construction industry tends to adopt near-miss management systems, but the procedure is relatively new and has not yet been fully explored or understood. Although the management of near-miss reporting systems in construction has been investigated, no effort appears to have been made to suggest a methodology for technically investigating the content of near-miss reported events. This paper reports a study that implemented both qualitative and quantitative analysis methods for a structured investigation of tower-crane-related incident stories (near misses and accidents). The study began by collecting a large number of incident stories (51 accidents and 161 near misses) that were qualitatively analyzed to form an incident database that served as raw material for further quantitative analyses. The database structure definitions contained categories, which in turn contained variables, such as defining the event severity outcomes on a six-point scale from ‘1’ – near miss to ‘6’ – fatality. Further quantitative analysis suggests comparing groups of similar or identical incidents. The groups (clusters) were established by implementing k-means clustering based on the database definitions as variables. The relative risk potential of each cluster was then quantified by comparing each cluster's severity outcome occurrences to those of the other clusters and to the entire database as well. Findings from the analyses suggest that technical failures are the most hazardous risk factors within the tower crane domain.

Air pollution is a significant health risk factor and causes many negative effects on the environment. Thus, arises the need for studying and assessing air-quality. Today, air-pollution assessment is mostly based on data acquired from Air Quality Monitoring (AQM) stations. These AQM stations provide continuous measurements and considered to be accurate; however, they are expensive to build and operate, thus scattered sparingly. To cope with this limitation, typically, the information obtained from those measurements is generalized with interpolation methods such as IDW or Kriging. Yet, the mathematical basis of those schemes defines that pollution extremum values are obtained at the measuring points. In addition, they are not considering the location of the pollution source or any physicochemical characteristics of pollutant hence do not reveal the real spatial air-pollution patterns. This research introduces a new interpolation scheme which breaks the interpolation process into two stages. At the first stage, the source of pollution and its estimated emission rate are inferred through a detection procedure which is based on the Hough Transform. At the second stage, based on the detected source location and emission, spatial dense pollution maps are created. The method requires, for its computation, to assume a dispersion model. To this end, any model can be used as sophisticated as it may be. Spatial maps created with simplified dispersion models in a computational simulation, show that the suggested interpolation scheme manages to create more accurate and more physically reasonable maps than the state-of-the-art.

Maintaining water quality is critical for any water distribution company. One of the major concerns in water quality assurance, is bacterial contamination in water sources. To date, bacteria growth models cannot predict with sufficient accuracy when a bacteria outburst will occur in a water well. This is partly due to the natural sparsity of the bacteria count time series, whic]h hinders the observation of deviations from normal behavior. This precludes the application of mathematical models nor statistical quality control methods for the detection of high bacteria counts before contamination occurs. As a result, currently a future outbreak prediction is a subjective process. This research developed a new cost-effective method that capitalizes on the sparsity of the bacteria count time series. The presented method first transforms the data into its spectral representation, where it is no longer sparse. Capitalizing on the spectral representation the dimensions of the problem are reduced. Machine learning methods are then applied on the reduced representations for predicting bacteria outbursts from the bacterial counts history of a well. The results show that these tools can be implemented by the water quality engineering community to create objective, more robust, quality control techniques to ensure safer water distribution.

Recent advances in sensory and communication technologies have made Wireless Distributed Environmental Sensory Networks (WDESN) technically and economically feasible. WDESNs present an unprecedented tool for studying many environmental processes in a new way. However, the WDESNs' calibration process is a major obstacle in them becoming the common practice. Here, we present a new, robust and efficient method for aggregating measurements acquired by an uncalibrated WDESN, and producing accurate estimates of the observed environmental variable's true levels rendering the network as self-calibrated. The suggested method presents novelty both in group-decision-making and in environmental sensing as it offers a most valuable tool for distributed environmental monitoring data aggregation. Applying the method on an extensive real-life air-pollution dataset showed markedly more accurate results than the common practice and the state-of-the-art.

This article investigates the feasibility of intelligent textile-reinforced concrete structural elements with sensing capabilities. The concept is based on dual use of glass and carbon fiber textiles as reinforcement and, at the same time, as a sensory agent. Experimental investigation demonstrates the feasibility of the concept in two applications: detecting strains in a mechanically loaded textile-reinforced concrete beam and monitoring the interaction of the structural element with a wet environment. By detecting the changes to the integrative electrical resistance of the carbon tow, the ability of the textile to sense strain and exposure to water is demonstrated. For strain sensing, the hybrid reinforcing textile provides electro-mechanical sensing with a gauge factor of the order of 1 and a detectable correlation with the load, strain, and displacement responses. For the detection of wetting, the implementation of the carbon tow in a Wheatstone bridge detects fractional resistance changes in the order of 10^'5, a figure that is effectively detected by monitoring the voltage across the bridge. The response to wetting, which is conditioned by the cracking of the beam and the exposure to ionic conductive solutions, provides a mean to monitor the functionality and the structural health of the textile-reinforced concrete beam.

Rapid advances in image acquisition and storage technology underline the need for real-time algorithms that are capable of solving large-scale image processing and computer-vision problems. The minimums–tcut problem, which is a classical combinatorial optimization problem, is a prominent building block in many vision and imaging algorithms such as video segmentation, co-segmentation, stereo vision, multi-view reconstruction, and surface fitting to name a few. That is why finding a real-time algorithm which optimally solves this problem is of great importance. In this paper, we introduce to computer vision the Hochbaum’s pseudoflow (HPF) algorithm, which optimally solves the minimum s–t cut problem. We compare the performance of HPF, in terms of execution times and memory utilization, with three leading published algorithms: (1) Goldberg’s and Tarjan’s Push-Relabel; (2) Boykov’s and Kolmogorov’s augmenting paths; and (3) Goldberg’s partial augment-relabel. While the common practice in computer-vision is to use either BK or PRF algorithms for solving the problem, our results demonstrate that, in general, HPF algorithm is more efficient and utilizes less memory than these three algorithms. This strongly suggests that HPF is a great option for many real-time computer-vision problems that require solving the minimum s–t cut problem.

The Macroscopic Fundamental Diagram (MFD) relates the number of circulating vehicles (or accumulation) to a neighbourhood's average speed or flow. In theory the MFD has a well-defined maximum which remains invariant over time. Recent studies, however, suggest that in practice this is not the case, and the MFD does present a variations over time. These variations in the MFD render traffic simulations, modelling and control schemes inaccurate, as these tools do not capture the dynamic nature of the MFD. This paper presents a dynamic model for estimating the MFD, so it does capture the MFD's time varying nature. A mathematical Kalman-filter based framework for solving the model and estimating the MFD are also presented. The application of the method on a small scale example shows the potential of the method.

In recent years the global data volume growth has been 40 percent each year, the number of bytes created daily is 10 to the power of 18 and the number of servers used by big data companies ranges up to millions. Space related research and space based observations are great contributors of big data. For example, satellite telemetry, sensor data, observation logs and manned space mission studies all produce enormous amounts of information that can be examined again and again to understand more about our universe, our solar system, our planet and our body. The International Space University (ISU) Space Studies Program (SSP) "Big Space Data" Team Project (TP) held in summer of 2016 examined the world of big data through the eyes of the space community; the Team Project mapped the data creators, the data depositories, the data managers and data consumers related to space and space based activities. The TP discussed ways to better the data flow from sensor to processor to general public. The leading question for the TP was "how do we make new conclusions on different matters from all the data available to me", and by it the TP evaluated ways of using and reusing data acquired by space based and terrestrial sensors to learn as much as possible about the universe around us. The TP examined both the technological challenges in data management as well as the legal and ethical considerations of balancing privacy, data security and intellectual property with the socioeconomic benefits of global information sharing and openness for the general good. This paper summarizes the findings and conclusions of the ISU SSP 2016 Big Space Data Team Project.

Air pollution has a proven impact on public health. Currently, pollutant levels are obtained by high-priced, sizeable, stationary Air Quality Monitoring (AQM) stations. Recent developments in sensory and communication technologies have made relatively low-cost, micro-sensing units (MSUs) feasible. Their lower power consumption and small size enable mobile sensing, deploying single or multiple units simultaneously. Recent studies have reported on measurements acquired by mobile MSUs, mounted on cars, bicycles and pedestrians. While these modes of transportation inherently present different velocity and acceleration regimes, the effect of the sensors' varying movement characteristics have not been previously accounted for. This research assesses the impact of sensor's motion on its functionality through laboratory measurements and a field campaign. The laboratory setup consists of a wind tunnel to assess the effect of air flow on the measurements of nitrogen dioxide and ozone at different velocities in a controlled environment, while the field campaign is based on three cars mounted with MSUs, measuring pollutants and environmental variables at different traveling speeds. In both experimental designs we can regard the MSUs as a moving object in the environment, i.e. having a distinct ego-motion. The results show that MSU's behavior is highly affected by variation in speed and sensor placement with respect to direction of movement, mainly due to the physical properties of installed sensors. This strongly suggests that any future design of MSU must account for the speed effect from the design stage all the way through deployment and results analysis. This is the first report examining the influence of airflow variations on MSU's ability to accurately measure pollutant levels.

Future water demand is a main consideration in water system management. Consequently, water demand models (WDMs) have evolved in past decades, identifying principal demand-generating factors and modeling their influence on water demand. Regional water systems serve consumers of various types (e.g., municipalities, farmers, industrial regions) and consumption patterns. Thus, one of the challenges in regional water demand modeling is the heterogeneity of the consumers served by the water system. When a high-resolution, regional WDM is desired, accounting for this heterogeneity becomes all the more important. This paper presents a novel approach to regional water demand modeling. The two-step approach includes aggregating the data set into groups of consumers having similar consumption characteristics, and developing a WDM for each homogeneous group. The development of WDMs is widely applied in the literature and thus, the focus of this paper is to discuss the first step of data aggregation. The research hypothesis is that water consumption records in their original or transformed form can provide a basis for aggregating the data set into groups of consumers with similar consumption characteristics. This paper presents a methodology for water consumption data clustering by comparing several data representation methods (termed Feature Vectors): monthly normalized average, monthly consumption coefficient of variation, a combination of the monthly average and monthly variation, and the autocorrelation coefficients of the consumption time series. Clustering using solely normalized monthly average provided homogeneous and distinct clusters with respect to monthly consumption, which succeed in capturing different consumer characteristics (water use, geographical location) that were not specified a-priori. Clustering using the monthly coefficient of variation provided different, yet homogeneous clusters, clustering consumers characterized by similar variation trends that were closely related to consumer water use type. The concatenation of these two Feature Vectors provided further insight into the relationship between consumption patterns and variability of consumers. An autocorrelation Feature Vector provided results that can form a basis for constructing a time-series model that is based on a group of resembling time series. The approaches presented here are steps toward utilizing the increasing amount of available water consumption data and data analysis techniques to facilitate the modeling of water demands in larger and heterogeneous regions with sufficient resolution.

Atmospheric turbulence causes a random blur in images due to random fluctuations of the refraction index of the air through which the light propagates. An algorithm is described that compensates image distortion due to the atmospheric turbulence in video while keeping the real moving objects in the video unharmed. The result of the restoration of the video sequence is a much more 'stable' sequence where real moving objects are easier to detect and track. This algorithm is based on Differential Elastic Image Registration which maps each turbulent image to a one good reference image derived from the sequence of the turbulent images. To preserve real motion in the scene, the moving objects are located and the compensation for the distortion of the turbulence is applied only to the static areas of images. The algorithm provides also a smooth transition between the static and the moving areas in the image.

Accurate evaluation of air pollution on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution levels but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Similarly, dedicated field campaigns can use tens of measurement devices and obtain highly dense spatial coverage but normally deployment has been limited to short periods of no more than few weeks. Nowadays, advances in communication and sensory technologies enable the deployment of dense grids of wireless distributed air monitoring nodes, yet their sensor ability to capture the spatiotemporal pollutant variability at the sub-neighborhood scale has never been thoroughly tested. This study reports ambient measurements of gaseous air pollutants by a network of six wireless multi-sensor miniature nodes that have been deployed in three urban sites, about 150. m apart. We demonstrate the network's capability to capture spatiotemporal concentration variations at an exceptional fine resolution but highlight the need for a frequent in-situ calibration to maintain the consistency of some sensors. Accordingly, a procedure for a field calibration is proposed and shown to improve the system's performance. Overall, our results support the compatibility of wireless distributed sensor networks for measuring urban air pollution at a sub-neighborhood spatial resolution, which suits the requirement for highly spatiotemporal resolved measurements at the breathing-height when assessing exposure to urban air pollution.

Distributed continuous in situ monitoring of the environment is an essential component in assessing environmental indicators as their changes take place on different spatial and temporal scales. Recent sensory and communication technological developments have led to the emergence of Wireless Distributed Environmental Sensing Networks (WDESNs) that consist mainly of Micro-Sensory-Units (MSUs). The set of skills and expertise that are called for developing these WDESNs are from different fields and research disciplines, where each discipline has its own jargon. To allow cross-disciplinary discussion, a comprehensive, yet simple framework for discussing data acquired by WDESNs is presented. The terminology presented here allows for describing complex multi-modal environmental sensory networks and the integration of the observed environmental indicators’ into a holistic understanding of the environment. The usage of the presented framework is demonstrated in describing a recently reported data acquired by air-pollution WDESN.

The detection of illicit nuclear materials is a major tool in preventing and deterring nuclear terrorism. The detection task is extremely difficult because of physical limitations of nuclear radiation detectors, shielding by intervening cargo materials, and the presence of background noise. We aim at enhancing the capabilities of detectors with algorithmic methods specifically tailored for nuclear data. This paper describes a novel graphtheory-based methodology for this task. This research considers for the first time the utilization of supervised normalized cut (SNC) for data mining and classification of measurements obtained from plastic scintillation detectors that are of particularly low resolution. Specifically, the situation considered here is for when both energy spectra and the time dependence of such data are acquired. We present here a computational study, comparing the supervised normalized cut method with alternative classification methods based on support vector machine (SVM), specialized feature-reducing SVMs (i.e., 1-norm SVM, recursive feature elimination SVM, and Newton linear program SVM), and linear discriminant analysis (LDA). The study evaluates the performance of the suggested method in binary and multiple classification problems of nuclear data. The results demonstrate that the new approach is on par or superior in terms of accuracy and much better in computational complexity to SVM (with or without dimension or feature reduction) and LDA with principal components analysis as preprocessing. For binary and multiple classifications, the SNC method is more accurate, more robust, and is computationally more efficient by a factor of 2-80 than the SVM-based and LDA methods.

This study examines and demonstrates the feasibility of a new class of smart textile reinforced concrete (TRC) structural elements with inherent sensing capabilities that are based on embedding metallic yarns in the textile mesh. The new approach combines the advantages of thin walled glass fiber based TRC with the electro-mechanical properties of the stainless steel fibers embedded in the textile matrix. To examine this concept and to demonstrate its potential feasibility, TRC beam specimens are tested and monitored under mechanical and environmental loading condition. The results of the tests demonstrate the features of the sensory/structural system, reveals its potential use as a basis for a combined structural and functional monitoring system, and highlights its spectrum of potential applications.

Video tracking is a fundamental problem in computer vision with many applications. The goal of video tracking is to isolate a target object from its background across a sequence of frames. Tracking is inherently a three dimensional problem in that it incorporates the time dimension. As such, the computational efficiency of video segmentation is a major challenge. In this paper we present a generic and robust graph-theory-based tracking scheme in videos. Unlike previous graph-based tracking methods, the suggested approach treats motion as a pixel's property (like color or position) rather than as consistency constraints (i.e., the location of the object in the current frame is constrained to appear around its location in the previous frame shifted by the estimated motion) and solves the tracking problem optimally (i.e., neither heuristics nor approximations are applied). The suggested scheme is so robust that it allows for incorporating the computationally cheaper MPEG-4 motion estimation schemes. Although block matching techniques generate noisy and coarse motion fields, their use allows faster computation times as broad variety of off-the-shelf software and hardware components that specialize in performing this task are available. The evaluation of the method on standard and non-standard benchmark videos shows that the suggested tracking algorithm can support a fast and accurate video tracking, thus making it amenable to real-time applications.

Size and concentration of airborne particulate matter (PM) are important indicators of air pollution events and public health risks. It is therefore important to monitor size resolved PM concentrations in the ambient air. This task, however, is hindered by the highly dynamic spatiotemporal variations of the PM concentrations. Satellite remote sensing is a common approach for gathering spatiotemporal data regarding aerosol events but its current spatial resolution is limited to a large grid that does not fit high varying urban areas. Moreover, satellite-borne remote sensing has limited revisit periods and it measures along vertical atmospheric columns. Thus, linking satellite-borne aerosol products to ground PM measurements is extremely challenging. In the last two decades visibility analysis is used by the US Environmental Protection Agency (US-EPA) to obtain quantitative representation of air quality in rural areas by horizontal imaging. However, significantly fewer efforts have been given to utilize the acquired scene characteristics (color, contrast, etc.) for quantitative parametric modeling of PM concentrations. We suggest utilizing the image effective bandwidth, a quantitative measure of image characteristics, for predicting PM concentrations. For validating the suggested method, we have assembled a large dataset that consists of time series imaging as well as measurements from air quality monitoring stations located in the study area that report PM concentrations and meteorological data (wind direction and velocity, relative humidity, etc.). Quantitative and qualitative statistical evaluation of the suggested method shows that dynamic changes of PM concentrations can be inferred from the acquired images.

The ability to track illicit radioactive source in an urban environment is critical in national security applications. To this end, two modes of operation are common: positioning individual portal monitors, and deploying a network of distributed sensors. We address here the use of multiple detectors, mounted on moving vehicles, for the purpose of detecting nuclear threats. An example scenario is that of multiple taxi cabs each carrying a detector. The detectors' positions are known in real-time as these are continuously reported from GPS data. The level of detected risk is then reported from each detector at each position. The problem is to delineate the presence of a potentially dangerous source and its approximate location by identifying a small area that has an elevated concentration of reported risk. This problem of using spatially deployed mobile detector networks to identify and locate risks is modeled and formulated here. The problem is shown to be solvable in polynomial time and with a combinatorial network flow algorithm. The efficiency of the algorithm enable its use in real time, and in areas containing a large number of deployed detectors. A simulation study, that takes into account false-positive and false-negatives reports from individual sensors, demonstrates the effectiveness of the algorithm in using the sensor network's detection capabilities.

Of the numerous factors that play a role in fatal pedestrian collisions, the time of day, day of the week, and time of year can be significant determinants. More than 60% of all pedestrian collisions in 2007 occurred at night, despite the presumed decrease in both pedestrian and automobile exposure during the night. Although this trend is partially explained by factors such as fatigue and alcohol consumption, prior analysis of the Fatality Analysis Reporting System database suggests that pedestrian fatalities increase as light decreases after controlling for other factors. This study applies graphical cross-tabulation, a novel visual assessment approach, to explore the relationships among collision variables. The results reveal that twilight and the first hour of darkness typically observe the greatest frequency of pedestrian fatal collisions. These hours are not necessarily the most risky on a per mile travelled basis, however, because pedestrian volumes are often still high. Additional analysis is needed to quantify the extent to which pedestrian exposure (walking/crossing activity) in these time periods plays a role in pedestrian crash involvement. Weekly patterns of pedestrian fatal collisions vary by time of year due to the seasonal changes in sunset time. In December, collisions are concentrated around twilight and the first hour of darkness throughout the week while, in June, collisions are most heavily concentrated around twilight and the first hours of darkness on Friday and Saturday. Friday and Saturday nights in June may be the most dangerous times for pedestrians. Knowing when pedestrian risk is highest is critically important for formulating effective mitigation strategies and for efficiently investing safety funds. This applied visual approach is a helpful tool for researchers intending to communicate with policy-makers and to identify relationships that can then be tested with more sophisticated statistical tools.

Color videos acquired with a single CCD through turbulent media can be enhanced in their resolution beyond the limit defined by the image sampling rate. We provide a mathematical justification for this claim, present an efficient superresolution algorithm and its experimental verification on a real-life video, and finally, discuss its potentials and limitations.

In many applications, sampled data are collected in irregular fashion or are partly lost or unavailable. In these cases, it is necessary to convert irregularly sampled signals to regularly sampled ones or to restore missing data. We address this problem in the framework of a discrete sampling theorem for band-limited discrete signals that have a limited number of nonzero transform coefficients in a certain transform domain. Conditions for the image unique recovery, from sparse samples, are formulated and then analyzed for various transforms. Applications are demonstrated on examples of image superresolution and image reconstruction from sparse projections.

One of the key aspects of 3D visualization is computation of depth maps. Depth maps enables synthesis of 3D video from 2D video and use of multi-view displays. Depth maps can be acquired in several ways. One method is to measure the real 3D properties of the scene objects. Other methods rely on using two cameras and computing the correspondence for each pixel. Once a depth map is acquired for every frame, it can be used to construct its artificial stereo pair. There are many known methods for computing the optical flow between adjacent video frames. The drawback of these methods is that they require extensive computation power and are not very well suited to high quality real-time 3D rendering. One efficient method for computing depth maps is extraction of motion vector information from standard video encoders. In this paper we present methods to improve the 3D visualization quality acquired from compression CODECS by spatial/temporal and logical operations and manipulations. We show how an efficient real time implementation of spatial-temporal local order statistics such as median and local adaptive filtering in 3D-DCT domain can substantially improve the quality of depth maps and consequently 3D video while retaining real-time rendering. Real-time performance is achived by utilizing multi-core technology using standard parallelization algorithms and libraries (OpenMP, IPP).

Visual surveillance for traffic systems requires short processing time, low processing cost and high reliability. Under those requirements, image processing technologies offer a variety of systems and methods for Intelligence Transportation Systems (ITS) as a platform for traffic Automatic Incident Detection (AID). There exist two classes of AID methods mainly studied: one is based on inductive loops, radars, infrared sonar and microwave detectors and the other is based on video images. The first class of methods suffers from drawbacks in that they are expensive to install and maintain and they are unable to detect slow or stationary vehicles. Video sensors, on the other hand, offer a relatively low installation cost with little traffic disruption during maintenance. Furthermore, they provide wide area monitoring allowing analysis of traffic flows and turning movements, speed measurement, multiple-point vehicle counts, vehicle classification and highway state assessment, based on precise scene motion analysis. This paper suggests the utilization of traffic models for real-time vision-based traffic analysis and automatic incident detection. First, the traffic flow variables, are introduced. Then, it is described how those variables can be measured from traffic video streams in real-time. Having the traffic variables measured, a robust automatic incident detection scheme is suggested. The results presented here, show a great potential for integration of traffic flow models into video based intelligent transportation systems. The system real time performance is achieved by utilizing multi-core technology using standard parallelization algorithms and libraries (OpenMP, IPP).

Image evaluation and quality measurements are fundamental components in all image processing applications and techniques. Recently, a no-reference perceptual blur metrics (PBM) was suggested for numerical evaluation of blur effects. The method is based on computing the intensity variations between neighboring pixels of the input image before and after low-pass filtering. The method was proved to demonstrate a very good correlation between the quantitative measure it provides and visual evaluation of perceptual image quality. However, this quantitative image blurriness measure has no intuitive meaning and has no association with conventionally accepted imaging system design parameters such as, for instance, image bandwidth. In this paper, we suggest an extended modification of this PBM-method that provides such a direct association and allows evaluation image in terms of the image efficient bandwidth. To this end we apply the PBM-method to a series of test pseudo-random images with uniform spectrum of different spread within the image base-band defined by the image sampling rate and map the image blur measurement results obtained for this set of test images to corresponding measures of their bandwidths. In this way we obtain a new image feature, which provides evaluation of image in terms of the image effective bandwidth measured in fractions, from 0 to 1, of the image base-band. In addition, we also show that the effective bandwidth measure provides a good estimation for the potential JPEG encoder compression rate, which allows one to choose the best compression quality for a requested compressed image size.

Optical flow algorithms for estimating image local motion in video sequences are based on the first term Taylor series expansion approximation of image variations caused by motion, which requires computing image spatial derivatives. In this paper we report an analytical assessment of lower bounds of optical flow estimation errors defined by the accuracy of the Taylor series expansion approximation of image variations and results of experimental comparison of performance of known optical flow methods, in which image differentiation was implemented through different commonly used numerical differentiation methods and through DFT/DCT based algorithms for precise differentiation of sampled data. The comparison tests were carried out using simulated sequences as well as real-life image sequences commonly used for comparison of optical flow methods. The simulated sequences were generated using, as test images, pseudo-random images with uniform spectrum within a certain fraction of the image base band specified by the image sampling rate, the fraction being a parameter specifying frequency contents of test images. The experiments have shown that performance of the optical flow methods can be significantly improved compared to the commonly used numerical differentiation methods by using the DFT/ DCT- based differentiation algorithms especially for images with substantial high-frequency content

It has been shown that one can make use of local instabilities in turbulent video frames to enhance image resolution beyond the limit defined by the image sampling rate. This paper outlines a real-time solution for the implementation of super-resolution algorithm on MPEG-4 platforms. The MPEG-4 video compression standard offer, in real-time, several features, such as motion extraction with quarter pixel accuracy, scene segmentation to video object planes, global motion compensation and de-blocking and de-ringing filters, which can be incorporated into the super-resolution process to produce enhanced visual output. Experimental verification on real-life videos is also provided.

This paper presents methods for intrachannel and interchannel fusion of thermal and visual sensors used in long-distance terrestrial observation systems. Intrachannel spatial and temporal fusion mechanisms used for image stabilization, super-resolution, denoising, and deblurring are supplemented by interchannel data fusion of visual- and thermal-range channels for generating fused videos intended for visual analysis by a human operator. Tests on synthetic, as well as on real-life, video sequences have confirmed the potential of the suggested methods.

A common distortion in videos acquired in long range observation systems is image instability in form of chaotic local displacements of image frames caused by fluctuations in the refraction index of the atmosphere turbulence. At the same time, such videos, which are designed to present moving objects on a stable background, contain tremendous redundancy that potentially can be used for image stabilization and perfecting provided reliable separation of stable background from true moving objects. Recently, it was proposed to use this redundancy for resolution enhancement of turbulent video through elastic registration, with sub-pixel accuracy, of segments of video frames that represent stable scenes. This paper presents results of investigation, by means of computer simulation, into how parameters of such a resolution enhancement process affect its performance and its potentials and limitations.

In order to synthesize 3D content, one needs to have either a stereo pair or an image and a depth map. Many methods exist to compute depth maps, however, these methods are usually very intensive in term of computational complexity. In this paper we focus on processing of depth maps acquired by simple motion extraction methods and concentrate on manipulating these coarse depth maps to yield high quality 3D images and video.

In this paper we present experimental evidence of the redundancy of depth maps for 3D visualization. To this end, we performed a series of statistical experiments, devised to measure the effect of depth map quantization and the resolving power of 3D perception. The results of these tests show that for good 3D perception and 3D visualization, one does not need to use depth map of the same resolution neither with the same quantization as the original images. These results indicate that depth map based visualization can be based on low resolution, coarsely quantized, depth maps without significant degradation in the perceived 3D image.

The paper presents a real-time algorithm that compensates image distortions due to atmospheric turbulence in video sequences, while keeping the real moving objects in the video unharmed. The algorithm involves (1) generation of a "reference" frame, (2) estimation, for each incoming video frame, of a local image displacement map with respect to the reference frame, (3) segmentation of the displacement map into two classes: stationary and moving objects; (4) turbulence compensation of stationary objects. Experiments with both simulated and real-life sequences have shown that the restored videos, generated in real-time using standard computer hardware, exhibit excellent stability for stationary objects while retaining real motion.

We present a real-time implementation of 2D to 3D video conversion using compressed video. In our method, compressed 2D video is analyzed by extracting motion vectors. Using the motion vector maps, depth maps are built for each frame and the frames are segmented to provide object-wise depth ordering. These data are then used to synthesize stereo pairs. 3D video synthesized in this fashion can be viewed using any stereoscopic display. In our implementation, anaglyph projection was selected as the 3D visualization method, because it is mostly suited to standard displays.

It is shown that one can make use of local instabilities in turbulent video frames to enhance image resolution beyond the limit defined by the image sampling rate. We outline the processing algorithm, present its experimental verification on simulated and real-life videos, and discuss its potentials and limitations.

We present experimental results of analysis of the impact of depth map blurring on 3D perception of synthetic 3D video. These results are then used to increase the quality of synthetic 3D video.

With the recent advancement in visualization devices over the last years, we are seeing a growing market for stereoscopic content. In order to synthesize 3D content, one needs to have either a stereo pair or an image and a depth map. Computing depth maps for images is a highly computationally intensive and time-consuming process. In this paper, we describe results of an experimental evaluation of depth map data redundancy in stereoscopic images. In the experiments with computer generated images, several observers visually tested the number of quantization levels required for comfortable and quantization unaffected stereoscopic vision. The experiments show that the number of depth quantization levels can be as low as only a couple of tens. This may have profound implication on the process of depth map estimation and 3D synthesis.

In this paper, we present an efficient method to synthesize 3D video from compressed 2D video. The 2D video is analyzed by computing frame-by-frame motion maps. For this computation, MPEG motion vectors extraction was performed. Using the extracted motion vector maps, the video undergoes analysis and the frames are segmented to provide object-wise depth ordering. The frames are then used to synthesize stereo pairs. This is performed by resampling the video frames on a grid that is governed by a corresponding depth-map. In order to improve the quality of the synthetic video, as well as to enable 2D viewing where 3D visualization is not possible, several techniques for image enhancement are used. In our test case, anaglyph projection was selected as the 3D visualization method, as the method is mostly suited to standard displays. The drawback of this method is ghosting artifacts. In our implementation we minimize these unwanted artifacts by modifying the computed depth-maps using non-linear transformations. Defocusing of one anaglyph color component was also used to counter such artifacts. Our results show that the suggested methods enable synthesis of high quality 3D videos in real-time.

Long Range Observation Systems is a domain, which carries a lot of interest in many fields such as astronomy (i.e. planet exploration), geology, ecology, traffic control, remote sensing, and homeland security (surveillance and military intelligence). Ideally, image quality would be limited only by the optical setup used, but, in such systems, the major cause for image distortion is atmospheric turbulence. The paper presents a real-time algorithm that compensates images distortion due to atmospheric turbulence in video sequences, while keeping the real moving objects in the video unharmed. The algorithm is based on moving objects extraction; hence turbulence distortion compensation is applied only to the static areas of images. For that purpose a hierarchical decision mechanism is suggested. First, a lightweight computational decision mechanism which extracts most stationary areas is applied. Then a second step improves accuracy by more computationally complex algorithms. Finally, all areas in the incoming frame that were tagged as stationary are replaced with an estimation of the stationary scene. The restored videos exhibit excellent stability for stationary objects while retaining real motion. This is achieved in real-time on standard computer hardware.

Image and video quality in Long Range Observation Systems (LOROS) suffer from atmospheric turbulence that causes small neighbourhoods in image frames to chaotically move in different directions and substantially hampers visual analysis of such image and video sequences. The paper presents a real-time algorithm for perfecting turbulence degraded videos by means of stabilization and resolution enhancement. The latter is achieved by exploiting the turbulent motion. The algorithm involves generation of a "reference" frame and estimation, for each incoming video frame, of a local image displacement map with respect to the reference frame; segmentation of the displacement map into two classes: stationary and moving objects and resolution enhancement of stationary objects, while preserving real motion. Experiments with synthetic and real-life sequences have shown that the enhanced videos, generated in real time, exhibit substantially better resolution and complete stabilization for stationary objects while retaining real motion.

A two stage fusion image processing system has been developed for visual and thermal range video streams. At the first stage, an intra-channel-inter- frame fusion is applied to each channel images independently, addressing each channel specific limitations. Noise removing processing by means of 3D (spatial-temporal) local DCT adaptive filtering is applied to the thermal channel video sequence. An algorithm that compensates image distortion due to the atmospheric turbulence is applied to the visual range channel video sequence. The second stage is interframe intra-channel fusion. At this stage, thermal and visual range channel image frames are fused frame-by-frame using a weighted average scheme with locally adapted weights. Experimental results obtained for both simulated and real life thermal and visual range image sequences are demonstrated.