Surveillance Problem Research Articles

A framework for multicriteria decision-aid analyses in animal health surveillance applied to periodic screening for French bovine tuberculosis.

In animal health surveillance, decision-makers must allocate limited financial and human resources, choosing appropriate protocols that consider technical, economic and human aspects (i.e. test sensitivity, cost benefits and policy acceptability respectively). Choosing in an objective manner and considering all these criteria can be challenging, especially where criteria have a tendency to be at odds with one another. In France, there are several mandatory protocols implemented to screen for bovine tuberculosis (TB), each with advantages and drawbacks concerning effectiveness, cost and acceptability. Previous studies have developed scenario tree models in order to evaluate mandatory periodic screening protocols and alternative protocols. Using these previously developed models, we estimated protocol sensitivity, costs at the level of State and farmer, probability of false suspects, and probability of culling an uninfected animal, which influence stakeholders' acceptability. We then assessed the level of difficulty for protocol implementation for veterinarians, farmers and State through the use of surveys. Using these criteria, we rank the protocols with the PROMETHEE method, a multicriteria decision-aid method, by considering the relative importance of each criterion from the decision-maker point of view in four administrative areas with contrasting epidemiological context. This method can be considered a tool to aid decision-makers in choosing the appropriate protocol to apply to a heard while considering the technical and socio-economic facets of the problem. Additionally, by adapting the criteria to specific issues with regards to decision-making, there is potential for applying the PROMETHEE method to other animal health surveillance problems.

Direct reconstruction of anatomical change in low-dose lung nodule surveillance.

Purpose: In sequential imaging studies, there exists rich information from past studies that can be used in prior-image-based reconstruction (PIBR) as a form of improved regularization to yield higher-quality images in subsequent studies. PIBR methods, such as reconstruction of difference (RoD), have demonstrated great improvements in the image quality of subsequent anatomy reconstruction even when CT data are acquired at very low-exposure settings. Approach: However, to effectively use information from past studies, two major elements are required: (1)registration, usually deformable, must be applied between the current and prior scans. Such registration is greatly complicated by potential ambiguity between patient motion and anatomical change-which is often the target of the followup study. (2)One must select regularization parameters for reliable and robust reconstruction of features. Results: We address these two major issues and apply a modified RoD framework to the clinical problem of lung nodule surveillance. Specifically, we develop a modified deformable registration approach that enforces a locally smooth/rigid registration around the change region and extend previous analytic expressions relating reconstructed contrast to the regularization parameter and other system dependencies for reliable representation of image features. We demonstrate the efficacy of this approach using a combination of realistic digital phantoms and clinical projection data. Performance is characterized as a function of the size of the locally smooth registration region of interest as well as x-ray exposure. Conclusions: This modified framework is effectively able to separate patient motion and anatomical change to directly highlight anatomical change in lung nodule surveillance.

Surveillance and Epidemiology of Dengue in Medellín, Colombia from 2009 to 2017.

Dengue is the most prevalent arthropod-borne viral disease in humans, primarily transmitted by the Aedes aegypti mosquito. We conducted a descriptive analysis of dengue cases from 2009 to 2017 in Medellín, Colombia, using data available from the Secretariat of Health. We analyzed the burden of outbreak years on the healthcare system, risk of cases exhibiting severe illness, potential disease surveillance problems, gender and age as risk factors, and spatiotemporal patterns of disease occurrence. Our data consisted of 50,083 cases, separated based on whether they were diagnostic test negative, diagnostic test positive (primarily IgM ELISA), clinically confirmed, epidemiologically linked, or probable. We used dengue incidence to analyze epidemiological trends between our study years, related to human movement patterns, between gender and age-groups, and spatiotemporally. We used risk to analyze the severity of dengue cases between the study years. We identified human movement could contributed to dengue spread, and male individuals (incidence rate: 0.86; 95% CI: 0.76-0.96) and individuals younger than 15 years (incidence rate: 1.24; 95% CI: 1.13-1.34) have higher incidence of dengue and located critical parts of the city where dengue incidence was high. Analysis was limited by participant diagnostic information, data concerning circulating strains, and a lack of phylogenetic information. Understanding the characteristics of dengue is a fundamental part of improving the health outcomes of at-risk populations. This analysis will be useful to support studies and initiatives to counteract dengue and provide context to the surveillance data collected by the health authorities in Medellín.

Navigating UAVs for Optimal Monitoring of Groups of Moving Pedestrians or Vehicles

This paper focuses on navigating a team of unmanned aerial vehicles (UAVs) equipped with cameras to monitor groups of ground pedestrians or vehicles that move along given paths with unkown, time-varying but bounded speeds. The objective is to deliver a high-quality surveillance of the pedestrians or vehicles. We formulate a surveillance problem which requires the UAVs to monitor the pedestrians or vehicles from as short as possible distances. We propose a navigation algorithm that enables each UAV to determine its movement locally with a minor participation of a central station. We prove that this algorithm is locally optimal. Simulations confirm its performance.

Stochastic Strategies for Robotic Surveillance as Stackelberg Games

This article studies a stochastic robotic surveillance problem where a mobile robot moves randomly on a graph to capture a potential intruder that strategically attacks a location on the graph. The intruder is assumed to be omniscient: It knows the current location of the mobile agent and can learn the surveillance strategy. The goal for the mobile robot is to design a stochastic strategy so as to maximize the probability of capturing the intruder. We model the strategic interactions between the surveillance robot and the intruder as a Stackelberg game; and optimal and suboptimal Markov chain based surveillance strategies in star, complete, and line graphs are studied. We first derive a universal upper bound on the capture probability, i.e., the performance limit for the surveillance agent. We show that this upper bound is tight in the complete graph and further provide suboptimality guarantees for the random walk. For the star and line graphs, we first characterize dominant strategies for the surveillance agent and the intruder. Then, we rigorously prove the optimal strategy for the surveillance agent.

A game of surveillance between an omnidirectional agent and a differential drive robot

ABSTRACT This paper addresses a surveillance problem between a Differential Drive Robot (DDR) and an Omnidirectional Agent (OA) in a Euclidean plane without obstacles. The agent is initially located inside the robot's detection region, and it wants to escape from it as soon as possible. The DDR, on the other hand, wants to delay the escape as much as possible. The pursuer's detection region is modelled as a circle centred at the pursuer's current location. In this work, the time-optimal motion strategies for the players are presented. Also, based on the players' initial configurations, we solve the decision problem of determining the winner of the game.

The meaning of Freedom after Covid-19.

Many governments have seen digital health technologies as promising tools to tackle the current COVID-19 pandemic. A much-talked example in this context involves the recent deluge of digital contact tracing apps (DCT) aimed at detecting Covid-19 exposure. In this short contribution we look at the bio-political justification of this phenomenon and reflect on whether DCT apps constitute, as it is often argued, a serious potential breach of our right to privacy. Despite praising efforts attempting to develop legal and ethical frameworks for DCT apps’ usage; we argue that such endeavours are not sufficient to tackle the more fundamental problem of mass surveillance, which will remain largely unaddressed unless we deal with the biopolitical arguments presented and resort to a technical and structural defence.

OABC: An Otsu-Artificial Bee Colony Multilevel Image Threshold Optimization for Liver Tumor Segmentation

Grey scale or color image conversion involves thresholding technique which can be either at single or multiple threshold levels. Resultant images have reduced pixel level classes. As level of thresholding required increases, the process becomes complex involving exhaustive search and thereby increasing computational resources and time complexity. Meta-heuristic optimization algorithms show promise to reduce time-complexity, especially the Artificial Bee Colony (ABC) algorithm mimicking honeybee foraging activities. It provides optimal results when sampling a large solution space. Since threshold-based image segmentation is a multidimensional discreet optimization problem, for fitness evaluation of threshold level candidates, Otsu’s thresholding method has been widely employed. In this paper, proposed is an Otsu-ABC multilevel image thresholding model for CT images using MATLAB for Liver tumor segmentation. The developed model is evaluated for increased dimensionality and computation time. It is also benchmarked against other commonly used algorithms. Results demonstrate that the proposed model is immune to dimensionality increase and outperforms all other models with regards to quality of images segmented and computation time proving that the model is ideal for optimizing multilevel image thresholding problems involving large thresholds for complex computer vision problems in medical diagnostics, biometrics, surveillance, satellite imagery and gaming.

Learning Transferable Driven and Drone Assisted Sustainable and Robust Regional Disease Surveillance for Smart Healthcare.

Smart healthcare has been applied in many fields such as disease surveillance and telemedicine, etc. However, there are some challenges for device deployment, data collection and guarantee of stainability in regional disease surveillance. First, it is difficult to deploy sensors and adjust the sensor network in unknown region for dynamic disease surveillance. Second, the limited life-cycle of sensor network may cause the loss of surveillance data. Thus, it is important to provide a sustainable and robust regional disease surveillance system. Given a set of Disease surveillance Area (DsA)s and Point of disease Surveillance (PoS)s, some sensors are deployed to monitor these PoSs, and a drone collect data from the sensors as well as charge the sensors to extend their life-cycles. The drone replenish its energy by relying on the bus network. We first formulate the drone assisted regional disease surveillance problem under the constraints of life-cycle of sensors and energy of drone, and propose an approximation algorithm to find a feasible cycle of drone to minimize the traveling time cost of drone. To satisfy the diversity requirements and dynamic scalability of regional disease surveillance, we deploy one robot in each DsA instead of sensors. We further formulate the learning transferable driven regional disease surveillance problem, and propose a joint schedule algorithm of drone and robots. The results of both theoretical analysis and extensive simulations show that the proposed algorithms can reduce the total time cost by 39.71 and 48.74 percent, average waiting time by 42.00 and 50.14 percent, and increase the average accessing ratio of PoSs by 15.53 and 22.30 percent, through the assistance of bus network and learning transferable features.

İş Yerinde Sağlık Gözetimi Sorunu: Bir Olgu Sunumu

İş Yerinde Sağlık Gözetimi Sorunu: Bir Olgu Sunumu

Analysis of heterogeneous genomic samples using image normalization and machine learning

BackgroundAnalysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for analysis of sequence data from such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datasets of different sizes and structures.ResultsWe propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important problems in molecular epidemiology: inference of viral infection stage and detection of viral transmission clusters using next-generation sequencing data. The infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.ConclusionsSequence image normalization method allows for a robust conversion of genomic data into numerical data and overcomes several issues associated with employing machine learning methods to viral populations. Image data also help in the visualization of genomic data. Experimental results demonstrate that the proposed method can be successfully applied to different problems in molecular epidemiology and surveillance of viral diseases. Simple binary classifiers and clustering techniques applied to the image data are equally or more accurate than other models.

Cost-Minimizing System Design for Surveillance of Large, Inaccessible Agricultural Areas Using Drones of Limited Range

Drones are used increasingly for agricultural surveillance. The limited flight range of drones poses a problem for surveillance of large, inaccessible areas. One possible solution is to place autonomous, solar-powered charging stations within the area of interest, where the drone can recharge during its mission. This paper designs and implements a software system for planning low-cost drone coverage of large areas. The software produces a feasible, cost-minimizing charging station placement, as well as a drone path specification. Multiple optimizations are required, which are formulated as integer linear programs. In extensive simulations, the resulting drone paths achieved 70–90 percent of theoretical optimal performance in terms of minimizing mission time for a given number of charging stations, for a variety of field configurations.

Energy-Efficient Autonomous Navigation of Solar-Powered UAVs for Surveillance of Mobile Ground Targets in Urban Environments

In this paper, we consider the navigation of a group of solar-powered unmanned aerial vehicles (UAVs) for periodical monitoring of a set of mobile ground targets in urban environments. We consider the scenario where the number of targets is larger than that of the UAVs, and the targets spread in the environment, so that the UAVs need to carry out a periodical surveillance. The existence of tall buildings in urban environments brings new challenges to the periodical surveillance mission. They may not only block the Line-of-Sight (LoS) between a UAV and a target, but also create some shadow region, so that the surveillance may become invalid, and the UAV may not be able to harvest energy from the sun. The periodical surveillance problem is formulated as an optimization problem to minimize the target revisit time while accounting for the impact of the urban environment. A nearest neighbour based navigation method is proposed to guide the movements of the UAVs. Moreover, we adopt a partitioning scheme to group targets for the purpose of narrowing UAVs’ moving space, which further reduces the target revisit time. The effectiveness of the proposed method is verified via computer simulations.

Stationary target localization and circumnavigation by a non‐holonomic differentially driven mobile robot: Algorithms and experiments

SummaryThis article addresses a surveillance problem where the goal is to achieve a circular motion around a target by a non‐holonomic differentially driven mobile robot. The available information for the mobile robot includes its own position, velocity with respect to the inertial frame, and the bearing angle of the target in its body frame. Since the target position is unavailable, an estimator is first proposed to localize it. Then a two‐step controller is given to drive the mobile robot to move onto a circular trajectory with a desired radius around it. It is shown that the proposed algorithm guarantees the convergence of the estimation error to a small neighborhood of zero and the motion of the robot to a small neighborhood of a designed radius. The performance of the proposed algorithm is first verified by simulations. Then, several experiments on a differentially driven mobile robot, Pioneer 3‐DX, are presented to further validate the proposed algorithm.

Airborne Maritime Surveillance Using Magnetic Anomaly Detection Signature

For an airborne sensor, there is a pressing need to be able to detect/track submerged submarines, shipwrecks, sea mines, unexploded explosive ordnance, and buried drums during maritime surveillance. Traditional usage is the magnetic anomaly detection (MAD), where the small changes in the earth's magnetic field caused by the ferrous components of the targets are measured. The primary means of long-range detection and classification of targets are with passive and active acoustic sensors, and MAD is used for accurate final localization. MAD could also be used for land-based targets but this is not common. Knowing the relationship between the magnetic signature and the kinematic parameters, the tracking problem can be formulated under a Bayesian framework. In this article, multiple nonlinear filters are used for a real single surface-target tracking problem in maritime surveillance using an airborne total-field sensor. The posterior Cramer–Rao lower bound for MAD is derived. Given the total-field measurements, these filters can estimate the kinematic states as well as the permanent moments and induced moments effectively. Results demonstrate the effectiveness of the proposed nonlinear filters as well as the impact of using MAD as part of airborne surveillance.

Perimeter Surveillance Based on Set-Invariance

A solution to the perimeter surveillance problem for one intruder and multiple surveillance robots based on set-invariance is presented. The surveillance robots, constrained to move on the perimeter of a polygonal region, intercept the intruders as they cross the perimeter. The proposed closed-form control laws only depend on the maximum speed of the robots and their distances to the endpoints of the line segments that make the sides of the polygon. The presented results allow for groups of robots with members of different characteristics, such as size and maximum speed, to defend polygonal regions. Simulations are used to show the application and effectiveness of the theoretical results.

Is Data Localization a Solution for Schrems II?

ABSTRACT For the second time this decade, the Court of Justice of the European Union has struck a blow against the principal mechanisms for personal data transfer to the USA. In Data Protection Commissioner v Facebook Ireland, Maximillian Schrems, the Court declared the European Union-US Privacy Shield invalid and placed significant hurdles to the process of transferring personal data from the European Union to the USA via the mechanism of Standard Contractual Clauses. Many have begun to suggest data localization as the solution to the problem of data transfer; that is, do not transfer the data at all. I argue that data localization neither solves the problem of foreign surveillance, nor enhances personal privacy, while undermining other values embraced by the European Union.

RMSRS: Rover Multi-purpose Surveillance Robotic System

The development of the internet of things (IoT) and the internet of robotics (IoR) are becoming more and more involved with our daily lives. It serves a variety of tasks some of them are essential to us. The main objective of SRR is to develop a surveillance system for detecting suspicious and targeted places for users without any loss of human life. This paper shows the design and implementation of a robotic surveillance platform for real-time monitoring with the help of image processing, which can explorer places of difficult access or high risk. The robotic live streaming is via two cameras, the first one is fixed straight on the road and the second one is dynamic with tilt-pan ability. All cameras have image processing capabilities to analyze, detect and track objects plus few other graphical functions. The components mentioned above built on top of the four-wheel vehicle system with high torque to provide mobility on rough terrain. This work is based on Raspberry Pi and can be controlled over Wi-Fi locally or publicly over the internet. The results show making a high potential, relatively low price robot with lots of features and functions that can perform multiple tasks simultaneously, all are crucial to surveillance and monitoring problems, controlled by a user from far distances and for a long time.

Ações para reduzir o risco de infecções relacionados à assistência à saúde

Identificar as ações realizadas pela equipe de SCIH e Gestão da Qualidade para alcançar a meta redução do risco de infecções relacionadas à assistência à saúde. Pesquisa qualitativa, exploratória e descritiva, realizada em duas instituições hospitalares. Houve triangulação de dados com entrevistas, observação e análise documental. Para a análise dos dados utilizou-se a análise de conteúdo. As ações mais realizadas foram educação permanente, identificação e correções de problemas em procedimentos, busca ativa, vigilância e higienização das mãos. Foi possível identificar a ocorrência de eventos adversos. A comunicação e os problemas estruturais aparecem como dificultadores no alcance da meta. As ações identificadas foram práticas tradicionais e muitas vezes ineficientes. A prevenção e o controle de infecções ainda são desafios para as instituições de saúde. Há necessidade de adoção de estratégias inovadoras para alcançar a meta de redução do risco de infecção relacionada à assistência à saúde.

Modeling and Path Planning for Persistent Surveillance by Unmanned Ground Vehicle

This article proposes a novel modeling and path planning framework to tackle a new problem, named road-network persistent surveillance problem (RPSP), in which the occurrence location and probability of the events are unknown. To capture such events, an unmanned ground vehicle (UGV) with certain detection ability must move along the road-network with different monitoring priorities to persistently monitor them. First, we presented an event-oriented modeling method to formulate the new problem. The monitoring effect of the road-network is quantified by uncertainty, which takes the detection ability of the sensor, the detection interval, and the monitoring weight into account. Based on the proposed model, we then designed a heuristic path planning algorithm from a decision-making perspective, which enables a UGV to persistently monitor the viewpoints of the road-network without traversing them. The simulation results and analysis demonstrate the feasibility and superiority of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by developing an effective solution for unmanned ground vehicle (UGV) to execute road-network persistent surveillance tasks under outdoor circumstances. To the best of our knowledge, there is no other consideration dealing with this problem. To capture spatiotemporal random events, we proposed a new approach that enables UGV to persistently monitor an area with road-network performing in the weighted mode. Distinguished from the traditional path planning methods in the literature, the proposed approach considered the detection ability of the UGV. Consequently, the so-called “area-to-area” planning approach does not require traversing all the viewpoints. The approach can be used in road-network with any topology. Moreover, the difficulty in modeling road-network from the actual environment is degraded, and compared with the existing patrolling methods, the monitoring efficiency of the proposed approach is improved obviously.