Surveillance Region Research Articles

Computation-distributed probability hypothesis density filter

Particle probability hypothesis density filtering has become a promising approach for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in a nonlinear, non-Gaussian system. However, its computational complexity linearly increases with the number of obtained observations and the number of particles, which can be very time consuming, particularly when numerous targets and clutter exist in the surveillance region. To address this issue, we present a distributed computation particle probability hypothesis density(PHD) filter for target tracking. It runs several local decomposed particle PHD filters in parallel while processing elements. Each processing element takes responsibility for a portion of particles but all measurements and provides local estimates. A central unit controls particle exchange among the processing elements and specifies a fusion rule to match and fuse the estimates from different local filters. The proposed framework is suitable for parallel implementation. Simulations verify that the proposed method can significantly accelerate and maintain a comparative accuracy compared to the standard particle PHD filter.

TrackT: Accurate tracking of RFID tags with mm-level accuracy using first-order taylor series approximation

Radio Frequency Identification (RFID) technology is widely used to achieve indoor object tracking and positioning. Currently, many methods need to deploy a large number of reference tags beforehand and some are limited by antennas’ spacing. Further, the signal propagation along Non-Line of Sight introduces multipath effects which will challenge the accuracy of RFID localization system. In this work, we propose a method based on measured phase to track mobile RFID tags with millimeter level (mm-level) accuracy. We first partition the surveillance region into square grids at mm-level and suppose that there is a virtual tag as the same as the tracked one in each grid. On this basis, for the case where the tags move along a known track with constant speed, we only need to locate the tag's initial position. We leverage phase periodicity to obtain some candidates and then eliminate position ambiguity by double difference true phase. And for the case where the tag's moving track is unknown to the system, we adopt a first-order Taylor series expansion to calculate the relative displacements of the tracked tag and then locate the initial position as the same process as tracking the known trajectory. In our experiment, our solution can achieve a mean error distance of 0.26cm and 0.55cm for known and unknown movement tracks respectively.

Performance Analysis of a Cognitive Phased Array Radar with Online Tracking Capability

This paper presents the performance analysis of a cognitive phased array radar with online tracking capability. The versatile model has the ability to sense the environment for adaptive selection of active number of array elements in the transmitter, as well as, in receiver arrays. An online extended Kalman filter with a kernel recursive least squares observer has been used to predict the future direction of target. The proposed radar avoids continuous scanning of the surveillance region. Instead, the transmitter, based on the feedback from the receiver, ensures maximum power in the target future direction. It saves a lot of power. Likewise, the intelligent selection of transmitting and receiving array elements, at each scan, results in less computational complexity and improved detection probability along with signal to interference plus noise ratio than that of a conventional phased array. Monte-Carlo based simulations have been conducted to validate the performance of the design.

Distributed Bernoulli Filters for Joint Detection and Tracking in Sensor Networks

We introduce in this paper a novel, fully distributed diffusion Bernoulli filter based on random information dissemination over a partially connected sensor network. The proposed algorithm allows the network nodes to cooperatively perform joint multiframe detection and tracking of an emitter that randomly appears in and disappears from a surveillance region. In addition, we also introduce an alternative flooding-based algorithm that reproduces exactly, in a fully distributed fashion, the optimal centralized Bernoulli filter based on all network node measurements. We derive two alternative low bandwidth implementations of the proposed filters, using respectively a marginalized sequential Monte Carlo (SMC) method and a hybrid Gaussian mixture model (GMM)/SMM scheme. The algorithms were evaluated in a simulated scenario where network nodes directly assimilate raw received signal strength measurements, subject only to possible measurement censoring.

Wildfire smoke detection based on local extremal region segmentation and surveillance

A novel video-based method is proposed for long-distance wildfire smoke detection. Since the long-distance wildfire smoke usually moves slowly and lacks salient features in the video, the detection is still a challenging problem. Unlike many traditional video-based methods that usually rely on the smoke color or motion for initial smoke region segmentation, we use the Maximally Stable Extremal Region (MSER) detection method to extract local extremal regions of the smoke. This makes the initial segmentation of possible smoke region less dependent on the motion and color information. Potential smoke regions are then selected from all the possible regions by using some static visual features of the smoke, helping to eliminate the non-smoke regions as many as possible. Once a potential smoke region is found, we keep tracking it by searching the best-matched extremal regions in the subsequent frames. At the same time, the propagating motions of the potential smoke region are monitored based on a novel cumulated region approach, which can be effectively used to identify the distinctive expanding and rising motions of smoke. This approach can also make the final smoke motion identification insensitive to image shaking. It was proved that the proposed method is able to reliably detect the long-distance wildfire smoke and simultaneously produce very few false alarms in actual applications.

Multisensor-multitarget bearing-only sensor registration

Bearing-only estimation is one of the fundamental and challenging problems in target tracking. As in the case of radar tracking, the presence of offset or position biases can exacerbate the challenges in bearing-only estimation. Modeling various sensor biases is not a trivial task, and not much has been done in the literature specifically for bearing-only tracking. This paper addresses the modeling of offset biases in bearing-only sensors and the ensuing multitarget tracking with bias compensation. Bias estimation is handled at the fusion node to which individual sensors report their local tracks in the form of associated measurement reports (AMRs) or angle-only tracks. The modeling is based on a multisensor approach that can effectively handle a time-varying number of targets in the surveillance region. The proposed algorithm leads to a maximum likelihood bias estimator. The corresponding Cram´er-Rao lower bound to quantify the theoretical accuracy that can be achieved by the proposed method or any other algorithm is also derived. Finally, simulation results on different distributed tracking scenarios are presented to demonstrate the capabilities of the proposed approach. In order to show that the proposed method can work even with false alarms and missed detections, simulation results on a centralized tracking scenario where the local sensors send all their measurements (not AMRs or local tracks) are also presented.

Abstract 5214: Stage IV colorectal cancer sub-site and patterns of distant metastases

Abstract Approximately 20% of individuals diagnosed with colorectal cancer (CRC) in the United States are found to have distant metastases at the time of their diagnosis, indicating an extremely poor prognosis. Although it is well known that primary CRC typically metastasizes to the liver and/or lungs, little is known regarding which factors might explain the anatomic pattern of CRC metastatic spread. Using population-based registry data from the Surveillance, Epidemiology and End Results (SEER) cancer registry of the Seattle-Puget Sound region, we assessed the relationship between primary CRC sub-site and the presence of synchronous metastases in the liver, lungs, and both the liver and lungs. All individuals with stage IV CRC who were diagnosed between 2010-2014 in the 13-county catchment area and who also had complete data on site of metastasis at diagnosis were included in analyses (N = 1287). In total, 703 (55%) patients had distant metastases confined to the liver at the time of diagnosis, 78 (6%) had lung-only metastases, 190 (15%) had metastases in both the liver and lungs but not at other sites, and 316 (25%) exhibited different patterns of metastatic spread, including to the bone, brain, and other anatomic sites. Using multiple logistic regression models, we compared individuals with different patterns of metastatic spread at diagnosis with respect to primary CRC site and with separate models comparing cases with liver-only metastases to all other metastatic cases, comparing cases with lung-only metastases to all other metastatic cases, and comparing cases with metastases to both the liver and lungs to all other metastatic CRC cases. All models were adjusted for age at diagnosis and sex, as well as for depth of primary tumor growth within the colorectum (T-stage) and extent of cancer spread to lymph nodes (N-stage). The odds of having a primary CRC tumor in the rectum, versus proximal colon, were significantly lower among patients with liver-only synchronous metastases relative to those with other patterns of metastatic spread at diagnosis [odds ratio (OR): 0.62, 95% confidence interval (CI): 0.46 - 0.82]. Conversely, the odds of having a primary CRC located in the rectum were significantly higher among those with metastatic disease confined to the lungs (OR: 2.48, 95% CI: 1.38 - 4.47) and those with metastases to the liver and lungs (OR: 1.97, 95% CI: 1.29 - 3.01). Our findings suggest that a patient's site of primary CRC may be informative with respect to possible pattern of metastatic spread. Citation Format: Jamaica R. Robinson, Polly A. Newcomb, Sheetal Hardikar, Stacey A. Cohen, Amanda I. Phipps. Stage IV colorectal cancer sub-site and patterns of distant metastases. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5214.

TMicroscope: Behavior Perception Based on the Slightest RFID Tag Motion

Behavior perception with fine granularity can offer much more valuable information, e.g. which and why products people are often interested in will not be purchased in shopping. In this paper, we propose a RFID-based method to perceive slight motion of a target product with RFID tag for mining customer’s behaviors. However, the interference from human movements in the reading zone is a challenge to result in false positives. To address this problem, we first compute virtual displacements of the target RFID tag and then adopt Support Vector Machines to recognize absence or presence of human motion near the tag. Next, we divide the surveillance region into mm-level grids to construct similarity matrixes of the tracked tag before and after human motion, which can accurately distinguish absence or presence of tag motion. Indeed, the optimization method based on phase-ambiguity is introduced to reduce computations. The results show that our method can achieve the average scenario classification accuracy of 96.3 % and the average slight tag motion perception accuracy of 100 % with the hypersensitivity of 1 mm. DOI: http://dx.doi.org/10.5755/j01.eie.22.2.14603

Conditional Survival in Anal Carcinoma Using the National Population-Based Survey of Epidemiology and End Results Database (1988-2012).

Conditional survival can provide valuable information for both patients and healthcare providers about the changing prognosis in surviving patients over time. This study estimated conditional survival for patients with anal cancer in the United States through analysis of a national population-based cancer registry. Log-rank test identified significant covariates of cause-specific survival (defined as time from diagnosis until death from anal cancer). Significant covariates were considered in the multivariable regression of cause-specific survival using Cox proportional hazards models. Covariates included cancer stage and demographic variables. Patients in Surveillance, Epidemiology, and End Results regions diagnosed with anal squamous cell carcinoma as their first and only cancer diagnosis from 1988 to 2012 were selected from this database, and 5145 patients were included in the retrospective cohort study. Five-year conditional survival stratified by each variable in the final Cox models was measured : The final multivariable models of overall and cause-specific survivals included stage, grade, sex, age, race, and relationship status. Over the first 6 years after diagnosis, conditional survival of distant stage increased from 37% to 89%, whereas regional stage increased from 65% to 93% and localized stage increased from 84% to 96%. The other variables had increasing prognosis as well, but the subgroups increased at a more similar rate over time. The data source used does not include information on chemotherapy treatment, patient comorbidities, or socioeconomic status. Conditional survival showed improvement over time. Patients with advanced stage had the greatest improvement in conditional survival. This is the first study to provide specific conditional survival probabilities for patients with anal cancer.

People-flow counting in complex environments by combining depth and color information

People-flow counting is one of the key techniques of intelligence video surveillance systems and the information of people-flow obtained from this technique is an very important evidence for many applications, such as business analysis, staff planning, security, etc. Traditionally, the color image information based methods encounter kinds of challenges, such as shadows, illumination changing, cloth color, etc., while the depth information based methods suffer from lack of texture. In this paper, we propose an effective approach of people-flow counting by combining color and depth information. First, we adopt a background subtraction technique to fast obtain the moving regions on depth images. Second, the water filling algorithm is used to effectively detect head candidates on the moving regions. Then we use the SVM to recognize the real heads from the candidates. Finally, we adopt a weighted K Nearest Neighbor based multi-target tracking method to track each confirmed head and count the people through the surveillance region. Four datasets constructed from two surveillance scenes are used to evaluate the proposed method. Experimental results show that our method outperform the state-of-the-art methods. Our method can work stably on condition of kinds of interruptions and can not only obtain high precisions, but also high recalls on four datasets.

Detection of West Nile and Usutu Viruses in Italian Free Areas: Entomological Surveillance in Piemonte and Liguria Regions, 2014.

West Nile virus and Usutu virus have established in different parts of Italy over the past 10 years. Piemonte and Liguria Regions (Northwestern Italy) are known to be nonendemic areas, despite the presence of competent vectors and environmental conditions conducive to maintaining infection. This work evidences for the first time, through an entomological surveillance implemented on the basis of risk factor approach, the presence of West Nile and Usutu viruses in Piemonte and Liguria Regions (Northwestern Italy).

Is molecular xenomonitoring of mosquitoes for Dirofilaria repens suitable for dirofilariosis surveillance in endemic regions?

Dirofilaria repens is a parasite of animals and humans, transferred by mosquitoes. The assessment of the presence of D. repens-infected vertebrate hosts in the investigated area can be performed by xenomonitoring—detection of the parasite in blood-feeding arthropods. Our study aimed to evaluate PCR xenomonitoring of mosquitoes as a tool for dirofilariosis surveillance in Poland. We were also interested whether inter-study comparisons at the international level would be possible. Mosquitoes were collected in a single locality in Mazowsze province in Poland, in which between 12 and 20% of dogs were infected with D. repens and autochthonous human dirofilariosis was confirmed. All captured female mosquitoes were divided into pools; alternatively, single mosquitoes were analyzed; DNA was isolated and subjected to PCR and real-time PCR for detection of D. repens. The estimations of infection rates of mosquitoes with D. repens, based on PCR results, varied from 0 to 1.57% even between assays for detection of distinct fragments of the same marker—cytochrome oxidase subunit one gene. Polymorphisms of the DNA sequence within binding sites of the primers used in D. repens xenomonitoring assays, applied in European studies, were identified. Non-specific amplification of Setaria tundra (Nematoda: Onchocercidae) DNA occurred. Surveillance of dirofilariosis by PCR mosquito xenomonitoring is possible; however, the efficiency of the approach on territories where the prevalence of the disease among definitive hosts is lower than 12% remains unknown. Furthermore, mosquito infection rate estimations can be PCR assay dependent, which makes inter-study comparisons difficult. The results obtained in independent European xenomonitoring studies were contradictory. International collaboration would be required to establish a standardized set of assays for sensitive and specific xenomonitoring-based dirofilariosis surveillance.

Studies of trypanosomiasis in the Luangwa valley, north-eastern Zambia

BackgroundThe present study, conducted in Zambia’s Luangwa valley where both animal African trypanosomiasis (AAT) and human African trypanosomiasis (HAT) are endemic, combined the use of microscopy and molecular techniques to determine the presence of trypanosome species in cattle, goats and tsetse flies.MethodsThis study was conducted between 2008 and 2010 in Petauke, Chama and Isoka districts, north-eastern Zambia. A total of 243 cattle, 36 goats and 546 tsetse flies, were examined for presence of trypanosome species using microscopy, PCR and loop-mediated isothermal amplification (LAMP).ResultsThere was poor agreement among the test methods used for detection of trypanosomes species in animal blood and tsetse flies. Trypanosomes were observed in 6.1 % (95 % CI: 3.3-8.9 %) of the animals sampled by microscopy, 7.5 % (95 % CI: 4.4–10.6 %) by PCR and 18.6 % (95 % CI: 13.6–23.6 %) by PFR-LAMP. PFR-LAMP was more sensitive for detecting Trypanozoon than KIN-PCR. The highest occurrence of AAT was recorded in cattle from Petauke (58.7 %, 95 % CI: 44.7–72.7 %) while the lowest was from Isoka (5.4 %, 95 % CI: 0.8–10.0 %). Infection of both cattle and goats with Trypanosoma congolense and T. vivax was associated with clinical AAT.ConclusionWhen selecting molecular techniques for AAT surveillance in endemic regions, the KIN-PCR and species-specific PCR may be recommended for screening animal or tsetse fly samples for T. congolense and T. vivax, respectively. On the other hand, species-specific PCR and/or LAMP might be of greater value in the screening of animal and human body fluids as well as tsetse fly samples for Trypanozoon.

Financial and Economic Costs of the Elimination and Eradication of Onchocerciasis (River Blindness) in Africa.

BackgroundOnchocerciasis (river blindness) is a parasitic disease transmitted by blackflies. Symptoms include severe itching, skin lesions, and vision impairment including blindness. More than 99% of all cases are concentrated in sub-Saharan Africa. Fortunately, vector control and community-directed treatment with ivermectin have significantly decreased morbidity, and the treatment goal is shifting from control to elimination in Africa.MethodsWe estimated financial resources and societal opportunity costs associated with scaling up community-directed treatment with ivermectin and implementing surveillance and response systems in endemic African regions for alternative treatment goals—control, elimination, and eradication. We used a micro-costing approach that allows adjustment for time-variant resource utilization and for the heterogeneity in the demographic, epidemiological, and political situation.ResultsThe elimination and eradication scenarios, which include scaling up treatments to hypo-endemic and operationally challenging areas at the latest by 2021 and implementing intensive surveillance, would allow savings of $1.5 billion and $1.6 billion over 2013–2045 as compared to the control scenario. Although the elimination and eradication scenarios would require higher surveillance costs ($215 million and $242 million) than the control scenario ($47 million), intensive surveillance would enable treatments to be safely stopped earlier, thereby saving unnecessary costs for prolonged treatments as in the control scenario lacking such surveillance and response systems.ConclusionsThe elimination and eradication of onchocerciasis are predicted to allow substantial cost-savings in the long run. To realize cost-savings, policymakers should keep empowering community volunteers, and pharmaceutical companies would need to continue drug donation. To sustain high surveillance costs required for elimination and eradication, endemic countries would need to enhance their domestic funding capacity. Societal and political will would be critical to sustaining all of these efforts in the long term.

Paraparesia espástica tropical autóctona en la Comunidad de Madrid. Experiencia en el cribado del virus linfotrópico de células T humanas tipo I

We have recently documented a case of tropical spastic paraparesis by HTLV-I in a Spanish patient. HTLV-I infection is rare in Europe, and hardly ever is accompanied by symptoms, but if it does it could trigger a major health issue. This case is presented here, as well as a discussion on the situations in which HTLV-I detection is justified. An analysis was made of the HTLV diagnostic requests at our centre during 2014-2015 (n=123). The diagnostic algorithm was: 1) Enzyme immunoassay, 2) Reverse hybridization, and 3) Proviral DNA detection by PCR. The results showed several situations of HTLV screening, emphasising those related to paraparesis (22%). Seven cases of HTLV-I infection were found: five in patients from endemic regions, one in an HIV-infected patient, and the case of TSP mentioned above. HTLV-I surveillance in non-endemic regions is a challenging issue, as the cost-benefit ratio is not well-established. This case report emphasises the importance of including HTLV within the differential diagnosis of insidious spastic paraparesis.

Continuous 2-D assignment for multitarget tracking with rotating radars

Mechanically steered scanning radars receive measurements continuously while sweeping the surveillance region. However, most target tracking algorithms, like the multiple hypothesis tracker (MHT) and the joint probabilistic data association (JPDA) techniques, wait for the end of a scan in order to process the measurements and to estimate targets states. This is due to the fundamental assumption of one-to-one association between tracks and measurements and the 360° physical limit of a scan. Associating measurements to initialized tracks and filtering at the end of a complete scan may cause significant delays in target state update. In addition, association may become imperfect due to longer intervals between updates. This issue becomes significant when tracking high-speed targets with low scan rate sensors as in the airborne early warning (AEW) system. In this paper, we present a new dynamic sector processing algorithm using two-dimensional (2-D) assignment for scanning radars that report measurements within the duration of a scan. The full scan is dynamically and adaptively divided into sectors, which could be as small as a single detection, depending on the scanning rate, sparsity of targets, and required target state update speed. Measurement-to-track association, filtering, and target state update are done dynamically while sweeping from one region to another, i.e., continuous track update, limited only by the intermeasurement interval, becomes possible. The proposed algorithm offers low latency while maintaining estimation accuracy in track updates as well as efficient utilization of computational resources compared with standard frame-based tracking algorithms. Experimental results based on rotating radars demonstrate the advantages of the proposed technique.

TARGET TRACKING AND RECEIVER PLACEMENT IN MIMO DVB-T BASED PCL

Recently many studies have been done on different issues of using multiple antennas at transmit and receive sides of a radar system in a MIMO (Multiple Input Multiple Output) configuration. In addition, for the electronic warfare‟s purposes, the utilization of available signals of the environment to detect the targets has gained considerable attention in recent years. Such passive radars use the transmitter of other purposes (e.g. the GSM or television stations) as their noncooperative transmitter. Moreover, the idea of using multiple of such illuminators to get the advantages of MIMO technology, besides the advantages of passive illumination, is new and attractive. An excellent candidate of such configuration is a DVB-T SFN (single frequency network). However, various obstacles and considerations appear when working with a MIMO DVB-T based passive radar system. In this paper, we consider two such problems: tracking multiple targets and choosing appropriate positions for the receive antennas in the surveillance region. First, based on the previously developed schemes to associate the receive data to the transmitters and targets, we propose a tracking algorithm to extract the true targets from the candidate targets. Then, we develop a procedure to place the receivers of such MIMO passive systems in such positions that result in better performance of the aforementioned association algorithm.

Feature selection for position estimation using an omnidirectional camera

This paper considers visual feature selection to implement position estimation using an omnidirectional camera. The localization is based on a maximum likelihood estimation (MLE) with a map from optimally selected visual features using Gaussian process (GP) regression. In particular, the collection of selected features over a surveillance region is modeled by a multivariate GP with unknown hyperparameters. The hyperparameters are identified through the learning process by an MLE, which are used for prediction in an empirical Bayes fashion. To select features, we apply a backward sequential elimination technique in order to improve the quality of the position estimation with compressed features for efficient localization. The excellent results of the proposed algorithm are illustrated by the experimental studies with different visual features under both indoor and outdoor real-world scenarios.

Itaya virus, a Novel Orthobunyavirus Associated with Human Febrile Illness, Peru.

Our genetic analyses of uncharacterized bunyaviruses isolated in Peru identified a possible reassortant virus containing small and large gene segment sequences closely related to the Caraparu virus and a medium gene segment sequence potentially derived from an unidentified group C orthobunyavirus. Neutralization tests confirmed serologic distinction among the newly identified virus and the prototype and Caraparu strains. This virus, named Itaya, was isolated in 1999 and 2006 from febrile patients in the cities of Iquitos and Yurimaguas in Peru. The geographic distance between the 2 cases suggests that the Itaya virus could be widely distributed throughout the Amazon basin in northeastern Peru. Identification of a new Orthobunyavirus species that causes febrile disease in humans reinforces the need to expand viral disease surveillance in tropical regions of South America.

Topology optimisation for energy management in underwater sensor networks

In general, battery-powered sensors in a sensor network are operable as long as they can communicate sensed data to a processing node. In this context, a sensor network has two competing objectives: (1) maximisation of the network performance with respect to the probability of successful search for a specified upper bound on the probability of false alarms, and (2) maximisation of the network’s operable life. As both sensing and communication of data consume battery energy at the sensing nodes of the sensor network, judicious use of sensing power and communication power is needed to improve the lifetime of the sensor network. This paper presents an adaptive energy management policy that will optimally allocate the available energy between sensing and communication at each sensing node to maximise the network performance subject to specified constraints. Under the assumptions of fixed total energy allocation for a sensor network operating for a specified time period, the problem is reduced to synthesis of an optimal network topology that maximises the probability of successful search (of a target) over a surveillance region. In a two-stage optimisation, a genetic algorithm-based meta-heuristic search is first used to efficiently explore the global design space, and then a local pattern search algorithm is used for convergence to an optimal solution. The results of performance optimisation are generated on a simulation test bed to validate the proposed concept. Adaptation to energy variations across the network is shown to be manifested as a change in the optimal network topology by using sensing and communication models for underwater environment. The approximate Pareto-optimal surface is obtained as a trade-off between network lifetime and probability of successful search over the surveillance region.