ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

Unravelling animal exposure profiles of human Q fever cases in Queensland, Australia, using natural language processing

Q fever, caused by the zoonotic bacterium Coxiella burnetii, is a globally distributed emerging infectious disease. Livestock are the most important zoonotic transmission sources, yet infection in people without livestock exposure is common. Identifying potential exposure pathways is necessary to design effective interventions and aid outbreak prevention. We used natural language processing and graphical network methods to provide insights into how Q fever notifications are associated with variation in patient occupations or lifestyles. Using an 18-year time-series of Q fever notifications in Queensland, Australia, we used topic models to test whether compositions of patient answers to follow-up exposure questionnaires varied between demographic groups or across geographical areas. To determine heterogeneity in possible zoonotic exposures, we explored patterns of livestock and game animal co-exposures using Markov Random Fields models. Finally, to identify possible correlates of Q fever case severity, we modelled patient probabilities of being hospitalized as a function of particular exposures. Different demographic groups consistently reported distinct sets of exposure terms and were concentrated in different areas of the state, suggesting the presence of multiple transmission pathways. Macropod exposure was commonly reported among Q fever cases, even when exposure to cattle, sheep or goats was absent. Males, older patients and those that reported macropod exposure were more likely to be hospitalized due to Q fever infection. Our study indicates that follow-up surveillance combined with text modelling is useful for unravelling exposure pathways in the battle to reduce Q fever incidence and associated morbidity.

Synchronous shedding of multiple bat paramyxoviruses coincides with peak periods of Hendra virus spillover

Within host-parasite communities, viral co-circulation and co-infections of hosts are the norm, yet studies of significant emerging zoonoses tend to focus on a single parasite species within the host. Using a multiplexed paramyxovirus bead-based PCR on urine samples from Australian flying foxes, we show that multi-viral shedding from flying fox populations is common. We detected up to nine bat paramyxoviruses shed synchronously. Multi-viral shedding infrequently coalesced into an extreme, brief and spatially restricted shedding pulse, coinciding with peak spillover of Hendra virus, an emerging fatal zoonotic pathogen of high interest. Such extreme pulses of multi-viral shedding could easily be missed during routine surveillance yet have potentially serious consequences for spillover of novel pathogens to humans and domestic animal hosts. We also detected co-occurrence patterns suggestive of the presence of interactions among viruses, such as facilitation and cross-immunity. We propose that multiple viruses may be interacting, influencing the shedding and spillover of zoonotic pathogens. Understanding these interactions in the context of broader scale drivers, such as habitat loss, may help predict shedding pulses of Hendra virus and other fatal zoonoses.

Tsallis-Entropy Segmentation through MRF and Alzheimer anatomic reference for Brain Magnetic Resonance Parcellation

Quantifying the intracranial tissue volume changes in magnetic resonance imaging (MRI) assists specialists to analyze the effects of natural or pathological changes. Since these changes can be subtle, the accuracy of the automatic compartmentalization method is always criticized by specialists. We propose and then evaluate an automatic segmentation method based on modified q-entropy (Mqe) through a modified Markov Random Field (MMRF) enhanced by Alzheimer anatomic reference (AAR) to provide a high accuracy brain tissues parcellation approach (Mqe-MMRF). We underwent two strategies to evaluate Mqe-MMRF; a simulation of different levels of noise and non-uniformity effect on MRI data (7 subjects) and a set of twenty MRI data available from MRBrainS13 as patient brain tissue segmentation challenge. We accessed eleven quality metrics compared to reference tissues delineations to evaluate Mqe-MMRF. MRI segmentation scores decreased by only 4.6% on quality metrics after noise and non-uniformity simulations of 40% and 9%, respectively. We found significant mean improvements in the metrics of the five training subjects, for whole-brain 0.86%, White Matter 3.20%, Gray Matter 3.99%, and Cerebrospinal Fluid 4.16% (p-values < 0.02) when Mqe-MMRF compared to the other reference methods. We also processed the Mqe-MMRF on 15 evaluation subjects group from MRBrainS13 online challenge, and the results held a higher rank than the reference tools; FreeSurfer, SPM, and FSL. Since the proposed method improved the precision of brain segmentation, specifically, for GM, and thus one can use it in quantitative and morphological brain studies.

Markov random field segmentation for industrial computed tomography with metal artefacts

X-ray Computed Tomography (XCT) has become an important tool for industrial measurement and quality control through its ability to measure internal structures and volumetric defects. Segmentation of constituent materials in the volume acquired through XCT is one of the most critical factors that influence its robustness and repeatability. Highly attenuating materials such as steel can introduce artefacts in CT images that adversely affect the segmentation process, and results in large errors during quantification. This paper presents a Markov Random Field (MRF) segmentation method as a suitable approach for industrial samples with metal artefacts. The advantages of employing the MRF segmentation method are shown in comparison with Otsu thresholding on CT data from two industrial objects.

Breast-lesion Segmentation Combining B-Mode and Elastography Ultrasound

Breast ultrasound (BUS) imaging has become a crucial modality, especially for providing a complementary view when other modalities (i.e., mammography) are not conclusive in the task of assessing lesions. The specificity in cancer detection using BUS imaging is low. These false-positive findings often lead to an increase of unnecessary biopsies. In addition, increasing sensitivity is also challenging given that the presence of artifacts in the B-mode ultrasound (US) images can interfere with lesion detection. To deal with these problems and improve diagnosis accuracy, ultrasound elastography was introduced. This paper validates a novel lesion segmentation framework that takes intensity (B-mode) and strain information into account using a Markov Random Field (MRF) and a Maximum a Posteriori (MAP) approach, by applying it to clinical data. A total of 33 images from two different hospitals are used, composed of 14 cancerous and 19 benign lesions. Results show that combining both the B-mode and strain data in a unique framework improves segmentation results for cancerous lesions (Dice Similarity Coefficient of 0.49 using B-mode, while including strain data reaches 0.70), which are difficult images where the lesions appear with blurred and not well-defined boundaries.

4D ACTIVE CUT: AN INTERACTIVE TOOL FOR PATHOLOGICAL ANATOMY MODELING

4D pathological anatomy modeling is key to understanding complex pathological brain images. It is a challenging problem due to the difficulties in detecting multiple appearing and disappearing lesions across time points and estimating dynamic changes and deformations between them. We propose a novel semi-supervised method, called 4D active cut, for lesion recognition and deformation estimation. Existing interactive segmentation methods passively wait for user to refine the segmentations which is a difficult task in 3D images that change over time. 4D active cut instead actively selects candidate regions for querying the user, and obtains the most informative user feedback. A user simply answers 'yes' or 'no' to a candidate object without having to refine the segmentation slice by slice. Compared to single-object detection of the existing methods, our method also detects multiple lesions with spatial coherence using Markov random fields constraints. Results show improvement on the lesion detection, which subsequently improves deformation estimation.

Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs

In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model.

The impact of Platelet Rich Plasma (PRP) in osseointegration of oral implants in dental panoramic radiography: texture based evaluation

PURPOSE

In this study the temporal texture differentiation associated with the bone formation properties, around loaded oral implants after Platelet Rich Plasma (PRP) employment, was investigated in Panoramic Radiographs.

MATERIALS AND METHODS

Thirty eligible patients are randomly assigned to two groups. The test group received PRP application around new implants, while in the control group no PRP treatment was made. The bone-to-implant contact region was analyzed in a clinical sample of 60 Digitized Panoramic Radiographs, 30 corresponding to immediate implant loading (Class-I) and 30 after an 8 month follow-up period (Class-II). This region was sampled by 1146 circular Regions-of-Interest (ROIs), resulting from a specifically designed segmentation scheme based on Markov-Random-Fields (MRF). From each ROI, 41 textural features were extracted, then reduced to a subset of 4 features due to redundancy and employed as input to Receiver-Operating-Characteristic (ROC) analysis, to assess the textural differentiation between two classes.

RESULTS

The selected subset, achieved Area-Under-Curve (AUC) values ranging from 0.77-0.81 in the PRP group, indicating the significant temporal textural differentiation has been made. In the control group, the AUC values ranged from 0.56-0.68 demonstrating lesser osseo integration activity.

CONCLUSION

This study provides evidences that PRP application may favor bone formation around loaded dental implants that could modify the dental treatment planning.

A New Markov Random Field Segmentation Method for Breast Lesion Segmentation in MR images

Breast cancer is a major public health problem for women in the Iran and many other parts of the world. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a pivotal role in breast cancer care, including detection, diagnosis, and treatment monitoring. But segmentation of these images which is seriously affected by intensity inhomogeneities created by radio-frequency coils is a challenging task. Markov Random Field (MRF) is used widely in medical image segmentation especially in MR images. It is because this method can model intensity inhomogeneities occurring in these images. But this method has two critical weaknesses: Computational complexity and sensitivity of the results to the models parameters. To overcome these problems, in this paper, we present Improved-Markov Random Field (I-MRF) method for breast lesion segmentation in MR images. Unlike the conventional MRF, in the proposed approach, we don't use the Iterative Conditional Mode (ICM) method or Simulated Annealing (SA) for class membership estimation of each pixel (lesion and non-lesion). The prior distribution of the class membership is modeled as a ratio of two conditional probability distributions in a neighborhood which is defined for each pixel: probability distribution of similar pixels and non-similar ones. Since our proposed approach don't use an iterative method for maximizing the posterior probability, above mentioned problems are solved. Experimental results show that performance of segmentation in this approach is higher than conventional MRF in terms of accuracy, precision, and Computational complexity.

An Evaluation of Radarsat-1 and ASTER Data for Mapping Veredas (Palm Swamps)

Veredas (palm swamps) are wetland complexes associated with the Brazilian savanna (cerrado) that often represent the only available source of water for the ecosystem during the dry months. Their extent and condition are mainly unknown and their cartography is an essential issue for their protection. This research article evaluates some of the fine resolution satellite data both in the radar (Radarsat-1) and optical domain (ASTER) for the delineation and characterization of veredas. Two separate approaches are evaluated. First, given the known potential of Radarsat-1 images for wetland inventories, the automatic delineation of veredas is tested using only Radarsat-1 data and a Markov random fields region-based segmentation. In this case, to increase performance, processing is limited to a buffer zone around the river network. Then, characterization of their type is attempted using traditional classification methods of ASTER optical data combined with Radarsat-1 data. The automatic classification of Radarsat data yielded results with an overall accuracy between 62 and 69%, that proved reliable enough for delineating wide and very humid veredas. Scenes from the wet season and with a smaller angle of incidence systematically yielded better results. For the classification of the main vegetation types, better results (overall success of 78.8%) were obtained by using only the visible and near infrared (VNIR) bands of the ASTER image. Radarsat data did not bring any improvement to these classification results. In fact, when using solely the Radarsat data from two different angle of incidence and two different dates, the classification results were low (50.8%) but remained powerful for delineating the permanently moist riparian forest portion of the veredas with an accuracy better than 75% in most cases. These results are considered good given the width of some types often less than 50 m wide compared with the resolution of the images (12.5 - 15 m). Comparing the classification results with the Radarsat-generated delineation allows an understanding of the relation between synthetic aperture radar (SAR) backscattering and vegetation types of the veredas.