Video surveillance systems-current status and future trends

Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance

Enabling Real-Time AI Edge Video Analytics

This paper introduces a novel distributed AI model for managing in real-time, edge based intelligent analytics, such as the ones required for smart video surveillance. The novelty relies on distributing the applications in several decomposed functions which are linked together, creating virtual chain func- tions, where both computational and communication limitations are considered. Both theoretical analysis and simulation analysis in a real-case scenario have shown that the proposed model can enable real-time surveillance analytics on a low-cost edge network. Finally, a caching mechanism is proposed and evaluated, reducing further the operational costs of the edge network

A Generic Framework for Deploying Video Analytic Services on the Edge

This paper introduces a novel distributed model for handling in real-time, edge-based Artificial Intelligence analytics, such as the ones required for smart video surveillance. The novelty of the model relies on decoupling and distributing the services into several decomposed functions which are linked together, creating virtual function chains (VFC model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the VFC model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the VFC model have shown that it can reduce the total edge cost, compared with a Monolithic and a Simple Frame Distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Spark ©) and an edge-deployement framework (Kubernetes©), have been deployed. A cloud service has also been considered. Experiments have shown that VFC can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a caching and a QoS monitoring service based on Long-Term-Short-Term models are introduced and evaluated

Image Analysis on Bacteria Time-Lapse Movies

This study has been developed a methodology for identifying accurately the boundaries of individual bacterial cells and tracking them from frame to frame so as to construct the cells’ genealogy (bacterial cell segmentation and lineage tree construction) even in large-size microbial communities where there is great difficulty in identifying the individual cell boundaries

An Intelligent model for supporting Edge Migration for Virtual Function Chains in Next Generation Internet of Things

The developments on next generation IoT sensing devices, with the advances on their low power computational capabilities and high speed networking has led to the introduction of the edge computing paradigm. Within an edge cloud environment, services may generate and consume data locally, without involving cloud computing infrastructures. Aiming to tackle the low computational resources of the IoT nodes, Virtual-Function-Chain has been proposed as an intelligent distribution model for exploiting the maximum of the computational power at the edge, thus enabling the support of demanding services. An intelligent migration model with the capacity to support Virtual-Function-Chains is introduced in this work. According to this model, migration at the edge can support individual features of a Virtual-Function-Chain. First, auto-healing can be implemented with cold migrations, if a Virtual Function fails unexpectedly. Second, a Quality of Service monitoring model can trigger live migrations, aiming to avoid edge devices overload. The evaluation studies of the proposed model revealed that it has the capacity to increase the robustness of an edge-based service on low-powered IoT devices. Finally, comparison with similar frameworks, like Kubernetes, showed that the migration model can effectively react on edge network fluctuations

Hybrid forecast and control chain for operation of flexibility assets in micro-grids

Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets

A Dynamic Bayesian Network Approach to Behavioral Modelling of Elderly People during a Home-based Augmented Reality Balance Physiotherapy Programme

In this study, we propose a dynamic Bayesian network (DBN)-based approach to behavioral modelling of community dwelling older adults at risk for falls during the daily sessions of a hologram-enabled vestibular rehabilitation therapy programme. The component of human behavior being modelled is the level of frustration experienced by the user at each exercise, as it is assessed by the NASA Task Load Index. Herein, we present the topology of the DBN and test its inference performance on real-patient data.Clinical Relevance- Precise behavioral modelling will provide an indicator for tailoring the rehabilitation programme to each individual's personal psychological needs

MRI assessment of the effects of acetazolamide and external lumbar drainage in idiopathic Normal Pressure Hydrocephalus

BACKGROUND: The objective was to identify changes in quantitative MRI measures in patients with idiopathic normal pressure hydrocephalus (iNPH) occurring in common after oral acetazolamide (ACZ) and external lumbar drainage (ELD) interventions. METHODS: A total of 25 iNPH patients from two clinical sites underwent serial MRIs and clinical assessments. Eight received ACZ (125-375 mg/day) over 3 months and 12 underwent ELD for up to 72 hours. Five clinically-stable iNPH patients who were scanned serially without interventions served as controls for the MRI component of the study. Subjects were divided into responders and non-responders to the intervention based on gait and cognition assessments made by clinicians blinded to MRI results. The MRI modalities analyzed included T1-weighted images, diffusion tensor Imaging (DTI) and arterial spin labelling (ASL) perfusion studies. Automated threshold techniques were used to define regions of T1 hypo-intensities. RESULTS: Decreased volume of T1-hypointensities and decreased mean diffusivity (MD) within remaining hypointensities was observed after ACZ and ELD but not in controls. Patients responding positively to these interventions had more extensive decreases in T1-hypointensites than non-responders: ACZ-responders (4,651 ± 2,909 mm(3)), ELD responders (2,338 ± 1,140 mm(3)), ELD non-responders (44 ± 1,188 mm(3)). Changes in DTI MD within T1-hypointensities were greater in ACZ-responders (7.9% ± 2%) and ELD-responders (8.2% ± 3.1%) compared to ELD non-responders (2.1% ± 3%). All the acetazolamide-responders showed increases in whole-brain-average cerebral blood flow (wbCBF) estimated by ASL (18.8% ± 8.7%). The only observed decrease in wbCBF (9.6%) occurred in an acetazolamide-non-responder. A possible association between cerebral atrophy and response was observed, with subjects having the least cortical atrophy (as indicated by a positive z-score on cortical thickness measurements) showing greater clinical improvement after ACZ and ELD. CONCLUSIONS: T1-hypointensity volume and DTI MD measures decreased in the brains of iNPH patients following oral ACZ and ELD. The magnitude of the decrease was greater in treatment responders than non-responders. Despite having different mechanisms of action, both ELD and ACZ may decrease interstitial brain water and increase cerebral blood flow in patients with iNPH. Quantitative MRI measurements appear useful for objectively monitoring response to acetazolamide, ELD and potentially other therapeutic interventions in patients with iNPH

Self-medication with antibiotics in rural population in Greece: a cross-sectional multicenter study

<p>Abstract</p> <p>Background</p> <p>Self-medication is an important driver of antimicrobial overuse as well as a worldwide problem. The aim of the present study was to estimate the use of antibiotics, without medical prescription, in a sample of rural population presenting in primary care in southern Greece.</p> <p>Methods</p> <p>The study included data from 1,139 randomly selected adults (545 men/594 women, mean age ± SD: 56.2 ± 19.8 years), who visited the 6 rural Health Centres of southern Greece, between November 2009 and January 2010. The eligible participants were sought out on a one-to-one basis and asked to answer an anonymous questionnaire.</p> <p>Results</p> <p>Use of antibiotics within the past 12 months was reported by 888 participants (77.9%). 508 individuals (44.6%) reported that they had received antibiotics without medical prescription at least one time. The major source of self-medication was the pharmacy without prescription (76.2%). The antibiotics most frequently used for self-medication were amoxicillin (18.3%), amoxicillin/clavulanic acid (15.4%), cefaclor (9.7%), cefuroxim (7.9%), cefprozil (4.7%) and ciprofloxacin (2.3%). Fever (41.2%), common cold (32.0%) and sore throat (20.6%) were the most frequent indications for the use of self-medicated antibiotics.</p> <p>Conclusion</p> <p>In Greece, despite the open and rapid access to primary care services, it appears that a high proportion of rural adult population use antibiotics without medical prescription preferably for fever and common cold.</p