Surveillance System For Monitoring Research Articles

A Wireless Surveillance and Safety Monitoring System for Mine Workers Using ZigBee

n the mining industry, ZigBee technology is used in wireless surveillance and safety monitoring systems specifically designed for mine workers. By continuously monitoring the ambient conditions and vital indicators of miners in real-time, the technology seeks to improve their safety and security. In order to detect dangerous situations in the mine unit, the proposed system consists of a variety of sensors that are integrated together. ZigBee-enabled base stations are used for data collecting and transmission. By means of Zigbee, the system enables smooth communication between the base stations and sensors, guaranteeing prompt alerts and reactions to possible safety hazards. Through the mining industry, where worker safety is of utmost significance, this research advances wireless monitoring systems in dangerous environments. [1]. The bell in an emergency signals a quick reaction from the base station. This research represents a significant advancement in wireless sensor networking, improving mine worker safety. Our goal is to improve the lives of mine workers facing dangerous circumstances by providing a rapid means of rescue through wireless communication.

Enhanced multi-model multi-scan data association and tracking algorithm via convex variational inference

Tracking multiple targets with unknown measurement-to-target association and uncertain target dynamics is a significant problem that arises in various applications such as surveillance monitoring and intelligent transportation systems. In this paper, we propose an enhanced multi-model multi-scan data association algorithm to address the problem of tracking multiple maneuvering targets. First, we use a probabilistic graphical model to represent the joint distribution of the dynamic model indices, target state, and multi-scan data association variables. This formulation transforms the inference of marginal distributions into a Bethe free energy (BFE) problem. Next, to transform the BFE problem into a convex one, we demonstrate that the BFE function can be made convex through re-weighting. Additionally, we decompose the re-weighted BFE function into a block-wise sum form. We prove that under certain regularization conditions, each block of the re-weighted BFE is convex, ensuring convergence of the primal–dual coordinate ascent algorithm to the minimum of the overall re-weighted BFE. Finally, we provide a particle implementation of the proposed algorithm, accompanied by an analysis of its complexity. Simulation results indicate that the proposed algorithm exhibits favorable performance when compared to both the single-model multi-scan algorithm and the multi-model single-scan algorithm.

Anomaly Detection Using CNN with I3D Feature Extraction

Anomaly detection is a critical task in various fields such as surveillance, healthcare, and industrial monitoring, aiming to identify patterns that deviate significantly from normal behavior.Video anomaly detection is inherently difficult due to visual complexity and variability. This work proposes a unique anomaly detection technique leveraging Convolutional Neural Networks (CNN) with Inflated 3D Convolutional Networks (I3D) for feature extraction. This involves training the CNN on a large dataset to learn normal behavior, enabling it to identify anomalies by recognizing deviations from learned patterns. Furthermore, our approach exhibits promising results in detecting various types of anomalies, including sudden changes, abnormal trajectories, and rare events. Upon detection of such activity, mail(notification) can be raised concerned people who can take immediate action.This research contributes a significant advancement in the field of anomaly detection, and holds potential for applications in surveillance, security, and industrial monitoring systems. Keywords—Anomaly detection,I3D(Inflated3D) feature extraction,Convolutional neural network, Spatio-Temporal Features,Normal and abnormal event detection.

Assessing the role of social determinants of health in health disparities: The need for data on work.

Work is a key social determinant of health. Without the collection of work-related information in public health data systems, the role of social determinants in creating and reinforcing health disparities cannot be fully assessed. The Centers for Disease Control and Prevention (CDC) maintains or supports a number of public health surveillance and health monitoring systems, including surveys, case-based disease and exposure systems, vital status records, and administrative data systems. We evaluated a convenience sample of these systems for inclusion of information in three work-related domains: employment status, industry and occupation, and working conditions. While 12 of 39 data systems were identified as collecting work-related data, this information was often minimal (e.g., only employment status), restricted to a subset of respondents, or only gathered periodically. Information on working conditions was particularly sparse. Historically, the limited and inconsistent collection of work-related information in public health data systems has hindered understanding of the role work plays in health disparities. Current CDC data modernization efforts present opportunities to enhance the identification and mitigation of health disparities by prioritizing inclusion of an expanded set of work-related data elements.

Birth prevalence and risk factors of neural tube defects in Ethiopia: a systematic review and meta-analysis

ObjectiveThis study aims to estimate the prevalence of neural tube defects (NTDs) and to identify potential risk factors in the Ethiopian context.Study designSystematic review and meta-analysis.Study participantsA total of 611...

Implementing stroke care in a lower-middle-income country: results and recommendations based on an implementation study within the Nepal Stroke Project.

Globally, the majority of strokes affect people residing in lower- and lower-middle-income countries (LMICs), but translating evidence-based knowledge into clinical practice in regions with limited healthcare resources remains challenging. As an LMIC in South Asia, stroke care has remained a healthcare problem previously unaddressed at a national scale in Nepal. The Nepal Stroke Project (NSP) aims to improve acute stroke care in the tertiary healthcare sector of Nepal. We hereby describe the methods applied and analyze the barriers and facilitators of the NSP after 18 months. The NSP follows a four-tier strategy: (1) quality improvement by training healthcare professionals in tertiary care centers; (2) implementation of in-hospital stroke surveillance and quality monitoring system; (3) raising public awareness of strokes; and (4) collaborating with political stakeholders to facilitate public funding for stroke care. We performed a qualitative, iterative analysis of observational data to analyze the output indicators and identify best practices. Both offline and online initiatives were undertaken to address quality improvement and public awareness. More than 1,000 healthcare professionals across nine tertiary care hospitals attended 26 stroke-related workshops conducted by Nepalese and international stroke experts. Monthly webinars were organized, and chat groups were made for better networking and cross-institutional case sharing. Social media-based public awareness campaigns reached more than 3 million individuals. Moreover, live events and other mass media campaigns were instituted. For quality monitoring, the Registry of Stroke Care Quality (RES-Q) was introduced. Collaboration with stakeholders (both national and international) has been initiated. We identified six actions that may support the development of tertiary care centers into essential stroke centers in a resource-limited setting. We believe that our experiences will contribute to the body of knowledge on translating evidence into practice in LMICs, although the impact of our results must be verified with process indicators of stroke care.

Multiple Object Detection on Surveillance Videos for Improving Accuracy Using Enhanced Faster R-CNN

Computer vision is a dynamic and rapidly evolving field within the broader domain of artificial intelligence. Within surveillance monitoring systems, one of the central tasks is object detection, which involves identifying and localizing objects of interest in video sequences to provide safety and security of the people. Detection of multiple objects is a challenging task in video sequences which interprets less accuracy and false Bounding box regression. In this paper, enhanced faster R-CNN model is proposed and trained to compute regional proposal through Convolutional layers on the different scene of the sequences in term of lighting, motion capture related to spatial analysis. These enhancements could encompass architectural improvements, novel training strategies, or the incorporation of additional data sources to improve the model's overall performance. Proposed model is experimented on pedestrian video gives an improved accuracy detection rate than single detector techniques.

Performance characteristics of a prototype dialysate turbidity monitoring system to detect peritonitis in patients receiving peritoneal dialysis.

The risk of peritonitis has limited wider adoption of peritoneal dialysis (PD) in the United States. We developed a prototype bedside dialysate turbidity monitoring system, aiming to improve diagnostic accuracy relative to conventional approaches which depend on visual inspection and reporting of insensitive and non-specific symptoms. The prototype system was tested in a single-centre, proof-of-principle clinical study in patients receiving intermittent PD. We obtained multiple effluent dialysate samples from each consenting participant. We compared turbidity measurements with diagnostic criteria endorsed by the International Society of Peritoneal Dialysis (ISPD). Overall, we analysed 983 specimens from 65 patients, including 105 samples from patients with peritonitis and 878 samples from patients without peritonitis. An operating point derived from a previous in vitro study yielded an unadjusted sensitivity and specificity of 95.2% and 91.5%, respectively. The majority of samples that did not meet ISPD diagnostic criteria were either cases detected before criteria were met or were related to active peritonitis treatment and resolution. This proof-of-principle study demonstrates the feasibility and diagnostic accuracy of a prototype dialysate turbidity monitoring system for peritonitis surveillance.

Education Research: Monitoring and Tracking Neurophobia: Evidence From a Temporal Analysis of Brazilian Medical Schools.

Neurologic disorders are common medical conditions. However, even with a higher demand for neurologic care, the capacity to train neurologists is impaired. The fear of neurosciences/neurology by medical students, known as neurophobia, may cause multiple adverse effects in neurologic assistance. The objectives of this study were to estimate the current prevalence and characteristics of neurophobia in medical students in Brazil and to compare neurophobic symptoms at 2 time points. This is a cross-sectional study conducted with students from 4 medical schools in Pará, Brazil, who matriculated into the preclinical stage, clinical stage, and internship were submitted to a questionnaire to assess the perception of clinical specialties, including neurology. Reasons for neurophobia, probable strategies to improve neurologic education, and a specific scale to detect neurophobia were also evaluated. Furthermore, we performed a temporal comparison of current results with those from a previous study from 2015. Neurophobia was detected in 63.3% of medical students. The perception of the difficulty in neurology/neurosciences was very high in all stages, and the perception of interest and quality of teaching worsened during the internship. The need to understand neuroanatomy and neurophysiology was cited as the most important reason for neurophobia. More and better bedside tutorials were the most mentioned suggestion to improve neurologic education. The temporal comparison between 2015 and 2022 showed that the level of knowledge, quality of teaching, and likelihood of pursuing a career in neurology has become more favorable in 2022. The prevalence of neurophobia in Brazil was higher than that in high-income countries. Unfavorable opinions about neurology tended to increase throughout the medical course, but the temporal comparison showed that the impact of neurophobia has decreased. Surveillance systems for monitoring and tracking neurophobia should be implemented in medical schools.

IoT Based Surveillance and Health Monitoring System for Elderly and Physically Challenged People

With the increase in global life expectancy and the advance of technology, the creation of age-friendly environments is a priority in the design of new products for elderly people healthcare. This paper presents a proposal for a real-time health monitoring system of older adults living in geriatric residences. This system was developed to help caregivers to have a better control in monitoring the health of their patients and have closer communication with their patients’ family members. To validate the feasibility and effectiveness of this proposal, a prototype was built, using a biometric bracelet connected to a mobile application, which allows real-time visualization of all the information generated by the sensors (heart rate, body temperature, and blood oxygenation) in the bracelet. Using these data, caregivers can make decisions about the health status of their patients. The evaluation found that the users perceived the system to be easy to learn and use, providing initial evidence that our proposal could improve the quality of the adult’s healthcare.

SARS-CoV-2 Wastewater Surveillance in Ten Cities from Mexico

We aimed to estimate the lead time and infection prevalence from SARS-CoV-2 wastewater (WW) monitoring compared with clinical surveillance data in Mexico to generate evidence about the feasibility of a large-scale WW surveillance system. We selected 10 WW treatment plants (WWTP) and 5 COVID-19 hospitals in major urban conglomerates in Mexico and collected biweekly 24-h flow-adjusted composite samples during October–November 2020. We concentrated WW samples by polyethylene glycol precipitation and employed quantitative PCR (RT-qPCR) assays, targeting the nucleoprotein (N1 and N2) genes. We detected and quantified SARS-CoV-2 RNA in 88% and 58% of the raw WW samples from WWTPs and COVID-19 hospitals, respectively. The WW RNA daily loads lead the active cases by more than one month in large and medium WWTP sites. WW estimated that cases were 2 to 20-fold higher than registered active cases. Developing a continuous monitoring surveillance system for SARS-CoV-2 community transmission through WW is feasible, informative, and recognizes three main challenges: (1) WW system data (catchment area, population served), (2) capacity to maintain the cold-chain and process samples, and (3) supplies and personnel to ensure standardized procedures.

An Intelligent Health Care System in Fog Platform with Optimized Performance

Cloud computing delivers services through the Internet and enables the deployment of a diversity of apps to provide services to many businesses. At present, the low scalability of these cloud frameworks is their primary obstacle. As a result, they are unable to satisfy the demands of centralized computer systems, which are based on the Internet of Things (IoT). Applications such as disease surveillance and tracking and monitoring systems, which are highly latency sensitive, demand the computation of the Big Data communicated to centralized databases and from databases to cloud data centers, resulting in system performance loss. Recent concepts, such as fog and edge computing, offer novel approaches to data processing by relocating the processing power and other resources closer to the end user, thereby reducing latency and maximizing energy efficiency. Existing fog models, on the other hand, have a number of limitations and tend to prioritize either the precision of their findings or a faster response time, but not both. For the purpose of applying a healthcare solution in the real world, we developed and implemented a one-of-a-kind architecture that integrates quartet deep learning with edge computing devices. The paradigm that has been developed delivers health management as a fog service through the Internet of Things (IoT) devices and efficiently organizes the data from patients based on the requirements of the user. FogBus, a fog-enabled cloud framework, is used to measure the effectiveness of the proposed structure in regards to resource usage, network throughput, congestion, precision, and runtime. To maximize the QoS or forecast the accuracy in different fog computing settings and for different user requirements, the suggested technique can be set up to run in a number of different modes.

Re-emergence of Rift Valley fever: an update and preventive measures.

Re-emergence of Rift Valley fever: an update and preventive measures.

STATIC AND DYNAMIC ASSESSMENTS OF INFORMATION SIGNS IN RECOGNITION OF SOURCES AND OBJECTS OF OBSERVATION IN THE PROCESS OF RADIO MONITORING

Background. The current state and problems of the surveillance and radio monitoring systems of Ukraine require fundamentally new approaches to increasing their efficiency and the level of informatization. At the same time, the informatization of the radio monitoring system should be understood as the process of implementation and application in various areas of their activity of methods and means of collecting, transmitting, processing, saving and using information in order to increase the effectiveness of conducting radio monitoring and meet the needs of national security based on the formation and use of information resources.
 Objective. The purpose of the paper is to increase the effectiveness of radio monitoring by using the calculation of estimates of dynamic and static informational features when recognizing sources and objects of radio radiation and determining their phase (operational) state and level of possible danger.
 Methods. Recognition is based on the method of least squares by calculating the degree of "similarity" (similarity coefficient) of the recognized object with objects whose classes are known. Both the researched and reference objects are presented as a set of values of informational features of various nature, some of which are unchanged over the entire period of observation, that is, static, while others change dynamically.
 Results. The structure of the automated system of classification and recognition of surveillance objects and the recognition algorithm based on the calculation of static and dynamic information features and the similarity coefficient are proposed.
 Conclusions. A distinctive feature of deciding whether an object or a source of information belongs to one or another class feature is the calculation of the degree of "similarity" (similarity coefficient) of the recognized object to objects whose classes are known. To eliminate recognition errors associated with a violation of the synchronicity of measurements of the values of dynamic informational features of reference objects and objects to be recognized, a calculation is required taking into account possible time shifts.

A proposal model using deep learning model integrated with knowledge graph for monitoring human behavior in forest protection

In conventional monitoring of human behavior in forest protection, deep learning approaches can be detected human behavior significantly since thousands of visitors’ forest protection is abnormal and normal behaviors coming to national or rural forests. This paper has presented a new approach using a deep learning model integrated with a knowledge graph for the surveillance monitoring system to be activated to confirm human behavior in a real-time video together with its tracking human profile. To confirm the proposed model, the proposed model has been tested with data sets through case studies with real-time video of a forest. The proposed model provides a novel approach using face recognition with its behavioral surveillance of the human profile integrated with the knowledge graph. Experimental results show that the proposed model has demonstrated the model’s effectiveness.

A Review of Antimicrobial Surveillance Networks Across the Globe

Introduction: Antimicrobial Resistance (AMR) poses a global public health threat with a wide gap between the knowledge and mechanism of resistance acquired by the pathogens. This increasing hazard of AMR has been recognized by the different geographical regions and responded to by implementing tough restrictions and AMR strengthening AMR surveillance systems for monitoring and prediction. Methodology: We searched data using National and International databases of different geographical AMR surveillance networks and extracted information from them. Results: In India, AMR surveillance reported fluoroquinolones and Aminoglycosides as the major group against which microbes develop resistance followed by Cephalosporins and Carbapenem whereas the WHO European Region, EU/EEA noted decreasing trend of AMR during 2016-2020 for several bacteria although an increasing trend was reported for Carbapenem-resistance in Klebsiella pneumoniae and represents region’s most serious concern. Conclusion: India currently has two independent AMR surveillance networks, whereas the European Surveillance system presents a trajectory of resistance data based on consumption data.

O3-4 Progress on addressing the cornerstones of physical activity policy for children and youth in England

BackgroundThere has been an increasing focus on the importance of national policy to address population levels of physical inactivity. It has been suggested that the four ?cornerstones' of policy comprise: 1) national guidelines on physical activity levels; 2) setting population goals and targets; 3) surveillance or health monitoring systems; and 4) public education. The aim of the current paper was to analyse the policy actions which have addressed each of these elements for children and youth in England and to identify areas of progress and remaining challenges.MethodsA literature search was undertaken to identify past and present documents relevant to physical activity policy for children and youth in England. Each document was analysed to identify content relevant to the four ?cornerstones' of policy.ResultsPhysical activity guidelines for children and youth have been in place since 1998 and reviewed periodically to ensure they reflect the latest scientific evidence. The setting of physical activity targets has focused on the provision of opportunities for physical activity, particularly through physical education (PE) in schools, rather than in relation to the proportion of children meeting recommended physical activity levels. There has been much surveillance of children's physical activity but this has been undertaken infrequently over time, by a wide range of organisations, and with varying inclusion of different domains of activity such as school PE, leisure time activity and active travel. There has only been one campaign in England targeted at children and their intermediaries (Change4Life), which was an obesity campaign focussing on dietary behaviour in combination with physical activity. Most recently an infographic supporting the physical activity guidelines for children and young people was developed, but details of its dissemination and usage are unknown.ConclusionsThere have been many developments in physical activity policy in England targeted at children and youth. The area of greatest progress is national physical activity guidelines. Establishing prevalence targets, streamlining surveillance systems, and investing in public education would strengthen national policy efforts to reduce physical inactivity.

Hybrid deep learning – picture fuzzy set model for monitoring human behaviour in forest protection

In conventional monitoring forest protection, detection methods use optical sensors or RGB cameras combine features including smokes, fires and human-destroyed forests at national forests. This paper has presented a new approach using Deep learning integrated with Picture Fuzzy Set for the surveillance monitoring system to be activated to confirm human behaviour in real-time in forest protection. Picture Fuzzy Graph (PFG) are applied to solve many complex problems in the real-world problems. The paper has presented a novel approach using deep learning with knowledge graphs to find a human profile including the detection of humans in large data. In the proposed model, digital human profiles are collected from conventional databases combination with social networks in real-time, and a knowledge graph is created to represent complex-relational user attributes of human profile in large datasets. PFG is applied to quantify the degree centrality of nodes. To confirm the proposed model, the proposed model has been tested with data sets through case studies of a forest. Experimental results show that the proposed model has been validated on real world datasets to demonstrate this method’s effectiveness.

META-HEURISTIC OPTIMISATION FOR OPTIMAL VIDEO QUALITY ENHANCEMENT

We have seen a rapid development in technology over the past few years, from basic mobile phones to highly advanced surveillance monitoring systems that can record and analyze video clips. The process of video capture inevitably leads to a decline in overall video recording quality. Inadequate lighting is due to either an open aperture or a slow shutter speed. Images taken in such conditions typically have poor contrast and noisy backgrounds. If the contrast in your video is off, it could be because of a malfunctioning imaging device or an untrained operator. These two outcomes are equally plausible. When recording videos, this causes a loss of the dynamic range that could have been captured. Because of this, the video may appear distorted or washed out, and some of the finer details may be lost. In order to lessen the impact of these problems on the viewer experience, contrast enhancement techniques can be used to boost the visual quality. Work presented here will take a two-pronged approach to addressing the aforementioned problems. Videos can be compressed and their contrast can be increased, both of which are useful techniques that complement one another. Video quality can be improved with the help of a method called ant colony optimisation ACO based image quality enhancement. The frames of a video can be analyzed in greater detail using this hybrid method than with the conventional method. The noise is further reduced when the non-divisible median filter is used. To do this, the study develops an optimisation to attain increased rate of peak signal to noise rate than the other existing methods. After examining available options, the researchers settled on the DLACDHE procedure as the best option. Based on the results, it is reasonable to infer that the proposed strategy offers better contrast enhancement than the conventional methods.

In-Cabin Monitoring System for Autonomous Vehicles.

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.