Surveillance Environment Research Articles

Judders, Jabs, and Slips: Automation, Anomalies, Movements

In May 2019, the “Automation and Me” event invited digital artists from around the world to come together in an unused shopping center unit in the heart of Leeds, United Kingdom, to critically respond to and explore the themes of automation, embodiment, and identity. In this article, we draw on Tobias Matzner’s argument that what algorithms are and do “emerges in a complex interplay of social practices, material properties, discourses, mathematical abstractions, code” to think through the event. In so doing, we explore a number of complex interplays as they were felt and lived by all of us who participated in it. We reflect on the experiences during the workshops through the particular framework of automation and feminist new materialism to explore how AI technologies and the complex surveillance of the smart environment replicated, on the one hand, existing and oppressive prejudice and, on the other hand, worked to constantly recalibrate and manipulate the context that humans and nonhumans navigate.

Study design of National Surveillance of the Work Environment of Employees in Denmark (NASWEED): Prospective cohort with register follow-up.

The aim is to report the design and baseline data of the 'National Surveillance of the Work Environment of Employees in Denmark' study (NASWEED). NASWEED consist of (a) bi-annual cross-sectional samples, based on probability samples of wage earners of the general working population in Denmark from 2021 onwards (surveillance), (b) a prospective cohort of all previous respondents every two years (epidemiology, questionnaire follow-up) and (c) longitudinal follow-up in Danish registers about work and health (epidemiology, register follow-up). Between February and May 2021, a stratified (38 occupational industries) probability sample of 63,391 Danish residents aged 15-69 years who were employed for at least 34 hours per month received an invitation to participate, of whom 30,099 (47.5%) completed the questionnaire, 897 (1.4%) partially completed the questionnaire and 32,395 (51.1%) did not respond. Baseline was completed in June 2021. NASWEED covers various topics about the work environment (psychosocial, ergonomic, chemical, biological, safety, accidents, working from home, etc), health behaviours and somatic and mental health-related conditions. Statistical analyses will mainly build on survey procedures with model-assisted weights to ensure that the sample will yield representative estimates of the general working population. NASWEED will monitor the development of the work environment and health in Denmark until 2030. The survey data will be included in epidemiological studies with repeated measurement of the work environment, health variables and covariates, and follow-ups in national registers to investigate the prospective association in the years and decades to come between the work environment and workers' health and labour market participation.

Users' Information Security Awareness of Home Closed-Circuit Television Surveillance

Closed-circuit television (CCTV) surveillance cameras are widely used in public and private areas around the world. It is primarily used for tracking individuals and preventing criminal activities. It is necessary to balance the benefits of video surveillance and the risks it poses to individuals' right to privacy. The existing studies raised privacy issues of installing CCTV in public places. However, there is a lack of studies investigating users’ awareness of information security and privacy limitations in installing CCTV in private places. Thus, in this study, the author evaluated users' information security awareness of the value of CCTV and other forms of video surveillance. In-person interviews were conducted in Riyadh province, Kingdom of Saudi Arabia. A total of 77 individuals responded to the interview. A qualitative analysis was conducted to evaluate the participants’ perception of CCTV usage. The outcome of the analysis revealed four themes: Privacy invasion, privacy awareness, dilemmas in implementing security, and preventive measures. The findings revealed that the participants required strict privacy policies for installing CCTV video monitoring systems in private areas. In addition, they understood that CCTV is effectively reducing the fear of crime. The research contributes to understanding users' general awareness of information security and offers the necessary steps to protect the user's privacy in a CCTV surveillance environment. In addition, a data-sharing framework is recommended to share the data in a secure environment. Furthermore, researchers can utilize the study findings in conducting further similar investigative studies.

Parasitological assessment of some fruits and vegetables commonly sold in retail outlets in the Mfoundi Division of Cameroon

BackgroundFruits and vegetables are very important for human diet as they provide all the nutrients needed to be healthy with just a daily-recommended intake of 400 to 600 mg. However, they constitute one of the major sources of human infectious agents. Thus monitoring of the microbial contaminants of the fruits and vegetables is very crucial for human safety. MethodA cross sectional study was conducted on fruits and vegetables in four markets (Mfoundi, Mokolo, Huitième and Acacia) of the Yaoundé city from October 2020 to March 2021. In all, 528 samples were purchased (carrots, cucumbers, cabbages, lettuces, leeks, green beans, okra, celeries, pepper, green peppers and tomatoes) and processed for infective agents using the centrifugation methods (Formalin, distilled and saline water). Seventy-four (74) soil/water samples collected from the sale environment were analysed using the same technics. ResultsOverall, 149/528 (28.21%) were contaminated by at least one infective agent: 130 (24.62%) and 19 (3.6%) having one and two pathogen species respectively. Vegetables had high contamination rate (22.34%) than fruits (5.87%). Lettuce (52.08%), carrot (41.66%) and cabbage (35.41%), were the most contaminated while okra was the least (6.25%). Candida spp. (14.01%) and larva of Strongyloides stercoralis (7.76%) were more observed while Hookworms (1.13%) was the least. Frequency of Strongyloides stercoralis (p = 0.001) and Candida spp. (p = 0.01) were statistically high than other pathogens. Contamination rates were similar for washed (27.65%) and unwashed (28.78%) samples before sale. Candida spp. (p = 0.001), Strongyloides stercoralis (p = 0.01) and Entamoeba histolytica/dispar (p = 0.017) showed significant contamination rates by month. Contamination trends were high in the rainy season (42.6%) than the dry season (15.1%). Correlation between environment and products sold revealed same pathogens in both cases. ConclusionThe study highlights that the sale environment and products constitute potential source of microbial contamination. These data raised the concern of stakeholders about health risk related to vegetables and fruits sold in some local markets in Cameroon. Thus the necessity for them to development more appropriate policies on the surveillance of sale environment and on the management of these products during the different process phases by the population.

MDNet: A Fusion Generative Adversarial Network for Underwater Image Enhancement

Underwater images are widely used in ocean resource exploration and ocean environment surveillance. However, due to the influence of light attenuation and noise, underwater images usually display degradation phenomena such as blurring and color deviation; an enhancement method is required to make the images more visible. Currently, there are two major approaches for image enhancement: the traditional methods based on physical or non-physical models, and the deep learning method. Inspired by the fusion-based idea, this paper attempts to combine traditional methods with deep learning and proposes a multi-input dense connection generator network (MDNet) for underwater image enhancement. Raw images and processed images are input into the network together, the shallow information is fully utilized by dense connection, and the network is trained in generative and adversarial manner. We also design a multiple loss function to improve the visual quality of the generated images. We conduct both qualitative and quantitative experiments, and then compare the results with state-of-the-art approaches comprehensively using three representative datasets. Results show that the proposed method can effectively improve the perceptual and statistical quality of underwater images.

Sequential attention mechanism for weakly supervised video anomaly detection

Surveillance cameras are installed across various sectors of a smart city in order to capture ongoing events for monitoring purposes. The analysis of these surveillance videos is an important research topic that involves activity recognition, object detection, anomaly recognition, and other problems. However, anomaly recognition is the most common task in a smart city, and has received significant attention with the aim of ensuring public safety and security. Many works have been published in this field, but these schemes have not been able to provide the desired detection outcomes. Mainstream anomaly recognition methods are heavily dependent on strong supervision to achieve satisfactory performance, which is time-consuming and impractical. With a particular focus on this problem, this article presents a deep convolution neural network (CNN)-based novel anomaly recognition model, in which deep features are extracted from surveillance video frames. These features are forwarded to the proposed temporal convolution network (TCN) that includes a multi-head attention module to enable it to recognise anomalies from these videos. The multi-head temporal attention mechanism enables the model to obtain more key temporal information about the complex surveillance environment. Experiments conducted on standard datasets and a comparison with state-of-the-art approaches demonstrate the effectiveness and superiority of the proposed framework, which achieves increases in accuracy of 0.9%, 1.9%, 0.65%, 0.27%, and 1.5% on the UCF-Crime2local, LAD-2000, RWF-2000, RLVS, and Crowd Violence datasets, respectively. These outcomes indicate the suitability of our method for deployment in real-time surveillance schemes.

Quantitative and Qualitative Airborne Mycobiota Surveillance in High-Risk Hospital Environment.

(1) Background: The primary aim of the presented study was to assess the prevalence of fungi in the indoor air of selected hospital wards, and the additional goal was to evaluate the susceptibility of cultured isolates of Aspergillus fumigatus to triazoles. (2) Methods: Three hematology departments and a hospital for lung diseases were surveyed in 2015 and/or 2019. Air samples were taken with a MicroBio MB1 air sampler on Sabouraud agar. The susceptibility of Aspergillus fumigatus isolates to voriconazole, posaconazole and itraconazole was tested with a microdilution method, according to EUCAST. (3) Results: The amount of fungi cultured from rooms equipped with sterile air circulation, as well as flow devices for air disinfection, was significantly lower compared to that from unprotected rooms. The areas most contaminated with fungi were corridors and bathrooms. The dominant species were Cladosporium and Penicillium. A. fumigatus was rare in hematological departments (6/61, 9.8% examinations performed in 2014 and 2/40, 5% in 2019), whereas in the hospital for lung diseases an outbreak of A. fumigatus spores with up to 300 CFU/m3 was noted in March 2015. No triazole-resistant A. fumigatus isolate was detected. (4) Conclusions: Regular microbiological testing of the hospital environment can contribute to the detection of spore outbreaks, and thus enable the implementation of corrective procedures (e.g., additional disinfection, changing of HEPA filters).

HeadPose-Softmax: Head pose adaptive curriculum learning loss for deep face recognition

Face recognition has been one of the most popular applications in the field of target detection. Currently, frontal faces can be easily detected, but multi-view face detection remains a difficult task because of various factors such as illumination, various poses, occlusions, and facial expressions. Margin-based loss functions are used to increase the feature margins between different classes, thus enhancing the discriminability of face recognition models, but the performance in face detection in complex scenes (e.g., high pitch angle face detection in surveillance environments) can be significantly degraded. Recently, the idea of a mining-based strategy to emphasize hard samples has been used to achieve good results in multi-view face detection. However, most of the existing methods do not explicitly emphasize samples based on their importance, resulting in the underutilization of hard samples. In this paper, we propose a curriculum learning loss function (HeadPose-Softmax) to classify the difficulty of a sample based on its facial pose, and embed the concept of curriculum learning into the loss function to implement a novel training strategy for deep face recognition. The loss function explicitly emphasizes the importance of the samples according to the different difficulty of each sample, which allows the model to make fuller use of hard samples, focus on learning pose invariant features, and improve the accuracy of the model in multi-view face detection tasks. Specifically, our HeadPose-Softmax dynamically adjusts the relative importance of the hard samples according to the pose angle of the face in the hard samples during the training phase. At each stage, different samples are assigned different importance according to their corresponding difficulty. Extensive experimental results under popular benchmarks show that our HeadPose-Softmax can enhance the accuracy of the model in multi-view face detection and outperform the state-of-the-art competitors.

Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics.

Comprehensive surveillance systems are the key to provide accurate data for effective modeling. Traditional symptom-based case surveillance has been joined with recent genomic, serologic, and environment surveillance to provide more integrated disease surveillance systems. A major gap in comprehensive disease surveillance is to accurately monitor potential population behavioral changes in real-time. Population-wide behaviors such as compliance with various interventions and vaccination acceptance significantly influence and drive the overall epidemic dynamics in the society. Original infoveillance utilizes online query data (e.g., Google and Wikipedia search of a specific content topic such as an epidemic) and later focuses on large volumes of online discourse data about the from social media platforms and further augments epidemic modeling. It mainly uses number of posts to approximate public awareness of the disease, and further compares with observed epidemic dynamics for better projection. The current COVID-19 pandemic shows that there is an urgency to further harness the rich, detailed content and sentiment information, which can provide more accurate and granular information on public awareness and perceptions toward multiple aspects of the disease, especially various interventions. In this perspective paper, we describe a novel conceptual analytical framework of content and sentiment infoveillance (CSI) and integration with epidemic modeling. This CSI framework includes data retrieval and pre-processing; information extraction via natural language processing to identify and quantify detailed time, location, content, and sentiment information; and integrating infoveillance with common epidemic modeling techniques of both mechanistic and data-driven methods. CSI complements and significantly enhances current epidemic models for more informed decision by integrating behavioral aspects from detailed, instantaneous infoveillance from massive social media data.

Nonuniform Background Correction for Wide-Field Surveillance Camera

Space environment surveillance is very important for space security, which is easy to be disturbed by stray light and hot pixels, and the image background presents a certain degree of nonuniformity. The existing methods can not achieve the accurate segmentation of weak targets while correcting the nonuniform background. To solve this problem, this paper presents an accurate and robust correction method for the wide-field surveillance camera, called the enhanced new top-hat transform (ENTHT). Firstly, we analyze the formation mechanism and influence of the nonuniformity background from multiple dimensions. Secondly, because of the dependence and limitations of the background suppression effect of the new top-hat transform on the selection of structural elements, we improve the new top-hat transform by designing a noise structure element (NSE). Finally, we analyze the performance and advantages of the ENTHT method. In the field experiment, the method can accurately correct the complex space nonuniform background, eliminate the stray light and hot pixels, and realize the accurate segmentation of weak targets. In the complex space environment, it brings great help to space-moving target recognition and tracking.

Vision transformer attention with multi-reservoir echo state network for anomaly recognition

Anomalous event recognition requires an instant response to reduce the loss of human life and property; however, existing automated systems show limited performance due to considerations related to the temporal domain of the videos and ignore the significant role of spatial information. Furthermore, although current surveillance systems can detect anomalous events, they require human intervention to recognise their nature and to select appropriate countermeasures, as there are no fully automatic surveillance techniques that can simultaneously detect and interpret anomalous events. Therefore, we present a framework called Vision Transformer Anomaly Recognition (ViT-ARN) that can detect and interpret anomalies in smart city surveillance videos. The framework consists of two stages: the first involves online anomaly detection, for which a customised, lightweight, one-class deep neural network is developed to detect anomalies in a surveillance environment, while in the second stage, the detected anomaly is further classified into the corresponding class. The size of our anomaly detection model is compressed using a filter pruning strategy based on a geometric median, with the aim of easy adaptability for resource-constrained devices. Anomaly classification is based on vision transformer features and is followed by a bottleneck attention mechanism to enhance the representation. The refined features are passed to a multi-reservoir echo state network for a detailed analysis of real-world anomalies such as vandalism and road accidents. A total of 858 and 1600 videos from two datasets are used to train the proposed model, and extensive experiments on the LAD-2000 and UCF-Crime datasets comprising 290 and 400 testing videos reveal that our framework can recognise anomalies more effectively, outperforming other state-of-the-art approaches with increases in accuracy of 10.14% and 3% on the LAD-2000 and UCF-Crime datasets, respectively.

CrimeNet: Neural Structured Learning using Vision Transformer for violence detection

The state of the art in violence detection in videos has improved in recent years thanks to deep learning models, but it is still below 90% of average precision in the most complex datasets, which may pose a problem of frequent false alarms in video surveillance environments and may cause security guards to disable the artificial intelligence system.In this study, we propose a new neural network based on Vision Transformer (ViT) and Neural Structured Learning (NSL) with adversarial training. This network, called CrimeNet, outperforms previous works by a large margin and reduces practically to zero the false positives. Our tests on the four most challenging violence-related datasets (binary and multi-class) show the effectiveness of CrimeNet, improving the state of the art from 9.4 to 22.17 percentage points in ROC AUC depending on the dataset. In addition, we present a generalisation study on our model by training and testing it on different datasets. The obtained results show that CrimeNet improves over competing methods with a gain of between 12.39 and 25.22 percentage points, showing remarkable robustness.

Efficacy of Active Rapid Molecular Screening and IPC Interventions on Carbapenem-Resistant Enterobacterales Infections in Emergency Intensive Care Units without Enough Single-Room Isolation.

To investigate whether rapid active molecular screening and infection prevention and control (IPC) interventions can reduce colonization or infection with carbapenem-resistant Enterobacterales (CRE) in a general emergency intensive care unit (EICU) without enough single-room isolation. The study was designed as a before-and-after quasi-experiment. Before the experimental period, the ward was rescheduled and the staff were trained. From May 2018 to April 2021, active screening was performed by seminested real-time fluorescent polymerase chain reaction (PCR) detection with rectal swabs from all patients on admission to the EICU, and the results were reported in 1 hour. Other IPC interventions including hand hygiene, contact precautions, patient isolation, environmental disinfection, environment surveillance, monitoring, auditing and feedback were conducted under strict supervision. The patients' clinical characteristics were collected simultaneously. In this 3-year study, 630 patients were enrolled and 19.84% of the patients were initially colonized or infected with CRE as shown by active molecular screening. The average drug resistance ratio to carbapenem shown by clinical culture detection of Klebsiella pneumoniae (KPN) before the study was performed was 71.43% in EICU. The drug resistance ratio decreased significantly from 75%, 66.67% to 46.67% in the next 3 years (p<0.05) during which active screening and IPC interventions were strictly executed. While the ratio gaps between EICU and the whole hospital were narrowed from 22.81%, 21.11% to 4.64%. Patients with invasive devices, skin barrier damage, and the recent use of antibiotics on admission were found to have a higher risk of being colonized or infected with CRE (p<0.05). Active rapid molecular screening and other IPC interventions may significantly reduce CRE nosocomial infections even in wards without enough single-room isolation. The key to reduce the spread of CRE in the EICU is the strict execution of IPC interventions by all medical staff and healthcare workers.

Lidar and AI Based Surveillance of Industrial Process Environments

Abstract The paper describes a system approach to use LiDAR sensors for capturing dynamic point cloud data in industrial process environments and to interpret the captured scenes with AI based object detection. The object detection is used to distinguish between humans and other mobile objects in safety relevant workspaces. Several AI methods relevant for such application are analysed. One method is applied with annotated test data and evaluated concerning its accuracy.

Trajectory optimization of laser-charged UAV to minimize the average age of information for wireless rechargeable sensor network

This paper considers the laser-charged Unmanned Aerial Vehicle (UAV) assisted Wireless Rechargeable Sensor Network (WRSN), where rechargeable sensors are deployed in surveillance environment to monitor information, a UAV is not only used as aerial wireless mobile collector for gathering data from sensors but also used as mobile charger to replenish energy for sensors, Laser Beam Directors (LBDs) are uniformly deployed in the monitoring environment to charge UAV by emitting laser beams. In such network, we study the average Age of Information Optimization (AoIO) problem whose objective is to minimize the average AoI of data collected from sensors such that all data of the network are transported to the base station and the remaining energy of any sensor exceeds a certain threshold. We prove that the AoIO problem is NP-hard. To solve the AoIO problem, we first study the Total Flight Time Minimizing of UAV (TFTM) problem, which aims at finding an optimal charging solution of UAV to minimize the flight time of UAV based on the order of sensors visited by UAV. Then we prove that the TFTM problem is also NP-hard. Afterwards, we propose a heuristic algorithm to solve the TFTM problem by optimizing flight path, data collection, energy power transfer and laser charging of UAV. Based on the solution for the TFTM problem, we propose an approximation algorithm to solve the AoIO problem. Finally, we conduct extensive simulation experiments to verify the effectiveness of the proposed algorithm.

Pigeon Inspired Optimization Based Centroid Selection in TGVFCMS for Energy-Efficient Wireless Sensor Network

Wireless Sensor Network (WSN) includes large quantities of sensing node used in various situations to collect data. WSN considers applications to collect data from remote locations, for example in environment surveillance, military, transport protection, etc. An energy efficient routing protocol is essential to handle the discharged electricity of the system and to minimize traffic and upstairs during data broadcast phases to fulfill the sensing, communication and processing tasks of the WSN. The biggest problem in the WSN is that energy resources are small. In this study, an energy-efficient routing protocol improves energy efficiency with the life span of sensor nodes by using optimization algorithm Pigeon Inspired Optimization (PIO). Clustering technique i.e. Total Generalized Variation Fuzzy c-means clustering (TGVFCMS) is used to select the Cluster Head (CH) and CH folds and bandage the information and direct it to the board node. By its fitness feature, PIO helps to initialize the centroid. After MATLAB simulation, the energy consumption rate was reduced to 25.03% while using PIO. Finally, comparisons are made between the work proposed and the existing current work to assess the effectiveness of the work proposed.

Elevada prevalência de doenças infecciosas entre trabalhadores da saúde indica a necessidade de melhorar a vigilância

Abstract Objective: to estimate the seroprevalence of arboviruses, syphilis, HIV, and hepatitis B (HBV) in Healthcare Workers (HCW). Methods: a survey among HCW among was randomly selected in a city in Bahia-Brazil. The research used a structured questionnaire, which collected sociodemographic, occupational, and health information in 2019. Rapid immunochromatographic tests were used to track infections. Thus, positive results for arboviruses, syphilis, HIV, and HBV indicated seropositivity for the respective infection. In addition, simultaneous detection of antibodies for zika (ZIKV) and dengue (DENV) viruses indicated seropositivity for flavivirus infection. Results: a total of 453 HCW were included, 82.8% were women. Most HCW (55.1%) reported contact with biological material; 5.2% reported injuries with biological material. The seroprevalence was 34.7% (95%CI:30.1-39.4) for dengue, 1.7% (95%CI:0.7-3.4) for zika, 9.9% (95%CI:7.2-13.2) for chikungunya, and 39.9% (95%CI:35.2-44.7) for flavivirus (ZIKV+DENV); with 21.9% (95%CI:18.1-26.2) being negative to all arboviruses. Seropositivity to arboviruses increased with age and was higher among endemic disease control agents. Three HCW tested positive for HBV (HBsAg); no HIV cases were detected. Conclusion: a high percentage of HCW was exposed to infectious agents (contact with biological material and/or exposure to arboviruses). Among the necessary conditions for preventing infections in the work environment, the following stand out: monitoring of infectious diseases among HCW, surveillance of work environments, and measures to control occupational exposures, such as the availability of repellents.

High Spatial Resolution Gap-Free Global and Regional Ocean Color Products

Satellite ocean color products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20, and the Ocean and Land Colour Instrument (OLCI) on the Sentinel-3A (S3A) and Sentinel-3B (S3B) have been widely used for surveillance of the ocean environment and research on ocean physical, biological, biogeochemical, and ecological processes. However, either VIIRS or OLCI daily ocean color images are often incomplete in spatial coverage due to cloud cover, contamination of high sun glint, narrow swath width, high sensor-zenith angle, high solar-zenith angle, and/or other unfavorable retrieval conditions. Although merging daily ocean color images from multiple satellite sensors can help reduce the number of invalid pixels, gap-filling methods such as the Data Interpolating Empirical Orthogonal Function (DINEOF) are often used to reconstruct invalid pixels and generate gap-free images. The 9-km spatial resolution global gap-free ocean color data have been routinely produced by the NOAA Ocean Color Team and distributed through NOAA CoastWatch (https://coastwatch.noaa.gov/cw/index.html). In this study, we aim to develop and produce improved spatial resolution gap-free products, including chlorophyll-a (Chl-a) concentration, diffuse attenuation coefficient at the wavelength of 490 nm [ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K_d</i> (490)], and suspended particulate matter (SPM) concentration for spatial resolutions of 0.5-km, 1-km, and 2-km. Two-sensor (VIIRS-SNPP and VIIRS-NOAA-20), three-sensor (two-sensor + OLCI-S3A), and four-sensor (three-sensor + OLCI-S3B) daily merged global Level-3 ocean color data are created and compared. It is found that by merging data from the two VIIRS sensors, ~38% more valid ocean product data are retrieved compared with a single sensor from either SNPP or NOAA-20. Adding OLCI-S3A to the two-sensor merged data can increase the number of valid pixels by ~12%; and adding OLCI-S3B to the three-sensor merged data can further increase the number of valid pixels by ~8%. The DINEOF method is applied to daily two-sensor, three-sensor, and four-sensor merged data to generate global 2-km resolution gap-free images. Results show that 2-km resolution gap-free images are able to resolve fine ocean features like coastal eddies and filaments, which are not available in the 9-km resolution images. While adding OLCI-S3A data significantly improves the three-sensor derived gap-free images over the two-sensor images, no significant enhancement is found in the four-sensor derived gap-free images by adding OLCI-S3B data. The DINEOF method is also applied to 1-km and 0.5-km resolution four-sensor merged Chl-a, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K_d</i> (490), and SPM images in the Gulf of Mexico and U.S. west coast region. It is found that both 0.5-km and 1-km resolution images show more detailed ocean structures and features in coastal regions.

Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

To prevent economic, social, and ecological damage, fire detection and management at an early stage are significant yet challenging. Although computationally complex networks have been developed, attention has been largely focused on improving accuracy, rather than focusing on real-time fire detection. Hence, in this study, the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment. The proposed model architecture is inspired by the VGG16 network, with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4. This results in higher performance with a reduced number of parameters and inference time. Moreover, smaller convolutional kernels are utilized, which are particularly designed to obtain the optimal details from input images, with numerous channels to assist in feature discrimination. In E-FireNet, three steps are involved: preprocessing of collected data, detection of fires using the proposed technique, and, if there is a fire, alarms are generated and transmitted to law enforcement, healthcare, and management departments. Moreover, E-FireNet achieves 0.98 accuracy, 1 precision, 0.99 recall, and 0.99 F1-score. A comprehensive investigation of various Convolutional Neural Network (CNN) models is conducted using the newly created Fire Surveillance SV-Fire dataset. The empirical results and comparison of numerous parameters establish that the proposed model shows convincing performance in terms of accuracy, model size, and execution time.

Smart Hybridized Routing Protocol for Animal Monitoring and Tracking Applications

Wireless sensor networks (WSN) have been exploited for {countless} application domains, most notably the surveillance of environments and habitats, which has already become a critical mission. As a result, WSNs have been implemented to monitor animal care and track their health status. However, excessive energy utilization and communication traffic on packet transmissions lead to system deterioration, especially whenever perceived information captured in the monitoring area is transferred to the access point over multiple dynamic sinks. Further to manage the energy and data transmission issue, the energy consumption and location aware routing protocol has been architected on the wireless Nano sensor nodes. In this article, a novel hybrid energy and location aware routing protocol to cloud enabled IoT based Wireless Sensor Network towards animal health monitoring and tracking has been proposed. However proposed data routing protocol incorporates the trace file for path selection for data transmission to base station using sink node. Trace file has been obtained on processing the cluster heads established in the network. Therefore, clustering of node in the network has to be achieved using LEACH protocol which enhances the network scalability and network lifetime by clustering the nodes with Metaheuristics constraints like location or node density comparability. The objective of the proposed model is to enhance the network scalability and energy consumption by establishing the multiple node clusters with high density cluster head through Metaheuristics Node Clustering optimization techniques. Metaheuristics based node clustering is been obtained using Improved Particle Swarm Optimization. Further it is employed to compute the optimal path for sensed data transmission to base station. Node clustering provides high energy consumption among the sensing nodes and to establish the high energy clusters towards sensed information dissemination to base station on dynamically reforming the nodes clusters with respect to Node density and node location. Simulation analysis of the proposed energy efficient routing protocol provides high performance in energy utilization, packet delivery ratio, packet loss and Average delay compared against the conventional protocols on propagation of the data through sink node to base station