Surveillance Domain Research Articles

DC characteristics of a dynamic vision sensor's photoreceptor circuit evaluated for event-based sensing in the mid-wave infrared

Forthcoming infrared event-based sensors will have utility in the space-based surveillance domain where they could potentially perform traditional sensing and tracking functions with significantly enhanced temporal resolution and reduced downstream datalink demands and power consumption. As a first step toward extending event-based sensing technology into the mid-wave infrared, a DC simulation of the conventional event-based sensor unit cell's photoreceptor circuit is performed, and the results are compared with measurements of a printed circuit board implementation of the same circuit to assess what design freedom is available to interface the photoreceptor with a mid-wave infrared photodetector. Detailed analysis of the circuit and measurements provides insight into which fundamental properties of the transistors drive the photoreceptor's dynamic range and demonstrates several characteristics that are relevant to mid-wave infrared sensing. These characteristics include the capability to produce a stable, low voltage bias on the infrared photodetector to maximize sensitivity, and that operating the circuit below room temperature increases the photoreceptor's dynamic range. Measurements show that even the simplest implementation of the photoreceptor circuit exhibits a dynamic range of logarithmic compression of 150 dB at room temperature. However, the dynamic range is ultimately limited by the mid-wave infrared photodetector's dark current activation energy, which is significantly lower than the threshold voltage (energy) of the photoreceptor's feedback transistor and, thus, there is an incentive for the photodetector's dark current to be diffusion-limited.

PicPatrolling: Smart Surveillance with ESP32CAM

Robotics is an exciting and developing branch of science. Robots are now common in society because of their high efficiency and durability, and they have many users. Security and surveillance robots, especially those with wheeled movements and agility, are important for multiplexes in security and surveillance domains, including public security, military, facilities, and home security. These robots carry built-in high-end sensors, cameras, and communication equipment that simplify real-time data collection in indoor and outdoor environments. "PicPatrolling: Smart Surveillance with ESP32CAM” is a technique that attempts to improve the current concept of security and surveillance. The proposed system incorporates ESP32CAM for image capture and transmission without delay and GPS for location tracking. The system includes sending photo e-mail alerts, making immediate reports, and guaranteeing adequate area coverage. The robot can move during operation in a complex environment using computer vision, machine learning, and sensor fusion to recognize objects, faces, and changes and monitor important regions.. Application areas for surveillance robots include commercial and populated regions, transport points and architectural structures, other soft regions that people cannot access, and other dangerous areas that humans cannot monitor for various reasons.

Adaptive Pitch-Tracking Control with Dynamic and Static Gains for Remotely Operated Towed Vehicles

The pitch angle regulation in Remotely Operated Towed Vehicles (ROTVs) is essential to ensure the robustness of emitted signals within the maritime surveillance domain. Characterized by inherent nonlinear dynamics and stochastic uncertainties, the pitch angle model poses significant challenges to conventional tracking controls relying on linearization. This study introduces an adaptive pitch-control algorithm designed for ROTVs, which adeptly manages nonlinear dynamics as well as unmeasurable states through a synergistic integration of dynamic and static gains. A key feature of our approach is the incorporation of a high-order observer that adeptly estimates the system’s unmeasurable states, thereby enhancing control precision. Our proposed algorithm greatly exceeds traditional PID and fuzzy PID methods in both settling time and steady-state error, particularly in high-order nonlinear and unmeasurable state scenarios. Compared to sliding mode control, the proposed control strategy improved the settling time by 74% and the steady-state error was enhanced from 10−6 to 10−8, as confirmed by numerical simulations. The efficacy of the algorithm in achieving the desired tracking trajectories highlights its potential for deep-water operations and fine-tuned attitude adjustments for ROTVs.

Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data

In the domain of maritime surveillance, the continuous tracking and monitoring of vessels are imperative for the early detection of potential threats. The Automatic Identification System (AIS) database, which collects vessel movement data over time, including timestamps and other motion details, plays a crucial role in real-time maritime monitoring. However, it frequently exhibits irregular intervals of data collection and intricate, intersecting trajectories, underscoring the importance of analyzing long-term temporal patterns for effective vessel tracking. While Kalman Filters and other physics-based models have been employed to tackle these issues, their effectiveness is limited by their inability to capture long-term dependence and non-linearity in the historical data. This paper introduces a novel approach that leverages Long Short-Term Memory (LSTM), a type of recurrent neural network, renowned for its proficiency in recognizing patterns over extended periods. Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. This combination harnesses the physical laws governing vessel movements alongside data driven pattern mining, thereby enhancing the predictive accuracy of vessel locations. To assess the performance of standalone and hybrid models, various scenarios with different levels of complexity are generated. Furthermore, to simulate real-world data loss conditions often encountered in maritime tracking, temporal data gaps are randomly introduced into the scenarios. The competing approaches are then evaluated using both with time gap and without time gap conditions. Our results show that, although the LSTM model performs better than the physics-based model, the hybrid model consistently outperforms both standalone models across all scenarios. Furthermore, while data gaps negatively impact the accuracy of all models, the performance reduction is minimal for the physics-infused model. In summary, this study not only demonstrates the potential of combining data-driven and physics-based approaches but also sets a new benchmark for maritime vessel tracking.

Essential public health competencies in nursing during the COVID-19 pandemic: A Delphi study

Background & Aim: Nursing practice, characterized by its multifaceted nature, encompasses diverse settings and situations in public health, making it difficult to clearly define the role of nurses in this setting. The study aimed to determine the applicability and relevance of the Regional Core Competency Framework for Public Health in nursing practice during the COVID-19 pandemic. Methods & Materials: Study based on the Delphi methodology, a structured approach to the systematic collectión of expert opinions, to obtain group agreements. Twenty-one nurses working in public health areas during the pandemic participated. Results: The competencies most applied by the nurses were in the domain of health situation analysis and surveillance and risk and damage control. 95% of the nurses were involved in care actions to prevent contagion; 71% analyzed social determinants of health, 100% were involved in the domain of policies, planning, regulation, and control, and 86% in the domain of health promotion. 89% made decisions to favor access to preventive and health care services. 78% valued social participation actions and 76% implemented immediate responses to COVID-19. Conclusion: The main public health competencies used by nursing for dealing with the COVID-19 pandemic are the management of educational processes, health promotion, primary health care, analysis of social determinants, management of health policies, information analysis, and management of immediate responses to challenges generated by COVID-19.

Security-centric approach in the use of digital technologies in EU migration and asylum policies

ABSTRACT The rapid evolution of digital technology, particularly in the domain of surveillance and identification, has brought forth a multitude of implications for the European Union’s approach to migration, asylum and border control. The use of digital technologies at the EU's external borders has emerged as a critical point of contention. By referring to ‘security-centric approach’ to describe the expansion of national security-related uses of digital technologies in migration and asylum, the article unveils the extent to which security considerations have permeated the use of digital technologies in this area. The analysis aims to demonstrate the predominance of this security focus and only a marginal role of efficiency, convenience and market needs objectives in the use of digital technologies in EU migration, asylum and border control policies.

No Drone’s Sky: Full Spectrum Drone Surveillance and Neutralisation Concept for Enhanced Counter-UAS Framework

Unmanned Aircraft Systems (UAS), commonly known as drones, have witnessed substantial global proliferation in the past decade. Their constructive applications hold the promise of being a useful, yet critical component in creating a more efficient society with the enhancement of safety, efficiency, and facilitating advancements in various domains, ultimately contributing to our modern daily lives. However, the escalating dependence on computer and communication technologies renders, especially small, UAS susceptible to various threats, posing risks to public safety, national security, and individual privacy. Addressing these concerns necessitates the development of innovative technologies designed to detect, track, identify, and eliminate UAS in a manner that upholds safety, security, and privacy. A Counter-Unmanned Aircraft System (C-UAS) is defined as a system or apparatus capable of legally and securely incapacitating, disrupting, or assuming control over an UAS. Recent years have witnessed significant research endeavours aimed at detecting and eliminate drone threats. Detection methodologies encompass acoustic, visual, passive radio frequency, radar, and data fusion techniques, while neutralisation strategies encompass physical capture and jamming approaches. This paper, delving into the realm of small drone surveillance, is the opening segment of a three-part series aims to envision a C-UAS framework; it provides an exhaustive review of existing literature in the domain of UAS surveillance, delineating the challenges associated with countering unauthorised or unsafe drone operations, and evaluating the trajectory of detection to prepare against UAS-induced threats. Therefore, the fundamental objective of this paper is to offer a comprehensive surveillance baseline for a structured vision to a C-UAS framework, thus fostering a research community dedicated to the secure integration of drones into the airspace system.

UAV-RGB-image-based aboveground biomass equation for planted forest in semi-arid Inner Mongolia, China

The acquisition of high-resolution aboveground biomass (AGB) data cost-effectively and expeditiously represents a formidable challenge within the domain of current ecosystem surveillance. Plot-based inventory, the conventional approach for estimating and validating remote sensing data, is nonetheless costly and constrained in terms of spatial coverage. The expeditious advancements in unmanned-aerial-vehicle (UAV) technology furnish the potential to devise AGB equations that transcend traditional diameter-height-based equations alongside techniques for quantifying forest structural parameters through standard RGB aerial imagery. Since the canopy diameter (CD) and tree height (H) can be directly ascertained from UAV-derived datasets, biomass equations parameterized by CD and H may be more valuable. In the present investigation, we established AGB equations predicated on data procured from a UAV outfitted with a high-resolution RGB camera, specifically for planted sparsely Pinus sylvestris forest in central Inner Mongolia, China. Utilizing the aerial imagery, we generated the digital terrain model (DTM), digital surface model (DSM) and the digital orthophoto image (DOM). Then, the canopy height model (CHM) was obtained by subtracting DSM from DTM to extract the H and CD of individual trees. This methodology's CD (R2 = 0.85, RMSE = 0.203 m) and H (R2 = 0.77 and RMSE = 0.671 m) obtained closely mirrored the in-situ measurements. Six prospective AGB equations were constructed for the Pinus sylvestris forest, taking H and CD extracted from UAV aerial survey datasets as the dependent variables. The accuracy of the AGB estimation was appraised by employing extant allometric growth equations, which were parameterized using the ground-measured tree diameter at breast height (DBH) and H. The most efficacious biomass equation, predicated on H and CD data extracted from UAV aerial surveys, was delineated as W=2.3442CD∗H0.9057(R2 = 0.731, RMSE = 2.46 kg), thus presenting a convenient tool for estimating the AGB of sparse Pinus sylvestris forests in semi-arid locales.

Queering trialectics among space, power, and the subject: spatial representations and practices of othered identities in Turkey

ABSTRACT This article analyzes spatial configurations of biopower in Turkey, in other words, of the politics of ‘supra-identity’. In the multi-identity social structure of the country, the subjectification process of citizens has been controlled with several exclusionary policies through the definition of ‘supra-identity’ – incorporating Turkish and Muslim identities and embodying heteronormativity – by the state as the institutional equivalent of the concept of power. In this process, space plays an essential role as a domain of closure, surveillance, prohibition, and punishment for the discipline of the body. This article deals with alternative spatial representations and practices in Turkey through a case study of LGBT-friendly spaces in the capital of the country. As a result of spatial observations, mappings, and interviews, it is noticed that these spaces, as encounter and solidarity domains for individuals with diverse ethnicities, beliefs, and genders, hold the capacity for queering bio-politics in Turkey.

Cancer Survivorship in Gulf Countries: An Updated Systematic Review

Introduction Cancer survivorship addresses attendant challenges beyond treatment, emphasizing outcomes of patient quality of life, costs, emergency services/hospitalization, and mortality. Nekhlyduov L et al., 2019 describe nine domains of cancer survivorship which, although represented distinctly, are interrelated and codependent. Despite the sustained worldwide increase in the burden of cancer in the past decade, there is a paucity of evidence regarding cancer survivorship pertaining to patient needs across the Gulf countries. Methods A systematic review adhering to the PRISMA 2020 guidelines was conducted to assess cancer survivorship across the 6 Gulf countries Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. We utilized search results across Medline, Embase, Cochrane, SciELO, and Web of Science published in English through April 1, 2023. Our search terms were inclusive of the country's names, cancer, survivors, neoplasm, survivorship, and follow-up studies were used. Included articles were then evaluated for nine domains of cancer survivorship outlined by Nekhlyduov L et al., 2019. Results Thirty-one articles were included in the systematic review. Cancer survivorship in the context of hematological malignancy (leukemia, lymphoma, and multiple myeloma) was observed in 48% (n = 15) of studies. Saudi Arabia accounted for most of the published literature, 67.7% (n = 21). Kuwait, Qatar, and Bahrain all reported the least on cancer survivorship, 3.2% (n = 1). Clinical structure, surveillance, and management of physical and psychosocial effects were the most reported domains across the included articles, 87.1% (n = 27), 74.2% (n = 23), and 67.7% (n = 21), respectively. Conversely, the surveillance and management of chronic medical conditions and patient and caregiver experience domains were the least reported,12.9% (n = 4) and 9.7% (n = 3), respectively. The clinical structure domain refers to the type of healthcare delivery environment, cancer survivorship providers' education and training status, availability, and access to needed specialty care, healthcare professionals, health information systems, and opportunities for research participation to patients. The surveillance and management of physical and psychosocial effects domains encompassed assessment of patient symptoms and conditions, referral of recommended evaluation, recommendations of and adherence to appropriate treatment and risk-reducing strategies, and reassessment of symptoms and/or conditions at defined intervals and/or treatment phases. The domain of surveillance and management of chronic medical conditions involves evaluating and treating noncancer comorbidities using disease-specific indications and medical reconciliation. The patient and caregiver experience domain assesses the patient's satisfaction with the healthcare provider, perceived timely access to healthcare and services, and follow-up regarding test results. Conclusion Our findings siren the need for a clear focus on the clinical structure domain. There are significant gaps in both the countries reporting on the topic and the domains of cancer survivorship included in their studies. Importantly, Oman, Kuwait, Qatar, and Bahrain require further cancer survivorship research. Moreover, all the countries need to emphasize domains of surveillance and management of chronic conditions and patient caregiver experience.

Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems

The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance.

On Wireless Sensor Network Models: A Cross-Layer Systematic Review

Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor, communication, sensor, and power supply modules, sending and receiving information of a covered area across a propagation medium. Given the increasing complexity of a WSN system, and in an effort to understand, comprehend and analyze an entire WSN, different metrics are used to characterize the performance of the network. To reduce the complexity of the WSN architecture, different approaches and techniques are implemented to capture (model) the properties and behavior of particular aspects of the system. Based on these WSN models, many research works propose solutions to the problem of abstracting and exporting network functionalities and capabilities to the final user. Modeling an entire WSN is a difficult task for researchers since they must consider all of the constraints that affect network metrics, devices and system administration, holistically, and the models developed in different research works are currently focused only on a specific network layer (physical, link, or transport layer), making the estimation of the WSN behavior a very difficult task. In this context, we present a systematic and comprehensive review focused on identifying the existing WSN models, classified into three main areas (node, network, and system-level) and their corresponding challenges. This review summarizes and analyzes the available literature, which allows for the general understanding of WSN modeling in a holistic view, using a proposed taxonomy and consolidating the research trends and open challenges in the area.

A Novel Framework to Generate Synthetic Video for Foreground Detection in Highway Surveillance Scenarios

Foreground detection (FD) plays an important role in the domain of video surveillance for highway. The design of advanced FD algorithms requires large-scale and diverse video dataset. However, collecting and labeling real dataset is still time-consuming, labor-intensive, and highly subjective. To address this issue, we first use computer graphics (CG) to clone real highway scenarios (HS) and generate synthetic multi-challenge video datasets, called “Synthetic-HS (CG)”, automatically labeled with accurate pixel-level ground truth. The Synthetic-HS (CG) dataset contains eight imaging condition sequences for computer vision research. Then, we design an image translation (IT) model that translates source domain (Synthetic-HS (CG)) to target domain (real). This model uses skip connections and attention module to generate realistic synthetic images “Synthetic-HS (IT)”. We use publicly available Synthetic-HS in combination with the corresponding real video sequence to conduct experiments. The experiment results suggest that: 1) The Synthetic-HS (CG) dataset enables us to provide precise quantitative evaluation of the drawbacks of foreground detection methods 2) The realistic Synthetic-HS (IT) images can be used to promote the visual perception in highway video surveillance.

Automatic Vehicle Pollution Detection Using Feedback Based Iterative Deep Learning

Air pollution is one of the major health hazards in modern times. Vehicle pollution is one of the major contributors to aerial contamination. Significant emphasis has been given by the researchers to identify the traffic pollutant sources. However, there is a need for a low-cost, automated solution for fast detection of polluting vehicles from the end of law enforcers. In this article, we have presented a novel deep learning based evaluation strategy that will identify the pollutant vehicle through on-road installed surveillance camera images. An enhanced image data set with notable variations has been prepared for training the models. Thereafter, a multi-model feedback process has been integrated. In contrast to the other deep learning approaches, the proposed framework has started with low labeled training data which is quite relevant in real-life scenarios. Subsequently, the size of the training samples has been increased using feedback. The evaluation of the framework has been performed with seven popular deep learning CNN models, i.e Inception-V3, MobileNet-V2, MobileNet-V3 (small), InceptionResNet-V2, VGG16, VGG19 and XceptionNet. Transfer learning has been exploited to distinguish the on-road pollutants and to provide proper surveillance in transport system. We have compared our method with recent state-of-the-art techniques. The results have demonstrated the superiority of the proposed framework in the road surveillance domain. To the best of our knowledge, this is the first attempt to apply a feedback based iterative deep learning model for vehicle pollution detection.

Video anomaly detection by fusing self-attention and autoencoder

Video anomaly detection by fusing self-attention and autoencoder

Mining regular behaviors based on spatiotemporal trajectory multi‐dimensional features

In the military and civilian surveillance domain, it is of great significance to mine regular behaviours of targets for situation awareness and command decision support. Most of the existing trajectory clustering algorithms only consider the similarity of spatial position of the trajectory, without sufficient multi-dimensional information such as time, course and velocity. Some approaches based on information fusion take these multi-dimensional information into account, but the features with different dimensions fused by weight coefficients are not robust and universal for different scenarios. In this paper, a regular behaviour mining method based on spatiotemporal trajectory multi-dimensional features and density clustering is proposed. Firstly, multi-dimensional Hausdorff similarity is defined to measure spatiotemporal trajectory from different feature dimensionalities. Different from methods based on information fusion, the proposed method defines trajectory density in feature similarity of different dimensions and adaptively determines parameters according to feature distribution in different dimensions. Experimental results in simulated and radar measured trajectory data show that the proposed method can be accurate and robust in clustering evaluation indexes such as Purity, Precision, Recall and Rand Index from different scenarios, which has a good application prospect in intelligent surveillance tasks.

The Polyopticon: a diagram for urban artificial intelligences

Smart city discourses often invoke the Panopticon, a disciplinary architecture designed by Jeremy Bentham and popularly theorized by Michel Foucault, as a model for understanding the social impact of AI technologies. This framing focuses attention almost exclusively on the negative ramifications of Urban AI, correlating ubiquitous surveillance, centralization, and data consolidation with AI development, and positioning technologies themselves as the driving factor shaping privacy, sociality, equity, access, and autonomy in the city. This paper describes an alternative diagram for Urban AI—the Polyopticon: a distributed, polyvalent, multi-modal network of synthetic intelligences. It posits that fourth industrial revolution technologies change the political, social, and psychodynamic relationships of sentience and witness in the city, shifting the effects of watching and watched beyond the exclusive domain of top-down surveillance and discipline. The Polyopticon poses a more expansive and ambivalent spectrum of possibilities for Urban AI scenarios, one that undermines the totalizing, singular, and cerebral notion of intelligence that so often characterizes Urban AI and smart city critiques.

A Conceptual Framework for Representing Events Under Public Health Surveillance.

Information integration across multiple event-based surveillance (EBS) systems has been shown to improve global disease surveillance in experimental settings. In practice, however, integration does not occur due to the lack of a common conceptual framework for encoding data within EBS systems. We aim to address this gap by proposing a candidate conceptual framework for representing events and related concepts in the domain of public health surveillance.

Multidrug-Resistant Bacteria in Hospital Wastewater of the Korle Bu Teaching Hospital in Accra, Ghana.

Background:Antimicrobial resistance (AMR) is one of the top 10 public health threats. One approach to tackling the AMR menace could involve expanding the range of AMR surveillance domains to include hospital wastewater (HWW), a domain that has largely been overlooked by researchers.Aim:To evaluate the occurrence of multidrug-resistant bacteria in hospital wastewater of the Korle Bu Teaching Hospital (KBTH).Methodology:This was a longitudinal study involving 288 HWW samples consecutively collected across 12 weeks from the pool of wastewater emanating from 2 critical care units of KBTH—The Child Health Unit and the Maternity Unit—on Mondays and Thursdays, each week. The samples were cultured for bacteria, which were identified using the Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) technique and subjected to antimicrobial susceptibility testing via the Kirby-Bauer method.Results:In total, 294 bacteria of 23 different types, all being Gram-negative, were isolated from the 288 samples. The predominant ones were Escherichia coli (30.6%, n = 90), Klebsiella pneumoniae (11.2%, n = 33), Citrobacter freundii (10.9%, n = 32), Alcaligenes faecalis (5.8%, n = 17), and Pseudomonas mendocina (5.4%, n = 16). The prevalence of multidrug resistance among the isolates was 55.4% (n = 163). Moreover, the prevalence of extended-spectrum beta-lactamase (ESBL) producers was 15.6% (n = 46). E. coli accounted for the most ESBL-producing organisms (28.9%, n = 26).Conclusion:The wastewater generated by the Maternity and Child Health Units of KBTH harbored a wide range of multidrug resistant bacteria, with a good proportion of these being ESBL producers, and the predominant one being E. coli. The study thus identifies the wastewater of KBTH as an important source of multidrug resistant organisms, and underscores the significance of appropriate treatment of wastewater of the hospital and other clinical, and related settings prior to its discharge.

The Library/Surveillance Interface

AbstractFew topics are more often discussed than surveillance, particularly in the context of surveillance technologies that reflect structural inequities. There is space, however, to bring more discussion of surveillance tech into the library literature. At the same time, literature on digital surveillance and associated systems such as Big Data, surveillance capitalism, and platform capitalism often discuss these phenomena as if they are novel rather than iterations of long‐standing inequitable circumstances. We propose that a dialogue between surveillance literature and critical library literature will benefit both areas: theories from the surveillance domain can strengthen examinations of structural oppression in libraries while theories from critical library literature can strengthen acknowledgment of surveillance techs' historical roots. Moreover, overlap exists between concepts used in surveillance and library literature, including concerns about neutrality and classification practices. Therefore, after reviewing surveillance theories and their applicability to libraries, we demonstrate how these scholarly areas may strengthen each other, with three major consequences: (a) moving library literature beyond considerations of the panopticon in favor of the surveillant assemblage; (b) recognizing that surveillance tech is a hyper‐visible form of historical oppression; and (c) acknowledging that the library ethos is critical to any fight for justice within information science.