Passive Detection Research Articles

Glaucoma visual field quantification with eye tracker

Glaucoma is a group of diseases that manifest as atrophy and depression of the optic papilla, visual field defects, and vision loss, representing one of the three leading causes of blindness worldwide. Traditional visual field examinations – an important diagnostic tool for glaucoma – present various challenges including patients’ inability to maintain fixed vision, delays in detecting vision loss, passive position detection, difficulty in detection, and limitations in reflecting physiological visual field damage. Early diagnosis and intervention are crucial for improving patients’ condition and enhancing their later-life abilities and life quality. Herein, we proposed two vision field detection systems to overcome these limitations. First, we establish a dynamic visual field detection system to reduce the complexity of traditional detection experiments and to enhance their operability. Instead of fixating on a central point, subjects are only required to search for the target in the picture. We analyze the heat map and trajectory map of visual attention for visual interpretation, and the analysis of experimental data reveals that the average finding time of subjects in the experimental task varies. In response to the scenario where visual field defects are not detected by the dynamic visual field detection system, we have developed a static visual field detection system based on the former. The system obtains eye movement data and automatically generates a map of the extent of the physiological blind spot without any action required from the patient. The experiment results provide evidence for the effectiveness of the static visual field detection system in detecting the physiological blind spot. Given the well-established association between glaucoma and an enlarged physiological blind spot, the use of an eye tracker to assess the extent of the subject’s blind spot represents an easy-to-use and reliable method for preliminary glaucoma screening.

Association between Livestock Ownership and Malaria Incidence in South-Central Ethiopia: A Cohort Study.

Zooprophylaxis is one of the possible environmental vector control strategies for malaria prevention. However, its effect on reducing malaria transmission has been questionable, requiring a detailed understanding of contextual factors. This study aims to evaluate the effect of keeping livestock on malaria incidence in south-central Ethiopia. A cohort of 34,548 people in a total of 6,071 households was followed for 121 weeks from October 2014 to January 2017. Baseline data were collected, including livestock ownership. Weekly home visits were done to actively search for malaria cases, and passive case detection was also carried out. Malaria was diagnosed with rapid diagnostic tests. Log binomial and parametric regression survival-time models were used to estimate effect measures. A total of 27,471 residents had complete follow-ups, and the majority (87.5%) lived in households owning livestock, including cattle, sheep, goats, and chickens. The overall incidence risk of malaria was 3.7%, and there was a 24% reduction in the risk of malaria among livestock owners. The total cohort contributed to 71,861.62 person-years of observation. The incidence rate of malaria was 14.7 cases per 1,000 person-years. There was a 17% reduction in the rate of malaria among livestock owners. Meanwhile, the protective effect of livestock ownership increased as the number of livestock or the livestock-to-human ratio increased. In conclusion, livestock owners had less malaria. In a setup where domestication of livestock is a common practice and the predominant malaria vector tends to feed more on livestock than humans, zooprophylaxis remains a promising strategy for malaria prevention.

A Simple and Cost-Effective Benchtop Setup for Assessing Ultrasound-Induced Inertial Cavitation Probability

Passive cavitation detection methods for measuring ultrasound cavitation events have been utilized for many years. They allow clinicians and researchers to measure cavitation device performance and monitor treatment in real-time. Many methods involve complex processing algorithms and/or expensive measurement hardware. Here we describe and provide open-source resources for a simple benchtop apparatus for measuring the acoustic emissions produced by cavitation events, especially in the context of histotripsy device characterization.

Injection Harmonic Current Differential Protection Based on Control-Protection Synergy for Distribution Networks with IBDGs

The high penetration of inverter-based distributed generations (IBDGs) leads to traditional distribution networks’ complex and variable fault characteristics. Traditional passive detection protection methods can hardly meet the stability and reliability requirements. To solve this problem, an injection harmonic current differential protection (IHCDP) scheme based on the idea of control-protection synergy is proposed in this paper. By analyzing the circuit structure, control strategy, low-voltage-ride-through characteristics of IBDGs, and short-circuit currents provided by IBDGs, an injection-control strategy of characteristic harmonic currents (CHCs) based on the d-axis and q-axis is proposed. According to the change of short-circuit currents provided by the IBDG, the amplitude of CHCs is adaptively corrected to strengthen the fault characteristics and ensure that the distribution network harmonic injection protocols are always satisfied. An IHCDP criterion based on the distortion rate of the negative-sequence component of CHCs is proposed by analyzing the fault characteristics of the CHCs after injection. To effectively eliminate the dead-zone problem during symmetrical short-circuit faults, the proposed IHCDP utilizes the distortion rate of A-phase CHCs. The feasibility of the proposed protection scheme is demonstrated by the theoretical analysis and simulation results in this paper.

A LiDAR-less approach to autonomous hazard detection and avoidance systems based on semantic segmentation

In this paper, a passive hazard detection and avoidance (HDA) system is presented, relying only on images as observations. To process these images, convolutional neural networks (CNNs) are used to perform semantic segmentation and identify hazards corresponding to three different layers, namely feature detection, shadow detection, and slope estimation. The absence of active sensors such as light detection and ranging (LiDAR) makes it challenging to assess the surface geometry of a celestial body, and the training of the neural networks in this work is oriented towards coping with that drawback. The image data set for the training is generated using blender, and different body shape models (also referred to as meshes) are included, onto which stochastic feature populations and illumination conditions are imposed to produce a more diverse database. The CNNs are trained following a transfer learning approach to reduce the training effort and take advantage of previously trained networks. The results accurately predict the hazards in the images belonging to the data set, but struggle to yield successful predictions for the slope estimation, when images external to the data set are used, indicating that including the geometry of the target body in the training phase makes an impact on the quality of these predictions. The obtained predictions are composed to create safety maps, which are meant to be given as input to the guidance block of the spacecraft to evaluate the need for a manoeuvre to avoid hazardous areas. Additionally, preliminary hardware-in-the-loop (HIL) test results are included, in which the algorithms developed are confronted against images taken using real hardware.

Finite-key security analysis of differential-phase-shift quantum key distribution

Differential-phase-shift (DPS) quantum key distribution (QKD) is one of the major QKD protocols that can be implemented with a simple setup using a laser source and a passive detection unit. Recently, an information-theoretic security proof of this protocol has been established in [npj Quant. Inf. 5, 87 (2019)] assuming the infinitely large number of emitted pulses. To implement the DPS protocol in a real-life world, it is indispensable to analyze the security with the finite number of emitted pulses. The extension of the security proof to the finite-size regime requires the accommodation of the statistical fluctuations to determine the amount of privacy amplification. In doing so, Azuma's inequality is often employed, but unfortunately we show that in the case of the DPS protocol, this results in a substantially low key rate. This low key rate is due to a loose estimation of the sum of probabilities regarding three-photon emission whose probability of occurrence is very small. The main contribution of our work is to show that this obstacle can be overcome by exploiting the recently found novel concentration inequality, Kato's inequality. As a result, the key rate of the DPS protocol is drastically improved. For instance, assuming typical experimental parameters, a 3 Mbit secret key can be generated over 77 km for 8.3 hours, which shows the feasibility of DPS QKD under a realistic setup.

WiFi-based indoor passive fall detection for medical Internet of Things

There are currently numerous automated fall detection devices available, but most require the deployment of additional specialized equipment, which can create inconvenience for elderly individuals in their daily lives. This paper proposes a solution that reuses existing smartphone and WiFi infrastructure, eliminating the need for extra equipment and greatly simplifying the implementation of fall event detection. Due to the lower quality of data collected by smartphones, we have developed an adaptive DBSCAN clustering algorithm to process and extract useful data. We then train a deep convolutional LSTM model to classify the collected data in real-time. When a fall is detected, the smartphone can transmit an alert via the internet to notify relatives or medical staff of the incident. Our experimental results demonstrate that utilizing smartphones for fall detection is both feasible and accurate.

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends.

Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos. Therefore, it's vital to distinguish between true and fake data. Digital video forgery detection techniques are used to validate and check the realness of digital video content. Deep learning algorithms lately sparked a lot of interest in the field of digital forensics, such as Recurrent Neural Networks (RNN), Deep Convolutional Neural Networks (DCNN), and Adaptive Neural Networks (ANN). In this paper, we give a soft taxonomy as well as a thorough overview of recent research on multimedia falsification detection systems. First, the basic knowledge needed to comprehend video forgery is provided. Then, a summary of active and passive video manipulation detection approaches is provided. Anti-forensics, compression video methods, datasets required for video forensics, and challenges of video detection approaches are also addressed. Following that, we presented an overview of deepfake, and the datasets required for detection were also provided. Also, helpful software packages and forensics tools for video detection are covered. In addition, this paper provides an overview of video analysis tools that are used in video forensic applications. Finally, we highlight research difficulties as well as interesting research avenues. In short, this survey provides detailed information and a broader investigation to extract data and detect fraud video contents under one umbrella.

Wavefront single-pixel imaging using a flexible SLM-based common-path interferometer

Wavefront single-pixel imaging (WSPI) is an important tool for simultaneously measuring the amplitude and phase of an unknown field, especially suitable for wavefront detecting in some special wavelengths where array detectors are immature or even unavailable. Here, a passive-detection-based WSPI method using a flexible SLM-based common-path interferometer is proposed, in which by detecting the zero-order diffraction of the SLM directly, the common-path phase-shift or off-axis technique can be flexibly realized by a “sandwich” structure consisting of a half-wave plate, a SLM, and a polarizer. The reference light can be conveniently chosen as the direct reflected light from the SLM, the unmodulated light from the pixels of the SLM, or the light from both of them. We experimentally verify the feasibility and advantages of the proposed method. We further demonstrate the method for wavefront measurement of a dragonfly wing, which suggests it can be used for quantitative phase imaging. It provides a high-resolution spatial wavefront passive detection technique, with a compact, stable, and flexible setup, which has lots of applications involving three-dimensional imaging, aberration correction, digital holographic measurement, and digital microscopy.

A modelling assessment of short- and medium-term risks of programme interruptions for gambiense human African trypanosomiasis in the DRC.

Gambiense human African trypanosomiasis (gHAT) is a deadly vector-borne, neglected tropical disease found in West and Central Africa targeted for elimination of transmission (EoT) by 2030. The recent pandemic has illustrated how it can be important to quantify the impact that unplanned disruption to programme activities may have in achieving EoT. We used a previously developed model of gHAT fitted to data from the Democratic Republic of the Congo, the country with the highest global case burden, to explore how interruptions to intervention activities, due to e.g. COVID-19, Ebola or political instability, could impact progress towards EoT and gHAT burden. We simulated transmission and reporting dynamics in 38 regions within Kwilu, Mai Ndombe and Kwango provinces under six interruption scenarios lasting for nine or twenty-one months. Included in the interruption scenarios are the cessation of active screening in all scenarios and a reduction in passive detection rates and a delay or suspension of vector control deployments in some scenarios. Our results indicate that, even under the most extreme 21-month interruption scenario, EoT is not predicted to be delayed by more than one additional year compared to the length of the interruption. If existing vector control deployments continue, we predict no delay in achieving EoT even when both active and passive screening activities are interrupted. If passive screening remains as functional as in 2019, we expect a marginal negative impact on transmission, however this depends on the strength of passive screening in each health zone. We predict a pronounced increase in additional gHAT disease burden (morbidity and mortality) in many health zones if both active and passive screening were interrupted compared to the interruption of active screening alone. The ability to continue existing vector control during medical activity interruption is also predicted to avert a moderate proportion of disease burden.

Intelligent Island detection method of DC microgrid based on Adaboost algorithm

Aiming at the problems of large detection blind areas and difficult threshold settings in traditional passive islanding detection methods, this paper proposes an intelligent passive islanding detection method based on the adaptive boosting (Adaboost) algorithm. This method first uses feature screening technology to obtain the key feature electrical quantities of DC microgrid islands and form a sample set, and then rely on the Adaboost algorithm to generate a high-precision island classification model to realize the detection and classification of DC microgrid operation status. It has the advantages of automatic setting threshold, small detection blind area, and high accuracy. In addition, aiming at the problem that the classification results of the intelligent islanding detection algorithm based on machine learning cannot be directly used as the islanding discrimination results, an islanding criterion method based on the sliding window algorithm is added on the basis of the classification algorithm. This method realizes the accurate detection of the DC microgrid islanding operation state by the secondary analysis and judgment of the classification results of the islanding classification model. Finally, a DC microgrid model is built to simulate and verify the proposed method. The results show that the method can quickly and accurately complete the islanding detection of the DC microgrid.

A Novel Passive Islanding Detection Method for a Microgrid Using Instantaneous Energy

A Novel Passive Islanding Detection Method for a Microgrid Using Instantaneous Energy

Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyber-physical systems subject to process disturbances and measurement noise. A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack model. The stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state values. In order to help detect such attacks, a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change, due to the attack, at a time prior to the attack occurrence time. A key characteristic of this residual is that its magnitude increases every time a new attack occurs. To estimate this unknown residual, an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting matrices. The smoother design guarantees robustness with respect to process disturbances and measurement noise, and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting matrices. The adaptive threshold is designed based on the estimated backward-in-time residual, and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed methodology. Finally, a simulation example is used to demonstrate the effectiveness of the developed methodology.

Dynamic Behavior of Polymer Microbubbles During Long Ultrasound Tone-Burst Excitation and Its Application for Sonoreperfusion Therapy.

Ultrasound (US)-targeted microbubble (MB) cavitation (UTMC)-mediated therapies have been found to restore perfusion and enhance drug/gene delivery. Because of the potentially longer circulation time and relative ease of storage and reconstitution of polymer-shelled MBs compared with lipid MBs, we investigated the dynamic behavior of polymer microbubbles and their therapeutic potential for sonoreperfusion (SRP) therapy. The fate of polymer MBs during a single long tone-burst exposure (1 MHz, 5 ms) at various acoustic pressures and MB concentrations was recorded via high-speed microscopy and passive cavitation detection (PCD). SRP efficacy of the polymer MBs was investigated in an in vitro flow system and compared with that of lipid MBs. Microscopy videos indicated that polymer MBs formed gas-filled clusters that continued to oscillate, fragment and form new gas-filled clusters during the single US burst. PCD confirmed continued acoustic activity throughout the 5-ms US excitation. SRP efficacy with polymer MBs increased with pulse duration and acoustic pressure similarly to that with lipid MBs but no significant differences were found between polymer and lipid MBs. These data suggest that persistent cavitation activity from polymer MBs during long tone-burst US excitation confers excellent reperfusion efficacy.

Ge-Doped Optical Fibers for Passive and Active Radiation Detection Modes

Silica-based optical fibers made by Modified Chemical Vapor Deposition (MCVD) technique have been doped with different concentrations of germanium (Ge). After characterization of the thermoluminescence (TL) process in each of these fibers, their real-time responses were studied by radioluminescence (RL) under X-rays. First, the same counterintuitive trend was observed for the intensity of the two phenomena as a function of the Ge content, namely the decrease in TL and RL signals with increasing Ge content. This behavior was explained by the existence of thermally disconnected traps. Then, the RL response as a function of the dose-rate of the fiber doped with 1% Ge was judiciously chosen and investigated in detail. The results show that up to Total Ionizing Dose (TID) of 1.9 kGy(SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), this response is not only insensitive to the accumulated dose effect but also exhibits an afterglow decay at the end of the radiation much faster than that of a Ce-doped fiber, a very useful feature where irradiations are repetitive as in the medical field of flash-therapy. In addition to the medical field where the Ge-doped fiber seems to be quite suitable, slightly larger core diameters would also allow this same fiber to meet the challenge of high sensitivity in the environmental radiation monitoring around nuclear power plants or radioactive waste storage areas.

Tightly Coupled INS/APS Passive Single Beacon Navigation

Unlike aerial or terrestrial navigation, the global navigation satellite system (GNSS) is not available underwater. This is a big challenge for underwater navigation. The inertial navigation system (INS) aided by the single-beacon acoustic positioning system (APS) provides one solution, but the long-range case is limited by low-SNR conditions. Inspired by passive synthetic aperture detection, we proposed a new tightly coupled navigation algorithm based on spatial synthesis and one-way-travel-time (OWTT) range measurement. We design two estimators: the DOA/range estimator using the model-based method and the tightly coupled INS/APS navigation estimator. Based on the improved UKF, all information is combined. Simulation is carried out in MATLAB. Compared with range-only tightly coupled INS/APS navigation, synthetic long baseline (SLBL) algorithm and Doppler velocity logger (DVL) aided centralized extended Kalman filter (CEKF) based single beacon INS/OWTT navigation, the proposed method’s performance is proven. The main contributions of this work are: (1). Propose a new architecture of underwater integrated navigation; (2). Apply the passive acoustic detecting method in the navigation to improve accuracy. (3). Apply the tightly coupled method to improve availability.

Compressive sensing-based correlation sidelobe suppression for passive water pipeline fault detection using ambient noise

The ambient noise in water pipelines are observed as spontaneous signal sources that can be used for pipe fault detection by correlation analysis. However, the limited bandwidth of these noise signal causes strong correlation sidelobes, which introduces significant ambiguities when extracting the system response from correlation results and this increases the risk of false alarms from fault detections. This paper proposes a compressive sensing based method that can extend the noise bandwidth and suppress the correlation sidelobes. Numerical and field experiment results have shown that with the recovered wider bandwidth, the correlation sidelobes can be significantly suppressed and the pipe faults can be identified with greater certainty. The impacts of fault size as well as noise bandwidth, strength and spectrum features on the proposed method are also assessed through numerical experiments.

1D recursive median filter based passive islanding detection strategy for grid‐connected distributed generations network

AbstractIn modern distribution networks, integration of distributed generations (DGs) at consumer premises is common. However, islanding detection in such a grid‐tied distributed generation (GTDG) network is the topmost challenge to ensure power quality and reliable operation. This paper presents a modified passive islanding detection scheme (MPIDS) for the GTDG network, using a One‐D recursive Median filter (ODRMF) algorithm. Firstly, at the point of common coupling (PCC), 3‐phase voltage is acquired. Next, the ODRMF is employed to extract 3rd harmonic and residuals from the acquired voltage signal. Afterward, two novel islanding detection indices developed are: (1) First change detection (FCD), which is calculated from the residuals, and (2) delta selected harmonic distortion (∆SHD), which is computed from 3rd harmonic content of the voltage signal. Finally, variations in the two indices are compared with the threshold to identify islanding. To validate the efficacy of the presented MPIDS, rigorous simulations in MATLAB/ Simulink are conducted on the UL‐1741 and IEEE 13‐bus GTDGs systems under different conditions. The results illustrate that the presented strategy is highly reliable in all assessed conditions and can detect the islanding as well as non‐islanding incidents effectively under balanced and unbalanced load/generation conditions with a very low non‐detection zone.

Asymptomatic Plasmodium falciparum carriage and clinical disease: a 5-year community-based longitudinal study in The Gambia

BackgroundCarriers of persistent asymptomatic Plasmodium falciparum infections constitute an infectious reservoir that maintains malaria transmission. Understanding the extent of carriage and characteristics of carriers specific to endemic areas could guide use of interventions to reduce infectious reservoir.MethodsIn eastern Gambia, an all-age cohort from four villages was followed up from 2012 to 2016. Each year, cross-sectional surveys were conducted at the end of the malaria transmission season (January) and just before the start of the next one (June) to determine asymptomatic P. falciparum carriage. Passive case detection was conducted during each transmission season (August to January) to determine incidence of clinical malaria. Association between carriage at the end of the season and at start of the next one and the risk factors for this were assessed. Effect of carriage before start of the season on risk of clinical malaria during the season was also examined.ResultsA total of 1403 individuals—1154 from a semi-urban village and 249 from three rural villages were enrolled; median age was 12 years (interquartile range [IQR] 6, 30) and 12 years (IQR 7, 27) respectively. In adjusted analysis, asymptomatic P. falciparum carriage at the end of a transmission season and carriage just before start of the next one were strongly associated (adjusted odds ratio [aOR] = 19.99; 95% CI 12.57–31.77, p < 0.001). The odds of persistent carriage (i.e. infected both in January and in June) were higher in rural villages (aOR = 13.0; 95% CI 6.33–26.88, p < 0.001) and in children aged 5–15 years (aOR = 5.03; 95% CI 2.47–10.23, p = < 0.001). In the rural villages, carriage before start of the season was associated with a lower risk of clinical malaria during the season (incidence risk ratio [IRR] 0.48, 95% CI 0.27–0.81, p = 0.007).ConclusionsAsymptomatic P. falciparum carriage at the end of a transmission season strongly predicted carriage just before start of the next one. Interventions that clear persistent asymptomatic infections when targeted at the subpopulation with high risk of carriage may reduce the infectious reservoir responsible for launching seasonal transmission.

LossDetection: Real-Time Packet Loss Monitoring System for Sampled Traffic Data

Packet loss is common in networks, which leads to network quality of service degradation. Packet loss is an essential and concerning symptom when the quality of service is degraded. Therefore, real-time passive packet loss detection is conducive to estimating network services. Existing passive packet loss detection methods mainly study the packet loss for TCP using header information from full traffic. However, it cannot infer packet loss status for UDP due to its limited header information and is too costly to perform full acquisition in real networks. To address these problems, we propose a framework called LossDetection based on packet sampling and Feature-Sketch to detect packet loss in real time for both TCP and UDP. The result shows that our methodology can detect packet loss with an accuracy of 98%-100% at a sampling rate of 1/16. Furthermore, our extensive evaluation demonstrates that LossDetection is easy to implement in a software router and achieves low memory and detection latency while providing real-time information about packet loss.