Passive Detection Research Articles

A Single-Hydrophone Coherent-Processing Method for Line-Spectrum Enhancement

Improving the line-spectrum detection capability of a single hydrophone is of great significance for the passive detection of small underwater platforms. In this paper, we propose a single-hydrophone cross-power spectrum (SHCS) method based on time-domain coherence. This method uses the coherence of the line spectrum and the non-coherence of the continuous spectrum noise to obtain coherent gain and improve the signal-to-noise ratio (SNR) of the line spectrum. The effects of the input SNR, number of averaging operations, and overlap ratio on the performance of the SHCS method under a background of Gaussian white noise are simulated and analyzed. The results show that when the overlap ratio is 0 and the number of averaging operations reaches saturation, the SHCS method can achieve the best performance and about 15 dB coherence gain is obtained. The performance of the SHCS method was verified by sea experiments. Under the extremely low input SNR, in which the line spectrum was almost completely submerged in the marine environmental noise, the SHCS method can obtain about 10 dB coherence gain. Under the conventional input SNR, in which the line spectrum could be observed, the SHCS method can obtain about 13 dB coherence gain. The results of processing the radiated noise from an actual cargo ship also demonstrate the effectiveness of the SHCS method.

Analysis of Space-Based Observed Infrared Characteristics of Aircraft in the Air

The space-based infrared observatory of aircraft in the air has the advantages of wide-area, full-time, and passive detection. The optical design parameters for space-based infrared sensors strongly rely on target observed radiation, but there is still a lack of insight into the causes of aircraft observation properties and the impact of instrument performance. A simulation model of space-based observed aircraft infrared characteristics was constructed for this provision, coupling the aircraft radiance with background radiance and instrument performance effects. It was validated by comparing the model predictions to data from both space-based and ground-based measurements. The validation results reveal the alignment between measurements and model predictions and the dependence of overall model accuracy on the background. Based on simulations, the radiance contributions of aircraft and background are quantitatively evaluated, and the detection spectral window for flying aircraft and its causes are discussed in association with instrumental performance effects. The analysis results indicate that the target-background (T-B) contrast is higher in the spectral ranges where aircraft radiation makes an important contribution. The background radiance plays a significant role overall, while the observed radiance at 2.5–3μm is mainly from skin reflection and plume radiance. The skin-reflected radiation absence affects the model reliability, and its reduction at nighttime reduces the T-B contrast. The difference in T-B self-radiation and the stronger atmospheric attenuation for background contribute to the higher contrast at 2.7 μm compared to the other spectral bands.

Recovering a function from its integrals over conical surfaces through relations with the Radon transform

This paper addresses the overdetermined problem of inverting the n-dimensional cone (or Compton) transform that integrates a function over conical surfaces in . The study of the cone transform originates from Compton camera imaging, a nuclear imaging method for the passive detection of gamma-ray sources. We present a new identity relating the n-dimensional cone and Radon transforms through spherical convolutions with arbitrary weight functions. This relationship, which generalizes a previously obtained identity, leads to various inversion formulas in n-dimensions under a mild assumption on the geometry of detectors. We present two such formulas along with the results of their numerical implementation using synthetic phantoms. Compared to our previously discovered inversion techniques, the new formulas are more stable and simpler to implement numerically.

Adolescent Social Communication Through Smartphones: Linguistic Features of Internalizing Symptoms and Daily Mood.

The increasing use of smartphone technology by adolescents has led to unprecedented opportunities to identify early indicators of shifting mental health. This intensive longitudinal study examined the extent to which differences in mental health and daily mood are associated with digital social communication in adolescence. In a sample of 30 adolescents (ages 11-15 years), we analyzed 22,152 messages from social media, email, and texting across one month. Lower daily mood was associated with linguistic features reflecting self-focus and reduced temporal distance. Adolescents with lower daily mood tended to send fewer positive emotion words on a daily basis, and more total words on low mood days. Adolescents with lower daily mood and higher depression symptoms tended to use more future focus words. Dynamic linguistic features of digital social communication that relate to changes in mental states may represent a novel target for passive detection of risk and early intervention in adolescence.

The impact of active community-based survey on dementia detection ratio in Taiwan: A cohort study with historical control.

Although early dementia detection is crucial to optimize the treatment outcomes and the management of associated symptoms, the published literature is scarce regarding the effectiveness of active screening protocols in enhancing dementia awareness and increasing the rate of early detection. The present study compared the detection ratio of an active community-based survey for dementia detection with the detection ratio of passive screening during routine clinical practice. Data for passive screening were obtained from the National Health Insurance (NHI) system, which was prospectively collected during the period from 2000 to 2003. A population-based cohort study with historical control. Taiwan. A total of 183 participants aged 65 years or older were involved in a community-based survey. Data from 1,921,308 subjects aged 65 years or older were retrieved from the NHI system. An adjusted detection ratio, defined as a ratio of dementia prevalence to incidence was used. The results showed that the dementia prevalence during the 2000-2003 period was 2.91% in the elderly population, compared with a prevalence of 6.59% when the active survey was conducted. The incidence of dementia in the active survey cohort was 1.83%. Overall, the dementia detection ratio was higher using active surveys [4.23, 95% confidence interval (CI): 2.68-6.69] than using passive detection (1.45, 95% CI: 1.43-1.47) for those aged 65-79 years. Similar findings were observed for those aged 80 years and older. The implementation of an active community-based survey led to a 3-fold increase in the detection rate of early dementia detection compared to passive screening during routine practice.

A Hybrid Method of Adaptive Cross Approximation Algorithm and Chebyshev Approximation Technique for Fast Broadband BCS Prediction Applicable to Passive Radar Detection

A hybrid method combining the adaptive cross approximation method (ACA) and the Chebyshev approximation technique (CAT) is presented for fast wideband BCS prediction of arbitrary-shaped 3D targets based on non-cooperative radiation sources. The incident and scattering angles can be computed by using their longitudes, latitudes and altitudes according to the relative positions of the satellite, the target and the passive bistatic radar. The ACA technique can be employed to reduce the memory requirement and computation time by compressing the low-rank matrix blocks. By exploiting the CAT into ACA, it is only required to calculate the currents at several Chebyshev–Gauss frequency sampling points instead of direct point-by-point simulations. Moreover, a wider frequency band can be obtained by using the Maehly approximation. Three numerical examples are presented to validate the accuracy and efficiency of the hybrid ACA-CAT method.

Acoustic methods improve the detection of the endangered African manatee

The African manatee (Trichechus senegalensis) is an elusive, data-deficient, and endangered species which inhabits marine and freshwater systems throughout Western and Central Africa. A major challenge in understanding the species ecology and distribution is the difficulty in detecting it using traditional visual surveys. The recent invasion of Giant Salvinia (Salvinia molesta) at the most important site for the species in Cameroon further limits their detectability and may restrict their movements and habitat use. To investigate methods’ effectiveness in detecting African manatees, we conducted monthly vessel surveys from which visual point scans, 360° sonar scans, and passive acoustic monitoring were conducted simultaneously at ten locations and over 12 months in Lake Ossa, Cameroon. Manatee detection frequency was calculated for each method and the influence of some environmental conditions on the methods’ effectiveness and manatee detection likelihood was assessed by fitting a binary logistic regression to our data. Detection frequencies were significantly different between methods (p < 0.01) with passive acoustics being the most successful (24.17%; n = 120), followed by the 360° sonar scan (11.67%; n = 120), and the visual point scan (3.33%; n = 120). The likelihood of detecting manatees in Lake Ossa was significantly influenced by water depth (p = 0.02) and transparency (p < 0.01). It was more likely to detect manatees in shallower water depths and higher water transparency. Passive acoustic detections were more effective in uninvaded areas of the Lake. We recommend using passive acoustics to enhance African manatee detections in future surveys.

Conceptual Model, Experiment and Numerical Simulation of Diaphragm Wall Leakage Detection Using Distributed Optical Fiber.

Leakage in the diaphragm wall is difficult to detect in deep foundation pits. In this study, the conceptual model of active and passive thermal leak detection methods was proposed according to the occurrence of temperature field anomalies caused by seepage. Experiments were performed using a heating system and an optical fiber temperature measurement system to verify the thermal leakage detection systems. Numerical simulations were performed to understand the mechanism of the detecting method. Results indicated that the optical cable could detect the low-temperature anomaly in the active temperature field leak detection. The arrangement method of the leakage detection system was also presented in actual engineering.

Passive and wireless NFC tag-type trimethylamine gas detection based on WO3/MXene composite sensors

In this paper, we developed a high-sensitive WO3/MXene composite based gas sensor. The morphology, crystal structure, and chemical composition of the pure WO3, MXene and WO3/MXene composite were analyzed by several characterization means include SEM, XRD, and XPS, proving that the successful synthesis of WO3 nanoparticles, MXene nanosheets and WO3/MXene composites. The trimethylamine sensing element was fabricated by spin coating the prepared sensitive materials on the interdigital electrodes. The gas-sensing performance of the sensor was investigated at 25 oC, 23% RH, and the results demonstrate that the WO3/MXene based sensor has excellent response (Ra/Rg = 277.78 @10 ppm), fast response/recovery time (2 s/3 s @ 2 ppm) towards trimethylamine. It also holds excellent repeatability, selectivity, short-term and long-term stability. As a result, we have found that the addition of MXene has greatly improved the performance of WO3 for trimethylamine detection at room temperature. Finally, a tag-type gas detection device based on NFC technology has been developed to achieve detection. When testing, we only need to place a sensor label without power supply in the gas environment, and the non-contact testing can be realized outside. This work presents a novel, safe method for realizing hazardous gas detection.

Active detection fault diagnosis and fault location technology for LVDC distribution networks

Under the rapid development of direct current (DC) transmission technology, the interconnection between converter stations in the DC network has been further improved, and a looped network topology has been gradually formed. Practical applications have shown that loop troubleshooting takes a long time to perform and requires substantial field experience, which has considerably reduced the efficiency of troubleshooting and the safety factor of low-voltage direct current (LVDC) distribution networks. The complex, transient characteristics and short duration of power electronic system faults limit the application of passive detection and protection technology, raising difficulties in DC line fault detection and location. To adapt to the development of power systems, this paper presents an active detection fault diagnosis and fault location technology for LVDC distribution networks based on a converter. After a line fault occurs, the fault type is identified by injecting detection signals into the fault line through the converter, and the fault location is determined by calculating harmonic impedance. The method can detect the fault of the system in time to reduce loss when the system on the converter side is faulty but can still run. It effectively utilizes the converter equipment in power systems and does not require the installation of additional detection devices, which is largely unaffected by fault resistance in fault location, and has accurate detection results and fast speed.

Epidemiology of Human Leptospirosis in Bantul District, Yogyakarta Province, Indonesia

Bantul District has the highest number of leptospirosis cases in Yogyakarta Province. Transmission of leptospirosis occurs primarily through exposure to water and soil contaminated with the urine of infected animals. This study aims to describe the epidemiological characteristics of leptospirosis in Bantul District, Yogyakarta. This is cross-sectional research. Leptospirosis cases were collected from active and passive detections. Selected positive cases underwent home visits for interviews about epidemiological data. Rat surveys were carried out to find out the existing rat species and successful traps in the study area. The most leptospirosis cases were found at Kasihan public health center. There were 74 suspect cases, 4 probable cases, and 15 cases positively confirmed. The cases increased from January to April and from November - December. Cases in males were more related to his work as a farmer. Fever, headache and muscle aches are the most common symptoms felt by leptospirosis sufferers. The most caught rats was Rattus tanezumi. Leptospirosis epidemiological information can be used as a basis or reference for Bantul District Health Office in prevention and control of leptospirosis in priority areas

HE‐DETR‐DC5 method to rapidly detect objects on passive THz images in human safety inspection

As worldwide counterterrorism operations grow, screening people for concealed objects in public places utilizing Terahertz security technology is becoming increasingly critical. In this letter, a passive THz Human security image Intelligent detection method (HE-DETR-DC5) is proposed that combines histogram equalization (HE) processing and DEtection TRansformer (DETR). The proposed method first performs histogram equalization on passive THz images to improve image contrast. In addition, the cavity convolution is added to the ResNet-50 backbone network to enhance the model's attention to the concealed object in the passive THz image and improve the accuracy. The corresponding model is called DETR-DC5. The experimental results show that the average precision mAP of the proposed method can achieve 99.9%. At the same time, compared to other image enhancement methods, the histogram equalization method used in this letter has better performance and higher detection accuracy. The detection speed of the proposed method is about 0.5 FPS, which can meet the demand for real-time detection. Therefore, the proposed model can efficiently and automatically detect hidden objects in passive THz human security images.

A Main-Auxiliary Structured Resonant Galvanometer for Noninvasive Passive Dual-Frequency Detection

A Main-Auxiliary Structured Resonant Galvanometer for Noninvasive Passive Dual-Frequency Detection

Harmonic Signature-Based One-Class Classifier for Islanding Detection in Microgrids

This article presents a new passive islanding detection technique in MGs that uses locally measured voltage signals at the PoC of DERs. The proposed method distinguishes islanding events from normal/non-islanding conditions by utilizing superimposed harmonic spectra extracted through a full-cycle discrete Fourier transform. Our solution utilizes a machine-learning-based one-class classifier to define and adjust thresholds for full harmonic spectra. Unlike other methods, our approach does not require data synchronization or communication infrastructure, nor does it suffer from common errors that often arise in current transformers. Moreover, our design is compatible with distributed and decentralized control strategies, as it relies solely on local voltage measurements at the PoC. Another advantage of this method is its low sampling frequency requirement, in the range of 1 kHz, making it cost-effective and implementable in most existing systems. In a comprehensive evaluation of a typical MG test system that included synchronous and inverter-based DERs, the proposed scheme demonstrated exceptional performance. Specifically, the scheme was able to detect 99.06% of different islanding events within the training range, with a detection time of just 10 to 21 ms. Additionally, the scheme remained 100% stable during various normal conditions, short-circuit faults, load changes, voltage changes, capacitor switching, and frequency changes.

Nonintrusive wind blade fault detection using a deep learning approach by exploring acoustic information.

Various physical characteristics, including ultrasonic waves, active acoustic emissions, vibrations, and thermal imaging, have been used for blade fault detection. In this work, we propose using the sound produced by spinning wind blades to identify faults. To the best of our knowledge, passive acoustic information has not yet been explored for this task. In particular, we develop three networks targeting different scenarios. The main contributions of this work are threefold. First, when normal and aberrant data are available for supervised learning, an attention-convolutional recurrent neural network is designed to show the feasibility of using passive sound information to conduct fault detection. Second, in the absence of abnormal training data, we build a normal-encoder network to learn the distributions of normal data through semisupervised learning, which avoids the requirement of abnormal training data. Third, when multiple devices are used to collect the data, due to different properties of devices, there is a domain mismatch issue. To overcome this, we create an adversarial domain adaptive network to close the gap between the source and target domains. Acoustic signal datasets of actual wind turbine operations are collected to evaluate our fault detection systems. The findings demonstrate that the proposed systems offer high classification accuracy and indicate the feasibility of passive acoustic signal-based wind turbine blade fault detection with one step close to automatic detection.

Status and Prospect of Islanding Detection in Active Distribution Network

A large number of distributed power sources are injected to the distribution network, which can cause island operation in planned or unplanned situations. The islanded operation caused by the fault will cause the inverter to go off the grid in serious fault conditions such as short circuit between phases and disconnection. After the island is formed, if the common connection point before the island exchanges more power, a sudden change in the voltage within the island occurs. The frequency and phase angle changes of the voltage in the island are closely related to the characteristics of the inverter phase-locked loop, and the frequency mutation generally does not cause sudden changes in frequency due to a certain inertia of the phase-locked loop. Due to sudden changes in load power angle, input fault ride-through, etc., sudden changes in the phase angle of voltage in islands may occur. In order to solve the possible problems of power quality deterioration in islands, passive, active, and remote islanding detection schemes can be adopted, and the three schemes have their own advantages and disadvantages. In actual operation, it is necessary to consider the cooperation relationship between island detection, protection, safe automatic control and fault ride through.

Multipolarization Image Evaluation for Passive Terahertz Imaging Detection

Multipolarization Image Evaluation for Passive Terahertz Imaging Detection

Comparison and Analysis of Payloads Performance for Active and Passive Spaceborne Atmospheric Detection

Comparison and Analysis of Payloads Performance for Active and Passive Spaceborne Atmospheric Detection

A Machine Learning Approach to Passive Human Motion Detection Using WiFi Measurements From Commodity IoT Devices

Human motion is a primary indicator of indoor occupancy and activity. Motion sensing has paramount importance in the energy management of modern smart buildings and is employed for automated controls of lighting and heating, ventilation and air conditioning (HVAC) equipments. The all-pervasive WiFi infrastructure in urban buildings offers an opportunistic method of human motion detection through passive sensing of WiFi received signal strength indicator (RSSI) and channel state information (CSI). This technique unfolds a plethora of Building IoT related services, in addition to sustainable energy utilization and reduced emission of greenhouse gases. In this paper, a device free human motion detection method through WiFi RSSI and CSI collected from commercial-off-the-shelf (COTS) IoT devices, is proposed. Utilizing a laptop, a smartphone and an ESP32 as receivers, WiFi RSSI and CSI samples were collected from two residential buildings, to constitute 6 datasets. A four dimensional feature vector that exploits the data spread in time domain, is extracted from the collected samples and utilized to train a two stage ensemble machine learning model. A comparison of various RSSI based datasets indicates a mean cross validation accuracy of up to 99.7% and 97.8% in line of sight (LoS) and through-the-wall scenarios, respectively. The detection accuracy in non LoS environments can be enhanced using CSI based features, enabling motion detection in different rooms using a single WiFi router.

Passive Multi-Channel Detection: A General First-Order Statistical Theory

Passive Multi-Channel Detection: A General First-Order Statistical Theory