Average Detection Probability Research Articles

Closed-form energy-based signal detection analysis in presence of a Lomax fading channel in full hyper-Rayleigh regime

The presented research examines the energy-based spectrum sensing problem of a signal transmitted over a wireless communication channel with severe fading. To account for the most challenging propagation conditions, a recently developed Lomax wireless channel model was utilized, which exhibits hyper-Rayleigh characteristics over the entire range of possible parameter values. For the channel model under consideration, the existence of the specific hyper-Rayleigh regimes (i.e., weak, strong, and full) are identified asymptotically and studied numerically for a finite signal-to-noise ratio. For the most severe fading conditions, the system’s performance was assessed in terms of the average probability of detection, receiver operating characteristics, and area under the curve. These metrics were derived in closed-form for the cases of Maximum Ratio Combining at the receiver and without diversity reception. The obtained expressions were analyzed via numerical and statistical simulation as functions of system and channel parameters, including the sensing base, average signal-to-noise ratio, and scale parameter for the signal-to-noise probability distribution. The results from numerical simulations were compared with existing regulations specified in the 5G standard for energy-based detection in medium access procedures.

Occupancy model reveals limited detectability of lichens in a standardised large‐scale monitoring

AbstractQuestionWhat are the extent and the possible causes of imperfect detection in lichens? Because lichens are sessile and lack seasonality, they should be easier to survey than animals that can move or plants and fungi with seasonal morphology, and one could therefore expect relatively high detection probabilities.Location826 standardised sampling plots across Switzerland.MethodsUsing repeated detection/non‐detection data from a national lichen survey conducted by professional lichenologists, we estimated the mean and variation in detectability for 373 tree‐living species with a multi‐species occupancy model. We also quantified the effect of species conspicuousness, identifiability and observer experience on detection probability.ResultsThe average detection probability for a single survey was unexpectedly low with an average of 0.49 (range across species: 0.25–0.74). Conspicuous species showed higher average detectability (0.56) than inconspicuous species (0.41), and identifiability as well as previous experience with a species substantially increased the probability of a person detecting it. Accounting for experience, the mean detection probabilities of observers ranged from 0.32 to 0.69.ConclusionsOur study confirms that detection probability per survey is often far below 1 also in sessile organisms, even when a standardised survey is conducted by experts. When species are seasonal (plants, fungi, etc.), survey areas are larger, or field personnel are less experienced, as is the case for many surveys and monitoring programs, detectabilities are likely to be substantially lower. We therefore argue that imperfect detection should systematically be considered in the survey design and data analysis also for sessile organisms.

Intelligent Reflecting Surface Framework for ED Based Spectrum Sensing

According to a recent study, adding intelligent reflecting surfaces (IRS) to propagation environments improves the performance of wireless devices. The capabilities of cognitive radio’s spectrum sensing over environments with assisted wireless propagation via IRS are examined in this research. Taking channel fading effects into consideration, we derive closed-form analytical expressions for the average probability of detection (APD) for single user and cooperative users. Further, the derived APD expression is used to derive the average area under the receiver operating characteristics curve (AUC¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{AUC}}$$\\end{document}) expression. Monte Carlo simulations are also carried out in order to validate the derived expressions. Results demonstarte the extent of detectability performance in cognitive radio on utilizing the IRS aided wireless propagation environment therefore witnessing its significance for upcoming wireless technological advancement by offering spectrum efficiency with good quality of service (QoS).

A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring

To efficiently detect aquatic invasive species early in an invasion when control may still be possible, predictions about which locations are likeliest to be occupied are needed at fine scales but are rarely available. Occupancy modeling could provide such predictions given data of sufficient quality and quantity. We assembled a data set for the macroalga starry stonewort (Nitellopsis obtusa) across Minnesota and Wisconsin, USA, where it is a new and high-priority invader. We used these data to construct a multi-season, single-species spatial occupancy model that included biotic, abiotic, and movement-related predictors. Distance to the nearest access was an important occurrence predictor, highlighting the likely role boats play in spreading starry stonewort. Fetch and water depth also predicted occupancy. We estimated an average detection probability of 63% at sites with mean non-N. obtusa plant cover, declining to ~ 38% at sites with abundant plant cover, especially that of other Characeae. We recommend that surveyors preferentially search for starry stonewort in areas of shallow depth and high fetch close to boat accesses. We also recommend searching during late summer/early fall when detection is likelier. This study illustrates the utility of fine-scale occupancy modeling for predicting the locations of nascent populations of difficult-to-detect species.

Assessing Nowcast Models in the Central Mexico Region Using Radar and GOES-16 Satellite Data

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region.

Comparative Analysis of Ultrasonic and Traditional Gas-Leak Detection Systems in the Process Industries: A Monte Carlo Approach

Gas leaks can cause disasters at process sites, including fires and explosions, and thus, effective gas-leak detection systems are required. This study investigated the limitations of conventional detectors and introduced an innovative ultrasonic sensor-based approach for continuous monitoring. A new configuration for a stationary remote ultrasonic gas-leak monitoring system is proposed. The selected material was 1-Butene. The detection probability was assessed through a simulation based on a gas-leak scenario, detailing the selection criteria for leak sites and simulation conditions. Computational fluid-dynamics (CFD) simulations were used to evaluate the detection capability of the existing system, whereas Monte Carlo simulations were used to compare it with the proposed ultrasonic system. The CFD simulation was performed by setting the lower detection limit of the concentration-measurement-type gas detector to 600 ppm, and the leak-detection time was approximately 8.895 s. A Monte Carlo simulation accounting for wind direction showed that the existing system’s average detection probability was 3.41%. By integrating the existing and new systems, the detection probability increased by 96.58% and 3.92% compared with that using only the concentration-measurement and ultrasonic measurement methods, respectively. These rates provide valuable insights for future applications in oil refining and petrochemical industries.

Comparing methods to estimate feral burro abundance

AbstractObtaining precise and unbiased estimates of feral burro (Equus asinus) abundance in the western United States is challenging due to their cryptic pelage and the rugged terrain they inhabit. Management agencies employ helicopter‐based, simultaneous double‐observer sightability surveys (hereafter denoted as DOS) to estimate abundance of burros; but the DOS method routinely produces negatively biased estimates due to residual heterogeneity in detection probability. Consequently, testing alternative methods to improve upon current procedures is warranted. Residual heterogeneity in DOS surveys can be minimized by including radio‐collared individuals in the population. Alternatively, if distance measurements are recorded, residual heterogeneity can also be reduced via a mark‐recapture distance sampling (MRDS) approach. Aerial infrared (IR) surveys offer a safer alternative than helicopter‐based surveys because they can be flown at a higher altitude and require fewer observers in the aircraft. Further, IR surveys using a distance sampling approach have been shown to generate accurate and precise estimates of feral horse (E. caballus) populations. Accordingly, we compared results of surveys using aerial IR distance sampling, the standard DOS survey, a DOS survey incorporating detections of radio‐collared individuals, and an MRDS analysis of a feral burro population with a known minimum population size in central Utah, winter 2015–2016 and spring 2016. The minimum number of burros known alive during the winter and spring surveys were 236 and 136, respectively. The average detection probability of IR surveys was P = 0.88 (SE = 0.16) and distance models produced estimates of 127 burros (95% CIs = 99–175) for the winter survey, and 94 burros (CIs = 72–134) for the spring survey. Mean detection probability of the standard DOS surveys was P = 0.78 (SE = 0.09), and model‐generated abundance estimates were 155 burros (CIs = 133–227) in winter, and 92 burros (CIs = 79–139) in spring. Incorporating detections of radio‐collared individuals in the DOS survey resulted in a decreased detection probability (P = 0.46; SE = 0.06) and increased abundance estimates to 267 (CIs = 169–571) and 155 (CIs = 128–263) for winter and spring, respectively. Mark‐recapture distance sampling produced a mean detection probability of P = 0.48 (SE = 0.12) and resulted in estimates of 282 (CIs = 178–385) and 169 (CIs = 73–310) burros in winter and spring, respectively. Our study demonstrated that aerial IR surveys conducted using standard distance sampling can produce precise estimates of burro population sizes; however, estimates were negatively biased relative to the known population size. Small sample size limits generalization of our results, but the IR‐based distance approach did not improve upon DOS surveys. Accounting for residual heterogeneity through use of radio‐collars and mark‐recapture distance sampling eliminated the negative bias from the standard DOS survey but decreased survey precision. Managers will need to decide whether unbiased but less precise abundance estimates are preferable compared to a more precise, but biased, estimate.

ED based spectrum sensing over IRS-assisted Rayleigh-FTR fading channels

A recent study has revealed that incorporating intelligent reflecting surfaces (IRS) into wireless systems improves their performance within various signal propagation conditions. This research paper focuses on evaluating the spectrum-sensing abilities of cognitive radio in wireless environments enhanced by IRS technology. To achieve this, the paper develops a statistical model to describe the connections between the primary user (PU) and IRS, as well as between IRS and the secondary user (SU). This model calculates a precise approximation of the probability density function (PDF) of the end-to-end instantaneous signal-to-noise ratio (SNR). The SNR considers both the PU-to-IRS link, which follows a Rayleigh fading distribution, and the IRS-to-SU link, which follows an FTR fading distribution. By utilizing this PDF distribution, the paper derives analytical expressions for the average probability of detection (APD) for single and cooperative secondary users. These APD expressions are then employed to establish the exact closed-form expression for the average area under the receiver operating characteristics (AUC), system throughput, and transmission probability of the SU. To validate these theoretical findings, the paper conducts Monte Carlo simulations. The results from these simulations indicate an improved detection probability when operating in an IRS-enhanced wireless propagation environment. Consequently, the study suggests that incorporating IRS technology can significantly enhance the reliability of communication systems based on cognitive radio principles.

Utilizing double-shadowed Rician fading over energy detector-based spectrum sensing with diversity reception

This study explores the capabilities of an Energy Detector (ED)-based Cognitive Radio (CR) device in a double shadowed (DS) Rician distribution environment for spectrum sensing. The effectiveness of the ED is evaluated using metrics such as average probability of detection (PD) and average area under the receiver operating characteristics curve (AUC). The investigation extends to single-input-multiple-output (SIMO) channel conditions, considering maximal ratio combining (MRC) and square-law selection (SLS) diversity techniques. To provide a comprehensive understanding of the system, the study also includes the asymptotic performance analysis of diversity techniques, including MRC, equal gain combining (EGC), and selection combining (SC). The findings indicated a notable enhancement in detection performance through the application of diversity combining techniques. Among the evaluated schemes, MRC demonstrated the most favorable results, followed EGC, while SC yielded comparatively weaker outcomes. To further minimize error probability, the detection threshold is optimized, resulting in improved energy detection statistics, even in scenarios involving diversity. The formulations are additionally applied to cooperative spectrum sensing (CSS). It further noted that fading parameter directly influence the array gain, while diversity gain only depends on the number of diversity branches. The accuracy and validity of the proposed results are confirmed through Monte-Carlo simulation.

An Antijamming Method Based on Multichannel Singular Spectrum Analysis and Affinity Propagation for UWB Ranging Sensors

Ultra-wideband impulse radio system has received extensive attention due to their high resolution and poor interception possibility. But the specific electromagnetic threat posed by its complex work environment cannot be ignored. In all kinds of active jamming signals, smart noise jamming plays an important role in modern radar electronic warfare, and most traditional anti-jamming methods have less suppression effect. In this paper, the mechanism of smart noise jamming on the UWB ranging sensor is discussed first. On this basis, an anti-smart noise jamming method based on multichannel singular spectrum analysis and kurtosis characteristic is proposed. Experiments show that smart noise jamming is more threatening to the UWB system than other noise modulation jamming. And the improved algorithm proposed in this paper can suppress the jamming better. After the anti-jamming processing, the average detection probability can reach more than 95%. Meanwhile, the method is improved by adding the affinity propagation (AP) clustering algorithm to solve the underdetermined signal separation problem in the case of compound jamming. The advantage is that it does not have to estimate the number of signals first. Even if the SJR is -10 dB, the waveform similarity between the separation signal and the source signal can reach 0.7 or more.

Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region

Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7–0.9 and 0.6–0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15–20%, while underestimating consecutive dry days by 5–10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January–April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions.

Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data

Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.

A Novel RIS-Aided Code Index Modulation Scheme With Low-Complexity Detection

In order to achieve high throughput and low-power consumption of the transmitter and improve the error performance of the receiver, a novel reconfigurable intelligent surface (RIS)-aided code index modulation (RIS-CIM) is proposed in wireless communications. RIS-CIM utilizes the index of a spreading code to convey additional information, which further improves the spectral efficiency (SE) and error performance of the receiver by adding and adjusting the reflected phase shift of low-cost reflective elements at the RIS-aided relay. To reduce the computational complexity incurred by the maximum likelihood (ML) detection, we propose two low-complexity detections, namely the match correlation (MC) detection and the optimal low complexity (OLC) detection. MC and OLC approaches separate the dispreading from the demodulation in the detection, which achieves a significant reduction of the computational complexity over ML detection demonstrated by the analysis of the computational complexity measurements. We present a thorough analysis for the upper bound on the average bit error probability (ABEP) of the ML detection and the bit error probability (BEP) of the OLC detection of RIS-CIM, which is verified by numerical simulations. Numerical simulations also demonstrate that the proposed RIS-CIM performs better in terms of the error performance of ML detection than several existing CIM and RIS-related schemes at a given SE setting. In addition, the proposed ML and OLC detections achieve an error performance close to the ML detection under various measures of signal-to-noise ratio (SNR) and the increasing SE in the simulations. Moreover, RIS-CIM can utilize a large number of reflective elements at the RIS-aided relay to offset the performance degradation caused by the improvement of SE.

Spatial density pattern of Himalayan Ibex (Capra sibirica) in Pakistan

Mountain ungulates perform a key role in maintaining the balance of ecosystems as they are the primary consumers of vegetation and prey for large predators. The mountain ranges of northern Pakistan are home to six species of mountain ungulates, and the Himalayan ibex (Capra sibirica), hereafter ibex, is the most abundant among them. This study was conducted in three administrative regions of northern Pakistan, viz. Gilgit-Baltistan (GB), Azad Jammu and Kashmir (AJK), and Khyber Pakhtunkhwa (KP), to generate a range-wide density pattern map of ibex. A double-observer survey was conducted in 25 study sites during 2018–2021 across the ibex distribution range, covering an area of about 35,307 km 2 , by walking transects totaling 1647 km. Within the ibex range where the survey was not conducted due to financial and logistical constraints, we obtained species population information from local wildlife departments’ most recent annual survey data. The aim was to generate a density map for the entire ibex range. Using the BBRecapture package in program R, we estimated an ibex population of 7639 (95 % CI) with a mean density of 0.21/km 2 in the surveyed area. Combining with the secondary data from un-surveyed areas, the total population estimate for the country came to 10,242 ibex. The largest population densities were observed in four valleys (Shimshal, Gulkin-Hussaini, Khyber, and Khunjerab) of the Karakoram-Pamir range, followed by the Hindu Kush range (Chitral Wildlife Division [WD]). The central and eastern parts of the Karakoram range had moderate to low densities, while the Himalayan range (e.g., Astore Valley) supported a small population. The mean herd size was 15 individuals (range: 5–41), and the average detection probability of observers A and B was 0.69 and 0.48, respectively. The average male and young ratios per 100 females were estimated to be 75 and 81, respectively. The range-wide density map developed during the study provided an evidence for the impact of trophy hunting programs and an objective tool for range-wide conservation planning of the species.

Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations

Abstract. Spatially dense and continuous information on avalanche occurrences is crucial for numerous safety-related applications such as avalanche warning, hazard zoning, hazard mitigation measures, forestry, risk management and numerical simulations. This information is today still collected in a non-systematic way by observers in the field. Current research has explored the application of remote sensing technology to fill this information gap by providing spatially continuous information on avalanche occurrences over large regions. Previous investigations have confirmed the high potential of avalanche mapping from remotely sensed imagery to complement existing databases. Currently, the bottleneck for fast data provision from optical data is the time-consuming manual mapping. In our study we deploy a slightly adapted DeepLabV3+, a state-of-the-art deep learning model, to automatically identify and map avalanches in SPOT 6/7 imagery from 24 January 2018 and 16 January 2019. We relied on 24 778 manually annotated avalanche polygons split into geographically disjointed regions for training, validating and testing. Additionally, we investigate generalization ability by testing our best model configuration on SPOT 6/7 data from 6 January 2018 and comparing it to avalanches we manually annotated for that purpose. To assess the quality of the model results, we investigate the probability of detection (POD), the positive predictive value (PPV) and the F1 score. Additionally, we assessed the reproducibility of manually annotated avalanches in a small subset of our data. We achieved an average POD of 0.610, PPV of 0.668 and an F1 score of 0.625 in our test areas and found an F1 score in the same range for avalanche outlines annotated by different experts. Our model and approach are an important step towards a fast and comprehensive documentation of avalanche periods from optical satellite imagery in the future, complementing existing avalanche databases. This will have a large impact on safety-related applications, making mountain regions safer.

Intelligent Reflecting Surfaces and Spectrum Sensing for Cognitive Radio Networks

In Cognitive Radio (CR) networks, Primary User (PU) and Secondary User (SU) coexist to efficiently share the spectrum. PU has the right to access its dedicated channel at any time, while SU, operating in an opportunistic mode. can access only when PU is absent. Thus, SU should continuously monitor the channel to avoid any interference with PU when transmitting. Several factors, such as fading and shadowing adversely impact the PU SNR at the SU receiver making the Spectrum Sensing (SS) process more challenging. Recently, Intelligent Reflecting Surface (IRS) has been proposed to control the propagation channel for wireless systems. Introducing IRS in CR networks impacts the SS performance because of altering the channel. In this paper, we investigate the effect of deploying IRS on the SS by considering two scenarios: in (S1) the IRS is configured to enhance the PU signal at SU, while in the second scenario (S2), the IRS is configured to assist the Primary Receiver (PR). First, we highlight several important challenges and research directions. Then, we derive the analytical average detection probability for both (S1) and (S2). Results show that deploying IRS can significantly enhance SS even when the IRS is deployed to assist the PR.

Investigating detection probability of mobile survey solutions for natural gas pipeline leaks under different atmospheric conditions

The 2015 Paris agreement aims to cut greenhouse gas emissions and keep global temperature rise below 2 °C above pre-industrial levels. Reducing CH4 emissions from leaking pipelines presents a relatively achievable objective. While walking and driving surveys are commonly used to detect leaks, the detection probability (DP) is poorly characterized. This study aims to investigate how leak rates, survey distance and speed, and atmospheric conditions affect the DP in controlled belowground conditions with release rates of 0.5–8.5 g min−1. Results show that DP is highly influenced by survey speed, atmospheric stability, and wind speed. The average DP in Pasquill–Gifford stability (PG) class A is 85% at a low survey speed (2–11 mph) and decreases to 68%, 63%, 65%, and 60% in PGSC B/C, D, E/F, and G respectively. It is generally less than 25% at a high survey speed (22–34 mph), regardless of stability conditions and leak rates. Using the measurement data, a validated DP model was further constructed and showed good performance (R2: 0.76). The options of modeled favorable weather conditions (i.e., PG stability class and wind speed) to have a high DP (e.g., >50%) are rapidly decreased with the increase in survey speed. Walking survey is applicable over a wider range of weather conditions, including PG stability class A to E/F and calm to medium winds (0–5 m s−1). A driving survey at a low speed (11 mph) can only be conducted under calm to low wind speed conditions (0–3 m s−1) to have an equivalent DP to a walking survey. Only calm wind conditions in PG A (0–1 m s−1) are appropriate for a high driving speed (34 mph). These findings showed that driving survey providers need to optimize the survey schemes to achieve a DP equivalence to the traditional walking survey.

Fusion Schemes Based on IRS-Enhanced Cooperative Spectrum Sensing for Cognitive Radio Networks

The detection performance of cooperative spectrum sensing (CSS) is poor when there are obstacles blocking. Therefore, fusion schemes based on intelligent reflecting surface (IRS)-enhanced CSS are investigated. Existing fusion schemes can be divided into the soft combination and the hard combination. In the soft combination, each secondary user (SU) uploads the decision statistic to a fusion center (FC). Comparatively, during the hard combination, each SU reports the local 0/1 decision result to FC instead of decision statistic. In this paper, the equal gain combination (EGC) and the selection combination (SC) schemes are studied for the soft combination. The weighted hard combination (WHC) scheme is studied for the hard combination, and an optimal set combination (OSC) scheme is proposed based on signal-to-noise ratio (SNR). By using the definition of the non-centrality chi-square distribution and the moment-matching method, the closed-form expressions for the average probability of detection of all the schemes are derived. Simulation results reveal that the detection performance of IRS-enhanced CSS obviously outperforms the decode and forward (DF) relay case and the no IRS case. In addition, our proposed OSC are superior to the K-rank in terms of the detection performance.

Multi-surveyor capture-mark-recapture as a powerful tool for butterfly population monitoring in the pre-imaginal stage.

For many elusive insect species, which are difficult to cover by standard monitoring schemes, innovative survey methods are needed to gain robust data on abundance and population trends. We suggest a monitoring of overwintering larvae for the endangered nymphalid butterfly Limenitis reducta. We tested different removal and capture‐mark‐recapture (CMR) approaches in a field study in the “Alb‐Donau” region, Germany. Classical removal and CMR studies require movement of the organisms under study, but in our approach, we replaced movement of the study organisms by random movement of multiple different surveyors. We tested the validity of the approach by comparing detection frequencies from our field data with simulated detections. Our results indicate that multi‐surveyor removal/CMR techniques are suitable for estimating abundance of overwintering L. reducta larvae. Depending on surveyor experience, the average detection probability ranged between 16% for novices and 35% for experts. The uncertainty of population estimates increased with a decrease in personnel expenditure. Estimated larval densities on a spruce clear‐cut varied between one and three individuals per 100 m2, probably related to habitat conditions. We suggest a CMR approach with three to four trained surveyors for the monitoring of L. reducta populations in the overwintering stage. Compared with previous sampling methods, our approach is a powerful tool with clear advantages: long survey period, estimates of the absolute population size accompanied by uncertainty measures, and estimates of overwinter mortality. The proposed method can be adapted and used for several different butterfly species, other insect taxa with specific immobile life stages, and some sessile organisms, for example, elusive plants, fungi, or corals.

Lot-to-lot reagent verification: Effect of sample size and replicate measurement on linear regression approaches

BackgroundWe investigate the simulated impact of varying sample size and replicate number using ordinary least squares (OLS) and Deming regression (DR) in both weighted and unweighted forms, when applied to paired measurements in lot-to-lot verification. MethodsSimulation parameter investigated in this study were: range ratio, analytical coefficient of variation, sample size, replicates, alpha (level of significance) and constant and proportional biases. For each simulation scenario, 10,000 iterations were performed, and the average probability of bias detection was determined. ResultsGenerally, the weighted forms of regression significantly outperformed the unweighted forms for bias detection. At the low range ratio (1:10), for both weighted OLS and DR, improved bias detection was observed with greater number of replicates, than increasing the number of comparison samples. At the high range ratio (1:1000), for both weighted OLS and DR, increasing the number of replicates above two is only slightly more advantageous in the scenarios examined. Increasing the numbers of comparison samples resulted in better detection of smaller biases between reagent lots. ConclusionsThe results of this study allow laboratories to determine a tailored approach to lot-to-lot verification studies, balancing the number of replicates and comparison samples with the analytical performance of measurement procedures involved.