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The diversity of sensors in remote sensing allows for faster and easier detection of changes and issues across different scales, in contrast to conventional ground-based systems. One of the most important distinguishing features among these sensors is their varying resolutions, contributing to the versatility of remote sensing technologies across diverse environmental applications. In this study, the effectiveness of PlanetScope and Sentinel-2 satellite images with different image resolutions in detecting damage caused by a harmful insect (beet webworm moth - Loxostege sticticalis) in sunflower fields in Lüleburgaz district of Kırklareli in the Trace region was evaluated. Damage rates in sunflower fields were analyzed using various spectral indices (Enhanced Vegetation Index and Chlorophyll Index Green) and spectral transformation (Tasseled Cap Greenness) in conjunction with in situ data. Based on the spectral analysis, the satellite image dated 26 July, which showed the most severe damage, was used in the damage assessment analysis. The damaged areas were compared by classifying both satellite images with the Random Forest algorithm. According to the results of the classification accuracy assessment, PlanetScope satellite imagery showed the highest accuracy, with 90% overall accuracy and 84% Kappa statistics, making it a more suitable sensor choice for agricultural applications. [ABSTRACT FROM AUTHOR]

Statistical modeling is at the heart of many engineering problems. The importance of statistical modeling emanates not only from the desire to accurately characterize stochastic events, but also from the fact that distributions are the central models utilized to derive sample processing theories and methods. The generalized Cauchy distribution (GCD) family has a closed-form pdf expression across the whole family as well as algebraic tails, which makes it suitable for modeling many real-life impulsive processes. This paper develops a GCD theory-based approach that allows challenging problems to be formulated in a robust fashion. Notably, the proposed framework subsumes generalized Gaussian distribution (GGD) family-based developments, thereby guaranteeing performance improvements over traditional GCD-based problem formulation techniques. This robust framework can be adapted to a variety of applications in signal processing. As examples, we formulate four practical applications under this framework: (1) filtering for power line communications, (2) estimation in sensor networks with noisy channels, (3) reconstruction methods for compressed sensing, and (4) fuzzy clustering.

The spectrum sensing performance of Cognitive Radios (CRs) considering noisy signal measurements and the time domain transmission statistics of the Primary User (PU) is considered in this paper. When the spectrum is linearly swept in the frequency domain continuously to detect the presence of the PU the time-domain statistics of the PU plays an important role in the detection performance. This is true especially when the PU's bandwidth is much smaller than the CR's scanning frequency range. We model the transmission statistics that is the temporal characteristics of the PU as a Poisson arrival process with a random occupancy time. The spectrum sensing performance at the CR node is then theoretically analyzed based on noisy envelope detection together with the time domain spectral occupancy statistics. The miss detection and false alarm probabilities are derived from the considered spectral occupancy model and the noise model, and we present simulation results to verify our theoretical analysis. We also study the minimum required sensing time for the wideband CR to reliably detect the narrowband PU with a given confidence level considering its temporal characteristics.

Recurrent pregnancy loss (RPL) is defined as three or more pregnancy losses before 20 weeks. RPL is a multifactorial condition with several etiologic factors including genetic abnormalities of the parents, anatomical, endocrinological, hematologic and immunologic abnormalities, infections, nutritional and environmental factors. The causes of pregnancy loss in about half of the women with RPL even after extensive investigations remain unknown. We analyzed IL-6 -174 G/C, -572 G/C, -597 G/A, -1363 G/T, -2954 G/C promoter region polymorphisms in 113 RPL patients and 113 healthy subjects by using polymerase chain reaction (PCR)-based restriction fragment length polymorphism (RFLP) assay. The -174G/C genotypic and -174C allelic frequency and the -2954G/C genotypic and -2954C allelic frequency of IL-6 was higher in RPL patients than healthy controls and a significant association was found between RPL and -174G/C, -2954G/C polymorphisms (p < 0.0001, OR: 0.28, 95% CI: 0.15-0.51, p < 0.034, OR: 0.16, 95% CI: 0.01-1.12 respectively). We found remarkably similar frequencies in RPL patients compared to controls for IL-6 -572G/C,-597G/A and -1363G/T genotypes/alleles and no association was observed between RPL and these polymorphisms. Our study supported that IL-6 -174G/C and -2954G/C polymorphisms were associated with an increased risk of RPL in Turkish patients (Tab. 3, Ref. 24).