Identification of new isomers in $$^{228}$$Ac: impact on dark matter searches

We report the identification of metastable isomeric states of $$^{228}$$ 228 Ac at 6.28 keV, 6.67 keV and 20.19 keV, with lifetimes of an order of 100 ns. These states are produced by the $$\beta $$ β -decay of $$^{228}$$ 228 Ra, a component of the $$^{232}$$ 232 Th decay chain, with $$\beta $$ β Q-values of 39.52 keV, 39.13 keV and 25.61 keV, respectively. Due to the low Q-value of $$^{228}$$ 228 Ra as well as the relative abundance of $$^{232}$$ 232 Th and their progeny in low background experiments, these observations potentially impact the low-energy background modeling of dark matter search experiments.

An effective motion object detection using adaptive background modeling mechanism in video surveillance system

Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure.

Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection

Although image change detection (ICD) methods provide good detection accuracy for many scenarios, most existing methods rely on place-specific background modeling. The time/space cost for such place-specific models is prohibitive for large-scale scenarios, such as long-term robotic visual simultaneous localization and mapping (SLAM). Therefore, we propose a novel ICD framework that is specifically customized for long-term SLAM. This study is inspired by the multi-map-based SLAM framework, where multiple maps can perform mutual diagnosis and hence do not require any explicit background modeling/model. We extend this multi-map-based diagnosis approach to a more generic single-map-based object-level diagnosis framework (i.e., ICD), where the self-localization module of SLAM, which is the change object indicator, can be used in its original form. Furthermore, we consider map diagnosis on a state-of-the-art deep convolutional neural network (DCN)-based SLAM system (instead of on conventional bag-of-words or landmark-based systems), in which the blackbox nature of the DCN complicates the diagnosis problem. Additionally, we consider a three-dimensional point cloud (PC)-based (instead of typical monocular color image-based) SLAM and adopt a state-of-the-art scan context PC descriptor for map diagnosis for the first time.

Technical comment on the paper of Dessert et al. "The dark matter interpretation of the 3.5 keV line is inconsistent with blank-sky observations"

An unidentified line at energy around 3.5 keV was detected in the spectra of dark matter-dominated objects. Recent work [1] used 30~Msec of XMM-Newton blank-sky observations to constrain the admissible line flux, challenging its dark matter decay origin. We demonstrate that these bounds are overestimated by more than an order of magnitude due to improper background modeling. Therefore, the dark matter interpretation of the 3.5~keV signal remains viable.

Kernel Density Estimation and Correntropy based Background Modeling and Camera Model Parameter Estimation for Underwater Video Object Detection

Underwater video object detection is challenging because of the complex background and the movement of the camera. In order to address this, we propose a novel scheme of simultaneously estimating the camera model parameters and detecting the object. The object detection phase includes background modeling and its learning. Background is modeled by the proposed Spatial Kernel Density Estimation (SKDE) model and the model learning happens in the SKDE feature space. Background modeling and its learning is pixel based approach. The model histograms learn the new pixel through its histogram representation. Our learning and classification strategy is different from the Heikkila et al. [17] in the context of similarity measure. We have proposed the correntropy based similarity measure that is used for model learning and pixel classification. The camera model parameters are estimated by 2D optimization method where we have used the corner features of an object at subpixel accuracy level. These subpixel level features are used in the proposed pipelining framework for model parameters estimation. The estimated model parameters are used to transform the input frame, which in turn is used for model learning and classification. The proposed scheme has been tested with underwater video frames from six data sets. The efficacy of the proposed scheme is compared with seven existing schemes and it is found that the proposed scheme exhibits improved performance as compared to the existing methods.