Medieval or Early Pre-Modern? Dating Several Fragments from a Judeo-Persian Manuscript (C40 Hebrew, the IOM RAS)

The article examines some paleographic and codicological aspects of several fragments of the undated manuscript C40 from the Hebrew Collection in the Institute of Oriental Manuscripts of the Russian Academy of Sciences (IOM RAS). This is a codex of Shahin's Musa-name, formed from some different, scattered fragments. Apparently, three large fragments of this manuscript are taken from medieval codices, which were gathered by one person (the so-called “restorer”) in the 19th century. Judging from the results of their comparative and historical analysis, one can suggest some possibilities for dating of each fragment. The author dates these fragments back to the late 15th early 16th centuries C.E. According to her, they were transcribed in the region where the cities of Qum and Kashan are located (the current provinces of Qum and Isfahan in Iran). Unfortunately, it is hardly possible to provide a more accurate localization. However, several dated and previously studied Jewish manuscripts from this period and this area have nearly the same attributes, quality, or characteristics of the writing material and the type of writing, as well as some textual pattern.

An Efficient Vehicle Localization Method by Using Monocular Vision

Accurate localization of surrounding vehicles helps drivers to perceive surrounding environment, which can be obtained by two parameters: depth and direction angle. This research aims to present a new efficient monocular vision based pipeline to get the vehicle’s location. We proposed a plug-and-play convolutional block combination with a basic target detection algorithm to improve the accuracy of vehicle’s bounding boxes. Then they were transformed to actual depth and angle through a conversion method which was deduced by monocular imaging geometry and camera parameters. Experimental results on KITTI dataset showed the high accuracy and efficiency of the proposed method. The mAP increased by about 2% with an additional inference time of less than 5 ms. The average depth error was about 4% for near distance objects and about 7% for far distance objects. The average angle error was about two degrees.

A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation

This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach.

A Paradigm Shift in Monitoring Pipeline Leaks - Combining Sensorless NPW with RTTM

Leak detection solutions in pipelines use several known methods and technologies. However, each method and its underlying technology has their benefits and drawbacks. This article will present and evaluate a hybrid solution that combines two methods based on different physical measurements and quantities to ensure a superior detection probability, short detection time, accurate localization of faults, and minimal false alarm rates. In addition, this solution also features preventive capabilities by pointing out problematic areas in a pipeline that may need more attention. The article presents a novel approach for pipeline monitoring using a combined solution with the strengths of real-time transient model (RTTM) technology and the power of next-generation fiber sensing geared towards leak detection. On top of acoustic sensing for leaks, it features continuous pipeline integrity monitoring where, using subtle characteristics of propagating negative pressure waves (NPW), pipeline sections signatures are tracked, aiming to detect changes that might expose pipeline integrity issues that can enable the operator to take preventive measures and plan maintenance events. Such a hybrid solution, from AVEVA™ (RTTM) and Prisma Photonics (fiber sensing), will obtain higher levels of performance and reliability. In addition, such a hybrid approach responds to the increasing regulatory demand to have two continuously working solutions based on different physical measures to ensure leak detection and prevention of substance spillage. This article intends to introduce such a hybrid solution with new applications in predictive maintenance for pipeline operators and shed more light on the benefits of such a solution facing further regulatory demands.

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Internet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

Accurate Localization of Oil Tanks in Remote Sensing Images via FGMRST-Based CNN

Object localization is an important application of remote sensing images and the basis of information extraction. The acquired accuracy is the key factor to improve the accuracy of object structure information inversion. The floating roof oil tank is a typical cylindrical artificial object, and its top cover fluctuates up and down with the change in oil storage. Taking the oil tank as an example, this study explores the localization by combining the traditional feature parameter method and convolutional neural networks (CNNs). In this study, an improved fast radial symmetry transform (FRST) algorithm called fast gradient modulus radial symmetry transform (FGMRST) is proposed and an approach based on FGMRST combined with CNN is proposed. It effectively adds the priori of circle features to the calculation process. Compared with only using CNN, it achieves higher precision localization with fewer network layers. The experimental results based on SkySat data show that the method can effectively improve the calculation accuracy and efficiency of the same order of magnitude network, and by increasing the network depth, the accuracy still has a significant improvement.

A novel guidewire technique for endobronchial silicone plug occlusion for bronchopleural fistula

Successful bronchoscopic bronchopleural fistula closure requires both accurate localization of the fistula and device implantation; placing a silicone plug requires experience and skill because of the limited endobronchial working space. We report a novel bronchoscopic silicone plug placement technique for a bronchopleural fistula that developed after a left upper lobectomy following induction chemoradiation therapy, which was then successfully treated by omentopexy.

Particle localization using local gradients and its application to nanometer stabilization of a microscope

Accurate localization of single particles plays an increasingly important role in a range of biological techniques, including single molecule tracking and localization-based superresolution microscopy. Such techniques require fast and accurate particle localization algorithms as well as nanometer-scale stability of the microscope. Here, we present a universal method for three-dimensional localization of single labeled and unlabeled particles based on local gradient calculation of microscopy images. The method outperforms current techniques in high noise conditions, and it is capable of nanometer accuracy localization of nano- and micro-particles with sub-ms calculation time. By localizing a fixed particle as fiducial mark and running a feedback loop, we demonstrate its applicability for active drift correction in sensitive nanomechanical measurements such as optical trapping and superresolution imaging. A multiplatform open software package comprising a set of tools for local gradient calculation in brightfield and fluorescence microscopy is shared to the scientific community.