Accurate indoor pedestrian positioning has attracted extensive research attention along with the rising popularity of indoor location-based service. Among the indoor positioning methods, pedestrian dead reckoning stands out for requiring neither expensive infrastructure nor laborious site survey. However, with time going on, the pedestrian dead reckoning method suffers from error accumulation problem, whereas the major cause of positioning error is usually heading deviation. To tackle the error-drifting problem, an FM-aided heuristic drift reduction method for pedestrian dead reckoning systems is designed and evaluate in this paper. First a random forest classifier is devised to achieve real-time recognition of straight-line and turning motion mode with data from frequency-modulated radio signal and inerital sensor signal. Based on the classification result, constraint is applied to headings in straight periods and accurate headings are extracted from accelerometer and magnetometer measurements to reduce heading deviation in straight periods. Field experiments have been conducted at three sites in two office buildings to evaluate the performance of the proposed system. Experiment results indicate error drifting can be effectively constrained by the proposed method.

The Global Positioning System (GPS) yields good precision and availability in open outdoor environment. However, the errors of GPS may suffer degradation in some complex environments, such as forests and urban canyons. To solve this problem, a new positioning method is designed integrating GPS, Digital Terrestrial Multimedia Broadcast (DTMB) and frequency-modulated (FM) radio signal. In this method, the DTMB transmitter acts as a pseudo-satellite to assist GPS positioning. Furthermore, the FM fingerprint positioning is used to correct the positioning bias. An adaptive selection scheme is proposed to provide an optimal integration mode of the sensors. Field experiments in complex environment were carried out for evaluation. Comparing to the GPS-only and GPS + DTMB approach, positioning accuracy was improved by at least $68.21\%$ and $21.27\%$ with the proposed method, respectively.

Recent advances in computational chemistry, machine learning, and large-scale virtual screening have rapidly expanded the accessible chemical space, increasing the need for interpretable molecular representations that capture the hierarchical topological structure of molecules. Existing formats, such as Simplified Molecular Input Line Entry System (SMILES) strings and MOL files, effectively encode molecular graphs but provide limited support for representing the multi-level structural information needed for complex downstream tasks. To address these challenges, we introduce HiFrAMes, a novel graph–theoretic hierarchical molecular fragmentation framework that decomposes molecular graphs into chemically meaningful substructures and organizes them into hierarchical scaffold representations. HiFrAMes is implemented as a four-stage pipeline consisting of leaf and ring chain extraction, ring mesh reduction, ring enumeration, and linker detection, which iteratively transforms raw molecular graphs into interpretable abstract objects. The framework decomposes molecules into chains, rings, linkers, and scaffolds while retaining global topological relationships. We apply HiFrAMes to both complex and drug-like molecules to generate molecular fragments and scaffold representations that capture structural motifs at multiple levels of abstraction. The resulting fragments are evaluated using selection criteria established in the fragment-based drug discovery literature and qualitative case studies to demonstrate their suitability for downstream computational tasks.

In scientific documents, molecule structure information is usually delivered by chemical structure images. Although convenient for human interpretation, the image‐based molecule structure depiction is not a machine‐readable format, blocking productivity improvements in many fields including chemical data mining and drug discovery. Molecule structures can be modeled as graphs with atoms as nodes and bonds as edges. Following this intuitive modeling, optical chemical structure recognition (OCSR) can be achieved by first detecting individual atoms and bonds and then assembling into a graph. However, the challenges in decision ambiguity due to false positives and spatial proximity during graph assembly is rarely explained and explicitly addressed. In this work, we present a rule‐based probabilistic OCSR model to explain and tackle the ambiguity challenges in graph assembly. We developed a novel line detection algorithm for precise bond line identification, and designed a candidate mechanism with probabilistic graph to resolve atom/bond ambiguity. The proposed model is evaluated with popular large image datasets and achieved outperforming recognition accuracy compared to state‐of‐the‐art solutions.

A z-curve is the set of points on Earth’s surface that have identical pseudoranges to Global Navigation Satellite Systems (GNSS) satellites (A, B). The zero Time Difference of Arrival (TDOA) model eliminates common mode errors in signal transmission and the clock bias of GNSS receivers. Building upon a static model of z-surfaces and z-curves, this paper presents a dynamic model for migration of z-surfaces and z-curves with respect to satellite motions and atmospheric changes. Specifically, we use geometric transformation matrices and numerical solvers to describe the migration of the satellite Terrestrial Service Volume (TSV) model, z-surface, and z-curve. Affine transformations are applied to TSV and z-surface migrations, while for z-curve migration, we adopt a modified Newton’s method and a 4th-order Runge-Kutta method to align z-curve points on ground and within satellite coverage. These migration functions are validated using Global Positioning System (GPS) data.