A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study compares distances for indoor WiFi mapping, finding Earth Mover's Distance effective.
Efficiently maps indoor magnetic fields with SKI and D-SKI.
The paper uses Bayesian Surprise to identify unexpected structures in indoor environments.
Improves magnetic field mapping using an array of magnetometers with noisy input.
New model estimates indoor radon distribution with higher spatial resolution.
We present an algorithm for converting an indoor spherical panorama into a photograph with a simulated overhead view. The resulting image will have an extremely wide field of view covering up to 4π steradians of the spherical panorama. We argue that our method complements the stereographic projection commonly used in t…
Fingerprinting based WLAN indoor positioning system (FWIPS) provides a promising indoor positioning solution to meet the growing interests for indoor location-based services (e.g., indoor way finding or geo-fencing). FWIPS is preferred because it requires no additional infrastructure for deploying an FWIPS and achievin…
A new method learns continuous occupancy fields efficiently using the Ising model.
DeepPos uses deep learning to improve indoor location accuracy.
Paper presents a WiFi-based indoor sensor localization technique.
Proposes efficient calibration for indoor localization models.
Paper presents a new Wi-Fi RSS and geomagnetic field database for indoor localization and trajectory estimation.
This paper improves indoor positioning accuracy by deploying reference nodes to ensure Line-of-Sight.
RETR improves indoor radar perception with a novel transformer model.
Autoencoder neural networks reconstruct missing indoor environment data.
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that di…
Paper uses RNNs for more accurate indoor WiFi localization.
Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.
New method combines neural networks and data assimilation for indoor air quality prediction.
Paper proposes a feature-wise change detection method for improving indoor positioning accuracy.
Paper proposes a SIMO DNN for indoor localization using Wi-Fi fingerprints.
SLAM-net learns to navigate visually in challenging indoor environments.
One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings --- e.g., a big shopping mall and a university campus --- is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural network…
This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations. …
Paper proposes CNN-LSTM for WiFi indoor localization.
We propose a reinforcement learning (RL) based closed loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells. The main contributions of our paper are to 1) use RL to solve performance tuning problems in an indoor cellular network for voic…
With the recent development in mobile computing devices and as the ubiquitous deployment of access points(APs) of Wireless Local Area Networks(WLANs), WLAN based indoor localization systems(WILSs) are of mounting concentration and are becoming more and more prevalent for they do not require additional infrastructure. A…
Two deep learning models improve indoor location prediction from WiFi fingerprints.
The paper optimizes predicting future window states in buildings using past climate data.
VNLA uses vision and language to guide agents in finding objects in indoor environments.
AGML model improves indoor localization with sparse fingerprints using meta-learning and graph neural networks.
We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable reward function is designed keeping in mind the cost and weight constraints for micro drone with minimum number of sensing modalities. Collection of small number of…
Paper proposes a new Markov model for efficient PLC system design.
Indoor localization based on SIngle Of Fingerprint (SIOF) is rather susceptible to the changing environment, multipath, and non-line-of-sight (NLOS) propagation. Building SIOF is also a very time-consuming process. Recently, we first proposed a GrOup Of Fingerprints (GOOF) to improve the localization accuracy and reduc…
Study classifies surface types for autonomous indoor robots using inertial data.
PRISM provides real-time SLAM with uncertainty estimates for agent and map states.
Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promis- ing approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a …
Most existing fingerprints-based indoor localization approaches are based on some single fingerprints, such as received signal strength (RSS), channel impulse response (CIR), and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environ…
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light lev…
This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfinding for people with disabilities (e.g., vision impairments, physical disabilities, etc.), path planni…
We propose a scheme to employ backpropagation neural networks (BPNNs) for both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal strengths (FWIPS): radio map construction during the offline stage, and localization during the online stage. Given a training radio map (TRM), i.e., a set of coordinat…
The paper improves safety in autonomous systems using adversarial learning.
Bayesian segmentation and uncertainty estimation improve 3D model accuracy for factory planning.
Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by…
Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting receiv…
Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environme…
MOAI evaluates indoor airflow's impact on COVID-19 transmission.
Paper develops a scalable distributed inference algorithm for sensor networks.