New model estimates indoor radon distribution with higher spatial resolution.
problem Accurate estimation of indoor radon concentration for health assessment.
method Quantile regression forest and probabilistic Monte Carlo sampling.
result Approximate lognormal distribution of indoor radon in Germany with specific exceedance probabilities.
Study compares distances for indoor WiFi mapping, finding Earth Mover's Distance effective.
problem Indoor localization and mapping using WiFi signals.
method Comparison of distance measures and kernel density estimation.
result Earth Mover's Distance is most beneficial for indoor localization.
DeepPos uses deep learning to improve indoor location accuracy.
problem Indoor GPS accuracy issues with mobile devices.
method Supervised autoencoder network for modeling CSI-based environments.
result Robust and efficient indoor localization system developed.
Paper presents a WiFi-based indoor sensor localization technique.
problem Indoor localization in wireless sensor networks.
method Zoning-based localization using statistical learning.
result Efficient sensor zone determination in indoor environments.
Paper proposes a method for faster object detection annotation in indoor scenes.
problem Efficiently annotate object detection datasets in indoor scenes.
method Two-stage annotation process: manual annotation of a part of the dataset, followed by automatic annotation of the rest.
result The two-stage method significantly reduces the total workload compared to manual annotation of the entire dataset.
RL-based power control improves VoLTE performance in indoor small cells.
problem Improving VoLTE performance in indoor small cells with RL.
method RL-based closed-loop power control algorithm.
result Significant improvement in voice retainability and MOS.
Proposes efficient calibration for indoor localization models.
problem Calibration data scarcity in wireless indoor localization.
method Uses synthetic labels and prediction sets to fine-tune a predictor and estimate bias.
result Yields rigorous coverage guarantees for prediction sets.
New DNN architecture for indoor localization in multi-story buildings.
problem Scalable indoor localization in complex multi-story buildings.
method Stacked autoencoder and feed-forward classifier for multi-label classification.
result Near state-of-the-art performance with lower complexity and energy consumption.
This paper improves indoor positioning accuracy by deploying reference nodes to ensure Line-of-Sight.
problem Systematic bias errors in indoor positioning due to non-LoS propagation.
method Model indoor service area as a graph, partition into cliques for reference nodes, set minimum distance and angle parameters.
result Guaranteed LoS to reference nodes improves indoor positioning accuracy and precision.
Paper tackles indoor localization issues with reduced dimensionality and improved fingerprint matching.
problem Curse of dimensionality and asymmetric matching in fingerprint-based indoor localization.
method Proposes a semi-supervised RSS dimensionality reduction algorithm and integrates it with a fingerprint-based algorithm.
result Improves indoor localization accuracy by reducing dimensionality and addressing matching issues.
NAVREN-RL uses deep reinforcement learning to teach drones indoor navigation.
problem Teaching drones to navigate indoor environments efficiently.
method End-to-end deep reinforcement learning with a custom reward function, expert data integration.
result Drones successfully navigate indoor arenas avoiding obstacles.
The paper uses Bayesian Surprise to identify unexpected structures in indoor environments.
problem Identifying unexpected structures in indoor environments.
method Bayesian Surprise applied to Isovist Analysis of 2D floor plans.
result Surprise regions in indoor environments can be used to focus on important areas in LBS.
RETR improves indoor radar perception with a novel transformer model.
problem Indoor radar perception lacks models tailored for multi-view radar settings.
method RETR extends DETR architecture with depth-prioritized feature similarity, tri-plane loss, and radar-to-camera transformation.
result RETR outperforms state-of-the-art methods by 15.38+ AP for object detection and 11.91+ IoU for instance segmentation.
Autoencoder neural networks reconstruct missing indoor environment data.
problem Missing data in building operation data sets.
method Three different autoencoder neural networks trained to reconstruct missing data.
result Reconstructing variables with average RMSEs of 0.42 °C, 1.30 % and 78.41 ppm.
Paper uses RNNs for more accurate indoor WiFi localization.
problem Accurate indoor WiFi localization using RSSI measurements.
method Proposes recurrent neural networks (RNNs) for trajectory positioning of RSSI data.
result Achieves an average localization error of 0.75 m with 80% under 1 m, outperforming conventional algorithms.
Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.
problem Challenges in deploying optical wireless networks due to LoS requirement and limited range.
method Graph modeling approach to identify minimum number of APs and their optimal locations.
result Optimal deployment of APs ensures connectivity and minimizes interference in indoor environments.
New method combines neural networks and data assimilation for indoor air quality prediction.
problem Accurate and fast prediction of indoor air quality using real-time data.
method Combines Data Assimilation and Machine Learning using a Convolutional neural network and Long-Short-Term-Memory.
result Improved accuracy of dynamic system representation by integrating real data.
Paper proposes a SIMO DNN for indoor localization using Wi-Fi fingerprints.
problem Indoor localization with high accuracy and efficiency.
method Single-input and multi-output deep neural network architecture.
result SIMO-DNN scheme outperforms existing methods in floor detection and location accuracy.
New method uses SVBI to quickly generate radio maps for indoor positioning.
problem Time-consuming site surveys for creating radio maps in large indoor spaces.
method Combines fingerprinting positioning and SVBI for efficient radio map generation.
result Significantly reduces the cost and time of creating radio maps for indoor positioning.
Paper proposes CNN-LSTM for WiFi indoor localization.
problem Classical WiFi localization methods using only spatial data.
method Combines CNN and LSTM for CSI feature extraction.
result Achieves 2.5~m average localization error with 80% under 4~m.
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…
Two deep learning models improve indoor location prediction from WiFi fingerprints.
problem Indoor location prediction from WiFi fingerprints.
method Convolutional mixture density recurrent neural network and VAE-based semi-supervised learning model.
result Proposed models outperform existing methods in real-world datasets.
Automatically infers high dynamic range illumination from a single indoor photo.
problem Predicting accurate indoor illumination from a single image.
method End-to-end deep neural network trained in three steps: lighting classifier, scene light localization, and fine-tuning for intensity prediction.
result Significantly outperforms previous methods in recovering high-quality HDR illumination.
Efficiently maps indoor magnetic fields with SKI and D-SKI.
problem Computing large-scale magnetic field maps in indoor environments.
method Structured kernel interpolation (SKI) with derivatives (D-SKI) for Gaussian process regression.
result Achieves better accuracy and faster computation than state-of-the-art methods.
The paper optimizes predicting future window states in buildings using past climate data.
problem Predicting future window opening actions based on past climate data.
method Trained a deep neural network to predict window states using indoor climate data sequences of 30-240 time-steps.
result Optimal predictive performance achieved with 60 time-steps of indoor climate data, and long sequences could be addressed efficiently.
VNLA uses vision and language to guide agents in finding objects in indoor environments.
problem Guiding agents in finding objects in indoor environments via language.
method Developed I3L framework for imitation learning with indirect intervention.
result Significantly improved success rate of learning agents over baselines.
AGML model improves indoor localization with sparse fingerprints using meta-learning and graph neural networks.
problem Maintaining high localization accuracy with extremely sparse fingerprints.
method Attentional Graph Neural Network (AGNN) and meta-learning framework with data augmentation strategies.
result AGML model consistently outperforms baseline methods across various metrics.
Paper proposes a new Markov model for efficient PLC system design.
problem Efficient estimation of Markov model parameters for bursty error channels.
method Introduced a Block Diagonal Markov model and a modified Baum-Welch algorithm.
result Efficient estimation of state transition matrix Λ for PLC system design. Paper presents a new Wi-Fi RSS and geomagnetic field database for indoor localization and trajectory estimation.
problem Indoor localization and trajectory estimation challenges.
method Convolutional neural network (CNN) for RSS data and LSTM network for geomagnetic field intensity.
result CNN and LSTM networks show feasibility for localization and trajectory estimation.
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…
Improves magnetic field mapping using an array of magnetometers with noisy input.
problem Improving magnetic field maps in indoor environments with noisy magnetometer data.
method Uses Gaussian process regression with an array of magnetometers, incorporating known array positions and relative magnetometer locations.
result The method produces higher quality magnetic field maps compared to using a single magnetometer.
Study classifies surface types for autonomous indoor robots using inertial data.
problem Classifying surface types for wheeled robots in indoor environments.
method Prepared a time series dataset of inertial measurements, used deep learning and ensemble machine learning models.
result Baseline model achieved over 68% accuracy on a nine-category surface type dataset.
Paper proposes a feature-wise change detection method for improving indoor positioning accuracy.
problem Improving the quality of reference fingerprint maps in indoor positioning systems.
method Inspired by RANSAC, the paper uses resampling of features to estimate intermediate locations and identifies candidate locations using MJI.
result The approach improves positioning accuracy by 20% and achieves 90% change detection accuracy.
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 …
Reduced computational complexity for indoor positioning.
problem Efficiently positioning users in large areas with limited data.
method Segmentation, Jaccard index, LASSO feature selection, Bayesian MAP estimation.
result Positioning accuracy with a subset of features and sub-regions is equivalent to full data, but computation time is reduced.
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…
Bayesian segmentation and uncertainty estimation improve 3D model accuracy for factory planning.
problem Generating accurate 3D models from outdated and incomplete 2D data.
method Bayesian neural network for point cloud segmentation and entropy-based uncertainty estimation.
result Bayesian segmentation network significantly improves model accuracy and object identification.
SLAM-net learns to navigate visually in challenging indoor environments.
problem Challenges in SLAM for visual robot navigation, especially in noisy conditions.
method Differentiable SLAM Network (SLAM-net) that encodes a particle filter SLAM algorithm in a differentiable graph and learns components through backpropagation.
result Significantly outperforms ORB-SLAM in noisy conditions and improves the Habitat Challenge 2020 PointNav task.
GANs improve path planning for smart mobility applications.
problem Improving path planning for smart mobility applications.
method Generative Adversarial Networks (GANs) for path planning.
result Generated paths are correct and reliable with high accuracy and quality.
A new method learns continuous occupancy fields efficiently using the Ising model.
problem Efficiently learning continuous occupancy fields for robot navigation.
method Generalizes the Ising model to model continuous occupancy fields, using a unique kernel for range measurements.
result The method is computationally efficient and requires only a small number of hyperparameters.
Paper proposes RL algorithms for optimizing cellular network performance.
problem Improving cellular network performance against wireless impairments.
method Formulated as a reinforcement learning problem and developed two algorithms: PC and SON.
result RL algorithms outperform industry standards in simulated environments.
MOAI evaluates indoor airflow's impact on COVID-19 transmission.
problem Understanding indoor airflow's role in COVID-19 transmission.
method Developed a privacy-preserving app and model to evaluate risk exposure.
result Quantified factors contributing to higher or lower contamination in settings.
CDM improves fingerprinting-based positioning accuracy.
problem Quantifying similarity of collections with missing attributes.
method Combining vector-based distance metrics and set operations.
result Improves positioning accuracy by 5% on average.
In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. We describe a local entropy approximation for this general formulation using duality, and derive an efficient messa…
Learning structured outputs with general structures is computationally challenging, except for tree-structured models. Thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. The idea is based on the realization that a graph is a superimposition of trees. Different from most existing work, our…
Improved robot navigation using multi-head attention for natural language instructions.
problem Improving robot navigation in unfamiliar environments.
method Proposes a multi-head attention mechanism blending layer in a neural network model.
result Significant performance gains in translating instructions for unseen environments.
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorit…