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.
Wi-Fringe recognizes named gestures using WiFi CSI without training data.
Paper presents a WiFi-based indoor sensor localization technique.
Deep learning model classifies concurrent human interactions from WiFi data with high accuracy.
CMDRNN predicts user location using WiFi fingerprints with deep learning.
Neural networks predict airport passenger behavior using WiFi traces.
Paper proposes CNN-LSTM for WiFi indoor localization.
Two deep learning models improve indoor location prediction from WiFi fingerprints.
For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In pa…
Paper uses RNNs for more accurate indoor WiFi localization.
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…
Deep learning uses WiFi CSI for reliable human presence detection.
We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the representation of WiFi signals. Using CSI-Net, we jointly solved two body characterization problems: biometrics estimation (including body fat, muscle, water, and bone rates) and person recognition. We also demonstrated the application of CSI-Net on tw…
We utilize Wi-Fi communications from smartphones to predict their mobility mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four strategic locations in a closed loop on streets in downtown Toronto. Deep neural network (Multilayer Perceptron) along with three decision tree based classifiers (Decisi…
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…
Population migration is valuable information which leads to proper decision in urban-planning strategy, massive investment, and many other fields. For instance, inter-city migration is a posterior evidence to see if the government's constrain of population works, and inter-community immigration might be a prior evidenc…
We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold. A simple two step approach to this problem is to: (i) estimate the manifold geodesic distance between any pair of points using both the labeled and unlabeled instances; and (ii) apply a k nearest neighbor regressor b…
The preponderance of connected devices provides unprecedented opportunities for fine-grained monitoring of the public infrastructure. However while classical models expect high quality application-specific data streams, the promise of the Internet of Things (IoT) is that of an abundance of disparate and noisy datasets …
Complex-valued neural networks improve wireless fingerprinting robustness.
Landmark2Vec maps unknown landmarks without GPS.
Gait is a person's natural walking style and a complex biological process that is unique to each person. Recently, the channel state information (CSI) of WiFi devices have been exploited to capture human gait biometrics for user identification. However, the performance of existing CSI-based gait identification systems …
Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness. Most previous work applies gaits extracted from WiFi distortions caused by the person walking to achieve the identification. However, to extract useful gait, a p…
Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200subjects, each using their personal Android mobile device for a period of at least 30…
Deep learning models can learn confounding factors instead of device fingerprints in wireless signals.
With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare o…
Paper proposes methods to localize sources in WSNs without knowing sensor parameters.
Method discovers user habits from mobile data.
Deep learning identifies wireless interference sources efficiently.
Study neural communication systems with bandwidth-limited channels.
A novel GPUM constructs Gaussian Processes for unknown manifolds with probabilistic metrics.
AppsPred predicts smartphone app usage based on context.
A new weighted dissimilarity measure reduces positioning errors in feature-based systems.
AGML model improves indoor localization with sparse fingerprints using meta-learning and graph neural networks.
Self-supervised learning from unlabeled sensor data improves model performance in federated learning.
This paper tackles efficient testing strategies for COVID-19 by using a partially observable MDP approach.