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.
Paper presents a new Wi-Fi RSS and geomagnetic field database for indoor localization and trajectory estimation.
The paper detects entrance positions using Wi-Fi and GPS signals.
Study develops a semi-supervised deep ResNet for Wi-Fi mode detection.
WiPIN uses Wi-Fi signals to identify people without requiring them to walk.
Deep learning predicts user identity, activity, and location from Wi-Fi signals.
Paper proposes a SIMO DNN for indoor localization using Wi-Fi fingerprints.
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…
Proposes Siegel neural networks for improved classification tasks.
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…
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…
Paper proposes DRL for unsupervised IoT localization.
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…
RecSim creates customizable simulation environments for RSs.
A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points. First, we obtain some {\…
New proof shows random neural networks contain sparse subnetworks.
Noise-ignorant empirical risk minimization achieves state-of-the-art performance on noisy data.
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…
Paper presents an energy-efficient RL method for sensor networks.
DeepPos uses deep learning to improve indoor location accuracy.
Gradient optimization improves preference elicitation for large item spaces.
Detects synchronized behavior in streaming data.
System states that are anomalous from the perspective of a domain expert occur frequently in some anomaly detection problems. The performance of commonly used unsupervised anomaly detection methods may suffer in that setting, because they use frequency as a proxy for anomaly. We propose a novel concept for anomaly dete…
Inspired by the question of identifying the start time of financial bubbles, we address the calibration of time series in which the inception of the latest regime of interest is unknown. By taking into account the tendency of a given model to overfit data, we introduce the Lagrange regularisation of the normalised …
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…
New job recommendation system improves job seekers' welfare through field experiments.
Quantum analysis tags news for sentiment and entities.
We study contact structures compatible with genus one open book decompositions with one boundary component. Any monodromy for such an open book can be written as a product of Dehn twists around dual non-separating curves in the once-punctured torus. Given such a product, we supply an algorithm to determine whether the …
Deep learning model classifies concurrent human interactions from WiFi data with high accuracy.
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than the…
In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of safety assurance --- wh…
LOCA learns standardized data coordinates from measurements.
Enhances network monitoring with interpretable data analysis.
Recursive filtering predicts wireless interference levels accurately.
Landmark2Vec maps unknown landmarks without GPS.
With the development and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. In order to counter security threats posed by rogue or unknown transmitters, it is important to identify RF transmitters not by the data cont…
Method trains vision and control policies on real robots quickly.
Hotel2vec learns hotel embeddings from multiple data sources.
Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable. A recent approach for finding a common representation of the two domains is via domain adversarial training (Ganin & Lempitsky, 2015), which attempt…
Shredder reduces inference privacy by adding noise to data without significantly affecting accuracy.