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 WiFi-based indoor sensor localization technique.
Paper proposes methods to localize sources in WSNs without knowing sensor parameters.
With the rising number of interconnected devices and sensors, modeling distributed sensor networks is of increasing interest. Recurrent neural networks (RNN) are considered particularly well suited for modeling sensory and streaming data. When predicting future behavior, incorporating information from neighboring senso…
Improved DOA estimation with distributed sensors across multiple frequencies.
Study uses Shapley value for sensor anomaly detection, proving its superiority in certain cases.
This letter presents an improved version of diffusion least mean ppower (LMP) algorithm for distributed estimation. Instead of sum of mean square errors, a weighted sum of mean square error is defined as the cost function for global and local cost functions of a network of sensors. The weight coefficients are updated b…
Many mobile robots rely on 2D laser scanners for localization, mapping, and navigation. However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring. In this work, instead of estimating the distance…
Optimizes resource allocation for distributed parameter estimation in sensor networks.
Proposes a neural network for handling multi-sensor time series with varying input dimensions.
Data-target pairing is an important step towards multi-target localization for the intelligent operation of unmanned systems. Target localization plays a crucial role in numerous applications, such as search, and rescue missions, traffic management and surveillance. The objective of this paper is to present an innovati…
Data-target association is an important step in multi-target localization for the intelligent operation of un- manned systems in numerous applications such as search and rescue, traffic management and surveillance. The objective of this paper is to present an innovative data association learning approach named multi-la…
This letter proposes a sparse diffusion steepest-descent algorithm for one bit compressed sensing in wireless sensor networks. The approach exploits the diffusion strategy from distributed learning in the one bit compressed sensing framework. To estimate a common sparse vector cooperatively from only the sign of measur…
Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning mod…
The paper optimizes sensor selection for network time series data.
Optimizes seismic monitoring networks using Bayesian OED.
Predicts traffic flow using reinforcement learning and sensor data.
This paper examines ADMM for network averaging, revealing its convergence rates and network topology impacts.
We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query bas…
We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regr…
Drone optimizes collaborative learning for neural networks.
This work builds a sensor graph from DC sensors for anomaly detection.
Predicts local AQI using mobile sensor data, improving accuracy by 71.654 MSE.
Low-cost sensors improve air quality prediction accuracy significantly.
New algorithms optimize decentralized convex optimization with near optimal communication and computation.
New method uses neural networks for accurate angle estimation in noisy conditions.
Deep RL predicts equipment maintenance from sensor data.
Neural networks outperform conventional filters in inertial sensor-based attitude estimation.
Study uses Bayes Hilbert framework to recover probability measure flows from sensors.
RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.
Deep learning improves sensor performance optimization.
VIDON learns operators with variable sensors, overcoming sensor limitations.
Radio tomographic imaging (RTI) is an emerging technology for localization of physical objects in a geographical area covered by wireless networks. With attenuation measurements collected at spatially distributed sensors, RTI capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at…
Paper proposes LATC for multivariate time series prediction and missing data imputation.
Machine Learning (ML) has been applied to enable many life-assisting appli-cations, such as abnormality detection and emdergency request for the soli-tary elderly. However, in most cases machine learning algorithms depend on the layout of the target Internet of Things (IoT) sensor network. Hence, to deploy an applicati…
Multimodal learning has been lacking principled ways of combining information from different modalities and learning a low-dimensional manifold of meaningful representations. We study multimodal learning and sensor fusion from a latent variable perspective. We first present a regularized recurrent attention filter for …
Self-attention model improves HAR from wearable sensors.
Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the r…
Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification. The likelihood functions of the hypotheses are often not known in advance, and decision rules have to be learned via supervised learning. A specific such algorithm is Fisher discriminant analysis (FDA),…
Paper develops robust neural network sensors for fuel injection quantities.
Study improves prediction accuracy and uncertainty for mobile sensor data using randomized neural networks.
We consider the exploration problem: an agent equipped with a depth sensor must map out a previously unknown environment using as few sensor measurements as possible. We propose an approach based on supervised learning of a greedy algorithm. We provide a bound on the optimality of the greedy algorithm using submodulari…
Optimal sensor placement minimizes information loss from simulations.
Detects which features have shifted in data distributions.
Optimizes sensor usage for detecting abrupt changes in sensor data.
In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps. A deep neural network architecture is used for mapping the vision and depth information, respectively, to steering commands. This fusion of information from two sensor sources allows to provide …
Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approac…
Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concen…
FSL-Net detects and localizes feature shifts in large, high-dimensional datasets.