21 papers · ranked by Valyu relevance
Kai Liu, Meng-Ying Cui, Peng Cao, Jiang-Bo Wang + 1 more
On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved…
Gautam Thakur
This paper develops a mathematical and computational framework for analyzing the expected performance of Bayesian data fusion, or joint statistical inference, within a sensor network. We use variational techniques to obtain the posterior expectation as the optimal fusion rule under a deterministic constraint and a…
Jos Elfring, Rein Appeldoorn, Sjoerd van den Dries, Maurice Kwakkernaat + 4 more
The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data…
Ihsan Ullah, Ju-Bong Kim, Youn-Hee Han, Antonio Puliafito + 1 more
'Alberto Gotta'] The objective of smart cities is to improve the quality of life for citizens by using Information and Communication Technology (ICT). The smart IoT environment consists of multiple sensor devices that continuously produce a large amount of data. In the IoT system, accurate inference from multi-sensor…
Muhammad Abu Bakr, Sukhan Lee
The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering…
Nisar Ahmed, Tsung-Lin Yang, Mark Campbell
—Recent advances in communications, mobile computing, and artificial intelligence have greatly expanded the application space of intelligent distributed sensor networks. This in turn motivates the development of generalized Bayesian decentralized data fusion (DDF) algorithms for robust and efficient information sharing…
Xiaoying Wang, Reza Hoseinnezhad, Amirali K. Gostar, Tharindu Rathnayake + 2 more
'Tharindu Rathnayake' 'Benlian Xu' 'Alireza Bab‐Hadiashar'] This paper presents a novel statistical information fusion method to integrate multiple-view sensor data in multi-object tracking applications. The proposed method overcomes the drawbacks of the commonly used Generalized Covariance Intersection method, which…
Tatiana Berlenko, Kirill Krinkin, Zheng Chen
Comparisons of Bayesian log-odds and Dempster’s combination rule for occupancy grid mapping typically parameterize the two sensor models independently, so that observed performance differences confound the fusion rule with the sensor parameterization. We develop a pignistic-transform-based matching methodology that…
S. C. J. Robertson, Corné E. van Daalen, Johan A. du Preez
In this paper, we propose two efficient, approximate formulations of the multi-sensor labelled multi-Bernoulli (LMB) filter, which both allow the sensors' measurement updates to be computed in parallel. Our first filter is based on the direct mathematical manipulation of the multi-sensor, multi-object Bayes filter's…
Máté Aller, Uta Noppeney
To form a percept of the environment, the brain needs to solve the binding problem – inferring whether signals come from a common cause and be integrated, or come from independent causes and be segregated. Behaviourally, humans solve this problem near-optimally as predicted by Bayesian Causal Inference; but, the neural…
Peter Scarfe
Sensory cue integration is one of the primary areas in which a normative mathematical framework has been used to define the “optimal” way in which to make decisions based upon ambiguous sensory information and compare these predictions to behaviour. The conclusion from such studies is that sensory cues are integrated…
James R.H. Cooke, W. Pieter Medendorp
Causal inference, the process of inferring the causes of our sensory input, is central to multisensory perception. While most computational models of causal inference focus on static perceptual tasks with no temporal or motor components, real-world behavior unfolds dynamically and often involves closed-loop control.…
Lechi Li, Chen Dai, Yuxuan Xia, Lennart Svensson
The fusion of multiple probability densities has important applications in many fields, including, for example, multi-sensor signal processing, robotics, and smart environments. In this paper, we demonstrate that deep learning based methods can be used to fuse multiobject densities. Given a scenario with several…
Seth W. Egger, Mehrdad Jazayeri
Bayesian models of behavior have advanced the idea that humans combine prior beliefs and sensory observations to minimize uncertainty. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent and manipulate probability…
Murat Üney, B. Mulgrew, Daniel E. Clark
—Multi-sensor state space models underpin fusion applications in networks of sensors. Estimation of latent parameters in these models has the potential to provide highly desirable capabilities such as network self-calibration. Conventional solutions to the problem pose difficulties in scaling with the number of sensors…
Oleksii Tsepa, Roman Burakov, Brokoslaw Laschowski, Alex Mihailidis
Unlike traditional hierarchical controllers for robotic leg prostheses and exoskeletons, continuous systems could allow persons with mobility impairments to walk more naturally in real-world environments without requiring high-level switching between locomotion modes. To support these next-generation controllers, we…
Callan M. Gillespie, Lesley R. Arant, Joshua D. Roth, Robb W. Colbrunn
To quantify the contributions of specific ligaments to overall joint biomechanics, the principle of superposition has been used for nearly 30 years. This principle relies on a robotic test system to move a biological joint to the same pose before and after transecting a ligament. The magnitude of the vector difference…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
Identifying molecular structure based on spectroscopic readings is a key task in a va- riety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…