Synthetic Sensor Data Generation Exploiting Deep Learning Techniques and Multimodal Information

Romanelli, Fabrizio; Martinelli, Francesco

doi:10.1109/lsens.2023.3290209

Cited by 5 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the following, several popular image processing methods and cell models are presented. Optical-based force sensors generally use a light source (e.g., light emitting diode [LED], laser, or halogen lamp) to illuminate a load-sensitive medium 32 Robotics for cell manipulation and characterization (e.g., microcantilever or grating) [4]. A photodetector (e.g., photodiode or CCD camera) is adopted to measure the ranks of illumination, refractive index, or spectrum of the reflected light from the load-sensitive medium.…”

Section: Robotics For Cell Manipulation and Characterizationmentioning

confidence: 99%

Survey on Sensor Application

Khalesi

2024

BJSTR

View full text Add to dashboard Cite

Measurement is a very important topic in every technical system. If we cannot measure some physical property in our system (temperature, pressure, humidity, force, torque, liquid level, etc.) with a desired precision and other characteristics, we cannot expect to have good control over the system. It is therefore very important to know how we can measure the desired property, what sensor principles we can use, what accuracy can we expect, where to place sensors to obtain correct readings, etc. The proper application of any process-monitoring device requires a good understanding of how sensors work and their advantages as well as their limitations. A chemical sensor is a self-contained device that is capable of providing realtime analytical information about a test sample. By chemical information we understand here the concentration of one or more chemical species in the sample. Ion sensors were the first type of chemical sensors to be developed and produced on a large scale. The pH glass electrode was the first widely used ion sensor. Enzymes form an important class of recognition receptors utilized in chemical sensors. Although isolated enzymes were initially used, it was soon realized that enzymes incorporated in biological materials (such as cells or tissues) can perform better due to the fact that they are in their natural environment.

show abstract

Section: Robotics For Cell Manipulation and Characterizationmentioning

confidence: 99%

Survey on Sensor Application

Khalesi

2024

BJSTR

View full text Add to dashboard Cite

show abstract

“…They can be trained on unlabeled data to learn meaningful features and then optimized for a particular supervised task, such as image classification or sentiment analysis. This process, known as pre-training, enables the model to leverage the learned representations and potentially improve performance on the supervised task [53,54].…”

Section: Autoencodersmentioning

confidence: 99%

Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids

Habbak,

Mahmoud,

Fouda

et al. 2023

Energies

View full text Add to dashboard Cite

In the smart grid, malicious consumers can hack their smart meters to report false power consumption readings to steal electricity. Developing a machine-learning based detector for identifying these readings is a challenge due to the unavailability of malicious datasets. Most of the existing works in the literature assume attacks to compute malicious data. These detectors are trained to identify these attacks, but they cannot identify new attacks, which creates a vulnerability. Very few papers in the literature tried to address this problem by investigating anomaly detectors trained solely on benign data, but they suffer from these limitations: (1) low detection accuracy and high false alarm; (2) the need for knowledge on the malicious data to compute good detection thresholds; and (3) they cannot capture the temporal correlations of the readings and do not address the class overlapping issue caused by some deceptive attacks. To address these limitations, this paper presents a deep support vector data description (DSVDD) based unsupervised detector for false data in smart grid. Time-series readings are transformed into images, and the detector is exclusively trained on benign images. Our experimental results demonstrate the superior performance of our detectors compared to existing approaches in the literature. Specifically, our proposed DSVDD-based schemes have exhibited improvements of 0.5% to 3% in terms of recall and 3% to 9% in terms of the Area Under the Curve (AUC) when compared to existing state-of-the-art detectors.

show abstract

Resilient Simultaneous Localization and Mapping Fusing Ultra Wide Band Range Measurements and Visual Odometry

Romanelli,

Martinelli,

Mattogno

2023

J Intell Robot Syst

View full text Add to dashboard Cite

In this paper we consider a Simultaneous Localization and Mapping (SLAM) problem for a moving agent using Visual Odometry (VO) while measuring the range from a set of Ultra Wide Band (UWB) antennas, deployed in unknown position in the environment. The solution approach is based on a switching observer which, under standard working conditions, for each observed UWB, uses a two dimensional Extended Kalman Filter (EKF) providing an estimate of the range and bearing of the observed UWB with respect to the agent. This information is then used in a Robust EKF algorithm which solves the SLAM problem with performances that, even before closing the loop, are comparable to the ones that a VO algorithm (namely ORB-SLAM2) would obtain only after closing the loop. Moreover, a resilient module is added to the algorithm to evaluate the reliability of the position estimate of each observed UWB. When the Visual Odometry is not available, the switching observer uses an auxiliary EKF to provide an estimate of the agent position. This makes the proposed approach robust with respect to several kinds of unmodeled disturbances, like multipath effects, and automatically adapts to sensor failures with resilience (e.g. when Visual Odometry or UWB measurements are not available).

show abstract

Synthetic Sensor Data Generation Exploiting Deep Learning Techniques and Multimodal Information

Cited by 5 publications

References 12 publications

Survey on Sensor Application

Survey on Sensor Application

Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids

Resilient Simultaneous Localization and Mapping Fusing Ultra Wide Band Range Measurements and Visual Odometry

Contact Info

Product

Resources

About