| 1. |
- Alqaysi, Hiba, et al.
(författare)
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Cost Optimized Design of Multi-Camera Domefor Volumetric Surveillance
- 2021
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Ingår i: IEEE Sensors Journal. - 1530-437X .- 1558-1748. ; 21:3, s. 3730-3737
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Tidskriftsartikel (refereegranskat)abstract
- A multi-camera dome consists of number ofcameras arranged in layers to monitor a hemisphere aroundits center. In volumetric surveillance,a 3D space is required tobemonitoredwhich can be achievedby implementing numberof multi-camera domes. A monitoring height is consideredas a constraint to ensure full coverage of the space belowit. Accordingly, the multi-camera dome can be redesignedinto a cylinder such that each of its multiple layers hasdifferent coverage radius. Minimum monitoring constraintsshould be met at all layers. This work is presenting a costoptimized design for the multi-camera dome that maximizesits coverage. The cost per node and number of squaremetersper dollar of multiple configurations are calculated using asearch space of cameras and considering a set of monitoring and coverage constraints. The proposed design is costoptimized per node and provides more coverage as compared to the hemispherical multi-camera dome.
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| 2. |
- An, Sining, 1991, et al.
(författare)
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Micrometer Accuracy Phase Modulated Radar for Distance Measurement and Monitoring
- 2020
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Ingår i: IEEE Sensors Journal. - 1558-1748 .- 1530-437X. ; 20:6, s. 2919-2927
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Tidskriftsartikel (refereegranskat)abstract
- An enhanced accuracy random binary phase modulated radar is proposed. It can be used in high accuracy monitoring in manufacturing. Compared with the traditional high accuracy radar using frequency modulated continuous wave (FMCW), the proposed radar system can be used in a multi-user scenario without occupying more bandwidth. A two-step distance estimation method is introduced to estimate the distance. First, the distance estimation accuracy is narrowed down to a half carrier wavelength by analyzing the envelope of the phase modulated signal. Then the carrier phase information increases the distance accuracy to several micrometers. An equalization method is introduced to solve the I/Q imbalance problem. The proposed radar system is demonstrated at a carrier frequency of 80 GHz with a bandwidth of 2 GHz. The measured distance error was within ±7 μm. In addition, a high measurement repetition rate of 500 kHz was reached which is suitable for real-time monitoring in automatic manufacturing.
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| 3. |
- Athanasiou, Vasileios, 1988, et al.
(författare)
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On Sensing Principles Using Temporally Extended Bar Codes
- 2020
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Ingår i: IEEE Sensors Journal. - 1558-1748 .- 1530-437X. ; 20:13, s. 6782-6791
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Tidskriftsartikel (refereegranskat)abstract
- The detection of ionic variation patterns could be a significant marker for the diagnosis of neurological and other diseases. This paper introduces a novel idea for training chemical sensors to recognise patterns of ionic variations. By using an external voltage signal, a sensor can be trained to output distinct time-series signals depending on the state of the ionic solution. Those sequences can be analysed by a relatively simple readout layer for diagnostic purposes. The idea is demonstrated on a chemical sensor that is sensitive to zinc ions with a simple goal of classifying zinc ionic variations as either stable or varying. The study features both theoretical and experimental results. By extensive numerical simulations, it has been shown that the proposed method works successfully in silico. Distinct time-series signals are found which occur with a high probability under only one class of ionic variations. The related experimental results point in the right direction.
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| 4. |
- Carlsson, Håkan, et al.
(författare)
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Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays
- 2021
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 21:17, s. 19362-19373
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Tidskriftsartikel (refereegranskat)abstract
- We present an algorithm to estimate and quantify the uncertainty of the accelerometers' relative geometry in an inertial sensor array. We formulate the calibration problem as a Bayesian estimation problem and propose an algorithm that samples the accelerometer positions' posterior distribution using Markov chain Monte Carlo. By identifying linear substructures of the measurement model, the unknown linear motion parameters are analytically marginalized, and the remaining non-linear motion parameters are numerically marginalized. The numerical marginalization occurs in a low dimensional space where the gyroscopes give information about the motion. This combination of information from gyroscopes and analytical marginalization allows the user to make no assumptions of the motion before the calibration. It thus enables the user to estimate the accelerometer positions' relative geometry by simply exposing the array to arbitrary twisting motion. We show that the calibration algorithm gives good results on both simulated and experimental data, despite sampling a high dimensional space.
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| 5. |
- Carlsson, Håkan, et al.
(författare)
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Self-Calibration of Inertial Sensor Arrays
- 2021
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 21:6, s. 8451-8463
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Tidskriftsartikel (refereegranskat)abstract
- A maximum likelihood estimator is presented for self-calibrating both accelerometers and gyroscopes in an inertial sensor array, including scale factors, misalignments, biases, and sensor positions. By simultaneous estimation of the calibration parameters and the motion dynamics of the array, external equipment is not required for the method. A computational efficient iterative optimization method is proposed where the calibration problem is divided into smaller subproblems. Further, an identifiability analysis of the calibration problem is presented. The analysis shows that it is sufficient to know the magnitude of the local gravity vector and the average scale factor gain of the gyroscopes, and that the array is exposed to two types of motions for the calibration problem to be well defined. The proposed estimator is evaluated by real-world experiments and by Monte Carlo simulations. The results show that the parameters can be consistently estimated and that the calibration significantly improves the accuracy of the motion estimation. This enables on-the-fly calibration of small inertial sensors arrays by simply twisting them by hand.
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| 6. |
- Chen, Guang, et al.
(författare)
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A Novel Visible Light Positioning System With Event-Based Neuromorphic Vision Sensor
- 2020
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 20:17, s. 10211-10219
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Tidskriftsartikel (refereegranskat)abstract
- With the advanced development of image processing technology, visible light positioning (VLP) system based on image sensors has attracted more and more attention. However, as a commonly used light receiver, traditional CMOS camera has limited dynamic range and high latency, which is susceptible to various lighting and environmental factors. Moreover, high computational cost from image processing is unavoidable for most of visible light positioning systems. In our work, a novel VLP system using an event-based neuromorphic vision sensor (event camera) as the light receiver is proposed. Due to the low latency and microsecond-level temporal resolution of the event camera, our VLP system is able to identify multiple high-frequency flickering LEDs in asynchronous events simultaneously leaving out the need for data association and traditional image processing methods. A multi-LED fusion method is applied and a high positioning accuracy of 3cm is achieved when the height between LEDs and the event camera is within 1m.
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| 7. |
- Chen, Guang, et al.
(författare)
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EDDD : Event-Based Drowsiness Driving Detection Through Facial Motion Analysis With Neuromorphic Vision Sensor
- 2020
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 20:11, s. 6170-6181
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Tidskriftsartikel (refereegranskat)abstract
- Drowsiness driving is a principal factor of many fatal traffic accidents. This paper presents the first event-based drowsiness driving detection (EDDD) system by using the recently developed neuromorphic vision sensor. Compared with traditional frame-based cameras, neuromorphic vision sensors, such as Dynamic Vision Sensors (DVS), have a high dynamic range and do not acquire full images at a fixed frame rate but rather have independent pixels that output intensity changes (called events) asynchronously at the time they occur. Since events are generated by moving edges in the scene, DVS is considered as an efficient and effective detector for the drowsiness driving-related motions. Based on this unique output, this work first proposes a highly efficient method to recognize and localize the driver's eyes and mouth motions from event streams. We further design and extract event-based drowsiness-related features directly from the event streams caused by eyes and mouths motions, then the EDDD model is established based on these features. Additionally, we provide the EDDD dataset, the first public dataset dedicated to event-based drowsiness driving detection. The EDDD dataset has 260 recordings in daytime and evening with several challenging scenes such as subjects wearing glasses/sunglasses. Experiments are conducted based on this dataset and demonstrate the high efficiency and accuracy of our method under different illumination conditions. As the first investigation of the usage of DVS in drowsiness driving detection applications, we hope that this work will inspire more event-based drowsiness driving detection research.
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| 8. |
- Choudhary, Anurag, et al.
(författare)
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Infrared Thermography-Based Fault Diagnosis of Induction Motor Bearings Using Machine Learning
- 2021
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 21:2, s. 1727-1734
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Tidskriftsartikel (refereegranskat)abstract
- Bearing is one of the most crucial parts in induction motor (IM) as a result there is a constant call for effective diagnosis of bearing faults for reliable operation. Infrared thermography (IRT) is appreciably used as a non-destructive and non-contact method to detect the bearing defects in a rotary machine. However, its performance is limited by insignificant information and string noise present in the infrared thermal image. To address this issue, an emergent two dimensional discrete wavelet transform (2D-DWT) based IRT method has been proposed in this article for diagnosing the different bearing faults in IM, namely, inner and outer race defects, and lack of lubrication. The dimensionality of the extracted features was reduced using principal component analysis (PCA) and thereafter the selected features were ranked in the order of most relevant features using the Mahalanobis distance (MD) method to achieve the optimal feature set. Finally these selected features have been passed to the complex decision tree (CDT), linear discriminant analysis (LDA) and support vector machine (SVM) for fault classification and performance evaluation. The classification results reveal that the SVM outperformed CDT and LDA. The proposed strategy can be used for self-adaptive recognition of bearing faults in IM which helps to avoid the unplanned and unwanted system shutdowns due to the bearing failure. © 2001-2012 IEEE.
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| 9. |
- Damiati, Samar, et al.
(författare)
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Flex Printed Circuit Board Implemented Grapene-Based DNA Sensor for Detection of SARS-CoV-2
- 2021
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 21:12, s. 13060-13067
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Tidskriftsartikel (refereegranskat)abstract
- Since the COVID-19 outbreak was declared a pandemic by the World Health Organization (WHO) in March 2020, ongoing efforts have been made to develop sensitive diagnostic platforms. Detection of viral RNA provides the highest sensitivity and specificity for detection of early and asymptomatic infections. Thus, this work aimed at developing a label-free genosensor composed of graphene as a working electrode that could be embedded into a flex printed circuit board (FPCB) for the rapid, sensitive, amplification-free and label-free detection of SARS-CoV-2. To facilitate liquid handling and ease of use, the developed biosensor was embedded with a user-friendly reservoir chamber. As a proof-of-concept, detection of a synthetic DNA strand matching the sequence of ORF1ab was performed as a two-step strategy involving the immobilization of a biotinylated complementary sequence on a streptavidin-modified surface, followed by hybridization with the target sequence recorded by the differential pulse voltammetric (DPV) technique in the presence of a ferro/ferricyanide redox couple. The effective design of the sensing platform improved its selectivity and sensitivity and allowed DNA quantification ranging from 100 fg/mL to 1 mu g/mL. Combining the electrochemical technique with FPCB enabled rapid detection of the target sequence using a small volume of the sample (5-20 mu L). We achieved a limit-of-detection of 100 fg/mL, whereas the predicted value was similar to 33 fg/mL, equivalent to approximately 5 x 10(5) copies/mL and comparable to sensitivities provided by isothermal nucleic acid amplification tests. We believe that the developed approach proves the ability of an FPCB-implemented DNA sensor to act as a potentially simpler and more affordable diagnostic assay for viral infections in Point-Of-Care (POC) applications.
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| 10. |
- Ghanem, Sally, et al.
(författare)
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Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion
- 2020
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Ingår i: IEEE Sensors Journal. - 1558-1748 .- 1530-437X. ; 20:20, s. 12307-12316
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Tidskriftsartikel (refereegranskat)abstract
- Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. We build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors' data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.
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