| 281. |
- Laureshyn, Aliaksei, et al.
(författare)
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From Speed Profile Data to Analysis of Behaviour
- 2009
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Ingår i: IATSS Research. - 0386-1112. ; 33:2, s. 88-98
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Tidskriftsartikel (refereegranskat)abstract
- Classification of speed profiles is necessary to allow interpretation of automatic speed measurements in terms of road user behaviour. Aggregation without considering variation in individual profile shapes easily leads to aggregation bias, while classification based on exogenous criteria runs the risk of loosing important information on behavioural (co-) variation. In this paper we test how three pattern recognition techniques (cluster analysis, supervised learning and dimension reduction) can be applied to automatically classify the shapes of speed profiles of individual vehicles into interpretable types, with a minimum of a priori assumptions. The data for the tests is obtained from an automated video analysis system and the results of automated classification are compared to the classification by a human observer done from the video. Normalisation of the speed profiles to a constant number of data points with the same spatial reference allows them to be treated as multidimensional vectors. The k-means clustering algorithm groups the vectors (profiles) based on their proximity in multidimensional space. The results are satisfactory, but still the least successful among the tested techniques. Supervised learning (nearest neighbour algorithm tested) uses a training dataset produced beforehand to assign a profile to a specific group. Manual selection of the profiles for the training dataset allows better control of the output results and the classification results are the most successful in the tests. Dimension reduction techniques decrease the amount of data representing each profile by extracting the most typical “features”, which allows for better data visualisation and simplifies the classification procedures afterwards. The singular value decomposition (SVD) used in the test performs quite satisfactorily. The general conclusion is that pattern recognition techniques perform well in automated classification of speed profiles compared to classification by a human observer. However, there are no given rules on which technique will perform best.
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| 282. |
- Li, Xuhong, et al.
(författare)
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Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components
- 2019
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Ingår i: IEEE Transactions on Wireless Communications. - 1536-1276. ; 18:9, s. 4254-4267
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multipleoutput (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and therefore also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40 MHz. The results show that high accuracy localization is possible even with such a low bandwidth.
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| 283. |
- Li, Xuhong, et al.
(författare)
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Robust Phase-Based Positioning Using Massive MIMO with Limited Bandwidth
- 2018
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Ingår i: 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017.. - 9781538635292 - 9781538635322
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Konferensbidrag (refereegranskat)abstract
- This paper presents a robust phase-based positioningframework using a massive multiple-input multiple-output(MIMO) system. The phase-based distance estimates of MPCstogether with other parameters are tracked with an ExtendedKalman Filter (EKF), the state dimension of which varies withthe birth-death processes of paths. The iterative maximumlikelihoodestimation algorithm (RIMAX) and the modeling ofdense multipath component (DMC) in the framework furtherenhance the quality of parameter tracking by providing anaccurate initial state and the underlying noise covariance.The tracked MPCs are fed into a time-of-arrival (TOA) selfcalibrationpositioning algorithm for simultaneous trajectoryand environment estimation. Throughout the positioning process,no prior knowledge of the surrounding environment andbase station position is needed. The performance is evaluatedwith the measurement of a 2D complex movement, which wasperformed in a sports hall with an antenna array with 128 portsas base station using a standard cellular bandwidth of 40 MHz.The positioning result shows that the mean deviation of theestimated user equipment trajectory from the ground truth is13 cm. In summary, the proposed framework is a promisinghigh-resolution radio-based positioning solution for current andnext generation cellular systems.
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| 284. |
- Li, Zhen, et al.
(författare)
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Cellular traction forces : a useful parameter in cancer research
- 2017
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Ingår i: Nanoscale. - : Royal Society of Chemistry (RSC). - 2040-3372 .- 2040-3364. ; 9:48, s. 19039-19044
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Tidskriftsartikel (refereegranskat)abstract
- The search for new cancer biomarkers is essential for fundamental research, diagnostics, as well as for patient treatment and monitoring. Whereas most cancer biomarkers are biomolecules, an increasing number of studies show that mechanical cues are promising biomarker candidates. Although cell deformability has been shown to be a possible cancer biomarker, cellular forces as cancer biomarkers have been left largely unexplored. Here, we measure traction forces of cancer and normal-like cells at high spatial resolution using a robust method based on dense vertical arrays of nanowires. A force map is created using automated image analysis based on the localization of the fluorescent tips of the nanowires. We show that the force distribution and magnitude differ between MCF7 breast cancer cells and MCF10A normal-like breast epithelial cells, and that monitoring traction forces can be used to investigate the effects of anticancer drugs.
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| 285. |
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| 286. |
- Linderoth, Magnus, et al.
(författare)
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Object Tracking with Measurements from Single or Multiple Cameras
- 2010
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. - 9781424450381 ; , s. 4525-4530
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Konferensbidrag (refereegranskat)abstract
- To be able to determine the position of a static object in 3D space by means of computer vision, it has to be seen by cameras from at least two different view points. The same applies for measuring the position of a moving object based on images captured at one single time instant. However, if the cameras are not synchronized in time, or if a moving object is not visible in all images, one can not rely on using matching pictures for making accurate position estimates of dynamical objects. This paper presents a strategy to track an object with known dynamical model, using a series of images where no pair has to be captured simultaneously. It even allows tracking of a point object in 3D space using a single static camera.
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| 287. |
- Linderoth, Magnus, et al.
(författare)
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Vision Based Tracker for Dart-Catching Robot
- 2009
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Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). - 2405-8963. ; 42:16, s. 717-722
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes how high-speed computer vision can be used in a motion control application. The specific application investigated is a dart catching robot. Computer vision is used to detect a flying dart and a filtering algorithm predicts its future trajectory. This will give data to a robot controller allowing it to catch the dart. The performance of the implemented components indicates that the dart catching application can be made to work well. Conclusions are also made about what features of the system are critical for performance.
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| 288. |
- Lindström, Johan, et al.
(författare)
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Background and Foreground Modeling Using an Online EM Algorithm
- 2006
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Ingår i: IEEE International Workshop on Visual Surveillance. ; VS2006, s. 9-16
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Konferensbidrag (refereegranskat)abstract
- A novel approach to background/foreground segmentation using an online EM algorithm is presented. The method models each layer as a Gaussian mixture, with local, per pixel, parameters for the background layer and global parameters for the foreground layer, utilising information from the entire scene when estimating the foreground. Additionally, the online EM algorithm uses a progressive learning rate where the relative update speed of each Gaussian component depends on how often the component has been observed. It is shown that the progressive learning rate follows naturally from introduction of a forgetting factor in the log-likelihood. To reduce the number of mixture components similar foreground components are merged using a method based on the Kullback-Leibler distance. A bias is introduced in the variance estimates to avoid the known problem of singularities in the log-likelihood of Gaussian mixtures when the variance tends to zero. To allow a decoupling of the learning rate of the Gaussian components and the speed at which stationary objects are incorporated into the background a CUSUM detector is used instead of the prevailing method that uses the ratio of prior probability to standard deviation. The algorithm is scale invariant and its properties on gray-scale and RGB videos, as well as on output from an edge detector, is compared to that of another algorithm. Especially for the edge detector video performance increases dramatically.
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| 289. |
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| 290. |
- Lomsky, Milan, 1948, et al.
(författare)
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A new automated method for analysis of gated-SPECT images based on a three-dimensional heart shaped model
- 2005
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Ingår i: Clin Physiol Funct Imaging. - 1475-0961. ; 25:4, s. 234-40
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Tidskriftsartikel (refereegranskat)abstract
- A new automated method for quantification of left ventricular function from gated-single photon emission computed tomography (SPECT) images has been developed. The method for quantification of cardiac function (CAFU) is based on a heart shaped model and the active shape algorithm. The model contains statistical information of the variability of left ventricular shape. CAFU was adjusted based on the results from the analysis of five simulated gated-SPECT studies with well defined volumes of the left ventricle. The digital phantom NURBS-based Cardiac-Torso (NCAT) and the Monte-Carlo method SIMIND were used to simulate the studies. Finally CAFU was validated on ten rest studies from patients referred for routine stress/rest myocardial perfusion scintigraphy and compared with Cedar-Sinai quantitative gated-SPECT (QGS), a commercially available program for quantification of gated-SPECT images. The maximal differences between the CAFU estimations and the true left ventricular volumes of the digital phantoms were 11 ml for the end-diastolic volume (EDV), 3 ml for the end-systolic volume (ESV) and 3% for the ejection fraction (EF). The largest differences were seen in the smallest heart. In the patient group the EDV calculated using QGS and CAFU showed good agreement for large hearts and higher CAFU values compared with QGS for the smaller hearts. In the larger hearts, ESV was much larger for QGS than for CAFU both in the phantom and patient studies. In the smallest hearts there was good agreement between QGS and CAFU. The findings of this study indicate that our new automated method for quantification of gated-SPECT images can accurately measure left ventricular volumes and EF.
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