| 1. |
- Karlsson, Alexander, et al.
(författare)
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Anomaly-Based Drone Classification Using a Model Trained Convolutional Neural Network Autoencoder on Radar Micro-Doppler
- 2023
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Ingår i: 2023 IEEE International Radar Conference, RADAR 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- We present an anomaly-based drone classification scheme. High dimensional spectrum data is encoded using a convolutional neural network autoencoder. This is trained on data generated from a generic mathematical drone model. Once encoded, we use quadratic discriminant analysis for non-drone classes and define anomalies in terms of the log likelihood and prior knowledge from the drone model. When integrating ten samples, we can discriminate drones from non-drone samples such as birds, with an average accuracy of 98% at 20 dB signal to noise ratio. This corresponds to an effective observation time of 90 ms.
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| 2. |
- Karlsson, Alexander, et al.
(författare)
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Data Driven Track Before Detect Using Artificial Neural Networks
- 2023
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Ingår i: 2023 IEEE International Radar Conference, RADAR 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- We present two neural network solutions for data driven track before detect applications. The detected tracks may be used to estimate good initial states for traditional trackers such as Kalman filters. We evaluate the method on different scenarios with multiple targets, non-linear trajectories, and different signal to noise ratio (SNR) values. Depending on scenario, the presented method achieves 99% detection probability on Swerling 3 and 4 targets at 5 - 13 dB SNR, with 0.04 - 0.001 false tracks per frame. The presented method is compared to a theoretically optimal detector.
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| 3. |
- Kenet, T., et al.
(författare)
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Disconnectivity of the cortical ocular motor control network in autism spectrum disorders
- 2012
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Ingår i: Neuroimage. - : Elsevier BV. - 1053-8119. ; 61:4, s. 1226-1234
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Tidskriftsartikel (refereegranskat)abstract
- Response inhibition, or the suppression of prepotent but contextually inappropriate behaviors, is essential to adaptive, flexible responding. Individuals with autism spectrum disorders (ASD) consistently show deficient response inhibition during antisaccades. In our prior functional MRI study, impaired antisaccade performance was accompanied by reduced functional connectivity between the frontal eye field (FEF) and dorsal anterior cingulate cortex (dACC), regions critical to volitional ocular motor control. Here we employed magnetoencephalography (MEG) to examine the spectral characteristics of this reduced connectivity. We focused on coherence between FEF and dACC during the preparatory period of antisaccade and prosaccade trials, which occurs after the presentation of the task cue and before the imperative stimulus. We found significant group differences in alpha band mediated coherence. Specifically, neurotypical participants showed significant alpha band coherence between the right inferior FEF and right dACC and between the left superior FEF and bilateral dACC across antisaccade, prosaccade, and fixation conditions. Relative to the neurotypical group, ASD participants showed reduced coherence between these regions in all three conditions. Moreover, while neurotypical participants showed increased coherence between the right inferior FEF and the right dACC in preparation for an antisaccade compared to a prosaccade or fixation, ASD participants failed to show a similar increase in preparation for the more demanding antisaccade. These findings demonstrate reduced long-range functional connectivity in ASD, specifically in the alpha band. The failure in the ASD group to increase alpha band coherence with increasing task demand may reflect deficient top-down recruitment of additional neural resources in preparation to perform a difficult task. (C) 2012 Elsevier Inc. All rights reserved.
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