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REVIEW 4 major objections 4 minor 50 references

DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read DynamicPose claims that a closed loop of visual-inertial odometry, depth-informed 2D tracking, and a VIO-guided Kalman filter keeps 6D object pose locked while both camera and object move rapidly.

desk verdict Plausible and well-motivated closed-loop tracker for fast-motion 6D pose tracking, but the only readable evidence is the abstract, so the empirical claims are unverified. read the letter →

arxiv 2508.11950 v1 pith:F3E7XUXO submitted 2025-08-16 cs.CV cs.RO

Tingbang Liang , Yixin Zeng , Jiatong Xie , Boyu Zhou This is my paper
classification cs.CVcs.RO
keywords 6Dposetrackingvisual-inertialodometryKalmanfilterdepth-informedregionofinterestfastmotionretraining-freeRGB-D
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

DynamicPose sets out to prove that a 6D object pose tracker can keep working when the camera and the object are both moving quickly, a regime where earlier trackers that assume static or quasi-static scenes lose the object. The framework combines three parts: visual-inertial odometry to shift the region of interest when the camera moves, a depth-informed 2D tracker to adjust for large object translations, and a VIO-guided Kalman filter that predicts object rotation and refines candidate poses. These parts feed into each other in a closed loop. The payoff, if correct, is a retraining-free tracker that maintains accurate 6D pose under fast motion using RGB-D plus inertial data, which matters for robotics, drones, and augmented reality where quick camera and object motion is the norm rather than the exception.

What carries the argument

The load-bearing mechanism is the closed loop among three complementary estimators: a VIO module that predicts where the object's ROI has shifted due to camera motion; a depth-informed 2D tracker that corrects the ROI for large object translation; and a VIO-guided Kalman filter that predicts object rotation, samples multiple candidate poses, and refines them hierarchically. The refined 6D pose is fed back to the 2D tracker and the filter, so the system re-anchors itself each frame. The key property is that each component corrects the errors the others cannot see, and the loop carries the pose across fast motions.

What would settle it

Run the system on a fast-motion sequence with the IMU signal artificially corrupted, for example by adding an angular-rate bias, while keeping the visual input fixed; if tracking degrades to the same level as the non-VIO baseline, or if the loop diverges rather than recovering, the central claim fails. Alternatively, measure the maximum angular velocity at which tracking stays within an error threshold and compare it with the baseline; the claim predicts a sharply higher threshold.

Watch

Extended reading notes

Core claim

The paper's central claim is that the deterioration of existing 6D pose trackers under fast camera and object motion is not a fundamental limitation but a coordination problem, and that coordinating inertial, depth, and RGB cues in a closed loop solves it. Specifically, visual-inertial odometry compensates for the ROI shift caused by camera ego-motion; depth-informed 2D tracking corrects the ROI deviation caused by the object's own translation; and a VIO-guided Kalman filter predicts object rotation, generates several candidate poses, and selects the final pose by hierarchical refinement. The resulting pose then guides the next 2D tracking and Kalman updates. The authors assert that this loo

Load-bearing premise

The whole loop stands on the premise that the VIO estimate and the depth-informed 2D tracker remain accurate enough in fast motion that their errors stay within the search range of the candidate-pose refinement; if odometry or depth drifts farther than that, the loop feeds a biased pose back into itself and there is no recovery mechanism.

Editorial extensions

If this is right

  • If the claim holds, an existing RGB-D tracker can be made resilient to fast motion without retraining, by wrapping it in this VIO/ROI/Kalman loop.
  • The closed-loop design means the 6D pose output at each frame directly improves the next frame's ROI and rotation prediction, so tracking errors should not accumulate purely from object speed.
  • The system is designed to run in real time, so it could support onboard perception for robots and drones rather than offline pose recovery.
  • The method offers a concrete path to 6D pose tracking in handheld and mobile settings where camera ego-motion dominates, a case that quasi-static trackers handle poorly.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the closed-loop coordination story is right, the same architecture could be carried into category-level pose trackers or hand-held augmented reality, where the dominant failure is camera ego-motion rather than object motion.
  • A natural stress test the paper does not report: sweep IMU noise and object speed separately to find the loop's divergence boundary; the claim predicts graceful degradation only while VIO stays unbiased.
  • Because the method is retraining-free, it can be applied to unseen object instances immediately, so the practical competition is not learned trackers but classical geometric trackers and their motion models.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The manuscript presents DynamicPose, a retraining-free 6D object pose tracking framework aimed at fast-moving camera and object scenarios. The method combines three components: VIO-based ROI shift compensation, depth-informed 2D tracking, and a VIO-guided Kalman filter that generates candidate poses followed by hierarchical refinement. The pose estimate is fed back into the 2D tracker and Kalman update, forming a closed loop. The abstract claims that simulation and real-world experiments demonstrate real-time, robust 6D pose tracking under fast motion. In the version supplied for review, only the abstract is fully readable; the main text and tables are heavily corrupted (mojibake), so the technical derivations and quantitative evidence cannot be verified.

Significance. If the claimed results hold, DynamicPose would be a useful retraining-free extension of 6D pose trackers beyond quasi-static scenes, with potential applications in augmented reality and robotics. The proposed closed-loop architecture is plausible in principle and worth evaluating. However, the supplied manuscript provides insufficient readable evidence to judge whether the central claim is true: no concrete error metrics, baseline comparisons, ablations, or runtime numbers are accessible, and the closed-loop stability concern is not addressed in the readable portion.

major comments (4)
  1. [Abstract (closed-loop guarantee)] The sentence 'forming a closed-loop system that ensures accurate pose initialization and precise pose tracking' states a strong guarantee without specifying an error floor, recovery mechanism, or operating envelope. If VIO drift, IMU noise, or depth error biases the ROI and the Kalman candidate generation, the true pose can fall outside the candidate search range, and feeding the biased pose back into the next update can cause divergence. Please state the conditions under which the guarantee holds (e.g., maximum angular/linear velocities, IMU noise levels, depth error bounds) and provide failure-mode or recovery experiments for cases where the candidate set misses the true pose.
  2. [Experiments (visible tables/abstract)] The central claim is an empirical performance claim, but the only readable evidence is the abstract. The experimental section and tables are corrupted in the supplied text; I cannot extract any translation/rotation error, ADD(-S) score, baseline comparison, variance bar, or FPS number. To verify 'improves tracking robustness', the manuscript needs a clean, readable experimental section with quantitative comparisons against at least two quasi-static trackers on fast-motion sequences, separate reporting for camera motion, object motion, and combined motion, per-sequence speed regimes, and runtime with hardware specifications.
  3. [Method (Sections 3–4, equations)] The body text and equations are largely unreadable in the supplied version. I cannot verify the Kalman measurement model, the candidate-pose generation distribution, the hierarchical refinement objective, or the exact coupling between VIO and the 2D tracker. A clean manuscript with notation definitions and algorithm pseudocode is required for reproducibility. This is load-bearing because the stability of the closed loop depends on these details.
  4. [Free parameters and ablations] The method relies on several free parameters: Kalman process/measurement noise covariances, number of candidate poses per frame, and hierarchical refinement thresholds/iterations. No ablation for these parameters is visible in the readable material. If the robustness claim depends on specific hand-tuned values, the sensitivity should be reported; otherwise the claim that the method is robust across fast-motion scenarios is not supported.
minor comments (4)
  1. [Introduction/Related Work] The phrase 'Previous work is mainly applicable to static or quasi-static scenes' is too vague; please name representative prior methods and specify the failure modes (e.g., ROI drift, motion blur) that motivate the three proposed components.
  2. [Tables/figures] Several table fragments are visible with rows labeled 'DynamicPose', 'VIO', '2D Tracker', etc. and red/checker cells, but no legend or numeric values are readable. If these are qualitative heatmaps, please replace them with numeric error tables or add an explicit legend.
  3. [Notation] The partially readable equations use symbols such as \Theta, R, t and various subscripts without complete definitions in the visible text. Please ensure all notation is introduced and defined consistently.
  4. [References] The reference list is not readable in the supplied text. Please ensure all citations are complete and correctly formatted in the final version.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: the closed-loop tracking architecture is recursive estimation, not a self-referential proof.

full rationale

The paper's visible derivation chain consists of three coupled components: VIO-based ROI compensation, a depth-informed 2D tracker, and a VIO-guided Kalman filter with candidate-pose generation and hierarchical refinement, with pose feedback into subsequent tracking and filtering. Each component is described as producing inputs for the next, and the final pose is obtained by refinement over candidate poses. No equation or stated definition in the visible material defines one component in terms of the output pose, and no fitted parameter is renamed as a prediction. The 'closed-loop system' is a recursive estimation architecture—pose estimates guide the 2D tracker, and the 2D tracker constrains the pose—which is standard filtering, not circular evidence. The abstract's phrase 'ensures accurate pose initialization and precise pose tracking' is an empirical robustness claim, not a derivation; the absence of an explicit error floor or recovery mechanism for VIO failure is a stability/correctness concern, not a circularity. The full text was received as unreadable mojibake, so an equation-level audit could not be completed, but the visible abstract and fragments exhibit no self-citation chain, no definitional reduction, and no fitted-input-called-prediction pattern. Therefore no circular step is identifiable.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

All listed parameters are tuning choices invisible in the abstract. The paper inherits standard components (VIO, Kalman filter, 2D trackers) as assembled modules; the central unstated premise is that these modules keep their error bounds inside the refinement search range in the fast-motion regime. No new physical entities are introduced.

free parameters (3)
  • Kalman filter process and measurement noise covariances
    State-space tuning parameters for the VIO-guided rotation prediction and pose updates; values are not stated in the abstract and are typically hand-tuned per sensor rig.
  • Number of candidate poses per frame
    The VIO-guided Kalman filter 'generates multiple candidate poses'; the candidate count is a chosen hyperparameter trading speed against robustness, not given in the abstract.
  • Hierarchical refinement thresholds and iterations
    Final pose selection by 'hierarchical refinement' implies inlier and iteration thresholds that are implementation choices, unspecified in the abstract.
assumptions (4)
  • domain assumption VIO provides bounded-drift camera motion estimates during fast motion, including motion blur and agile trajectories
    Load-bearing for ROI compensation and rotation prediction; if VIO degrades in the target regime the loop is biased. Entered at component (1) and the closed-loop description.
  • domain assumption Depth data from the RGB-D sensor is accurate and time-aligned with the RGB stream
    The depth-informed 2D tracker corrects ROI deviations from object translation; corrupted or misaligned depth would break that correction.
  • domain assumption A usable initial 6D pose and object model are available at track start
    Tracking with candidate generation and refinement is initialization-dependent; the abstract does not describe re-detection or recovery from lost tracking.
  • domain assumption The object remains inside the corrected ROI so the 2D tracker and pose refinement receive valid observations
    The closed loop feeds pose estimates back into tracking; the abstract describes no mechanism for the object leaving the ROI.

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Cite this review

Pith. "Pith review of DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects." pith.science (2026). https://pith.science/paper/F3E7XUXO

@misc{pith2026250811950,
  author       = {Pith},
  title        = {Pith review of: DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F3E7XUXO}},
  note         = {Machine review of arXiv:2508.11950}
}
read the original abstract

We present DynamicPose, a retraining-free 6D pose tracking framework that improves tracking robustness in fast-moving camera and object scenarios. Previous work is mainly applicable to static or quasi-static scenes, and its performance significantly deteriorates when both the object and the camera move rapidly. To overcome these challenges, we propose three synergistic components: (1) A visual-inertial odometry compensates for the shift in the Region of Interest (ROI) caused by camera motion; (2) A depth-informed 2D tracker corrects ROI deviations caused by large object translation; (3) A VIO-guided Kalman filter predicts object rotation, generates multiple candidate poses, and then obtains the final pose by hierarchical refinement. The 6D pose tracking results guide subsequent 2D tracking and Kalman filter updates, forming a closed-loop system that ensures accurate pose initialization and precise pose tracking. Simulation and real-world experiments demonstrate the effectiveness of our method, achieving real-time and robust 6D pose tracking for fast-moving cameras and objects.

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Reference graph

Works this paper leans on

50 extracted references · 25 canonical work pages

  1. [1]

    11em plus .33em minus .07em @technote 4000 4000 100 4000 4000 500 `\.=1000 = #1 #1 #1 0pt [0pt][0pt] #1 * \| ** #1 \@IEEEauthorblockNstyle \@IEEEauthorblockAstyle \@IEEEauthordefaulttextstyle \@IEEEauthorblockconfadjspace -0.25em \@IEEEauthorblockNtopspace 0.0ex \@IEEEauthorblockAtopspace 0.0ex \@IEEEauthorblockNinterlinespace 2.6ex \@IEEEauthorblockAinte...

    work page

  2. [2]

    C. Wu, L. Chen, Z. He, and J. Jiang, ``Pseudo-siamese graph matching network for textureless objects' 6-d pose estimation,'' IEEE Transactions on Industrial Electronics, no. 69-3, 2022

    work page 2022

  3. [3]

    M. A. Fischler and R. C. Bolles, ``Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,'' vol. 24, no. 6, 1981. [Online]. Available: https://doi.org/10.1145/358669.358692

    work page arXiv 1981

  4. [4]

    Stoiber, M

    M. Stoiber, M. Pfanne, K. H. Strobl, R. Triebel, and A. Albu-Schaeffer, ``Srt3d: A sparse region-based 3d object tracking approach for the real world,'' International Journal of Computer Vision, vol. 130, no. 4, pp. 1008--1030, April 2022

    work page 2022

  5. [5]

    Georgakis, S

    G. Georgakis, S. Karanam, Z. Wu, and J. Kosecka, ``Learning local rgb-to-cad correspondences for object pose estimation,'' pp. 8966--8975, 2019

    work page 2019

  6. [6]

    B. Wen, W. Yang, J. Kautz, and S. Birchfield, ``Foundationpose: Unified 6d pose estimation and tracking of novel objects,'' in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 17\,868--17\,879

    work page 2024

  7. [7]

    B. Wen, C. Mitash, B. Ren, and K. E. Bekris, ``se(3)-tracknet: Data-driven 6d pose tracking by calibrating image residuals in synthetic domains,'' 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct 2020. [Online]. Available: http://dx.doi.org/10.1109/IROS45743.2020.9341314

    work page arXiv 2020

  8. [8]

    Y. He, W. Sun, H. Huang, J. Liu, H. Fan, and J. Sun, ``Pvn3d: A deep point-wise 3d keypoints voting network for 6dof pose estimation,'' June 2020

    work page 2020

Show all 50 references
  1. [9]

    Y. He, H. Huang, H. Fan, Q. Chen, and J. Sun, ``Ffb6d: A full flow bidirectional fusion network for 6d pose estimation,'' June 2021

    2021

  2. [10]

    Labbe , J

    Y. Labbe , J. Carpentier , M. Aubry , and J. Sivic , ``Cosypose: Consistent multi-view multi-object 6d pose estimation,'' in Proceedings of the European Conference on Computer Vision (ECCV), 2020

    2020

  3. [11]

    K. Park, T. Patten, and M. Vincze, ``Pix2pose: Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation,'' in The IEEE International Conference on Computer Vision (ICCV), Oct 2019

    2019

  4. [12]

    Z. Li, G. Wang, and X. Ji, ``Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation,'' in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019

    2019

  5. [13]

    Xiang, T

    Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox, ``Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes,'' arXiv preprint arXiv:1711.00199, 2017

    2017 arXiv

  6. [14]

    Labb \'e , L

    Y. Labb \'e , L. Manuelli, A. Mousavian, S. Tyree, S. Birchfield, J. Tremblay, J. Carpentier, M. Aubry, D. Fox, and J. Sivic, ``Megapose: 6d pose estimation of novel objects via render & compare,'' arXiv preprint arXiv:2212.06870, 2022

    2022 arXiv

  7. [15]

    Shugurov, F

    I. Shugurov, F. Li, B. Busam, and S. Ilic, ``Osop: A multi-stage one shot object pose estimation framework,'' in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 6825--6834

    2022

  8. [16]

    X. Deng, A. Mousavian, Y. Xiang, F. Xia, T. Bretl, and D. Fox, ``Poserbpf: A rao--blackwellized particle filter for 6-d object pose tracking,'' IEEE Transactions on Robotics, vol. 37, no. 5, pp. 1328--1342, 2021

    2021

  9. [17]

    Y. Li, G. Wang, X. Ji, Y. Xiang, and D. Fox, ``Deepim: Deep iterative matching for 6d pose estimation,'' in European Conference on Computer Vision (ECCV), 2018

    2018

  10. [18]

    Y. Lin, J. Tremblay, S. Tyree, P. A. Vela, and S. Birchfield, ``Keypoint-based category-level object pose tracking from an RGB sequence with uncertainty estimation,'' in IEEE International Conference on Robotics and Automation (ICRA), 2022

    2022

  11. [19]

    C. Wang, R. Mart \' n-Mart \' n, D. Xu, J. Lv, C. Lu, L. Fei-Fei, S. Savarese, and Y. Zhu, ``6-pack: Category-level 6d pose tracker with anchor-based keypoints,'' 2020

    2020

  12. [20]

    Pauwels and D

    K. Pauwels and D. Kragic, ``Simtrack: A simulation-based framework for scalable real-time object pose detection and tracking,'' in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015, pp. 1300--1307

    2015

  13. [21]

    D. J. Tan, F. Tombari, S. Ilic, and N. Navab, ``A versatile learning-based 3d temporal tracker: Scalable, robust, online,'' in 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 693--701

    2015

  14. [22]

    Zhong and L

    L. Zhong and L. Zhang, ``A robust monocular 3d object tracking method combining statistical and photometric constraints,'' Int. J. Comput. Vision, vol. 127, no. 8, p. 973–992, Aug. 2019. [Online]. Available: https://doi.org/10.1007/s11263-018-1119-x

    2019 doi

  15. [23]

    Tjaden, U

    H. Tjaden, U. Schwanecke, E. Schömer, and D. Cremers, ``A region-based gauss-newton approach to real-time monocular multiple object tracking,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 8, pp. 1797--1812, 2019

    2019

  16. [24]

    Calli, A

    B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar, ``The ycb object and model set: Towards common benchmarks for manipulation research,'' in 2015 International Conference on Advanced Robotics (ICAR), 2015, pp. 510--517

    2015

  17. [25]

    Calli, A

    B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar, ``Benchmarking in manipulation research: Using the yale-cmu-berkeley object and model set,'' IEEE Robotics & Automation Magazine, vol. 22, no. 3, pp. 36--52, 2015

    2015

  18. [26]

    J. Liu, W. Sun, H. Yang, Z. Zeng, C. Liu, J. Zheng, X. Liu, H. Rahmani, N. Sebe, and A. Mian, ``Deep learning-based object pose estimation: A comprehensive survey,'' arXiv preprint arXiv:2405.07801, 2024

    2024

  19. [27]

    K. He, X. Zhang, S. Ren, and J. Sun, ``Deep residual learning for image recognition,'' in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770--778

    2016

  20. [28]

    N. Ravi, V. Gabeur, Y.-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R \"a dle, C. Rolland, L. Gustafson, E. Mintun, J. Pan, K. V. Alwala, N. Carion, C.-Y. Wu, R. Girshick, P. Doll \'a r, and C. Feichtenhofer, ``Sam 2: Segment anything in images and videos,'' arXiv preprint arXi...

    2024 arXiv

  21. [29]

    T. Qin, S. Cao, J. Pan, and S. Shen, ``A general optimization-based framework for global pose estimation with multiple sensors,'' 2019

    2019

  22. [30]

    Hinterstoisser, V

    S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. Bradski, K. Konolige, and N. Navab, ``Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes,'' ser. ACCV'12. 1em plus 0.5em minus 0.4em Berlin, Heidelberg: Springer-Verla...

    2012 doi

  23. [31]

    G. O. Young, ÒSynthetic structure of industrial plastics (Book style with paper title and editor),Ó in Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15Ð64

    1964

  24. [32]

    Chen, Linear Networks and Systems (Book style)

    W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123Ð135

    1993

  25. [33]

    Poor, An Introduction to Signal Detection and Estimation

    H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4

    1985

  26. [34]

    Smith, ÒAn approach to graphs of linear forms (Unpublished work style),Ó unpublished

    B. Smith, ÒAn approach to graphs of linear forms (Unpublished work style),Ó unpublished

  27. [35]

    E. H. Miller, ÒA note on reflector arrays (Periodical styleÑAccepted for publication),Ó IEEE Trans. Antennas Propagat., to be publised

  28. [36]

    Wang, ÒFundamentals of erbium-doped fiber amplifiers arrays (Periodical styleÑSubmitted for publication),Ó IEEE J

    J. Wang, ÒFundamentals of erbium-doped fiber amplifiers arrays (Periodical styleÑSubmitted for publication),Ó IEEE J. Quantum Electron., submitted for publication

  29. [37]

    C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995

    1995

  30. [38]

    Yorozu, M

    Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, ÒElectron spectroscopy studies on magneto-optical media and plastic substrate interfaces(Translation Journals style),Ó IEEE Transl. J. Magn.Jpn., vol. 2, Aug. 1987, pp. 740Ð741 [Dig. 9th Annu. Conf. Magnetics Japan, 1982, p. 301]

    1987

  31. [39]

    Young, The Techincal Writers Handbook

    M. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989

    1989

  32. [40]

    J. U. Duncombe, ÒInfrared navigationÑPart I: An assessment of feasibility (Periodical style),Ó IEEE Trans. Electron Devices, vol. ED-11, pp. 34Ð39, Jan. 1959

    1959

  33. [41]

    S. Chen, B. Mulgrew, and P. M. Grant, ÒA clustering technique for digital communications channel equalization using radial basis function networks,Ó IEEE Trans. Neural Networks, vol. 4, pp. 570Ð578, July 1993

    1993

  34. [42]

    R. W. Lucky, ÒAutomatic equalization for digital communication,Ó Bell Syst. Tech. J., vol. 44, no. 4, pp. 547Ð588, Apr. 1965

    1965

  35. [43]

    S. P. Bingulac, ÒOn the compatibility of adaptive controllers (Published Conference Proceedings style),Ó in Proc. 4th Annu. Allerton Conf. Circuits and Systems Theory, New York, 1994, pp. 8Ð16

    1994

  36. [44]

    G. R. Faulhaber, ÒDesign of service systems with priority reservation,Ó in Conf. Rec. 1995 IEEE Int. Conf. Communications, pp. 3Ð8

    1995

  37. [45]

    W. D. Doyle, ÒMagnetization reversal in films with biaxial anisotropy,Ó in 1987 Proc. INTERMAG Conf., pp. 2.2-1Ð2.2-6

    1987

  38. [46]

    G. W. Juette and L. E. Zeffanella, ÒRadio noise currents n short sections on bundle conductors (Presented Conference Paper style),Ó presented at the IEEE Summer power Meeting, Dallas, TX, June 22Ð27, 1990, Paper 90 SM 690-0 PWRS

    1990

  39. [47]

    J. G. Kreifeldt, ÒAn analysis of surface-detected EMG as an amplitude-modulated noise,Ó presented at the 1989 Int. Conf. Medicine and Biological Engineering, Chicago, IL

    1989

  40. [48]

    Williams, ÒNarrow-band analyzer (Thesis or Dissertation style),Ó Ph.D

    J. Williams, ÒNarrow-band analyzer (Thesis or Dissertation style),Ó Ph.D. dissertation, Dept. Elect. Eng., Harvard Univ., Cambridge, MA, 1993

    1993

  41. [49]

    Kawasaki, ÒParametric study of thermal and chemical nonequilibrium nozzle flow,Ó M.S

    N. Kawasaki, ÒParametric study of thermal and chemical nonequilibrium nozzle flow,Ó M.S. thesis, Dept. Electron. Eng., Osaka Univ., Osaka, Japan, 1993

    1993

  42. [50]

    J. P. Wilkinson, ÒNonlinear resonant circuit devices (Patent style),Ó U.S. Patent 3 624 12, July 16, 1990

    1990

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