IEEE Transactions on Magnetics Institutional Listings

Guest editorial: Decision making and control for connected and automated vehicles

This paper reviews the current state of conductive adhesive technology. Most work to date has been carried out with isotropically-conductive adhesives which conduct electricity in any direction. In this review, particular attention has been paid to recently-developed anisotropically-conductive adhesives which are electrically conductive along one axis only. Patents filed in this area have been surveyed and the key points relating to the technology employed are summarized. A survey of the market was carried out and is presented. Adhesive processing techniques were studied and reliability issues relating to adhesives in general and to conductive adhesives in particular investigated. Voids in the adhesive leading to reduced adhesion and stress concentration were seen to be an area of concern while the effect of moisture leading to increased joint resistance and reduced strength was concluded to be the key limiting factor in the long-term reliability of conductive adhesives.

Bibliography

Distributed Cooperative Guidance for Multivehicle Simultaneous Arrival Without Numerical Singularities

Representation of cardiac electrical activity by a moving dipole for normal and ectopic beats in the intact dog.

Combining Power Allocation and Superposition Coding for an Underlay Two-way Decode-and-forward Scheme

Passivity-Based Iterative Learning Control for Spacecraft Attitude Tracking on SO(3)

About Our Authors

References 1 Suzuki, M. and Miura, M., Feedback Controllers for Singularly Perturbed Linear Constant Systems, IEEE Transactions on Automatic Control, Vol. AC-21, 1976, pp. 123-124. 2 Porter, B., Singular Perturbations Methods in the Design of Stabilizing Feedback Controllers for Multivariable Linear Systems, International Journal of Control, Vol. 20, 1974, pp. 689-692. Kalman, R., and Bucy, R., New Results in Linear Flitering and Prediction Journal of Basic Engineering, Transactions of ASME, Ser. D., Vol. 83, 1961, pp. 95-108. Luenberger, D., Observing the State of a Linear System, IEEE Transactions on Military Electronics, Vol. 8, 1964, pp. 74-80. Klimushchev, A. and Krasovskii, N., Uniform Asymptotic of Systems of Differential Equations with Small Parameter in the Derivative Terms, Journal of Applied Mathematics and Mechanics, Vol. 25, 1961, pp. 1011-1025. Wonham, W., On Pole Assignment in Multi-input Controllable Linear Systems, IEEE Transactions on Automatic Control, Vol. AC-12, 1967, pp. 660-665. Kokotovic, P., O'Malley, R., and Sannuti, P., Singular Perturbations and Order Reduction in Control Theory—An Overview, Automatica, Vol. 12, 1976, pp. 123-132. Shensa, M., Parasitics and the of Equilibrium Points of Nonlinear Networks, IEEE Transactions on Circuit Theory, Vol. CT-18, 1971, pp. 481-484. Desoer, C. and Shensa, M., with Very Small and Very Large Parasitics: Natural Frequencies and Stability, Proceedings of the IEEE, Vol. 58, 1970, pp. 1933-1938. Wilde, R.R. and Kokotovic, P.V., Stability of Singularity Perturbed Systems and Networks with Parasitics, IEEE Transactions on Automatic Control, Vol. AC-17, 1972, pp. 245-246.


 
 
 Object segmentation is an important task which is widely employed in many computer vision applications such as object detection, tracking, recognition, and retrieval. It can be seen as a two-phase process: object detection and segmentation. Object segmentation becomes more challenging in case there is no prior knowledge about the object in the scene. In such conditions, visual attention analysis via saliency mapping may offer a mean to predict the object location by using visual contrast, local or global, to identify regions that draw strong attention in the image. However, in such situations as clutter background, highly varied object surface, or shadow, regular and salient object segmentation approaches based on a single image feature such as color or brightness have shown to be insufficient for the task. This work proposes a new salient object segmentation method which uses a depth map obtained from the input image for enhancing the accuracy of saliency mapping. A deep learning-based method is employed for depth map estimation. Our experiments showed that the proposed method outperforms other state-of-the-art object segmentation algorithms in terms of recall and precision.
 KeywordsSaliency map, Depth map, deep learning, object segmentation
 References[1] Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on pattern analysis and machine intelligence 20(11) (1998) 1254-1259.[2] Goferman, L. Zelnik-Manor, A. Tal, Context-aware saliency detection, IEEE transactions on pattern analysis and machine intelligence 34(10) (2012) 1915-1926.[3] Kanan, M.H. Tong, L. Zhang, G.W. Cottrell, Sun: Top-down saliency using natural statistics, Visual cognition 17(6-7) (2009) 979-1003.[4] Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, H.-Y. Shum, Learning to detect a salient object, IEEE Transactions on Pattern analysis and machine intelligence 33(2) (2011) 353-367.[5] Perazzi, P. Krähenbühl, Y. Pritch, A. Hornung, Saliency filters: Contrast based filtering for salient region detection, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 2012, pp. 733-740.[6] M. Cheng, N.J. Mitra, X. Huang, P.H. Torr, S.M. Hu, Global contrast based salient region detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3) (2015) 569-582.[7] Borji, L. Itti, State-of-the-art in visual attention modeling, IEEE transactions on pattern analysis and machine intelligence 35(1) (2013) 185-207.[8] Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, arXiv preprint arXiv:1312.6034.[9] Li, Y. Yu, Visual saliency based on multiscale deep features, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 5455-5463.[10] Liu, J. Han, Dhsnet: Deep hierarchical saliency network for salient object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 678-686.[11] Achanta, S. Hemami, F. Estrada, S. Susstrunk, Frequency-tuned saliency detection model, CVPR: Proc IEEE, 2009, pp. 1597-604.Fu, J. Cheng, Z. Li, H. Lu, Saliency cuts: An automatic approach to object segmentation, in: Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, IEEE, 2008, pp. 1-4Borenstein, J. Malik, Shape guided object segmentation, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 1, IEEE, 2006, pp. 969-976.Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, S. Li, Automatic salient object segmentation based on context and shape prior., in: BMVC. 6 (2011) 9.Ciptadi, T. Hermans, J.M. Rehg, An in depth view of saliency, Georgia Institute of Technology, 2013.Desingh, K.M. Krishna, D. Rajan, C. Jawahar, Depth really matters: Improving visual salient region detection with depth., in: BMVC, 2013.Li, J. Ye, Y. Ji, H. Ling, J. Yu, Saliency detection on light field, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2806-2813.Koch, S. Ullman, Shifts in selective visual attention: towards the underlying neural circuitry, in: Matters of intelligence, Springer, 1987, pp. 115-141.Laina, C. Rupprecht, V. Belagiannis, F. Tombari, N. Navab, Deeper depth prediction with fully convolutional residual networks, in: 3D Vision (3DV), 2016 Fourth International Conference on, IEEE, 2016, pp. 239-248.Bruce, J. Tsotsos, Saliency based on information maximization, in: Advances in neural information processing systems, 2006, pp. 155-162.Ren, X. Gong, L. Yu, W. Zhou, M. Ying Yang, Exploiting global priors for rgb-d saliency detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 25-32.Fang, J. Wang, M. Narwaria, P. Le Callet, W. Lin, Saliency detection for stereoscopic images., IEEE Trans. Image Processing 23(6) (2014) 2625-2636.Hou, L. Zhang, Saliency detection: A spectral residual approach, in: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, IEEE, 2007, pp. 1-8.Guo, Q. Ma, L. Zhang, Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform, in: Computer vision and pattern recognition, 2008. cvpr 2008. ieee conference on, IEEE, 2008, pp. 1-8.Fang, W. Lin, B.S. Lee, C.T. Lau, Z. Chen, C.W. Lin, Bottom-up saliency detection model based on human visual sensitivity and amplitude spectrum, IEEE Transactions on Multimedia 14(1) (2012) 187-198.Lang, T.V. Nguyen, H. Katti, K. Yadati, M. Kankanhalli, S. Yan, Depth matters: Influence of depth cues on visual saliency, in: Computer vision-ECCV 2012, Springer, 2012, pp. 101-115.Zhang, G. Jiang, M. Yu, K. Chen, Stereoscopic visual attention model for 3d video, in: International Conference on Multimedia Modeling, Springer, 2010, pp. 314-324.Wang, M.P. Da Silva, P. Le Callet, V. Ricordel, Computational model of stereoscopic 3d visual saliency, IEEE Transactions on Image Processing 22(6) (2013) 2151-2165.Peng, B. Li, W. Xiong, W. Hu, R. Ji, Rgbd salient object detection: A benchmark and algorithms, in: European Conference on Computer Vision (ECCV), 2014, pp. 92-109.Wu, L. Duan, L. Kong, Rgb-d salient object detection via feature fusion and multi-scale enhancement, in: CCF Chinese Conference on Computer Vision, Springer, 2015, pp. 359-368.Xue, Y. Gu, Y. Li, J. Yang, Rgb-d saliency detection via mutual guided manifold ranking, in: Image Processing (ICIP), 2015 IEEE International Conference on, IEEE, 2015, pp. 666-670.Katz, A. Adler, Depth camera based on structured light and stereo vision, uS Patent App. 12/877,595 (Mar. 8 2012).Chatterjee, G. Molina, D. Lelescu, Systems and methods for determining depth from multiple views of a scene that include aliasing using hypothesized fusion, uS Patent App. 13/623,091 (Mar. 21 2013).Matthies, T. Kanade, R. Szeliski, Kalman filter-based algorithms for estimating depth from image sequences, International Journal of Computer Vision 3(3) (1989) 209-238.Y. Schechner, N. Kiryati, Depth from defocus vs. stereo: How different really are they?, International Journal of Computer Vision 39(2) (2000) 141-162.Delage, H. Lee, A.Y. Ng, A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 2, IEEE, 2006, pp. 2418-2428.Saxena, M. Sun, A.Y. Ng, Make3d: Learning 3d scene structure from a single still image, IEEE transactions on pattern analysis and machine intelligence 31(5) (2009) 824-840.Hedau, D. Hoiem, D. Forsyth, Recovering the spatial layout of cluttered rooms, in: Computer vision, 2009 IEEE 12th international conference on, IEEE, 2009, pp. 1849-1856.Liu, S. Gould, D. Koller, Single image depth estimation from predicted semantic labels, in: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, IEEE, 2010, pp. 1253-1260.Ladicky, J. Shi, M. Pollefeys, Pulling things out of perspective, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 89-96.K. Nathan Silberman, Derek Hoiem, R. Fergus, Indoor segmentation and support inference from rgbd images, in: ECCV, 2012.Liu, J. Yuen, A. Torralba, Sift flow: Dense correspondence across scenes and its applications, IEEE transactions on pattern analysis and machine intelligence 33(5) (2011) 978-994.Konrad, M. Wang, P. Ishwar, 2d-to-3d image conversion by learning depth from examples, in: Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, IEEE, 2012, pp. 16-22.Liu, C. Shen, G. Lin, Deep convolutional neural fields for depth estimation from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5162-5170.Wang, X. Shen, Z. Lin, S. Cohen, B. Price, A.L. Yuille, Towards unified depth and semantic prediction from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2800-2809.Geiger, P. Lenz, C. Stiller, R. Urtasun, Vision meets robotics: The kitti dataset, International Journal of Robotics Research (IJRR).Achanta, S. Süsstrunk, Saliency detection using maximum symmetric surround, in: Image processing (ICIP), 2010 17th IEEE international conference on, IEEE, 2010, pp. 2653-2656.E. Rahtu, J. Kannala, M. Salo, J. Heikkilä, Segmenting salient objects from images and videos, in: Computer Vision-ECCV 2010, Springer, 2010, pp. 366-37.
 
 

This paper presents a method for all-digital background calibration of multiple channel mismatches including offset, gain and timing mismatches in time-interleaved analog-to-digital converters (TIADCs). The average technique is used to remove offset mismatch at each channel. The gain mismatch is calibrated by calculating the power ratio of the sub-ADC over the reference ADC. The timing skew mismatch is calibrated by using Hadamard transform for error correction and LMS for timing mismatch estimation. The performance improvement of TIADCs employing these techniques is demonstrated through numerical simulations. Besides, achievement results on the field-programmable gate array (FPGA) hardware have demonstrated the effectiveness of the proposed techniques.
 KeywordsTime-interleaved analog-to-digital converter (TIADC), channel mismatches, all-digital background calibration
 References[1] Melamed and S. Toledo, A robust, selective, and flexible RF front-end for wideband sampling receivers, ICT Express 3(2) (2017) 96-100.[2] C. Black and D. A. Hodges, Time interleaved converter arrays, IEEE Journal of Solid-state circuits 15(6) (1980) 1022-1029.[3] Razavi, Design considerations for interleaved ADCs, IEEE Journal of Solid-State Circuits 48(8) (2013) 1806-1817.[4] Kurosawa, H. Kobayashi, K. Maruyama, H. Sugawara, and K. Kobayashi, Explicit analysis of channel mismatch effects in time-interleaved ADC systems, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 48(3) (2001) 261-271.[5] Vogel, The impact of combined channel mismatch effects in time-interleaved ADCs, IEEE transactions on instrumentation and measurement 54(1) (2005) 415-427.[6] J. Harpe, J. A. Hegt, and A. H. van Roermund, Analog calibration of channel mismatches in time‐interleaved ADCs, International Journal of Circuit Theory and Applications 37(2) (2009) 301-318.[7] Camarero, K. B. Kalaia, J.-F. Naviner, and P. Loumeau, Mixed-signal clock-skew calibration technique for time-interleaved ADCs, IEEE Transactions on Circuits and Systems I: Regular Papers 55(11) (2008) 3676-3687. DOI: 10.1109/TCSI.2008.926314.[8] Le Duc, D. M. Nguyen, C. Jabbour, T. Graba, P. Desgreys, and O. Jamin, All-digital calibration of timing skews for TIADCs using the polyphase decomposition, IEEE Transactions on Circuits and Systems II: Express Briefs 63(1) (2015) 99-103.[9] Le Duc, D. M. Nguyen, C. Jabbour, T. Graba, P. Desgreys, and O. Jamin, Hardware implementation of all digital calibration for undersampling TIADCs, in 2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015, pp. 2181-2184.[10] Guo, S. Tian, and Z. Wang, Estimation and correction of gain mismatch and timing error in time-interleaved ADCs based on DFT, Metrology and Measurement Systems 21(3) (2014) 535-544.[11] Qiu, Y.-J. Liu, J. Zhou, G. Zhang, D. Chen, and N. Du, All-digital blind background calibration technique for any channel time-interleaved ADC, IEEE Transactions on Circuits and Systems I: Regular Papers 65(8) (2018) 2503-2514.[12] Matsuno, T. Yamaji, M. Furuta, and T. Itakura, All-digital background calibration technique for time-interleaved ADC using pseudo aliasing signal, IEEE Transactions on Circuits and Systems I: Regular Papers 60(5) (2013) 1113-1121.[13] Saleem and C. Vogel, On blind identification of gain and timing mismatches in time-interleaved analog-to-digital converters, in 33rd International Conference on Telecommunications and Signal Processing, Baden (Austria), 2010, pp. 151-155.[14] W. Kang, H.-K. Hong, S. Park, K.-J. Kim, K.-H. Ahn, and S.-T. Ryu, A sign-equality-based background timing-mismatch calibration algorithm for time-interleaved ADCs, IEEE Transactions on Circuits and Systems II: Express Briefs 63(6) (2016) 518-522.[15] H. Chen, J. Lee, and J.-T. Chen, Digital background calibration for timing mismatch in time-interleaved ADCs, Electronics Letters 42(2) (2006) 74-75.[16] Liu, N. Lv, H. Ma, and A. Zhu, Adaptive semiblind background calibration of timing mismatches in a two-channel time-interleaved analog-to-digital converter, Analog Integrated Circuits and Signal Processing 90(1) (2017) 1-7.[17] Chen, Y. Pan, Y. Yin, and F. Lin, All-digital background calibration technique for timing mismatch of time-interleaved ADCs, Integration 57 (2017) 45-51.[18] Van-Thanh, H. Van-Phuc, and X. Tran, All-Digital Background Calibration Technique for Offset, Gain and Timing Mismatches in Time-Interleaved ADCs, EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 6(21) (2019). DOI: 10.4108/eai.24-10-2019.160983.[19] Le Dortz et al., 22.5 A 1.62 GS/s time-interleaved SAR ADC with digital background mismatch calibration achieving interleaving spurs below 70dBFS, in 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), IEEE, 2014, pp. 386-388.[2] Tertinek and C. Vogel, Reconstruction of nonuniformly sampled bandlimited signals using a differentiator–multiplier cascade, IEEE Transactions on Circuits and Systems I: Regular Papers 55(8) (2008) 2273-2286.[21] Cho et al., Calibration of channel mismatch in time-interleaved real-time digital oscilloscopes, in 2015 85th Microwave Measurement Conference (ARFTG), IEEE, 2015, pp. 1-5.

Adaptive Formation Tracking Control of Directed Networked Vehicles in a Time-Varying Flowfield

We present a new algorithm for the numerical solution of problems of electromagnetic or acoustic scattering by large, convex obstacles. This algorithm combines the use of an ansatz for the unknown density in a boundary-integral formulation of the scattering problem with an extension of the ideas of the method of stationary phase. We include numerical results illustrating the high-order convergence of our algorithm as well as its asymptotically bounded computational cost as the frequency increases.

Cultures of the Central Highlands, New Guinea

The authors investigate the existence and the global stability of periodic solution for dynamical systems with periodic interconnections, inputs and self-inhibitions. The model is very general, the conditions are quite weak and the results obtained are universal.