1. A. Antoniou,Digital Filters: Analysis, Design, and Signal Processing Applications. New York: McGraw-Hill, 2018.

   Google Scholar 

2. W.-S. Lu and A. Antoniou,Two-Dimensional Digital Filters. New York: Marcel Dekker, 1992.

   MATH  Google Scholar 

3. J. W. Adams, “FIR digital filters with least-squares stopbands subject to peak-gain constraints,”IEEE Trans. Circuits Syst., vol. 38, pp. 376–388, Apr. 1991.

   Article  Google Scholar 

4. W.-S. Lu, “Design of nonlinear-phase FIR digital filters: A semidefinite programming approach,” inProc. IEEE Int. Symp. Circuits and Systems, vol. III, Orlando, FL, May 1999, pp. 263–266.

   Google Scholar 

5. A. G. Deczky, “Synthesis of recursive digital filters using the minimum\(p\)-error criterion,”IEEE Trans. Audio Electroacoust., vol. 20, pp. 257–263, 1972.

   Article  Google Scholar 

6. A. T. Chottra and G. A. Jullien, “A linear programming approach to recursive digital filter design with linear phase,”IEEE Trans. Circuits Syst., vol. 29, pp. 139–149, Mar. 1982.

   Article  Google Scholar 

7. W.-S. Lu, S.-C. Pei, and C.-C. Tseng, “A weighted least-squares method for the design of stable 1-D and 2-D IIR filters,”IEEE Trans. Signal Processing, vol. 46, pp. 1–10, Jan. 1998.

   Google Scholar 

8. M. Lang, “Weighted least squares IIR filter design with arbitrary magnitude and phase responses and specified stability margin,” inIEEE Symp. on Advances in Digital Filtering and Signal Processing, Victoria, BC, June 1998, pp. 82–86.

   Google Scholar 

9. T. Kailath,Linear Systems. Englewood Cliffs, NJ: Prentice-Hall, 1980.

   MATH  Google Scholar 

10. R. C. Nongpiur, D. J. Shpak, and A. Antoniou, “Improved design method for nearly linear-phase IIR filters using constrained optimization,”IEEE Trans. Signal Processing, vol. 61, no. 4, pp. 895–906, Feb. 2013.

    Article MathSciNet  Google Scholar 

11. C. E. Garcia, D. M. Prett, and M. Morari, “Model predictive control: Theory and practice — a survey,”Automatica, vol. 25, pp. 335–348, 1989.

    Article  Google Scholar 

12. M. V. Kothare, V. Balakrishnan, and M. Morari, “Robust constrained model predictive control using linear matrix inequalities,”Automatica, vol. 32, no. 6, pp. 1361–1379, 1996.

    Article MathSciNet  Google Scholar 

13. H. Kwakernaak and R. Sivan,Linear Optimal Control Systems. New York: Wiley, 1972.

    MATH  Google Scholar 

14. J. Kerr and B. Roth, “Analysis of multifingered hands,”Int. J. Robotics Research, vol. 4, no. 4, pp. 3–17, 1986.

    Article  Google Scholar 

15. D. E. Orin and F.-T. Cheng, “General dynamic formulation of the force distribution equations,” inProc. 4th Int. Conf. on Advanced Robotics, Columbus, Ohio, June 13-15, 1989, pp. 525–546.

    Google Scholar 

16. F.-T. Cheng and D. E. Orin, “Efficient algorithm for optimal force distribution — The compact-dual LP method,”IEEE Trans. Robotics and Automation, vol. 6, pp. 178–187, 1990.

    Google Scholar 

17. E. S. Venkaraman and T. Iberall,Dextrous Robot Hands. New York: Springer-Verlag, 1990.

    Book  Google Scholar 

18. M. Buss, H. Hashimoto, and J. B. Moore, “Dextrous hand grasping force optimization,”IEEE Trans. Robotics and Automation, vol. 12, pp. 406–418, June 1996.

    Article  Google Scholar 

19. K. Shimoga, “Robot grasp synthesis algorithms: A survey,”Int. J. Robotics Research, vol. 15, pp. 230–266, June 1996.

    Article  Google Scholar 

20. J. G. Proakis,Digital Communications, 3rd ed. New York: McGraw-Hill, 1995.

    MATH  Google Scholar 

21. S. Verdú,Multiuser Detection. New York: Cambridge University Press, 1998.

    MATH  Google Scholar 

22. S. Verdú, “Minimum probability of error for asynchronous Gaussian multiple-access channels,”IEEE Trans. Inform. Theory, vol. 32, pp. 85–96, Jan. 1986.

    Article MathSciNet  Google Scholar 

23. X. M. Wang, W.-S. Lu, and A. Antoniou, “A near-optimal multiuser detector for CDMA channels using semidefinite programming relaxation,” inProc. IEEE Int. Symp. Circuits and Systems, Sydney, Australia, June 2001, pp. 525–546.

    Google Scholar 

24. X. F. Wang, W.-S. Lu, and A. Antoniou, “Constrained minimum-BER multiuser detection,”IEEE Trans. Signal Processing, vol. 48, pp. 2903–2909, Oct. 2000.

    Article  Google Scholar 

25. M. X. Geomans and D. P. Williamson, “Improved approximation algorithms for maximum cut and satisfiability problem using semidefinite programming,”J. ACM, vol. 42, pp. 1115–1145, 1985.

    Article MathSciNet  Google Scholar 

26. M. X. Geomans and D. P. Williamson, “.878-approximation algorithm for MAX-CUT and MAX-2SAT,” inProc. 26th ACM Symp. Theory of Computing, 1994, pp. 422–431.

    Google Scholar 

27. L. Vandenberghe and S. Boyd, “Semidefinite programming,”SIAM Review, vol. 38, pp. 49–95, Mar. 1996.

    Article MathSciNet  Google Scholar 

28. H. Wolkowicz, R. Saigal, and L. Vandenberghe,Handbook on Semidefinite Programming. MA: Kluwer Academic Publishers, 2000.

    Book  Google Scholar 

29. G. W. Stewart,Introduction to Matrix Computations. New York: Academic Press, 1973.

    MATH  Google Scholar 

30. K. C. Toh, R. H. Tütüncü, and M. J. Todd, “On the implementation of SDPT3 version 3.1 — a MATLAB software package for semidefinite-quadratic-linear programming,” inProc. IEEE Conf. on Computer-Aided Control System Design, Sept. 2004.

    Google Scholar 

31. A. Nemirovski and P. Gahinet, “The projective method for solving linear matrix inequalities,”Math. Programming, Series B, vol. 77, pp. 163–190, 1997.

    MATH  Google Scholar 

Download references