M. Shensa

2.8k total citations · 1 hit paper
30 papers, 1.9k citations indexed

About

M. Shensa is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, M. Shensa has authored 30 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Signal Processing, 9 papers in Artificial Intelligence and 8 papers in Computer Networks and Communications. Recurrent topics in M. Shensa's work include Advanced Adaptive Filtering Techniques (8 papers), Speech and Audio Processing (6 papers) and Blind Source Separation Techniques (6 papers). M. Shensa is often cited by papers focused on Advanced Adaptive Filtering Techniques (8 papers), Speech and Audio Processing (6 papers) and Blind Source Separation Techniques (6 papers). M. Shensa collaborates with scholars based in United States, Brazil and Norway. M. Shensa's co-authors include C. Desoer, Leonard J. Trejo, Ralf Meyer, L.B. Milstein, Michael L. Honig, S.L. Miller, J. Zeidler and E. Satorius and has published in prestigious journals such as IEEE Transactions on Automatic Control, Proceedings of the IEEE and IEEE Transactions on Signal Processing.

In The Last Decade

M. Shensa

27 papers receiving 1.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The discrete wavelet transform: wedding the a trous and Mallat algorithms

1992 1.5k citations M. Shensa profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
M. Shensa United States	14	888	421	332	270	255	30	1.9k
Jan E. Odegard United States	12	1.1k 1.2×	431 1.0×	416 1.3×	283 1.0×	248 1.0×	34	2.1k
Haitao Guo United States	12	803 0.9×	291 0.7×	356 1.1×	243 0.9×	257 1.0×	30	1.7k
R.A. Haddad United States	13	1.3k 1.5×	526 1.2×	583 1.8×	107 0.4×	182 0.7×	49	2.0k
Bruce W. Suter United States	18	765 0.9×	217 0.5×	364 1.1×	221 0.8×	373 1.5×	96	2.0k
NG Kingsbury United Kingdom	5	1.6k 1.8×	810 1.9×	384 1.2×	121 0.4×	133 0.5×	12	2.2k
N.K. Bose United States	32	1.4k 1.5×	638 1.5×	367 1.1×	559 2.1×	343 1.3×	119	3.3k
G.L. Wise United States	15	589 0.7×	99 0.2×	310 0.9×	171 0.6×	189 0.7×	78	1.5k
Saishang Zhong China	12	1.5k 1.7×	629 1.5×	302 0.9×	137 0.5×	126 0.5×	30	2.3k
Neal C. Gallagher United States	26	2.0k 2.3×	680 1.6×	778 2.3×	207 0.8×	475 1.9×	104	3.5k
Raghuveer Rao United States	20	769 0.9×	288 0.7×	320 1.0×	158 0.6×	352 1.4×	110	2.1k

Countries citing papers authored by M. Shensa

Since Specialization

Citations

This map shows the geographic impact of M. Shensa's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. Shensa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Shensa more than expected).

Fields of papers citing papers by M. Shensa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Shensa. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. Shensa. The network helps show where M. Shensa may publish in the future.

Co-authorship network of co-authors of M. Shensa

This figure shows the co-authorship network connecting the top 25 collaborators of M. Shensa. A scholar is included among the top collaborators of M. Shensa based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. Shensa. M. Shensa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Shensa, M.. (2005). The spectral dynamics of evolving LMS adaptive filters. 4. 950–953. 3 indexed citations

2.

Miller, S.L., Michael L. Honig, M. Shensa, & L.B. Milstein. (2002). MMSE reception of DS-CDMA for frequency-selective fading channels. 51–51. 1 indexed citations

3.

Honig, Michael L., S.L. Miller, M. Shensa, & L.B. Milstein. (2001). Performance of adaptive linear interference suppression in the presence of dynamic fading. IEEE Transactions on Communications. 49(4). 635–645. 33 indexed citations

4.

Trejo, Leonard J. & M. Shensa. (1999). Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring. Brain and Language. 66(1). 89–107. 37 indexed citations

5.

Shensa, M.. (1994). Discrete inverses for nonorthogonal wavelet transforms. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 2242. 314–314.

6.

Shensa, M.. (1989). A computational structure for the propositional calculus. International Joint Conference on Artificial Intelligence. 384–388. 8 indexed citations

7.

Shensa, M., et al.. (1986). Polytopes, a novel approach to tracking. 1749–1752. 23 indexed citations

8.

Shensa, M., et al.. (1986). Bearings Tracking of Targets with Unobservable States. 1850–1854. 4 indexed citations

9.

Shensa, M., et al.. (1984). Algebraic Techniques in Systolic Array Design.. Defense Technical Information Center (DTIC). 1 indexed citations

10.

Shensa, M., et al.. (1982). Remarks on a displacement-rank inversion method for Toeplitz systems. Linear Algebra and its Applications. 45. 127–130. 7 indexed citations

11.

Shensa, M.. (1981). The MLE Tracking Algorithm: A Summary and Error Analysis.. Defense Technical Information Center (DTIC). 2 indexed citations

12.

Shensa, M.. (1981). Recursive least squares lattice algorithms--A geometrical approach. IEEE Transactions on Automatic Control. 26(3). 695–702. 46 indexed citations

13.

Zeidler, J., et al.. (1981). A performance analysis of adaptive line enhancer-augmented spectral detectors. IEEE Transactions on Circuits and Systems. 28(6). 534–541. 4 indexed citations

14.

Shensa, M.. (1981). On the uniqueness of Doppler tracking. The Journal of the Acoustical Society of America. 70(4). 1062–1064. 14 indexed citations

15.

Zeidler, J., et al.. (1981). A performance analysis of adaptive line enhancer-augmented spectral detectors. IEEE Transactions on Acoustics Speech and Signal Processing. 29(3). 694–701. 21 indexed citations

16.

Satorius, E. & M. Shensa. (1980). Recursive lattice filters - A brief overview. 955–959. 6 indexed citations

17.

Shensa, M.. (1980). Non-Wiener solutions of the adaptive noise canceller with a noisy reference. IEEE Transactions on Acoustics Speech and Signal Processing. 28(4). 468–473. 26 indexed citations

18.

Shensa, M.. (1979). Performance of the Adaptive Noise Canceller with a Noisy Reference - Non-Wiener Solutions.. Defense Technical Information Center (DTIC). 1 indexed citations

19.

Shensa, M.. (1976). Complex Exponential Weighting Applied to Homomorphic Deconvolution. Geophysical Journal International. 44(2). 379–387. 2 indexed citations

20.

Desoer, C. & M. Shensa. (1970). Networks with very small and very large parasitics: Natural frequencies and stability. Proceedings of the IEEE. 58(12). 1933–1938. 41 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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