Akio Tsuneda
About
In The Last Decade
Akio Tsuneda
72 papers receiving 715 citations
Peers
Comparison fields: 5 of 63
- Statistical and Nonlinear Physics 313
- Computer Vision and Pattern Recognition 265
- Computer Networks and Communications 263
- Artificial Intelligence 240
- Computational Theory and Mathematics 240
Countries citing papers authored by Akio Tsuneda
This map shows the geographic impact of Akio Tsuneda'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 Akio Tsuneda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akio Tsuneda more than expected).
Fields of papers citing papers by Akio Tsuneda
This network shows the impact of papers produced by Akio Tsuneda. 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 Akio Tsuneda. The network helps show where Akio Tsuneda may publish in the future.
Co-authorship network of co-authors of Akio Tsuneda
This figure shows the co-authorship network connecting the top 25 collaborators of Akio Tsuneda. A scholar is included among the top collaborators of Akio Tsuneda 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 Akio Tsuneda. Akio Tsuneda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | Performance Evaluation of Spreading Codes with Negative Auto-Correlation Based on M-sequences and Chaos Theory | 1 |
| 3 | Design and Evaluation of Spreading Sequences with Negative Auto-correlation Based on Chaos Theory and M-Sequences | 2 |
| 4 | Spreading Sequences with Negative Auto-correlations Generated by LFSRs Based on Chaos Theory of Modulo-2 Added Sequences | 2 |
| 5 | Design of Spreading Sequences with Negative Auto-correlations Based on LFSR Sequences | 1 |
| 6 | PERFORMANCE OF ASYNCHRONOUS DS-CDMA SYSTEMS ON MULTIPATH PROPAGATION CHANNELS WITH DIGITALIZED CHAOTIC SEQUENCES | 0 |
| 7 | CORRELATION PROPERTIES OF ORTHOGONAL SEQUENCES GENERATED BY NFSR AND WALSH FUNCTIONS | 1 |
| 8 | Characteristics of Feedback-Limited Nonlinear shift Register Sequences | 1 |
| 9 | Characteristics of Orthogonalized Maximal-Period Sequences | 3 |
| 10 | Maximal-Period Sequences with Negative Auto-Correlations and Their Application to Asynchronous DS-CDMA Systems | 8 |
| 11 | New Maximal-Period Sequences Using Extended Nonlinear Feedback Shift Registers Based on Chaotic Maps | 18 |
| 12 | Search Algorithm of Maximal-Period Sequences Based on One-Dimensional Maps with Finite Bits and Its Application to DS-CDMA Systems | 1 |
| 13 | A New Stochastic Binary Neural Network Based on Hopfield Model and Its Application | 3 |
| 14 | Design of Maximal-Period Sequences with Prescribed Auto-Correlation Properties Based on One-Dimensional Maps with Finite Bits | 1 |
| 15 | Synthesis and analysis of a digital chaos circuit generating multiple-scroll strange attractors | 4 |
| 16 | Properties of Chaotic Sequences Generated by Finite-Bit Operations | 1 |
| 17 | FPGA Implementation of a Digital Chaos Circuit Realizing a 3-Dimensional Chaos Model | 2 |
| 18 | On a Neural-Network Type Binary-Image Noise Elimination Filter | 0 |
| 19 | Explicit evaluations of correlation functions of Chebyshev binary and bit sequences based on Perron-Frobenius operator | 25 |
| 20 | Pseudonoise Sequences by Chaotic Nonlinear Maps and Their Correlation Properties | 69 |
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.