Books
- 1.
Haykin, S. (2008). Neural Networks and Learning Machines. In Pearson Prentice Hall New Jersey USA 936 pLinks (3rd ed., Vol. 3). Pearson.
- 2.
Russell, S., & Norvig, P. (2010). Artificial Intelligence A Modern Approach Third Edition. In Pearson.
https://doi.org/10.1017/S0269888900007724.
- 3.
Oviedo, J., Vandewalle, J., Wertz, V.: Fuzzy Logic, Identification and Predictive Control. Advances in Industrial Control. Springer London (2004)
- 4.
Coppin, B. (2004). Artificial intelligence illuminated. Jones & Bartlett Learning.
- 5.
Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms. John Wiley & Sons.
- 6.
Adams, Ansel. The Camera (Book 1) (Little Brown & Co, 1995)
- 7.
Adams, Ansel. The Negative (Book 2) (Little Brown & Co, 1995) [the Zone system is thoroughly discussed in this volume.]
- 8.
Adams, Ansel. The Print (Book 3) (Little Brown & Co, 1995)
- 9.
Adams, Ansel. The Portfolios of Ansel Adams, introduced by John Szarkowski (Little Brown & Co., 1981)
- 10.
Adams, Ansel. The Grand Canyon and the Southwest (Little Brown & Co, 2000)
- 11.
Adams, Ansel and Mary Street Alinder. Ansel Adams: An Autobiography (New York Graphic Society, 1985)
- 12.
Alinder, Mary Street and Andrea Gray Stillman (eds). Ansel Adams: Letters 1916-1984 (Little Brown & Co., 2001)
- 13.
Alinder, Mary Street. Ansel Adams: A Biography (Henry Holt & Co., 1996)
- 14.
Spaulding, Jonathan. Ansel Adams and the American Landscape: A Biography (University of California Press, 1995)
- 15.
Barthes, Roland, La Chambre claire. Note sur la photographie (Paris: Seuil, 1980)
- 16.
Sontag, Susan.On Photography. Penguin Classics. 2008.
- 17.
Szwarcfiter, Jayme Luis e Markenzon, Lilian. Estruturas de Dados e Seus Algoritmos.
- 18.
Wirth, Niklaus. Algoritmos e Estruturas de Dados, Prentice Hall.
- 19.
Sedgewick, Robert, Algorithms in C. Addison Wesley.
- 20.
Weiss, Mark A., Data Structures and Algorithm Analysis in C++. Addisson Wesley-Pearson.
- 21.
Brian W. Kernighan, Dennis Ritchie. C Programming Language.
- 22.
Stroustrup, B. (2023) A tour of C++. Boston: Addison-Wesley Professional.
- 23.
Gunther, M. (2004) Zurich axioms (The investment classic). Harriman House Ltd.
Papers
- 1.
Leonardo A. Dias, Augusto M.P. Damasceno, Elena Gaura, Marcelo A.C. Fernandes,
A full-parallel implementation of Self-Organizing Maps on hardware,
Neural Networks,
Volume 143,
2021,
Pages 818-827,
ISSN 0893-6080,
https://doi.org/10.1016/j.neunet.2021.05.021.
(https://www.sciencedirect.com/science/article/pii/S0893608021002173)
- 2.
Aggarwal, C. C., Hinneburg, A., & Keim, D. A. (2001). On the surprising behavior
of distance metrics in high dimensional space. In International conference on
database theory (pp. 420–434). Springer.
- 3.
Araujo, A. F., & Santana, O. V. (2014). Self-organizing map with time-varying
structure to plan and control artificial locomotion. IEEE Transactions on Neural
Networks and Learning Systems, 26(8), 1594–1607.
- 4.
Ayani, S., Moulaei, K., Khanehsari, S. D., Jahanbakhsh, M., & Sadeghi, F. (2019). A
systematic review of big data potential to make synergies between sciences
for achieving sustainable health: Challenges and solutions. Applied Medical
Informatics, 41(2), 53–64.
- 5.
Ben Khalifa, K., Blaiech, A. G., & Bedoui, M. H. (2019). A novel hardware
systolic architecture of a self-organizing map neural network. Computational
Intelligence and Neuroscience, 2019.
- 6.
Cardarilli, G. C., Di Nunzio, L., Fazzolari, R., Re, M., & Spanó, S. (2019). AW-SOM,
an algorithm for high-speed learning in hardware self-organizing maps. IEEE
Transactions on Circuits and Systems II: Express Briefs.
- 7.
Chen, N., Chen, L., Ma, Y., & Chen, A. (2019). Regional disaster risk assessment
of China based on self-organizing map: Clustering, visualization and ranking.
International Journal of Disaster Risk Reduction, 33, 196–206.
- 8.
Choi, Y., & So, H. K. H. (2014). Map-reduce processing of k-means algorithm
with FPGA-accelerated computer cluster. In 2014 IEEE 25th international
conference on application-specific systems, architectures and processors (pp.
9–16). http://dx.doi.org/10.1109/ASAP.2014.6868624.
- 9.
Delibasis, K. K., Goudas, T., & Maglogiannis, I. (2016). A novel robust ap-
proach for handling illumination changes in video segmentation. Engineering
Applications of Artificial Intelligence, 49, 43–60. http://dx.doi.org/10.1016/j.
engappai.2015.11.006, URL http://www.sciencedirect.com/science/article/pii/
S0952197615002614.
- 10.
Dias, L., Coutinho, M. G. F., Gaura, E., & Fernandes, M. A. C. (2020). A new
hardware approach to self-organizing maps. In 2020 IEEE 31st international
conference on application-specific systems, architectures and processors (pp.
205–212). http://dx.doi.org/10.1109/ASAP49362.2020.00041.
- 11.
Dias, L. A., Ferreira, J. C., & Fernandes, M. A. (2020). Parallel implementation of
K-means algorithm on FPGA. IEEE Access, 8, 41071–41084.
- 12.
Hikawa, H. (2019). Nested hardware architecture for self-organizing map. In 2019
international joint conference on neural networks (pp. 1–7). IEEE.
- 13.
Hikawa, H., & Kaida, K. (2015). Novel FPGA implementation of hand sign
recognition system with SOM–Hebb classifier. IEEE Transactions on Circuits
and Systems for Video Technology, 25(1), 153–166. http://dx.doi.org/10.1109/
TCSVT.2014.2335831.
- 14.
Hussain, H., Benkrid, K., Erdogan, A. T., & Seker, H. (2011). Highly parameterized
k-means clustering on FPGAs: Comparative results with GPPs and GPUs.
In 2011 international conference on reconfigurable computing and FPGAs (pp.
475–480). http://dx.doi.org/10.1109/ReConFig.2011.49.
- 15.
Karkare, V., Gibson, S., & Marković, D. (2013). A 75-µW, 16-channel neural spike-
sorting processor with unsupervised clustering. IEEE Journal of Solid-State
Circuits, 48(9), 2230–2238.
- 16.
Khalifa, K. B., & Bedoui, M. H. (2019). A massively parallel implementation of
a modular self-organizing map on FPGAs. Journal of Circuits, Systems, and
Computers, 28(03), Article 1950054.
- 17.
Kolasa, M., Długosz, R., Pedrycz, W., & Szulc, M. (2012). A programmable triangu-
lar neighborhood function for a Kohonen self-organizing map implemented
on chip. Neural Networks, 25, 146–160.
- 18.
Kolesnikov, A., Trichina, E., & Kauranne, T. (2015). Estimating the number of
clusters in a numerical data set via quantization error modeling. Pattern
Recognition, 48(3), 941–952. http://dx.doi.org/10.1016/j.patcog.2014.09.017,
URL http://www.sciencedirect.com/science/article/pii/S0031320314003781.
- 19.
Koseleva, N., & Ropaite, G. (2017). Big data in building energy efficiency:
Understanding of big data and main challenges. Procedia Engineering, 172,
544–549. http://dx.doi.org/10.1016/j.proeng.2017.02.064.
- 20.
Kriegel, H. -P., Kröger, P., & Zimek, A. (2009). Clustering high-dimensional data:
A survey on subspace clustering, pattern-based clustering, and correlation
clustering. ACM Transactions on Knowledge Discovery from Data, 3(1), http:
//dx.doi.org/10.1145/1497577.1497578.
- 21.
Lachmair, J., Mieth, T., Griessl, R., Hagemeyer, J., & Porrmann, M. (2017). From
CPU to FPGA—Acceleration of self-organizing maps for data mining. In 2017
international joint conference on neural networks (pp. 4299–4308). IEEE.
- 22.
MATLAB (2012). version 8 (R2012). Natick, Massachusetts: The MathWorks Inc..
Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F.,
et al. (2014). A million spiking-neuron integrated circuit with a scalable
communication network and interface. Science, 345(6197), 668–673.
- 23.
Musci, M., Parigi, G., Cantoni, V., & Piastra, M. (2020). A scalable multi-signal
approach for the parallelization of self-organizing neural networks. Neural
Networks, 123, 108–117. http://dx.doi.org/10.1016/j.neunet.2019.11.016, URL
http://www.sciencedirect.com/science/article/pii/S0893608019303752.
- 24.
Nathan, B. J., & Lary, D. J. (2019). Combining domain filling with a self-organizing
map to analyze multi-species hydrocarbon signatures on a regional scale.
Environmental Monitoring and Assessment, 191(2), 337.
- 25.
Nedjah, N., & de Macedo Mourelle, L. (2007). An efficient problem-independent
hardware implementation of genetic algorithms. Neurocomputing, 71(1–3),
88–94.
- 26.
Patel, K. A., & Thakral, P. (2016). The best clustering algorithms in data mining.
In 2016 international conference on communication and signal processing (pp.
2042–2046). IEEE.
- 27.
Pölzlbauer, G. (2004). Survey and comparison of quality measures for
self-organizing maps. na.
- 28.
Rast, A., Galluppi, F., Davies, S., Plana, L., Patterson, C., Sharp, T., et al. (2011). Con-
current heterogeneous neural model simulation on real-time neuromimetic
hardware. Neural Networks, 24(9), 961–978.
- 29.
Rodríguez, A., Navarro, A., Asenjo, R., Corbera, F., Gran, R., Suárez, D., et al. (2019).
Exploring heterogeneous scheduling for edge computing with CPU and FPGA
mpsocs. Journal of Systems Architecture, 98, 27–40.
- 30.
Saraswati, A., Nguyen, V. T., Hagenbuchner, M., & Tsoi, A. C. (2018). High-
resolution self-organizing maps for advanced visualization and dimen-
sion reduction. Neural Networks, 105, 166–184. http://dx.doi.org/10.1016/
j.neunet.2018.04.011, URL https://www.sciencedirect.com/science/article/pii/
S0893608018301333.
- 31.
Shao, Q., Du, L., & Wang, L. (2018). Modular hardware implementation of SOM
neural network based on FPGA. DEStech Transactions on Computer Science and
Engineering, (iciti).
- 32.
de Sousa, M. A. d. A., & Del-Moral-Hernandez, E. (2017). An FPGA distributed
implementation model for embedded SOM with on-line learning. In 2017
international joint conference on neural networks (pp. 3930–3937). IEEE.
- 33.
de Sousa, M. A. d. A., Pires, R., & Del-Moral-Hernandez, E. (2020). SOMprocessor:
A high throughput FPGA-based architecture for implementing self-organizing
maps and its application to video processing. Neural Networks.
- 34.
Suzuki, A., Morie, T., & Tamukoh, H. (2018). A shared synapse architecture for
efficient FPGA implementation of autoencoders. PLoS One, 13(3).
- 35.
Tanaka, Y., & Tamukoh, H. (2019). Hardware implementation of brain-inspired
amygdala model. In 2019 IEEE international symposium on circuits and systems
(pp. 1–5). http://dx.doi.org/10.1109/ISCAS.2019.8702430.
- 36.
Tirumalai, V., Ricks, K. G., & Woodbury, K. A. (2007). Using parallelization and
hardware concurrency to improve the performance of a genetic algorithm.
Concurrency Computations: Practice and Experience, 19(4), 443–462.
- 37.
Tisan, A., & Cirstea, M. (2013). Som neural network design–A new Simulink
library based approach targeting FPGA implementation. Mathematics and
Computers in Simulation, 91, 134–149.
- 38.
Vesanto, J., & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE
Transactions on Neural Networks, 11(3), 586–600. http://dx.doi.org/10.1109/
72.846731.
- 39.
Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B.,
et al. (2016). Big data: From beginning to future. International Journal of
Information Management, 36(6, Part B), 1231–1247. http://dx.doi.org/10.1016/
j.ijinfomgt.2016.07.009.
- 40.
Ding, B., Luo, X., stability research of discrete-time takagisugeno fuzzy control systems. In: IEEE ICCA 2010. (June 2010) 411–416
- 41.
Poli, V.S.R.: Fuzzy data mining and web intelligencIts Applications (iFUZZY), 2015 International Conference on, IEEE (2015) 74–79
- 42.
Nasrollahzadeh, A., Karimian, G., Mehrafsa, A.: Implementation of neuro-fuzzy systperformance genetic algorithm on embedded systems. Applied Soft Computing (2017)
- 43.
Lucileide M. D. da Silva, Maria G. F. Coutinho, Carlos E. B. SanLuiz Affonso Guedes, M. Dolores Ruiz, Marcelo A. C. Fernandes, Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection. arXiv prep2020.
- 44.
A. L. X. Da Costa, C. A. D. Silva, M. F. Torquato and M. A. C. Fernandes, "Parallel Implementation of Particle Swarm Optimization on FPGA," in IEEE TransacSystems II: Express Briefs, vol. 66, no. 11, pp. 1875-1879, Nov. 2019.
- 45.
Torquato, M.F., Fernandes, M.A.C. High-Performance Parallel Implementation of Genetic AlgoSyst Signal Process 38, 4014–4039 (2019).
- 46.
M. G. F. Coutinho, M. F. Torquato and M. A. C. Fernandes, "Deep Neural Network Hardware Implementation Based on SAutoencoder," in IEEE Access, vol. 7, pp. 40674-40694, 2019.
- 47.
Lopes, F.F.; Ferreira, J.C.; Fernandes, M.A.C. Parallel Implementation on FPGA of Support Vector MGradient Descent. Electronics 2019, 8, 631.
- 48.
Noronha, D. H., Torquato, M. F., & Fernandes, M. A. (2019). A parallel implementation of sequential minimal optimizatioMicroprocessors and Microsystems, 69, 138-151.
- 49.
Silva, S.N.; Lopes, F.F.; Valderrama, C.; Fernandes, M.A.C. Proposal of Takagi–Sugeno Fuzzy-PI Controller Hard1996.
- 50.
H. Kung, "Why Systolic Architectures?" in Computer, vol. 15, no. 01, pp. 37-46, 1982.
- 51.
Sun, Y.; Tang, S.; Meng, Z.; Zhao, Y.; Yang, Y. A scalable accuracy{FPGA}. Expert Syst. Appl. 2015, 42, 6658–6673
- 52.
Ontiveros-Robles, E.; Gonzalez-Vazquez, J.L.; Castro, J.R.; Castillo, O. A hardware architecture for real-time edgeinterval type-2 fuzzy logic. In Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, Canada, 24–29 July 2016; pp. 8
- 53.
Telbany, M.E.; Zekry, A. Reconfigurable generic FPGA implementation of fuzzy logic controller for MPPT of PV systems. Renew. Sustain. Energy Rev. 2018, 82, 1313–1J.
- 54.
Antonio-Méndez, R.; Salazar-Pereyra, M. Fuzzy logic control on FPGA for two axes solar tracking. Neural Comput. Appl. 2019, 31, 2469–2483
- 55.
Krim, S.; Gdaim, M.F. Contribution of the FPGAs for Complex Control Algorithms: Sensorless DTFC with an EKF of an Induction Motor. Int. J. Autom. Comput. 2019, 16, 226–237
- 56.
“Artificial Intelligence (Chipsets) Market worth 16.06 Billion USD by 2022”, tractica.com. http://www.marketsandmarkets.com/PressReleases/artificial-intelligence.asp
- 57.
A.C.D. de Souza and M.A.C. Fernandes, Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA. Sensors 2014, 14, 18223-18243.
- 58.
K. KO. Mutlu and C. Zhang, "FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off," 2017 IEEE 25th Annual International Symposium onCustom Computing Machines (FCCM), Napa, CA, 2017, pp. 160-167.
- 59.
F. Shaikh, I. H. Kalwar, T. D. Memon and S. Sheikh, "Design and analysis of linear phase FIR falgorithm," 2017 6th Mediterranean Conference on Embedded Computing (MECO), Bar, 2017, pp. 1-4.
- 60.
A. Rodríguez and F. Moreno, "Evolutionary Computing and PHardware-Based Motion Estimation System," in IEEE Transactions on Computers, vol. 64, no. 11, pp. 3140-3152, Nov. 1 2015.
- 61.
Tirumalai, Vijay, Kenneth G Ricks & KUsing parallelization and hardware concurrency to improve the performance of a genetic algorithm, Concurrency and Computation: Practice and Experience 19(4), 443–
- 62.
David J Mulvaney & Vassilios A Chouliaras (2007), Hardware implementation of a novel genetic algorithm’, Neurocomputing 71(1), 95–106.
- 63.
D. Anguita, S. Pischiutta,"Feed-Forward Support Vector Machine Without Multipliers," in IEEE Transactions on Neural Networks, vol. 17, no. 5, pp. 1328-1331, Sept. 2006.
- 64.
S. Venkateshan, A"Hybrid Working Set Algorithm for SVM Learning With a Kernel Coprocessor on FPGA," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 23, nOct. 2015.
- 65.
S. R. Chowdhury and H. Saha, "A High-Performance FPGA-Based Fuzzy Processor Architecture for Medical Diagnosis," in IEEE Micro, vol. 28, no. 5, pp
- 66.
C. C. Chung and Y. H. Wang, "Hadoop cluster with FPGA-based hardware accelerators for K-means clustering algorithm," 2017 IEEE International Conference on CTaiwan (ICCE-TW), Taipei, 2017, pp. 143-144.
- 67.
C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie and X. Zhou, "DLAU: A Scalable Deep Learning Accelerator Unit on FPGA," inComputer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 3, pp. 513-517, March 2017.
- 68.
M. Alawad and M. Lin, "Stochastic-Based Deep ConvolutionalReconfigurable Logic Fabric," in IEEE Transactions on Multi-Scale Computing Systems, vol. 2, no. 4, pp. 242-256, Oct.-Dec., 2016.
- 69.
Samuel, A. L. (1959). Some Studies in Machine Learning. IBM Journal of Research and Development, 3(3), 210–229.
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5392560
- 70.
Pereira, R., Couto, M., Ribeiro, F., Rua, R., Cunha, J., Fernandes, J. P., &
Saraiva, J. (2017). Energy efficiency across programming languages:
How do energy, time, and memory relate? SLE 2017 - Proceedings of the 10th ACM
SIGPLAN International Conference on Software Language Engineering, Co-Located
with SPLASH 2017, 256–267.
https://doi.org/10.1145/3136014.3136031
- 71.
Cooper, R., Naclerio, F., Allgrove, J., & Jimenez, A. (2012). Creatine supplementation with specific view to exercise/sports performance: An update. In Journal of the International Society of Sports Nutrition (Vol. 9).
https://doi.org/10.1186/1550-2783-9-33
