Relevant bibliographies by topics / SNN

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'SNN'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "SNN"

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Novy, Diane. "Effectiveness of Splanchnic Nerve Neurolysis for Targeting Location of Cancer Pain: Using the Pain Drawing as an Outcome Variable." July 2016 6;19, no. 6;7 (2016): 397–403. http://dx.doi.org/10.36076/ppj/2016.19.397.

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The effectiveness of splanchnic nerve neurolysis (SNN) for cancer-related abdominal pain has been investigated using numeric pain intensity rating as an outcome variable. The outcome variable in this study used the grid method for obtaining a targeted pain drawing score on 60 patients with pain from pancreatic or gastro-intestinal primary cancers or metastatic disease to the abdominal region. Results demonstrate excellent inter-rater agreement (intra-class correlation [ICC] coefficient at pre-SNN = 0.97 and ICC at within one month post-SNN = 0.98) for the grid method of scoring the pain drawin
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Moritz, Christian P., Yannick Tholance, Pierre-Baptiste Vallayer, et al. "Anti-AGO1 Antibodies Identify a Subset of Autoimmune Sensory Neuronopathy." Neurology - Neuroimmunology Neuroinflammation 10, no. 3 (2023): e200105. http://dx.doi.org/10.1212/nxi.0000000000200105.

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Background and ObjectivesAutoantibodies (Abs) improve diagnosis and treatment decisions of idiopathic neurologic disorders. Recently, we identified Abs against Argonaute (AGO) proteins as potential autoimmunity biomarkers in neurologic disorders. In this study, we aim to reveal (1) the frequency of AGO1 Abs in sensory neuronopathy (SNN), (2) titers and IgG subclasses, and (3) their clinical pattern including response to treatment.MethodsThis retrospective multicentric case/control study screened 132 patients with SNN, 301 with non-SNN neuropathies, 274 with autoimmune diseases (AIDs), and 116
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Al-Hamid, Ali A., and HyungWon Kim. "Optimization of Spiking Neural Networks Based on Binary Streamed Rate Coding." Electronics 9, no. 10 (2020): 1599. http://dx.doi.org/10.3390/electronics9101599.

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Spiking neural networks (SNN) increasingly attract attention for their similarity to the biological neural system. Hardware implementation of spiking neural networks, however, remains a great challenge due to their excessive complexity and circuit size. This work introduces a novel optimization method for hardware friendly SNN architecture based on a modified rate coding scheme called Binary Streamed Rate Coding (BSRC). BSRC combines the features of both rate and temporal coding. In addition, by employing a built-in randomizer, the BSRC SNN model provides a higher accuracy and faster training.
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Pawar, Anuradha, and Nidhi Tiwari. "A Novel Approach of DDOS Attack Classification with Genetic Algorithm-optimized Spiking Neural Network." International Journal of Computer Network and Information Security 16, no. 2 (2024): 103–16. http://dx.doi.org/10.5815/ijcnis.2024.02.09.

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Spiking Neural Network (SNN) use spiking neurons that transmit information through discrete spikes, similar to the way biological neurons communicate through action potentials. This unique property of SNNs makes them suitable for applications that require real-time processing and low power consumption. This paper proposes a new method for detecting DDoS attacks using a spiking neural network (SNN) with a distance-based rate coding mechanism and optimizing the SNN using a genetic algorithm (GA). The proposed GA-SNN approach achieved a remarkable accuracy rate of 99.98% in detecting DDoS attacks
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Neiva, Flávia Cristina Brisque, and Cléa Rodrigues Leone. "Sucção em recém-nascidos pré-termo e estimulação da sucção." Pró-Fono Revista de Atualização Científica 18, no. 2 (2006): 141–50. http://dx.doi.org/10.1590/s0104-56872006000200003.

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TEMA: a estimulação da sucção não-nutritiva pode antecipar o início da alimentação por via oral e influenciar a evolução da sucção em recém-nascidos pré-termo. OBJETIVO: descrever a evolução do padrão de sucção e os efeitos da estimulação da sucção não-nutritiva (SNN). MÉTODO: foram estudados 95 recém-nascidos pré-termo (RNPT) adequados para a idade gestacional (IG), com IG ao nascer menor ou igual a 33 semanas, distribuídos de forma aleatória em três grupos: Grupo 1 (G1), grupo controle, sem estimulação da SNN; Grupo 2 (G2), com estimulação da SNN com chupeta ortodôntica para prematuros NUK®
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Galán-Prado, Fabio, Alejandro Morán, Joan Font, Miquel Roca, and Josep L. Rosselló. "Compact Hardware Synthesis of Stochastic Spiking Neural Networks." International Journal of Neural Systems 29, no. 08 (2019): 1950004. http://dx.doi.org/10.1142/s0129065719500047.

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Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biologically-inspired SNN is currently a complex challenge of high scientific and technical interest. In this work, we propose a compact digital design for the implementation of high-volume SNN that considers the intrinsic stochastic processes present in biological neurons and enables high-density hardware implementation. The proposed stochastic SNN mo
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Putra, Rizal Kusuma, Gusti Ahmad Fanshuri Alfarisy, Faizal Widya Nugraha, and Aninditya Anggari Nuryono. "Automatic Plant Disease Classification with Unknown Class Rejection using Siamese Networks." Buletin Ilmiah Sarjana Teknik Elektro 6, no. 3 (2024): 308–16. https://doi.org/10.12928/biste.v6i3.11619.

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Potatoes are one of the horticultural commodities with significant trade value both domestically and internationally. To produce high-quality potatoes, healthy and disease-free potato plants are essential. The most common diseases affecting potato plants are late blight and early blight. These diseases appear randomly in different positions and sizes on potato leaves, resulting in numerous combinations of infected leaves. This study proposes an architecture focused on a similarity-based approach, namely the Siamese Neural Network (SNN). SNN can recognize images by comparing two or more images
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Liu, Yang, Meng Tian, Ruijia Liu, et al. "Spike-Based Approximate Backpropagation Algorithm of Brain-Inspired Deep SNN for Sonar Target Classification." Computational Intelligence and Neuroscience 2022 (October 20, 2022): 1–11. http://dx.doi.org/10.1155/2022/1633946.

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With the development of neuromorphic computing, more and more attention has been paid to a brain-inspired spiking neural network (SNN) because of its ultralow energy consumption and high-performance spatiotemporal information processing. Due to the discontinuity of the spiking neuronal activation function, it is still a difficult problem to train brain-inspired deep SNN directly, so SNN has not yet shown performance comparable to that of an artificial neural network. For this reason, the spike-based approximate backpropagation (SABP) algorithm and a general brain-inspired SNN framework are pro
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Ma, Zansong, Xiangbing Shu, Jie Huang, Haiyan Zhang, Zhen Xiao, and Li Zhang. "Salvia-Nelumbinis Naturalis Formula Improved Inflammation in LPS Stressed Macrophages via Upregulating MicroRNA-152." Mediators of Inflammation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5842747.

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Salvia-Nelumbinis naturalis (SNN) formula is an effective agent in treating nonalcoholic steatohepatitis (NASH); however, the precise mechanisms are still undefined. Activation of Kupffer cells by gut-derived lipopolysaccharide (LPS) plays a central role in the pathogenesis of NASH. In the present study, we aimed to explore the epigenetic regulation of microRNAs under the beneficial effects of SNN-containing serum in LPS stressed macrophages. Kupffer cells were isolated from C57BL/6 mice and treated with LPS or LPS and SNN-containing serum; the mRNA expression of tumor necrosis factor-α (TNF-α
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Li, Cuixia, Zhiquan Shang, Li Shi, Wenlong Gao, and Shuyan Zhang. "IC-SNN: Optimal ANN2SNN Conversion at Low Latency." Mathematics 11, no. 1 (2022): 58. http://dx.doi.org/10.3390/math11010058.

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The spiking neural network (SNN) has attracted the attention of many researchers because of its low energy consumption and strong bionics. However, when the network conversion method is used to solve the difficulty of network training caused by its discrete, too-long inference time, it may hinder the practical application of SNN. This paper proposes a novel model named the SNN with Initialized Membrane Potential and Coding Compensation (IC-SNN) to solve this problem. The model focuses on the effect of residual membrane potential and rate encoding on the target SNN. After analyzing the conversi
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Dissertations / Theses on the topic "SNN"

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Parana-Liyanage, Krishani. "Outlier detection in spatial data using the m-SNN algorithm." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 2013. http://digitalcommons.auctr.edu/dissertations/1299.

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Outlier detection is an important topic in data analysis because of its applications to numerous domains. Its application to spatial data, and in particular spatial distribution in path distributions, has recently attracted much interest. This recent trend can be seen as a reflection of the massive amounts of spatial data being gathered through mobile devices, sensors and social networks. In this thesis we propose a nearest neighbor distance based method the Modified-Shared Nearest Neighbor outlier detection (m-SNN) developed for outlier detection in spatial domains. We modify the SNN techniqu
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Itani, Aashish. "COMPARISON OF ADVERSARIAL ROBUSTNESS OF ANN AND SNN TOWARDS BLACKBOX ATTACKS." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2864.

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n recent years, the vulnerability of neural networks to adversarial samples has gained wide attention from machine learning and deep learning communities. Addition of small and imperceptible perturbations to the input samples can cause neural network models to make incorrect prediction with high confidence. As the employment of neural networks on safety critical application is rising, this vulnerability of traditional neural networks to the adversarial samples demand for more robust alternative neural network models. Spiking Neural Network (SNN), is a special class of ANN, which mimics the
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Parker, William Chesluk. "CENTRALITY DEPENDENCE OF BULK FIREBALL PROPERTIES IN /Snn=62.4 and 200 GeV." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/192559.

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Zapata, Rodriguez Mireya. "Arquitectura escalable SIMD con conectividad jerárquica y reconfigurable para la emulación de SNN." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461085.

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A biological neural system consists of millions of highly integrated neurons with multiple dynamic functions operating in coordination with each other. Its structural organization is characterized by highly hierarchical assemblies. These assemblies are distinguished by locally dense and globally ispersed connections communicated by spikes traveling through the axon to the target neuron. In the last century, approaching the biological complexity of the cortex by means of hardware architectures has continued to be a challenge still unattainable. This is not only due to the massively parallel pr
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Quintero, Amilkar. "MEASUREMENT OF CHARM MESON PRODUCTION IN Au+Au COLLISIONS ATsqrt(SNN) =200 GeV." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1460734511.

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Johnson, Ian Jeffrey. "Photon and [pi]⁰ production in ¹⁹⁷Au+¹⁹⁷Au collisions at [square root]SNN=130GeV /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2002. http://uclibs.org/PID/11984.

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Appelt, Eric. "Measurements of Charged-Particle Transverse Momentum Spectra in PbPb Collisions at Square Root of SNN = 2|76 TeV and in pPb Collisions at Square Root of SNN = 5|02 TeV with the CMS Detector." Thesis, Vanderbilt University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3584408.

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Ahmed, Samah. "Measurement of Neutral Mesons in p-Pb collisions at √sNN = 8.16TeV with the ALICE detector." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31492.

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The measured transverse momentum spectra of neutral pion π 0 and η mesons are presented for p-Pb collisions at √ sNN = 8.16 TeV using the photon conversion method for the signal extraction. This method uses the tracking and particle identification capabilities of the central barrel detectors of ALICE. Signal extracted down to 0.3 GeV/c and 0.7 GeV/c for π 0 and η respectively. The resulting spectra are presented and systematic uncertainties have been evaluated. A suppression of the yield compared to pp collisions at the same center of mass energy is observed in RpA for both mesons. Comparisons
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Castillo, Javier. "Production de particules doublement étranges dans les collisions d'ions lourds ultra-relativistes à √SNN = 130 GeV." Paris 7, 2002. http://www.theses.fr/2002PA077043.

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Raisig, Pascal [Verfasser], Harald [Gutachter] Appelshäuser та Christoph [Gutachter] Blume. "J/ψ production in √sNN = 5.02 TeV Pb−Pb collisions / Pascal Raisig ; Gutachter: Harald Appelshäuser, Christoph Blume". Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2020. http://d-nb.info/1212509749/34.

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Books on the topic "SNN"

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Schuchmann, Simone. Modification of K0s and Lambda(AntiLambda) Transverse Momentum Spectra in Pb-Pb Collisions at √sNN = 2.76 TeV with ALICE. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43458-2.

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SNN Symposium on Neural Networks (3rd 1995 Nijmegen, Netherlands). Neural networks: Artificial intelligence and industrial applications : proceedings of the Third Annual SNN Symposium on Neural Networks, Nijmegen, The Netherlands, 14-15 September 1995. Springer, 1995.

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Kappen, Bert. Neural Networks: Artificial Intelligence and Industrial Applications: Proceedings of the Third Annual SNN Symposium on Neural Networks, Nijmegen, the Netherlands, 14-15 September 1995. Springer London, 1995.

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Holka, Peter. Sen o sne. Slovenský spisovatel̕, 1998.

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yi, Duan Zhang qu, ed. Hu wa san jin sen lin. Xin shi ji chu ban she, 2016.

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Quanzheng, Qiu, Fu Zhixing, and Chen Changguang, eds. Sun Zhongshan: Mian wei qi nan de ge ming jia. Zhongguo hua qiao chu ban she, 1996.

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Janet, Lloyd, ed. Sun Yat-sen. Stanford University Press, 1998.

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Liu, Cixin. San ti II: Hei an sen lin. Chongqing chu ban she., 2008.

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Kim, Ho-sang. Nangsan: Sin ŭi sup, wang ŭi san. Chisik kwa Kamsŏng#, 2021.

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Ch'oe, Chong-sŏng oe. Sin kwa in'gan i mannanŭn kot, san. Ihaksa, 2020.

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Book chapters on the topic "SNN"

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Baumann, Norbert, Hans-Jürgen Fachmann, Reimund Jotter, and Alfons Kubny. "Dinitrogen Sulfide, SNN." In S Sulfur-Nitrogen Compounds. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-06351-4_5.

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Baumann, Norbert, Hans-Jürgen Fachmann, Reimund Jotter, and Alfons Kubny. "Dinitrogen Sulfide, SNN." In Sulfur-Nitrogen Compounds. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-06354-5_5.

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Penkov, Dimitar, Petia Koprinkova-Hristova, Nikola Kasabov, Simona Nedelcheva, Sofiya Ivanovska, and Svetlozar Yordanov. "Grid Search Optimization of Novel SNN-ESN Classifier on a Supercomputer Platform." In Large-Scale Scientific Computations. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56208-2_45.

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AbstractThis work is demonstrating the use of a supercomputer platform to optimise hyper-parameters of a proposed by the team novel SNN-ESN computational model, that combines a brain template of spiking neurons in a spiking neural network (SNN) for feature extraction and an Echo State Network (ESN) for dynamic data series classification. A case study problem and data are used to illustrate the functionalities of the SNN-ESN. The overall SNN-ESN classifier has several hyper-parameters that are subject to refinement, such as: spiking threshold, duration of the refractory period and STDP learning
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Aguilar, Jesús S., Roberto Ruiz, José C. Riquelme, and Raúl Giráldez. "SNN: A Supervised Clustering Algorithm." In Engineering of Intelligent Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45517-5_24.

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Maji, Prasenjit, Ramapati Patra, Kunal Dhibar, and Hemanta Kumar Mondal. "SNN Based Neuromorphic Computing Towards Healthcare Applications." In Internet of Things. Advances in Information and Communication Technology. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45878-1_18.

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Dabbous, Ali, Ali Ibrahim, and Maurizio Valle. "Feed-Forward SNN for Touch Modality Prediction." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16281-7_21.

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Kasabov, Nikola K. "Brain-Computer Interfaces Using Brain-Inspired SNN." In Springer Series on Bio- and Neurosystems. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57715-8_14.

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Meng, Minrui, Xingbo Wang, and Xiaotao Wang. "Adaptive SNN Torque Control for Tendon-Driven Fingers." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6370-1_23.

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Safa, Ali, Lars Keuninckx, Georges Gielen, and Francky Catthoor. "A Top-Down Approach to SNN-STDP Networks." In Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63565-6_4.

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Stasenko, Sergey V., and Victor B. Kazantsev. "Astrocyte Controlled SNN Dynamic Induced by Sensor Input." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52470-7_23.

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Conference papers on the topic "SNN"

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Li, Jiajun. "SNN Implementation Based on Systolic Arrays." In 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). IEEE, 2024. http://dx.doi.org/10.1109/iist62526.2024.00107.

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Kim, Namji, Jongpil Jeong, Chaegyu Lee, Dokyung Lee, Jaewon Seo, and Jaewon Jung. "SNN Based Anomaly Detection Using ESVAE." In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2024. https://doi.org/10.1109/ictc62082.2024.10827115.

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Plagwitz, Patrick, Frank Hannig, Jürgen Teich, and Oliver Keszocze. "DSL-Based SNN Accelerator Design Using Chisel." In 2024 27th Euromicro Conference on Digital System Design (DSD). IEEE, 2024. http://dx.doi.org/10.1109/dsd64264.2024.00032.

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Fang, Zerui, Jia Li, and Weibing Wang. "CASNN: Continuous Adaptive SNN for Human Activity Recognition." In 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2024. http://dx.doi.org/10.1109/iciea61579.2024.10665045.

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Kapoor, Hemangee K., Imlijungla Longchar, and Binayak Behera. "E-DOSA: Efficient Dataflow for Optimising SNN Acceleration." In 2025 38th International Conference on VLSI Design and 2025 24th International Conference on Embedded Systems (VLSID). IEEE, 2025. https://doi.org/10.1109/vlsid64188.2025.00055.

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Warr, Katy, Jonathon Hare, and David Thomas. "Dedicated Class Subnetworks for SNN Class Incremental Learning." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11064919.

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Nguyen-Dinh, Huong, Lan Dang-Hoang, Huy Tran-Quang, Linh Nguyen-Phuong, Chi Hoang-Phuong, and Minh Nguyen-Duc. "A Novel NoC-based SNN Architecture with Optimized Routers." In 2024 Tenth International Conference on Communications and Electronics (ICCE). IEEE, 2024. http://dx.doi.org/10.1109/icce62051.2024.10634604.

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Zhao, Wenbin, Zhouyou Gu, Branka Vucetic, and Wibowo Hardjawana. "SNN-Based Early HARQ Predictor Design For 5G Networks." In 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall). IEEE, 2024. https://doi.org/10.1109/vtc2024-fall63153.2024.10757795.

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Gao, Yuxuan, Bingli Guo, Jingxiang Wang, Yuan Liu, and Shanguo Huang. "Fault Tracing of Optical Networks Based on CPSO-SNN." In 2024 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC). IEEE, 2024. https://doi.org/10.1109/acp/ipoc63121.2024.10809816.

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Li, Shuailong, Qing Zhang, and Feng Lin. "DEP-SNN-RL:Spiking Neural Networks Reinforcement Learning in Musculoskeletal Systems." In 2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2024. http://dx.doi.org/10.1109/cis-ram61939.2024.10672803.

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Reports on the topic "SNN"

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KURIHARA, N., H. HAMAGAKI, K. OZAWA, T. SAKAGUCHI, T. CHUJO, and S. ESUMI. MEASUREMENT OF CHARGED HADRON SPECTRA IN AU+AU COLLISION AT SNN = 62.4 GEV AT RHIC-PHENIX. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/859893.

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Wang, Felix, Nick Alonso, and Corinne Teeter. Combining Spike Time Dependent Plasticity (STDP) and Backpropagation (BP) for Robust and Data Efficient Spiking Neural Networks (SNN). Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1902866.

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LINDENBAUM, S. J., and R. S. LONGACRE. PARTON BUBBLE MODEL COMPARED WITH RHIC CENTRAL AU+AU DELTA PHI DELTA ETA CORRELATIONS AT SQRT SNN = 200 GEV. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/890922.

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Bryan, Charles R., and Eric John Schindelholz. FY18 Status Report: SNL Research into Stress Corrosion Cracking of SNF Interim Storage Canisters. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1481507.

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Schaller, Rebecca, Andrew William Knight, Charles R. Bryan, and Eric John Schindelholz. FY19 Status Report: SNL Research into Stress Corrosion Cracking of SNF Dry Storage Canisters. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1569157.

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Yen, Hung-Ju. Organic Synthetic Advanced Materials for Optoelectronic and Energy Applications (at National Sun Yat-sen University). Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1332217.

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Diaz, Aaron A., David L. Baldwin, Anthony D. Cinson, et al. Identify and Quantify the Mechanistic Sources of Sensor Performance Variation Between Individual Sensors SN1 and SN2. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1339935.

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Pretorius, Philip Christo. Various Facets of Populist, Authoritarian and Nationalist Trends in Japan and Taiwan. European Center for Populism Studies (ECPS), 2024. http://dx.doi.org/10.55271/rp0052.

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This report provides a brief overview of the tenth event in ECPS’s monthly Mapping European Populism (MEP) panel series, titled "Various Facets of Populist, Authoritarian and Nationalist Trends in Japan and Taiwan" held online on February 29, 2024. Moderated by Dr. Dachi Liao, Emeritus Professor at the Institute of Political Science at National Sun Yat-sen University in Taiwan, the panel featured speakers Dr. Yoshida Toru, Full Professor of Comparative Politics at Doshisha University in Japan, Dr. Airo Hino, Professor, School of Political Science and Economics, Waseda University, Dr. Szu-Yun H
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Stevens, Michael. SSN 774 Virginia Class Submarine (SSN 774). Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1019531.

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NAVY PEO (SUBMARINES) WASHINGTON NAVY YARD DC. SSN 774 Virginia Class Submarine (SSN 774). Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada615033.

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