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Статті в журналах з теми "DNN architecture"
Roorda, Esther, Seyedramin Rasoulinezhad, Philip H. W. Leong, and Steven J. E. Wilton. "FPGA Architecture Exploration for DNN Acceleration." ACM Transactions on Reconfigurable Technology and Systems 15, no. 3 (September 30, 2022): 1–37. http://dx.doi.org/10.1145/3503465.
Повний текст джерелаElola, Andoni, Elisabete Aramendi, Unai Irusta, Artzai Picón, Erik Alonso, Pamela Owens, and Ahamed Idris. "Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest." Entropy 21, no. 3 (March 21, 2019): 305. http://dx.doi.org/10.3390/e21030305.
Повний текст джерелаTran, Van Duy, Duc Khai Lam, and Thi Hong Tran. "Hardware-Based Architecture for DNN Wireless Communication Models." Sensors 23, no. 3 (January 23, 2023): 1302. http://dx.doi.org/10.3390/s23031302.
Повний текст джерелаTurner, Daniel, Pedro J. S. Cardoso, and João M. F. Rodrigues. "Modular Dynamic Neural Network: A Continual Learning Architecture." Applied Sciences 11, no. 24 (December 18, 2021): 12078. http://dx.doi.org/10.3390/app112412078.
Повний текст джерелаLee, Junghwan, Huanli Sun, Yuxia Liu, Xue Li, Yixin Liu, and Myungjun Kim. "State-of-Health Estimation and Anomaly Detection in Li-Ion Batteries Based on a Novel Architecture with Machine Learning." Batteries 9, no. 5 (May 8, 2023): 264. http://dx.doi.org/10.3390/batteries9050264.
Повний текст джерелаMudgil, Pooja, Pooja Gupta, Iti Mathur, and Nisheeth Joshi. "An ontological architecture for context data retrieval and ranking using SVM and DNN." Journal of Information and Optimization Sciences 44, no. 3 (2023): 369–82. http://dx.doi.org/10.47974/jios-1347.
Повний текст джерелаElsisi, Mahmoud, and Minh-Quang Tran. "Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles." Sensors 21, no. 24 (December 18, 2021): 8467. http://dx.doi.org/10.3390/s21248467.
Повний текст джерелаP, Shanmugavadivu, Mary Shanthi Rani M, Chitra P, Lakshmanan S, Nagaraja P, and Vignesh U. "Bio-Optimization of Deep Learning Network Architectures." Security and Communication Networks 2022 (September 20, 2022): 1–11. http://dx.doi.org/10.1155/2022/3718340.
Повний текст джерелаKrishnan, Gokul, Sumit K. Mandal, Chaitali Chakrabarti, Jae-Sun Seo, Umit Y. Ogras, and Yu Cao. "Impact of On-chip Interconnect on In-memory Acceleration of Deep Neural Networks." ACM Journal on Emerging Technologies in Computing Systems 18, no. 2 (April 30, 2022): 1–22. http://dx.doi.org/10.1145/3460233.
Повний текст джерелаZhao, Jiaqi, Ming Xu, Yunzhi Chen, and Guoliang Xu. "A DNN Architecture Generation Method for DDoS Detection via Genetic Alogrithm." Future Internet 15, no. 4 (March 26, 2023): 122. http://dx.doi.org/10.3390/fi15040122.
Повний текст джерелаДисертації з теми "DNN architecture"
Azam, Md Ali. "Energy Efficient Spintronic Device for Neuromorphic Computation." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6036.
Повний текст джерелаRiera, Villanueva Marc. "Low-power accelerators for cognitive computing." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669828.
Повний текст джерелаLes xarxes neuronals profundes (DNN) han aconseguit un èxit enorme en aplicacions cognitives, i són especialment eficients en problemes de classificació i presa de decisions com ara reconeixement de veu o traducció automàtica. Els dispositius mòbils depenen cada cop més de les DNNs per entendre el món. Els telèfons i rellotges intel·ligents, o fins i tot els cotxes, realitzen diàriament tasques discriminatòries com ara el reconeixement de rostres o objectes. Malgrat la popularitat creixent de les DNNs, el seu funcionament en sistemes mòbils presenta diversos reptes: proporcionar una alta precisió i rendiment amb un petit pressupost de memòria i energia. Les DNNs modernes consisteixen en milions de paràmetres que requereixen recursos computacionals i de memòria enormes i, per tant, no es poden utilitzar directament en sistemes de baixa potència amb recursos limitats. L'objectiu d'aquesta tesi és abordar aquests problemes i proposar noves solucions per tal de dissenyar acceleradors eficients per a sistemes de computació cognitiva basats en DNNs. En primer lloc, ens centrem en optimitzar la inferència de les DNNs per a aplicacions de processament de seqüències. Realitzem una anàlisi de la similitud de les entrades entre execucions consecutives de les DNNs. A continuació, proposem DISC, un accelerador que implementa una tècnica de càlcul diferencial, basat en l'alt grau de semblança de les entrades, per reutilitzar els càlculs de l'execució anterior, en lloc de computar tota la xarxa. Observem que, de mitjana, més del 60% de les entrades de qualsevol capa de les DNNs utilitzades presenten canvis menors respecte a l'execució anterior. Evitar els accessos de memòria i càlculs d'aquestes entrades comporta un estalvi d'energia del 63% de mitjana. En segon lloc, proposem optimitzar la inferència de les DNNs basades en capes FC. Primer analitzem el nombre de pesos únics per neurona d'entrada en diverses xarxes. Aprofitant optimitzacions comunes com la quantització lineal, observem un nombre molt reduït de pesos únics per entrada en diverses capes FC de DNNs modernes. A continuació, per millorar l'eficiència energètica del càlcul de les capes FC, presentem CREW, un accelerador que implementa un eficient mecanisme de reutilització de càlculs i emmagatzematge dels pesos. CREW redueix el nombre de multiplicacions i proporciona estalvis importants en l'ús de la memòria. Avaluem CREW en un conjunt divers de DNNs modernes. CREW proporciona, de mitjana, una millora en rendiment de 2,61x i un estalvi d'energia de 2,42x. En tercer lloc, proposem un mecanisme per optimitzar la inferència de les RNNs. Les cel·les de les xarxes recurrents realitzen multiplicacions element a element de les activacions de diferents comportes, sigmoides i tanh sent les funcions habituals d'activació. Realitzem una anàlisi dels valors de les funcions d'activació i mostrem que una fracció significativa està saturada cap a zero o un en un conjunto d'RNNs populars. A continuació, proposem CGPA per podar dinàmicament les activacions de les RNNs a una granularitat gruixuda. CGPA evita l'avaluació de neurones senceres cada vegada que les sortides de neurones parelles estan saturades. CGPA redueix significativament la quantitat de càlculs i accessos a la memòria, aconseguint en mitjana un 12% de millora en el rendiment i estalvi d'energia. Finalment, en l'última contribució d'aquesta tesi ens centrem en metodologies de poda estàtica de les DNNs. La poda redueix la petjada de memòria i el treball computacional mitjançant l'eliminació de connexions o neurones redundants. Tanmateix, mostrem que els esquemes de poda previs fan servir un procés iteratiu molt llarg que requereix l'entrenament de les DNNs moltes vegades per ajustar els paràmetres de poda. A continuació, proposem un esquema de poda basat en l'anàlisi de components principals i la importància relativa de les connexions de cada neurona que optimitza automàticament el DNN optimitzat en un sol tret sense necessitat de sintonitzar manualment múltiples paràmetres
Heath, Felicity. "Variable architecture polymers for DNA delivery." Thesis, University of Nottingham, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.539162.
Повний текст джерелаDing, Ke. "Architectures of DNA block copolymers." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=98214217X.
Повний текст джерелаMarriott, Hannah. "Genome architecture and DNA replication in Haloferax volcanii." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/50190/.
Повний текст джерелаWei, Diming. "The beauty of DNA architecture : the design and applications in DNA nanotechnology /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CBME%202009%20WEI.
Повний текст джерелаSchilter, David. "Synthesis and DNA-binding of Metallocyclic Architectures." Thesis, The University of Sydney, 2009. http://hdl.handle.net/2123/5317.
Повний текст джерелаSchilter, David. "Synthesis and DNA-binding of Metallocyclic Architectures." University of Sydney, 2009. http://hdl.handle.net/2123/5317.
Повний текст джерелаA new family of cationic N-heterocyclic ligand derivatives was prepared and characterised. Among these compounds are halide salts of the dications [Y(spacer)Y]2+, each of which comprise two N heterocyclic donor groups (Y = 4,4′-bipy, pyz, apyz, apym) linked by a conformationally flexible spacer such as (CH2)n, α,α′-xylylene, 2,6-lutidylene or thiabicyclo[3.3.1]nonane-2,6 diyl. The diquaternary halide salts were converted to NO3- and PF6- salts, and interaction of these bridging ligands with labile palladium(II) and platinum(II) precursors afforded several multinuclear complexes. Bis(4,4′-bipyridinium) dications were incorporated into the dinuclear macrocycles [M2(2,2′ bipy)2{4,4′ bipy(CH2)n4,4′-bipy}2]8+ (M = Pd, Pt; n = 4, 6), cis [Pd2Cl4{4,4′ bipy(CH2)34,4′-bipy}2]4+, [Pt2(dppp)2{4,4′-bipy(1,2-xylylene)4,4′-bipy}2]8+ and cis-[Pt2Cl4{4,4′-bipy(1,2-xylylene)4,4′-bipy}2]4+. While bis(pyrazinium) analogues were unreactive towards the palladium(II) and platinum(II) precursors, the doubly deprotonated bis(3 aminopyrazinium) and bis(2 aminopyrimidinium) derivatives served as charge-neutral quadruply-bridging ligands in the complexes [Pt4(2,2′ bipy)4{apyz(CH2)6apyz–2H}2]8+ and [Pt4(2,2′ bipy)4{apym(CH2)5apym–2H}2]8+, both of which feature Pt(II). Pt(II) interactions. Larger species formed when the diamine O,O′-bis(2-aminoethyl)octadeca(ethylene glycol) (PEGda) was treated with cis dinitratopalladium(II) and platinum(II) precursors. The resulting complexes [M(N,N)(PEGda)]2+ (M = Pd, Pt; N,N = 2,2′-bipy, en, tmeda) possessed great size (62 membered chelate rings) and aqueous solubility. DNA-binding studies were conducted with selected complexes in order to investigate the types of interactions these species might participate in. Equimolar mixtures containing either the 16mer duplex DNA D2 or the single strand D2a and palladium(II)/platinum(II) complexes were prepared and analysed by negative-ion ESI MS. Studies of D2/Pd(II) mixtures suggested extensive fragmentation was occuring, and the use of [Pd(tmeda)(PEGda)]2+ and [Pd2(2,2′-bipy)2{4,4′-bipy(CH2)44,4′-bipy}2]8+ resulted in D2 adducts of [Pd(tmeda)]2+ and [4,4′-bipy(CH2)44,4′-bipy]2+, respectively. Decomposition also occurred when D2a was used, although 1 : 1 adducts were observed with [Pd(tmeda)(PEGda)]2+, [Pd(2,2′ bipy)(PEGda)]2+ and [Pd2(2,2′-bipy)2{4,4′-bipy(CH2)44,4′-bipy}2]8+. The low intensities of these adducts indicated that they are unstable towards ESI MS. Analogous ESI-MS experiments using platinum(II) derivatives were performed and, in contrast to those with palladium(II), indicated that the complexes remained largely intact. ESI-MS analysis of D2/Pt(II) mixtures allowed for the detection of 1 : 1 D2 adducts of [Pt(en)(PEGda)]2+, [Pt(tmeda)(PEGda)]2+ and [Pt2(2,2′-bipy)2{4,4′-bipy(CH2)44,4′-bipy}2]8+. Intensities of the adduct ions suggested the greater charge and aryl surface area allow the dinuclear species to bind D2 most strongly. Both [Pt(2,2′-bipy)(Mebipy)2]4+ and [Pt(2,2′ bipy)(NH3)2]2+ gave rise to 1 : 2 adducts of D2, although the latter was found to be a weaker binder, perhaps owing to its lower charge. Data obtained using 1 : 5 (D2 : complex) mixtures were consistent with the results above and suggested that D2 can bind more molecules of daunomycin than any of the platinum(II) species. Analyses of D2a/Pt(II) mixtures gave results similar to those obtained with D2, although fragmentation was more pronounced, indicating that the nucleobases in D2a play more significant roles in mediating decomposition than those in D2, in which they are paired in a complementary manner. Investigations into the effects of selected platinum(II) complexes on the thermal denaturation of calf-thymus DNA (CT-DNA) in solution were conducted. Both [Pt2(2,2′ bipy)2{4,4′-bipy(CH2)64,4′-bipy}2]8+ and [Pt(2,2′-bipy)(Mebipy)2]4+ greatly stabilised CT-DNA, most likely by intercalation. In contrast, [Pt(tmeda)(PEGda)]2+ and [Pt(en)(PEGda)]2+ (as well as PEGda) caused negligible changes in melting temperature (∆Tm), suggesting that these interact weakly with CT-DNA. Data for [Pt(2,2′ bipy)(PEGda)]2+ and [Pt(2,2′-bipy)(NH3)2]2+ indicated that these species perhaps intercalate CT-DNA, with similar ∆Tm values for both complexes implying that PEGda does not play a major role in binding. While findings from ESI-MS experiments were similar to those from the thermal denaturation experiments, discrepancies between results from the two methods could be found. In particular, fragmentation of cyclic species during ESI-MS caused the binding strength of the species to be underestimated when this method was employed.
Yu, Zhiling. "Interactions and architecture of human MCM proteins in vitro and in vivo /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?BICH%202003%20YU.
Повний текст джерелаIncludes bibliographical references (leaves 118-137). Also available in electronic version. Access restricted to campus users.
van, der Merwe Mariè. "Enzyme architecture and flexibility affect DNA topoisomerase I function." View the abstract Download the full-text PDF version, 2007. http://etd.utmem.edu/ABSTRACTS/2007-026-van_der_Merwe-Index.html.
Повний текст джерелаTitle from title page screen (viewed on July 29, 2008). Research advisor: Mary-Ann Bjornsti, Ph.D. Document formatted into pages (xiii, 175 p. : ill.). Vita. Abstract. Includes bibliographical references (p. 161-175).
Книги з теми "DNN architecture"
Charre, Alain. Dan Graham. Paris: Dis Voir, 1995.
Знайти повний текст джерелаCharre, Alain. Dan Graham. Paris: Editions Dis voir, 1995.
Знайти повний текст джерела1944-, Budihardjo Eko, ed. Pengaruh budaya dan iklim dalam perancangan arsitektur. Bandung: Alumni, 2009.
Знайти повний текст джерела1944-, Budihardjo Eko, ed. Pengaruh budaya dan iklim dalam perancangan arsitektur. Bandung: Alumni, 2009.
Знайти повний текст джерелаTiantian, Xu, ed. Architecture as transformer: DnA-Design and Architecture, Beijing : projects 2004-2018. Berlin: Aedes, 2018.
Знайти повний текст джерелаÜber den Minderwertigkeitskomplex der deutschen Architektur: Ursachen einer Kontroverse. Göttingen: Optimus Verlag, 2010.
Знайти повний текст джерелаMayr, Fingerle Christoph, ed. Neues Bauen in den Alpen: Architekturpreis 2006 = Architettura alpina contemporanea : premio d'architettura 2006 = New Alpine architecture : architectural prize 2006. Basel: Birkhäuser, 2008.
Знайти повний текст джерелаvan, Boven Cees, Freijser Victor, and Vaillant Christiaan, eds. Gids van de moderne architectuur in Den Haag =: Guide to modern architecture in The Hague. 2nd ed. Den Haag: Uitgeverij Ulysses, 1998.
Знайти повний текст джерела1931-, Böhm Elisabeth, ed. Gottfried Böhm: Bauten und Projekte : Auszug aus den Jahren 1985-2000 = Buildings and projects : a selection of works 1985-2000. Tübingen: Wasmuth, 2001.
Знайти повний текст джерелаFoundation, Solomon R. Guggenheim, ed. Dan Flavin: The architecture of light. New York: The Solomon R. Guggenheim Foundation, 1999.
Знайти повний текст джерелаЧастини книг з теми "DNN architecture"
Wang, Liang, and Jianxin Zhao. "Deep Neural Networks." In Architecture of Advanced Numerical Analysis Systems, 121–47. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8853-5_5.
Повний текст джерелаBartz-Beielstein, Thomas, Sowmya Chandrasekaran, and Frederik Rehbach. "Case Study III: Tuning of Deep Neural Networks." In Hyperparameter Tuning for Machine and Deep Learning with R, 235–69. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5170-1_10.
Повний текст джерелаHan, Donghyeon, and Hoi-Jun Yoo. "An Overview of Energy-Efficient DNN Training Processors." In On-Chip Training NPU - Algorithm, Architecture and SoC Design, 183–210. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34237-0_8.
Повний текст джерелаAnjum, Muhammad, Moizzah Asif, and Jonathan Williams. "Towards an Optimal Deep Neural Network for SOC Estimation of Electric-Vehicle Lithium-Ion Battery Cells." In Springer Proceedings in Energy, 11–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_2.
Повний текст джерелаPontes, Felipe Arruda, and Edward Curry. "Cloud-Edge Microservice Architecture for DNN-based Distributed Multimedia Event Processing." In Communications in Computer and Information Science, 65–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71906-7_6.
Повний текст джерелаHan, Donghyeon, and Hoi-Jun Yoo. "HNPU-V1: An Adaptive DNN Training Processor Utilizing Stochastic Dynamic Fixed-Point and Active Bit-Precision Searching." In On-Chip Training NPU - Algorithm, Architecture and SoC Design, 121–61. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34237-0_6.
Повний текст джерелаHan, Donghyeon, and Hoi-Jun Yoo. "HNPU-V2: An Energy-Efficient DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation." In On-Chip Training NPU - Algorithm, Architecture and SoC Design, 163–82. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34237-0_7.
Повний текст джерелаSinha, Bam Bahadur, Gurvinder Singh Yadav, and Sagar Badrish Kudkelwar. "Modified-PIP with Deep Neural Network (DNN) Architecture: A Coherent Recommendation Framework for Capturing User Behaviour." In Studies in Big Data, 121–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10869-3_7.
Повний текст джерелаAlsuhli, Ghada, Vasilis Sakellariou, Hani Saleh, Mahmoud Al-Qutayri, Baker Mohammad, and Thanos Stouraitis. "BFP for DNN Architectures." In Synthesis Lectures on Engineering, Science, and Technology, 61–72. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38133-1_6.
Повний текст джерелаAlsuhli, Ghada, Vasilis Sakellariou, Hani Saleh, Mahmoud Al-Qutayri, Baker Mohammad, and Thanos Stouraitis. "Posit for DNN Architectures." In Synthesis Lectures on Engineering, Science, and Technology, 81–88. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38133-1_8.
Повний текст джерелаТези доповідей конференцій з теми "DNN architecture"
Krishnan, Gokul, Zhenyu Wang, Li Yang, Injune Yeo, Jian Meng, Rajiv V. Joshi, Nathaniel C. Cady, Deliang Fan, Jae-Sun Seo, and Yu Cao. "IMC Architecture for Robust DNN Acceleration." In 2022 IEEE 16th International Conference on Solid-State & Integrated Circuit Technology (ICSICT). IEEE, 2022. http://dx.doi.org/10.1109/icsict55466.2022.9963165.
Повний текст джерелаYemini, Yochai, Shlomo E. Chazan, Jacob Goldberger, and Sharon Gannot. "A Composite DNN Architecture for Speech Enhancement." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053821.
Повний текст джерелаKim, Jinhan, Nargiz Humbatova, Gunel Jahangirova, Paolo Tonella, and Shin Yoo. "Repairing DNN Architecture: Are We There Yet?" In 2023 IEEE Conference on Software Testing, Verification and Validation (ICST). IEEE, 2023. http://dx.doi.org/10.1109/icst57152.2023.00030.
Повний текст джерелаHe, Zhezhi. "Session details: Architecture for DNN Acceleration (Virtual)." In ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3578439.
Повний текст джерелаYazdani, Reza, Marc Riera, Jose-Maria Arnau, and Antonio Gonzalez. "The Dark Side of DNN Pruning." In 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA). IEEE, 2018. http://dx.doi.org/10.1109/isca.2018.00071.
Повний текст джерелаKwon, Hyoukjun, Liangzhen Lai, Michael Pellauer, Tushar Krishna, Yu-Hsin Chen, and Vikas Chandra. "Heterogeneous Dataflow Accelerators for Multi-DNN Workloads." In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 2021. http://dx.doi.org/10.1109/hpca51647.2021.00016.
Повний текст джерелаCai, Chengtao, and Dongning Guo. "CNN-Self-Attention-DNN Architecture For Mandarin Recognition." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164333.
Повний текст джерелаGeng, Wei, Dongyu Liu, and Xiu Cao. "A Power Anomaly Detection Architecture Based on DNN." In the 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3331453.3361641.
Повний текст джерелаGlint, Tom, Chandan Kumar Jha, Manu Awasthi, and Joycee Mekie. "Analysis of Quantization Across DNN Accelerator Architecture Paradigms." In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2023. http://dx.doi.org/10.23919/date56975.2023.10136899.
Повний текст джерелаJanfaza, Vahid, Kevin Weston, Moein Razavi, Shantanu Mandal, Farabi Mahmud, Alex Hilty, and Abdullah Muzahid. "MERCURY: Accelerating DNN Training By Exploiting Input Similarity." In 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 2023. http://dx.doi.org/10.1109/hpca56546.2023.10071051.
Повний текст джерелаЗвіти організацій з теми "DNN architecture"
Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.
Повний текст джерелаBenner, Steven A. Design Automation Software for DNA-Based Nano-Sensor Architecture. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada582334.
Повний текст джерелаClark, Paul C., Timothy E. Lavin, Cynthia E. Irvine, and David J. Shifflett. DNS and Multilevel Secure Networks: Architectures and Recommendations. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada498511.
Повний текст джерелаPeterson, J., O. Kolkman, H. Tschofenig, and B. Aboba. Architectural Considerations on Application Features in the DNS. RFC Editor, October 2013. http://dx.doi.org/10.17487/rfc6950.
Повний текст джерелаMacula, Anthony, Russell Deaton, and Junghuei Chen. A Two-Dimensional Deoxyribonucleic Acid (DNA) Matrix Based Biomolecular Computing and Memory Architecture. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada494650.
Повний текст джерелаYu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.
Повний текст джерелаJoel, Daniel M., Steven J. Knapp, and Yaakov Tadmor. Genomic Approaches for Understanding Virulence and Resistance in the Sunflower-Orobanche Host-Parasite Interaction. United States Department of Agriculture, August 2011. http://dx.doi.org/10.32747/2011.7592655.bard.
Повний текст джерелаCrowley, David E., Dror Minz, and Yitzhak Hadar. Shaping Plant Beneficial Rhizosphere Communities. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7594387.bard.
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