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Thematische Bibliographien / Edge devices

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Auswahl der wissenschaftlichen Literatur zum Thema „Edge devices“

Autor: Grafiati

Veröffentlicht am 29. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel zum Thema "Edge devices"

1

van Dijke, Koen, Gert Veldhuis, Karin Schroën und Remko Boom. „Parallelized edge-based droplet generation (EDGE) devices“. Lab on a Chip 9, Nr. 19 (2009): 2824. http://dx.doi.org/10.1039/b906098g.

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Mahmod, Md Jubayer al, und Ujjwal Guin. „A Robust, Low-Cost and Secure Authentication Scheme for IoT Applications“. Cryptography 4, Nr. 1 (08.03.2020): 8. http://dx.doi.org/10.3390/cryptography4010008.

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The edge devices connected to the Internet of Things (IoT) infrastructures are increasingly susceptible to piracy. These pirated edge devices pose a serious threat to security, as an adversary can get access to the private network through these non-authentic devices. It is necessary to authenticate an edge device over an unsecured channel to safeguard the network from being infiltrated through these fake devices. The implementation of security features demands extensive computational power and a large hardware/software overhead, both of which are difficult to satisfy because of inherent resource limitation in the IoT edge devices. This paper presents a low-cost authentication protocol for IoT edge devices that exploits power-up states of built-in SRAM for device fingerprint generations. Unclonable ID generated from the on-chip SRAM could be unreliable, and to circumvent this issue, we propose a novel ID matching scheme that alleviates the need for enhancing the reliability of the IDs generated from on-chip SRAMs. Security and different attack analysis show that the probability of impersonating an edge device by an adversary is insignificant. The protocol is implemented using a commercial microcontroller, which requires a small code overhead. However, no modification of device hardware is necessary.

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Lee, Dongkyu, Hyeongyun Moon, Sejong Oh und Daejin Park. „mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications“. Sensors 20, Nr. 12 (12.06.2020): 3337. http://dx.doi.org/10.3390/s20123337.

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As the Internet of Things (IoT) is becoming more pervasive in our daily lives, the number of devices that connect to IoT edges and data generated at the edges are rapidly increasing. On account of the bottlenecks in servers, due to the increase in data, as well as security and privacy issues, the IoT paradigm has shifted from cloud computing to edge computing. Pursuant to this trend, embedded devices require complex computation capabilities. However, due to various constraints, edge devices cannot equip enough hardware to process data, so the flexibility of operation is reduced, because of the limitations of fixed hardware functions, relative to cloud computing. Recently, as application fields and collected data types diversify, and, in particular, applications requiring complex computation such as artificial intelligence (AI) and signal processing are applied to edges, flexible processing and computation capabilities based on hardware acceleration are required. In this paper, to meet these needs, we propose a new IoT platform, called a metamorphic IoT (mIoT) platform, which can various hardware acceleration with limited hardware platform resources, through on-demand transmission and reconfiguration of required hardware at edges instead of via transference of sensing data to a server. The proposed platform reconfigures the edge’s hardware with minimal overhead, based on a probabilistic value, known as callability. The mIoT consists of reconfigurable edge devices based on RISC-V architecture and a server that manages the reconfiguration of edge devices based on callability. Through various experimental results, we confirmed that the callability-based mIoT platform can provide the hardware required by the edge device in real time. In addition, by performing various functions with small hardware, power consumption, which is a major constraint of IoT, can be reduced.

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Linke, Markus, und Juan García-Manrique. „Contribution to Reduce the Influence of the Free Sliding Edge on Compression-After-Impact Testing of Thin-Walled Undamaged Composites Plates“. Materials 11, Nr. 9 (13.09.2018): 1708. http://dx.doi.org/10.3390/ma11091708.

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Standard Compression-After-Impact test devices show a weakening effect on thin-walled specimens due to a free panel edge that is required for compression. As a result, thin-walled undamaged samples do not break in the free measuring area but near the free edge and along the supports. They also show a strength reduction due to the free edge which can become potentially relevant for very weakly damaged panels. In order to reduce the free edge influence on the measured strength, a modified Compression-After-Impact test device has been developed. In an experimental investigation with carbon fiber reinforced plastics, the modified device is compared with a standard device. It is shown that thin-walled undamaged specimens investigated with the modified device now mainly break within the free measuring area and no longer at the free edge and along the bearings as it is the case for standard test devices. The modified device does not cause a free edge weakening effect in comparison to standard devices. The modified device is therefore more suitable for determining the compression strengths of undamaged thin-walled composite plates.

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Douglas, Antonyo, Richard Holloway, Jonathan Lohr, Elijah Morgan und Khaled Harfoush. „Blockchains for constrained edge devices“. Blockchain: Research and Applications 1, Nr. 1-2 (Dezember 2020): 100004. http://dx.doi.org/10.1016/j.bcra.2020.100004.

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TYAGI, PAWAN. „MOLECULAR SPIN DEVICES: CURRENT UNDERSTANDING AND NEW TERRITORIES“. Nano 04, Nr. 06 (Dezember 2009): 325–38. http://dx.doi.org/10.1142/s1793292009001903.

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Molecular spin devices (MSDs) are the most promising candidate for futuristic quantum computation, having potential to resolve spin scattering issue which compromise the utility of conventional spin devices. The MSDs have been extensively reviewed from the view points of device physics and the application of target molecules, such as single molecular magnets. Fabrication of a competent MSD still remains an intractable task. In this review, we first describe the experimental studies where spin state of molecule and/or electrode affected the device transport, especially under magnetic field. Then, we correlated the number of theoretical and experimental results from various domains of nanomagnetism to highlight the scope and future directions panoramically. Finally, the key designs of various MSDs, including our recently developed multilayer edge molecular electrode, have been discussed. A multilayer edge molecular electrode, prepared by bridging the molecular clusters on the exposed edges of a customized ferromagnet–insulator–ferromagnet junction, can be a promising platform for testing the variety of molecular magnets.

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Shafiq, Muhammad, Zhihong Tian, Ali Kashif Bashir, Korhan Cengiz und Adnan Tahir. „SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique“. Wireless Communications and Mobile Computing 2020 (09.10.2020): 1–10. http://dx.doi.org/10.1155/2020/8864301.

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The Internet of Things (IoT) is growing day by day, and new IoT devices are introduced and interconnected. Due to this rapid growth, IoT faces several issues related to communication in the edge computing network. The critical issue in these networks is the effective edge computing IoT device selection whenever there are several edge nodes to carry information. To overcome this problem, in this paper, we proposed a new framework model named SoftSystem based on the soft set technique that recommends useful IIoT devices. Then, we proposed an algorithm named Softsystemalgo. For the proposed system, three different parameters are selected: IoT Device Security (IDSC), IoT Device Storage (IDST), and IoT Device Communication Speed (IDCS). We also find out the most significant parameters from the given set of parameters. It is evident that our proposed system is effective for the selection of edge computing devices in the IoT network.

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Santa, José, Pedro J. Fernández, Ramon Sanchez-Iborra, Jordi Ortiz und Antonio F. Skarmeta. „Offloading Positioning onto Network Edge“. Wireless Communications and Mobile Computing 2018 (23.10.2018): 1–13. http://dx.doi.org/10.1155/2018/7868796.

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While satellite or cellular positioning implies dedicated hardware or network infrastructure functions, indoor navigation or novel IoT positioning techniques include flexible storage and computation requirements that can be fulfilled by both end-devices or cloud back-ends. Hybrid positioning systems support the integration of several algorithms and technologies; however, the common trend of delegating position calculation and storage of local geoinformation to mobile devices or centralized servers causes performance degradation in terms of delay, battery usage, and waste of network resources. The strategy followed in this work is offloading this computation effort onto the network edge, following a Mobile Edge Computing (MEC) approach. MEC nodes in the access network of the mobile device are in charge of receiving navigation data coming from both the smart infrastructure and mobile devices, in order to compute the final position following a hybrid approach. With the aim of supporting mobility and the access to multiple networks, an Information Centric Networking (ICN) solution is used to access generic position information resources. The presented system currently supports WiFi, Bluetooth LE, GPS, cellular and NFC technologies, involving both indoor and outdoor positioning, using fingerprinting and proximity for indoor navigation, and the integration of smart infrastructure data sources such as the door opening system within real smart campus deployment. Evaluations carried out reveal latency improvements of 50%, as compared with a regular configuration where position fixes are computed by mobile devices; at the same time the MEC solution offers extra flexibility features to manage positioning databases and algorithms and move extensive computation from constrained devices to the edge.

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Liang, Tyng-Yeu, und You-Jie Li. „A Location-Aware Service Deployment Algorithm Based on K-Means for Cloudlets“. Mobile Information Systems 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8342859.

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Cloudlet recently was proposed to push data centers towards network edges for reducing the network latency of delivering cloud services to mobile devices. For the sake of user mobility, it is necessary to deploy and hand off services anytime anywhere for achieving the minimal network latency for users’ service requests. However, the cost of this solution usually is too high for service providers and is not effective for resource exploitation. To resolve this problem, we propose a location-aware service deployment algorithm based on K-means for cloudlets in this paper. Simply speaking, the proposed algorithm divides mobile devices into a number of device clusters according to the geographical location of mobile devices and then deploys service instances onto the edge cloud servers nearest to the centers of device clusters. Our performance evaluation has shown that the proposed algorithm can effectively reduce not only the network latency of edge cloud services but also the number of service instances used for satisfying the condition of tolerable network latency.

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Zeng, Xin, Xiaomei Zhang, Shuqun Yang, Zhicai Shi und Chihung Chi. „Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices“. Sensors 21, Nr. 13 (05.07.2021): 4592. http://dx.doi.org/10.3390/s21134592.

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Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

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Dissertationen zum Thema "Edge devices"

1

Choi, Myung Cheon. „Traffic flow management for RSVP/ATM edge devices“. Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15520.

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Barnes, Brian E. „Real-time resource management for RSVP/ATM edge devices“. Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15395.

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Pinkney, Thomas David. „Wound-edge protection devices to reduce surgical site infection“. Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7588/.

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This thesis provides an overview of the design, conduct and analysis of a multicentre phase III trial exploring the clinical effectiveness of a novel in-theatre intervention to try to reduce rates of post-operative surgical site infection (SSI). The pitfalls inherent in the conduct of research into SSI are discussed, along with measures to try and overcome these. The wound-edge protection device (WEPD) intervention is defined and the published evidence assessing its clinical effectiveness systematically appraised. Clinical surgical research and its difficulties are described, and the paradigm-shift bought about by the new trainee-led research collaborative model introduced. The design considerations involved creating a pragmatic and simple trial within the complex intervention that is surgery are explored in the context of the creation of the ROSSINI trial. This trial successfully recruited ahead of time and target and robustly proved that WEPDs are not clinically effective in reducing SSI. It also demonstrated the power and ability of this new collaborative model, as witnessed by both the citations of the results paper and the exponential growth in similar collaborative ventures. Finally, lessons learned about SSI research and clinical surgical research are summarised, and plans for future research presented.

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Kaarlela, H. (Heikki). „Edge adaptive filtering of depth maps for mobile devices“. Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201910313045.

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Abstract. Mobile phone cameras have an almost unlimited depth of field, and therefore the images captured with them have wide areas in focus. When the depth of field is digitally manipulated through image processing, accurate perception of depth in a captured scene is important. Capturing depth data requires advanced imaging methods. In case a stereo lens system is used, depth information is calculated from the disparities between stereo frames. The resulting depth map is often noisy or doesn’t have information for every pixel. Therefore it has to be filtered before it is used for emphasizing depth. Edges must be taken into account in this process to create natural-looking shallow depth of field images. In this study five filtering methods are compared with each other. The main focus is the Fast Bilateral Solver, because of its novelty and high reported quality. Mobile imaging requires fast filtering in uncontrolled environments, so optimizing the processing time of the filters is essential. In the evaluations the depth maps are filtered, and the quality and the speed is determined for every method. The results show that the Fast Bilateral Solver filters the depth maps well, and can handle noisy depth maps better than the other evaluated methods. However, in mobile imaging it is slow and needs further optimization.Reunatietoinen syvyyskarttojen suodatus mobiililaitteilla. Tiivistelmä. Matkapuhelimien kameroissa on lähes rajoittamaton syväterävyysalue, ja siksi niillä otetuissa kuvissa laajat alueet näkyvät tarkennettuina. Digitaalisessa syvyysterävyysalueen muokkauksessa tarvitaan luotettava syvyystieto. Syvyysdatan hankinta vaatii edistyneitä kuvausmenetelmiä. Käytettäessä stereokameroita syvyystieto lasketaan kuvien välisistä dispariteeteista. Tuloksena syntyvä syvyyskartta on usein kohinainen, tai se ei sisällä syvyystietoa joka pikselille. Tästä syystä se on suodatettava ennen käyttöä syvyyden korostamiseen. Tässä prosessissa reunat ovat otettava huomioon, jotta saadaan luotua luonnollisen näköisiä kapean syväterävyysalueen kuvia. Tässä tutkimuksessa verrataan viittä suodatusmenetelmää keskenään. Eniten keskitytään nopeaan bilateraaliseen ratkaisijaan, johtuen sen uutuudesta ja korkeasta tuloksen laadusta. Mobiililaitteella kuvantamisen vaatimuksena on nopea suodatus hallitsemattomissa olosuhteissa, joten suodattimien prosessointiajan optimointi on erittäin tärkeää. Vertailuissa syvyyskuvat suodatetaan ja suodatuksen laatu ja nopeus mitataan jokaiselle menetelmälle. Tulokset osoittavat, että nopea bilateraalinen ratkaisija suodattaa syvyyskarttoja hyvin ja osaa käsitellä kohinaisia syvyyskarttoja paremmin kuin muut tarkastellut menetelmät. Mobiilikuvantamiseen se on kuitenkin hidas ja tarvitsee pidemmälle menevää optimointia.

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Brada, Matej. „Graphene electronic devices in magnetic field“. Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/21800.

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This thesis discusses the two dimensional allotrope of carbon known as graphene in presence of magnetic field, with special focus on edge states. The structure of graphene is described in detail and from the structure, two models are formed. The Dirac equation is a good description of graphene for large samples, far away from edges, where the boundaries can be ignored. However, it causes problems with most types of edge and hard wall approximation has to be implemented. The Dirac equation is described in detail and used to obtain an energy spectrum, wavefunction and density of states for graphene edge in a strong magnetic field. For comparison, a Bohr-Sommerfield approximation was used to find the dispersion relation and compare it to the results obtained numerically from the Dirac equation. The second model, better fitting for nano-scale systems, is the tight binding model. This model was utilized to find Energy spectrum for graphene flakes in magnetic field, which resembles Hofstadter's butterfly spectrum. The spectrum was analyzed and periodic oscillations of magnetisation dependent on magnetic field (known as the de Haas-van Alphen effect) were described. The oscillation of magnetisation depends on the shape of the dot, even though the main properties remain the same: at low magnetic field, periodic oscillations due to Aharonov-Bohm effect, turning into more chaotic oscillations depending on the boundary conditions of the given quantum dot.

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Sankin, Igor. „Edge termination and RESURF technology in power silicon carbide devices“. Diss., Mississippi State : Mississippi State University, 2006. http://library.msstate.edu/etd/show.asp?etd=etd-12162005-141206.

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Hansson, Gustav. „Computation offloading of 5G devices at the Edge using WebAssembly“. Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85898.

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With an ever-increasing percentage of the human population connected to the internet, the amount of data produced and processed is at an all-time high. Edge Computing has emerged as a paradigm to handle this growth and, combined with 5G, enables complex time-sensitive applications running on resource-restricted devices. This master thesis investigates the use of WebAssembly in the context of computa¬tional offloading at the Edge. The focus is on utilizing WebAssembly to move computa¬tional heavy parts of a system from an end device to an Edge Server. An objective is to improve program performance by reducing the execution time and energy consumption on the end device. A proof-of-concept offloading system is developed to research this. The system is evaluated on three different use cases; calculating Fibonacci numbers, matrix multipli¬cation, and image recognition. Each use case is tested on a Raspberry Pi 3 and Pi 4 comparing execution of the WebAssembly module both locally and offloaded. Each test will also run natively on both the server and the end device to provide some baseline for comparison.

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Rajakaruna, A. (Archana). „Lightweight edge-based networking architecture for low-power IoT devices“. Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906072483.

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Abstract. The involvement of low power Internet of Things (IoT) devices in the Wireless Sensor Networks (WSN) allow enhanced autonomous monitoring capability in many application areas. Recently, the principles of edge computing paradigm have been used to cater onsite processing and managing actions in WSNs. However, WSNs deployed in remote sites require human involvement in data collection process since internet accessibility is still limited to population dense areas. Nowadays, researchers propose UAVs for monitoring applications where human involvement is required frequently. In this thesis work, we introduce an edge-based architecture which create end-to-end secure communication between IoT sensors in a remote WSN and central cloud via UAV, which assist the data collection, processing and managing procedures of the remote WSN. Since power is a limited resource, we propose Bluetooth Low Energy (BLE) as the communication media between UAV and sensors in the WSN, where BLE is considered as an ultra-low power radio access technology. To examine the performance of the system model, we have presented a simulation analysis considering three sensor nodes array types that can realize in the practical environment. The impact of BLE data rate, impact of speed of the UAV, impact of distance between adjacent sensors and impact of data generation rate of the sensor node have been analysed to examine the performance of system. Moreover, to observe the practical functionality of the proposed architecture, prototype implementation is presented using commercially available off-the-shelf devices. The prototype of the system is implemented assuming ideal environment.

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Wood, Michael G. „Active Silicon Photonic Devices Based on Degenerate Band Edge Resonances“. The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480432902683812.

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Tania, Zannatun Nayem. „Machine Learning with Reconfigurable Privacy on Resource-Limited Edge Computing Devices“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292105.

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Distributed computing allows effective data storage, processing and retrieval but it poses security and privacy issues. Sensors are the cornerstone of the IoT-based pipelines, since they constantly capture data until it can be analyzed at the central cloud resources. However, these sensor nodes are often constrained by limited resources. Ideally, it is desired to make all the collected data features private but due to resource limitations, it may not always be possible. Making all the features private may cause overutilization of resources, which would in turn affect the performance of the whole system. In this thesis, we design and implement a system that is capable of finding the optimal set of data features to make private, given the device’s maximum resource constraints and the desired performance or accuracy of the system. Using the generalization techniques for data anonymization, we create user-defined injective privacy encoder functions to make each feature of the dataset private. Regardless of the resource availability, some data features are defined by the user as essential features to make private. All other data features that may pose privacy threat are termed as the non-essential features. We propose Dynamic Iterative Greedy Search (DIGS), a greedy search algorithm that takes the resource consumption for each non-essential feature as input and returns the most optimal set of non-essential features that can be private given the available resources. The most optimal set contains the features which consume the least resources. We evaluate our system on a Fitbit dataset containing 17 data features, 4 of which are essential private features for a given classification application. Our results show that we can provide 9 additional private features apart from the 4 essential features of the Fitbit dataset containing 1663 records. Furthermore, we can save 26:21% memory as compared to making all the features private. We also test our method on a larger dataset generated with Generative Adversarial Network (GAN). However, the chosen edge device, Raspberry Pi, is unable to cater to the scale of the large dataset due to insufficient resources. Our evaluations using 1=8th of the GAN dataset result in 3 extra private features with up to 62:74% memory savings as compared to all private data features. Maintaining privacy not only requires additional resources, but also has consequences on the performance of the designed applications. However, we discover that privacy encoding has a positive impact on the accuracy of the classification model for our chosen classification application.
Distribuerad databehandling möjliggör effektiv datalagring, bearbetning och hämtning men det medför säkerhets- och sekretessproblem. Sensorer är hörnstenen i de IoT-baserade rörledningarna, eftersom de ständigt samlar in data tills de kan analyseras på de centrala molnresurserna. Dessa sensornoder begränsas dock ofta av begränsade resurser. Helst är det önskvärt att göra alla insamlade datafunktioner privata, men på grund av resursbegränsningar kanske det inte alltid är möjligt. Att göra alla funktioner privata kan orsaka överutnyttjande av resurser, vilket i sin tur skulle påverka prestanda för hela systemet. I denna avhandling designar och implementerar vi ett system som kan hitta den optimala uppsättningen datafunktioner för att göra privata, med tanke på begränsningar av enhetsresurserna och systemets önskade prestanda eller noggrannhet. Med hjälp av generaliseringsteknikerna för data-anonymisering skapar vi användardefinierade injicerbara sekretess-kodningsfunktioner för att göra varje funktion i datasetet privat. Oavsett resurstillgänglighet definieras vissa datafunktioner av användaren som viktiga funktioner för att göra privat. Alla andra datafunktioner som kan utgöra ett integritetshot kallas de icke-väsentliga funktionerna. Vi föreslår Dynamic Iterative Greedy Search (DIGS), en girig sökalgoritm som tar resursförbrukningen för varje icke-väsentlig funktion som inmatning och ger den mest optimala uppsättningen icke-väsentliga funktioner som kan vara privata med tanke på tillgängliga resurser. Den mest optimala uppsättningen innehåller de funktioner som förbrukar minst resurser. Vi utvärderar vårt system på en Fitbit-dataset som innehåller 17 datafunktioner, varav 4 är viktiga privata funktioner för en viss klassificeringsapplikation. Våra resultat visar att vi kan erbjuda ytterligare 9 privata funktioner förutom de 4 viktiga funktionerna i Fitbit-datasetet som innehåller 1663 poster. Dessutom kan vi spara 26; 21% minne jämfört med att göra alla funktioner privata. Vi testar också vår metod på en större dataset som genereras med Generative Adversarial Network (GAN). Den valda kantenheten, Raspberry Pi, kan dock inte tillgodose storleken på den stora datasetet på grund av otillräckliga resurser. Våra utvärderingar med 1=8th av GAN-datasetet resulterar i 3 extra privata funktioner med upp till 62; 74% minnesbesparingar jämfört med alla privata datafunktioner. Att upprätthålla integritet kräver inte bara ytterligare resurser utan har också konsekvenser för de designade applikationernas prestanda. Vi upptäcker dock att integritetskodning har en positiv inverkan på noggrannheten i klassificeringsmodellen för vår valda klassificeringsapplikation.

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Bücher zum Thema "Edge devices"

1

Krasheninnikov, Sergei, Andrei Smolyakov und Andrei Kukushkin. On the Edge of Magnetic Fusion Devices. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49594-7.

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2

Hirose, Akira. Collisionless edge instability in tokamaks. Saskatoon, Sask: Plasma Physics Laboratory, University of Saskatchewan, 1995.

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3

Zhang, Wei. Improved confinement and edge plasma fluctuations in the STOR-M tokamak. Saskatoon, Sask: University of Saskatchewan, Plasma Physics Laboratory, 1991.

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4

Springett, Alan. Do colour measuring devices maximise a printing company's competitive edge?. London: LCP, 2001.

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5

Scott, Samuel J. Effects of leading-edge devices on the low-speed aerodynamic characteristics of a highly-swept arrow-wing. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1985.

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6

Nakano, Hiroshi. DC-SQUIDs fabricated by shapes of sputtering deposition at step edge and anodic oxidization. Fukuoka, Japan: [Kyushu University Research Institute of Fundamental Information Science, 1986.

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7

Pitcher, Charles Spencer. Tokamak plasma interaction with limiters. Downsview, Ont: Institute for Aerospace Studies, 1988.

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8

Rudolph, Peter K. C. High-lift systems on commercial subsonic airliners. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1996.

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9

Stephen, Booth. The devil's edge: A Cooper & Fry mystery. New York]: Witness Impulse, an imprint of HarperCollinsPublisher, 2014.

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10

Cheruvu, Sunil. Demystifying Internet of Things Security: Successful IoT Device/Edge and Platform Security Deployment. Berkeley, CA: Springer Nature, 2020.

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Mehr Quellen

Buchteile zum Thema "Edge devices"

1

Fordham, Stuart. „Edge Devices“. In Learning SD-WAN with Cisco, 149–82. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7347-0_7.

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2

Stackowiak, Robert. „IoT Edge Devices and Microsoft“. In Azure Internet of Things Revealed, 55–72. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5470-7_3.

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3

Shin, Changhwan. „Line Edge Roughness (LER)“. In Variation-Aware Advanced CMOS Devices and SRAM, 19–35. Dordrecht: Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-017-7597-7_2.

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4

Van Vaerenbergh, Kevin, und Tom Tourwé. „Distributed Data Compression for Edge Devices“. In Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops, 293–304. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79157-5_24.

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5

Rogalla, H. „YBCO/PBCO/YBCO Edge Junctions and DC-SQUIDs“. In Superconducting Devices and Their Applications, 106–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77457-7_18.

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6

Krasheninnikov, Sergei, Andrei Smolyakov und Andrei Kukushkin. „Edge Plasma Issues in Magnetic Fusion Devices“. In Springer Series in Plasma Science and Technology, 1–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49594-7_1.

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7

Siegel, M., F. Schmidl, E. Heinz, K. Zach, J. Fuchs, E. B. Kley und P. Seidel. „DC-SQUID with Step Edge Junctions on (100) SrTiO3“. In Superconducting Devices and Their Applications, 138–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77457-7_22.

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8

Nguyen-Duc, Manh, Anh Le-Tuan, Jean-Paul Calbimonte, Manfred Hauswirth und Danh Le-Phuoc. „Autonomous RDF Stream Processing for IoT Edge Devices“. In Semantic Technology, 304–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41407-8_20.

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9

Fathoni, Halim, Chao-Tung Yang, Chih-Hung Chang und Chin-Yin Huang. „Performance Comparison of Lightweight Kubernetes in Edge Devices“. In Pervasive Systems, Algorithms and Networks, 304–9. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30143-9_25.

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10

Meghana, V., B. S. Anisha und P. Ramakanth Kumar. „Monitoring Health of Edge Devices in Real Time“. In Advances in Intelligent Systems and Computing, 555–66. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8443-5_47.

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Konferenzberichte zum Thema "Edge devices"

1

Sharma, Ragini, Saman Biookaghazadeh, Baoxin Li und Ming Zhao. „Are Existing Knowledge Transfer Techniques Effective for Deep Learning with Edge Devices?“ In 2018 IEEE International Conference on Edge Computing (EDGE). IEEE, 2018. http://dx.doi.org/10.1109/edge.2018.00013.

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2

Pandey, Manish, und Young-Woo Kwon. „Middleware for Edge Devices in Mobile Edge Computing“. In 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). IEEE, 2021. http://dx.doi.org/10.1109/itc-cscc52171.2021.9501419.

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3

Jo, Jongmin, Sucheol Jeong und Pilsung Kang. „Benchmarking GPU-Accelerated Edge Devices“. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2020. http://dx.doi.org/10.1109/bigcomp48618.2020.00-89.

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4

Fan, Shu-Hao, Daniel Guidotti, Claudio Estevez, Gee-Kung Chang, Ying-Jung Chang und Daoqiang Daniel Lu. „Short-reach flexible optical interconnection using embedded edge-emitting lasers and edge-viewing detectors“. In Integrated Optoelectronic Devices 2008, herausgegeben von Alexei L. Glebov und Ray T. Chen. SPIE, 2008. http://dx.doi.org/10.1117/12.763336.

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5

Gibson, Perry, Jose Cano, Jack Turner, Elliot J. Crowley, Michael O'Boyle und Amos Storkey. „Optimizing Grouped Convolutions on Edge Devices“. In 2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP). IEEE, 2020. http://dx.doi.org/10.1109/asap49362.2020.00039.

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6

Prabhu, Narendra, Daksha Naik und Fatima Anwar. „Trusted Video Streaming on Edge Devices“. In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2021. http://dx.doi.org/10.1109/percomworkshops51409.2021.9431058.

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7

Topno, Preeti, und Govind Murmu. „An Improved Edge Detection Method based on Median Filter“. In 2019 Devices for Integrated Circuit (DevIC). IEEE, 2019. http://dx.doi.org/10.1109/devic.2019.8783450.

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8

Liu, Shiya, Lingjia Liu und Yang Yi. „Quantized Reservoir Computing on Edge Devices for Communication Applications“. In 2020 IEEE/ACM Symposium on Edge Computing (SEC). IEEE, 2020. http://dx.doi.org/10.1109/sec50012.2020.00068.

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9

Benditkis, Daniel, Aviv Keren, Liron Mor-Yosef, Tomer Avidor, Neta Shoham und Nadav Tal-Israel. „Distributed deep neural network training on edge devices“. In SEC '19: The Fourth ACM/IEEE Symposium on Edge Computing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3318216.3363324.

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10

Qin, Zhuwei, Fuxun Yu und Xiang Chen. „Task-adaptive incremental learning for intelligent edge devices“. In SEC '19: The Fourth ACM/IEEE Symposium on Edge Computing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3318216.3363373.

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Berichte der Organisationen zum Thema "Edge devices"

1

Miller, L. S., S. Huang und G. A. Quandt. Atmospheric tests of trailing-edge aerodynamic devices. Office of Scientific and Technical Information (OSTI), Januar 1998. http://dx.doi.org/10.2172/565655.

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2

Dickens, John B., und Dean R. Dukes. Innovative Decentralized Decision-Making Enabling Capability on Mobile Edge Devices. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ad1008918.

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3

Griem, Hans R. High Resolution Spectroscopy in the Divertor and Edge Regions of Alcator-C Mode and Measurement of Radiative Transfer in Vacuum-UV Line Emission from Magnetic Fusion Devices. Office of Scientific and Technical Information (OSTI), März 2005. http://dx.doi.org/10.2172/1046049.

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4

Ammar H Hakim. Final Report for "Extending BOUT++ for Solution of Edge Plasma Equations for Use in Whole Device Simulation of Tokamaks". Office of Scientific and Technical Information (OSTI), Oktober 2011. http://dx.doi.org/10.2172/1027635.

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