Journal articles: 'Edge devices'

1

van Dijke, Koen, Gert Veldhuis, Karin Schroën, and Remko Boom. "Parallelized edge-based droplet generation (EDGE) devices." Lab on a Chip 9, no. 19 (2009): 2824. http://dx.doi.org/10.1039/b906098g.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Mahmod, Md Jubayer al, and Ujjwal Guin. "A Robust, Low-Cost and Secure Authentication Scheme for IoT Applications." Cryptography 4, no. 1 (March 8, 2020): 8. http://dx.doi.org/10.3390/cryptography4010008.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

Lee, Dongkyu, Hyeongyun Moon, Sejong Oh, and Daejin Park. "mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications." Sensors 20, no. 12 (June 12, 2020): 3337. http://dx.doi.org/10.3390/s20123337.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

Linke, Markus, and 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, no. 9 (September 13, 2018): 1708. http://dx.doi.org/10.3390/ma11091708.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

Douglas, Antonyo, Richard Holloway, Jonathan Lohr, Elijah Morgan, and Khaled Harfoush. "Blockchains for constrained edge devices." Blockchain: Research and Applications 1, no. 1-2 (December 2020): 100004. http://dx.doi.org/10.1016/j.bcra.2020.100004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

TYAGI, PAWAN. "MOLECULAR SPIN DEVICES: CURRENT UNDERSTANDING AND NEW TERRITORIES." Nano 04, no. 06 (December 2009): 325–38. http://dx.doi.org/10.1142/s1793292009001903.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

7

Shafiq, Muhammad, Zhihong Tian, Ali Kashif Bashir, Korhan Cengiz, and Adnan Tahir. "SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique." Wireless Communications and Mobile Computing 2020 (October 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/8864301.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

Santa, José, Pedro J. Fernández, Ramon Sanchez-Iborra, Jordi Ortiz, and Antonio F. Skarmeta. "Offloading Positioning onto Network Edge." Wireless Communications and Mobile Computing 2018 (October 23, 2018): 1–13. http://dx.doi.org/10.1155/2018/7868796.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

Liang, Tyng-Yeu, and 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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

Zeng, Xin, Xiaomei Zhang, Shuqun Yang, Zhicai Shi, and Chihung Chi. "Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices." Sensors 21, no. 13 (July 5, 2021): 4592. http://dx.doi.org/10.3390/s21134592.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

11

Bansal, Malti, and Harshit. "IoT based Edge Computing." December 2020 2, no. 4 (January 5, 2021): 204–10. http://dx.doi.org/10.36548/jtcsst.2020.4.005.

Full text

Abstract:

Edge computing is a new way of calculating where most computer and storage devices are located on the internet, near mobile devices, sensors, end users, and internet of things devices. This physical approach improves delays, bandwidth, trust and survival.

APA, Harvard, Vancouver, ISO, and other styles

12

Konishi, Akihito, Yasukazu Hirao, Hiroyuki Kurata, Takashi Kubo, Masayoshi Nakano, and Kenji Kamada. "Anthenes: Model systems for understanding the edge state of graphene nanoribbons." Pure and Applied Chemistry 86, no. 4 (April 17, 2014): 497–505. http://dx.doi.org/10.1515/pac-2013-0811.

Full text

Abstract:

AbstractThe edge state, which is a peculiar magnetic state in zigzag-edged graphene nanoribbons (ZGNRs) originating from an electron–electron correlation in an edge-localized π-state, has promising applications for magnetic and spintronics devices and has attracted much attention of physicists, chemists, and engineers. For deeper understanding of the edge state, precise fabrication of edge structures in ZGNRs has been highly demanded. We focus on anthenes, which are peri-condensed anthracenes that have zigzag and armchair edges on the molecular periphery, as model systems for understanding, and indeed prepare and characterize them. This paper summarizes our recent studies on the origin of the edge state by investigating anthenes in terms of the relationship between the molecular structure and spin-localizing character.

APA, Harvard, Vancouver, ISO, and other styles

13

Kim, Svetlana, Jieun Kang, and YongIk Yoon. "Linked-Object Dynamic Offloading (LODO) for the Cooperation of Data and Tasks on Edge Computing Environment." Electronics 10, no. 17 (September 3, 2021): 2156. http://dx.doi.org/10.3390/electronics10172156.

Full text

Abstract:

With the evolution of the Internet of Things (IoT), edge computing technology is using to process data rapidly increasing from various IoT devices efficiently. Edge computing offloading reduces data processing time and bandwidth usage by processing data in real-time on the device where the data is generating or on a nearby server. Previous studies have proposed offloading between IoT devices through local-edge collaboration from resource-constrained edge servers. However, they did not consider nearby edge servers in the same layer with computing resources. Consequently, quality of service (QoS) degrade due to restricted resources of edge computing and higher execution latency due to congestion. To handle offloaded tasks in a rapidly changing dynamic environment, finding an optimal target server is still challenging. Therefore, a new cooperative offloading method to control edge computing resources is needed to allocate limited resources between distributed edges efficiently. This paper suggests the LODO (linked-object dynamic offloading) algorithm that provides an ideal balance between edges by considering the ready state or running state. LODO algorithm carries out tasks in the list in the order of high correlation between data and tasks through linked objects. Furthermore, dynamic offloading considers the running status of all cooperative terminals and decides to schedule task distribution. That can decrease the average delayed time and average power consumption of terminals. In addition, the resource shortage problem can settle by reducing task processing using its distributions.

APA, Harvard, Vancouver, ISO, and other styles

14

Park, Sihyeong, Jemin Lee, and Hyungshin Kim. "Hardware Resource Analysis in Distributed Training with Edge Devices." Electronics 9, no. 1 (December 26, 2019): 28. http://dx.doi.org/10.3390/electronics9010028.

Full text

Abstract:

When training a deep learning model with distributed training, the hardware resource utilization of each device depends on the model structure and the number of devices used for training. Distributed training has recently been applied to edge computing. Since edge devices have hardware resource limitations such as memory, there is a need for training methods that use hardware resources efficiently. Previous research focused on reducing training time by optimizing the synchronization process between edge devices or by compressing the models. In this paper, we monitored hardware resource usage based on the number of layers and the batch size of the model during distributed training with edge devices. We analyzed memory usage and training time variability as the batch size and number of layers increased. Experimental results demonstrated that, the larger the batch size, the fewer synchronizations between devices, resulting in less accurate training. In the shallow model, training time increased as the number of devices used for training increased because the synchronization between devices took more time than the computation time of training. This paper finds that efficient use of hardware resources for distributed training requires selecting devices in the context of model complexity and that fewer layers and smaller batches are required for efficient hardware use.

APA, Harvard, Vancouver, ISO, and other styles

15

Mehrabi, Mahshid, Shiwei Shen, Yilun Hai, Vincent Latzko, George Koudouridis, Xavier Gelabert, Martin Reisslein, and Frank Fitzek. "Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks." Network 1, no. 2 (September 4, 2021): 191–214. http://dx.doi.org/10.3390/network1020012.

Full text

Abstract:

Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in edge networks with sliced computing resources has mainly been studied for end devices (helper nodes) that are stationary (or follow predetermined mobility paths) and for independent computation tasks. However, end devices are often mobile, and a given application request commonly requires a set of dependent computation tasks. We formulate a novel model for the cooperative edge offloading of dependent computation tasks to mobile helper nodes. We model the task dependencies with a general task dependency graph. Our model employs the state-of-the-art deep-learning-based PECNet mobility model and offloads a task only when the sojourn time in the coverage area of a helper node or Multi-access Edge Computing (MEC) server is sufficiently long. We formulate the minimization problem for the consumed battery energy for task execution, task data transmission, and waiting for offloaded task results on end devices. We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. The numerical evaluations indicate that the EETO approach consistently reduces the battery energy consumption across a wide range of task complexities and task completion deadlines and can thus extend the battery lifetimes of mobile devices operating with sliced edge computing resources.

APA, Harvard, Vancouver, ISO, and other styles

16

Shimazaki, Ayako, Hiroki Sakurai, Masao Iwase, Reiko Yoshimura, and Tsukasa Tada. "Metallic Contamination Control in Leading-Edge ULSI Manufacturing." Solid State Phenomena 145-146 (January 2009): 115–21. http://dx.doi.org/10.4028/www.scientific.net/ssp.145-146.115.

Full text

Abstract:

Contamination control has become a high-centered issue for the fabrication yield, performance and reliability of leading-edge ULSI devices. With the progress of sizing down dimensions in higher-density devices, complicated device structures and various novel electronic materials have been introduced, particularly in the latest devices such as CMOS and nonvolatile memory LSIs (Table I). On the other hand, high productivity is a necessity when you consider QTAT (quick turnaround time) and cost-effective flexible ULSI manufacturing lines. Therefore, effective contamination control coupled with adequate protocol has become essential in such production lines. The point of the protocol is minimization of damage caused by impurity metals diffused from these novel electronic materials [1-5].

APA, Harvard, Vancouver, ISO, and other styles

17

Shiraishi, Yoichi. "Latest Trend of Edge AI Devices." Journal of The Japan Institute of Electronics Packaging 23, no. 2 (March 1, 2020): 145–49. http://dx.doi.org/10.5104/jiep.23.145.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Felfernig, Alexander, Seda Polat Erdeniz, Michael Jeran, Arda Akcay, Paolo Azzoni, Matteo Maiero, and Charalampos Doukas. "Recommendation Technologies for IoT Edge Devices." Procedia Computer Science 110 (2017): 504–9. http://dx.doi.org/10.1016/j.procs.2017.06.135.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Stangeby, P. C. "Plasma edge theory in fusion devices." Nuclear Fusion 32, no. 11 (November 1992): 2059–63. http://dx.doi.org/10.1088/0029-5515/32/11/424.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Muller, Erik M., Mengjia Gaowei, Ilan Ben-Zvi, Dimitre A. Dimitrov, and John Smedley. "Carbon edge response of diamond devices." Applied Physics Letters 104, no. 9 (March 3, 2014): 093515. http://dx.doi.org/10.1063/1.4868135.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Moore, M. R., and M. A. Buckner. "Learning-from-signals on edge devices." IEEE Instrumentation & Measurement Magazine 15, no. 2 (April 2012): 40–44. http://dx.doi.org/10.1109/mim.2012.6174579.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Liu, Shiya, Dong Sam Ha, Fangyang Shen, and Yang Yi. "Efficient neural networks for edge devices." Computers & Electrical Engineering 92 (June 2021): 107121. http://dx.doi.org/10.1016/j.compeleceng.2021.107121.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Zhao, Xiao-ping, Yong-hong Zhang, and Fan Shao. "A Multifault Diagnosis Method of Gear Box Running on Edge Equipment." Security and Communication Networks 2020 (August 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/8854236.

Full text

Abstract:

In recent years, a large number of edge computing devices have been used to monitor the operating state of industrial equipment and perform fault diagnosis analysis. Therefore, the fault diagnosis algorithm in the edge computing device is particularly important. With the increase in the number of device detection points and the sampling frequency, mechanical health monitoring has entered the era of big data. Edge computing can process and analyze data in real time or faster, making data processing closer to the source, rather than the external data center or cloud, which can shorten the delay time. After using 8 bits and 16 bits to quantify the deep measurement learning model, there is no obvious loss of accuracy compared with the original floating-point model, which shows that the model can be deployed and reasoned on the edge device, while ensuring real time. Compared with using servers for deployment, using edge devices not only reduces costs but also makes deployment more flexible.

APA, Harvard, Vancouver, ISO, and other styles

24

Park, BoSun, and Seog Chung Seo. "Efficient Implementation of NIST LWC ESTATE Algorithm Using OpenCL and Web Assembly for Secure Communication in Edge Computing Environment." Sensors 21, no. 6 (March 11, 2021): 1987. http://dx.doi.org/10.3390/s21061987.

Full text

Abstract:

In edge computing service, edge devices collect data from a number of embedded devices, like sensors, CCTVs (Closed-circuit Television), and so on, and communicate with application servers. Since a large portion of communication in edge computing services are conducted in wireless, the transmitted data needs to be properly encrypted. Furthermore, the application servers (resp. edge devices) are responsible for encrypting or decrypting a large amount of data from edge devices (resp. terminal devices), the cryptographic operation needs to be optimized on both server side and edge device side. Actually, the confidentiality and integrity of data are essential for secure communication. In this paper, we present two versions of security software which can be used on edge device side and server side for secure communication between them in edge computing environment. Our softwares are basically web-based application because of its universality where the softwares can be executed on any web browsers. Our softwares make use of ESTATE (Energy efficient and Single-state Tweakable block cipher based MAC-Then-Encrypt)algorithm, which is a promising candidate of NIST LWC (National Institute of Standards and Technology LightWeight Cryptography) competition and it provides not only data confidentiality but also data authentication. It also implements the ESTATE algorithm using Web Assembly for efficient use on edge devices, and optimizes the performance of the algorithm using the properties of the underlying block cipher. Several methods are applied to efficiently operate the ESTATE algorithm. We use conditional statements to XOR the extended tweak values during the operation of the ESTATE algorithm. To eliminate this unnecessary process, we use a method of expanding and storing the tweak value through pre-computation. The measured results of the ESTATE algorithm implemented with Web Assembly and the reference C/C++ ESTATE algorithm are compared. ESTATE implemented as Web Assembly is measured in web browsers Chrome, FireFox, and Microsoft Edge. For efficiency on server side, we make use of OpenCL which is parallel computing framework in order to process a number of data simultaneously. In addition, when implementing with OpenCL, using conditional statements causes performance degradation. We eliminated the conditional statement using the loop unrolling method to eliminate the performance degradation. In addition, OpenCL operates by moving the data to be encrypted to the local memory because the local memory has a high operation speed. TweAES-128 and TweAES-128-6, which have the same structure as AES algorithm, can apply the previously existing studied T-table method. In addition, the input value 16-byte is processed in parallel and calculated. In addition, since it may be vulnerable to cache-timing attack, it is safely operated by applying the previously existing studied T-table shuffling method. Our softwares cover the necessary security service from edge devices to servers in edge computing services and they can be easily used for various types of edge computing devices because they are all web-based applications.

APA, Harvard, Vancouver, ISO, and other styles

25

Galletta, Antonino, Armando Ruggeri, Maria Fazio, Gianluca Dini, and Massimo Villari. "MeSmart-Pro: Advanced Processing at the Edge for Smart Urban Monitoring and Reconfigurable Services." Journal of Sensor and Actuator Networks 9, no. 4 (December 4, 2020): 55. http://dx.doi.org/10.3390/jsan9040055.

Full text

Abstract:

With reference to the MeSmart project, the Municipality of Messina is making a great investments to deploy several types of cameras and digital devices across the city for carrying out different tasks related to mobility management, such as traffic flow monitoring, number plate recognition, video surveillance etc. To this aim, exploiting specific devices for each task increases infrastructure and management costs, reducing flexibility. On the contrary, using general-purpose devices customized to accomplish multiple tasks at the same time can be a more efficient solution. Another important approach that can improve the efficiency of mobility services is moving computation tasks at the Edge of the managed system instead of in remote centralized serves, so reducing delays in event detection and processing and making the system more scalable. In this paper, we investigate the adoption of Edge devices connected to high-resolution cameras to create a general-purpose solution for performing different tasks. In particular, we use the Function as a Service (FaaS) paradigm to easily configure the Edge device and set up new services. The key results of our work is deploying versatile, scalable and adaptable systems able to respond to smart city’s needs, even if such needs change over time. We tested the proposed solution setting up a vehicle counting solution based on OpenCV, and automatically deploying necessary functions into an Edge device. From experimental results, it results that computing performance at the Edge overtakes the performance of a device specifically designed for vehicle counting under certain conditions and thanks to our reconfigurable functions.

APA, Harvard, Vancouver, ISO, and other styles

26

Huang, Peng, Minjiang Deng, Zhiliang Kang, Qinshan Liu, and Lijia Xu. "Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems." Entropy 23, no. 9 (August 31, 2021): 1146. http://dx.doi.org/10.3390/e23091146.

Full text

Abstract:

Mobile edge computing (MEC) focuses on transferring computing resources close to the user’s device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality; it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.

APA, Harvard, Vancouver, ISO, and other styles

27

Li, Peng, Chenchen Shu, and Jiao Feng. "A Reciprocal-Selection-Based ‘Win–Win’ Overlay Spectrum-Sharing Scheme for Device-to-Device-Enabled Cellular Network." Algorithms 11, no. 11 (November 6, 2018): 179. http://dx.doi.org/10.3390/a11110179.

Full text

Abstract:

This paper proposes a reciprocal-selection-based ‘Win–Win’ overlay spectrum-sharing scheme for device-to-Device-enabled cellular networks to address the resource sharing between Device-to-Device devices and the cellular users by using an overlay approach. Based on the proposed scheme, the cell edge users intend to lease part of its spectrum resource to Device-to-Device transmission pairs. However, the Device-to-Device users have to provide the cooperative transmission assistance for the cell edge users in order to improve the Quality of Service of the uplink transmission from the cell edge users to the base station. Compared to the underlay spectrum-sharing scheme, overlay spectrum-sharing scheme may reduce spectrum efficiency. Hence, Non-Orthogonal Multiple Access technology is invoked at the Device-to-Device transmitter in order to improve the spectrum efficiency. The Stackelberg game is exploited to model the behaviours of the cell edge users and Device-to-Device devices. Moreover, based on matching theory, the cell edge users and Device-to-Device pairs form one-to-one matching and the stability of matching is analysed. The simulation results show that the proposed reciprocal-selection-based ‘Win–Win’ overlay spectrum-sharing scheme is capable of providing considerable rate improvements for both EUs and D2D pairs and reducing transmit power dissipated by the D2D transmitter to forward data for the EU compared with the existing methods.

APA, Harvard, Vancouver, ISO, and other styles

28

Chen, Zhongmin, Zhiwei Xu, Jianxiong Wan, Jie Tian, Limin Liu, and Yujun Zhang. "Conflict-Resilient Incremental Offloading of Deep Neural Networks to the Edge of Smart Environment." Mathematical Problems in Engineering 2021 (June 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/9985006.

Full text

Abstract:

Novel smart environments, such as smart home, smart city, and intelligent transportation, are driving increasing interest in deploying deep neural networks (DNN) in edge devices. Unfortunately, deploying DNN at resource-constrained edge devices poses a huge challenge. These workloads are computationally intensive. Moreover, the edge server-based approach may be affected by incidental factors, such as network jitters and conflicts, when multiple tasks are offloaded to the same device. A rational workload scheduling for smart environments is highly desired. In this work, we propose a Conflict-resilient Incremental Offloading of Deep Neural Networks at Edge (CIODE) for improving the efficiency of DNN inference in the edge smart environment. CIODE divides the DNN model into several partitions by layer and incrementally uploads them to local edge nodes. We design a waiting lock-based scheduling paradigm to choose edge devices for DNN layers to be offloaded. In detail, an advanced lock mechanism is proposed to handle concurrency conflicts. Real-world testbed-based experiments demonstrate that, compared with other state-of-the-art baselines, CIODE outperforms the DNN inference performance of these popular baselines by 20 % to 70 % and significantly improves the robustness under the insight of neighboring collaboration.

APA, Harvard, Vancouver, ISO, and other styles

29

Jeong, Jonghun, Jong Sung Park, and Hoeseok Yang. "Communication Failure Resilient Distributed Neural Network for Edge Devices." Electronics 10, no. 14 (July 6, 2021): 1614. http://dx.doi.org/10.3390/electronics10141614.

Full text

Abstract:

Recently, the necessity to run high-performance neural networks (NN) is increasing even in resource-constrained embedded systems such as wearable devices. However, due to the high computational and memory requirements of the NN applications, it is typically infeasible to execute them on a single device. Instead, it has been proposed to run a single NN application cooperatively on top of multiple devices, a so-called distributed neural network. In the distributed neural network, workloads of a single big NN application are distributed over multiple tiny devices. While the computation overhead could effectively be alleviated by this approach, the existing distributed NN techniques, such as MoDNN, still suffer from large traffics between the devices and vulnerability to communication failures. In order to get rid of such big communication overheads, a knowledge distillation based distributed NN, called Network of Neural Networks (NoNN), was proposed, which partitions the filters in the final convolutional layer of the original NN into multiple independent subsets and derives smaller NNs out of each subset. However, NoNN also has limitations in that the partitioning result may be unbalanced and it considerably compromises the correlation between filters in the original NN, which may result in an unacceptable accuracy degradation in case of communication failure. In this paper, in order to overcome these issues, we propose to enhance the partitioning strategy of NoNN in two aspects. First, we enhance the redundancy of the filters that are used to derive multiple smaller NNs by means of averaging to increase the immunity of the distributed NN to communication failure. Second, we propose a novel partitioning technique, modified from Eigenvector-based partitioning, to preserve the correlation between filters as much as possible while keeping the consistent number of filters distributed to each device. Throughout extensive experiments with the CIFAR-100 (Canadian Institute For Advanced Research-100) dataset, it has been observed that the proposed approach maintains high inference accuracy (over 70%, 1.53× improvement over the state-of-the-art approach), on average, even when a half of eight devices in a distributed NN fail to deliver their partial inference results.

APA, Harvard, Vancouver, ISO, and other styles

30

Xu, Rongxu, Wenquan Jin, Yonggeun Hong, and Do-Hyeun Kim. "Intelligent Optimization Mechanism Based on an Objective Function for Efficient Home Appliances Control in an Embedded Edge Platform." Electronics 10, no. 12 (June 18, 2021): 1460. http://dx.doi.org/10.3390/electronics10121460.

Full text

Abstract:

In recent years the ever-expanding internet of things (IoT) is becoming more empowered to revolutionize our world with the advent of cutting-edge features and intelligence in an IoT ecosystem. Thanks to the development of the IoT, researchers have devoted themselves to technologies that convert a conventional home into an intelligent occupants-aware place to manage electric resources with autonomous devices to deal with excess energy consumption and providing a comfortable living environment. There are studies to supplement the innate shortcomings of the IoT and improve intelligence by using cloud computing and machine learning. However, the machine learning-based autonomous control devices lack flexibility, and cloud computing is challenging with latency and security. In this paper, we propose a rule-based optimization mechanism on an embedded edge platform to provide dynamic home appliance control and advanced intelligence in a smart home. To provide actional control ability, we design and developed a rule-based objective function in the EdgeX edge computing platform to control the temperature states of the smart home. Compared to cloud computing, edge computing can provide faster response and higher quality of services. The edge computing paradigm provides better analysis, processing, and storage abilities to the data generated from the IoT sensors to enhance the capability of IoT devices concerning computing, storage, and network resources. In order to satisfy the paradigm of distributed edge computing, all the services are implemented as microservices. The microservices are connected to each other through REST APIs based on the constrained IoT devices to provide all the functionalities that accomplish a trade-off between energy consumption and occupant-desired environment setting for the smart home appliances. We simulated our proposed system to control the temperature of a smart home; through experimental findings, we investigated the application against the delay time and overall memory consumption by the embedded edge system of EdgeX. The result of this research work suggests that the implemented services operated efficiently in the raspberry pi 3 hardware of IoT devices.

APA, Harvard, Vancouver, ISO, and other styles

31

Ogawa, Yoshihiro. "Cleaning Technology for Advanced Devices beyond 20 nm Node." Solid State Phenomena 195 (December 2012): 7–12. http://dx.doi.org/10.4028/www.scientific.net/ssp.195.7.

Full text

Abstract:

Several attempts have recently been made to use novel high-k dielectric materials, such as AlxOy, HfxAlyOz, HfxSiyOz, and HfxOy, to improve electrical device characteristics of advanced devices. Moreover, it is becoming increasingly important in the ULSI manufacturing process to suppress contamination by metal or particles from the wafer backside or edge. This paper reviews the wafer backside/edge control technology for suppression of metal contamination.

APA, Harvard, Vancouver, ISO, and other styles

32

He, Gaofeng, Bingfeng Xu, Lu Zhang, and Haiting Zhu. "On-Device Detection of Repackaged Android Malware via Traffic Clustering." Security and Communication Networks 2020 (May 31, 2020): 1–19. http://dx.doi.org/10.1155/2020/8630748.

Full text

Abstract:

Malware has become a significant problem on the Android platform. To defend against Android malware, researchers have proposed several on-device detection methods. Typically, these on-device detection methods are composed of two steps: (i) extracting the apps’ behavior features from the mobile devices and (ii) sending the extracted features to remote servers (such as a cloud platform) for analysis. By monitoring the behaviors of the apps that are running on mobile devices, available methods can detect suspicious applications (simply, apps) accurately. However, mobile devices are typically resource limited. The feature extraction and massive data transmission might consume substantial power and CPU resources; thus, the performance of mobile devices will be degraded. To address this issue, we propose a novel method for detecting Android malware by clustering apps’ traffic at the edge computing nodes. First, a new integrated architecture of the cloud, edge, and mobile devices for Android malware detection is presented. Then, for repackaged Android malware, the network traffic content and statistics are extracted at the edge as detection features. Finally, in the cloud, similarities between apps are calculated, and the similarity values are automatically clustered to separate the original apps and the malware. The experimental results demonstrate that the proposed method can detect repackaged Android malware with high precision and with a minimal impact on the performance of mobile devices.

APA, Harvard, Vancouver, ISO, and other styles

33

Pandelea, Vlad, Edoardo Ragusa, Tommaso Apicella, Paolo Gastaldo, and Erik Cambria. "Emotion Recognition on Edge Devices: Training and Deployment." Sensors 21, no. 13 (June 30, 2021): 4496. http://dx.doi.org/10.3390/s21134496.

Full text

Abstract:

Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature extractors, and simple hardware-friendly classifiers based on linear separators can achieve competitive performance while allowing real-time inference and fast training. Various solutions including batch and Online Sequential Learning are analyzed. Additionally, our experiments show that latency and performance can be further improved via dimensionality reduction and pre-training, respectively. The resulting system is implemented on two types of edge device, namely an edge accelerator and two smartphones.

APA, Harvard, Vancouver, ISO, and other styles

34

Ke, Haotao, Yifan Jiang, Adam J. Morgan, and Douglas C. Hopkins. "Investigation of Package Effects on the Edge Termination E-Field for HV WBG Power Semiconductors." International Symposium on Microelectronics 2017, no. 1 (October 1, 2017): 000224–30. http://dx.doi.org/10.4071/isom-2017-wa32_092.

Full text

Abstract:

Abstract The edge termination of a power semiconductor is defined as the spatial junction terminations around the edges of the power devices. Guard rings are used to contour the internal depletion regions and E-fields as they terminate at the edge termination, i.e. the intersection of the depletion regions and the wafer saw line where the crystal damage is located. Since there is no specific package for WBG power devices, wire bonds are still widely used to interconnect to the topside metal pads of the power devices. From previous research it is shown that wire bonding will not affect the E-field around the guard rings on a WBG device. However, planar power package, such as double-sided and power flip-chip device packaging could be a problem where the close distance between the topside of the power device and conducting plane may negatively affect the E-field distribution of the guard rings, which in turn lowers the reverse blocking capability of the WBG power device and increases leakage current creating greater on-state power loss, or even early break down. Few works have shown the Electric field distribution in embedded power modules. Therefore, a more detailed investigation and possible solution is needed for the proliferation of double-sided power packages. To investigate this packaging problem simulations were performed in Sentaurus TCAD and COMSOL based on the device physics and package geometries. Guard ring structures in 1.2kV and 10kV SiC Schottky Barrier Diode (SBD) were built and simulated in various double-sided package geometries, together with the thermal and mechanical evaluation of the package, to observe the influence on the E-field distribution in and out the WBG device. Different double-sided package structures were evaluated and a guideline (spacing/pad size/etc.) summarized for double-sided design. Moreover, a new bevel edge termination method was evaluated for double-sided WBG power semiconductor devices. Experimental reverse blocking test results will be reported in various temperature (from 25°C to 175°C) to verify the function of the package. The tests are on 1200V/50A SiC SBD (Schottky Barrier Diode) from Global Power Technology, which has double-sided Ag on both sides.

APA, Harvard, Vancouver, ISO, and other styles

35

Zhang, Jinnan, Changqi Lu, Gang Cheng, Teng Guo, Jian Kang, Xia Zhang, Xueguang Yuan, and Xin Yan. "A Blockchain-Based Trusted Edge Platform in Edge Computing Environment." Sensors 21, no. 6 (March 18, 2021): 2126. http://dx.doi.org/10.3390/s21062126.

Full text

Abstract:

Edge computing is a product of the evolution of IoT and the development of cloud computing technology, providing computing, storage, network, and other infrastructure close to users. Compared with the centralized deployment model of traditional cloud computing, edge computing solves the problems of extended communication time and high convergence traffic, providing better support for low latency and high bandwidth services. With the increasing amount of data generated by users and devices in IoT, security and privacy issues in the edge computing environment have become concerns. Blockchain, a security technology developed rapidly in recent years, has been adopted by many industries, such as finance and insurance. With the edge computing capability, deploying blockchain platforms/applications on edge computing platforms can provide security services for network edge environments. Although there are already solutions for integrating edge computing with blockchain in many IoT application scenarios, they slightly lack scalability, portability, and heterogeneous data processing. In this paper, we propose a trusted edge platform to integrate the edge computing framework and blockchain network for building an edge security environment. The proposed platform aims to preserve the data privacy of the edge computing client. The design based on the microservice architecture makes the platform lighter. To improve the portability of the platform, we introduce the Edgex Foundry framework and design an edge application module on the platform to improve the business capability of Edgex. Simultaneously, we designed a series of well-defined security authentication microservices. These microservices use the Hyperledger Fabric blockchain network to build a reliable security mechanism in the edge environment. Finally, we build an edge computing network using different hardware devices and deploy the trusted edge platform on multiple network nodes. The usability of the proposed platform is demonstrated by testing the round-trip time (RTT) of several important workflows. The experimental results demonstrate that the platform can meet the availability requirements in real-world usage scenarios.

APA, Harvard, Vancouver, ISO, and other styles

36

Venčkauskas, Algimantas, Nerijus Morkevicius, Vaidas Jukavičius, Robertas Damaševičius, Jevgenijus Toldinas, and Šarūnas Grigaliūnas. "An Edge-Fog Secure Self-Authenticable Data Transfer Protocol." Sensors 19, no. 16 (August 19, 2019): 3612. http://dx.doi.org/10.3390/s19163612.

Full text

Abstract:

Development of the Internet of Things (IoT) opens many new challenges. As IoT devices are getting smaller and smaller, the problems of so-called “constrained devices” arise. The traditional Internet protocols are not very well suited for constrained devices comprising localized network nodes with tens of devices primarily communicating with each other (e.g., various sensors in Body Area Network communicating with each other). These devices have very limited memory, processing, and power resources, so traditional security protocols and architectures also do not fit well. To address these challenges the Fog computing paradigm is used in which all constrained devices, or Edge nodes, primarily communicate only with less-constrained Fog node device, which collects all data, processes it and communicates with the outside world. We present a new lightweight secure self-authenticable transfer protocol (SSATP) for communications between Edge nodes and Fog nodes. The primary target of the proposed protocol is to use it as a secure transport for CoAP (Constrained Application Protocol) in place of UDP (User Datagram Protocol) and DTLS (Datagram Transport Layer Security), which are traditional choices in this scenario. SSATP uses modified header fields of standard UDP packets to transfer additional protocol handling and data flow management information as well as user data authentication information. The optional redundant data may be used to provide increased resistance to data losses when protocol is used in unreliable networks. The results of experiments presented in this paper show that SSATP is a better choice than UDP with DTLS in the cases, where the CoAP block transfer mode is used and/or in lossy networks.

APA, Harvard, Vancouver, ISO, and other styles

37

Qin, Zhenquan, Zanping Cheng, Chuan Lin, Zhaoyi Lu, and Lei Wang. "Optimal Workload Allocation for Edge Computing Network Using Application Prediction." Wireless Communications and Mobile Computing 2021 (March 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/5520455.

Full text

Abstract:

By deploying edge servers on the network edge, mobile edge computing network strengthens the real-time processing ability near the end devices and releases the huge load pressure of the core network. Considering the limited computing or storage resources on the edge server side, the workload allocation among edge servers for each Internet of Things (IoT) application affects the response time of the application’s requests. Hence, when the access devices of the edge server are deployed intensively, the workload allocation becomes a key factor affecting the quality of user experience (QoE). To solve this problem, this paper proposes an edge workload allocation scheme, which uses application prediction (AP) algorithm to minimize response delay. This problem has been proved to be a NP hard problem. First, in the application prediction model, long short-term memory (LSTM) method is proposed to predict the tasks of future access devices. Second, based on the prediction results, the edge workload allocation is divided into two subproblems to solve, which are the task assignment subproblem and the resource allocation subproblem. Using historical execution data, we can solve the problem in linear time. The simulation results show that the proposed AP algorithm can effectively reduce the response delay of the device and the average completion time of the task sequence and approach the theoretical optimal allocation results.

APA, Harvard, Vancouver, ISO, and other styles

38

Yuan, Jie, Erxia Li, Chaoqun Kang, Fangyuan Chang, and Xiaoyong Li. "Review of the D2D Trusted Cooperative Mechanism in Mobile Edge Computing." Information 10, no. 8 (August 15, 2019): 259. http://dx.doi.org/10.3390/info10080259.

Full text

Abstract:

Mobile edge computing (MEC) effectively integrates wireless network and Internet technologies and adds computing, storage, and processing functions to the edge of cellular networks. This new network architecture model can deliver services directly from the cloud to the very edge of the network while providing the best efficiency in mobile networks. However, due to the dynamic, open, and collaborative nature of MEC network environments, network security issues have become increasingly complex. Devices cannot easily ensure obtaining satisfactory and safe services because of the numerous, dynamic, and collaborative character of MEC devices and the lack of trust between devices. The trusted cooperative mechanism can help solve this problem. In this paper, we analyze the MEC network structure and device-to-device (D2D) trusted cooperative mechanism and their challenging issues and then discuss and compare different ways to establish the D2D trusted cooperative relationship in MEC, such as social trust, reputation, authentication techniques, and intrusion detection. All these ways focus on enhancing the efficiency, stability, and security of MEC services in presenting trustworthy services.

APA, Harvard, Vancouver, ISO, and other styles

39

Yazici, Mahmut, Shadi Basurra, and Mohamed Gaber. "Edge Machine Learning: Enabling Smart Internet of Things Applications." Big Data and Cognitive Computing 2, no. 3 (September 3, 2018): 26. http://dx.doi.org/10.3390/bdcc2030026.

Full text

Abstract:

Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten diverse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.

APA, Harvard, Vancouver, ISO, and other styles

40

Hossen, Md Rajib, and Mohammad A. Islam. "Mobile Task Offloading Under Unreliable Edge Performance." ACM SIGMETRICS Performance Evaluation Review 48, no. 4 (May 17, 2021): 29–32. http://dx.doi.org/10.1145/3466826.3466838.

Full text

Abstract:

Offloading resource-hungry tasks from mobile devices to an edge server has been explored recently to improve task com- pletion time as well as save battery energy. The low la- tency computing resource from edge servers are a perfect companion to realize such task offloading. However, edge servers may su er from unreliable performance due to its rapid workload variation and reliance on intermittent re- newable energy. Further, batteries in mobile devices make online optimum offloading decisions challenging since it in- tertwines offloading decisions across di erent tasks. In this paper, we propose a deep Q-learning based task offloading solution, DeepTO, for online task offloading. DeepTO learns edge server performance in a model-free manner and takes future battery needs of the mobile device into account. Us- ing a simulation-based evaluation, we show that DeepTO can perform close to the optimum solution that has com- plete future knowledge.

APA, Harvard, Vancouver, ISO, and other styles

41

Kim, Hyoung Wook, Wook Bahng, Geun Ho Song, Sang Cheol Kim, Nam Kyun Kim, and Eun Dong Kim. "Edge Termination Technique for SiC Power Devices." Materials Science Forum 457-460 (June 2004): 1241–44. http://dx.doi.org/10.4028/www.scientific.net/msf.457-460.1241.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Hwang, Ho-Young, Seung-Cheon Kim, and Kwang-Hyun Ro. "Edge Security System for Factory Automation Devices." Journal of the Institute of Webcasting, Internet and Telecommunication 12, no. 2 (April 30, 2012): 251–58. http://dx.doi.org/10.7236/jiwit.2012.12.2.251.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Tyagi, Pawan. "Multilayer edge molecular electronics devices: a review." Journal of Materials Chemistry 21, no. 13 (2011): 4733. http://dx.doi.org/10.1039/c0jm03291c.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Muhammad, Ghulam, M. Shamim Hossain, and Abdulsalam Yassine. "Tree-Based Deep Networks for Edge Devices." IEEE Transactions on Industrial Informatics 16, no. 3 (March 2020): 2022–28. http://dx.doi.org/10.1109/tii.2019.2950326.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Lee, Eduardo J. H., Kannan Balasubramanian, Ralf Thomas Weitz, Marko Burghard, and Klaus Kern. "Contact and edge effects in graphene devices." Nature Nanotechnology 3, no. 8 (June 29, 2008): 486–90. http://dx.doi.org/10.1038/nnano.2008.172.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Van Guido, Oost. "Advanced Probe Edge Diagnostics for Fusion Devices." Fusion Science and Technology 53, no. 2T (February 2008): 387–97. http://dx.doi.org/10.13182/fst08-a1724.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

SUGAI, Kazumi. "CMP Processes for Leading-edge Semiconductor Devices." Journal of the Japan Society for Precision Engineering 78, no. 11 (2012): 928–31. http://dx.doi.org/10.2493/jjspe.78.928.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Kang, Seok-Hoon. "Effectiveness of Edge Selection on Mobile Devices." Journal of the Korea Society of Computer and Information 16, no. 7 (July 31, 2011): 149–56. http://dx.doi.org/10.9708/jksci.2011.16.7.149.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Van Oost, G. "Advanced probe edge diagnostics for fusion devices." Journal of Physics: Conference Series 666 (January 11, 2016): 012001. http://dx.doi.org/10.1088/1742-6596/666/1/012001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Zweben, S. J., J. A. Boedo, O. Grulke, C. Hidalgo, B. LaBombard, R. J. Maqueda, P. Scarin, and J. L. Terry. "Edge turbulence measurements in toroidal fusion devices." Plasma Physics and Controlled Fusion 49, no. 7 (June 5, 2007): S1—S23. http://dx.doi.org/10.1088/0741-3335/49/7/s01.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Edge devices'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles