Relevant bibliographies by topics / Frame network

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

Academic literature on the topic 'Frame network'

Author: Grafiati
Published: 3 June 2025
Last updated: 20 July 2025

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Journal articles on the topic "Frame network"

1

Yan, Bo, Chuming Lin, and Weimin Tan. "Frame and Feature-Context Video Super-Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5597–604. http://dx.doi.org/10.1609/aaai.v33i01.33015597.

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For video super-resolution, current state-of-the-art approaches either process multiple low-resolution (LR) frames to produce each output high-resolution (HR) frame separately in a sliding window fashion or recurrently exploit the previously estimated HR frames to super-resolve the following frame. The main weaknesses of these approaches are: 1) separately generating each output frame may obtain high-quality HR estimates while resulting in unsatisfactory flickering artifacts, and 2) combining previously generated HR frames can produce temporally consistent results in the case of short information flow, but it will cause significant jitter and jagged artifacts because the previous super-resolving errors are constantly accumulated to the subsequent frames.In this paper, we propose a fully end-to-end trainable frame and feature-context video super-resolution (FFCVSR) network that consists of two key sub-networks: local network and context network, where the first one explicitly utilizes a sequence of consecutive LR frames to generate local feature and local SR frame, and the other combines the outputs of local network and the previously estimated HR frames and features to super-resolve the subsequent frame. Our approach takes full advantage of the inter-frame information from multiple LR frames and the context information from previously predicted HR frames, producing temporally consistent highquality results while maintaining real-time speed by directly reusing previous features and frames. Extensive evaluations and comparisons demonstrate that our approach produces state-of-the-art results on a standard benchmark dataset, with advantages in terms of accuracy, efficiency, and visual quality over the existing approaches.
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Lu, Fan, Guang Chen, Sanqing Qu, Zhijun Li, Yinlong Liu, and Alois Knoll. "PointINet: Point Cloud Frame Interpolation Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (2021): 2251–59. http://dx.doi.org/10.1609/aaai.v35i3.16324.

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LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras. To overcome the temporal limitations of LiDAR sensors, a novel task named Point Cloud Frame Interpolation is studied in this paper. Given two consecutive point cloud frames, Point Cloud Frame Interpolation aims to generate intermediate frame(s) between them. To achieve that, we propose a novel framework, namely Point Cloud Frame Interpolation Network (PointINet). Based on the proposed method, the low frame rate point cloud streams can be upsampled to higher frame rates. We start by estimating bi-directional 3D scene flow between the two point clouds and then warp them to the given time step based on the 3D scene flow. To fuse the two warped frames and generate intermediate point cloud(s), we propose a novel learning-based points fusion module, which simultaneously takes two warped point clouds into consideration. We design both quantitative and qualitative experiments to evaluate the performance of the point cloud frame interpolation method and extensive experiments on two large scale outdoor LiDAR datasets demonstrate the effectiveness of the proposed PointINet. Our code is available at https://github.com/ispc-lab/PointINet.git.
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Ouyang, Ning, Zhishan Ou, and Leping Lin. "Video Super-Resolution Network with Gated High-Low Resolution Frames." Applied Sciences 13, no. 14 (2023): 8299. http://dx.doi.org/10.3390/app13148299.

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In scenes with large inter-frame motion variations, distant targets, and blurred targets, the lack of inter-frame alignment can greatly affect the effectiveness of subsequent video super-resolution reconstruction. How to perform inter-frame alignment in such scenes is the key to super-resolution reconstruction. In this paper, a new motion compensation method is proposed to design an alignment network based on gated high-low resolution frames. The core idea is to introduce a gating mechanism while using the information of high-low resolution neighboring frames to perform motion compensation adaptively. Meanwhile, within this alignment framework, we further introduce a pre-initial hidden state network and a local scale hierarchical salient feature fusion network. The pre-initial hidden state network is mainly used to reduce the impact of unbalanced quality effects between frames that occur in one-way cyclical networks; the local scale hierarchical salient feature fusion network is used to fuse the features of aligned video frames to extract contextual information and locally salient features to improve the reconstruction quality of the video. Compared with existing video super-resolution methods, this method achieves good performance and clearer edge and texture details.
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Jadhav, Savita, and Sangeeta Jadhav. "VHFRP: Virtual Hexagonal Frame Routing Protocol for Wireless Sensor Network." International journal of Computer Networks & Communications 15, no. 2 (2023): 39–55. http://dx.doi.org/10.5121/ijcnc.2023.15203.

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As physical and digital worlds become increasingly intertwined, wireless sensor networks are becoming an indispensable technology. A mobile sink may be required for some applications in the sensor field, where incomplete and/or delayed data delivery can lead to inappropriate conclusions. Therefore, latency and packet delivery ratios must be of high quality. In most existing schemes, mobile sinks are used to extend network lifetimes. By partitioning the sensor field into k equal sized frames, the proposed scheme creates a virtual hexagonal structure. Each frame header (FH) is linked together through the creation of a virtual backbone network. Frame headers are assigned to nodes near the centre of each frame. The virtual backbone network enables data collection from members of the frame and delivers it to the mobile sink. The proposed Virtual Hexagonal Frame Routing Protocol (VHFRP) improves throughput by 25%, energy consumption by 30% and delay by 9% as compared with static sink scenario.
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Li, Jiaao, Qunbo Lv, Wenjian Zhang, Yu Zhang, and Zheng Tan. "Burst-Enhanced Super-Resolution Network (BESR)." Sensors 24, no. 7 (2024): 2052. http://dx.doi.org/10.3390/s24072052.

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Multi-frame super-resolution (MFSR) leverages complementary information between image sequences of the same scene to increase the resolution of the reconstructed image. As a branch of MFSR, burst super-resolution aims to restore image details by leveraging the complementary information between noisy sequences. In this paper, we propose an efficient burst-enhanced super-resolution network (BESR). Specifically, we introduce Geformer, a gate-enhanced transformer, and construct an enhanced CNN-Transformer block (ECTB) by combining convolutions to enhance local perception. ECTB efficiently aggregates intra-frame context and inter-frame correlation information, yielding an enhanced feature representation. Additionally, we leverage reference features to facilitate inter-frame communication, enhancing spatiotemporal coherence among multiple frames. To address the critical processes of inter-frame alignment and feature fusion, we propose optimized pyramid alignment (OPA) and hybrid feature fusion (HFF) modules to capture and utilize complementary information between multiple frames to recover more high-frequency details. Extensive experiments demonstrate that, compared to state-of-the-art methods, BESR achieves higher efficiency and competitively superior reconstruction results. On the synthetic dataset and real-world dataset of BurstSR, our BESR achieves PSNR values of 42.79 dB and 48.86 dB, respectively, outperforming other MFSR models significantly.
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Nakayama, Yu, and Kaoru Sezaki. "Per-Flow Throughput Fairness in Ring Aggregation Network with Multiple Edge Routers." Big Data and Cognitive Computing 2, no. 3 (2018): 17. http://dx.doi.org/10.3390/bdcc2030017.

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Ring aggregation networks are often employed by network carriers because of their efficiency and high fault tolerance. A fairness scheme is required in ring aggregation to achieve per-flow throughput fairness and bufferbloat avoidance, because frames are forwarded along multiple ring nodes. N Rate N + 1 Color Marking (NRN + 1CM) was proposed to achieve fairness in ring aggregation networks consisting of Layer-2 Switches (SWs). With NRN + 1CM, frames are selectively discarded based on color and the frame-dropping threshold. To avoid the accumulation of a queuing delay, frames are discarded at upstream nodes in advance through the notification process for the frame-dropping threshold. However, in the previous works, NRN + 1CM was assumed to be employed in a logical daisy chain topology linked to one Edge Router (ER). The currently available threshold notification process of NRN + 1CM cannot be employed for ring networks with multiple ERs. Therefore, this paper proposes a method for applying NRN + 1CM to a ring aggregation network with multiple ERs. With the proposed algorithm, an SW dynamically selects the dropping threshold to send in order to avoid excess frame discarding. The performance of the proposed scheme was confirmed through computer simulations.
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Volodymyr, Khandetskyi, and Karpenko Nadiia. "Analysis of the efficiency of block frame transmission in IEEE 802.11 computer networks." System technologies 1, no. 150 (2024): 158–65. http://dx.doi.org/10.34185/1562-9945-1-150-2024-16.

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In infrastructure schemes of WLANs wireless computer networks, which are based on the use of the DSF (distributed coordination function) function and the CSMA/CA mechanism, the station STA (STAtion) sends a frame if the transmission channel is released after waiting for the end of the DIFS (distributed interframe space) interval and operation of the slot selec-tion mechanism for transmission (backoff mechanism). In case of collisions or damage to the frame by interference, the AP cannot decode the frame and does not send it back to the ACS station. The sending station STA waits for the re-ceipt of a potential ASK before the expiration of the corresponding timeout, and in case of receipt, starts the backoff procedure before transmission. The duration of the slot used in the backoff process depends on the speed of the physical layer technology. To reduce overhead, a transmission mechanism using frame blocks was proposed. This mechanism assumes that a block of frames intended for one recipient can be sent without con-firming the fact of correct reception by the AP access point of each frame separately. In this case, the sender (STA) competes for access to the channel for the first frame of the block. If it wins the access competition, the transmission of the first frame begins, and after receiving the ACK acknowledgment for it and a short SIFS separation interval, the STA transmits a whole block of frames, which is accompanied by a BAR service frame. The mechanism of frames block transmission in wireless computer networks IEEE 802.11 DCF with infrastructure topology has been analyzed. In protected mode, the depend-encies of network throughput on data transmission rate and the number of frames in a block are determined. It is shown that the mechanism of block transmission significantly increases the network throughput, especially in the range of higher transmission rates. With a marked increase in the intensity of interference the transmission of blocks becomes more complicated. Already with BER=10^-6 and the frames number of the order of 10 and more, in some cases there is a need to retransmit distorted in a block frames, and at BER=10^-5 there are many re-transmissions, so the resulting network throughput becomes small.
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Lan, Meng, Jing Zhang, Fengxiang He, and Lefei Zhang. "Siamese Network with Interactive Transformer for Video Object Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1228–36. http://dx.doi.org/10.1609/aaai.v36i2.20009.

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Semi-supervised video object segmentation (VOS) refers to segmenting the target object in remaining frames given its annotation in the first frame, which has been actively studied in recent years. The key challenge lies in finding effective ways to exploit the spatio-temporal context of past frames to help learn discriminative target representation of current frame. In this paper, we propose a novel Siamese network with a specifically designed interactive transformer, called SITVOS, to enable effective context propagation from historical to current frames. Technically, we use the transformer encoder and decoder to handle the past frames and current frame separately, i.e., the encoder encodes robust spatio-temporal context of target object from the past frames, while the decoder takes the feature embedding of current frame as the query to retrieve the target from the encoder output. To further enhance the target representation, a feature interaction module (FIM) is devised to promote the information flow between the encoder and decoder. Moreover, we employ the Siamese architecture to extract backbone features of both past and current frames, which enables feature reuse and is more efficient than existing methods. Experimental results on three challenging benchmarks validate the superiority of SITVOS over state-of-the-art methods. Code is available at https://github.com/LANMNG/SITVOS.
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Kim, Jeongmin, and Yong Ju Jung. "Multi-Stage Network for Event-Based Video Deblurring with Residual Hint Attention." Sensors 23, no. 6 (2023): 2880. http://dx.doi.org/10.3390/s23062880.

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Video deblurring aims at removing the motion blur caused by the movement of objects or camera shake. Traditional video deblurring methods have mainly focused on frame-based deblurring, which takes only blurry frames as the input to produce sharp frames. However, frame-based deblurring has shown poor picture quality in challenging cases of video restoration where severely blurred frames are provided as the input. To overcome this issue, recent studies have begun to explore the event-based approach, which uses the event sequence captured by an event camera for motion deblurring. Event cameras have several advantages compared to conventional frame cameras. Among these advantages, event cameras have a low latency in imaging data acquisition (0.001 ms for event cameras vs. 10 ms for frame cameras). Hence, event data can be acquired at a high acquisition rate (up to one microsecond). This means that the event sequence contains more accurate motion information than video frames. Additionally, event data can be acquired with less motion blur. Due to these advantages, the use of event data is highly beneficial for achieving improvements in the quality of deblurred frames. Accordingly, the results of event-based video deblurring are superior to those of frame-based deblurring methods, even for severely blurred video frames. However, the direct use of event data can often generate visual artifacts in the final output frame (e.g., image noise and incorrect textures), because event data intrinsically contain insufficient textures and event noise. To tackle this issue in event-based deblurring, we propose a two-stage coarse-refinement network by adding a frame-based refinement stage that utilizes all the available frames with more abundant textures to further improve the picture quality of the first-stage coarse output. Specifically, a coarse intermediate frame is estimated by performing event-based video deblurring in the first-stage network. A residual hint attention (RHA) module is also proposed to extract useful attention information from the coarse output and all the available frames. This module connects the first and second stages and effectively guides the frame-based refinement of the coarse output. The final deblurred frame is then obtained by refining the coarse output using the residual hint attention and all the available frame information in the second-stage network. We validated the deblurring performance of the proposed network on the GoPro synthetic dataset (33 videos and 4702 frames) and the HQF real dataset (11 videos and 2212 frames). Compared to the state-of-the-art method (D2Net), we achieved a performance improvement of 1 dB in PSNR and 0.05 in SSIM on the GoPro dataset, and an improvement of 1.7 dB in PSNR and 0.03 in SSIM on the HQF dataset.
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Xing, Guansheng, and Ziming Zhu. "Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation." Sensors 21, no. 21 (2021): 7156. http://dx.doi.org/10.3390/s21217156.

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Lane and road marker segmentation is crucial in autonomous driving, and many related methods have been proposed in this field. However, most of them are based on single-frame prediction, which causes unstable results between frames. Some semantic multi-frame segmentation methods produce error accumulation and are not fast enough. Therefore, we propose a deep learning algorithm that takes into account the continuity information of adjacent image frames, including image sequence processing and an end-to-end trainable multi-input single-output network to jointly process the segmentation of lanes and road markers. In order to emphasize the location of the target with high probability in the adjacent frames and to refine the segmentation result of the current frame, we explicitly consider the time consistency between frames, expand the segmentation region of the previous frame, and use the optical flow of the adjacent frames to reverse the past prediction, then use it as an additional input of the network in training and reasoning, thereby improving the network’s attention to the target area of the past frame. We segmented lanes and road markers on the Baidu Apolloscape lanemark segmentation dataset and CULane dataset, and present benchmarks for different networks. The experimental results show that this method accelerates the segmentation speed of video lanes and road markers by 2.5 times, increases accuracy by 1.4%, and reduces temporal consistency by only 2.2% at most.
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Dissertations / Theses on the topic "Frame network"

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Massey, David E. "Neural Network Application to Telemetry Frame Synchronization." International Foundation for Telemetering, 1992. http://hdl.handle.net/10150/611917.

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International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California<br>This paper looks into the use of neural network software as applied to the classical signal to noise concern when dealing with space to ground data communications. Use of a digital neural network to extend the correlation range of Pulse Code Modulation (PCM) down into noise is investigated. Conventional synchronization pattern correlation is done with digital logic comparisons on a sliding window with a set number of bit mismatch errors allowed. Correlation with a neural network does pattern recognition with a weighted network of artificial neurons that have been trained to recognize the sync pattern within noise. The output of such a neural network will produce a best guess of the correct pattern.
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Joakim, Carlsson. "CloudMAC Frame Prioritization : QoS and routing of IEEE802.11 frames in a Opendaylight controlled network." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-36313.

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Wireless networks are common in large organisations that can cover multiple floors and buildings. Wireless networks become expensive as they grow and more control and coordination is needed to operate and management them. This thesis describes how CloudMAC, a software defined networking solution (SDN), were implemented in OpenDaylight Hydrogen, a SDN controller. CloudMAC reduces complexity in large wireless local area networks. CloudMAC splits access points (AP) into, a physical (accesses the wireless medium) and a logical (handles the processing of data) part. These two part are then placed in different locations in a wired network. The parts are connected by making tunnels through the network. Some of the communications in wireless networks are time sensitive. Such time sensitive communication is easily disturbed during congestion. To improve CloudMAC, quality of service (QoS) was implemented. QoS was used both in the wired network and in accessing the wireless medium. Evaluations shows how to evaluate queues utilization and performance.
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Chakravorty, Sham. "An optimization analysis of frame architecture in selected protocols." Master's thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-04272010-020044/.

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Brown, Kyle Russell. "Packet level frame discard for MPEG-2 video in an active network." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1000109.

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Thesis (M.S.)--University of Florida, 2002.<br>Title from title page of source document. Document formatted into pages; contains vii, 67 p.; also contains graphics. Includes vita. Includes bibliographical references.
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Bester, Manfred, and Brett Stroozas. "TELEMETRY AND COMMAND FRAME ROUTING IN A MULTI-MISSION ENVIRONMENT." International Foundation for Telemetering, 2007. http://hdl.handle.net/10150/604572.

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ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada<br>In a modern ground control network for space communications, secure peer-to-peer TCP/IP network socket connections are typically used to transfer real-time telemetry and command frames between satellite operations centers and remote ground stations. Reliable and timely reconfiguration of data paths for upcoming pass supports becomes rather complex when many spacecraft and ground stations are involved. This paper describes a routing software application that was developed to facilitate switching of telemetry and command data paths between multiple ground stations and spacecraft command and control systems, and to forward telemetry streams to multiple client applications in parallel. Fully automated configuration and monitoring of the data flows is accomplished via a remote control interface that is tied into a pass scheduling system. The software is part of the SatTrack Suite and currently supports multi-mission flight operations, including those of the recently launched THEMIS constellation mission at Space Sciences Laboratory, University of California at Berkeley.
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Kanthla, Arjun Reddy. "Network Performance Improvement for Cloud Computing using Jumbo Frames." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143806.

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The surge in the cloud computing is due to its cost effective benefits and the rapid scalability of computing resources, and the crux of this is virtualization.  Virtualization technology enables a single physical machine to be shared by multiple operating systems. This increases the eciency of the hardware, hence decreases the cost of cloud computing. However, as the load in the guest operating system increases, at some point the physical resources cannot support all the applications efficiently. Input and output services, especially network applications, must share the same total bandwidth and this sharing can be negatively affected by virtualization overheads. Network packets may undergo additional processing and have to wait until the virtual machine is scheduled by the underlying hypervisor before reaching the final service application, such as a web server.In a virtualized environment it is not the load (due to the processing of the user data) but the network overhead, that is the major problem. Modern network interface cards have enhanced network virtualization by handling IP packets more intelligently through TCP segmentation offload, interrupt coalescence, and other virtualization specific hardware. Jumbo frames have long been proposed for their advantages in traditional environment. They increase network throughput and decrease CPU utilization.  Jumbo frames can better exploit Gigabit Ethernet and offer great enhancements to the virtualized environment by utilizing the bandwidth more effectively while lowering processor overhead. This thesis shows a network performance improvement of 4.7% in a Xen virtualized environment by using jumbo frames.  Additionally the thesis examines TCP's performance in Xen and compares Xen with the same operations running on a native Linux system.<br>Den kraftiga ökningen i datormoln är på grund av dess kostnadseffektiva fördelar och den snabba skalbarhet av datorresurser, och kärnan i detta är virtualisering. Virtualiseringsteknik möjliggör att man kan köra era operativsystem på en enda fysisk maskin. Detta ökar effektiviteten av hårdvaran, vilket gör att kostnaden minskar för datormoln. Men eftersom lasten i gästoperativsystemet ökar, gör att de fysiska resurserna inte kan stödja alla program på ett effektivt sätt. In-och utgångstjänster, speciellt nätverksapplikationer, måste dela samma totala bandbredd gör att denna delning kan påverkas negativt av virtualisering. Nätverkspaket kan genomgå ytterligare behandling och måste vänta tills den virtuella maskinen är planerad av den underliggande hypervisor innan den slutliga services applikation, till exempel en webbserver. I en virtuell miljö är det inte belastningen (på grund av behandlingen av användarens data) utan nätverket overhead, som är det största problemet. Moderna nätverkskort har förbättrat nätverk virtualisering genom att hantera IP-paket mer intelligent genom TCP- segmenterings avlastning, avbrotts sammansmältning och genom en annan hårdvara som är specifik för virtualisering. Jumboramar har länge föreslagits för sina fördelar i traditionell miljö. De ökar nätverk genomströmning och minska CPU-användning. Genom att använda Jumbo frames kan Gigabit Ethernet användandet förbättras samt erbjuda stora förbättringar för virtualiserad miljö genom att utnyttja bandbredden mer effektivt samtidigt sänka processor overhead. Det här examensarbetet visar ett nätverk prestandaförbättring på 4,7% i en Xen virtualiserad miljö genom att använda jumbo frames. Dessutom undersöker det TCP prestanda i Xen och jämför Xen med samma funktion som körs på en Linux system.
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Jun, Zhang, Qiu Yi, Du Yan, and Zhang Qishan. "The Realization of a Digital Correlation Detector of Telemetry Frame-Synchronization-Pattern Using a Neural Network." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611476.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California<br>In this paper, a method for digital correlation detector that takes advantage of the frame-synchronization-pattern feature of coincidence rate and adopts a multiple-bit detection window is proposed. Based on this method, a new digital correlation detector with a neural network is designed. It can recognizes frame-synchronization-pattern with error bits and slippage bits correctly, which has been approved practically according to the experimental results.
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Панасюк, А. В. "Інформаційна технологія проектування мереж операторів зв'язку на основі технології Frame-Relay та MPLS". Master's thesis, Сумський державний університет, 2018. http://essuir.sumdu.edu.ua/handle/123456789/72179.

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Розроблена програмно-інформаційна система, яка має візуальний інтерфейс, що дозволяє на основі введення інформації про інтерфейси отримати код для налаштування роутерів та зручно скопіювати його або в мережу Cisco Packet Tracer або на реальне обладнання. Це дозволить, по перше, реалізувати правильне, кероване, захищене функціонування мережевого обладнання, а по друге, оптимізувати роботу оператора, а також покращити можливості навчання інженерів- операторів мереж. Представлене рішення має ряд переваг, серед яких: оптимізація роботі інженера-оператора по налаштуванню мережі, можливість легкого копіювання коду, зручність заповнення вхідних данних, зрозумілий інтерфейс. Таким чином використавши дане рішення ми отримуємо зручність і економію часу при налаштуванні мереж за технологіями Frame-Relay та MPLS.
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Schauss, Daniel S. "Implementation of a performance tracking system for a Wide Area Network Provider's Frame Relay Services group." Master's thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-02162010-020342/.

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Puri, Amit, Siragan Ozkan, Peter Schaefer, Bob Anderson, and Mike Williams. "NEW TELEMETRY HARDWARE FOR THE DEEP SPACE NETWORK TELEMETRY PROCESSOR SYSTEM." International Foundation for Telemetering, 2000. http://hdl.handle.net/10150/607706.

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International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California<br>This paper describes the new Telemetry Processor Hardware (TPH) that Avtec Systems has developed for the Deep Space Network (DSN) Telemetry Processor (TLP) system. Avtec is providing the Telemetry Processor Hardware to RTLogic! for integration into the Telemetry Processor system. The Deep Space Network (DSN) is an international network of antennas that supports interplanetary spacecraft missions for exploration of the solar system and the universe. The Jet Propulsion Laboratory manages the DSN for NASA. The TLP system provides the capability to acquire, process, decode and distribute deep space probe and Earth orbiter telemetry data. The new TLP systems will be deployed at each of the three deep-space communications facilities placed approximately 120 degrees apart around the world: at Goldstone, California; near Madrid, Spain; and near Canberra, Australia. The Telemetry Processor Hardware (TPH) supports both CCSDS and TDM telemetry data formats. The TPH performs the following processing steps: soft-symbol input selection and measurement; convolutional decoding; routing to external decoders; time tagging; frame synchronization; derandomization; and Reed-Solomon decoding. The TPH consists of a VME Viterbi Decoder/MCD III Interface board (VM-7001) and a PCI-mezzanine Frame Synchronizer/Reed-Solomon Decoder (PMC- 6130-J) board. The new Telemetry Processor Hardware is implemented using the latest Field Programmable Gate Array (FPGA) technology to provide the density and speed to meet the current requirements as well as the flexibility to accommodate processing enhancements in the future.
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Books on the topic "Frame network"

1

Platt, Amelia. System management in a frame relay network. De Montfort University, 1994.

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2

Ibe, Oliver C. Converged network architectures: Delivering voice over IP, ATM, and frame relay. Wiley, 2002.

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3

IEEE Computer Society. LAN/MAN Standards Committee. and IEEE-SA Standards Board, eds. Supplement to carrier sense multiple access with collision detection (CSMA/CD) access method and physical layer specifications: Frame extensions for virtual bridged local area network (VLAN) tagging on 802.3 networks. Institute of Electrical and Electronics Engineers, 1998.

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Henderson, Liza. Frame relay internetworking. Sybex, Inc., 1999.

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Black, Uyless D. Frame relay networks: Specifications and implementations. McGraw-Hill, 1994.

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Black, Uyless D. Frame relay networks: Specifications and implementations. 2nd ed. McGraw-Hill, 1996.

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Black, Uyless D. Frame relay networks: Specifictions and implementations. McGraw-Hill, 1993.

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Mark, Miller. Analyzing broadband networks: Frame relay, SMDS & ATM. M&T Books, 1994.

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Mark, Miller. Analyzing broadband networks: ISDN, frame relay, SMDS, & ATM. 2nd ed. M&T Books, 1997.

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Held, Gilbert. Frame relay networking. Wiley, 1999.

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Book chapters on the topic "Frame network"

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Walton, D. "Frame Relay to ATM Interworking." In Data Network Engineering. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5215-4_8.

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Sujanani, Anish, and Shashidhar Pai. "802.11 Frame-level Network IDS for Public Wireless Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8289-9_44.

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Kim, Wonjik, Masayuki Tanaka, Masatoshi Okutomi, and Yoko Sasaki. "Adaptive Future Frame Prediction with Ensemble Network." In Pattern Recognition. ICPR International Workshops and Challenges. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68787-8_1.

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Das, Santanu, Sourav Dey Roy, Priya Saha, and Mrinal Kanti Bhowmik. "FvFc-Net: Forged Video Frame Classification Network." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9762-2_9.

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Ng, Wei Lun, Chee Kyun Ng, Borhanuddin Mohd. Ali, and Nor Kamariah Noordin. "Wireless Controller Area Network Using Token Frame Scheme." In Informatics Engineering and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25462-8_49.

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Boutilier, Craig, and Moisés Goldszmidt. "The Frame Problem and Bayesian Network Action Representations*." In Logical Foundations for Cognitive Agents. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60211-5_5.

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Lin, Xuhu, Lili Zhao, Xi Liu, and Jianwen Chen. "MVFI-Net: Motion-Aware Video Frame Interpolation Network." In Computer Vision – ACCV 2022. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26313-2_21.

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Piquer, Jos’e Miguel, and Javier Bustos-Jim’enez. "Frame Allocation Algorithms for Multi-threaded Network Cameras." In Euro-Par 2010 - Parallel Processing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15277-1_53.

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Boutilier, Craig, and Moisés Goldszmidt. "The frame problem and Bayesian network action representations." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61291-2_42.

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Mathai, Varghese, Arun Baby, Akhila Sabu, Jeexson Jose, and Bineeth Kuriakose. "Video Frame Interpolation Using Deep Convolutional Neural Network." In Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00665-5_82.

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Conference papers on the topic "Frame network"

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Shaik, Sameerunnisa, and J. Jabez. "Associated Video Frame Steganography Model based on Frame Integrity Verification using GAN Network." In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2024. http://dx.doi.org/10.1109/icosec61587.2024.10722460.

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S, Ashok, Alex Joseph K, Kessavan M, Susendhiran E, Prabhu V, and Venkat S. "Video Frame Transmission Using Semantic Frame Segmentation Over Deep Series Network with AWGN Channel." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026159.

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Chikh, Khaled, and Roberto Cavicchioli. "Adaptive Frame-Aware Network for Driver Monitoring Systems." In 2024 IEEE/ACM Symposium on Edge Computing (SEC). IEEE, 2024. https://doi.org/10.1109/sec62691.2024.00035.

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Li, Bokai, Wangwang Xu, Houying Qin, Lei Jin, Jing Wang, and Zeliang Zhao. "Micro-Expression Spotting and Recognition Network based on Apex Frame." In 2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML). IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779771.

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Lei, Pengcheng, Zaoming Yan, Tingting Wang, Faming Fang, and Guixu Zhang. "Three-Stage Temporal Deformable Network for Blurry Video Frame Interpolation." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687742.

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Yang, Q., W. Li, Z. Huang, et al. "A frame prediction network for total-body dynamic PET imaging." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10656700.

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Chiang, Chi-Hsun, Chung-Yi Wang, and Gwo-Hwa Ju. "FUN-VFI: Flow-Upscaling Network for 4K Video Frame Interpolation." In 2024 2nd International Conference on Artificial Intelligence and Power Engineering (AIPE). IEEE, 2024. https://doi.org/10.1109/aipe63225.2024.00013.

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Jia, Zhidong, Yihang Zhang, Qingyang Li, and Xinggong Zhang. "Tackling Bit-Rate Variation of RTC Through Frame-Bursting Congestion Control." In 2024 IEEE 32nd International Conference on Network Protocols (ICNP). IEEE, 2024. https://doi.org/10.1109/icnp61940.2024.10858541.

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Zhang, Yitian, Yue Bai, Chang Liu, Huan Wang, Sheng Li, and Yun Fu. "Frame Flexible Network." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01012.

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Jankowski, Bartosz, Wojciech Mazurczyk, and Krzysztof Szczypiorski. "Information Hiding Using Improper frame padding." In 2010 14th International Telecommunications Network Strategy and Planning Symposium (NETWORKS). IEEE, 2010. http://dx.doi.org/10.1109/netwks.2010.5624901.

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Reports on the topic "Frame network"

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Ward, Kimiora. Sierra Nevada Network high elevation white pine monitoring: 2021 annual report. National Park Service, 2024. http://dx.doi.org/10.36967/2302327.

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Five-needle white pines (Family Pinaceae, Genus Pinus, Subgenus Strobus), and in particular whitebark pine (Pinus albicaulis), limber pine (P. flexilis), and foxtail pine (P. balfouriana) are foundation species in upper subalpine and treeline forests of several National Park Service Pacific West Region parks, including Sequoia and Kings Canyon National Parks (SEKI) and Yosemite National Park (YOSE). The Sierra Nevada Network Inventory &amp; Monitoring Program, in collaboration with the Klamath Network, Upper Columbia Basin Network, and Mojave Desert Network have implemented a joint long-term monitoring protocol to assess the current status and future trends in high elevation white pine communities. Key demographic parameters within white pine forest communities will be estimated by monitoring individual trees within permanent plots through time. This report documents the results of the 2021 field season, which was the eighth year of monitoring in Sequoia and Kings Canyon National Parks (SEKI) and Yosemite National Park (YOSE). The 2021 goal was to complete the third full re-measure of the second of three rotating panels (Panel 2) for each species-park population: YOSE-whitebark pine, SEKI-whitebark pine, and SEKI-foxtail pine. Each panel consists of 12 permanent 50 x 50 m (2,500 m2) plots that were randomly selected for each of the three populations. The full sampling array thus includes a total of 36 whitebark pine plots in YOSE, 36 whitebark pine plots in SEKI, and 36 foxtail pine plots in SEKI. Data from plot surveys will be used to characterize white pine forest community dynamics in SEKI and YOSE, including changes in tree species composition, forest structure, forest health, and demographics. The first full measure of all Panel 2 plots was completed over two years in 2013-2014, then a full remeasure of both parks? whitebark pine Panel 2 was conducted in 2016, with 10 of 12 SEKI-foxtail plots sampled that year. A third remeasure of all Panel 2 plots was not possible in 2021 because a smaller crew size was necessary during the COVID-19 pandemic. In total, the crew visited 37 sites, and sampled 31, during the 2021 field season. One plot in the YOSE whitebark pine frame was uninstalled before reading and one plot in the SEKI whitebark pine frame was uninstalled after reading, both for safety concerns. Four plots were not visited due to lack of capacity with the reduced crew size: one in each of the YOSE and SEKI whitebark frames, and three in the SEKI foxtail frame. A plot from Panel 3 in each of the parks? whitebark frames was measured, for a total of 11 plots measured in each whitebark pine frame. Nine plots were measured in the SEKI foxtail pine frame. Within the 31 plots completed, a total of 5,728 trees was measured. Species composition, forest structure, and factors affecting tree health and reproduction, including incidence and severity of white pine blister rust (Cronartium ribicola) infection, mountain pine beetle (Dendroctonus ponderosae) infestation, dwarf mistletoe (Arceuthobium spp.) infection, canopy kill, female cone production and regeneration were recorded. During the 2021 field season, crews continued to count the total number of mature cones per tree for whitebark and foxtail pine, use crown condition codes to assess crown health, and tag individual seedlings to be tracked through time. All three of these procedures started in 2017 and are to be evaluated by each of the three participating networks over several years, to determine whether they should become permanent changes to the monitoring protocol. In YOSE, 11 whitebark pine plots were re-measured, from Panels 2 and 3. A total of 2,810 trees were sampled, which included 586 live whitebark pine trees and 2,097 other live conifers. An additional 127 trees (including 17 whitebark pine) were recorded as dead. The forest crew noted little sign of white pine blister rust (WPBR) in Yosemite in 2021, and just a single inactive canker was observed on one whitebark pine in Panel 3, Plot 42, near Dana Meadows. This infection was new to plot 42, and it expands the total number of plots where white pine blister rust has been documented in Yosemite to six. The crew also noted little mountain pine beetle activity, documenting beetle galleries on 15 lodgepole pines in three Panel 2 plots. Dwarf mistletoe was not observed. The average number of live whitebark pine trees per plot was 53 (SD = 56). This was a low cone crop year for whitebark pine, with two percent of live whitebark pine trees producing female cones. Cone bearing trees averaged 2 (SD = 1) cones per tree. Whitebark pine seedling density averaged 90 (SD = 157) seedlings per hectare. The largest number of whitebark pine seedlings found in a plot was four, and three of the eleven plots contained whitebark seedlings. In SEKI, 10 of 12 Panel 2, and one Panel 3, whitebark pine plots were re-measured. Within these plots, 1,246 live whitebark pine, 30 live foxtail pine, and 861 other live conifers were sampled. WPBR was infrequently documented in the SEKI whitebark frame as well, with indicators of infection in Plot 31 near Window Creek and Plot 44 near Upper State Lake. These were the first infections documented in these plots, bringing the number of plots where WPBR has been documented in the SEKI whitebark panel to nine. Although WPBR was documented in Plot 27 near Charlotte Dome in 2016, it was not documented this year because putative cankers showing three signs of infection in 2016 showed only two or fewer signs in 2021. Mountain pine beetle activity was observed in one live lodgepole pine and two recently dead whitebark pine, within three plots in the SEKI whitebark sample frame. An exception to the low levels of mountain pine beetle activity was outside Plot 31 in the Window Creek area, where the forest crew noted many recently dead whitebark pine with signs of beetle activity. Dwarf mistletoe was not encountered. The average number of live whitebark pine trees per plot was 113 (SD = 86). Less than one percent of live whitebark pine trees produced female cones, each producing on average 2 (SD = 1) cones. Whitebark seedling regeneration averaged 303 (SD = 319) seedlings per hectare. The largest number of whitebark seedlings found in a plot was eight, and eight of the 11 plots contained whitebark seedlings. Nine of the 12 SEKI foxtail Panel 3 plots were remeasured. Within these plots, 413 live foxtail pine, 67 live whitebark pine, and 402 other live conifers were sampled. Ninety-two dead or recently dead trees were also documented, 65 of which were foxtail pine. No signs of blister rust infection, mistletoe, or mountain pine beetle were observed in the foxtail plots sampled. The average number of foxtail pine trees per plot was 46 (SD = 33). Fifty-four percent of the foxtail pine trees produced female cones, averaging 14 (SD =15) cones/tree. Only one foxtail pine seedling was recorded within the 9 foxtail pine plots, resulting in an estimated 14 (SD = 41) seedlings per hectare. Eight whitebark pine seedlings were also found within two plots.
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Kull, Kathleen, Craig Young, Jennifer Haack-Gaynor, Lloyd Morrison, and Michael DeBacker. Problematic plant monitoring protocol for the Heartland Inventory and Monitoring Network: Narrative, version 2.0. National Park Service, 2022. http://dx.doi.org/10.36967/nrr-2293355.

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Problematic species, which include invasive, exotic, and harmful species, fragment native ecosystems, displace native plants and animals, and alter ecosystem function. In National Parks, such species negatively affect park resources and visitor enjoyment by altering landscapes and fire regimes, reducing native plant and animal habitat, and increasing trail maintenance needs. Recognizing these challenges, Heartland Inventory and Monitoring (I&amp;M) Network parks identified problematic plants as the highest-ranking vital sign across the network. Given the need to provide early detection of potential problematic plants (ProPs) and the size of network parks, the Heartland I&amp;M Network opted to allocate available sampling effort to maximize the area searched. With this approach and the available sampling effort in mind, we developed realistic objectives for the ProP monitoring protocol. The monitoring objectives are: 1. Create a watch list of ProPs known to occur in network parks and a watch list of potential ProPs that may invade network parks in the future, and occasionally update these two lists as new information is made available. 2. Provide early detection monitoring for all ProPs on the watch lists. 3. Search at least 0.75% and up to 40% of the reference frame for ProP occurrences in each park. 4. Estimate/calculate and report the abundance and frequency of ProPs in each park. 5. To the extent possible, identify temporal changes in the distribution and abundance of ProPs known to occur in network parks. ProP watch lists are developed using the best available and most relevant state, regional, and national exotic plant lists. The lists are generated using the PriorityDB database. We designed the park reference frames (i.e., the area to be monitored) to focus on accessible natural and restored areas. The field methods vary for small parks and large parks, defined as parks with reference frames less than and greater than 350 acres (142 ha), respectively. For small parks, surveyors make three equidistant passes through polygon search units that are approximately 2-acres (0.8 ha) in size. For large parks, surveyors record each ProP encountered along 200-m or 400-m line search units. The cover of each ProP taxa encountered in search units is estimated using the following cover scale: 0 = 0, 1 = 0.1-0.9 m2, 2 = 1-9.9 m2, 3 = 10-49.9 m2, 4 = 50-99.9 m2, 5 = 100-499.9 m2, 6 = 499.9-999.9 m2, and 7 = 1,000-4,999.9 m2. The field data are managed in the FieldDB database. Monitoring is scheduled to revisit most parks every four years. The network will report the results to park managers and superintendents after completing ProP monitoring.
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deSouza, O., and M. Rodrigues. Guidelines for Running OSPF Over Frame Relay Networks. RFC Editor, 1994. http://dx.doi.org/10.17487/rfc1586.

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Conta, A., A. Malis, and M. Mueller. Transmission of IPv6 Packets over Frame Relay Networks Specification. RFC Editor, 1999. http://dx.doi.org/10.17487/rfc2590.

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Conta, A., P. Doolan, and A. Malis. Use of Label Switching on Frame Relay Networks Specification. RFC Editor, 2001. http://dx.doi.org/10.17487/rfc3034.

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Tayeb, Shahab. Protecting Our Community from the Hidden Vulnerabilities of Today’s Intelligent Transportation Systems. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2132.

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The ever-evolving technology interwoven into the transportation industry leaves it frequently at risk for cyber-attacks. This study analyzes the security of a common in-vehicle network, the Controller Area Network (CAN), standard in most vehicles being manufactured today. Like many other networks, CAN comes with inherent vulnerabilities that leave CAN implementations at risk of being targeted by cybercriminals. Such vulnerabilities range from eavesdropping, where the attacker can read the raw data traversing the vehicle, to spoofing, where the attacker can place fabricated traffic on the network. The research team initially performed a simulation of CAN traffic generation followed by hardware implementation of the same on a test vehicle. Due to the concealed and untransparent nature of CAN, the team reverse-engineered the missing parameters through a series of passive "sniffing attacks" (attacks using data reading utilities called packet sniffers) on the network and then demonstrated the feasibility of the attack by placing fabricated frames on the CAN.
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McDonald, Jacob, Eric Starkey, and Wendy Wright. Wadeable stream suitability assessment for long-term monitoring: Ocmulgee National Monument. National Park Service, 2019. https://doi.org/10.36967/2259872.

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The Southeast Coast Network Monitoring Wadeable Stream Habitat Conditions in Southeast Coast Network Parks: Protocol Narrative (McDonald 2018a) was developed to provide insight into the status of, and trends in, stream and near-channel habitat conditions in wadeable streams at national parks, recreation areas, battle fields, and monuments in Alabama, Georgia, and South Carolina (McDonald et al. 2018a). These parks include Chattahoochee River National Recreation Area (CHAT), Kennesaw Mountain National Battlefield Park (KEMO), Congaree National Park (CONG), Horseshoe Bend National Military Park (HOBE), and Ocmulgee National Monument (OCMU). The number of reaches surveyed at each park is dependent on the size of the park and the number of wadeable streams within park boundaries. Reaches selected for monitoring (1) are representative of the processes influencing the streams in each park; (2) can address current and anticipated management concerns, and (3) offer the most utility for future complementary studies. The habitat assessment methods outlined in the protocol rely on standard data collection methods and standard operating procedures currently in use by the U.S. Geological Survey, U.S. Environmental Protection Agency, and U.S. Forest Service, modified to meet the needs of National Park Service (NPS) managers. he purpose of this report is to document the stream suitability survey conducted at Ocmulgee National Monument to determine which streams should be chosen for long-term monitoring. Over the course of three days (January 17, 2017; May 22, 2017; and December 6, 2018), twelve stream segments were assessed for suitability for long-term monitoring. Stream segment suitability was based on whether the stream (1) could be safely accessed, (2) was part of a single channel, wadeable stream system, and (3) was not greatly influenced by upstream impoundments. Of the twelve stream segments identified in GIS or in consultation with park staff, three were determined to be suitable for long-term monitoring, one was classified as potentially suitable (listed as “maybe”), three were determined to not be suitable for long-term monitoring, and five did not meet the criteria used to define the stream channel sample frame outlined in the protocol (i.e., perennial, single channel, wadeable stream). The streams selected will provide comparisons between similar streams and/or be representative of similar streams within the park and/or network. The following report provides an overview of the methods used to assess the suitability of each stream segment and provides a description of the streams assessed.
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Martini, L., C. Kawa, and A. Malis, eds. Encapsulation Methods for Transport of Frame Relay over Multiprotocol Label Switching (MPLS) Networks. RFC Editor, 2006. http://dx.doi.org/10.17487/rfc4619.

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Martini, L., E. Rosen, and N. El-Aawar, eds. Encapsulation Methods for Transport of Layer 2 Frames over MPLS Networks. RFC Editor, 2007. http://dx.doi.org/10.17487/rfc4905.

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Debroux, Patrick. The Use of Adjacent Video Frames to Increase Convolutional Neural Network Classification Robustness in Stressed Environments. DEVCOM Analaysis Center, 2023. http://dx.doi.org/10.21236/ad1205367.

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