Performance Evaluation Of Networks Research Articles

Vehicular Networks Performance Evaluation Based on Downlink Scheduling Algorithms for High-Speed Long Term Evolution – Vehicle

<p class="Abstract"><span lang="EN-US">Moving is the key to modern life. Most things are in moving such as vehicles and user mobiles, so the need for high-speed wireless networks to serve the high demand of the wireless application becomes essential for any wireless network design. The use of web browsing, online gaming, and on-time data exchange like video calls as an example means that users need a high data rate and fewer error communication links. To satisfy this, increasing the bandwidth available for each network will enhance the throughput of the communication, but the bandwidth available is a limited resource which means that thinking about techniques to be used to increase the throughput of the network is very important. One of the techniques used is the spectrum sharing between the available networks, but the problem here is when there is no available channel to connect with. This encourages researchers to think about using scheduling as a technique to serve the high capacity on the network. Studying scheduling techniques depends on the Quality-of-Service (QoS) of the network, so the throughput performance is the metric of this paper. In this paper, an improved Best-CQI scheduling algorithm is proposed to enhance the throughput of the network. The proposed algorithm was compared with three </span><span lang="MS">user scheduling algorithms to evaluate the throughput performance which are Round Robin (RR), Proportional Fair (PF), and Best-CQI algorithms. The study is performed under Line-of-Sight (LoS) link at carrier frequency 2.6 GHz to satisfy the Vehicular Long Term Evolution (LTE-V) with the high-speed scenario. The simulation results show that the proposed algorithm outperforms the throughput performance of the other algorithms.</span></p>

On Beamforming Gain Models for Performance Evaluation and Analysis of Narrowband and Wideband Wireless Networks

Directional antennas and beamforming techniques that bring promising transmission gain to the wireless links are widely incorporated in the system-level analysis of wireless networks. In many existing studies, the beamforming gain model to calculate the beamforming gain considers beamforming pattern and aligned as well as misaligned cases. However, the channel properties, e.g., the $K$ factor and the spatial distribution of multi-paths, are neglected, which could significantly influence the beamforming gain. In this paper, a general beamforming gain model is appropriately defined, while the traditional beamforming gain model is proved to be only a special case in the proposed general model by considering an oversimplified channel with no angular spread. In light of this, expressions of the received signal amplitude and the beamforming gain are rigorously derived for narrowband fading and wideband statistical mmWave channels, respectively. Thorough comparison between the proposed beamforming gain model and the traditional beamforming gain is provided, which demonstrates and validates that the traditional model incorrectly captures the beamforming gain and thereby, leads to inaccurate system-level network analysis. To this end, the effectiveness and importance of the proposed general beamforming gain model are revealed, particularly for millimeter-wave wideband systems.

User Grouping and Energy Harvesting in UAV-NOMA System With AF/DF Relaying

The development of unmanned aerial vehicles (UAVs) is one of the driving forces for revolutionizing wireless communications in the modern era. Although there are many unique features of UAV networks, their widespread use is still hampered by the short battery life. As a result, most of the research efforts in this domain mainly have focused on the efficient use of energy through trajectory planning and cooperative communication. While there have been a few proposals to use energy harvesting techniques for UAV networks, a complete understanding of the performance limits of such networks is still missing in the literature and needs further investigations. Within this context, our paper provides a comprehensive performance evaluation of UAV relay networks (UAVRNs) employing non-orthogonal multiple access (NOMA) technique. Specifically, a cooperative communication system, where a UAV serves as a mobile relay, is fully considered. The UAV relay is considered to be wirelessly-powered and harvests energy from the radio signals received from a nearby base station. For the sake of comparison, both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols are considered. Subsequently, the closed-form expressions of outage probabilities and ergodic capacities are provided for each UAV relaying protocol. Extensive simulations are performed to verify the accuracy of the derived closed-form expressions. The results provided in this work characterize the outage and ergodic capacity performance of NOMA-enabled UAVRNs.

Network for AI and AI for Network: Challenges and Opportunities for Learning-Oriented Networks

The “data pipe” model used by the existing Internet protocol stack is no longer ideal for many emerging applications, due to multimedia, multicast, mobility, machine learning, and network management challenges. A new learning-oriented network architecture is required to deal with these challenges and serve learning-centric applications in data centers, around network edges, and on mobile devices. This article focuses on the network for AI and AI for network for learning-oriented network architecture. This is done by leveraging, improving, and creating new learning techniques to determine and optimize protocol mechanisms and control policies. The new network architecture can provide ample research opportunities in network topology control, protocol design, and performance evaluation, aiming to network a truly dependable cyber-infrastructure. The learning-oriented network can also learn from applications and communications automatically and continuously while running on different infrastructures to support diverse requirements. In addition, the network can keep evolving its protocol mechanisms and control policies in an online manner. It does this while maintaining protocol security and preserving user privacy, to learn and perform more effectively and efficiently. Finally, the main challenges and opportunities of learning-oriented network are discussed, encouraging further research.

A surface cracks detection method for nuclear fuel pellets using an improved fully convolutional network

ABSTRACT Surface cracks, one of the main quality defects in nuclear fuel pellets, are usually subtle and hidden in complex backgrounds, making them difficult to detect accurately and threatening reactor safety. In order to achieve accurate cracks detection, a fuel pellet crack detection (FPCD) network is proposed based on the fully convolutional network with the VGG16 network structure as the encoder’s backbone (FCN-VGG16). The new architecture modifies the combination mode of convolution layers and pooling layers, and optimizes the size and number of convolution kernels. And a large weight is added for crack samples in the loss function to solve the class imbalance problem. Subsequently, a self-acquired dataset containing 2240 pellet surface crack images is used for training and performance evaluation of networks. Experiments show that the proposed method achieved an F1-score of 88.89% and an intersection over union (IoU) of 80.30%, obtaining better detection results for complex background crack images.

Resource Allocation Schemes for 5G Network: A Systematic Review.

Fifth-generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, lower power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising device-to-device networks, macro-cells, and different categories of small cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need for cellular service and the limited resources to provide it, capably handling network traffic and operation has become a problem of resource distribution. One of the utmost serious problems is to alleviate the jamming in the network in support of having a better QoS. However, although a limited number of review papers have been written on resource distribution, no review papers have been written specifically on 5G resource allocation. Hence, this article analyzes the issue of resource allocation by classifying the various resource allocation schemes in 5G that have been reported in the literature and assessing their ability to enhance service quality. This survey bases its discussion on the metrics that are used to evaluate network performance. After consideration of the current evidence on resource allocation methods in 5G, the review hopes to empower scholars by suggesting future research areas on which to focus.

Performance evaluation of campus network involving VLAN and broadband multimedia wireless networks using OPNET modeler

Modern technology is essential for all healthy, economic, and educational sectors. Using modern technology involves high-performance networks in terms of quality of service (QoS) parameters such as delay, throughput, bandwidth, and security. A campus network involves wire and the wireless environment with and without virtual local area network (VLAN) technology has been simulated using optimized network engineering tools (OPNET). The simulation model includes four scenarios that involve heavy-loaded File transfer protocol (FTP) and web browsing applications with two logical groups of users. The simulation results show that the VLAN overcome LAN networks in terms of bandwidth and security. This has been done by reducing the throughput in both sending and receiving levels to the confidential servers. The VLAN has also reduced the broadcast domain which results in a high-power efficiency. Moreover, the VLAN network technology has been proved to have lower values of delay in transferring files and packets than LAN network technology. In addition, VLAN technology generally has been decreased values of throughput regardless of the way of connection between servers and workstations. This, in turn, allows taking advantage of wireless connection features in terms of flexibility of installation and speed of configuration.

SuperPruner: Automatic Neural Network Pruning via Super Network

Most network pruning methods rely on rule-of-thumb for human experts to prune the unimportant channels. This is time-consuming and can lead to suboptimal pruning. In this paper, we propose an effective SuperPruner algorithm, which aims to find optimal pruned structure instead of pruning unimportant channels. We first train a VerifyNet, a kind of super network, which is able to roughly evaluate the performance of any given network structure. The particle swarm optimization algorithm is then used to search for optimal network structure. Lastly, the weights in the VerifyNet are used as the initial weights of the optimal pruned structure to make fine-tuning. VerifyNet is a network performance evaluation; our algorithm can quickly prune the network under any hardware constraints. Our algorithm can be applied in multiple fields such as object recognition and semantic segmentation. Extensive experiment results demonstrate the effectiveness of SuperPruner. For example, on CIFAR-10, the pruned VGG16 achieves 93.18% Top-1 accuracy and reduces 74.19% of FLOPs and 89.25% of parameters. Compared with state-of-the-art methods, our algorithm can achieve higher pruned ratio with less accuracy cost.

Dynamic Graph Optimization and Performance Evaluation for Delay-Tolerant Aeronautical Ad Hoc Network

With the growing interest in providing internet access and cellular connectivity in the commercial aircraft, the compatibility between Air-to-Air (A2A) communications and current Air-to-Ground (A2G) macro-cellular communications is necessary to form an Aeronautical Ad hoc Network (AANet). Due to the typical features of airborne communications, such as high mobility, long transmission range and three-dimensional macro-cells, AANet is affected by intermittent links, which results in variant delayed data transmissions. To this end, we exploit a dynamic graph approach to model such an AANet with rapidly changing topology under a realistic airborne scenario. Targeting the problem of intermittent transmissions, by employing A2A, an AANet can bear three transmission modes: direct transmission, connected relay transmission and opportunistic transmission. To investigate the potential gain of A2A transmissions in terms of the end-to-end data flow transmission delay and the data traffic amount, we formulate an integer non-linear programming problem that minimizes the average end-to-end delay from Internet Gateways (IGWs) on shore to the aircraft receivers through multiple options of the transmission modes. Through solving this minimization problem based on the realistic dynamic graph model, the tradeoffs between the end-to-end transmission delay and other key network factors related to AANet formation and resource allocation can be obtained without much compromise on the transmission delay. The results show that the average end-to-end delay of the AANet is comparable to the direct transmission delay when having a tradeoff with the data traffic amount, buffer size and the spectrum sharing.

Fine-Grained Analysis on Downlink LEO Satellite-Terrestrial mmWave Relay Networks

This letter investigates the downlink integrated low earth orbit (LEO) satellite-terrestrial millimeter-wave decode-and-forward relay network, where the satellite and terrestrial relays locate in uniform distribution over the spherical cap surface of the LEO and in Poisson point process over the ground, respectively. In order to obtain fine-grained information on the coverage performance, the meta distribution (MD) of signal-to-interference-to-noise ratio (SINR) is adopted for network performance evaluation, which provides the probability that certain percentage of satellite-relays-user links are able to reach a target SINR threshold. The closed-form and analytical expression of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${b}$ </tex-math></inline-formula> -th moments of the complementary cumulative distribution functions (CCDFs) of SINR for the satellite-to-relay link and relay-to-user link are then derived, respectively. In addition, we further give the analytical MD expression via the beta approximation. Finally, numerical results are provided to validate our analytical results.

A quick BAT for evaluating the reliability of binary-state networks

Network structures and models have been widely adopted, and many are based on the binary-state network. Reliability is the most commonly used tool to evaluate network performance. Efficient algorithms to evaluate binary-state network reliability are continually being developed. The motivation of this study is to propose an efficient algorithm called the quick BAT to evaluate binary-state network reliability. The propose quick BAT is based on the binary-addition tree algorithm (BAT) and employs three novel concepts: the first connected vector, the last disconnected vector, and super vectors. These super vectors narrow the search space and the calculations of their occurrent probabilities simplify the probability calculations to reduce the run time of the algorithm. Moreover, we show that replacing each undirected arc with two directed arcs, which is required in traditional direct methods, is unnecessary in the proposed algorithm. We call this novel concept the undirected vectors. The advantage and performance of the proposed quick BAT algorithm was verified experimentally by solving 20 benchmark problems and compared to the binary decision diagram (BDD), quick inclusion–exclusion technology (QIE), and BAT.

Preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradation

The extraction processes of palm oil from palm fruit bunch for industrial and domestic applications generate the palm oil mill effluent (POME) and it is considered a huge environmental challenge. The present study investigates the application of the POME as a suitable bio-stimulating organic nutrient in hydrocarbon contaminated soil (HCCS). The wet POME (WPOME) and dried POME (DPOME) were investigated in this study. The physiochemical and microbial characteristics in the HCCS showed the inadequacies of the required organic nutrients in the HCCS while its microbial population indicated well-acclimatized microorganisms. The DPOME was able to stimulate the microbial population and caused a reduction from the initial HC concentration of 4,248mg/kg to 1,600.35mg/kg while the WPOME reduced the initial HC concentration from 4,248mg/kg to 2,600.56mg/kg. The organic nutrient levels and pH in POME need to be adjusted to be within the range for optimum HC degradation and microbial activity. Data from the first-order kinetics interpretation confirmed that the DPOME was more beneficial to the HCCS as an organic nutrient. Performance evaluation of the artificial neural network (ANN) model through the root mean square error (RMSE), mean square error (MSE), and correlation coefficient (R²) confirmed that the DPOME with lower RMSE, MSE, and a higher R² performed better in the HC degradation process. Finally, this study showed that the nutrient present in POME could potentially stimulate microbial growth and serve as an effective organic nutrient in HCCS degradation.

Performance evaluation of artificial neural networks for identification of failure modes in composite plates

Abstract The aim of this work is to identify failure modes of double pinned sandwich composite plates by using artificial neural networks learning algorithms and then analyze their accuracies for identification. Mechanically pinned specimens with two serial pins/bolts for sandwich composite plates were used for recognition of failure modes which were obtained in previous experimental studies. In addition, the empirical data of the preceding work was determined with various geometric parameters for various applied preload moments. In this study, these geometric parameters and fastened/bolted joint forms were used for training by artificial neural networks. Consequently, ten different backpropagation training algorithms of artificial neural network were applied for classification by using one hundred data values containing three geometrical parameters. According to obtained results, it was seen that the Levenberg-Marquardt backpropagation training algorithm was the most successful algorithm with 93 % accuracy rate and it was appropriate for modeling of this problem. Additionally, performances of all backpropagation training algorithms were discussed taking into account accuracy and error ratios.

LTE network performance evaluation based on effects of various parameters on the cell range and MAPL

These days long term evaluation (LTE) is considered the common mobile technology around the world and there is a need to maximize the network performance to satisfy the increased demand in terms of the cell capacity and coverage. These are many parameters in the network configuration and in the surrounded environment, which have great effects on the network performance. Examples of parameters are the system overhead rations, the required capacity of the network, neighbor cell load, and link budget parameters. The determination of the optimum configuration parameters, which achieve the best network performance, is a main step in the planning process in addition to it is continuous step in network optimization phase. In this study, the effects of some parameters will be investigating to get the best parameters that achieve the best network performance in terms of capacity of the cells and coverage area. The study will start by discussing introduction about LTE network components and protocols, and then the main parameters of the protocols will be revising. The study will display the results of changing many parameters related to LTE protocols and surrounding environment parameters on the LTE network performance.

Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes.

We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance.

Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures

The current state of security and availability of the Internet is far from being commensurate with its importance. The number and strength of DDoS attacks conducted at the network layer have been steadily increasing. However, the single path (SP) routing used in today’s Internet lacks a mitigation scheme to rapidly recover from network attacks or link failure. In case of a link failure occurs, it can take several minutes until failover. In contrast, multi-path routing can take advantage of multiple alternative paths and rapidly switch to another working path. According to the level of available path control, we classfy the multi-path routing into two types, first-hop multi-path (FMP) and multi-hop multi-path (MMP) routing. Although FMP routing supported by networks, such as SD-WAN, shows marginal improvements over the current SP routing of the Internet, MMP routing supported by a global Internet architecture provides strong improvement under network attacks and link failure. MMP routing enables changing to alternate paths to mitigate the network problem in other hops, which cannot be controlled by FMP routing. To show this comparison with practical outcome, we evaluate network performance in terms of latency and loss rate to show that MMP routing can mitigate Internet hazards and provide high availability on global networks by 18 participating ASes in six countries. Our evaluation of global networks shows that, if network attacks or failures occur in other autonomous systems (ASes) that FMP routing cannot avoid, it is feasible to deal with such problems by switching to alternative paths by using MMP routing. When the global evaluation is under a transit-link DDoS attack, the loss rates of FMP that pass the transit-link are affected significantly by a transit-link DDoS attack, but the other alternative MMP paths show stable status under the DDoS attack with proper operation.

Binaural Audio-Visual Localization

Localizing sound sources in a visual scene has many important applications and quite a few traditional or learning-based methods have been proposed for this task. Humans have the ability to roughly localize sound sources within or beyond the range of the vision using their binaural system. However most existing methods use monaural audio, instead of binaural audio, as a modality to help the localization. In addition, prior works usually localize sound sources in the form of object-level bounding boxes in images or videos and evaluate the localization accuracy by examining the overlap between the ground-truth and predicted bounding boxes. This is too rough since a real sound source is often only a part of an object. In this paper, we propose a deep learning method for pixel-level sound source localization by leveraging both binaural recordings and the corresponding videos. Specifically, we design a novel Binaural Audio-Visual Network (BAVNet), which concurrently extracts and integrates features from binaural recordings and videos. We also propose a point-annotation strategy to construct pixel-level ground truth for network training and performance evaluation. Experimental results on Fair-Play and YT-Music datasets demonstrate the effectiveness of the proposed method and show that binaural audio can greatly improve the performance of localizing the sound sources, especially when the quality of the visual information is limited.

Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset

Nine previously proposed segmentation evaluation metrics, targeting medical relevance, accounting for holes, and added regions or differentiating over- and under-segmentation, were compared with 24 traditional metrics to identify those which better capture the requirements for clinical segmentation evaluation. Evaluation was first performed using 2D synthetic shapes to highlight features and pitfalls of the metrics with known ground truths (GTs) and machine segmentations (MSs). Clinical evaluation was then performed using publicly-available prostate images of 20 subjects with MSs generated by 3 different deep learning networks (DenseVNet, HighRes3DNet, and ScaleNet) and GTs drawn by 2 readers. The same readers also performed the 2D visual assessment of the MSs using a dual negative-positive grading of −5 to 5 to reflect over- and under-estimation. Nine metrics that correlated well with visual assessment were selected for further evaluation using 3 different network ranking methods - based on a single metric, normalizing the metric using 2 GTs, and ranking the network based on a metric then averaging, including leave-one-out evaluation. These metrics yielded consistent ranking with HighRes3DNet ranked first then DenseVNet and ScaleNet using all ranking methods. Relative volume difference yielded the best positivity-agreement and correlation with dual visual assessment, and thus is better for providing over- and under-estimation. Interclass Correlation yielded the strongest correlation with the absolute visual assessment (0–5). Symmetric-boundary dice consistently yielded good discrimination of the networks for all three ranking methods with relatively small variations within network. Good rank discrimination may be an additional metric feature required for better network performance evaluation.

Comprehensive Evaluation of Urban Road Network Resilience Facing Earthquakes

The road network’s transport capacity and traffic function will be directly reduced if urban roads are damaged by earthquakes. To effectively improve the resistance and recovery ability of urban road networks facing earthquake disasters, the establishment of an aseismic resilience evaluation method for the urban road network is the research goal. This paper’s novelty introduces the concept of engineering resilience into the aseismic performance evaluation of urban road networks. It reveals the internal influence principle of nodes and independent pathways on the aseismic resilience of the network. This paper’s significant contribution is to establish a reasonable and comprehensive urban road network aseismic resilience evaluation method. This method can realize the calculation of the aseismic resilience for the existing network, reconstruction network, and new network and propose the optimization, transformation, and layout for the network. The MATLAB program for the whole process calculation of aseismic resilience is developed. The overall network’s aseismic resilience is obtained by the sum of the product of the node importance and the average number of the reliable independent pathways.

Performance Evaluation of Vehicular Ad-Hoc Network based on Proactive and Reactive Routing Protocols

Performance Evaluation of Vehicular Ad-Hoc Network based on Proactive and Reactive Routing Protocols