Network Interface Research Articles

Segmented transmission delay based decoupling for parallel simulation of a distribution network

The introduction of inverters and power electronic devices increases the amount of calculation needed for electromagnetic transient simulation in distribution networks with a high proportion of distributed generation. Hence, the parallel calculation of electromagnetic transient simulation has become an effective way to improve its efficiency. To realize efficient parallel simulation, a method to decouple transmission lines in the distribution network based on the segmented transmission delay model is proposed, which divides the line into two segments according to different transmission delays. The transmission delay of one segment is an integral multiple of the simulation step used for line decoupling, whereas the other segment is a lumped parameter model, which makes it equivalent to the original line. In addition, an interface for the distribution network based on the segmented transmission delay model is designed and implemented, and the decoupling simulation of a distribution network is performed. Finally, using the simulation examples of IEEE 34 and IEEE 123 node test feeders, the proposed decoupling method is compared with other established methods. It is found to be more accurate and efficient as well as more suitable for the parallel computation of a distribution network simulation than the comparison methods.

Enhancement of ionic conduction and mechanical properties for all-solid-state polymer electrolyte systems through ionic and physical bonding

Solid polymer electrolytes (SPEs) are receiving significant attention as the next-generation electrolyte for battery applications, owing to their excellent safety aspects and potential load-bearing capability, as opposed to conventional organic electrolytes. Here, cyclohexanedimethanol (CHDM) was used as a new solid additive which can be replaced with most common organic or plastic crystals such as succinonitrile (SN) and polycarbonate (PC) to assist dissociation of lithium salts as well as to enhance an ionic conductivity in an epoxy-based poly (ethylene glycol) diglycidyl ether (PEGDGE) electrolyte. The PEGDGE-15 wt% bis(trifluoromethane)sulfonimide lithium salt-30 wt% CHDM formulation (Li15-CHDM30), which enables the highest ion conductivities of 0.014 mS/cm and 0.2 mS/cm at 25 °C and 90 °C, respectively, with an increased Li+ transference number (tLi+) of 0.71, was considered the optimized PEGDGE electrolyte system. This ionic conductivity at 90 °C is 13.6% and 61.7% higher than PEGDGE electrolyte systems containing 30 wt% SN (Li15-SN30) and 30 wt% PC (Li15-PC30), alongside 8.4% and 22.5% improvements in tLi+ values, respectively. It is proposed that the increase in ionic conductivity was facilitated by providing multidimensional channels at the cross-linking network interface between PEGDGE and CHDM as well as protonated groups in CHDM which attract more b is(trifluoromethane)sulfonimide anion (TFSI)− anions. Interestingly, increasing the CHDM content in the optimized PEGDGE electrolyte from 10 wt% to 30 wt% increased not only the elongation at break from 6.4% to 12.9% but also the tensile strength and toughness from 3.4 to 7 MPa and from 0.12 to 0.47 MJ/m3, respectively, which are significantly higher than those of values for Li15-SN30 and Li15-PC30 electrolyte systems. The high ductility and ionic conductivity of the Li15-CHDM30 are owing to physical bonding of CHDM via hydrogen bond and ion-dipole interactions with the resin matrix as evidenced by fourier-transform infrared (FTIR), Raman, and 7Li-NMR spectroscopic analyses.

Combined Stateful Classification and Session Splicing for High-Speed NFV Service Chaining

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Network functions</i> such as firewalls, NAT, DPI, content-aware optimizers, and load-balancers are increasingly realized as software to reduce costs and enable outsourcing. To meet performance requirements these <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">virtual</i> network functions (VNFs) often bypass the kernel and use their own user-space networking stack. A naïve realization of a chain of VNFs will exchange raw packets, leading to many redundant operations, wasting resources. In this work, we design a system to execute a pipeline of VNFs. We provide the user facilities to define (i) a traffic class of interest for the VNF, (ii) a session to group the packets (such as the TCP 4-tuple), and (iii) the amount of space per session. The system synthesizes a classifier and builds an efficient flow table that when possible will automatically be partially offloaded and accelerated by the network interface. We utilize an abstract view of flows to support seamless inspection and modification of the content of any flow (such as TCP or HTTP). By applying only surgical modifications to the protocol headers, we avoid the need for a complex, hard-to-maintain user-space TCP stack and can chain multiple VNFs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without re-constructing the stream multiple times</i> , allowing up to 5x improvement over standard approaches.

Instance: Soma-based multi-user interaction design for the telematic sonic arts

The telematic work instance is a performance for viola and dance that digitally connects performers in Vancouver and Cape Town. The network interface enables a violist and a dancer to simultaneously play multi-user digital music-dance instruments over the internet with music and dance. The composition, design and performance interaction of instance draw from acoustic multi-user instrument paradigms and music-dance interactions in the African performing arts to explore the idiosyncrasies of the telematic performance space. The iterative design process implements soma-based research methods to inspire sonic compositional material with the body and to explore the performers’ embodied experience of sonic aesthetics during their interaction.

Design and simulation of video monitoring structure over TCP/IP system using MATLAB

Video monitoring systems are undergoing an evolution from conventional analog to digital clarification to provide better rate and security over internet protocols. In addition, analog surveillance becomes insufficient to face enormous demand of security of system contains more than hundreds of camera often deployed in hotels environments far away from room control. This paper presents the design and simulation of a video monitoring scheme in excess of a transmission control protocol/internet protocol (TCP/IP) system using MATLAB. Sophisticated cameras could record directly high-definition digital videos based on digital technology which simply communicate the control room relaying on ordinary internet protocol infrastructure networks. This technology provides a flexible network interface over a wide variety of heterogeneous technology networks. Though, the acceptance of IP designed for video monitoring pretense severe difficulties in terms of power processing, system dependability, required bandwidth, and security of networks. The advantage of IP based video monitoring system has been investigated over conventional analog systems and the challenges of the method are described. The open research issues are still requiring a final solution to permits complete abandon against conventional technology of analog methods. In conclusion, the method to tackle the purpose of video monitoring in actual operation is proposed and verified properly by means of model simulation.

Evaluating the joint use of GPR and ERT on mapping shallow subsurface features of karst critical zone in southwest China

AbstractThe soils and underlying weathered carbonate rock in karstic regions play an important role in the infiltration, storage, and retention of water and nutrients. Because of significant heterogeneity of the karst, the use of individual geophysical techniques is often not sufficient for unambiguous assessment of the irregular distributions of soils and underlying fractures. In this study, ground penetrating radar (GPR) and electrical resistivity tomography (ERT) are jointly used with additional observations to delineate the shallow subsurface structure in two exposed profiles. The results show that ERT is effective for detecting the soil–rock interfaces, even for irregular terrain and fracture structures, such as a funnel‐shaped doline, as the soils and rocks show a large resistivity contrast. Although ERT may be able to sense the presence of extensive fracturing, it cannot detect individual small aperture fractures. Joint use of different frequencies of the GPR antenna (e.g., 100 and 500 MHz in this study) allowed the detection of most fractures at different depths in the study sites. However, forward modeling of typical weathered rock features illustrates that the GPR data cannot resolve any reflection signals of the vertical fractures, so the features of vertically enlarged fractures filled by soils cannot be seen from the GPR images. Moreover, large uncertainties of resistivity at the interface between soils or fractures and bedrock limit the identification of an irregularly distributed subsurface structure. Despite the limitations of individual techniques, the combination of ERT and GPR enhances the delineation of the soil–bedrock interface and identification of the fracture network, which can allow an enhanced geological interpretation of shallow subsurface features in the karst areas.

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.

100 Gb/s dynamically programmable SDN-enabled hardware encryptor for optical networks

We have successfully demonstrated an on-demand programmable hardware encryptor for optical networks enabled by the software-defined network (SDN) and tested its application in a quantum key distribution (QKD) use case. We believe our proposed encryptor is unique in the way that it combines on-demand programmable encryption algorithms with a 100G Ethernet network interface to provide a high-transmission capacity with flexible security. We have successfully formed an on-demand encryption library consisting of AES-256, AES-192, AES-128, Camellia-256, XOR, and no-encryption configurations. Our system has shown a network throughput of 91.3 Gb/s for AES variations and 90.2 Gb/s for Camellia-256, while theoretical encryption throughputs were 160 Gb/s. In addition, the goodputs of all the encryption schemes were measured as at least 90.4 Gb/s. For a faster reconfiguration of the field-programmable gate array (FPGA), partial reconfiguration technology has been used, and reconfiguration times of 2.6 s for encryption and 2 s for decryption have been achieved. The FPGA configuration rate was 3.35 MB/s. When our proposed design was tested in a QKD use case with 256-bit keys, the highest achieved key consumption rate was 27 keys/s, which corresponded to the minimum granularity of 474 MB per key. Thus, with a 256-bit key, up to a 6912 b/s key consumption rate was achieved. In addition, the encryptor’s end-to-end network latency has been tested using 300k standard Internet Control Message Protocol (ICMP) pings. For all encryption schemes, the latency was measured as 0.093 m s ± 0.028 m s on average, but the encryption/decryption processes did not have a meaningful latency impact with microsecond precision.

Distance-aware Approximate Nanophotonic Interconnect

The energy consumption of manycore architectures is dominated by data movement, which calls for energy-efficient and high-bandwidth interconnects. To overcome the bandwidth limitation of electrical interconnects, integrated optics appear as a promising technology. However, it suffers from high power overhead related to low laser efficiency, which calls for the use of techniques and methods to improve its energy costs. Besides, approximate computing is emerging as an efficient method to reduce energy consumption and improve execution speed of embedded computing systems. It relies on allowing accuracy reduction on data at the cost of tolerable application output error. In this context, the work presented in this article exploits both features by defining approximate communications for error-tolerant applications. We propose a method to design realistic and scalable nanophotonic interconnect supporting approximate data transmission and power adaption according to the communication distance to improve the energy efficiency. For this purpose, the data can be sent by mixing low optical power signal and truncation for the Least Significant Bits (LSB) of the floating-point numbers, while the overall power is adapted according to the communication distance. We define two ranges of communications, short and long, which require only four power levels. This reduces area and power overhead to control the laser output power. A transmission model allows estimating the laser power according to the targeted BER and the number of truncated bits, while the optical network interface allows configuring, at runtime, the number of approximated and truncated bits and the laser output powers. We explore the energy efficiency provided by each communication scheme, and we investigate the error resilience of the benchmarks over several approximation and truncation schemes. The simulation results of ApproxBench applications show that, compared to an interconnect involving only robust communications, approximations in the optical transmission led to up to 53% laser power reduction with a limited degradation at the application level with less than 9% of output error. Finally, we show that our solution is scalable and leads to 10% reduction in the total energy consumption, 35× reduction in the laser driver size, and 10× reduction in the laser controller compared to state-of-the-art solution.

Digital Video Image Preprocessing Algorithm Based on Embedded System

To solve the massive noise contained in the images acquired under low illumination, we designed a digital video image Preprocessing device with the denoising function. Based on the embedded CPU and operating system, video images are acquired by the camera. The noise contained in the video images is filtered by the improved median filtering algorithm and wavelet image denoising. Subsequently, the images are transmitted through USB and network interface, and the storage function of image files is implemented. The device can remove the noise contained in videos effectively, which is conducive to performing more advanced processing on the images.

Vulnerability Recognition and Resurgence in Network based on Prediction Model and Cognitive based Elucidation

The challengeable factor in current network issue is identification of vulnerability and the same can be prevented before it occurs. Many traditional measures applied to keep track of assessing the system in terms of misconduct calculation. There are two different kind application running via network interface which are known process application and unknown process application. Known applications can be managed with the help of existing approaches whereas resolving problems of unknown applications are questionable. The proposed solution addresses this issue by applying cognitive based solutions and supervised learning model. Traffic parameters considered here as major concern and feature extraction is done against parameters flow of information does after pre-process the data. Training, Automation and detection is a sequence of process is used to find vulnerability misconduct in network and simulated with the help of python.

Protection switching schemes and mapping strategies for fail-operational hard real-time NoCs

Communication infrastructures designed for mixed-critical MPSoCs must provide isolation of traffic, hard real-time guarantees, and fault-tolerance. In previous work, we proposed the combination of protection-switching with a hybrid Time-Division-Multiplexed (TDM) and packet-switched Network-on-Chip (NoC) to achieve all three goals. In this paper, we present an FPGA implementation of such a NoC with all its features. We give synthesis results for the hybrid NoC, including the network interface, and show that our router uses over 32% fewer LUTs and registers than a competitive state-of-the-art router for mixed-critical MPSoC. We then explore different channel and task mapping strategies for critical applications which use protection switching and evaluate the effect these mappings have on the best-effort (BE) traffic in the system. Results show, that spreading out the critical traffic rather than naively dividing the system in critical and non-critical application domains is advantageous or even necessary in many cases and can allow for up to 13% more BE traffic. We give a comprehensive trade-off analysis of three protection switching schemes—1:n, 1:1, and 1+1—and show that 1+1 protection has less than half the worst case latency for critical traffic that 1:n and 1:1 protection have. At the same time, 1+1 protection, on average, only causes a 1.18% earlier saturation rate for BE traffic, which we consider to be affordable. We conclude that 1+1 protection is ideally suited for use in mixed-critical systems with high safety requirements.

Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. The modeling was correlated with four parameters. To train and test the projected models, data for more than 150 samples were collected from the literature. Furthermore, mortar samples with varying proportions of volcanic ash were prepared in the laboratory and tested, and the results were used to validate the models. The performance of the developed models was assessed using numerous statistical measures. The results show that both the ANN and ANFIS models accurately predict the compressive strength of VAM with R-square above 0.9 and lower error statistics. The permutation feature analysis confirmed that the age of specimens affects the strength of VAM the most, followed by the water-to-cement ratio, curing temperature, and percentage of volcanic ash.

Polyurethane-templated 3D BN network for enhanced thermally conductive property of epoxy composites

Utilizing three-dimensional filler has been deem as a promising approach to improve the thermal conductivity of polymer composites. In this research, we developed a polyurethane sacrificial template technique to fabricate a novel three-dimensional porous carbon bonded BN foam (3D-CBNF), aiming to improve the thermal performance of polymers (epoxy). The as-prepared 3D-CBNF/epoxy composite exhibits a high thermal conductivity (TC) of 2.11 Wm−1K−1 at a relatively low BN loading of 24.7 wt%, corresponding to a TC enhancement (TCE) of 1072% compared with the pure epoxy resin. The analysis of the thermal conductivity mechanism based on EMT and Foygel models and finite element simulation indicated that the high thermal conductivity of composites was due to the efficient heat transfer pathways provided by the 3D interconnected BN network and lower interface thermal resistance in internal of it. In addition, the as-obtained composite possessed a relatively low dielectric constant (below 4.5 at 1 MHz) and a high volume electrical resistivity (beyond 1015 Ω·cm). Overall, a novel and promising method of preparation for 3D construction with thermally conductive filler, which is designed to attain composites with high thermal conductivity, has been proposed in this work.

Towards an Integrated Vehicle Management System in DriveOS

Modern automotive systems feature dozens of electronic control units (ECUs) for chassis, body and powertrain functions. These systems are costly and inflexible to upgrade, requiring ever increasing numbers of ECUs to support new features such as advanced driver assistance (ADAS), autonomous technologies, and infotainment. To counter these challenges, we propose DriveOS, a safe, secure, extensible, and timing-predictable system for modern vehicle management in a centralized platform. DriveOS is based on a separation kernel, where timing and safety-critical ECU functions are implemented in a real-time OS (RTOS) alongside non-critical software in Linux or Android. The system enforces the separation, or partitioning, of both software and hardware among different OSes. DriveOS runs on a relatively low-cost embedded PC-class platform, supporting multiple cores and hardware virtualization capabilities. Instrument cluster, in-vehicle infotainment and advanced driver assistance system services are implemented in a Yocto Linux guest, which communicates with critical real-time services via secure shared memory. The RTOS manages a real-time controller area network (CAN) interface that is inaccessible to Linux services except via well-defined and legitimate communication channels. In this work, we integrate three Qt-based services written for Yocto Linux, running in parallel with a real-time longitudinal controller task and multiple CAN bus concentrators, for vehicular sensor data processing and actuation. We demonstrate the benefits and performance of DriveOS with a hardware-in-the-loop CARLA simulation using a real car dataset.

Conducting network interface modulated rate performance in LiFePO4/C cathode materials

Conducting network interface modulated rate performance in LiFePO4/C cathode materials

In-situ growth of MAX phase coatings on carbonised wood and their terahertz shielding properties

Electromagnetic interference (EMI) shielding materials have received considerable attention in recent years. The EMI shielding effectiveness (SE) of materials depends on not only their composition but also their microstructures. Among various microstructure prototypes, porous structures provide the advantages of low density and high terahertz wave absorption. In this study, by using carbonised wood (CW) as a template, 1-mm-thick MAX@CW composites (Ti2AlC@CW, V2AlC@CW, and Cr2AlC@CW) with a porous structure were fabricated through the molten salt method. The MAX@CW composites led to the formation of a conductive network and multilayer interface, which resulted in improved EMI SE. The average EMI SE values of the three MAX@CW composites were > 45 dB in the frequency of 0.6–1.6 THz. Among the composites, V2AlC@CW exhibited the highest average EMI SE of 55 dB.

Dynamic scheduling mechanism for software-defined security resources based on multi-mode load balancing

Rather than the traditional rigid and decentralized security mechanism, software-defined security provides a flexible and centralized solution by extracting security mechanisms from the security hardware layer to the software layer, but the existing software-defined security mechanism is prone to the problem of high load on physical devices performing security functions leading to performance limitations. To address these problems, this paper proposes a dynamic scheduling mechanism for security resources based on load balancing, while designing the southbound interface of software-defined networks to be scheduled by the control plane of software-defined security for flows according to established policies and logical topologies. The mechanism can match the load resources required by the security components, thus avoiding the problem of overall performance degradation due to excessive load and long queuing time of individual devices.

Magnetic TiN composites for efficient microwave absorption: Nanoribbons vs nanoparticles

The rational structure-effect relationship design of microwave absorption devices provides tremendous potential for the elimination of electromagnetic radiation contamination. Here, we report on the efficient microwave absorbers (MA) consisting of TiN/Fe2Ni2N nanoribbons (NRs) and nanoparticles (NPs) as a functional MA filler. The cross-linked network and multiple heterogeneous interface polarization of TiN/Fe2Ni2N NRs contributes to improved conductance loss and polarization loss and is favorable for obtaining the lightweight MA absorber at a low filler content of 15 wt%. As a comparison, due to the enhanced permeability and balanced permittivity, the maximum reflection loss of the NPs filled absorber can reach −31.9 dB at a filler thickness of 1.3 mm at a high filler content of 50 wt%. This work deepens the understanding for designing the high-performance MA through tuning and optimizing the dielectric and magnetic loss.

A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning

The increasing ubiquity of network traffic and the new online applications’ deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their networks. However, modern network traffic uses dynamic ports and is transported over secure application-layer protocols (e.g., HTTPS, SSL, and SSH). This makes it a challenging task for network administrators to identify online applications using traditional port-based approaches. One way for classifying the modern network traffic is to use machine learning (ML) to distinguish between the different traffic attributes such as packet count and size, packet inter-arrival time, packet send–receive ratio, etc. This paper presents the design and implementation of NetScrapper, a flow-based network traffic classifier for online applications. NetScrapper uses three ML models, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Network (ANN), for classifying the most popular 53 online applications, including Amazon, Youtube, Google, Twitter, and many others. We collected a network traffic dataset containing 3,577,296 packet flows with different 87 features for training, validating, and testing the ML models. A web-based user-friendly interface is developed to enable users to either upload a snapshot of their network traffic to NetScrapper or sniff the network traffic directly from the network interface card in real time. Additionally, we created a middleware pipeline for interfacing the three models with the Flask GUI. Finally, we evaluated NetScrapper using various performance metrics such as classification accuracy and prediction time. Most notably, we found that our ANN model achieves an overall classification accuracy of 99.86% in recognizing the online applications in our dataset.