scholarly journals Computation of Regeneration Sites in Survivable Cost-efficient Translucent Optical Networks

2020 ◽  
Vol 8 (5) ◽  
pp. 4024-4033
Keyword(s):  
Optical Networks ◽  
Optimal Solution ◽  
Optical Signal ◽  
Wdm Networks ◽  
Link Failure ◽  
Placement Problem ◽  
Translucent Optical Networks ◽  
Fiber Link ◽  
Cost Efficient ◽  
The Impact

Survivability refers to the ability of networks to withstand failures and is one of the most important aspects of network planning and design. If the network is designed to survive failures then it will be capable of providing continuous services without disruption. In this paper, we address the problem of designing translucent optical WDM networks capable of withstanding any arbitrary single fiber-link failure. If an optical signal is propagated beyond a permissible distance also known as optical reach its quality degrades to a level which demands re-amplification, re-shaping and re-timing, a process known as 3R-regeneration. Since employing nodes with regeneration capability incur additional cost, ideally only a subset of the nodes in the network must be identified as regeneration sites. A cost-efficient survivable network design must then ensure that there is minimum number of regeneration sites. Since the Regenerator Placement Problem (RPP) is NP-Hard [1]; we propose heuristics for computing as few regeneration sites as possible to make a translucent network survive the impact of a fiber-link failure. We propose an ILP for getting optimal solution in small networks. We also propose two heuristic strategies namely; Survivable Link based computation of regenerator sites (SLCRS) and Survivable Segment based computation of regenerator sites (SSCRS). We compare the performance of the proposed strategies with some of the existing strategies. Performance comparisons show that our proposed SSCRS can be used to design survivable translucent optical networks with fewer regeneration sites

Role of Self Phase Modulation and Cross Phase Modulation on Quality of Signal in Optical Links

2019 ◽  
Vol 13 ◽  
Author(s):  
Karamjit Kaur ◽  
Anil Kumar
Keyword(s):  
Optical Networks ◽  
Phase Modulation ◽  
Input Power ◽  
Wdm Networks ◽  
Signal Power ◽  
Optical Links ◽  
System Parameters ◽  
Quality Of Transmission ◽  
The Impact

Background: In WDM networks, there is a crucial need to monitor signal degradation factors in order to maintain the quality of transmission. This is more critical in dynamic optical networks as non-linear impairments are network state dependent. Moreover, PLIs are accumulative in nature, so the overall impact is increased tremendously as the length of signal path is increased. The interactions between different impairments along the path also influence their overall impact. Objective: Among the different impairments, the present work focus on phase modulations owing to intensities of signals themselves as well as the neighboring signals. It includes the influence of SPM, SPM and XPM, system parameters like signal power, wavelength and fiber parameters like attenuation coefficient, dispersion coefficient and their influence on Q-value and BER. Method: The analysis is done through a single and two-channel transmitter system with varied power, wavelengths and system parameters. The corresponding optical spectrums are analysed. Result & Conclusion: It has been found that SPM and XPM pose broadening effect on spectrum without any effect on temporal distributions. The magnitude of signal power is among the parameters significantly influencing the broadening of spectrum. Higher is the power, more is the magnitude of broadening. It has been found that in order to neglect the impact of input power; its magnitude must be kept below 20 mW. Also, the dispersion and attenuation value need to be carefully as they pose counteracting effect to SPM and XPM for certain values and hence can be used as compensation measure without any additional cost.

scholarly journals PERFORMANCE ANALYSIS OF WSXC AND WIXC SSM OXC IN WDM OPTICAL NETWORKS

2014 ◽  
pp. 7-11
Author(s):  
MD. ISHTIAQUE AZIZ ZAHED ◽  
MD. SHAH AFRAN
Keyword(s):  
Optical Networks ◽  
Wavelength Division Multiplexing ◽  
Wavelength Conversion ◽  
Optical Network ◽  
Propagation Delay ◽  
Optical Signal ◽  
Wdm Optical Networks ◽  
Power Penalty ◽  
Time Duration ◽  
The Impact

The impact of inband crosstalk on an optical signal passing through optical cross-connect nodes (OXC’s) in wavelength division multiplexing (WDM) optical network, is studied from the equation of electric field with crosstalk and the corresponding current. The analysis has been done for two SSM (space switching matrix) OXC architecture namely WSXC & WIXC where later one has full wavelength conversion capability. Although WIXC attenuates more crosstalk though it is found that depending on the values of optical propagation delay differences, coherent time of lasers and time duration of one bit of the signal, the required power penalty in WIXC may be greater than that of WSXC in some cases. The analysis has been performed on the measures of Bit Error Rate (BER) and Power Penalty.

scholarly journals A comparative study of multiobjective computational intelligence algorithms to find the solution to the RWA problem in WDM networks

DYNA ◽  
2015 ◽  
Vol 82 (194) ◽  
pp. 221-229
Author(s):  
Bryan Montes Castañeda ◽  
Jorge Patiño Garzón ◽  
Gustavo Puerto Leguizamón
Keyword(s):  
Comparative Study ◽  
Optical Networks ◽  
Computational Intelligence ◽  
Simulated Annealing Algorithm ◽  
Wavelength Conversion ◽  
Search Space ◽  
Wavelength Assignment ◽  
Wdm Networks ◽  
Differential Evolutionary Algorithm ◽  
The Impact

This paper presents a comparative study of multiobjective algorithms to solve the routing and wavelength assignment problem in optical networks. The study evaluates five computational intelligence algorithms, namely: the Firefly Algorithm, the Differential Evolutionary Algorithm, the Simulated Annealing Algorithm and two versions of the Particle Swarm Optimization algorithm. Each algorithm is assessed based on the performance provided by two different network topologies under different data traffic loads and with a different number of wavelengths available in the network. The impact of implementing wavelength conversion processes is also taken into account in this study. Simulated results show that, in general, the evaluated algorithms appropriately solve the problem in small-sized networks in which a similar performance was found. However, key differences were found when the size of the network is significant. This means that more suitable algorithms optimize the search space and the fall into local minimums is avoided.

scholarly journals Empirical Stochastic Model of Multi-GNSS Measurements

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4566
Author(s):  
Dominik Prochniewicz ◽  
Kinga Wezka ◽  
Joanna Kozuchowska
Keyword(s):  
Stochastic Model ◽  
Density Ratio ◽  
Functional Model ◽  
Optimal Solution ◽  
Global Navigation Satellite Systems ◽  
Unknown Parameters ◽  
Short Baseline ◽  
Stochastic Parameters ◽  
Dependent Model ◽  
The Impact

The stochastic model, together with the functional model, form the mathematical model of observation that enables the estimation of the unknown parameters. In Global Navigation Satellite Systems (GNSS), the stochastic model is an especially important element as it affects not only the accuracy of the positioning model solution, but also the reliability of the carrier-phase ambiguity resolution (AR). In this paper, we study in detail the stochastic modeling problem for Multi-GNSS positioning models, for which the standard approach used so far was to adopt stochastic parameters from the Global Positioning System (GPS). The aim of this work is to develop an individual, empirical stochastic model for each signal and each satellite block for GPS, GLONASS, Galileo and BeiDou systems. The realistic stochastic model is created in the form of a fully populated variance-covariance (VC) matrix that takes into account, in addition to the Carrier-to-Noise density Ratio (C/N0)-dependent variance function, also the cross- and time-correlations between the observations. The weekly measurements from a zero-length and very short baseline are utilized to derive stochastic parameters. The impact on the AR and solution accuracy is analyzed for different positioning scenarios using the modified Kalman Filter. Comparing the positioning results obtained for the created model with respect to the results for the standard elevation-dependent model allows to conclude that the individual empirical stochastic model increases the accuracy of positioning solution and the efficiency of AR. The optimal solution is achieved for four-system Multi-GNSS solution using fully populated empirical model individual for satellite blocks, which provides a 2% increase in the effectiveness of the AR (up to 100%), an increase in the number of solutions with errors below 5 mm by 37% and a reduction in the maximum error by 6 mm compared to the Multi-GNSS solution using the elevation-dependent model with neglected measurements correlations.

scholarly journals WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 539
Author(s):  
Saleh Seyedzadeh ◽  
Andrew Agapiou ◽  
Majid Moghaddasi ◽  
Milan Dado ◽  
Ivan Glesk
Keyword(s):  
Optical Networks ◽  
Video Surveillance ◽  
Multiple Access ◽  
Optical Sensing ◽  
Multiple Access Interference ◽  
Service Differentiation ◽  
Sensor Data ◽  
Variable Weight ◽  
Code Division Multiple ◽  
The Impact

The growing demand for extensive and reliable structural health monitoring resulted in the development of advanced optical sensing systems (OSS) that in conjunction with wireless optical networks (WON) are capable of extending the reach of optical sensing to places where fibre provision is not feasible. To support this effort, the paper proposes a new type of a variable weight code called multiweight zero cross-correlation (MW-ZCC) code for its application in wireless optical networks based optical code division multiple access (WON-OCDMA). The code provides improved quality of service (QoS) and better support for simultaneous transmission of video surveillance, comms and sensor data by reducing the impact of multiple access interference (MAI). The MW-ZCC code’s power of two code-weight properties provide enhanced support for the needed service differentiation provisioning. The performance of this novel code has been studied by simulations. This investigation revealed that for a minimum allowable bit error rate of 10−3, 10−9 and 10−12 when supporting triple-play services (sensing, datacomms and video surveillance, respectively), the proposed WON-OCDMA using MW-ZCC codes could support up to 32 simultaneous services over transmission distances up to 32 km in the presence of moderate atmospheric turbulence.

scholarly journals Radiopharmaceutical and Eu3+ doped gadolinium oxide nanoparticles mediated triple-excited fluorescence imaging and image-guided surgery

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xiaojing Shi ◽  
Caiguang Cao ◽  
Zeyu Zhang ◽  
Jie Tian ◽  
Zhenhua Hu
Keyword(s):  
Fluorescent Probes ◽  
Signal Intensity ◽  
Tumor Imaging ◽  
Tumor Resection ◽  
Optical Signal ◽  
Gadolinium Oxide ◽  
Image Guided Surgery ◽  
Guided Surgery ◽  
Image Guided ◽  
The Impact

AbstractCerenkov luminescence imaging (CLI) is a novel optical imaging technique that has been applied in clinic using various radionuclides and radiopharmaceuticals. However, clinical application of CLI has been limited by weak optical signal and restricted tissue penetration depth. Various fluorescent probes have been combined with radiopharmaceuticals for improved imaging performances. However, as most of these probes only interact with Cerenkov luminescence (CL), the low photon fluence of CL greatly restricted it’s interaction with fluorescent probes for in vivo imaging. Therefore, it is important to develop probes that can effectively convert energy beyond CL such as β and γ to the low energy optical signals. In this study, a Eu3+ doped gadolinium oxide (Gd2O3:Eu) was synthesized and combined with radiopharmaceuticals to achieve a red-shifted optical spectrum with less tissue scattering and enhanced optical signal intensity in this study. The interaction between Gd2O3:Eu and radiopharmaceutical were investigated using 18F-fluorodeoxyglucose (18F-FDG). The ex vivo optical signal intensity of the mixture of Gd2O3:Eu and 18F-FDG reached 369 times as high as that of CLI using 18F-FDG alone. To achieve improved biocompatibility, the Gd2O3:Eu nanoparticles were then modified with polyvinyl alcohol (PVA), and the resulted nanoprobe PVA modified Gd2O3:Eu (Gd2O3:Eu@PVA) was applied in intraoperative tumor imaging. Compared with 18F-FDG alone, intraoperative administration of Gd2O3:Eu@PVA and 18F-FDG combination achieved a much higher tumor-to-normal tissue ratio (TNR, 10.24 ± 2.24 vs. 1.87 ± 0.73, P = 0.0030). The use of Gd2O3:Eu@PVA and 18F-FDG also assisted intraoperative detection of tumors that were omitted by preoperative positron emission tomography (PET) imaging. Further experiment of image-guided surgery demonstrated feasibility of image-guided tumor resection using Gd2O3:Eu@PVA and 18F-FDG. In summary, Gd2O3:Eu can achieve significantly optimized imaging property when combined with 18F-FDG in intraoperative tumor imaging and image-guided tumor resection surgery. It is expected that the development of the Gd2O3:Eu nanoparticle will promote investigation and application of novel nanoparticles that can interact with radiopharmaceuticals for improved imaging properties. This work highlighted the impact of the nanoprobe that can be excited by radiopharmaceuticals emitting CL, β, and γ radiation for precisely imaging of tumor and intraoperatively guide tumor resection.

scholarly journals Improving PV Resilience by Dynamic Reconfiguration in Distribution Grids: Problem Complexity and Computation Requirements

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 830
Author(s):  
Filipe F. C. Silva ◽  
Pedro M. S. Carvalho ◽  
Luís A. F. M. Ferreira
Keyword(s):  
Distributed Generation ◽  
Optimal Solution ◽  
Dynamic Reconfiguration ◽  
Scheduling Problem ◽  
Low Carbon ◽  
Distribution Grid ◽  
Medium Voltage ◽  
Classical Computing ◽  
The Impact ◽  
Distribution Grids

The dissemination of low-carbon technologies, such as urban photovoltaic distributed generation, imposes new challenges to the operation of distribution grids. Distributed generation may introduce significant net-load asymmetries between feeders in the course of the day, resulting in higher losses. The dynamic reconfiguration of the grid could mitigate daily losses and be used to minimize or defer the need for network reinforcement. Yet, dynamic reconfiguration has to be carried out in near real-time in order to make use of the most updated load and generation forecast, this way maximizing operational benefits. Given the need to quickly find and update reconfiguration decisions, the computational complexity of the underlying optimal scheduling problem is studied in this paper. The problem is formulated and the impact of sub-optimal solutions is illustrated using a real medium-voltage distribution grid operated under a heavy generation scenario. The complexity of the scheduling problem is discussed to conclude that its optimal solution is infeasible in practical terms if relying upon classical computing. Quantum computing is finally proposed as a way to handle this kind of problem in the future.

scholarly journals Exploring optimal control of epidemic spread using reinforcement learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Abu Quwsar Ohi ◽  
M. F. Mridha ◽  
Muhammad Mostafa Monowar ◽  
Md. Abdul Hamid
Keyword(s):  
Reinforcement Learning ◽  
Economic Crisis ◽  
Transmission Rate ◽  
Optimal Solution ◽  
Short Length ◽  
Reproduction Rate ◽  
Economic Factors ◽  
Epidemic Spread ◽  
The Impact ◽  
Individual Human

AbstractPandemic defines the global outbreak of a disease having a high transmission rate. The impact of a pandemic situation can be lessened by restricting the movement of the mass. However, one of its concomitant circumstances is an economic crisis. In this article, we demonstrate what actions an agent (trained using reinforcement learning) may take in different possible scenarios of a pandemic depending on the spread of disease and economic factors. To train the agent, we design a virtual pandemic scenario closely related to the present COVID-19 crisis. Then, we apply reinforcement learning, a branch of artificial intelligence, that deals with how an individual (human/machine) should interact on an environment (real/virtual) to achieve the cherished goal. Finally, we demonstrate what optimal actions the agent perform to reduce the spread of disease while considering the economic factors. In our experiment, we let the agent find an optimal solution without providing any prior knowledge. After training, we observed that the agent places a long length lockdown to reduce the first surge of a disease. Furthermore, the agent places a combination of cyclic lockdowns and short length lockdowns to halt the resurgence of the disease. Analyzing the agent’s performed actions, we discover that the agent decides movement restrictions not only based on the number of the infectious population but also considering the reproduction rate of the disease. The estimation and policy of the agent may improve the human-strategy of placing lockdown so that an economic crisis may be avoided while mitigating an infectious disease.

scholarly journals Time-jerk optimal trajectory planning of hydraulic robotic excavator

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110346
Author(s):  
Yunyue Zhang ◽  
Zhiyi Sun ◽  
Qianlai Sun ◽  
Yin Wang ◽  
Xiaosong Li ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
High Efficiency ◽  
Optimal Solution ◽  
Optimal Time ◽  
Target Point ◽  
Robotic Excavator ◽  
The Impact ◽  
Optimal Trajectory Planning

Due to the fact that intelligent algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) are susceptible to local optima and the efficiency of solving an optimal solution is low when solving the optimal trajectory, this paper uses the Sequential Quadratic Programming (SQP) algorithm for the optimal trajectory planning of a hydraulic robotic excavator. To achieve high efficiency and stationarity during the operation of the hydraulic robotic excavator, the trade-off between the time and jerk is considered. Cubic splines were used to interpolate in joint space, and the optimal time-jerk trajectory was obtained using the SQP with joint angular velocity, angular acceleration, and jerk as constraints. The optimal angle curves of each joint were obtained, and the optimal time-jerk trajectory planning of the excavator was realized. Experimental results show that the SQP method under the same weight is more efficient in solving the optimal solution and the optimal excavating trajectory is smoother, and each joint can reach the target point with smaller angular velocity, and acceleration change, which avoids the impact of each joint during operation and conserves working time. Finally, the excavator autonomous operation becomes more stable and efficient.

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