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A new disintegrated channelization approach for the backward pumped nonlinear distributed Raman amplifier is proposed by enacting the differential coupled equations of Raman amplification, in fiber, trailing by the solution of NLSE using the first integral method first time to the best of our knowledge. A nonlinear channel modeling-based simulation is adapted for the approximation of the Raman gain profile. To realize the real-time dynamic system, 16 multiplexed channels of prime C-band are employed in a QPSK/NRZ modulation environment with a 100 Gbps bit rate for optical network reconfiguration. A non-uniform gain is reported while the disparity in BER is observed from 10−12 to 10−8. [ABSTRACT FROM AUTHOR]

In this article, the proposed Distributed Raman Amplifier (DRA) simulator is designed on the MATLAB Simulink platform. 16 channels DWDM in C-band (1544–1559 nm) and 32 channels CWDM system in S-, C- and L band (1460–1625 nm) are executed with data rate of 100 Gb/s. The designed standalone system realizes the various effects that may arise at the receiver output due to reconfiguration of channels in real-time dynamic WDM optical traffic. Gain excursion assessment, and bit error rate (BER) are investigated for 200 km long Fiber cable and 45 km of Raman amplifier in co-pumping, counter pumping and bidirectional pumping propagation. [ABSTRACT FROM AUTHOR]

Industrial weight measurement is essential to monitoring and optimizing the quality and production cost. Fiber Bragg gratings (FBGs) are among the most widely used fiber optic sensors because they have a high sensitivity to temperature and strain fluctuations, making them ideal for various industrial applications. This paper proposes an optical weight measurement system that includes four FBG based load cells with a detailed analysis of their performance using different machine learning algorithms to improve accuracy and measurement range. Here, the shift in Bragg wavelength caused by strain generated on cantilever type load cell is acquired using an optical interrogator, giving the wavelength encoded FBG data. The data is acquired on PC, where specific Machine Learning models such as Linear Regression (LR), Support vector machines (SVMs), Neural networks (NN), Gaussian Process regression (GPR) are optimally selected and are utilized to directly learn the mapping between changes in load cell (Bragg wavelength) and applied weight. The experimental analysis shows that the GPR algorithm provides more accurate weight measurement than any other standard approach. The model ensures the practicality of the proposed method with improvement in measurement speed and accuracy of the FBG based weight measurement system. [ABSTRACT FROM AUTHOR]

Elastic optical network is the promising solution to improve spectrum efficiency by utilizing flex-grid optical orthogonal frequency division multiplexing technology and facilitating the flexible bandwidth allocation to services with heterogeneous demands. Future generation needs a reliable and fast transfer of information which leads to increase in demand bandwidth and therefore the necessity of optimized and improved spectrum management occurs. High resolution remote sensing have been adapted to sample growing crop in the hilly or high altitude field (like tea or apple bagan) to keep track of the crops growing in the field and also for the wild life monitoring in sanctuary or in national park to observe their health and behavior. One of the main challenges over here is the development of robust calibration and validation methodologies for transmission of these critical information through optical network for fast and reliable communication. In this article, authors have proposed Grey Wolf Optimization (GWO) based algorithm to provide the solution for the above mentioned challenge. The simulation results show that proposed approach provides significant improvement for spectrum efficiency with respect to reference methods. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm for multipath connection provisioning in elastic optical network (EON) has been proposed. Initially, the algorithm prefers the single-path routing for service provisioning. But when single-path routing is not adequate to serve a dynamic connection, the algorithm switches to the connection request fragmentation. Its computation is based on the parameters such as capacity_constant and capacity_allowed to fragment the connection request on disjoint paths. Simulation results clearly state that the proposed algorithm performs well in service provisioning as compared to the traditional single-path routing algorithms and improves the average network throughput. Thereafter, we have investigated the limitation of Erlang B traffic model in EON for calculation of link blocking probability using routing and spectrum assignment (RSA) algorithm. It is verified by the following two ways: (i) effect on the blocking probability in case of constant load and (ii) effect of slot width on the blocking probability. Our simulation results indicate that in EON due to dynamic RSA, blocking probability is not constant in case of proportionate varying of call arrival and service rate giving constant load and blocking probability depends on the number of slots per link, but in Erlang B traffic model blocking probability is always constant in case of constant load and it considers wavelength per link instead of slots per link. This is attributed to the fact that Erlang B traffic model fails to calculate blocking probability accurately in EON. We have computed the carried traffic on 14 nodes, 21-link National Science Foundation Network (NSFNET) topology. [ABSTRACT FROM AUTHOR]

In traditional networks, the pre-routed packets are dropped during the link failure leading to a huge data loss. Survivability techniques such as protection and restoration are available to provide the solution before and after the link failure. But, the new flow entries that are to be added in the flow table increase the initial network demand leading to an increase in memory demand per switch. The saved data not only reduces the network speed but also demands of repeated processing of entries. In this paper, we propose a new scheme of Group Table based Rerouting (GTR) technique to find the response against single link failure through Fast Fail-over (FF) group table feature provided by OpenFlow. This scheme provides equal roles for both controllers and forwarding OpenFlow enabled switches. Here, the controller maintains a look-up table which is updated periodically according to the change in network structure. Also, it has to update the FF group table simultaneously corresponding to every active port of the switches. The controller relabels the packets and updates the flow entries on respective switches. [ABSTRACT FROM AUTHOR]

Fiber Bragg Gratings (FBGs) are known for their high sensitivity towards strain and temperature variation. In this manuscript, the strain sensing property of FBG is utilized and explored for measuring weight using a common binocular shaped cantilever type load cell. The shift in Bragg wavelength due to load application is converted into optical power variation using edge filter detection technique. Furthermore, change in optical power is converted into voltage variation and is feed to the Raspberry Pi via analog to digital converter (ADC ADS1115) for real-time and continuous online monitoring of weights. The optical load cell has shown linear response for applied weight up to 8kg. Mechanical properties of optical load cell such as nonlinearity, hysteresis, repeatability, creep and safe over load are studied in detail. Message Queuing Telemetry Transport (MQTT) protocol is used to send data from Raspberry Pi to IoT cloud. The system has achieved resolution of 20g with error of ± 50g. [ABSTRACT FROM AUTHOR]

In recent years, vehicular ad hoc network (VANET) is an emerging technology for intelligent transportation system by providing its wireless network services to increase the demands of high data rate and traffic. VANET supports for various applications that include safety, vehicular distribution speed, traffic efficiency and information of data services. In this paper, we propose the stochastic method for multi-hop connectivity through vehicle to infrastructure and vehicle to vehicle in inter-vehicular communication system to improve the performance of the network. We have evaluated the performance analysis of the queuing system by measuring the traffic flow in highway lanes. In highway lanes, the collision between any two vehicles leads to traffic jam with heavy congestion in the highway lanes which are in the VANET system. Due to the stagnation of accumulated vehicles on the highway will further result in increasing the delay in clearance of the traffic on the road. We have evaluated the performance of the VANET system through the M/M/1 queuing model for analyzing parameters such as the end to end delay, waiting time and congestion on express highways at peak hours for multi-hop connectivity. [ABSTRACT FROM AUTHOR]

We have investigated the theoretical and principal analysis of cascaded electroabsorption modulators (EAMs) and Mach-Zehnder modulator (MZM). We have proposed a technique to generate an ultraflat and power efficient optical frequency comb (OFC) by the serial cascading of two EAMs and one single-drive MZM. Continuous wave (CW) light source is modulated and spectrum broadened by two EAMs and MZM, respectively. Here, EAMs in cascaded mode produce ultrashort pulses followed by MZM, which is introduced to perform intensity modulation and tuned the power variation of even and odd order sidebands at the same level for obtaining a flat optical spectrum at the output. The first EAM acts as a subcarrier generator and the latter one acts as a subcarrier enhancer, which is followed by the MZM acting as subcarrier flatter. By this proposed technique, we have generated 63 subcarriers with 10-GHz spacing and within 2-dB power fluctuation. The generated OFC has a bandwidth of 630 GHz. This technique generates OFC with an appreciable power level and flattened optical spectrum, which is very much essential in dense wavelength division multiplexing and elastic optical networks. [ABSTRACT FROM AUTHOR]

Traditional dense wavelength division multiplexing (DWDM) technology is no longer enough to meet the extensive growth in the demand of bandwidth in an efficient and effective manner under the fixed grid environment. Thus, a technology is needed to overcome the challenges raised by the ever-increasing demand of bandwidth in an effective manner with efficient utilization of spectral resources. To solve this problem, an elastic optical network (EON) paradigm comes into existence. It uses a flexible grid technology that removes the limitations of fixed grid DWDM technology and increases the channel capacity to meet current bandwidth demand with proper utilization of optical spectrum. We present the evolution of EON from the DWDM network. We have discussed the main components and characteristics of EON that includes superchannel and sliceable bandwidth variable transponder (SBVT). Thereafter, we discuss the different modulation and transmission techniques used in EON. Next, we present the recently proposed architectures of SBVT followed by its applications. Lastly, we have discussed various subcarrier generation techniques in brief. [ABSTRACT FROM AUTHOR]

• An advanced deep learning model is proposed to predict the quality of transmission (QoT) (Q-factor, chromatic dispersion) of a light-path. • The proposed model was tested on a data-set that contains data extracted from Microsoft's optical backbone network in North America. • The performance of the proposed model is compared with reference approaches viz; SVM, MLP, NN, and LSTM to show if such models could be considered for predicting the estimated received Q-factor and chromatic dispersion of established light-paths. • Results exhibit that the proposed model achieves the best performance across multiple channels of this data-set and even in a case when the size of the training data-set is small. Increasing traffic demand in the backbone optical network urges the initiation of new solutions that allow an increment in the transmission/network capacity without a considerable rise in service costs. To attain this objective, simpler and quicker network reconfiguration processes is required. A significant issue intrinsically correlated with network reconfiguration is the quality of transmission (QoT) (BER, SNR, Q-factor, chromatic dispersion and polarization mode dispersion) estimation. Predicting the QoT of already established light-paths enable network operators to respond proactively to performance deterioration, which helps in enhancing or sustaining quality of service (QoS). Recently, several deep learning-based approaches have been presented to estimate the QoT of an optical network. This paper proposes an advanced deep learning model comprising a long short-term memory (LSTM) network along with three fully connected layers that leverage the transmission quality metrics collected from the network's control plane to predict QoT of light-path. The three additional fully connected layers help the model learn the more complex patterns in the data that a basic LSTM cannot learn on its own and improve the model's performance by three orders of magnitude. Moreover, the proposed model doesn't require a large data-set to provide high accuracy results, which decreases the control plane's load. Our model was tested on a data-set that contains data extracted from Microsoft's optical backbone network in North America. We compare the performance of the proposed model with reference approaches viz; SVM, MLP, NN, and LSTM to show if such models could be considered for predicting the estimated received Q-factor and chromatic dispersion of established light-paths. Results exhibit that the proposed model achieves the best performance across multiple channels of this data-set and even in a case when the size of the training data-set is small. The proposed model provides high prediction accuracy, which is perfect for the next-generation agile optical network. [ABSTRACT FROM AUTHOR]

• Direct intensity modulation based edge filter detection technique is proposed. • The proposed technique increases system performance of weight measurement system. • It increases the sensitivity and resolution of the system up to two times. • The proposed technique can be integrated with any EFD interrogation system. Fiber Bragg Gratings (FBGs) are extensively used as strain and temperature sensors in various industrial applications. The key task for FBG based sensing system is to convert the optical sensing data into user friendly information that can be interpreted with ease. Edge filter detection (EFD) interrogation technique is commonly used to convert wavelength encoded sensing data into optical power variation. This technique is simple and cost effective as compared to other interrogation technique. While using FBG as both edge filter and sensing element the sensitivity of the system depends upon the slope of its reflection or transmission spectrum. Therefore, there is always a tradeoff between sensitivity and measurement range in this interrogation technique. Direct Intensity Modulation (D-IM) based EFD is proposed in this manuscript, where a tunable laser source is modulated using different message signals and is tuned in the linear region of FBG transmission spectrum. This technique observes the change in amplitude of message signal at the photodetector output due to shift in Bragg wavelength. Later the proposed technique is utilized in optical load cell based weight measurement system to observe the system performance. Maximum sensitivity of 196.7 mV/kg and SNR of 63 dB is obtained by using the proposed technique. Decrease in percent error with enhancement in system resolution is observed, as compared to previously reported conventional EFD technique based optical weight measurement. The proposed technique has shown significant improvement in system performance with nominal increase in system complexity that makes this technique more effective for industrial applications. [ABSTRACT FROM AUTHOR]