iosr-JVSP   Volume-12 ~ Issue-6 ~ Nov. - Dec. 2022

ABSTRACT: The need for high capacity long haul telecommunication system to carry huge traffic demands in recent times has lead to the use of optic fiber communication system because of its high capacity carrying advantage over wireless systems. But optic fiber signals suffer some signal impairment issues such as nonlinearity which tends to degrade its transmission performance. This paper proposed the use of adaptive optical equalizer to mitigate such impairments. To achieve that, a simulink model of the system was first developed for simulation experiments. Then the impact of out-of-bound nonlinear signal on the three key performance indicators (Q Factor, Bit Error Ratio and Eye Height) studied was evaluated. An adaptive optical equalizer system was them applied to the network and......

Keywords: Nonlinear optic fiber, self-phase modulation, Kerr effect, refractive index, nonlinearity mitigation

[1]. Paul E. and Green, Jr, 2003 " Fiber Optic Networks", Prince Hall, Englewood Cliffs, New Jersey,
[2]. Agrawal, G. P.,2001, "Nonlinear Fiber Optics", 3rd edition, Academic Press, San Diego, CA, 2001.
[3]. Poggiolini, P.; Jiang, Y. "Recent Advances in the Modeling of the Impact of Nonlinear Fiber Propagation Effects on Uncompensated Coherent Transmission Systems". J. Lightw. Technol. 2017, 35, 458–480. [CrossRef]
[4]. Golani, O.; Feder, M.; Shtaif, M. Kalman, "MLSE equalization of nonlinear noise". In Proceedings of the 2017 Optical Fiber Communications Conference and Exhibition (OFC), Los Angeles, CA, USA, 19–23 March 2017; Optical Society of America: Washington, DC, USA, 2017.
[5]. Golani, O.; Elson, D.; Lavery, D.; Galdino, L.; Killey, R.; Bayvel, P.; Shtaif, M. "Experimental characterization of nonlinear interference noise as a process of intersymbol Interference". Opt. Lett. 2018, 43, 1123–1126..

ABSTRACT: In this work, image processing and deep learning mechanisms are used to locate and classify the White Blood Cells based on their categories. The White Blood Cells which are classified are counted and compared with the standard range of the types available in the human blood sample. By comparing the availability of White Blood Cells types, the normal and the abnormal blood samples are predicted accordingly. The dataset of the normal blood sample is obtained from the laboratory in biotechnology department and the datasets used for training in Convolutional Neural Network are attained from the website Leukocyte Images for Segmentation and Classification (LISC). This will increase efficiency and reduce the doctor's burden as traditional manual counting is dull, tedious, and possibly subjective. Background: White Blood Cells (WBCs) are also called leukocytes or.....

Keywords: Magnetic Resonance Imaging (MRI), Convolutional Neural Network (CNN), Blood Cells, etc

[1]. T. Rosyadi, A. Arif, Nopriadi, B. Achmad and Faridah, "Classification of Leukocyte Images Using K-Means Clustering Based on Geometry Features," in 6th International Annual Engineering Seminar (InAES), Yogyakarta, Indonesia, 2016.
[2]. N. M. Salem, "Segmentation of White Blood Cells from Microscopic Images using K-means clustering," in 2014 31st National Radio Science Conference (NRSC), 2014.
[3]. A. Gautam and H. Bhadauria, "White Blood Nucleus Extraction Using K-Mean Clustering and Mathematical Morphing," in 5th international Conference- Confluence the Nect Generation Information Technology Summit (Confluence), 2014.
[4]. O. Ryabchykov, A. Ramoji, T. Bocklitz, M. Foerster, S. Hagel, C. Kroegel, M. Bauer, U. Neugebauer and J. Popp, "Leukocyte subtypes classification by means of image processing," Proceedings of the Federal Conference on Computer Science and Information Systems, vol. 8, no. 2300-5963, pp. 309-316, 2016.
[5]. Kroegel, M. Bauer, U. Neugebauer and J. Popp, "Leukocyte subtypes classification by means of image processing," Proceedings of the Federal Confeence on Computer Science and Information Systems, vol. 8, no. 2300-5963, pp. 309-316, 2016

ABSTRACT: The RAW (Restricted Access Window) component of the IoT network is deployed to reduce traffic and channel contention in dense and heterogeneous sensor network environment. It divides sensor nodes into groups and slots, allowing channel access only to one RAW slot at a time. Several algorithms and improved channel utilization optimization models have been proposed to optimize the RAW parameters, to ensure a contention free network or at least, minimally reduce it. These techniques often rely on previous traffic demands schedules, collision analysis and send/receive matrices to accurately predict the future of stations' interactions in an IoT environment. Thus systematically adjusting its operations to reduce contention among the stations and the Access Point(AP), thereby ensuring....

Keywords: Resource allocation, station, network, nodes, simulation

[1]. Tian, L., Famaey, J., &Latre, S, "Evaluation of the IEEE 802.11ah Restricted Access Window mechanism for dense IoT networks", IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), PP-1-10, 2016.
[2]. Slaoui, S. C., Dafir, Z., &Lamari, Y. (2018). E-Transitive: an enhanced version of the Transitive heuristic for clustering categorical data. Procedia Computer Science, 127, 26–34.
[3]. Gohar, A., Kyong H.K., & Ki-II, K. (2002). Adaptive TDMA Scheduling for Real-Time Flows in Cluster-Based Wireless Sensor Networks. Computer Science and Information System 13(2):475-492.
[4]. U., S., & A. V., B. (2018). Performance analysis of IEEE 802.11ah wireless local area network under the restricted access window-based mechanism. International Journal of Communication Systems, e3888
[5]. Michael Collins (2015). The Forward-Backward Algorithm. International Journal of Communication System, Volume 32, Issue 7, PP-1-20, 2019.