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.

ABSTRACT: An eigenmode projection technique is utilized to solve the problems ofthe electromagnetic wave propagation in shielded transmission lines. The technique isadopted to solve theproblems ofinfinite length rectangular shaped loaded lineswhere a fictitious canonical cavity surrounded by perfect electric surfaceis chosen to enclose the line and the fields inside are expanded in terms ofthe cavitysolenoidal and irrotational eigenmodes where they are considered as a complete set torepresent any vector field inside the cavity. The fields in Maxwell's equations inside theenclosed region are then expanded using the cavity eigenmodes. Finally, a set of equationsfor the eigenmodes are resulted by using thefields expansions in Maxwell's equations ofthe cavity where mode projections are done. This set of equations aresolved together toget the line dispersion curve and the propagating modes.

Index Terms: Eigenmodes; Resonance; Transmission Lines; Microstrip Lines; Coplanar Waveguides

[1]. L. Rayleigh, "On the passage of electric waves through tubes," PhilosophicalMagazine, vol. 43, 1897.
[2]. K. S. Packard, "The origin of waveguides: A case of multiple rediscovery," IEEETransactions on Microwave Theory and Techniques, MTT-32, Sep. 1984.
[3]. D. M. Pozar, "Microwave engineering," in, 4th ed. John Wiley and Sons, Inc., 2012,ch. 3.
[4]. C. A. Balanis, Advanced Engineering Electromagnetics. John Wiley and Sons, Inc.,2012.
[5]. R. M. Barrett, "Microwave printed circuits a historical survey," IRE Trans. Microwave Theory Tech., MTT-3, Mar. 1955.

ABSTRACT: This paper aimed at a comparative study of the performance of the Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms of the adaptive beam forming antenna on the CDMA based network. The smart antenna test-bed used include a single input multiple output system which consists of one transmitter and six receivers. During experimentation, the interference and noise reduction capabilities of the adaptive antenna were investigated using the LMS and RLS algorithms. Also, the two adaptive algorithms were simulated and evaluated for a 6 uniform linear array elements with inter-element spacing of 0.5λ on the CDMA based network using MATLAB version 7.5. In the simulation, the angle of arrival of the desired signal and the interfering signal were at 30˚ and 60˚ respectively.........

Keywords: Smart antenna, adaptive beamforming, RLS algorithm, LMS algorithm , adaptive antenna, antenna

[1]. Applebaum, S.(2006). "Adaptive Arrays", IEEE Transactions on Antenna array, Vol 24,No 5, pp 585-598.
[2]. Boukalov, A. (2009),"Introduction to smart Antenna Technologies and Algorithms", workshop on smart Antenna Technology and Application, RAWCON, Pg 62-70.
[3]. Garg V.K., (2000) "CDMA and CDMA2000, Cellular/PCS System Implementation", Prentice Hall PTR, Pg 86.
[4]. Ifeagwu E.N, (2015), "Analysis of Least Mean Square Adaptive Beam forming Algorithm of the Adaptive Antenna for Improving the Performance of the CDMA20001X Base Mobile Radio Network"
[5]. Nwalozie G.C, Okorogu V.N., Maduadichie S.S., Adenola A (2013), " A simple comparative Evaluation of Adaptive beamforming Algorithms" International journal of Engineering and Innovative Technology.Vol2, Issue 7, Pg 417-424