iosr-JECE   Volume-17 ~ Issue-4 ~ Jul. - Aug. 2022

Abstract:With the increase of world population, the availability of food to all inhabitants on globe is one of the significant challenges. These challenges need to be addressed by adopting innovative options to improve the soil capacity and the safety of environmental resources. The availability of real-time vital parameters related to farming such as moisture, temperature, weather, and water management as well as predictive actions against the changes in parameters can provide great help to deal with these challenges. Internet of Things (IoT) is an evolving technology, has great potential to play and prevail its miraculous role in almost every field. IoT is a network........

Key Word: Node Mcu, Sensor Data, Iot based automation

[1]. Jaideep Nuvvula1, Srivatsa Adiraju2, Shaik Mubin2, Shahana Bano1, VenkataSubba Rao Valisetty1 Environmental smart Agriculture monitoring system using internet of things K L University, Department of ComputerScience and Engineering, GunturAndhra Pradesh, India. International Journal of Pure and Applied Mathematics Volume 115 No6 2017, 313-320
[2]. K. Jyostsna Vanaja1, Aala Suresh2, S. Srilatha3, K. Vijay Kumar4, M. Bharath5 IOT based Agriculture System Using Node MCU.
International Research Journal of Engineering and Technology (IRJET). Volume: 05 Issue: 03 | Mar-2018, e-ISSN: 2395-0056.
[3]. Wang N, Zhang N P, Wang M H. Wireless sensors in agriculture and food industry-Recent development and future Perspective [J]. Computers and Electronics in Agriculture, 2006.
[4]. Chan, M., Campo, E., Esteve, D., Fourniols, J.Y., "Smart homes-current features and future Perspectives," Maturitas, vol. 64, Issue 2, pp. 90-97, 2009..

Abstract: Wireless sensor networks (WSNs) have gotten a lot of interest from researchers in recent years because it plays an important role in a variety of applications. WSN's primary function is to process and transmit extracted data to remote destinations. A sizable number of sensor nodes are installed in the monitoring region. For research, it is imperative to deploy the fewest number of nodes necessary to maintain connectivity and full coverage. In addition to maximizing network lifetime, coverage and connectivity difficulties represented the primary concern to be taken into account in this survey. One of the key architectural challenges in creating a Wireless Sensor Network is maximizing the network lifetime (WSN).In recent years, numerous methods have been created to address this issue,........

Key Word: PSO, GA, HETEROGENEOUS, WSN, Multi-hop.

[1]. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "Wireless Sensor Networks:a survey," Computer Networks Journal, Elsevier Science, March 2002, vol. 38, pp. 393-422.
[2]. Ming Liu, Jiannong Cao, Wei Lou, Li-jun Chen and Xie Li, "Coverage Analysis for Wireless Sensor Networks," Lecture notes in Computer Science (LNCS)-3794, pp. 711-720, 2005, Springer-Verlag Berlin Heidelberg.
[3]. A. Ghosal, S. Halder, Md. Mobashir, R.K. Saraogi, S. DasBit, A Jamming Defending Data-Forwarding Scheme for Delay Sensitive Applications in WSN, Proc. Second Int'l Conf. Wireless Vitae'11, IEEE press (Best paper award), (2011), 1-5.
[4]. A. Ghosal, S. Halder, S. Sur, A. Dan, S. DasBit, Ensuring Basic Security and Preventing Replay Attack in a Query Processing Application Domain in WSN, Proc. Tenth Int'l Conf.
[5]. He T, Stankovic J A, Lu C, et al. A spatiotemporal communication prtotocol for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 2005, 16(10): 995-1006.

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 measurements.....

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: This study describes the design and simulation of a compact antenna based on a substrate integrated waveguide (SIW) utilizing the slot section technique. Using a full wave electromagnetic simulation tool HFSS based on Finite elements methods. The antenna is meant to operate in the millimeter wave (mm-wave) frequency bands. The radiating patch is printed on a 7.5 x 30 x 0.254 mm3 Rogers's RT 5880 substrate with a relative permittivity of 2.2 and a loss tangent of 0.0009. The antenna shows the multiband behaviour at the frequencies of 26 GHz, 28 GHz,30.7 GHz, and 32GHz. The various parameters of the proposed antenna such as return loss, VSWR and radiation pattern, gain and radiation efficiency etc. are investigated and analyzed.

Key Words: Multiband, SIW, Slotted

[1]. Giuseppe Venanzoni , DavideMencarelli , Antonio Morini, Marco Farina and Francesco Prudenzano, "Review of Substrate Integrated Waveguide Circuits for Beam-Forming Networks Working in X-Band", Appl. Sci. 2019, 9, 1003;
[2]. Deslandes, D.; Wu, K. Single-substrate integration technique for planar circuits and waveguide filters.IEEE Trans. Microw. Theory Tech. 2003, 51, 593–596.
[3]. Deslandes, D.; Wu, K. Accurate modelling, wave mechanism and design considerations of a substrateintegrated waveguide. IEEE Trans. Microw. Theory Tech. 2006, 54, 2516–2526.
[4]. Bozzi, M.; Georgiadis, A.; Wu, K. Review of substrate-integrated waveguide circuits and antennas.IETMicrow. Antennas Propag. 2011, 5, 909–920
[5]. Kumar S and De A, "Design and analysis of sinusoidally modulated substrate integrated waveguide and filters" Int. J of RF microwcomput Aided Eng.2022, 32(e), 22912,.

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......

Key Word: 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: Agricultural productivity is an important factor in the Indian economy. Therefore, the contribution of food and cash crops is very important to both the environment and people. Each year, crops succumb to several diseases. The diagnosis of such diseases is inadequate, and many plants die due to ignorance of the symptoms of the disease and its treatment. This is done using image processing techniques. A total of 15 cases were fed to the model, 12 of which were Bell Paper Bacterial Spot, Potato Early Bright, Potato Rate Bright, Tomato Target Spot, Tomato Mosaic Virus, Tomato Yellow Leaf Curl Virus, and Tomato Bacteria. Three cases of Spot, Tomato Early Bright, Tomato Late Bright, Tomato Leaf Mold, Tomato Septoria Leaf Spot and Tomato Spider Mite and Healthy Leaves: Bell Paper Healthy, Potato Healthy and Tomato Healthy. The test accuracy is 94.80%. Different performance matrices are derived for the same thing......

Key Word: Convolutional Neural Network (CNN), Leaf Disease, etc.

[1]. Sardogan, M., Tuncer, A., and Ozen, Y.: Plant Leaf Disease Detection and ClassificationBasedonCNNwiththeLVQAlgorithm.In:3rdInt.Conf.Comput.Sci.Eng.(2018)382–385
[2]. Wallelign, S., Polceanu, M., and Buche, C.: Soybean plant disease identification using aconvolutionalneuralnetwork.In:Proc.31stInt.FloridaArtif.Intell.Res.Soc.Conf.
[3]. FLAIRS2018(2018),146–151
[4]. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., and Stefanovic, D.: Deep NeuralNetworks Based Recognition of Plant Diseases by Leaf Image Classification. Comput.Intell.Neurosci. 2016(2016)
[5]. Fuentes, A., Yoon, S., Kim, S. C., and Park, D. S.: A robust deep-learning-based detectorforreal-timetomatoplantdiseasesandpestsrecognition.Sensors(Switzerland)17(2017).

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......

Key Word: 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

Abstract: In the future, hologram technology might support the learning process as it produces a three-dimensional (3D) picture. The 360° holographic display produces holographic images that can be viewed from any angle. A hologram will possess all the visual depth cues as if it were a real object. The actual size of models helps students to learn. Students are motivated by realistic reproductions. This paper tries to shed some more light on the original hologram technique that had developed gradually and rapidly to reach the main object in life, "education". this part made more attention to the varieties in education media and tried to close the distance.......

Keywords: 3D hologram, in-line hologram, off-axis hologram, Fourier plane hologram

[1]. Vacca, John R. "Holograms & Holography: Design, Techniques, & Commercial Applications, CHARLES RIVER MEDIA." (2001).
[2]. Hariharan, Parameswaran. Basics of holography. Cambridge university press, 2002.
[3]. Lugt, A. Vander. "Signal detection by complex spatial filtering." IEEE Transactions on information theory 10.2 (1964): 139-145.
[4]. Denisyuk, Yu N. "Photographic reconstruction of the optical properties of an object in its own scattered radiation field." Soviet Physics Doklady. Vol. 7. 1962.
[5]. Nadila, Nadila, et al. "Smart Three-Dimensional (3D) Hologram as an Innovative Teaching Tool in Virtual Learning Environment during Exigent Circumstances." Pancaran Pendidikan 10.4 (2021).