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Electronic wallet technology provides efficiency, flexibility, and security in every electronic transaction. OVO is one of several electronic wallets in Indonesia. Other electronic wallets in Indonesia are Jenius, Gopay, and Dana. This research aims to examine the usability parameters of OVO electronic wallet application using usability testing method and usefulness, satisfaction, and ease of use questionnaire approach. This research begins with some user reviews on google playstore mention that using OVO application to make payments is considered less effective and efficient. Data in usability testing collected through task testing and interviews with 9 respondents. Usefulness, satisfaction, and ease of use questionnaire approach done by distribute the questionnaire to 100 respondents. Qualitative data is presented by descriptive sentences, while quantitative data is processed by statistically and mathematically using tools such as Microsoft Excel and SPSS software. Results of this research are values for 7 parameters. The process status parameter has a success rate of 86 percent, while the process time parameter has an efficiency of 0.03 goals per second. Interview results represent the problem parameters by showing OVO applications deficiency on notification side, login method, and interface layout consistency. Usability of OVO applications according to usefulness, satisfaction, and ease of use questionnaire results is 81.46 percent. The parameters of usefulness, ease of use, ease of learning, and satisfaction produce a good value for OVO applications.

Electronic wallet, Usability testing, Usefulness satisfaction and ease of use questionnaire, User experience, OVO

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[2] K. Aelani, Falahah, "Pengukuran Usability Sistem Menggunakan USE Questionnaire (Studi Kasus Aplikasi Perwalian Online STMIK "AMIKBandung")", In the Proceedings of the 2012 Seminar Nasional Aplikasi Teknologi Informasi (SNATI 2012), Indonesia, 2012.
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[6] K. A. Meshram, M. Puri, "Usability testing of Moodle application in the Context of M-learning in HE in India with Special Reference to MBA and MCA courses", International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.21-27, 2019.
[7] K. Jaganeshwari, S. Djodilatchoumy, "A Study on Quality and Reliability of websites using Functional testing and Usability testing", International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.344-348, 2018.
[8] S. W. Ningrum, I. Akrunanda, A. R. Perdanakusuma, "Evaluasi dan Perbaikan Usability Aplikasi Mobile Ojesy Menggunakan Metode Usability Testing dan USE Questionnaire", Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol.3, Issue.5, pp.4825-4834, 2019.

Handika Cahyo Prakoso, Mohammad Iqbal, Ana Kurniawati, "Usability Evaluation OVO Electronic Wallet Application Using Usability Testing and Usefulness, Satisfaction, Ease Of Use Questionnaire," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.1-6, 2020.

ChatBot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in instead of providing direct contact with a live human. However, An FAQ (Frequently Asked Questions) chatBot is a type of internet bot or software application that is beneficial for answering some of the most frequently asked questions your customers may have. FAQ bots help direct customers to the right website pages and provide answers easily any time of the day. In this paper, we present the architecture and prototype of a FAQ chatBot

ChatBot; Communication; Pattern Matching; Request; Response, FAQ

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[2] Y. Zhou, X. Ziyu, A. W. Black, A. I. Rudnicky, “ChatBot Evaluation and Database Expansion via Crowdsourcing”, Proc. Of the ChatBot Workshop of LREC, US, pp. 16-19, 2016.
[3] C. R. Anik, C. Jacob, A. Mohanan, “A Survey on Web Based Conversational Bot Design”, JETIR, Vol.3, Issue.10, pp. 96-99, 2016.
[4] J. Jia, “The Study of the Application of a Keywords-based ChatBot System on the Teaching of Foreign Languages”, Report of University of Augsburg, Augsburg, pp.1-36, 2003.

Farhana Sethi , "FAQ (Frequently Asked Questions) ChatBot for Conversation," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.7-10, 2020.

Industrialization ,and urbanization lead to a change in land-use patterns and an increase in the utilization of water resources. In biogeochemical cycles, it requires good estimates of the areal extent and shape of water bodies. So timely monitoring of surface water and delivering data on the dynamics of surface water are essential for policy and decision-making processes. Change detection based on multispectral and multi-temporal remote sensing data is one of the most acceptable and ever-growing surface water change detection mechanisms in recent years. In this paper, a study has been conducted and we present an automated procedure that allows extraction of water body from a multispectral image based on its spectral data and spatial information.

Multispectral Image, Spectral Data, Spatial Data, Remote Sensing Data, Water Body Extraction, Bio Geochemical cycles.

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Devashri Jalwadi, "Water Body Extraction from Multispectral Image Based on Spectral and Spatial Data," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.11-16, 2020.

Traffic light control has been a significant test in most major roads in Nigeria. The control of traffic has been so poor in certain spots in Nigeria to such an extent that more timing is being distributed to zones with lesser vehicles while little timing is being allotted to zones of more vehicles. This paper presents an Agent-based system to determine the control of traffic light signals using Reinforcement Learning algorithm by applying Deep Q Learning Techniques. The Reinforcement learning algorithm was trained using a Deep Q-learning technique with a total of 4 input layers, a batch size of 100, learning rate of 0.001 and a training epoch of 800 and a gamma of 0.97. The learning environment was made up with a maximum number of steps of 5400, total numbers of car generated to be 1000, green light duration in 10, yellow light duration to be 4. The number of actions taken by the agent equals 4 on 80 different states. The system helps in reducing traffic congestion by adapting to the learning environment, therefore knowing lanes with more vehicles during and without rush hours. By this, system optimizes the green time effectively by allocating more time to lane with more vehicles during and with rush hours, therefore, reducing the average cumulative delays and average cumulative queued length of vehicles. The result showed that system is efficient in traffic signal control with an average queued vehicle length of 5 to 20 vehicle

Reinforcement learning, Deep Learning, Traffic, Agent, Environment, Stimulation

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[4].G. Wade and R. Saiedeh “Asynchronous n-step Q-learning adaptive traffic signal control”, Journal of Intelligent Transportation Systems, Vol.23 issue.4, pp.319-331, 2019.
[5]. J. Kim, J. Sangchul, K. Kwangsik, L. Seungjae “The Real-time Traffic Signal Control System for the Minimum Emission using Reinforcement Learning in V2X Environment” Intellian Association of Chemical Engineering Vol. 72, pp.91-96, 2019.
[6]. G. Wade and R. Saiedeh “Policy Analysis of Adaptive Traffic Signal Control Using Reinforcement Learning” Journal of Computing in Civil Engineering Vol.34 issue 1 pp.1-5, 2020.
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O.E. Taylor, P. S. Ezekiel, V.T. Emmah, "An Agent-Based Traffic Signal Control Using Reinforcement Learning Algorithm," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.17-22, 2020.

In this work, we aim to detect and classify different dynamic textures representing scenes of outdoor and indoor environments from video sequences. These scenes constitute the vast majority of events in the world, and their detection offers a wide range of applications. Optical flow is one of the most popular methods for motion estimation due to its efficiency and low computational cost. It is based on the brightness constancy assumption, which assumes a constant brightness of the objects between each two frames over time. However, this assumption is not always verified for dynamic textures with non-uniform surface brightness, due to reflections, shadows, transparency or material diffusion. As an alternative, we propose a new flow estimation method based on texture constancy assumption, which describes the spatial texture components motion. The spatial texture of each point of the image, computed using the LBP operator, is assumed to be constant over time. The resulting flow is called texture flow. From its velocity vectors, we extract the magnitude and orientation, which we combine with the texture spatial features to form a shallow hybrid spatiotemporal descriptor. Experimental results on a benchmark database demonstrate both the ability of our method to distinguish between different types of dynamic textures, and its stability with respect to inter and intra-class differences.

Dynamic texture, Flow estimation, Motion analysis, Texture constancy, Spatiotemporal descriptors

[1] D. Chetverikov and R. Péteri, "A brief survey of dynamic texture description and recognition," In: Computer Recognition Systems. Springer, p. 17–26, 2005.
[2]G. Doretto, E. Jones and S. Soatto, "Spatially homogeneous dynamic textures," In: Computer Vision-ECCV 2004. Springer, pp. 591-602, 2004.
[3] P. Turaga, R. Chellappa and A. Veeraraghavan, "Advances in video-based human activity analysis: challenges and approaches," Advances in Computers, vol. 80, pp. 237-290, 2010.
[4] R. Péteri, S. Fazekas and Mark J. Huiskes, "DynTex: A comprehensive database of dynamic textures," Pattern Recognition Letters, vol. 31, Issue. 12, pp. 1627-1632, 2010.
[5]F. Yang, G.-S. Xia, G. Liu, L. Zhang and X. Huang, "Dynamic texture recognition by aggregating spatial and temporal features via ensemble {SVMs}," Neurocomputing, vol. 173, pp. 1310-1321, 2016.
[6]G. Doretto, A. Chiuso, Y. Wu and S. Soatto, "Dynamic Textures," International Journal of Computer Vision, vol. 51, Issue. 2, pp. 91-109, 2003.
[7] A. Chan and N. Vasconcelos, "Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures," IEEE transactions on pattern analysis and machine intelligence, vol. 30, Issue. 5, pp. 909-926, 2008.
[8] T. Ojala, M. Pietikainen and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," Pattern Anal. Mach. Intell., vol. 24, Issue. 7, p. 971–987, 2002.
[9] O. Lahdenoja, J. Poikonen and M. Laiho, "Towards understanding the formation of uniform local binary patterns," ISRN Machine Vision, 2013.
[10] Z. Guo, L. Zhang and D. Zhang, "A completed modeling of local binary pattern operator for texture classi?cation," IEEE Transactions on Image Processing, vol. 19, Issue. 6, p. 1657–1663, 2010.
[11] D. Tiwari and V. Tyagi, "Dynamic texture recognition based on completed volume local binary pattern," Multidimensional Systems and Signal Processing, vol. 27, pp. 563-575, 2016.
[12]L. Liu, L. Zhao, Y. Long, G. Kuang and P. Fieguth, "Extended local binary patterns for texture classification," Image and Vision Computing, vol. 30, p. 86–99, 2012.
[13] R. Maurya, "Melanoma Detection Using Modified Extended LBP," International Journal of Computer Sciences and Engineering, vol. 6, Issue. 7, pp. 698-703, 2018.
[14]G. Zhao and M. Pietikäinen, "Dynamic texture recognition using volume local binary patterns," In: Dynamic Vision. Springer, pp. 165-177, 2007.
[15]G. Zhao and M. Pietikäinen, "Dynamic texture recognition using local binary patterns with an application to facial expressions," IEEE transactions on pattern analysis and machine intelligence, vol. 29, Issue. 6, pp. 915-928, 2007.
[16] C.-H. Peh and L.-F. Cheong, "Synergizing spatial and temporal texture," IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, pp. 1179-1191, 2002.
[17]R. Péteri and D. Chetverikov, "Dynamic Texture Recognition Using Normal Flow and Texture Regularity," Iberian Conference on Pattern Recognition and Image Analysis, pp. 223-230, 2005.
[18] S. Fazekas and D. Chetverikov, "Dynamic texture recognition using optical flow features and temporal periodicity," In: 2007 International Workshop on Content-Based Multimedia Indexing, pp. 25-32, 2007.
[19] S. Fazekas, T. Amiaz, D. Chetverikov and N. Kiryati, "Dynamic Texture Detection Based on Motion Analysis," International Journal of Computer Vision, vol. 82, pp. 48-63, 2009.
[20]J. Chen, G. Zhao, M. Salo, E. Rahtu and M. Pietikainen, "Automatic dynamic texture segmentation using local descriptors and optical flow," IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, vol. 22, Issue. 1, pp. 326-339, 2013.
[21] R. Chaudhry, A. Ravichandran, G. Hager and R. Vidal, "Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions," In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1932-1939, 2009.
[22]J. Pers, V. Kenk, M. Kristan, M. Perše, K. Polanec and S. Kova?i?, "Histograms of optical flow for efficient representation of body motion," Pattern Recognition Letters, vol. 31, pp. 1369-1376, 2010.
[23] S. S and A. E. Mendonca, "Unusual Events Detection via Global Optical Flow and SVM," International Journal of Computer Sciences and Engineering, vol. 4, Issue. 3, pp. 179-183, 2016.
[24]V. Kaltsa, K. Avgerinakis, A. Briassouli, I. Kompatsiaris and M. Strintzis, "Dynamic texture recognition and localization in machine vision for outdoor environments," Computers in Industry, vol. 98, 2018.
[25] B. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision (IJCAI)," Proceedings of Imaging Understanding Workshop, pp. 121-130, 1981.
[26] N. Papenberg, A. Bruhn, T. Brox, S. Didas and J. Weickert, "Highly Accurate Optic Flow Computation with Theoretically Justified Warping," International Journal of Computer Vision, vol. 67, pp. 141-158, 2006.
[27] Y. Mileva, A. Bruhn and J. Weickert, "Illumination-Robust Variational Optical Flow with Photometric Invariants," Pattern Recognition, pp. 152-162, 2007.
[28]P. Mettes, R. T. Tan and R. C. Veltkamp, "Water detection through spatio-temporal invariant descriptors," Computer Vision and Image Understanding, vol. 154, pp. 182-191, 2017.

A. Mançour-Billah, E. H. Ait Laasri, A. Abenaou, D. Agliz, "Dynamic Texture Detection using Flow Estimation based on Texture Constancy," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.23-30, 2020.

This paper presents a design and development of slot and stub loaded multi band and triple notch band tunable monopole circular microstrip antenna for different microwave communication applications. The antenna is loaded with U-slot, two identical I-slots and J-slot over the radiating patch .When the spacing between two identical I-slots is 0.21 and 0.11cm then the antenna tunes to the four required multi bands with three alternate notch bands. This tunable antenna is the replacement to the broadband antenna. The single antenna can be used to tune different microwave applications over a large range of frequencies. Thus, the antenna produces four useful bands with three alternate notch bands. The four useful bands tunes from 1.675-1.6525GHz, 2.53-2.71GHz, 5.095-5.4775GHz and 79075-7.0975GHz and three alternate notch bands tunes from 2.2375-2.3725GHz, 4.555-4.4875GHz and 5.7475-6.0625GHz respectively. These notch bands resolve the problem of crowding in the frequency spectrum and rejects Wi-Fi, UMTS, LTE, Wi-MAX, WLAN and C band frequencies. The maximum impedance bandwidth of proposed antennas for two spacing between identical I-slots is found to be 37.79 % and 48.33 % having peak gain of 2.14 dBi and 2.39 dBi respectively. The radiation patterns are omni directional in nature in both E and H plane.

IBW, Notch bands, slots and stubs

[1]. R.C. Jain , M. M. Kadam, “A Novel Design of Compact Planner UWB Antenna with Multiple Band Rejection” , International Journal of Scientific Research in Network Security and Communication. Vol.5, No. 2. May 2017.
[2]. Anukriti Chauhan, B K Singh, R P S Gangwar and Shakti Singh, “Penta band Slotted Microstrip Patch Antenna for Wireless Applications”, International Journal of Computer Sciencess and Enginering”, Vol.2, Issue 11, pp.1-5, Nov. 2014.
[3]. Balanis C. A., “Antenna theory, Analysis and design” , 2nd Ed, Wiley, New York, 1997.
[4]. Garg, R. P. Bhartia , I. Bhal and Hipibon, “Microstrip Antenna Design Hand Book” , Artech Inc., 2001.
[5]. A. E. Daniel & G. Kumar, “Tunable dual & triple frequency stub loaded rectangular microstrip antenna (MSA)” , Proc. IEEE antennas propagation symposium, pp. 2140-2143, 1995.
[6]. G. Kumar and K. P. Ray, “Broad Band Microstrip Antennas” , Artech House, 2003.
[7]. Y. S. Li and W. X. Li, “A Circular Slot Antenna with Wide Tunable and Reconfigurable Frequency Rejection Characteristic Using Capacitance Loaded Split-Ring Resonator for UWB Applications” , Wireless Personal Communications, Vol. 78, No.1, pp.137-149, March 2014.
[8]. P. Tilanthe1, P. C. Sharma and T. K. Bandopadhyay, “A Monopole microstrip antenna with enhanced dual band rejection for UWB applications” , Progress In Electromagnetics Research B, Vol. 38, pp.315-331, 2012.
[9]. K. G. Jangid, Ajay Tiwari, Vijay Sharma, V.S. Kulhar, V. K. Sexena and D. Bhatnagar, “Circular patch antenna with defected ground for UWB communication with WLAN band rejection”, Defence Science Journal, Vol.66, No. 2, pp.162-167, March 2016.
[10]. Jia-wei Dai, Hong Li Peng, Yao-ping Zhang and Jun Fa Mao, “A tunable microstrip patch antenna using liquid crystal” , Progress In Electromagnetics Research C , Vol.71, pp.101-109, 2017.
[11]. K. P. Ray, S. Nikhil and A. Nair, “Compact Tunable and Dual band Circular Microstrip Antenna for GPS Bluetooth Applications”, International Journal of Microwave and Optical Technology, Vol.4, No.4, pp.205-210, July 2009.
[12]. D. Sarkar, K. V. Srivastava and K. Saurav, “A Compact Microstrip-Fed Triple Band-Notched UWB Monopole Antenna” , IEEE Antennas and Wireless Propag. Lett., Vol.13, pp.396-399, 2014.
[13]. Yingsong Li, Wenxing Li and QiuboYe, “A Reconfigurable Triple Notch-band Antenna integrated with defected Microstrip structure band stop filter for Ultra-Wideband cognitive Radio applications” , International Journal of Antenna and Propagation , Vol. 2013, pp. 1-14, 12 June 2013.
[14]. V. Harsha,Ram Keerthi, Dr. Habibullah Khan and Dr. P. Srinivasulu, “Design of C-band Microstrip Patch Antenna for Radar Applications using IE3D” , Vol.5, Issue. 6, pp. 49-58, Mar-Apr. 2013.
[15]. Kosuru Murthy, Kodidasu Umakantham and Korlapati Satyanarayana Murthy, “Reconfigurable Notch band Monopole slot antenna for WLAN/IEEE-802.11n applications” , International Journal of Intelligent Engineering and systems, Vol.10, No.6, pp.166-173, 2017.
[16]. Y. S. Li and W. X. Li, “A Circular Slot Antenna with Wide Tunable and Reconfigurable Frequency Rejection Characteristic Using Capacitance Loaded Split-Ring Resonator for UWB Applications” , Wireless Personal Communications, Vol. 78, No.1, pp.137-149, March 2014.

Biradar Rajendra, S.N. Mulgi, "Multi band and Triple Notch band Tunable Monopole Circular Microstrip Antenna for Wireless Applications," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.31-35, 2020.

This paper provides a model-based tracking framework for classifying the leukocytes in bone marrow images. Detecting and classifying the leukocytes is an established problem in image analysis and machine learning. This system aims to automate the process of detecting, differentiating and classifying the leukocytes. The process of automatic recognition requires the extraction of individual cells, generation of features and finally the classification using ARTMAP Neural Network classifier. For many illnesses, classifying the blood cell is used as a diagnostic technique in the detection of many illnesses, particularly leukemia. This system uses two phase methodology. The first phase includes the preprocessing methods of the bone marrow images. The second phase, classifies these features using pattern recognition techniques into a number of given families. This system uses the ARTMAP Neural Network classifiers for characterizing different types of cells.

Classifier, ARTMAP, Neural Network

[1] Farhad Pourpanah1 •Chee Peng Lim2 • Qi Hao1 ,“A reinforced fuzzy ARTMAP model for data classification”, International Journal of Machine Learning and Cybernetics, June 2018
[2] Priyanka Banerjee, Souvik Sur, Kashi Nath Dey, Samir Kumar Bandyopadhyay, “A Proposed method for fruit grading from fruit images using SVM”, International Journal of Computer Science and Engineering, Vol 8, Issue 3, March 2020
[3]Deepak Pandey, Amit Yerpude, Toran Verma, “Dental Biometrics for Human Identification using Bag of Features”, International Journal of Computer Science and Engineering, Vol 6, Issue 6, June 2018
[4] Farhad Pourpanaha , Ran Wanga,b, Chee Peng Limc , Xizhao Wang d, Manjeevan Seera e , Choo Jun Tan,” An improved fuzzy ARTMAP and Q-learning agent model for pattern classification”,June 2019
[5] Nimesh Patel, Ashutosh Mishra, “Automated Leukemia Detection using Microscopic Images”, ISSN 1877-0509
[6] Hemad Heidari Jobaneh,” Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping”, ISSN: 2637-5672 (Print); ISSN: 2637-5680 (Online)
[7] Gail a. Carpenter, Stephen Grossberg and John h. Reynolds, “ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network”, Neural Networks, Vol. 4, pp, 565-588, 1991.
[8] Carolina Reta1,Leopoldo Altamirano1, Jesus A. Gonzalez1, Raquel Diaz-Hernandez1,Hayde Peregrina1, Ivan Olmos, Jose E. Alonso, Ruben Lobato, “Segmentation and Classification of BoneMarrow Cells Images Using Contextual Information for Medical Diagnosis of AcuteLeukemias”, Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS 2010) .
Segmentation and Classification of Bone
Marrow Cells Images Using Contextual
Information for Medical Diagnosis of Acute
Leukemias
Carolina Reta
1
*, Leopoldo Altamirano
1
, Jesus A. Gonzalez
1
, Raquel Diaz-Hernandez
1
,
Hayde Peregrina
1
, Ivan Olmos
2
, Jose E. Alonso
3
, Ruben Lobato
[9] Chen Pan, Xiangguo Yan, and Chongxun Zheng, “Recognition of blood and bone marrow cells using kernel-based image retrieval”, IJCSNS International Journal of Computer Science and Network Security, VOL.6 No.10, October 2006.
[10] M. Egmont-Petersen, U. Schreiner, S. C. Tromp, T. M. Lehmann, “Detection of Leukocytes in Contact with the Vessel Wall from In Vivo Microscope Recordings Using a Neural Network”, IEEE Transactions On Biomedical Engineering, Vol. 47, No. 7, July 2000.
[11] D.K. Fragoulis, J.N. Avaritsiotis and C.N. Papaodysseus, “Timbre Recognition Of Single Notes Using An Artmap Neural Network”.
[12] Gregory P. Amis and Gail A. Carpenter, “Default ARTMAP2”,CAS/CNS Technical Report TR-2007-003, IJCNN’07, Orlando.
[13] Keith Breden Taylor and Julian B. Schorr, “Blood”, Colliers Encyclopaedia, Vol 4.
[14] O. Parsons and G. A. Carpenter, “ARTMAP neural networks for information fusion and data mining: map production and target recognition methodologies,” Neural Networks, vol. 16, pp.1075?1089, 2003.

R. Arthi, "Detection and Classification of Leukocytes in Bone Marrow Images Using ARTMAP Neural Network," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.36-39, 2020.

The traditional way of monitoring water quality of aquaculture ponds like fish farms and shrimp farms is to do it every few hours so as to minimize the stress to the animal and to minimize the mortality rate. The entire process is a tedious task involving manpower for collection of water samples and performing several lab tests. This results in unnecessary requirement of man power and in efficient usage of time thereby affecting the farmer’s economic returns and ultimate sustenance. This proposed IoT System uses Arduino development board with sensors for cost effectiveness and provides a real time monitoring environment whereby data is collected from certain specified areas of the pond every few hours and sent as an SMS via the GSM module to the farmer’s mobile along with a warning in case any of the parameters have crossed the defined range. The System will be powered by Solar Panels so that the device need not be charged manually thus automating the system.

Aquaculture ponds, IoT, Real-Time monitoring, Arduino, GSM, Water Quality Analysis

[1] Mourvika Shirode, Monika Adaling, Jyoti Biradar, Trupti Mate, IOT Based Water Quality Monitoring System, © 2018 IJSRCSEIT | Volume 3 | Issue 1 | ISSN : 2456-3307
[2] Mohammad Salah Uddin Chowdurya, Talha Bin Emranb, Subhasish Ghosha , Abhijit Pathaka , Mohd. Manjur Alama , Nurul Absara , Karl Anderssonc , Mohammad Shahadat Hossain, IoT Based Real-time River Water Quality Monitoring System, Procedia Computer Science 155 (2019) 161–168, The 16th International Conference on Mobile Systems and Pervasive Computing (MobiSPC) August 19-21, 2019, Halifax, Canada
[3] V Venkateswarlu, PV Seshaiah, P Arun and PC Behra, A study on water quality parameters in shrimp L. vannamei semi-intensive grow out culture farms in coastal districts of Andhra Pradesh, India, International Journal of Fisheries and Aquatic Studies 2019; 7(4): 394-399, E-ISSN: 2347-5129 P-ISSN: 2394-0506 (ICV-Poland) Impact Value: 5.62 (GIF) Impact Factor: 0.549 IJFAS 2019; 7(4): 394-399 © 2019 IJFAS
[4] Wiyoto, Sukenda, Enang Harris, Kukuh Nirmala and Daniel Djokosetiyanto, Water Quality and Sediment Profile in Shrimp Culture with Different Sediment Redox Potential and Stocking Densities Under Laboratory Condition, ILMU KELAUTAN Indonesian Journal of Marine Sciences, June 2016 Vol 21(2):65-76, ISSN 0853-7291
[5] Gowthamy J, Chinta Rohith Reddy, Pijush Meher, Saransh Shrivastava4, Guddu Kumar-Smart Water Monitoring System using IoT - International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
[6] Manoharan.S, Sathiyaraj.G, Thiruvenkadakrishnan.K, Vetriselvan.G.V, Praveenkishor - Water Quality Analyzer using IoT - International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8, Issue-8S, June 2019
[7] Prasenjit Barman1 , Partha Bandyopadhyay2 , Keshab Chandra Mondal1 , Pradeep Kumar Das Mohapatra1 * - Water quality improvement of Penaeus monodon culture pond for higher productivity through bioremediation - Volume 59(2):169-177, 2015 Acta Biologica Szegediensis
[8] Vaishnavi V. Daigavane and Dr. M.A Gaikwad - Water Quality Monitoring System Based on IOT - Advances in Wireless and Mobile Communications. ISSN 0973-6972 Volume 10, Number 5 (2017), pp. 1107-1116 © Research India Publications
[9] Mr. Aakash Pramod Adake, Dr. Manasi Dixit - Water Quality Monitoring System using RC Boat with Wireless Sensor Network - International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 6.887 Volume 7 Issue IV, Apr 2019- Available at www.ijraset.com
[10] S. Geetha* and S. Gouthami - Internet of things enabled real time water quality monitoring system – SpringerOpen - Geetha and Gouthami Smart Water (2017) 2:1 DOI 10.1186/s40713-017-0005-y
[11] Bhupendra Singh Rawat, Gaurav Phulwari, Trimurti Narayan Pandey, Water Quality Monitoring Using of Wireless Sensor, International Journal of Computer Sciences and Engineering Open Access, Research Paper Volume-5, Special Issue-2, Dec 2017 E-ISSN: 2347-2693
[12] A.Gupta, B.Gupta, K.K. Gola, Internet of Things (IoT) for Detecting, Monitoring and Measuring Water Quality, International Journal of Computer Sciences and Engineering Open Access Survey Paper Vol.-7, Issue-4, April 2019 E-ISSN: 2347-2693

Aishwarya Girish Menon, Prabhakar M., "Automated Water Quality Monitoring IOT System for Small-scale Aquaculture Farms," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.40-47, 2020.

This paper presents a design and development of slot and stub loaded tunable monopole circular microstrip antenna for different frequency of microwave communication applications. The antenna proposed is loaded with a U-slot, two I-slots and J- slot over the patch. The dimensions of U-slot, J-slot and upper I-slot are fixed. The one of the I-slot loaded over the patch on opposite side of J–slot is varied to achieve tuning in the WLAN, C and X band frequency ranges. When dimensions of I-slot is changed to 0.722cm, 0.755cm and 0.76 cm and corresponding resonant frequencies are lies at 5.815GHz, 4.825GHz and 4.69GHz at left band where as for right band the resonant frequencies are lies at 7.84GHz, 7.7725GHz and 7.8625GHz respectively. The bandwidth required for WLAN band of 5-22MHz is fulfilled in designed antenna and also for C and X bands. The optimum impedance bandwidth (IBW) of proposed antennas for left and right band is 29.43 % and 42.89 % having peak gain of 2.11 dBi and 2.72 dBi respectively. The radiation patterns are omni directional in nature in both E and H plane.

IBW, Notch bands, slots and stubs

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[2]. Y. S. Li and W. X. Li, “A Circular Slot Antenna with Wide Tunable and Reconfigurable Frequency Rejection Characteristic Using Capacitance Loaded Split-Ring Resonator for UWB Applications”, Wireless Personal Communications, Vol. 78, No.1, pp.137-149, March 2014.
[3]. Pooja J and and Divyanshu Gupta, “ Design and Analysis of Half I-U, Half I-F, and Half I-B Microstrip Patch Antenna for Wireless Applications”, International Journal of Computer Sciencess and Engineering, Vol. 7, Issue 4, pp.294-300, 2019.
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[12]. M. Venkata Narayana, Govardhani Immadi,K. Rajkamal and M.S.R.S.Tejeswi, “Microstrip Patch Antenna for C-band RADAR applications with coaxial fed”, International Journal of Engineering Research and Applications”, Vol. 2, pp. 118-122, May-Jun. 2012.
[13]. K. P. Ray, S. Nikhil and A. Nair, “Compact Tunable and Dual band Circular Microstrip Antenna for GPS Bluetooth Applications” , International Journal of Microwave and Optical Technology, Vol.4, No.4, pp.205-210, July 2009.
[14]. Jia-wei Dai, Hong Li Peng, Yao-ping Zhang and Jun Fa Mao, “A tunable microstrip patch antenna using liquid crystal”, Progress In Electromagnetics Research C , Vol.71, pp.101-109, 2017.
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Biradar Rajendra, S.N. Mulgi, "Tunable Monopole Circular Microstrip Antenna for tuning WLAN, Radar and Satellite Communication Applications," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.48-51, 2020.

In a mobile cellular network, users are located at a random position inside the cell. Sometimes users are highly concentrated in a region for example vicinity of a growth center on a holiday or near a stadium when a big football match is going on. In this case, a group of users under a convex hull can be covered by the beam of the adaptive Array Antenna System (AAS). Therefore the desired signal will be directional hence very low interference caused by the side lobes to other users. In this paper, an algorithm in the formation of a quick convex hull of a user group, determination of the inscribed angle of the convex hull from the center of the cell/Node-B, and formation of an adaptive beam to cover the convex hull. The paper reveals the results integrating the above three phenomena to achieve the communication of WAN (wide area network) with minimum interference. The concept of the paper is applicable in massive MIMO (multiple input and multiple output) of the 5G mobile cellular network.

Beamformer, DOA, AAS, SINR, and Quickhull algorithm

[1] Vivek Kumar, Deepak Rajouria, Manju Jain and Vikas Kumar, ‘Performance Analysis of LMS Adaptive Beamforming Algorithm’, International Journal of Electronics & Communication Technology, Vol. 4, Issue 5, pp.47-51, July - Sept 2013.
[2] Daniel Gaydos, Payam Nayeri, and Randy Haupt, ‘Experimental Demonstration of a Software-DefinedRadio Adaptive Beamformer’, Proceedings of the 48th European Microwave Conference, pp.1581-1584, 26-28 Sept. 2018, Madrid, Spain.
[3] Shaowei Dai, Minghui Li, Qammer H Abbasi, and Muhammad Imran, ‘Hardware Efficient Adaptive Beamformer Based on Cyclic Variable Step Size’, 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, pp.191-192, 8-13 July 2018, Boston, MA, USA.
[4] Shujie Lei , Yingchun Wu, Xuebo Wang, Zhao Wang, Shicong Yang, ‘Adaptive Beamformer Based on Effectiveness of Reconstruction’, IEEE 2nd International Conference on Electronic Information and Communication Technology, pp.353-357, 20-22 Jan. 2019, Harbin, China.
[5] Ronghua Xu, Hongjun Dai , Fengyu Wang and Zhiping Jia , ‘A Convex Hull Based Optimization to Reduce the Data Delivery Latency of the Mobile Elements in Wireless Sensor Networks’, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing pp. 2245 – 2252 13-15 Nov. 2013, Zhangjiajie, China.
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[10] Alexey A. Erokhin, Yury P. Salomatov, Evgeniy R. Gafarov and Victor N. Ushakov, ‘Simple Pre-Steering Constraints for Wideband LCMV-beamformer’, 2019 International Siberian Conference on Control and Communications (SIBCON), 18-20 April 2019, Tomsk, Russia, Russia
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[13] Md Abul Kalam Azad, Md. Imdadul Islam, Muhammad R. A. Khandaker and Jugal K. Das, ‘Comparison of Performance of Cognitive Radio Network under Single and Multi-User Scenario’, 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 3-5 May 2019, Dhaka, Bangladesh

Bishal Gautam, Md. Rafsan Jani, Bulbul Ahammad, Rahmina Rubaiat, Md. Imdadul Islam, "Convex-hull of Users under Adaptive Beam in WAN to Minimize Interference," International Journal of Computer Sciences and Engineering, Vol.8, Issue.10, pp.52-59, 2020.