Face Recognition Based Security Scanner using PCA
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.1-4, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.14
Abstract
Immediately This paper proposes a novel Face Recognition based Security Scanner for keeping track of people moving in and out of a workplace using Haar Cascade and Principal Component Analysis. The proposed system trains the system using images and then detects and recognises the person and gives details about the person with the date and time.
Key-Words / Index Term
Face Detection, Face Extraction, Face Recognition, Principal Component Analysis, Haar Cascade
References
[1] Santhosh Shetty, Paritosh Kelkar,K Manikantan, S Ramachandran, “Shift Invariance based Feature Extraction and Weighted BPSO based Feature Selection for Enhanced Face Recognition”, International Conference on Computational Intelligence: Modeling, Techniques and Applications(CIMTA) 2013 Procedia Technology 10(2013)822-830
[2] R. Jafri, H. R. Arabnia, “A Survey of Face Recognition Techniques”, Journal of Information Processing Systems, Vol.5, No.2, June 2009
[3] Zhao , W. chellappa , R. Phillips, P.J Rosenfeld , “Face Recognition: A Literature Survey”, ACM Computing Surverys (CSUR), V.35, Issue 4, pp.399-458 2003
[4] Chan A.B. Liang Z.S.J, Vaconcelos N.,” Privacy preserving crowd monitoring: Counting people without people models or tracking //Computer Vision and Pattern Recognition”, 2008. CVPR 2008. IEEE Conference on—IEEE,2008-C. 1-7
[5] T.Chandrasekhar, Dr.K GIRIBABU, Dr. Ch. Sumanth Kumar, “A Novel Approach of Face Recognition using Statistical Features and Neural Networks”, GITAM University, Visakhapatnam V.V.I.T Nambur, International Jouranl of Engineering and Innovative Technology (IJEIT) Volume 4, Issue11, May 2015
Citation
Amrita Naik, Amrita Nagekar, Michael Monte, Aditya Parab, Ronan Revadkar, "Face Recognition Based Security Scanner using PCA", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.1-4, 2018.
Cardiac Patient Remote Health Monitoring (CP-RHM) using Digital Monitoring
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.5-8, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.58
Abstract
Digital health is an application of digital and innovative technologies in health industry. It is a multi-disciplinary domain which involves Scientists, Researchers, Doctors and Technicians. Therefore, a new smart platform used by the Digital health is Internet of Things (IoT). An open, standards based framework enables innovators to create new ideas for e-healthcare. IoT allows innovators to design accessible, robust, affordable and secure healthcare i.e., Patient Remote Monitoring Systems which is used by Doctors, or Healthcare Practitioners to improve clinical care, research and public health. The main objective of this paper is to design remotely operated Patient Monitoring System for Cardiac patient at affordable cost.
Key-Words / Index Term
Cardiac, Health Remote Monitoring, Classification, Internet of Technology
References
[1] Bandana Mallick, Ajit Kumar Patro, “HEART RATE MONITORING SYSTEM USING FINGER TIP THROUGH ARDUINO AND PROCESSING SOFTWARE “- International Journal of Science, Engineering and Technology Research (IJSETR), Volume 5, Issue 1, January 2016
[2] Ramalatha Marimuthu, V. Deepak , S. Gowtham , V. Ram Prasad, “ REMOTE HEART RATE MONITORING SYSTEM” -International Journal of Electronics & Communication Engineering Research (IJECER)Vol. 1 Issue 3, August – 2013
[3] Lovenish Sharma“ PC BASED HEART RATE SENSOR”-Emerging trends in Engineering & Management for Sustainable Development 2016 International conference, Feb 2016
[4] Embedded Lab. Introducing Easy Pulse: A DIY photoplethysmographic sensor for measuring heart rate. http://embedded-lab.com/blog/?p=5508, 2012.
[5] Ufoaroh S.U , Oranugo C.O, Uchechukwu M.E “HEARTBEAT MONITORING AND ALERT SYSTEM USING GSM TECHNOLOGY” - International Journal of Engineering Research and General Science Volume 3, Issue 4, July-August, 2015
[6] Deepti Ameta, Kalpana Mudaliar and Palak Patel, “MEDICATION REMINDER AND HEALTHCARE – AN ANDROID APPLICATION ” - International Journal of Managing Public Sector Information and Communication Technologies (IJMPICT) Vol. 6, No. 2, June 2015
[7] Rajalakhshmi.S S.Nikilla “Real Time Health Monitoring System using Arduino” -Department of ECE, Sri Krishna College of Engineering and Technology Coimbatore, Tamilnadu, India.- South Asian Journal of Engineering and Technology Vol.2, No.18 (2016) 52–60
[8] D.Senthil Kumar, G.Sathyadevi and S.Sivanesh,“ Decision Support System for Medical Diagnosis Using Data Mining” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 1, May 2011.
Citation
Kiran Waghmare, Anuj Acodkar, Rohit Gavali, Rahul Tiwari, Prathamesh Pavnoji, Sidharth Naik, "Cardiac Patient Remote Health Monitoring (CP-RHM) using Digital Monitoring", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.5-8, 2018.
Using Proximity and Semantic Similarity in Question Answering
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.9-17, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.917
Abstract
This paper deals with the process of Question Answering, using news articles crawled from ‘THE HINDU’ newspaper website of the year 2017. We make use of corpus of close to 10,000 articles/documents crawled categorically into Sports, Science and Tech., Business and Entertainment. We have implemented a system that extracts documents based on relevance to the question a user asks through the tf-idf ranking. For the processing phase, we made use of methods initially implemented for simpler systems, such as document extraction and checking sentence similarity between two short sentences. We managed to implement the techniques to extract coherent answers by extracting the passages with the best likelihood of containing the answer and the process these passages for the answer based on their similarity with the question. To implement these, we have made use of various Natural Language Processing (NLP) techniques along with the Wordnet knowledge base. We have tested the system with different corpus sizes and different coefficient of cosine similarity to explore this technique.
Key-Words / Index Term
Question-Answering, Proximity, Semantic Similarity, Natural Language Processing and Synonyms
References
[1] Yuhua Li, David McLean, Zuhair A. Bandar, James D. O’Shea, and Keeley Crockett, “Sentence Similarity Based on Semantic Nets and Corpus Statistics”, in IEEE Transactions on Knowledge and Data Engineering, VOL. 18, NO. 8, AUGUST 2006.
[2] Daniel Jurafsky & James H. Martin, “Speech and Language Processing”,
[3] Man-Hung Jong, Chong-Han Ri, Hyok-Chol Choe, Chol-Jun Hwang, A Method of Passage-Based Document Retrieval in Question Answering System. https://arxiv.org/ftp/arxiv/papers/1512/1512.05437.pdf.
[4] Apra Mishra, Santosh Vishwakarma, Analysis of TF-IDF Model and its Variant for Document Retrieval, ,2015.
[5] Vicedo, Jose Luis & Ferrández, Antonio, A Semantic approach to Question Answering systems.
Citation
Shaunak S. Phaldessai, Amey D. S. Kerkar, "Using Proximity and Semantic Similarity in Question Answering", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.9-17, 2018.
Optical Music Recognition using Image Processing and Machine Learning
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.18-23, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.1823
Abstract
The ability to understand music score is a basic requirement for learning music. This paper proposes a mathematical method to find the pitch of a musical note from digital image of sheet music and a classification-based method for detecting the duration of a music note. In a sheet music, the horizontal direction can be associated with the notes starting time, whilst the vertical direction can be associated with pitch. The symbols used for a note represents its duration. Music scores sometimes need to be transposed or slightly modified, having the score in a digital format greatly reduces the time and effort required to do these. In this paper, we make use of techniques such as Run Length Encoding (RLE), Horizontal projection and Vertical Projection (X & Y projections) for Segmentation and attribute extraction. For note recognition, a classifier based system is used which returns the duration of the given input symbol. The pitch, duration and position of notes are finally given as input to a midi generation module, which generates a MIDI file corresponding to the given input music notation. There are several other applications to Optical Music Recognition (OMR) systems. Converting music scores in Braille code for the blind is yet another application of an OMR system.
Key-Words / Index Term
Optical Music Recognition, Image Processing, RLE, Classification, Machine Learning
References
[1] A. F. Desaedeleer, “Reading sheet music – openomr”,
Imperial College London,(University of London), http://sourceforge.net/projects/openomr/.
[2] R. J. Baugh Earl Gose, “Pattern Recognition and Image Analysis”, In Pattern Recognition and Image Analysis, volume 1, 2011.
[3] M. Hall, E. Frank , G. Holmes , B. Pfahringer , P. Reutemann , H. Ian : “The WEKA Data Mining Software: An Update”. SIGKDD Explorations 11, 2009.
[4] C.E. Shannon: “A Mathematical Theory of Communication”.
The Bell System Technical Journal 27, 379–423, 623–656, July, October 1948.
[5] Online. “Note names, MIDI numbers and frequencies”. http://www.phys.unsw.edu.au/jw/notes.html, June 2005.
[6] Online. “Midiutil - A Python interface for writing multi-track MIDI Files”. https://code.google.com/p/midiutil/, December 2013.
[7] Online. “PythonInMusic”. https://wiki.python.org/moin/PythonInMusic,December 2013.
[8] Online. “Note value”. http://en.wikipedia.org/wiki/Note_value, March 2014.
[9] Online. “Attribute-Relation File Format (ARFF)” https://www.cs.waikato.ac.nz/ml/weka/arff.html, November 2008.
Citation
Prince Mathew, Rahul Vijayakumar, Aju Tom Kuriakose, Jesmy Sunny, Ramani Bai V, "Optical Music Recognition using Image Processing and Machine Learning", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.18-23, 2018.
Using Computer Vision and Machine Learning Algorithm to Prevent and Detect Vehicular Stalking
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.24-27, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.2427
Abstract
Vehicular stalking is a can be termed when a motorist is deliberately following another motorist for various reasons such as investigative, spying or stalking. Stalking has been proved to be expensive as it breaches the privacy of the victim and can be used for other illicit activities. The victim is mostly unaware that they are being stalked and it becomes difficult to report stalking to law-enforcement authorities as victims do not have strong evidence to prove. The vehicle stalking is generally in close proximity or is in the length of sight to the victim being stalked. This paper introduces an idea which can help the victim know if they are being stalked by using Computer vision and Machine learning algorithm to extract information like number plate details, vehicle type, time, colour, brand and model from the vehicles on the road during travelling to predict the stalker’s vehicle based on the occurrence and alert the victim with a report.
Key-Words / Index Term
Computer Vision, Machine Learning, Humanitarian Technology,Stalking
References
[1] National Institute of Justice, “Stalking”, Web Publication,USA, 2007.
[2] US Department of Justice, “Stalking Victims in the United States-Revised”, The Bureau of Justice Statistics, USA, pp.1-10, 2012.
[3] Patel, Chirag & Shah, D & Patel, Atul. (2013). “Automatic Number Plate Recognition System (ANPR): A Survey”. International Journal of Computer Applications (IJCA).Vol.69, No.9, pp.21-23, 2013.
[4] M.M.Shidore,S.P.Narote “Number Plate Recognition for Indian Vehicles”, International Journal of Computer Science and Network Security, Vol.11, Issue.2, pp.143-146, 2011.
[5] S.Kousalya , Dr. Antony Selvadoss Thanamani, “Image Color Extraction and Retrieval Using Classification Techniques”, nternational Journal of Computer Science and Mobile Computing, Vol.2, No.8, pp.169-173, 2013.
[6] Reshu Kumari, Surya Prakash Sharma, “A Machine Learning Algorithm for Automatic Number Plate Recognition”, International Journal of Computer Applications, Vol.174, No.1, pp.6-9, 2017.
[7] Burhanuddin Bharmal, Gopal Thakur, Kshama Tiwari, “Auto-detection and Masking of Vehicle License Plate Using Machine Learning”, International Research Journal of Engineering and Technology, Vol.5, Issue.7, pp.2128-2134, 2018.
Citation
Shonal Fernandes, "Using Computer Vision and Machine Learning Algorithm to Prevent and Detect Vehicular Stalking", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.24-27, 2018.
Automating the Centralised Admission Process with Analytical Platform
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.28-31, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.2831
Abstract
Admission Processes for degree colleges conduct the admissions in courses like Medicine, Dentistry, Engineering, Pharmacy, Architecture, Homeopathy, Bachelor of Science in Nursing, Ayurveda and Allied Health Science. As there is an increase in the number of students to seek admissions in various courses, it causes a terrible pressure on the admission committee to manage and control the admission process when done manually. Hence, our proposed computer-aided system helps the administrative body in various tasks of the admission process which includes a collection of student’s application forms, storing data safely and automatic sorting of data according to various categories etc. The automated computer-aided system facilitates the student to perform tasks online that include form filling and viewing the merit and eligibility list, checking the seat availability at the time of counseling. The system also provides an analytical mock test to the students. The students, who are not sure of which courses they can have a good aptitude at, can answer the same.
Key-Words / Index Term
E-admission, CET, Analytics, Admission process
References
[1] Chandraganga Mirji, Vaibhavi Deshpande, Supriya Walunj, Ankita Ambavane, “E-Admission System”, IOSR Journal of Computer Engineering, Vol.16, Issue.2, pp.01-03, 2014.
[2] Mehul Gupta, K.Kartik Iyer, Mani Ratnam Singh, A.K.Kadam, “Automated Online College Admission Management System”, International Journal of Computer Science Trends and Technology, Vol.5, Issue.3, pp.1-4, 2017.
[3] Abdul Hamid M Ragab, Abdul Fatah S. Mashat, Ahmed M. Khedra, “Design and Implementation of a Hybrid Recommender System for Predicting College Admission”, International Journal of Computer Information Systems and Industrial Management Applications, Vol.6 ,pp. 35 - 44,2012
Citation
Salvador Fernandes, Ruchi Ghantkar, Pranita Desai, Megha Nayak, "Automating the Centralised Admission Process with Analytical Platform", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.28-31, 2018.
BOT–ENGINEER: A system to Create Chatbots
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.32-36, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.3236
Abstract
Digital Assistants driven by Artificial Intelligence (A.I.) are becoming increasingly popular .These assistants can send users updates on live data, help them know about the traffic situation on the way to work or home. All that the user has to do is ask. Due to increasing technologies, large amount of data gets generated. Digital assistants are changing the way people search for information, making it part of regular conversation. In this paper, we present a BOT-ENGINEER, a system that is designed to create chatbots. It uses intent classification and entity extraction to find what the user has said and meaningful keywords that represent actual data respectively.
Key-Words / Index Term
Casual intents, business intents, utterance
References
[1] Adhitya Bhawiyuga, M. Ali Fauzi, Eko Sakti Pramukantoro, Widhi Yahya, "Design of E-commerce chat robot for automatically answering customer question", Sustainable Information Engineering and Technology (SIET) 2017 International Conference on, pp. 159-162, 2017
[2] Emanuela Haller ; Traian Rebedea, “Designing a Chat-bot that Simulates an Historical Figure”, 2013 19th International Conference on Control Systems and Computer Science,
ISSN: 2379-0474
[3] Salto Martinez Rodrigo ; Jacques Garcia Fausto Abraham, “Development and Implementation of a Chat Bot in a Social Network”, 2012 Ninth International Conference on Information Technology - New Generations, ISBN: 978-1-4673-0798-7
Citation
Ravino De Souza, Kai DaCosta, Deston Cardozo, Maria Christina Barretto, "BOT–ENGINEER: A system to Create Chatbots", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.32-36, 2018.
Sharp Filters To Extract Absence Seizures EEG Signals
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.37-41, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.3741
Abstract
Reliable and accurate analysis of the electroencephalogram (EEG) waveforms can be very important to the current medical research and clinical fraternity to analyze the EEG signals and accordingly treat the subjects for any neurological abnormalities such as seizures etc. This paper introduces the types of EEG and pin points the absence seizure (or petit mal seizures) among children. The paper analyses the use of a novel method of using a sharp finite impulse response (FIR) filter that can extract time domain EEG signals of patients who are inflicted with absence seizures. The sharp FIR filter comfortably extracts the noise free delta EEG frequency of 3.04Hz for a cutoff of 3Hz. The filter displays flat passband and stopband attenuation, and also possesses a linear phase response. A transition width of 1 Hz is achieved using our sharp filter
Key-Words / Index Term
Electroencephalogram, absence seizure, petit mal seizure, linear phase, FIR filters
References
[1] D.P Subha, P.K Joseph, R. Acharya, C.M. Lim, “EEG signal analysis: a survey”, Journal of Medical Systems, Vol. 34, Issue. 2, pp.195-212, 2010.
[2] P. Vrielynck, “Current and emerging treatments for absence seizures in young patients”, Neuropsychiatric disease and treatment, Vol. 9, pp. 963, 2013.
[3] G. L. Holmes, M. McKeever, M. Adamson, M, “Absence seizures in children: clinical and electroencephalographic features, Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, Vol. 21, Issue. 3, pp.268-273 , 1987.
[4] D.F. Silva, M.M Lima, R. Anghinah, E. Zanoteli, J.G.C. Lima, “Atypical EEG pattern in children with absence seizures”, Arquivos de neuro-psiquiatria, Vol. 53, Issue. 2, pp. 258-261, 1995.
[5] J.R. Tenney, T.A. Glauser, “The current state of absence epilepsy: can we have your attention?”, Epilepsy currents, Vol. 13, Issue. 3, pp.135-140, 2013.
[6] D.M Kaufman, H.L Geyer, M..J. Milstein, “Kaufman`s Clinical Neurology for Psychiatrists” E-Book. Elsevier Health Sciences, 8th Edition, pp 197- 233.
[7] E. Posner, “Absence seizures in children”. BMJ clinical evidence, 2013.
[8] J. Kaur, A. Kaur, “A review on analysis of EEG signals”. In Computer Engineering and Applications (ICACEA), 2015 IEEE International Conference on Advances, pp. 957-960, 2015
[9] N. Marchon, G. Naik, K. R Pai, “Linear Phase Sharp Transition BPF to Detect Noninvasive Maternal and Fetal Heart Rate” Journal of healthcare engineering, 2018.
[10] N. Marchon, G. Naik, K. R. Pai, “Monitoring of fetal heart rate using sharp transition FIR filter” Biomedical Signal Processing and Control, Vol. 44, pp. 191-199 , 2018.
[11] E. Kabir, Y. Zhang, “Epileptic seizure detection from EEG signals using logistic model trees”. Brain informatics, Vol. 3, Issue 2, pp. 93-100, 2016.
[12] P.R. Carney, S. Myers, J.D Geyer, “Seizure prediction: methods”. Epilepsy & behavior, Vol. 22, pp.S94-S101, 2011.
[13] J.G. Proakis, D.G Manolakis, “Digital signal processing-principles, algorithms and applications”, pp. 621-623, 1992.
[14] E.C. Ifeachor, B.W. Jervis, “Digital signal processing: a practical approach”. Pearson Education, pp. 367-379, 2002.
Citation
Niyan Marchon, Gourish Naik, "Sharp Filters To Extract Absence Seizures EEG Signals", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.37-41, 2018.
Ultrasonic Distance Measurement
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.42-44, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.4244
Abstract
Measuring distance of an obstacle in the direction of a person, stationary or moving object is important parameter. Measuring the distance using ultrasonic sensors is one of the cheapest among various options. In this paper measuring distance of an obstacle in the direction of a person done by ultrasonic sensor. 40 kHz ultrasonic transducer is used for scaling the outpost between obstacle and the person. Ultrasonic sensor with a microcontroller it calibrates the distance which is displayed on an android device connected via Bluetooth. An android application displays the distance in various scaling units. The scaling units are Centimetre, Meter, Inches, Yards, Foot.
Key-Words / Index Term
Transducer, Microcontroller, Android device, Bluetooth, scaling units
References
[1]. Spasov Peter, Microcontroller Technology the 68HC11 and 68HC12 Upper Saddle River, Pear-son Prentice Hall, Fifth Edition, 2004.
[2] Sinclair Ian R. and Dunton John, Practical Electronic Handbook, 6th Edition, 2007.
[3] Horton Ivor, Beginning C, Wrox Press Ltd, Birmingham, U.K, 2nd Edition, 2002.
[4] Brown Forrest John, Embedded Systems Programming in C and Assembly, Van Nostrand Rein-hold, N.Y, Prentice-Hall, 2003.
[5] Deshmukh V Ajay, Microcontrollers Theory and Applications, New Delhi, Tata McGraw-Hill Publishing Co. Ltd, 2005.
Citation
Reeja S R, Venkat Durga Sriram, Tarun Reddy R, Venkatamanu, Rino Cherian, "Ultrasonic Distance Measurement", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.42-44, 2018.
A Mechanized Harvesting and Automated Grading Technology for Oil Palm Fruit
Research Paper | Journal Paper
Vol.06 , Issue.10 , pp.45-52, Nov-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6si10.4552
Abstract
The purpose of this research is to design and fabricate an automated grading cum harvesting device for Oil Palm Fruits. The presence of the VIRESCENS or NIGRESCENS gene in oil palm fruits renders it possible to determine the ripeness of the fruit by mere inspection. However, this method of inspection by workers is time-consuming and error-prone. Harvesting of oil palm fruits has been aided by numerous technologies such as the pole and knife method, aluminium pole and knife (APK) method and the like. These manual methods, besides consuming the time and energy of the harvester, pose a serious threat to the safety of the harvester, as he/she runs the risk of being hit by a falling oil palm branch.
Key-Words / Index Term
il palm, agriculture, UAV, drone, harvester, grading, image classification, VGGnet, ripeness, classification model
References
[1] Kalidas Potineni and Saravanan, L. 2013. Natural enemies of oil palm defoliators and their impact on pest population. Pest Management in Horticultural Eco systems.19(2):179-184
[2] D. Adetan, L. Adekoya and K. Oladejo. “An Improved Pole-and-Knife Method of Harvesting the Oil Palm”. Agricultural Engineering International: the CIGR Ejournal. Manuscript PM 06 027. Vol. IX. June, 2007.
[3] N. Sridhar1 & A. Surendrakumar, Performance Evaluation and Modification of Shredder Cutting Mechanism, International Journal of Agricultural Science and Research (IJASR) ISSN (P): 2250-0057; ISSN (E): 2321-0087 Vol. 7, Issue 5, Oct 2017, 227-242 © TJPRC Pvt. Ltd.
[4] Qiang Wang. “The Current Research Status and Prospect of Multi-Rotor UAV.” IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), vol. 14, no. 4, 2017, pp. 31–35.
[5] Li Chenglong. Research on flight stability control of multi rotor UAV at high altitude, Zhejiang University, 2016:3-4
[6] P.D.P.R.Harsh Vardhan, S.Dheepak, P.T.Aditya, Sanjivi Arul. “Development Of Automated Aerial Pesticide Sprayer”. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
[7] Gabriel M. Hoffmann, Haomiao Huang Steven L. Waslander Claire J. Tomlin "Quad rotor Helicopter Flight Dynamics and Control: Theory and Experiment".
[8] AbdallahZaid Al Kilani, SaifallahQasim Search-And-Rescue Remote Sensing Quad rotor UAV , German-Jordanian University, Project Entry Documentation For National Technology Parade 2010.
[9] Karen Simonyan_ & Andrew Zisserman, VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION, Published as a conference paper at ICLR 2015
[10] http://www.mpoc.org.my/The_Oil_Palm_Tree.aspx
[11] https://www.researchgate.net/figure/280134906_fig1_Figure-171-Current-method-of-harvesting-using-sickle
[12] http://greenpalm.org/about-palm-oil/what-is-palm-oil
[13] http://www.rea.co.uk/markets/oils-and-fats/uses-palm-oil
[14] https://www.thestar.com.my/lifestyle/features/2014/09/01/colours-of-distinction-when-oil-palm-is-right-for-picking/
[15] https://oscarliang.com/
[16] Pixwak x4 2.4.8. https://pixhawk.org
Citation
Akshay Saraf, Rajdutt Kenkre, Nigel Pinto, Blossom Fernandes, Amey Tilve, Gaurang Patkar, "A Mechanized Harvesting and Automated Grading Technology for Oil Palm Fruit", International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.45-52, 2018.