Journal of Advancements in Robotics
https://computers.stmjournals.com/index.php?journal=JoARB
<p align="center"><strong>Journal of Advancements in Robotics (JoARB)</strong></p><p align="center"><strong> </strong></p><p align="center"><strong>ISSN: 2455-1872</strong></p><p align="center"> </p><p align="center">Click <strong><a href="/index.php?journal=JoARB&page=about&op=editorialTeam">here </a></strong>for complete Editorial Board</p><p align="center"> </p><p align="center"><strong>Scientific Journal Impact Factor (SJIF):</strong> 6.05<strong></strong></p><p><strong> </strong></p><p><strong>Journal of Advancements in Robotics (JoARB)</strong> is a journal focused towards the rapid publication of fundamental research papers on all areas of Robotics and its advancements. It's a triannual journal, started in 2014.</p><p><strong> </strong></p><p><strong>Journal DOI no</strong>.: 10.37591/ JoARB</p><p><strong> </strong></p><p><strong>Focus and Scope Cover</strong></p><ul><li>Human Robot Interaction and Social Robotics</li><li>Sensor Integration Robot</li><li>Vision.- Robot Programming</li><li>Medical Robotics</li><li>Humanoid Robots</li><li>Autonomous Helicopters</li><li>Dynamics and kinematics of robot</li><li>Kinematics, dynamics, and simulation of robots and autonomous intelligent systems.</li><li>Design of robotic mechanisms.</li><li>Man-machine interface and integration.</li><li>Robotics-related computer hardware, software, and architectures.</li><li>Active sensory processing and control.</li><li>Machine learning and artificial intelligence for robotics.</li><li>Medical and Assistive Robotics.</li><li>Bio-mimetic and Bio-inspired Robotic Systems.</li></ul><p><strong> </strong></p><p><strong>Readership:</strong> Graduate, Postgraduate, Research Scholar, Faculties, Institutions, and in IT Companies.</p><p><strong> </strong></p><p><strong>Indexing: </strong>The Journal is index in DRJI, Google Scholar</p><p> </p><p> </p><p> </p><p><strong>Submission of Paper: </strong></p><p>All contributions to the journal are rigorously refereed and are selected on the basis of quality and originality of the work. The journal publishes the most significant new research papers or any other original contribution in the form of reviews and reports on new concepts in all areas pertaining to its scope and research being done in the world, thus ensuring its scientific priority and significance.</p><p> </p><p>Manuscripts are invited from academicians, students, research scholars and faculties for publication consideration.</p><p> </p><p>Papers are accepted for editorial consideration through email info@stmjournals.com or nikita@stmjournals.com</p><p><strong> </strong></p><p><br /> <strong>Abbreviation: JoARB</strong><em></em></p><p><em><br /> <br /> </em><strong></strong></p><p><strong> </strong></p><p><strong>Frequency</strong>: Three issues per year</p><p> </p><p><strong><a href="/index.php?journal=JoARB&page=about&op=editorialPolicies#peerReviewProcess">Peer Reviewed Policy</a></strong></p><p> </p><p align="center"><strong> </strong></p><p><strong><span style="text-decoration: underline;"><a href="/index.php?journal=JoARB&page=about&op=editorialTeam">Editorial Board</a></span></strong></p><p> </p><p><strong><a href="http://stmjournals.com/pdf/Author-Guidelines-stmjournals.pdf">Instructions to Authors</a></strong></p><p> </p><p><strong>Publisher:</strong> STM Journals A division of: Consortium eLearning Network Private Ltd</p><p><strong>Address:</strong> A-118, 1<sup>st</sup> Floor, Sector-63, Noida, Uttar Pradesh-201301, India</p><p><strong>Phone no.:</strong> 0120-4746-214/ Email: nikita@stmjournals.com</p>en-USJournal of Advancements in Robotics2455-1872<p align="center"><strong>Declaration and Copyright Transfer Form</strong></p><p align="center">(to be completed by authors)</p><p>I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is <span>not</span> published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is <strong><span>not</span></strong> under consideration of publication elsewhere.</p><ul><li>I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.</li><li>I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).</li><li>I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.</li><li> I/We also agree to the authorship of the article in the following order:</li></ul><p>Author’s name </p><p> </p><p>1. ________________</p><p>2. ________________</p><p>3. ________________</p><p>4. ________________</p><table width="100%" border="0" cellpadding="0"><tbody><tr><td valign="top" width="5%"><p align="center"> </p></td><td valign="top" width="95%"><p>We Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.</p></td></tr></tbody></table>Facial Expression Recognition using Convolutional Neural Network
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2749
<p><em>Machine learning is a subset of Artificial intelligence. Machine learning enables self-learning from past data and predicts the future. Machine learning is used in many applications such as self-driving cars, Google translate etc. Deep learning is a subset of machine learning. CNN is a convolutional neural network. The CNN approach is very effective in image classification. The target of this exploration is to assist a look acknowledgment framework dependent on convolutional neural organization. The conventional CNN network module is used to extract primary expressional vector (EV). The expressional vector (EV) is generated by tracking down relevant facial points of importance</em><em>.</em><em>In the proposed solution, gray scale image in Kaggle. This methodology empowers to order seven fundamental feelings which comprise of furious, disdain, dread, glad, unbiased, dismal and shock from Kaggle dataset. TensorFlow framework is used in facial expression with the accuracy of 92%.</em></p>Hamsavahan HLohit S.Nandakishore T. J.Kalaiselvan V2021-06-112021-06-118Automate Voice Controlled Robot Using Raspberry Pi 3b+
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2707
<p class="Default"><em>As, robotics and artificial intelligence (AI) is a subfield of engineering dedicated to research and development. There are a lot of researchers working on to enhance the connection between robots and humans through AI. The proposed system presents the research of the designing and development of a voice controlled talking robot using AI and IOT device. The proposed system is based on microcontroller Raspberry Pi. The client will utilize an android worked PDA to provide voice order. The command can be fetched using a Flutter based app which will convert the voice command into text. The phone will be connected with microcontroller using a Bluetooth module. After conversation of the voice command into text by Flutter based app, it will send necessary data to the microcontroller using Bluetooth of the phone and microcontroller will receive the data using Bluetooth module. As per the order, the robot will push ahead, in reverse, left, right or completely self-ruling. Development of the robot and it will work with the microcontroller to control two distinctive engines of left and right by controlling the course of turn of engines. An ultrasonic sensor will be interfaced to distinguish snag and assist robot with working.</em></p>Vikash SharmaVrutik ParvadiyaRohan Naik2021-06-112021-06-118Self Driving Car Simulation Using Deep Learning
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2748
<em>Every year, traffic accidents account for 2.2% of worldwide deaths. That accumulates to a few millions a year. On prime of this, some individuals are seriously lacerate in auto-related accidents annually in large numbers mostly due to human error. From distracted driving to drunk driving to reckless driving to careless driving, one poor or inattentive call may be the distinction between a typical drive and a critical state of affairs. However what if we tend to might neutralize human error from the equation?The proposed system of ‘Self Driving Autonomous Car’ is designed using Deep Learning and CNN and Computer Vision. Imagine stepping into your car and however speaking a location into your vehicle’s interface, then lease it drive you to your destination whereas you read a book, surf the net, or nap without fear concerning something. Computer Vision techniques via Open-CV are applied to establish lane lines for a self-driving car. CNN is trained to spot numerous traffic signs using Keras (open-source platform).To generalize the behavior of cars on different tracks, it is not possible to collect and process a huge amount of data, that is why augmentation is done which generalizes data on new tracks. Augmentation can be done using various techniques like crop, zooming, Flipping, and changing brightness</em>Aniket A. KanganeArti K. GorePournima N. BhosaleDhananjay K. Bhadke2021-06-112021-06-118Food Recipe Finder Web Application Based on Similarity of Materials
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2684
<p><em>This project report is entitled “Food recipe finder web application based on similarity of materials”. The main objective of the study is to solve the problems faced by many people regarding cooking. Cooking has been a huge burden for many people, mostly bachelors. So, to conquer this problem, our team is developing a web application to make cooking easier for these people. This application is an online platform especially for the people facing cooking issues. These people mostly suffer three scenarios. Different modules included in this project will help people facing different scenarios. Except for home and login page, there will be total three modules included in this project. In the first module, the user will input all the ingredients he/she have at the moment and what and how can he/she prepare using those ingredients, will appear on their screen. In the second module, the user will input the main dish and all the ingredients will appear on his/her screen. Inthe third module, the user just has to upload a picture of the prepared dish along with the ingredients and the exact or similar dish will appear on the user’s screen. First two modules will take data set and third module will take machine learning algorithm. The study recommends to solving cooking issues and to make people a great cook oneday.</em></p><p> </p>Usaid KaleskarZiyad SurveSaquib ShaikhBabita Bhagat2021-06-112021-06-118Advancement in Technology During COVID
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2683
<h4><em>Technology has played an indispensable role in survival during pandemic situations. From the initial stages of the COVID till the invention of the vaccine, technology has provided various means to live in such a pandemic situation. Technology has availed us with highly proficient resources to detect the COVID symptoms. It has also played a key role in WFH in various companies, industries, Business and also in education and learning. Many precautionary steps have been taken with the help of new technology. Various Contact tracing applications have been developed in different companies to have a check on the data like number of infectants, treatments, mortality etc. Misinformation also became a survival threat during the pandemic. Telecom services have also made efforts to stop the spread of COVID. AI, Internet, 5G, Cloud etc. were the main contributing domains of the technology. Mostly every domain of the Technology has performed a vital role in survival during COVID. As we all know ‘Necessity is the mother of all inventions’ and here in this scenario our necessity was to make preventive measures for survival during the pandemic. And innovation in the field of Tech to provide needed conditions. Thus, the increase in needs of the population is the Advancement of Technology.</em><em></em></h4>Hemant Dadhich2021-05-262021-05-268Half Adder Using Different Design Styles: A Review on Comparative Study
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2638
<p align="center"><strong><em>Abstract</em></strong></p><p><em>A half adder is a digital logic circuit that performs addition of two single bit binary numbers. Generally, in various types of processors, adders are used to perform arithmetic and logical operations .In this paper, working of half adder is analysed by designing it by using three different logic styles, i.e., CMOS logic, Transmission Gate Logic (TGL) and Pass Transistor Logic (PTL). The comparison has been made between the circuits on the basis of their power consumption and transistor count. Simulation of the circuits is carried out using HSPICE tool at 45 nm, 32 nm and 16 nm technologies at 1V power supply. After simulation it is observed that power consumption is lowest when the adder is implemented by transmission gate as compared to CMOS and PTL design styles whereas transistor count is minimum in case of PTL design style. It is also inferred that CMOS gives the best performance out of the three design styles.</em></p>Anju RajputTripti DuaDr. Renu KumawatDr. Avireni Srinivasulu2021-02-092021-02-098A Comparative Study on Emotion and Gender Prediction using OpenCV Library and Deep Learning
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2636
<p align="center"><strong><em>Abstract</em></strong><strong></strong></p><p><em>In this paper, we are going to study and develop a convolutional neural network (CNN) using EmoPy python Library for training our dataset, which performs human facial recognition as well as their gender prediction with real-time emotions. This model will work with OpenCV, Keras 2.3.1 and EmoPy. A fully connected layer of convolutional neural network which identifies the gender as well as the art of emotion of the human for which lots of data training has been done along with our own images.</em></p>Awanit KumarAditi SrivastavaMancy Gupta2021-02-092021-02-098A Simulation Analysis of Ripple Correlation Control MPPT for Low Power PV Systems
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2623
<p class="Abstract" align="center"><em><span>Abstract</span></em></p><p class="Abstract"><em><span>In this work, a simulation study of Maximum Power Point Tracking (MPPT) with Ripple Correlation Control (RCC) technique has been done. </span></em><span class="fontstyle01"><em><span>Photovoltaic (PV) array along with the DC/DC buck converter and its Maximum Power Point Tracking (MPPT) control strategy has been implemented with RCC for various solar radiations at 25<sup>o</sup>C, and simulations results are presented. RCC with low pass filter and tuned with Proportional Controller (PI) has been implemented. Simulation results show RCC removes all ripples from voltage, current and power outputs, which was the major concern of conventional MPPT techniques.</span></em></span></p>Pankaj SahuAshish SharmaDr. Rajiv Dey2021-02-092021-02-0982:1 Multiplexer Using Different Design Styles: Comparative Analysis
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2637
<p class="Abstract" align="center"><em>Abstract</em><em></em></p><p class="Abstract"><em>This paper presents a comparative analysis of 2:1 multiplexer using different logic styles (transmission gate, pass transistor and CMOS logic), with three different technologies i.e. 45nm, 32nm and 16nm. Simulation is done using Synopsys HSPICE tool at 1V power supply.</em><em> As a result, it is found that </em><em>the least power is consumed by 2:1 multiplexer implemented using TGL. </em><em>It consumes 99.7% less power than pass transistor logic and PTL consumes 99% more power than CMOS.</em><em> </em><em>Since multiplexer implemented by PTL utilizes minimum number of transistors, i.e., 2 ,therefore it is the area efficient logic circuit for 2:1 MUX but its performance is low as its output is somewhat distorted.</em></p><p class="Abstract" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-align: center; text-indent: 0in; line-height: 105%;" align="center"><em><span style="font-size: 12.0pt; line-height: 105%;">Abstract</span></em><em></em></p><p class="Abstract" style="margin-top: 0in; margin-right: .5in; margin-bottom: .0001pt; margin-left: .5in; text-indent: 0in; line-height: 105%;"><em><span style="font-size: 10.0pt; line-height: 105%; font-weight: normal;">This paper presents a comparative analysis of 2:1 multiplexer using different logic styles (transmission gate, pass transistor and CMOS logic), with three different technologies i.e. 45nm, 32nm and 16nm. Simulation is done using Synopsys HSPICE tool at 1V power supply.</span></em><em><span style="font-size: 10.0pt; line-height: 105%; mso-ansi-language: EN-IN; font-weight: normal;" lang="EN-IN"> As a result, it is found that </span></em><em><span style="font-size: 10.0pt; line-height: 105%; font-weight: normal;">the least power is consumed by 2:1 multiplexer implemented using TGL. </span></em><em><span style="font-size: 10.0pt; line-height: 105%; mso-ansi-language: EN-IN; font-weight: normal;" lang="EN-IN">It consumes 99.7% less power than pass transistor logic and PTL consumes 99% more power than CMOS.</span></em><em></em><em><span style="font-size: 10.0pt; line-height: 105%; mso-ansi-language: EN-IN; font-weight: normal;" lang="EN-IN">Since multiplexer implemented by PTL utilizes minimum number of transistors, i.e., 2 ,therefore it is the area efficient logic circuit for 2:1 MUX but its performance is low as its output is somewhat distorted.</span></em></p>Tripti DuaAnju Rajput2021-02-092021-02-098Review: Detection of Motor bike Accident to provide Emergency Assistance Using Narrowband-Internet of Things (NB-IoT) System
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2624
<p><strong>Abstract</strong><br />An accident is an unpredicted and unintentional event. Considering the alarming increase in the number of motor bike riders and the number of accidents happening in our country, this system ensures to make the two-wheeler driving safer than before for the rider. The lack of treatment in proper time is the major reason for half of the deaths in road accidents. Many Accident detection systems is based on 3G and 4G wireless communication technology, which requires broad network resources and large amounts of available electricity, was designed for high-speed Internet usage. Most narrowband-Internet of Things (NB-IoT) devices do not require high-speed Internet because most IoTs work by sending commands to and receiving commands from a server only. NB-IoT is a low-power wide area (LPWA) technology that was developed to accommodate a broad variety of new IoT devices. Our newly developed motor bike accident detection system recognize a fall of bike and heartbeats of Victim during the accident, If heartbeats are not in normal conditions then automatically alert up to 4 contacts about the accident . This system aims at providing early detection of accidents and communicating the information immediately to the emergency responses on time to provide quick assistance to the injured person.</p><p><strong>Keywords:</strong> low-power wide area (LPWA), Narrowband-Internet of Things (NB-IoT)</p>Shivani YadavShailesh KhaparkarNirdesh Jain2021-02-042021-02-048Artificial Neural Network and Its Application to Diagnosis
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2518
<p>In this paper, we are giving an excellent idea for the designing of the standard diagnosis key diagnosis systems, which security is based on the well known number theoretic problem, i.e., elliptic curve discrete logarithm problem in multiplicative group of the finite field. But now we are moving in the revolutionary idea, “The Automatic groups G converted into the multiplicative groups G’ of the finite field Zp* of the order p-1, where p is the prime number, If G and G’ are one-to-one and onto isomorphic (Theorem).” By using this theorem, we design the new diagnosis key diagnosis system, whose security or encoding scheme is strictly different as compare to the other traditional encoding scheme.</p><p>Keywords: Diagnosis, Automatic Groups, Diagnosis, Finite Field, Groups, Encoding Scheme.</p><p>Cite this Article: Sunil Kumar Kashyap, Deepshikha Sharma. Artificial Neural Network and Its Application to Diagnosis. Journal of Advancements in Robotics. 2020; 7(2): 15–22p.</p>Sunil Kumar KashyapDeepshikha Sharma2020-08-312020-08-318Fuzzy and Cryptosystem
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2517
<p>Zadeh’s theory of fuzzy for grading the objects is reviewed in this paper under the context to develop the cryptosystem. This paper proposes a cryptosystem whose security comprises with the membership function of fuzzy. The security lies with the linguistic and numeric representation of information and its transformation by fuzzy. The one way hash function is substituted to membership function. The key generation and encryption comprises with fuzzy set and fuzzy logic respectively. Thus, the proposed system provides the security against all attacks by linguistic transformation rather than numeric representation. This is also an efficient cryptosystem by grading.</p><p>Keywords: Fuzzy Set, Fuzzy Logic, Cryptosystem, Membership Function, Encryption.</p><p>Cite this Article: Sunil Kumar Kashyap, Ashutosh Pandey. Fuzzy and Cryptosystem. Journal of Advancements in Robotics. 2020; 7(2): 1–8p.</p>Sunil Kumar KashyapAshutosh Pandey2020-08-122020-08-128Spider Robot
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2521
<p>In the current age, the robots and electronic gadgets are getting more sophisticated, reliable and miniaturized. That makes robotic systems increasingly suitable for spying and law enforcement purposes. The need for flexi-controlled smart robot, equipped with appropriate sensors for movement, is increasing day by day due to growing risks and the concern for human safety. Spy-robots can be used for a wide range of applications, from collecting sensitive intelligence data to completing covert missions. This paper propose a spy robot with suitable sensors and cameras to perform different tasks, which can be operated remotely for reconnaissance or patrolling tasks, where it can relay the videos and images captured back to the operator, through wireless communication.</p><p>Keywords: Spider, robot, image processing, spying, technology.</p>Cite this Article: Ragini Sharma, Monish Adhikari, Sanket Deshmuk, Chetan Mhatre. Spider Robot. Journal of Advancements in Robotics. 2020; 7(2): 29–34p.Ragini SharmaMonish AdhikariSanket DeshmukChetan Mhatre2020-08-062020-08-068Hand Gesture Controlled Drone
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2520
<p>Unmanned Aerial Vehicles, also known as drones, are the aircrafts that can be operated without a human pilot on board of the aerial vehicle. Drones can be navigated from the ground, using a GPS tracking system. Drones are traditionally operated using remote controllers, joysticks, mobile applications, and embedded computers. Some important problems previously faced with these approaches are that drone controlling is limited to the range of electromagnetic radiation and is susceptible to interference noise. In this, the use of hand gestures is considered as another technology to control drones. Hand gestures are used to control the movement of the drone and an onboard camera is used for streaming an aerial view of the surrounding. This UAV (Unmanned aerial vehicles) are becoming very popular and are widely used for photogrammetry and remote sensing applications. This paper basically describes about how the surveillance drone is made and then discusses about its-components and it’s working. The main objective of this project is to provide convenient and efficient surveillance at an affordable price, so that the drones can be used for both private and government purposes.</p><p>Keywords: Drone, Hand gesture, Quadcopter, Aerial view, Gesture controllers, Aerial surveillance.</p>Cite this Article: Abhinav Vilas Dhanawade, Ninad Dwarkanath Patil, Mukesh Sanjay Dhanure, Rina Bora. Hand Gesture Controlled Drone. Journal of Advancements in Robotics. 2020; 7(2): 23–28p.Abhinav Vilas DhanawadeNinad Dwarkanath PatilMukesh Sanjay DhanureRina Bora2020-08-062020-08-068Decode Human Immune System for Blood Disease Detection using Machine Learning Algorithms
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2519
<p>Quick and accurate medical diagnosis are crucial for the successful treatment of diseases. Using machine learning algorithm and based on laboratory blood test result, we have built a model to predict hematology diseases. It is a predictive model which uses various symptoms causing the disease and a few blood parameters to detect the disease. Blood analysis is an essential indicator for many diseases. It contains several parameters which are sign for various specific blood diseases. For predicting the disease according to blood analysis, symptoms that leads to identifying the disease precisely should be recognized. Our model uses machine learning which is the field responsible for building models for predicting the output based on previous data. Accuracy of the machine learning algorithm is based on quality of data collected for learning process.</p><p>Keywords: Disease, Symptoms, Blood Parameters, technology, human immune system, algorithm.</p>Cite this Article: Ria Vijay Nagaonkar, Suraj Rajaram Jare, Rishi Vinod Pandey, Suhasini Parvatikar. Decode Human Immune System for Blood Disease Detection using Machine Learning Algorithms. Journal of Advancements in Robotics. 2020; 7(2): 9–14p.Ria Vijay NagaonkarSuraj Rajaram JareRishi Vinod PandeySuhasini Parvatikar2020-08-062020-08-068Design of Low Power Preamplifier for Hearing Aid Applications
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2406
<p>A microphone preamplifier having low power, low noise and high phase margin for use in hearing aid applications is proposed. The preamplifier uses two-stage topology utilizing self cascode transistors for improving the gain by decreasing the channel length modulation effect and increasing the output impedance. Simulations have been performed in Cadence Virtuoso using SCL 180 nm technology parameters and comparative analysis with other state of art preamplifier design is performed. Simulation result shows that the proposed preamplifier achieves a gain of 20 dB with a bandwidth of 19.2 kHz. The power reported is 9.8 µW which is lower than most of the reported designs which further demonstrates the effectiveness of the proposed design.</p><p>Keywords: Preamplifier, low power, high gain, low noise, microphone applications, low frequency</p><p>Cite this Article Richa Neog, Naushad Manzoor Laskar, Koushik Guha, Saurav Nath, K.L. Baishnab. Design of Low Power Preamplifier for Hearing Aid Applications. Journal of Advancements in Robotics. 2020; 7(1): 1–7p.</p>Richa NeogNaushad Manzoor LaskarKoushik GuhaSaurav NathK.L. Baishnab2020-05-052020-05-058Unsupervised Learning Techniques in Neural Network for Face Recognition
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2387
<p>This paper represents the review of systematic comparison between of unsupervised learning algorithms for face recognition. In neural network, different algorithms are used for face recognition. Each algorithm has different accuracy factors. Commonly these methods asset a set of base images and serve faces as linear combo of images. In our proposed review, firstly we discuss about the face detection using neural network along with the concept of digital image processing. In this paper, different era algorithms: K-means, C-means, PCA (principle component analysis), single SVM (support vector machine), ensemble SVM, RBF (redial basis function), and LVQ (learning vector quantization) are compared on the basis of recognition rate, error rate and identify the best algorithm.</p><p>Keywords: Face recognition (FR), recognition rate (RR), error rate (ER), principle component analysis (PCA), support vector machine (SVM), radial basis function (RBF), learning vector quantization (LVQ), K-means, C-means</p><p>Cite this Article Prince Verma, Jagriti, Manpreet Kaur. Unsupervised Learning Techniques in Neural Network for Face Recognition. Journal of Advancements in Robotics. 2020; 7(1): 25–29p.</p>Prince VermaJagriti .Manpreet Kaur2020-05-042020-05-048Event Detection and Analysis of Online Social Image Measurement by Using SNMF
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2405
<p>Conventional sites were driven by human-altered occasions which lead to immense web search traffic. This paper is an overview led for distinguishing the different occasion recognition strategies which are helpful for occasion mining. We picked picture examine log as the advantage for event mining, as chase logs can genuinely reflect person's inclinations. Late investigations have demonstrated that an observable level of web search traffic is about get-togethers. We picked picture look log as the asset for occasion mining, as search logs can legitimately mirror individuals' inclinations. Besides, time factor is considered as a significant component for occasion recognition as various occasions create at various times. What is more, to give an outwardly engaging storyboard, every occasion is mapped with a lot of pertinent pictures orchestrated along a timetable. We utilize acclaimed individuals as our test territory, which takes a broad degree of picture look for bargains. Investigations involving web look traffic on 200 acclaimed individuals, for a period of a half year, show amazingly encouraging results differentiated and top-notch production storyboards.</p><p>Keywords: SNMF, photo selection, event detection, NMF, crawling</p><p>Cite this Article Nalli Vinaya Kumari, Gouri Arun Lahare, Naresh Kumar Budugu, Vamshi Krishna Kallem. Event Detection and Analysis of Online Social Image Measurement by Using SNMF. Journal of Advancements in Robotics. 2020; 7(1): 19–24p.</p>Nalli Vinaya KumariGouri Arun LahareNaresh Kumar BuduguVamshi Krishna Kallem2020-05-042020-05-048Blue Eye Technology
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2416
<p>In social interaction with other human beings which delivers information by using emotion and facial expression play about their mood that is important part in communication. The “Blue Eyes Technology” creates sensory abilities like those of human beings which enable the computer to gather facts about humans and interact with others. This paper implements the detection of feelings or emotion like fear, surprised, happy, sad and anger; it defines by human eye expressions and by using an emotion. Blue Eyes Technology aims allow people to interact with computers in a more natural manner. This technology BLUE define for Bluetooth, which enables reliable wireless communication and EYES define the movement of the eye that enables us to see lot of interesting and important information. It objective at creating computational machines that have perceptual and sensory ability. In this technology, emotions and action can be identify by using camcorder. The technologies used for manual and artificial intelligent speech recognition, gaze input cascaded, recognition, simple user interest tracker, the eye movement sensor. Its main applications are automobile industry, video games, medical diagnosis and lie detector tests. It is an emerging technology and in future it is expected to reduce the gap between electronic and physical world. The emotion mouse obtains physiological data and emotional state of a person through the single touch of mouse having different sensors.</p><p>Keywords: Emotion mouse, emotion recognition, eye expressions, support vector machine (SVM), Hidden Markov Model (HMM), blue eyes, images, magic pointing, image processing, sense</p><p>Cite this Article Sushama Sainwar, Shalini Kulshrestha, Anil Kumar Sharma. Blue Eye Technology. Journal of Advancements in Robotics. 2020; 7(1): 15–18p.</p>Sushama SainwarDr. Shalini KulshresthaAnil Kumar Sharma2020-05-042020-05-048Application of Machine Learning Techniques for Tongue Diagnosis in Ayurveda
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=2407
<p>The analysis of tongue image is a very crucial approach in order to evaluate human health in Ayurveda medication. As a result of the modification in tongue color might counsel physical or mental disorders. Many tongue color quantification strategies for tongue diagnosis are published by many researchers in Chinese medication. However, reliable tongue color analysis algorithms are limited for Ayurveda medicine. The main objective of this paper is to apply advanced techniques and algorithms of digital image processing and Machine learning to quantify and verify clinical knowledge of tongue color identification by characterizing variations in tongue features. Tongue images are captured from good quality camera with sufficient lighting conditions, and collected about 60 tongue images. Active contour segmentation algorithm based on edge information is applied on input image to segment the tongue area, and then apply clustering technique using K-means. Clustering approach is applied to separate tongue-body and coating area. The result of segmenting tongue body and coating is very good in CIELAB color space.</p><p>Keywords: K-means clustering, CIELAB, tongue diagnosis, adaptive segmentation, tongue images</p><p>Cite this Article Sumanth N.S., N. Satish Kumar, Harshvardhan Tiwari, Balaji S., Prabhanjan S., Meenakshi Malhothra, Pallavi C.V. Application of Machine Learning Techniques for Tongue Diagnosis in Ayurveda. Journal of Advancements in Robotics. 2020; 7(1): 8–14p.</p>Sumanth N SN. Satish KumarHarshvardhan TiwariBalaji SPrabhanjan SMeenakshi MalhothraPallavi C V2020-05-042020-05-048Machine Vision Robot with Real Time Sensing
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=240
<p>Till now by giving the commands through voice we were able to control the robot but we can’t be there every where all the time so to give command through the voice some time we may face difficulty. So in our paper we are giving the commands through the text images. The robot captures it, after processing the image it react to the command which is in the text image. So here no need of using voice command. We have described an efficient method for localizing a mobile robot in an environment with landmarks. We assume that the robot can identify landmarks along with that it is able measure its bearings relatively. The inputs are given from the environment with the help of a camera. The image processing has been done using matlab. Different feature extraction techniques are used to process the image. We present results of simulations, with that we propose how to use our method for robot navigation. We are using Arduino in designing of the robot. In our paper we are going to add speedometer to the robot so that the distance travelled by the robot will be measured. Using the GSM module the distance travelled by the robot is sent to the user mobile. Also the robot sends the message to the user mobile whether the robot reached the destination successfully or not using the GSM module.</p><p><strong>Keywords</strong>: Image processing, land mark identification, GSM module, feature extraction, robot</p>Ganesh V. N.Sachin Gururaj AcharyaSubramanya BhatYashas S. V.2020-02-252020-02-258Tracking Control of Robotic Manipulator using PID, Computed Torque Control and Sliding Mode Control Techniques
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=239
<p>In the last decades, the robotic manipulators have been extensively used in the industrial applications such as paint spraying, welding, accurate positioning system etc. In these applications, joint angles of robotic manipulators are directed to follow some given trajectories as close as possible. Therefore, Inspite of long years of research, trajectory tracking problem of robotic manipulators is the most significant and fundamental task for researchers to work upon. Robotic manipulator systems are inevitably subject to structured and unstructured uncertainties which result in imprecision of dynamical models of robotic manipulators and it is difficult to obtain a suitable mathematical model for the robotic control scheme. Robotic manipulators are dynamically coupled, multi-input-multi-output, non-linear and time variant complex systems. This paper presents the dynamics of two link robotic manipulator. In this paper PID (Proportional Integral Derivative), CTC (Computed Torque Control) and SMC (Sliding Mode Controller) controllers are designed and implemented to the joint position control of two link robotic manipulator for pre-defined trajectory tracking control. Simulated results for different controllers are compared to show reduction in tracking error and performance improvement of two link robotic manipulators. The simulation work is carried out in MATLAB environment.</p><p><strong>Keywords</strong>: Robotic manipulator, tracking control, PID controller, CTC control and SMC control</p>Sheilza JainAnika Chhabra2020-02-252020-02-258Surveillance Patrol Robot for People Tracking in Indoor Environments
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=206
<p>There is a great challenge that a mobile robot reliably and continuously tracks a specific person in indoor environments. In this paper, a novel method is presented, which can effectively recognize and reliably track a target person based on mobile robot vision. Such a robot is equipped with a camera which senses a moving object and starts tracking the object. The on-board camera develops a computer vision system for detection of the object/target to control and guide the movement of mobile robot. In order to effectively track the specific person, upper body color clothes region is proposed for extracting the pattern features. The system applies center-of-mass based computation, filtering and color segmentation algorithm to locate the target and the position of the robot. Artificial neural network (ANN) is introduced for controlling the robot to follow the person with voice-aided instructions from the robot. Experimental results validate the robustness and the reliability of this approach.</p><p><strong>Keywords</strong>: Mobile robot, people tracking, color segmentation, filtering</p>Neerparaj RaiShakti DharRupam Kakati2020-02-252020-02-258Adaptive Tree Climbing Robot
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=207
<p>This paper is proposed to design a tree climbing robot which makes the robot to realize the environment and climb on tree autonomously. The proposed robot is inspired by locomotion of inchworms. By the use of limit switches, the algorithm reconstructs the shape of the tree which reveals how the environment can be realized with limited information. Since the shape of trees is irregular and complex, it is a challenging task to design the robot. So the A pair of grippers attached to the robot enable it to adhere on variety of trees with wide range of gripping curvatures. The body of the robot is designed such that it can climb the tree with limited sensing resources. The robot is well-designed such that it can climb regular and irregular shaped trees.</p><p><strong>Keywords</strong>: Continuum body, front gripper, rear gripper, limit switch</p>Santosh KUNagaraj R GudimathPrajwal AK2020-02-252020-02-258Active Vision Approach for Controlling Educational Robotic Arm with Autonomous Object Manipulation
https://computers.stmjournals.com/index.php?journal=JoARB&page=article&op=view&path%5B%5D=221
<p class="Els-Abstract-head"><em>This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a six-degree-of-freedom (DOF) serial robotic arm. Two on-board cameras </em><em>developed a computer vision system for detection and autonomous object/target manipulation placed randomly on a target surface and controlling an educational robotic arm (ERA) to pick it up and move it to a predefined destination. Force sensitive resistor (FSR) has been used as a sensory element for handling soft and sturdy objects. </em><em>The system applies centre-of-mass based computation, filtering and color segmentation algorithm to locate the target and position of the robotic arm. The proposed platform finds its potential to teach technical courses (like Robotics, Control systems, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. Experimental results demonstrated the effectiveness and robustness of the system.</em></p><p class="Els-Abstract-head"><em><strong>Keywords</strong>: computer vision, robotic arm, autonomous system</em></p><p><strong><em> </em></strong></p>Neerparaj RaiBijay Rai2020-02-252020-02-258