NCIL : Gesture Controlled Robot for Remote Applications

Gesture Controlled Robot for Remote Applications

Author(s):

Harshit Goel , Maharaja Agrasen College (University of Delhi) Delhi - 110096

Keywords:

Gesture Controlled Robot, Remote Applications, XBee Radios

Abstract

The paper describes robustness of Sensors based Gesture Controlled Robot which is a kind of robot that can be controlled by our hand gestures rather than an ordinary old switches or keypad. In future there is a chance of making robots that can interact with humans in a natural manner. Hence my target interest is with hand motion based gesture interfaces. An innovative formula for gesture recognition is developed for identifying the distinct action signs made through hand movements. An Acceleration Sensor was used to carry out this and also a Flex sensor for convinced operation. For communication between hand and robot XBee, radio module has been used which has a significant transmission range, consumes low power and has multiple advantages over conventional RF or Bluetooth transmission. In order to full-fill our requirement a software program has been written and executed using a microcontroller on the robot. Gestures control robots can extensively be employed in human non-verbal communication. They allow to express orders (e.g. stop), mood state (e.g. -"victory" gesture), or to transmit some basic cardinal information (e.g. "two"). In addition, in some special situations they can be the only way of communicating, as in the cases of deaf people (sign language) and police's traffic coordination in the absence of traffic lights, a real-time continuous gesture recognition system for sign language Face and Gesture recognition. Upon noticing the results of experimentation proves that our gesture formula is very competent and it also enhances the natural way of intelligence and is also assembled in a cheap simple hardware circuit. INTODUCTION:   Technology today is imbibed for accomplishment of several tasks of varied complexity, in almost all walks of life. The society as a whole is exquisitely dependent on science and technology. Technology has played a very significant role in improving the quality of life. The increase in human machine interactions in our daily lives has made user interface technology progressively more important. Physical gestures as intuitive expressions will greatly ease the interaction process and enable humans to more naturally command computers or machines.  For more information click here...

NCIL – 2015 #IJSRD Publication Partner

NCIL - 2015

National Conference on "Student-driven Research for Inspired Learning" in Science and Technology

Organised by ESRC and Dept of Electronics

Publication Partner International Journal for scientific research & Development (IJSRD)

Date: 16-17 October 2015

http://ncilindia.in/

http://mac.du.ac.in/

Ijsrd Call For Paper Data Mining

Special Issue For Data Mining 

Dear Researchers/Authors,
IJSRD is promoting a new field of this Digital Generation-“Data Mining”. In accordance to it IJSRD is inviting research Papers from you on subject of Data Mining. This is under special Issue Publication by IJSRD. In addition to this authors will have a chance to win the Best Paper Award under this category.
To submit your research paper on Data Mining Click here

Best 25 papers will be published online. Participate in this special issue and get a chance to win the Best Paper Award for Data Mining. Also other authors will have special prizes to be won.

• Submit Special Issue Manuscript Online

• Last Date For Paper Submission of Special Issue : 25th September 2015

What is Data Mining..?

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Data mining (the analysis step of the "Knowledge Discovery in Databases" process. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records, unusual records and dependencies.The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:
(1) Selection
(2) Pre-processing
(3) Transformation
(4) Data Mining
(5) Interpretation/Evaluation.

To know more…….

Data mining involves six common classes of tasks:

Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.

Association rule learning (Dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".

Regression – attempts to find a function which models the data with the least error.

Summarization – providing a more compact representation of the data set, including visualization and report generation.

Application Areas….

Games

            They are used to store human strategies into databases and based on that new tactics are designed by Computer ( in association with Machine Learning, Artificial Intelligence)

Business

            Businesses employing data mining may see a return on investment. In situations where a large number of models need to be maintained, some businesses turn to more automated data mining methodologies.In business, data mining is the analysis of historical business activities, stored as static data in data warehouse databases. The goal is to reveal hidden patterns and trends. Data mining software uses advanced pattern recognition algorithms to sift through large amounts of data to assist in discovering previously unknown strategic business information. Examples of what businesses use data mining for include performing market analysis to identify new product bundles, finding the root cause of manufacturing problems, to prevent customer attrition and acquire new customers, cross-selling to existing customers, and profiling customers with more accuracy.

Science and engineering

            In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering.

Human rights

            Data mining of government records – especially records of the justice system (i.e., courts, prisons) – empowers the revelation of systemic human rights infringement in association with era and publication of invalid or deceitful lawful records by different government organizations

Medical data mining

            Some machine learning algorithms can be applied in medical field as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in databases.

Spatial data mining

            Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis. Data mining offers great potential benefits for GIS-based applied decision-making.

Temporal data mining

            Data may contain attributes generated and recorded at different times. In this case finding meaningful relationships in the data may require considering the temporal order of the attributes.

Sensor data mining

            By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.

Visual data mining

            During the time spent transforming from analogical into computerized, vast datasets have been created, gathered, and stored finding measurable patterns, trends and information which is covered up in real data, with a specific end goal to manufacture prescient formations(patterns).

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