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Dr. Amlan Chakrabarti


About : Dean, Faculty of Eng., UoK.
Software-Hardware Co-design for new Generation IoTs

Hardware-software codesign is a recent technological trend among the system designers and design engineers to facilitate the design of small embedded systems especially in the internet of things (IoT) space. Codesign methodologies, implemented as new "types" of electronic design and automation tools, are intended to give relief to designers struggling with provisional divisions of hardware and software components, and the attendant integration problems. It tries to exploit the synergy of hardware and software with the promisel to optimize and/or satisfy design targets in terms of cost, performance, and power of the final product. At the same time, it aims to reduce the time-to-market frame considerably. In the IoT ecosystem, optimization in terms of maximizing throughput considering real time need, reducing hardware resources and power requirement is of utmost importance and thus hardware- software codesign is an essential pathway.In this pre-conference tutorial, the emphasis is given on fundamental as well as advanced strategies of hardware software co-design and its application in developing intelligent and robust IoT infrastructure. The key aspects to be covered in this tutorial are categorized into three types.

  1. Models and methodologies of system design
  2. Hardware software partitioning and scheduling
  3. Co-simulation, synthesis and verifications
  4. System on chip for IoT Applications
  5. Reconfigurable and partially reconfigurable targets for IoT applications.

A case study of IoT application involving hardware-software codesign may be covered

Dr. Gopi Krishna Durbhaka


About:(Senior Member IEEE, Fellow of ISECE)
Predictive Maintenance of Wind Turbines based on the Fault Behavior Pattern Analysis applying Machine Learning
Effective and efficient usage of renewable energies like Wind, Solar, Hydro and Tidal power should be the vital issue of the decade to meet the current energy requirements, not to speak of the growing energy demand. Wind power, of late has been the focus to offset the power crisis faced due to shortages in the grid. However, the challenges faced during the operation and maintenance of wind farms with number of wind turbines scattered and positioned in remote areas has been very difficult for a quick access and is also expensive. In this plenary session, a custom recommendation system model which performs prognosis and diagnosis behavior pattern analysis with data from the sensors distributed wide across each turbine in the wind farms shall be explained herewith. This not only helps monitoring the health of the turbines but also helps to take preventive action before critical & catastrophic failures take place. A case study, wherein a sample fault data of gearbox has been used for analysis of the dynamics of the faulty gearbox using supervised as well as unsupervised machine learning models for prognostic and diagnostic processes of the device classifying the faults accordingly.

Dr. Mansi Patwardhan


About:(Dean of Research and Industrial Relations at Vishwakarma Institute of Technology, Pune) Data Analytics R&D Opp
Data Science, under its umbrella covers many aspects starting from statistics and probability theory to data mining, machine learning, etc. This talk would begin with providing a complete understanding of different subareas data science as the domain talks about. It would provide a perspective towards how data science in short is applied statistics by providing some case studies. The later half of the talk would be about advance data analytics techniques such as data mining, machine learning, deep learning, etc and its current applications.

Chaitanya Pandya


About:(Project Manager, Eternus Solutions)
The Use of Collaborative Bots (co-bots) in Information Technology
Usage of robots within manufacturing units and healthcare are a common reality; as is, usage of cloud based enterprise systems across multiple industry verticals due to associated benefits. Advances in cloud computing and its availability at reduced costs for enterprises, paired with, reducing cost of manufacturing robots are a key catalyst for making collaborative robotics popular within enterprises.
Having witnessed a shift from legacy systems, to cloud based architecture, cloud enterprise systems are now easily available and affordable for small and medium sized enterprises. The minimal turnaround time to on-board an organization onto the cloud paired, with advancements in industrial internet of things, has opened up infinite possibilities for application and use of collaborative robotics within cloud based enterprise environment.
This tutorial is aimed at exploring the possibilities associated with using this mashup of cloud computing and collaborative robotics, within the information technology domain. The objective is to evaluate the application of collaborative robotic beyond the scope of manual and repetitive tasks, within the information technology industry vertical.
Key words: Collaborative robotics, Cloud Computing, Cloud Robotics, Industrial Internet of Things, Internet of Things
Target Audience: Open for all
Learning objectives: After the tutorial, attendees will:
1: Know what is cloud robotics, collaborative robotics, industrial internet of things.
2: Be able to understand the benefits of usage of co-bots across industries.
3: Evaluate and establish the feasibility of usage of co-bots across industries.

Dr. Surekha Deshmukh


About:(Treasurer IEEE Pune Section, PVG’s COET)
Research Opportunities in Use of Artificial Intelligence in Data Analytics
The Restructured Indian Power Sector has opened numerous research opportunities, in the area of data analytics, wherein power system-data is a treasure to understand the real picture of power system. With advancement in smart metering, smart sensors, smart instrumentation, it is very much possible to collect huge data with minimum time line, but the real challenge is to analyze the data in respect of utilizing it for gaining real time solutions. The power system data has characteristics of being random, uncertain and volatile in many of the cases. The artificial intelligent tool has an ability to understand, adopt, incorporate and generalize these features while analyzing it, which gives improved accuracy in data analytics.
The aim of this tutorial is to introduce
1: The significance of variety of power system-data with vide features
2: The necessity of huge data collection and data analytics
3: The role of Artificial Intelligence (AI) in power system data analytics
4: Real time applications in domains as short term planning, short term forecasting, economics of power generation, real time markets, power trading

Col. Indrajeet singh


About:(Technology Evangelist Solution Architect and a Mentor)
IoT Security: Implementation Challenges in Smart Cities

Dr. Shashidhar


About:National Institute of Technology Karnataka, Surathkal
Feature Ext. & Analysis from Speech Data
For any pattern recognition and machine learning task, the first and an important phase is “Feature Extraction”. Features are basically compact and enriched representation of the whole signal. Many times features are specific to the task. Extraction of proper features fairly ensures better performance of the developed system.
In this pre-conference tutorial, the emphasis is given on different features and their characteristics those are normally being extracted and popularly being used for implementing various speech tasks. The features covered in this tutorial are categorized into three types.
1: Excitation source features
2: System features
3: Prosodic features
Speech is a stochastic signal produced by the time varying filter excited by the time varying excitation. The features extracted from the time varying excitation source such as vocal folds’ vibration are known as excitation source features. The features derived from the varying shapes and sizes of the oral cavity (representing time varying filter) are known as system features. The features extracted from longer speech segments representing paralinguistic information are known as prosodic features.

Dr. G. S. Mani


About:Chair IEEE Pune Section
Technology Management
Three Long haired hippies, entered an office and proposed -- “We have an idea that‟s going to change the world” .They looked 21-22,inexperienced,no business plans, just had one prototype, didn‟t know how to sell, obviously unable to get any investor interested. No doubt, they were thrown out unceremoniously. Same three guys founded the company „APPLE‟, developed world‟s first PC much later and in July 2001 had a market cap of $7.8 Billion.
In this pre-conference tutorial, the emphasis is given on different features and their characteristics those are normally being extracted and popularly being used for implementing various speech tasks. The features covered in this tutorial are categorized into three types.
This tutorial will present key concepts forming part of Technology Management --- Technology Selection, Technology Transfer/Absorption, Technology Life Cycle and Deployment, Technology Safeguarding etc. Case study on how these concepts have been applied by DRDO for India‟s indigenous Defence products will also form part of the tutorial.
Mention will also be made of Tech and Biz schools offering educational programs to develop knowledge on the subject. Future scope of the subject will also be indicated.The tutorial will help
1: Management / Technology students wanting to specialize on this emerging discipline
2: Aspiring entrepreneurs who dream of setting up a venture on their own and turning it into a reality
3: Academicians who want to learn about the subject and start courses on this emerging discipline
4: Experienced businessmen intending to apply the principles for expanding their business
Key words:Technology Management, Technology Selection, Technology Transfer/Absorption, Technology Life Cycle and Deployment, Technology Safeguarding, Enterprising.
Target AudienceManagement / Technology students, Entrepreneurs, Academicians, Businessmen and others interested in this emerging subject
Learning objectives:
1: Learning key concepts of Technology Management and its relevance in today‟s changing environment
2: Motivating students to learn about the subject, which has a vast potential
3: Motivating Educational entrepreneurs to start courses on this subject and be one of the first-movers in India

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