SCIENTISTS STUDY THE WORLD AS IT IS

ENGINEERS MAKE THE WORLD A BETTER PLACE TO LIVE IN
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Welcome

I have been a Software Engineer for the past two decades - and have recently started blogging here. Most of the time I work for company projects - that are proprietary in nature and cannot be published. I would say, about 90% of my time goes in doing this kind of work, that I can only talk about when I have left the company. Sometimes I come across interesting subjects that are not surrounded with legal restrictions, and I put them here.

Recently I have found new interest in Robotics, that used to be my passion back in the late eighties and early nineties. That subject had been suppressed in the Technology world due to lack of computing power and tools - though industrial robots had progressed at a decent pace in the manufacturing industry. However, in the past few years interest in that subject has resurfaced within the common public due to the advancement of software tools like Deep Learning, Reinforcement Learning, Robot Operating System and various open-source tools put up by companies in the Autonomous Vehicle space, and also advancement in hardware like Graphic Processing Units. My quest to pick up knowledge on all these subjects will show up in these blogs with time.

I believe it makes sense again to gear up for the new generation by brushing up on all the old knowledge and applying them to the new world. One really cannot watch this revolution from the sidelines - and not be part of it.

ABOUT ME                          MY BLOGS

Recent Articles

Selected articles picked up from my blog site.

  • Written by
    Driver Signatures from Car Diagnostic Data captured using a Raspberry Pi: Part 1 (Building your Raspberry Pi setup)
    The Raspberry Pi is an extremely interesting invention. It is a full-fledged Linux box (literally can be caged inside a plastic box) and it basically allows you to run any program to connect to any other communicating device around it through cables or a Bluetooth adapter. I am going to show you how to build your own system to hook up a Raspberry Pi to your car, then extract diagnostic information from the CAN bus, upload that to the cloud, and then use a streaming API to predict who is driving the vehicle using a learning model created through a…
    Driver Signatures from Car Diagnostic Data captured using a Raspberry Pi: Part 1 (Building your Raspberry Pi setup)
    Written on Wednesday, 04 July 2018 04:14
  • Written by
    Driver Signatures from Car Diagnostic Data captured using a Raspberry Pi: Part 2 (Reading real-time data and uploading to the cloud)
    This is the second article of the series to determine driver signatures from OBD data using a Raspberry Pi. In the first article I had described in detail how to construct your Raspberry Pi. Now let us write some code to read data from your car and put it to the test. In this second article I will describe the software needed to read data from your car's CAN bus, including some data captured from the GPS antenna attached to your Raspberry Pi, combine it into one packet and send it over to the cloud. I will show you the…
    Driver Signatures from Car Diagnostic Data captured using a Raspberry Pi: Part 2 (Reading real-time data and uploading to the cloud)
    Written on Wednesday, 04 July 2018 17:28
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    Driver Signatures from Car Diagnostic Data captured using a Raspberry Pi: Part 3 (Building a data model and predicting driver)
    In the first and second part of this series I described how to set up the hardware to read data from the OBD port using a Raspberry Pi and then upload it to the cloud. You should read the first part and second part of this series before you read this article. Having done all the work to capture and transport all the data to the cloud, let us figure out what can be done on the cloud to introduce Machine Learning. To understand the concepts given in this article you will need to be familiar with Javascript and Python.…
    Driver Signatures from Car Diagnostic Data captured using a Raspberry Pi: Part 3 (Building a data model and predicting driver)
    Written on Monday, 09 July 2018 02:08
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    Managing effectiveness of Email campaigns
    A Behavior Analysis Problem Despite the growth of alternate communication means like Chat applications (Whatsapp, Slack, Snapchat), Social web-sites (Facebook, Google Plus, Twitter), communicating via icons (Emojis) and images (Instagram), the good old Email is not yet dead. Almost all companies still rely on Email to not only run their business, but also to do marketing to their customers. This, in itself, has evolved into a field of study. The main concern facing any marketing manager is to control the amount of email to send to their clients so that the customer stays interested in the company and does not…
    Managing effectiveness of Email campaigns
    Written on Saturday, 18 August 2018 21:57
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    Predicting sensor failure based on voltage readings
    Let us try to apply the principles of churn for a use-case involving sensors and devices. In this post we will apply some machine learning principles to predict which devices are likely to fail in future. The scenario is as follows: Problem Description A company is in the business of allocating car parking space to visitors. For that, it needs to have sensors installed at each parking space. These sensors are installed on the ground right under where the car is supposed to park. When a car comes over it, it senses the presence of the car through a set…
    Predicting sensor failure based on voltage readings
    Written on Saturday, 23 June 2018 16:30