Field Day 2018 #ARRLFD

2018ARRLFieldDayLogoDOWNLOADJune 23rd- 24th (starting at 2PM Eastern) is the annual Amateur Radio event called Field Day, where radio operators from around North America exercise their emergency response skills. It is also a contest to contact as many stations as possible in 24 hours, following a well defined set of rules for exchanging information. One thing different this year will be the exchange of more detailed geographic information than in the past.

Field Day is also ham radio’s open house, where groups of radio operators come together in a very visible way and interact with the public. Every June, more than 40,000 hams throughout North America set up temporary transmitting stations in public places demonstrating ham radio’s science, skill and service to our communities and nation. It combines public service, emergency preparedness, community outreach, and technical skills all in a single event. Field Day has been an annual event since 1933 and remains the most popular event in ham radio in the Americas.

This year I will be operating with the KE4HAM group at Sun City Hilton Head. We are planning to be running on all battery or generator power and string up a number of temporary antennas. I hope to be operating mainly FT8 (a relatively new digital mode).

You should be able to see a live update on the Internet of both those hearing KE4HAM as well as those I am hearing via PSKReporter.

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Update to Morse Code training for Android

antennaI wrote a program that simulated conversations between hams (QSOs) to help improve Morse code skillsQSOSender3. Believe it or not, Morse code is as popular now as it has ever been, in amateur radio.

QSOSender3 has a 5 out of 5 rating in the Play Store and has been installed on almost a thousand Android devices. It’s useful, since the code you hear on the air is usually quite different than what practice programs provide.

I received a request the other day to support the Farnsworth method, so something close to that has now been added and the program released on the Google Play store. If you find any other features you’d like to see added, let me know.

It will not help with field day this year, but may improve your skills for the future.

 

 

Things are not always what they seem – a discussion about analytics

evaluationHave you ever been in a discussion about a topic thinking you’re talking about one area but later find out it was about something else altogether?

We’ve probably all had that conversation with a child, where they say something like “That’s a really nice ice cream cone you have there.” Which sounds like a compliment on your dairy delight selection but in reality is a subtle way of saying “Can I have a bite?”

I was in a discussion with an organization about a need they had. They asked me a series of questions and I provided a quick stream of consciousness response…The further I got into the interaction the less I understood about what was going on. This is a summary of the interaction:

1) How do you keep up to speed in new data science technology? I read and write blogs on technical topics as well as read trade publications. I also do some recreational programming to keep up on trends and topics. On occasion I have audited classes on both EdX and Coursera (examples include gamification, Python, cloud management/deployment, R…)

2) Describe what success looks like in the context data science projects? Success related analytics efforts is the definition, understanding, development of insight on and the addressing of business goals using available data and business strategies. Sometimes this may only involve the development of better strategies and plans, but in other cases the creation of contextual understanding and actionable insight allows for continuous improvement of existing or newly developed processes.

3) Describe how do you measure the value of a successful data science application. I measure the value based on the business impact through the change in behavior or business results. It is not about increased insight but about actions taken.

4) Describe successful methods or techniques you have used to explain the value of data science, machine learning, advanced analytics to business people. I have demonstrated the impact of a gamification effort by using previously performed business process metrics and then the direct relationship with post implementation performance. Granted correlation does not prove causation but by having multiple instances of base cases and being able to validate performance improvement from a range a trials and processes improvements, a strong business case can be developed using a recursive process based on the definition of mechanics, measurement, behavior expectations, and rewards.

I’ve used a similar approach in the IoT space, where I’ve worked on and off with machine data collection and data analysis since entering the work force in the 1980s.

5) Describe the importance of model governance (model risk management) in the context of data science, advanced analytics, etc. in financial services. Without a solid governance model, you don’t have the controls and cannot develop the foundational level of understanding. The model should provide the rigor sufficient to move from supposition to knowledge. The organization needs to be careful not to have too rigid a process though, since you need to take advantage of any information learned along the way and make adjustment, to take latency out of the decision making/improvement process. Like most efforts today a flexible/agile approach should be applied.

6) Describe who did you (team, function, person) interact with in your current role, on average, and roughly what percent of time did you spend with each type of function/people/team. In various roles I spent time with CEO/COOs and senior technical decision makers in fortune 500 companies (when I was the chief technologist of Americas application development with HP: 70-80% of my time). Most recently when with Raytheon IT, I spend about 50% of my time with senior technical architects and 50% of my time with IT organization directors.

7) Describe how data science will evolve during the next 3 to 5 years. What will improve? What will change? Every organization should have in place a plan to leverage both improve machine learning and analytics algorithms based on the abundance of data, networking and intellectual property available. Cloud computing techniques will also provide an abundance of computing capabilities that can be brought to bear on the enterprise environment. For most organizations, small sprint project efforts need to be applied to both understanding the possibilities and the implications. Enterprise efforts will still take place but they will likely not have the short term impact that smaller, agile efforts will deliver. I wrote a blog post about this topic earlier this month. Both the scope and style of projects will likely need to change. It may also involve the use more contract labor to get the depth of experience in the short term to address the needs of the organization. The understanding and analysis of the meta-data (block chains, related processes, machines.…) will also play an ever increasing role, since they will supplement the depth and breadth of contextual understanding.

8) Describe how do you think about choosing technical design of data science solutions (what algorithms, techniques, etc.).

I view the approach to be similar to any other architectural technical design. You need to understand:

  • the vision (what is to be accomplished)
  • the current data and systems in place (current situation analysis)
  • understand the skills of the personnel involved (resource assessment)
  • define the measurement approach to be used (so that you have both a leading and lagging indicator of performance)

then you can develop a plan and implement your effort, validating and adjusting as you move along.

How do you measure the value/impact of your choice?

You need to have a measurement approach that is both tactical (progress against leading indicators) as well as strategic (validation by lagging indicators of accomplishment). Leading indicators look ahead to make sure you are on the right road, where lagging indicators look behind to validate where you’ve been.

9) Describe your experience explaining complex data to business users. What do you focus on?

The most important aspect of explaining complex data is to describe it in terms the audience will understand. No one cares how hard it was to do the analysis, they just want to know the business impact, value and how it can be applied.

Data visualization needs to take this into account and explain the data to the correct audience – not everyone consumes data using the same techniques. Some people will only respond to spreadsheets, while others would like to have nice graphics… Still others want business simulations and augmented reality techniques to be used whenever possible. If I were to have 3 rules related to explaining technical topics, they would be:

  1. Answer the question asked
  2. Display it in a way the audience will understand (use their terminology)
  3.  Use the right data

At the end of that exchange I wasn’t sure if I’d just provided some free consulting, went through a job interview or was just chewing the fat with another technologist. Thoughts???

Is AI a distraction???

AutomationI was recently in an exchange with a respected industry analyst where they stated that AI is not living up to its hype – they called AI ‘incremental’ and a ‘distraction’. This caught me a bit my surprise, since my view is that there are more capabilities and approaches available for AI practitioners than ever before. It may be the business and tech decision makers approach that is at fault.

It got me thinking about the differences in ‘small’ AI efforts vs. Enterprise AI efforts. Small AI are those innovative, quick efforts that can prove a point and deliver value and understanding in the near term. Big AI (and automation efforts) are those that are associated with ERP and other enterprise systems that take years to implement. These are likely the kinds of efforts that the analyst was involved with.

Many of the newer approaches enable the use of the abundance of capabilities available to mine the value out of the existing data that lies fallow in most organizations. These technologies can be tried out and applied in well defined, short sprints whose success criteria can be well-defined. If along the way, the answers were not quite what was expected, adjustments can be made, assumptions changed, and value can still be generated. The key is going into these projects with expectations but still flexible enough to change based on what is known rather than just supposition.

These approaches can be implemented across the range of business processes (e.g., budgeting, billing, support) as well as information sources (IoT, existing ERP or CRM). They can automate the mundane and free up high-value personnel to focus on generating even greater value and better service. Many times, these focused issues can be unique to an organization or industry and provide immediate return. This is not the generally not the focus of Enterprise IT solutions.

This may be the reason some senior IT leaders are disillusioned with the progress of AI in their enterprise. The smaller, high-value project’s contributions are round off error to their scope. They are looking for the big hit and by its very nature will be a compromise, if not a value to really move the ball in any definitive way – everyone who is deploying the same enterprise solution, will have access to the same tools…

My advice to those leaders disenchanted with the return from AI is to shift their focus. Get a small team out there experimenting with ‘the possible’. Give them clear problems (and expectations) but allow them the flexibility to bring in some new tools and approaches. Make them show progress but be flexible enough to understand that if their results point in a different direction, to shift expectations based on facts and results. There is the possibility of fundamentally different levels of costs and value generation.  

The keys are:

1)      Think about the large problems but act on those that can be validated and addressed quickly – invest in the small wins

2)      Have expectations that can be quantified and focus on value – Projects are not a ‘science fair’ or a strategic campaign just a part of the business

3)      Be flexible and adjust as insight is developed – just because you want the answer to be ‘yes’ doesn’t mean it will be, but any answer is valuable when compared to a guess

Sure, this approach may be ‘incremental’ (to start) but it should make up for that with momentum and results. If the approach is based on expectations, value generation and is done right, it should never be a ‘distraction’.

An SUV I couldn’t stand

This weekend, we rented a car to go to a high school graduation in Texas. When we arrived in Houston, I hopped out of the rental car bus looking forward to my choice of cars. There at the end of the aisle was a Mercedes SUV. I thought “I’ve never had one of those, now’s the Time.” We jumped in and off we went.

The first thing I noticed was that there were 4 (count them four) levers on the steering column. Who needs a porcupine for a steering column.

It was foggy. The first encounter with this little design gem was that one of the levers was located in exactly the same location as the wiper controls on my wife’s Chevy Equinox. The big difference in experience was that if you touch this lever, it drops you into neutral and one time even into park.

There is also a button on the console between the seats that when your hand brushes it, you are now in manual transmission mode rather than automatic. That happened twice before I figured out what was causing it. Flappy paddle shifting can be fun, but only when you expect it.

A bonus feature was that along my drive between Houston and Austin, the following screen kept popping up:

I like a leasurely drive as much as the next person, but to remind me to stop and take a break every 30 minutes is a bit much. I am driving across Texas, not to the grocery store. There is only so much coffee one can drink.

Another thing I found truly disappointing was the entertainment system. In the Equinox, Chevy has made it so the phone can display maps… on the large screen built into the car, allowing me to see the map that I am trying to follow or the progress in the podcast. Not on the Mercedes!

The final bow was when I was about half an hour from the rental return, the car decided it had enough of the Texas heat, and the window cracked, halfway across the drivers side.

I must say that for a car that costs as much as a down payment for a family home, I was a bit disappointed. Maybe that’s because the folks who buy this are looking for status and not convenient transportation. I’m out.

First flight in months

Headed to Round Rock, TX for a graduation. This is my first flight since October and after all the issues and controversy about flying lately, I am hoping for an uneventful flight.

Based on the level of traffic at the Starbucks in the Savannah airport, it should be a quiet flight.

Hopefully, I’ll be home before the first tropical depression of the year can make things interesting.

Woodworking and Yard Dice

diceAn interesting game my daughter recently showed me was Lawn Farkle or Lawn Yahtzee. For these games, you’ll need 5-6 lawn size dice. When I looked on-line, these dice ranged from about $30 up to $100 for a set of 6. Naturally, I thought I should be able to make these with stuff I had lying around. It did turn out to be a bit more work than I expected.

This post is a summary to make these (not so little) gems. I have a more detailed version (with pictures) if anyone is interested.

1)      First, you start with a 24” 4×4 or two glued together 2x4s to make about 24 inches that are 3 ½ inches on a side, since we’re going to turn them into 3 ½ inch wooden cubes. Be aware of where the knots in the wood are located, since you may need a longer piece of wood to work around these flaws.

2)      If you are using 2x4s.

  1. If you have a joiner, join the two faces of the 2×4 that you plan to glue together. This will enable an almost seamless connection.
  2. Glue both faces of the 2x4s you plan to glue together to ensure a strong seam. Make sure there is smooth even coat of water proof glue, since the dice will be used outside.
  3. Clamp them together and wait about 24 hours for the glue to set. You may need to clamp in two dimensions to ensure that boards are aligned. You may want to look at the grain of the wood and how they come together before you glue, to make the desired grain pattern. The end grain of the glued together board formed a pattern like this: )( Essentially, forming an X.

3)      If you have a joiner, clean up the side of the wood to make it smooth, square and ready to rip. The joined face is the one that will be placed against the fence of the table saw.

4)      You’ll need to rip the board (based on their shortest dimension) to form the square cross-section. Measure the smallest dimension and use that to rip the other dimension to the same size. If you made this out of 2x4s glued together, you’ll end up with a 3×3 (approximately) board after ripping. You may be tempted to use a ¼ inch roundover router bit right now to round off the edges, while the board is still large, but wait — since you’ll likely need to resurface the edges later.

5)      Measure the board’s width and use the smallest dimension to cut the board into cubes, using a chop saw. I found it best to measure again after each cut, don’t try to make all the cuts at once, since the saw will take out some of the wood each cut.

7)      Next, find a set of dice and use one as a reference and mark the wooden cubes with a pencil, where the faces should be. Use an awl and mark the cubes, where the spots should be located. The following illustration should useful.

template

 

8)      There are several ways to tackle placing the spots on the dice. You could use a ½” Forstner bit, wood burning, paint, epoxy… This description, will use the Forstner bit to make the hole and then put in a contrasting colored plug.  Keep in mind, you’re going to need a large number of these plugs since each dice has 21 spots.

9) Drill the holes in the cube where the awl marks are located. I used a drill press to make the holes and made them about 1/8” in depth.

10) Next you’ll need to use a ½” plug cutter to make the plugs for dots. In my case, I used some scrap walnut strips. The wood will need to be a bit longer than the hole depth cut in the previous step. Keep in mind, you’re going to need quite a bit of whatever material you choose. Go slow when cutting the holes, since it is easy to tear out.

The other choice is to use an appropriately sized dowel and cut plugs to the correct length using a scroll saw. This was much faster than making the plugs.

11) Place some glue in the holes of the cube and insert the plugs you’ve cut. You may need a hammer at this point. I recommend using a waterproof glue since these are going to be used outside.

12) Next, you’ll likely need to trim off the excess wood from the plugs (with a band saw) and sand the face until smooth

13)  Finally, you can route the edges with a ¼ inch roundover router bit.

14)  You can stain the cubes and use polyurethane or Danish oil… to protect them from the elements. Since two of the faces will be end cut, you may want to use some glue size on these surfaces to minimize the amount of stain that’s absorbed. If you don’t, these faces will be significantly darker. You can either buy glue size or just make it by diluting your wood glue by 90% and painting it on the ends, to fill up the end grain.

15) To roll a set of dice, you’ll need some kind of bucket, large enough to hold them all.

Tools required:

  1. Pencil
  2. Table saw to rip the boards into square boards
  3. Chop saw to make the cubes
  4. Band saw to trim the spots on the cubes
  5. Drill Press
  6. Drill ½” bit or equivalent for the spots (and dowels or a plug cutter if you’re going to fill them in with wood)
  7. Sander
  8. Sand paper
  9. Tape measure
  10. Rubber Mallet
  11. Waterproof glue
  12. Joiner

Games

And pretty much any other dice game you can think of.