This article explains how to use the Arduino toolchain to program microcontrollers from the Arduino IDE using their bootloaders, and also burning bootloaders directly onto bare microcontroller chips. It covers developing interactively with Forth (rapid prototyping), and moving your creations from a development board (Nano, Uno) to a standalone, low-cost, low-power, small footprint chip such as the ATMega328P or ATTiny85 or ATTiny84. Each of these microcontrollers is powerful, inexpensive, and allows using 3V batteries directly without the need to boost voltage to 5V. Additionally, we describe how to build an inexpensive (under £5), standalone 3-chip Atmel AVR universal bootloading programmer that you can use to program all of the chips above. Continue reading this article…
Voice controlled hardware requires four capabilities: (1) vocal response to trigger events (sensors/calculations-to-brain), (2) speech generation (brain-to-mouth), (3) speech recognition (ear-to-brain), and (4) speech understanding (brain-to-database, aka learning). These capabilities can increasingly be implemented using off-the-shelf modules, due to progress in advanced low-cost silicon capable of digital signal processing (DSP) and statistical learning/machine learning/AI.
In this article we look at the value chain involved in building voice control into hardware. We cover highlights in the history of artificial speech. And we show how to convert an ordinary sensor into a talking sensor for less than £5. We demonstrate this by building a Talking Passive Infra-Red (PIR) motion sensor deployed as part of an April Fool’s Day prank (jump to the design video and demonstration video).
The same design pattern can be used to create any talking sensor, with applications abounding around home, school, work, shop, factory, industrial site, mass-transit, public space, or interactive art/engineering/museum display.
Bringing Junk Model Robots to life with Talking Motion Sensors (April Fools Prank, 2021)
Abstract This brief note explores the use of fuzzy classifiers, with membership functions chosen using a statistical heuristic (quantile statistics), to monitor time-series metrics. The time series can arise from environmental measurements, industrial process control data, or sensor system outputs. We demonstrate implementation using the R language on an example dataset (ozone levels in New York City). Click here to skip straight to the coded solution), or read on for the discussion.
Fuzzy classification into 5 classes using p10 and p90 levels to achieve an 80-20 rule in the outermost classes and graded class membership in the inner three classes. Comparison with crisp classifier using the same 80-20 rule is shown in the bottom panel of the figure.
This article looks at Propositional Logic, also called Statement Calculus, from a combinatorial and algebraic point of view (Sections 3-6), its implementation in software (Section 7), and its application to digital electronics (Section 10). Historical sections cover the shift in viewpoint from classical logic based on Aristotle’s syllogism to modern symbolic logic (Section 2) and the axiomatization of logic (Section 9). (See logic sourcebook for the original source papers (1830-1881) that drove this shift.)
In Section 7, we implement the grammar of the statement calculus in the Symbolic Logic Simulator (SLS), a program written in 28 lines of Forth code, that allows computer-aided verification of any theorem in Propositional Logic (see Appendix 1 for source code). The program makes it straight-forward to explore non-obvious logical identities, and verify any propositional logic theorem or conjecture, in particular see Appendix 2 for key identities in the statement calculus (duality, algebraic, and canonical identities).
The concept of linguistic adequacy is developed in Section 8 and the NAND Adequacy Theorem is proved showing that NAND can generate all logical operations. A corollary is that any digital logic circuit can be built up entirely using NAND gates, illustrated using the free Digital Works software.
Before domain-specific languages (DSLs) and REPL environments (read, execute, print, loop) became fashionable, computing pioneer Charles (Chuck) Moore had built, by 1968, what he viewed as the perfect computer programming language, which he named FORTH (for fourth generation language). What he had kept in view during its creation was an extreme austerity in syntax and structure as he searched for the minimalist system required to interact with a computer and be able to write custom problem-oriented languages to solve them. This approach is what he considered to be “programming”: you solve your problem by developing an application specific language with multiple levels of abstraction giving you in the end a small dictionary of simple words (in code) which represents the solution cleanly and in overall the fewest lines of code. Let’s look at this idea, how it has worked out over the years, and how you can apply this, regardless of the language you choose to (or have to) work with. This article looks at Forth, Lisp & Ruby, language that make it easy to solve classes of problems by writing your own DSL, i.e. by programming a specific “problem-oriented language” in which to solve your problem.
Written July 7th, 2012, Revised Jan 12th, 2013, Updated & extended Jan 25th, 2020
There are at least seven distinct fields of computer programming that have less to do with languages and more to do with the target platform, desired functionality, and intended user. This article provides a short introduction to each, intended as a brief orienting survey. These are:
(1) Bare metal programming, not requiring an operating system,
(2) Application programming, in which an operating system is presumed,
(3) Mathematical computing and algorithms, from matrix computations and statistical learning to wavelet compression and cryptography,
(4) Web or Client-Server programming, in which the application lives in a client browser in communication with content generated on-the-fly from programs running on central servers,
(5) Mobile or App programming,
(6) Cloud programming, and
(7) Exotic programming (traditional super-computing, quantum computing, biological computing/soft robotics, deep machine learning).
I made these by taking still photographs of the action with my dad’s iPhone and then recording an audio soundtrack (for the second animation). My dad then assembled the stills into an animation and layered on the soundtrack (see below for how). I’m working to add music to it — check again soon!
The Basket (1m 18s long)(silent film) The Pets find a cozy basket. But amidst all the hustle and bustle, will anyone be able to take a nap?
Rapid prototyping can be an accelerator for hardware or embedded solutions. But for a successful outcome, follow the recommended checklist / task sequence given below, as all of these steps will likely need to be touched upon at some point in the process. Continue reading this article…
For under £10, you can put together a microcontroller development platform, ready to program directly from your PC over USB using free Arduino software. Once programmed, your microcontroller will run autonomously, untethered from your PC, powered by as small a battery power supply as a single 1.5V AAA or 3V CR2032 coin cell. You can have it interact with its environment using dozens of low-cost sensors and motors. Everything you need to explore the exciting world of embedded systems is available to you, typically for less than a day pass on the London underground.
A homebrew Arduino Nano microcontroller development kit for under £12 (including optional OLED display)
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