By Assad Ebrahim, on May 28th, 2024 (153 views) | Enter your password to view comments.
Topic: Education, SWEng--Programming, Technology
By Assad Ebrahim, on May 27th, 2024 (144 views) | Enter your password to view comments.
Topic: Education, SWEng--Programming, Technology
If you haven’t done so already, you may want to start by reading the Preface to Knowledge Engineering & Emerging Technologies.
January 31st, 2024 (4th ed)
When designing a system, what should you optimize? If it is a user-interface or process, you should be minimizing clicks, or process steps. But for hardware-software systems, the answer is not obvious, and a common mistake is to fail to consider the end-to-end problem. This article explores what is involved in optimizing end-to-end in hardware-software systems. The goal here is to minimize the overall complexity of the system, i.e. of the triple hardware-software-user combination. The following remarks set the stage for our discussion:
- “Any [one] can make things bigger, more complex. It takes a touch of genius, and a lot of courage, to move in the opposite direction.” – Ernst F. Schumacher, 1973, from “Small is Beautiful: A Study of Economics As If People Mattered”.
- “The goal [is] simple: to minimize the complexity of the hardware-software combination. [Apart from] some lip service perhaps, no-one is trying to minimize the complexity of anything and that is of great concern to me.” – Chuck Moore, [Moore, 1999] (For a succinct introduction to Chuck Moore’s minimalism, see Less is Moore by Sam Gentle, [Gentle, 2015]
- “We are reaching the stage of development [in computer science] where each new generation of participants is unaware both of their overall technological ancestry and the history of the development of their speciality, and have no past to build upon.” – J.A.N. Lee, [Lee, 1996, p.54].
- “The arc of change is long, but it bends towards simplicity”, paraphrasing Martin Luther King.
 Between complexity and simplicity, progress, and new layers of abstraction. Continue reading this article…
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) Continue reading this article…
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.
Continue reading this article…
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.
Continue reading this article…
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).
Continue reading this article…
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