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…
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. Continue reading this article…
By Assad Ebrahim, on May 15th, 2020 (4,425 views) |
Topic: R for Statistical Computing: Quick Start & Useful Coding Snippets
Assad Ebrahim, http://www.mathscitech.org/articles/computing-toolkits/r-for-stats
2003-2020
This page provides notes/references to using R for statistical computing.
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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.
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In this article we look at the ideas of George Peacock whose 700-page opus A Treatise on Algebra (1830) transformed classical algebra into its modern form as an abstract symbolic science, free from the physical interpretation of quantity that had previously restricted it.
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By Assad Ebrahim, on March 18th, 2020 (4,125 views) |
Topic: Maths--General Interest
Revised Nov 2022, Jan 2023
In this article we look at the evolution of logic from its earliest form in the demonstration of truths to the rapid development of mathematical logic in the 1800s at the start of the “golden century” of logic (1850-1950). We also look at the rise and surprising dashing of hopes for the formalist program.
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By Assad Ebrahim, on February 18th, 2020 (381 views) |
Topic: Maths--History
This is Part 3 in the Ancient Mathematics series. (To read earlier parts: Part 1: Prehistoric Origins of Mathematics, Part 2: The Mathematics of Uruk and Susa).
This article explores what the people of Mesopotamia wrote about, counted and produced in the last part of the fourth millenium BCE. It does so by examining the frequency of signs in the proto-cuneiform tablets from the period c.3500-3000 BCE. For those wishing to build up experiential understanding of life in the Neolithic and early Bronze Age near east, this article provides suitable references as well as practical explorations of the economic and productive activities of the people: rope-making from grass, basket-weaving from reads, baking, weaving of cloth from linen, breaking ground, sowing, reaping, making flour, etc.
The first part of the article look closely at the collection of artifacts in each period that are associated with the evolution of writing and mathematics in ancient Mesopotamia, examining noteworthy individual artifacts that showcase a key development.
The study uses the CDLI database of cuneiform documents, and software I’ve written that parse the files in the CDLI database, extracting counts, parsing signs, generating frequency distributions of signs, creating a proto-cuneiform glossary, and assisting in the quantitative analysis of artifacts and semi-automated translation (see CDLI parser software library written in Ruby).
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By Assad Ebrahim, on January 30th, 2020 (18,140 views) |
Topic: SWEng--Programming
2nd ed., Feb 1, 2024, 1st ed. Jan 9th, 2010
A common misconception is that assembly language programming is a relic of the past. This is certainly not the case, and assembly language remains a core knowledge area for embedded systems development, digital design, and algorithm development in the 21st century.
A second misconception, especially amongst those who are only familiar with higher level languages (Python, Ruby, C#/.NET, Perl), is that assembly language is a defective programming language and therefore not worth the time to invest in.
But assembly language is more than ‘just another general purpose programming language’. It is actually the control signal specification for the microprocessor or microcontroller that will be running the instructions, and whose digital design must be reasonably well understood in order to get it to work successfully.
Higher level languages typically hide the underlying toolchains behind turnkey integrated development environments (IDEs). But the toolchains are valuable in their own right, comprising various software components (pre-processor, compiler, assembler, linker, loader) which take the high level code and transform it to executable machine code that can run on the target processor, optionally producing assembly code for inspection along the way. Familiarity with this toolchain can help evaluate how much overhead the high-level tools introduce on the code, which is an important part of understanding how much you’re trading off.
In this article, we’ll look first take a look at the software toolchain involved in general terms, before turning to specific tools you can use on a modern Windows computer (through Windows 11) to target an x86 chip (no longer in your PC but in a DOS Emulator). Similar skills and approaches carry over to the toolchain for the Atmel 328P and ATTiny 85 with a graphics application (TinyPhoto) on the ATTiny85 here.
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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.
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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).
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