Everything you should have known about Systems before you started the course! The universe, planet earth, life forms, us, and everything we create and use constitute systems that are capable of transforming energy, matter and information at some micro and/or macro level. As such they span the basic, simple, linear and well behaved, through to the complicated, complex, non-linear and unpredictable. Moreover, they encompass the cosmological, geological, biological, mechanical, electrical, electronic, atomic and life systems + the more abstract economics, networking and sociology et al. “All known and studied systems obey the basic laws of physics and to one degree or another enjoy an underlying number of principles that lend them to a reasonably common set of analytic, modelling and mathematical techniques” Sadly, it appears to be badly taught and understood at an early stage in the education process and students often arrive at college and university with a partial or confused picture of the basic principles. This ‘Systems’ tutorial is therefore designed to correct any earlier failings and misconceptions, and to furnish students with the basic thinking and tools necessary for the wider lecture and research programs at The University of Suffolk.

1. Wed 28 March 2018 16:00 – 18:00
Out of hours public lecture
Presented by Professor
Peter Cochrane OBE
Ipswich Waterfront Building
Animated tutorial style with,
demonstrations, videos &
provoking propositions
Organised and hosted by the
UoS Innovation Centre
Systems 1.0
Everything you needed to know about the subject
before you started the course !
A detailed look at some of the fundamental principals
and key differences between natural and designed
systems on which we all dependant

2. "Simple can be harder than complex. You have to work hard
to get your thinking clean to make it simple"
Steve Jobs
"Global terrorism is extreme both in its lack of realistic goals
and in its cynical exploitation of the vulnerability of complex
systems"
Jurgen Habermas
"Today the network of relationships linking the human race to
itself and to the rest of the biosphere is so complex that all
aspects affect all others to an extraordinary degree. Someone
should be studying the whole system, however crudely that
has to be done, because no gluing together of partial studies
of a complex nonlinear system can give a good idea of the
behavior of the whole"
Murray Gell-Mann

3. Definitions & AXIOMS
Can we describe what we mean by a system ?
This singularly simple question directs us to an
ongoing philosophical debate that is less than
helpful for us as engineers and scientists
We need something both meaningful and
concise that affords us sufficient licence
to assist our understanding in designing
and building machines and networks
that invoke positive advantage and
progress at minimal risk employing
progressively less energy & material

4. “A group of interacting, interrelated, or interdependent
elements forming a complex whole”
The most concise definition I can find but not entirely
satisfactory for our purposes…
literature seaRch
Thousands of references/discourses on the topic
* The whole may be simple, complicated or complex, and there
may or may not be any interdependence !

5. A functionally related group of elements, especially:
- The human body regarded as a functional physiological unit
- An organism as a whole, especially with regard to its vital processes or functions
- A group of physiologically or anatomically complementary organs or parts
- A group of interacting mechanical or electrical components
- A network of structures and channels, as for communication, travel, or distribution
- A network of related computer software, hardware, and data transmission devices
Correct but inconcise, incomplete, and unsuitable:
literature seaRch
Thousands of references/discourses on the topic

6. An organised set of interrelated ideas or principles
-A social, economic, or political organisational form
- A naturally occurring group of objects or phenomena: the solar system.
- A set of objects or phenomena grouped together for classification or analysis
- A condition of harmonious, orderly interaction
- An organized and coordinated method; a procedure
Correct but inconcise, incomplete, and unsuitable:
literature seaRch
Thousands of references/discourses on the topic

7. ‘A system takes energy, matter, information, and transforms
their nature’
* Ergo: All Systems are Entropic
Not Published
My tight and sufficient (?) definition

8. A W IDER LENS
Only art, science, and engineering
Divisions: Mathematics, Physics Chemistry, Biology, Art are all artificial silos
inflicted sometime after the reformation and whilst accelerating our
progress & understanding they now sees widespread limited thinking and a
lack of mutual understanding that is disadvantageous
Galileo
Galilei
Michael
Angelo
Leonardo
da Vinci

9. Taking an interest in every system known to mankind pays dividends in
providing us with insights and challenging concepts and occasionally , really
useful results...
…we no longer design, deploy and operate our systems in isolation...we live in a
world of natural and unnatural systems... evolved and designed...
...the way they connect coexist and interact is important especially when life
dependency and mission critical issues are at stake !
JUST GOOD PRACTICE
For completeness of enquiry we need a better radar

10. Systems are never stronger than their weakest element
Systems are never simpler than their most complex elements
Systems are always more complex than their most complex elements
G E N E R A L A X I O M S
For individual & connected/networked systems

11. • Complex systems never get easier to characterise
• Simple systems tend to get more difficult to characterise
• Complex Systems are never rendered simpler - without incurring costs !
• Simple systems tend toward being rendered more complex !
• Simple systems don’t make the complex simpler
• Complex systems always make the simple more complex
• There are no simple solutions to complex problems
• There is a huge difference between complicated and complex systems
G E N E R A L A X I O M S
For individual & connected/networked systems

12. • Simple systems tend to migrate toward complication
• Complicated systems tend to migrate toward complexity
• Complex systems can comprise simple and/or complicated elements
• Entropy always follows the direction (1, 2, 3) and never the reverse
• The converse of the construct (1, 2, 3) seldom occurs - if ever !
• Cluster of simple/complicated systems can become complex a whole
Our lack of understanding never deterred us from exploiting anything, but
we have often witnessed some pretty big mistakes in the process!
G E N E R A L A X I O M S
For individual & connected/networked systems

13. Mother Natures propensity for Simple Systems resulting in Complex Outcomes
WHAT WE KNOW FOR SURE
A natural & designed world complexity - simplicity inversion

14. Humans propensity for Complex Systems resulting in Simple Outcomes
WHAT WE KNOW FOR SURE
A natural & designed world complexity - simplicity inversion

15. DominantSystemTrend
WHAT WE KNOW FOR SURE
We cannot manage 21C societies with 17C thinking/systems

16. V I S I B L E T R E N D
Irreversible progression to complexity
DESIGNED EVOLVED
Well behaved Out of control
Well Understood Knowledge Gap
TOOLS MODELS
Physical Laws Emulation
Low Combinatorics
Certainty the Norm
High Predictability
High Combinatorics
Uncertainty the Norm
Emergent Behaviour
Mathematics Simulations
Plasma
Biology
Physics
Weather
Universe
Genetics
Ecologies
Chemistry
Proteomics
Combustion
Earthquakes
Global Warming
Immune Systems
Quantum Mechanics
National Security
Search Engines
Globalisation
Management
Mobile Nets
Leadership
Intelligence
Economics
Nano-Tech
Bio-Tech
Conflict
M&A
COMPLEX
Telephone
Car Engine
Jet Turbine
MRI Scanner
Rocket Motor
Air Conditioner
Server Farm
Computer
Lap-top
Tablet
Radio
TV
COMPLICATED
Long Bow
Catapult
Pulley
Canal
Mill
SIMPLE
Drill
Lathe
Bicycle
Ratchet

17. syst e ms g e n e ra l it i es
Sureties, actualities, challenges, opportunities
Digital
Analogue
Hybrid Analogue//Digital
Our knowledge base
Our understanding
Made by mankind
Made by machine
Our species survival
Our planets survival
Machine intelligence
Symbiosis necessary
Challenges to be addressed
dominant
remains a vital core
ubiquitous & growing
advancing and accelerating
limited by our technologies
vital to our survival and prosperity
vital to our survival and prosperity
depends upon good systems *****
depends upon good stewardship/tech
overtaking us in many areas
man-machine partnership underway *****
formidable but interesting and vital *****

18. General Observations
All systems share similar mathematical/analysis frameworks
Ergo: Electrical, Electronic, Mechanical, Civil Engineering & Physics share the
same/very similar equations sets…’know one and you know the other’

19. Differences: Chemistry, Biology, Sociology, Information, Network Systems/
Engineering tends to observe through a different lens but share some similarities
General Observations
All systems share similar mathematical/analysis frameworks

20. ‘SIMPLEst ’ SYSTEMS
Lie within the grasp of one human mind & hands Designed top down
Always predictable
Easy to specify
Easy to design
Easy to model
Easy to realise
Easy to control
Easy to operate
Do not evolve
Do not adapt
Unchanging
Stable/well behaved
Essentially Linear
Laws of physics apply
Mathematics works
Linear behaviour
Predictable
Conceived, designed and produced by one human

21. S I M P L E S YS T E MS
Within the span of ‘a’ human mind or team
Designed top down
Always predictable
Easy to specify
Easy to design
Easy to model
Easy to realise
Easy to control
Easy to operate
Do not evolve
Do not adapt
Unchanging
Stable/well behaved
Essentially Linear
Laws of physics apply
Mathematics works
Linear behaviour
Predictable

22. Difficult to understand
Designed top down
Difficult to specify
Difficult to design
Hard to model
Hard to realise
Hard to control
Hard to operate
Seldom non-linear
Generally predictable
Do not evolve - stable
Stable/well behaved
Essentially Linear
Laws of physics apply
Maths ‘mostly’ works(ish)
Unnatural materials required
Computer modelling a necessity
Complicated SYSTEMS
A deep understanding beyond the grasp of ‘a’ human

23. Complicated

24. Initial/intuitive top down design
Evolution rapidly dominates
Modelling a major challenge
Accurate specification hard
Fundamentally stochastic
Design; a tough challenge
Realisation is easy/hard
Operation is easy/hard
Control is easy/hard
Non-linear/chaotic
Complex SYSTEMS
Beyond human wisdoms and mental abilities
Established wisdoms do not apply
Mathematics non-contributor
Emergent behaviours rule
No general laws

25. C O M P L E x
The concatenation of large numbers
of simple and/or complicated sub-
systems can lead to unpredicted
non-linearities and thus surprises -
unexpected emergent behaviours
due to the whole becoming complex

26. M ac h i n e D ES I G N E D
We are in (just) a realm where we have no clue!
AI + AL derived AL evolution
Impossible (?) to understand
Seeded from the bottom up
Accurate spec impossible
Outcome unpredictable
Beyond (?) modelling
Very easy to realise
No human control
Autonomous code
‘Always’ non-linear
Only modest compute power
Good network connectivity
Maths nears irrelevancy
Now ‘breeding’ malware

27. Stability
Two extremes/bounds
Conditionally Stable
Conditionally Unstable
Only becomes unstable when
there is some system failure
Under any and all conditions

28. Output
Input
Linear : NON-LINEAR
Confusing and often confused
These are all non-linear
responses as they give
wildly different outputs
for the same input
every time
These are all linear
responses in that an
input gives the same
output every time
Established wisdoms do not apply
Mathematics non-contributor
Emergent behaviours rule
No general laws

29. THESE DAYS HAVE Long GONE
We a r e n o w m o s t d e f i n i t e l y i n a m a c h i n e a g e

30. In a simple
disconnected
world... ...we can
address
problems
in isolation
...and simple
solutions
mostly work
In a complex
connected
world...
...we have
to consider
the whole
...simple
solutions
never work
AI
...modelling,
simulation,
decision
support
...BIG
DATA
...everything
online, and
networked
...a living
organism...
R E A L I T Y
Simple is now a rarity
AL
…evolved
designoid
solutions

31. BIGGEST WORRY
The majority do not comprehend
Simple linear and
a well behaved arena:
intuition, experience, &
wisdoms mostly worked
well and were adequate
across the most societies
A universe characterised by a
limited range of relationships,
concepts and equations that
were readily understood
Non-linear, complex and
highly unpredictable arena:
intuition, experience, wisdoms
are mostly unreliable at best &
highly dangerous at worse
A universe characterised by
a wide range of immature and
developing concepts/computer
models not readily understood
by lay people, politicians, +++
PAST TO DAY
FUTURE
Economics & market forces are failing
Ignorance and bigotry on the rise
Politics & democracy is in peril

32. LEMMING LIKE
Doing what they thing is right

33. Compounding

34. Fourier + Laplace - time and frequency
g(t)
f(t)❋g(t)f(t)
F(ω)
G(ω)
F(ω).G(ω)
Simple Multiplication
Complicated Convolution
Laplace forces integrals to converge - best used on transients signals
Fourier can see integrals to diverge - best used on repetitive signals
BASIC SYSTEM
Nomenclature - Methodology
Frequency
Domain
Descriptor
Time
Domain
Descriptor
This is the original analogue version we
have now rendered discrete and digital -
Digital Fourier/Laplace Transform

35. Time
Domain
Frequency
Domain
Real
Mathematical Abstraction
Reality CHECK

36. Temporal
Function
Frequency
Function
Frequency
Function
Temporal
Function
Applicable to time and
s p a c e , h e a t fl o w ,
mechanical systems,
probability theory +++
TRANSFORMATIONS
Difficult problems rendered easy(ier)
Fourier: time frequency domain
=
Laplace: time frequency
domain transient signals

37. T RA NS FO R M AT I O NS
Analogue and Digital/Discrete) formulations

38. ENVIRONMENT?
s(t) h(t) o(t)
Other systems of the same or differing type may be sharing the same
space or some part of it, and therefore there can be many obvious and
hidden opportunities for aliasing....
Air
Water
Earth
Machines
Lifeforms
Fluids
Solids
Chemicals
Radiation
Information
Chemical
Physical
Information/Data Processing
Mathematical
Natural
Unnatural
Biological
Electrical
Electronic
Mechanical
Computational
Optical
Acoustic
Organic
Inorganic
Life forms
+++

39. W hat ’s in THE BOX ?
What are the limits to what we describe and define
s(t) h(t) o(t)
What can we describe and define
Optical
Acoustic
+++
+++
Life forms
o(t) = h[s(t)] = h(s) for ease of notation
o = a + bt + ct2 + dt3 et4 + ft5
is the largest polynomial we
can solve for very limited and
narrow range of cases
In the absence of a closed form solution we often reduced to using polynomial or some
other form of approximate descriptor

40. AND the output ?
s(t) h(t) o(t)
In the general case it impacts/changes the
environment and the input and is often a
grossly non-linear series of loops
e(t)
f(t)

41. FEEDBACK & FEEDBACK ?
s(t) h(t) o(t)
FB o(t)
FF s(t)
Noise reduction loop
-ve FeedBack induces stability
+ve FeedBack induces instability
- an oscillator
Mathematical tractability reduced by the # loops
Stability and response
shaping loop

42. Fourier + Laplace - time and frequency
g(t)
f(t)❋g(t)f(t)
F(ω)
G(ω)
F(ω).G(ω)
Simple Multiplication
Complicated Convolution
Laplace forces integrals to converge - best used on transients signals
Fourier can see integrals to diverge - best used on repetitive signals
BASIC SYSTEM
Nomenclature - Methodology
Frequency
Domain
Descriptor
Time
Domain
Descriptor
This is the original analogue version
we have now rendered discrete and
Digital Fourier/Laplace Transform

43. General SYSTEM traits
s(t) h(t) o(t)
s1(t)
s2(t)
s3(t)
si(t)
o1(t)
ok(t)
o3(t)
o2(t)
hi(t)
Simple
Singular
Linear
Complicated
/Complex
Multi - I/O
Linear
Non-Linear
In general can be
fully tested and
characterised
In general cannot
be fully tested
and characterised

44. All known, understood, well described and
characterised, bounded, and well behaved
with causality preserved
Contained/bounded in/by some
known, or well defined,
environment/conditions
Simple System - Key Features 1
s(t) h(t) o(t)
s(t) = Stimulus
h(t) = Operator
o(t) = Output }
s(t) and o(t) originate
and terminate within the
environment

45. Response matches need
Symbiotic with the environment
Predictable, reliable, with a fast recovery time
Upgrades and changes not traumatic or risky
Shocks are not terminal or unduly debilitating
Reproducible, easy to deploy and maintain/repair/replace
Simple System - Key Features II
s(t) h(t) o(t)
Sometimes we cannot satisfy this wish list 100%

46. All known, understood, well described and
characterised, bounded, and well behaved
with causality preserved
Contained/bounded in/by some
known, or well defined,
environment/conditions
Complicated/Complex - Features I
s(t) = Stimulus
h(t) = Operator
o(t) = Output }
s(t) and o(t) originate
and terminate within the
environment
s1(t)
s2(t)
s3(t)
si(t)
o1(t)
ok(t)
o3(t)
o2(t)
hi(t)
X
Any one or more
or all of these
conditions may no
longer true
X X

47. s1(t)
s2(t)
s3(t)
si(t)
o1(t)
ok(t)
o3(t)
o2(t)
hi(t)
One or more of these
conditions no longer holds
true
Response matches need
Symbiotic with the environment
Predictable, reliable, with a fast recovery time
Upgrades and changes not traumatic or risky
Shocks are not terminal or unduly debilitating
Reproducible, easy to deploy and maintain/repair/replace
Almost by definition we cannot satisfy this wish list 100%
X
Complicated/Complex - Features II

48. Man and Mother Nature…
Design, understanding, desire, intent v evolution and chance
Only we design
Only we optimise
Only we do centralised control
Only we comprehend and assume responsibility
Design v evolution
Top down v bottom up
Optimisation v good enough

49. Mother Nature…proviso
Only evolves systems
Only builds bottom up and never top down
Only goes for ‘good enough’ and optimises nothing
She conceals her underlying
complexity at every level
of her constructs
and activity...
Extremely
complex cells
and reasonably
simple constructs
with distributed control

50. Hundreds of diverse inputs and outputs: cannot be
fully flood, or combinatorially tested…
Hundreds/thousands of feedback & feedforward
loops along with memory and adaptation…
R EA L ITY B UT ES !
Complexity in all things becoming the norm

51. “Perfection is the enemy of Good Enough”
Defining ‘good enough’ is not always trivial
and is generally the biggest challenge !
~80% of the need satisfied by ~20% of the
effort….and then often destroyed/devalued
by specification creep….
APEING Nature
We are building evolved systems
AI, AL, IoT, Internet, Security are now evolutionary
This future will be full of surprises -
‘emergent behaviours’ - and we are
having to abandon the idea of 100%
testability and characterisation - and
we will surely lose control too!
New mind sets and new system
thinking is going to be essential

52. U N R E A L
Not realistic/impossible

53. T R A D E O F F S
You can’t have it all - not forever anyway

54. Size
Scale
Complexity
Connectivity
Sophistication
Connectivity
MTBF
Speed
Agility
Reliability
Testability
Predicability
Responsivity
Cost MTTR Latency
Power Heat Resources
Often difficult
to define
with
any great
precision
Common/General system traits

55. A long storage life and very
short operational activity
HUH ??

56. Short storage life and very
long operational activity
HUH ??

57. Brittleness ALWAYS rules
Reliability/resilience and optimisation are mutually exclusive
Highly optimised components result
in mission critical failures

58. η
Failure free
operating cost
with efficiency f1(𝓷)
Failure cost with
efficiency f2(𝓷)
€ € €
Efficiency
𝓷
f1’(𝓷) + f2’(𝓷) = 0
For the specific exponential
case:
C = Aexp(-a. 𝓷) + Bexp(b. 𝓷)
𝓷o =
b - a
__1 loge Aa
Bb
_
The optimum operating
efficiency is found to be:
Efficiency is ‘easy’
People overlook the cost of failure!

59. Bathtub Reliability Curve
Failure Rate
End of life
wear out
failures
Normal/useful life
low level failures
Time
Infant mortality
- early failures
Failure
Rate

60. 0.001
0.01
0.1
1
10
100
1,000
10,000
100,000
1,000,000
1 10 100 1,000 10,000 100,000 1,000,000
MTBF - hours
Mean Time Between Failures
MTTR - seconds
Mean Time To Repair
99.999%
99.99%
99.9%
99%
90%
0.1
1 year 10 years1 month
1 week
1 day
1 minute
1 hour
1 day
Availability
Offsetting inevitable failures
MTBF = 11.4 years
1 second

61. System 1
System 2
System N
?
Not co-located
Not the same power feed
Not the same software
Not the same network connections
No centralised control
Switch
Or Sum
Voting
A =1- (1 - Av)N
Av
Av
Av
A = Availability
Hot standby
Offsetting inevitable failures

62. MTBF = Mean Time Between Failures
MTTR = Mean Time To Repair
Mean = Average - and Averages don’t tell us much!
Distribution is key !
Availability = Av = MTBF/(MTBF + MTTR)
Unavailability = Uv = 1 - Av = 1 - MTBF/(MTBF + MTTR)
= MTTR/(MTBF + MTTR)
Relationships
At the most basic ‘average’ level

63. Resilience = The capacity to recover
Failure modes: Graceful/Phased
Predominant in the world of analogue system - precursors evident

64. Resilience = The capacity to recover
Failure modes: catastrophic/instant
Predominant in the world of digital systems - precursors often hidden

65. SOme still to do’s
Specifically for the digital domain
Failure precursor detection
Integrates graceful fail mode
Pre-emptive failure switchover
Golden ‘brick’ reference models
Comprehensive test methodologies
Brittleness threshold/onset detection
Abnormal system operation identification
No-GO realm/region/mode recognition
Autonomous failure recover modes
Penetration/illegal act detection
Full outcomes characterisations
Decision ‘post mortem’ facility
Species comparators ++++

66. Real
System
Design
Example

67. Sniper Locating
M u l t i p l e a c o u s t i c s e n s o r s

68. Sniper Locating
with acoustic sensorsAcoustic Cone of
U n c e r t a i n t y
3 - 5o
Sensor
Unit
War Zone
Ambient
Noise
Dynamic
R a n g e
Limited
Today’s Performance
~200m

69. Acoustic Cone
of
U n c e r t a i n t
y
3 - 5o
Sensor
Unit Dynamic
R a n g e
Limited
~200m
In Combat Multi-Mic ArrayMODERN SYSTEM

70. Filtering
Post Microphone analogue/digital filtering/
processing
~~~
DSP
~~~
Noise Filtering
Directivity - Focus
Voice Characteristics
Anticipated Subject
Matter
Correlated sniper
shot signal - a
matched filter by
any other name
Dynamic range limiting
background noise
T i m e
waveform
and spectrum
o f s i n g l e
sniper shot

71. Primary dynamic range limiter - the
diaphragm
Diaphragm driven to
mechanical saturation by
noise and thereby limiting
dynamic range for the
wanted/weak signal
T i m e
waveform
and spectrum
o f s i n g l e
sniper shot
Pre-Filter

72. Noise Filtering
Directivity - Focus
Voice Characteristics
Multiple analogue resonant cavity
filtering
Pre-Diaphragm acoustic filter
reduces noise input to improves
dynamic range and directivity
T i m e
waveform
and spectrum
o f s i n g l e
sniper shot
Filter Config

73. Filter Characteristic
A wide range of selectivity configurations is
possible
Naked Microphone Insert
Filter 1
Filter 2

74. Experimental
Multi-Array

75. E a c h f i lt e r/m i c
a s s e m b l y i s
m e c h a n i c a l l y
isolated by foam -
n o a c o u s t i c
linkage assembly to
assembly
Detail

76. Machine gun background

77. Sniper rifle

78. Ambient Battlefield Noise
Sniper Rifle Spectrum
Containment > 95% of Sniper Spectral Energy
Filter Rejection ZoneThis region is not generally critical to
detection but can contain energy vital
to identification
PeaktoPeakhighly
distancedependent
2 10 100 1k 5kHz

79. Ambient Battlefield Noise
Sniper Rifle Spectrum
Containment > 95% of Sniper Spectral Energy
Filter Rejection Zone
PeaktoPeakhighly
distancedependent
2 10 100 1k 5kHz
Relative and non specific energy levels
for the purpose of illustration - all
location and situation defined
Prototype Filter Response

80. Sniper rifle buried in
machine gun signal

81. PRE-MICROPHONE ACOUSTIC FILTERS WITH GAIN
Sniper rifle detected with pre-microphone filter
detection

82. Sniper Rifle
+
Machine Gun
Autocorrelation
Sniper Detected
Pre mic acoustic filtered signal
Processing Delay

83. “Anti-intellectualism has been a constant thread winding its
way through our political and cultural life, nurtured by the false
notion that democracy means that 'my ignorance is just as good
as your knowledge” ― Isaac Asimov
ENGINEERING CHALLENGE
We face a tidal wave on skepticism based on ignorance
“This a ‘systemic failure’ and it is beholden to all the professions and
educationalists to counter this trend through the clear and justified
provision of the raw facts and truth of a situation or prospect - truth
and knowledge are the foundation of our technological society - they
are vital to our survival as a species”

84. Any further questions
or thoughts ??
cochrane.org.uk