Presentation of my Thesis with the title "Coastal erosion management using image processing and Node Oriented Programming" for achieving my Masters Degree in Computer and Automation Engineering from Siena University, Italy.

1. Coastal erosion management
using image processing and
Node Oriented Programming
Relatore:
Prof. Alessandro Mecocci
Corelatore:
Prof. Alessandro Pozzebon
University of Siena
Department of Engineering
Candidato:
AbdAllah Aly Saad

2. Outline
 Introduction
 Current methodologies
 The distributed system
 Node Oriented Programming
 Conclusions
 System overview
 Object distance measurement
 Object detection
 Rover-boat pinpointing
 NOP programming Model
 Examples
 DM14 programming language
 Results and Scenario
 Limitation
 Conclusions
 Future work
 Problem definition
 Results
 Problem definition
 Results
 LiDAR and ARGUS

3. Coastal erosion
 The encroachment of land by the sea, periodically
measured and averaged to ensure the elimination of
the impacts of weather, storm events and local
sediment dynamics.
Figure 1. Beach prfile

4. Reasons for coastal erosion
 There are many reasons aresult in coastal erosion,
both natural and human induced, both types existing
as a combination with several factors.
 Coastal engineering
 Land claim
 Dredging
 Vegetation clearing
 Gas mining and water extraction
 Winds
 Storms
 Near-shore currents
 Relative sea level rise

5. Results of coastal erosion
 Losing and seriously impacting land
 Destruction of the natural sea defenses
 loss of European coastal wetlands

6. Why costal erosion monitoring ?
 “Information lies at the heart of good decision making"
 Help anticipate future trends and risks
 Inadequate decisions are being made altready !

7. Methodolgies
 LIDAR
 Uses laser with Airborne support and RTK
GPS
 Costs 500-700 euro/km2
with resolution of
0,1 meter.
 Germany, Netherlands, the US and the Uk
 ARGUS
 3-6 synchronized cameras covering up
to 5 kilometers
 Costs 20-30 euro per km2
with 1 meter
resolution,
 NL, the US, Australia and the UK
Figure 2. Lidar System Figure 3. ARGUS system

8. Distributed Coastal erosion
management
 Use image processing for data extraction
 Use distributed programming for data processing
Figure 4. overview system

9. Object detection
 Use color segmentation.
 Select only some color ranges
 Use multiple ranges for multiple weather conditions
 Shape detection
 Extract circular shaped contours.
 Use different algorithms and techniques to remove irrelevant noise
Figure 5. Color HSV presentation

10. Object distance measurement
 Based on the basic concept of a camera
 Provides to similar triangles
 Becomes clear then that Distance is D=
F*I
P
Figure 6.A. triangles of the real image and inverted image Figure 6.B. Point of convegence

11. Rover-boat pinpointing
 Use trilateration
 Nodes represent different circles
 The circles intersect on the exact location of the rover-boat
 Nodes know their locations
 Apply trilateration using similar triangles technique
R1 . x= C0 . x± T
(C1 . y− C2 . y )
D1
R1 . y= C0 . y∓T
(C1 . x− C2 . x )
D1
Figure 7. Trilateration of two circles

12. Node Oriented Programming
 A Distributed Programming Model
 Represent a program as a graph
 Parition a program into a set of nodes to construct a graph
 Edges represent the data flow beween the nodes
x int := 10
y int
z backward int
print (z)
distribute
y int := x + 10
distribute
z := x + y
Diagram 1. simple program in NOP

13. Distribute !
 The key primitve to distribute the program
 Used to distrubte program segments to different nodes
Listing 1. the distribute primitive
x int := 10
y int
z backward int
print (z)
distribute
y int := x + 10
distribute
z := x + y

14. Data distributing primitives
 Actual dataflow between nodes is the overall system
execution flow
 Defines the edges of each node
noblock
recurrent
forward flow
backprop backward flow
channel two way communication
nodist local variable
Table 1. NOP distributed data primitives

15. Forward and backward propagation
Forward propagation
 Forward daraflow
 implicit dataflow direction
x int := 10
distribute
print(x)
x backward int
print(x)
distribute
x := 10
Backward propagation
 backward daraflow
 Explicit dataflow direction
Diagram 2. forward and backward propagation

16. Channel and recurrent
channel
 Different value for each read
 Bidrectional communication
 buffered
x int := 10
distribute
print(x)
x recurrent int c
x := 10
distribute
print(x)
print(x)
recurrent
 different values for each read
 One direction
 buffered
Diagram 3. normal and recurrent variables

17. Block and noblock
block
 Strong edges
 implicit
x int := 10
distribute
print(x)
x noblock int x =
x =: 10
distribute
print(x)
noblock
 Weak edge
 Explicit
Diagram 4. block and noblock variables

18. DM14 programming language
 Compiled Imperative structured distributed
 Weak typed with static type-checking
 Uses NOP as the main programming model/paradigm
 Modular with extentions
 Ad-hoc Scanner and LL* parser
 Single phase compiler
with io use io
main(->)
{
nspill("Hello World");
}
Listing 2. Hello World in DM14

19. Ping Pong in DM14
with io use *
main (->)
{
x int;
spill("please enter X :");
get(x);
Resetnode(1);
distribute;
spill("X = ");
nspill(x);
resetnode(0);
}
Diagram 5. ping pong example in DM14 Listing 3. ping pong in DM14

20. Results of distance measreument
Device Environement Resolution Distance CM Detected Accuracy
Samsung S4 indoor 4128x3096 58 58 100%
Samsung S4 indoor 4128x3096 100 99 99%
Samsung S4 indoor 4128x3096 150 150 100%
Samsung S4 indoor 4128x3096 242 243 99.5%
Samsung S4 indoor 640x480 58 58 100%
Samsung S4 indoor 640x480 100 99 99%
Samsung S4 indoor 640x480 200 196 98%
Samsung S4 indoor 640x480 150 149 99.3%
Samsung S4 indoor 2048x1536 100 103 97%
Samsung S4 indoor 2048x1536 158 160 98.75%
Samsung S4 indoor 2048x1536 210 212 99%
Table 2. results summary of distance measurment

21. Runtime Scenario
 Two nodes running
 One node is at (0,0) Detects the roverboat at 100 cm
 One node at (70, 0) Detects the rovers boat at 55 cm
 Rover-boat is either at (81.8, 55,765) or (81.8, -55.765)
Figure 8. trilateration scenario of two nodes

22. Conclusions
 The system presented could be a real alternative to the
current used systems as it would cost much less, easier
to maintain, scalable, and produces raw data.
 Very low operating and maintance cost
 NOP permits writing distributed programs in an
orthogonal manner using only the basic primitives.

23. Limitations
 WIFI ad-hoc network is of range 100 meter max :
margin of nodes is constrained by the connectivity
range
 camera resolution : affects the field view or depth of
field , so nodes must be as close to the foreshore …
 weather changes : such as clouds could effects the
rover-boat detection significantly
 the sysem architecture is fixed : once the graph is built it
is not possible to change it.

24. Future Work
 Use LoRa for communication
 Weight the results of each node with its camera
resolution
 Send different signals to extend the knowledge
base

25. Thank You