keynote ic3e2- august 2015
TRANSCRIPT
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Smart Embedded Solutions for
Connected Health
Prof. Abbes Amira
The 2015 International Conference on Electrical and Electronic Engineering (IC3E 2015)
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© 2015 Abbes Amira
• Connected health is the convergence of medical devices,
security devices, and communication technologies
• It enables patients to be monitored and treated remotely
from their home or primary care facility rather than
attend outpatient clinics or be admitted to hospital
Connected Health?
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© 2015 Abbes Amira
Healthcare Lifestyle
Technology
Sweet spot
Context
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© 2015 Abbes Amira
Wireless Body Area Network
• Acquisition and transmission
• Physiological data
• Fall detection, ECG, Gait analysis
Some Challenges
• Extreme energy efficiency
• Data fusion, analysis and security
• Data classification and transmission
Compressive sensing
• Reduce acquired data
• Reduce power consumption
• Increase performance
• Complexity in the reconstruction side
Context
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© 2015 Abbes Amira
Research: Focus Areas
• Video, image and signal
processing
• Reconfigurable computing and
HPC
• Connected health systems
• Medical imaging
• Data visualisation, analysis and
processing
• Biometrics and security
• Cloud computing
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• Research focuses on pioneering future directions and
innovation software tools and intelligent embedded
systems with applications in video and image
processing, connected health and biomedical signal
processing
Research: Focus Areas
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• The research aims at the development of software tools
and hardware accelerators for multidimensional data
acquisition, visualisation and analysis using advanced
compression standards, pattern recognition and
classification algorithms
Research: Focus Areas
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Funding and Collaborations
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Funding and Collaborations
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Outline
• Concepts and Challenges
• Emerging Technologies
• Smart Connected Health Solutions
• Fall Detection
• ECG Monitoring
• Medical Imaging
• Other Applications
• The Future?
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Concepts and Challenges
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© 2015 Abbes Amira
Instrumented
Measure, sense and see
the exact condition
Interconnected
Communicate and interact
with each other
Intelligent
Respond to change,
predict and optimize for
future events
Embedded Sensors,
automatic capturing of
data, embedded computing
Real-time communications
Data analysis, pattern
recognition, use rules and
logic, intelligent reporting
Through greater levels of instrumentation, interconnectivity and intelligence,
smarter health monitoring solutions are possible
Connected Health: Concepts
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© 2015 Abbes Amira
• Connected health is a new model for health management which
puts the correct information in the correct hands at the correct
time
• Patients and clinicians can make better decisions that can save
lives, save money and ensure a better quality of life during and
after treatment
• Not just about technologies but also about connecting people
and information within a system
• Includes terms such as eHealth, Digital Health, mHealth,
Telehealth, Telecare, remote care, and assisted living
Connected Health: Concepts
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Connected Health: Concepts
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Usage shifting toward prevention
and management
Connected Health: Concepts
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• Big Data
• Wearable devices and Internet of Things
• Data Analytics and Security
Connected Health: Challenges
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Connected Health: Challenges
• Data is growing and moving faster than
healthcare organizations can consume it
• 80% of medical data (EMRs, images,
physicians notes…etc) is unstructured and
is clinically relevant
• Healthcare organizations are leveraging big
data technology to capture all of the
information about a patient
• Successfully harnessing big data unleashes
the potential to achieve the critical
objectives for healthcare transformation
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Connected Health: Challenges
IoT, Big data key to overcoming
healthcare bottlenecks by 2025
Source: Survey, Firstpost. Friday, July 31, 2015
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• Big Data Analytics can Prevent Healthcare Fraud
• Scenario: If doctors and pharmacies can access controlled
substance history information quickly and at the point of care, it can
help them make better prescribing decisions and identify potential
prescription drug abuse. With the ability to combine multiple data
sources, analyze data and quickly deliver insights, pharmacies,
doctor offices and hospitals can track abnormal activity to mitigate
prescription drug abuse. Rather than just delivering raw data to
healthcare professionals, sophisticated databases will give
healthcare professionals a larger picture that allows them to address
why, where, when and how these issues are arising
Connected Health: Challenges
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© 2015 Abbes Amira
• Applications of Emerging Technologies such as Social
Media, Cloud and Mobile for Healthcare
• Electronic Health Records
• Knowledge Generation and Analytics in Healthcare
Settings (data mining, knowledge management, decision
support systems, data visualisation)
• Health Information Systems for Chronic Disease
Management
• Mobile Apps, Healthcare and Analytics
Connected Health: Challenges
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• Evaluation and Assessment of Health Information Systems
and Technologies
• Confidentiality, Privacy and Data Security in Health
Informatics
• Remote or Resource Poor (international and local use of
Health Informatics in remote and resource poor locations)
• Acceptance of Information Technologies for Healthcare
Systems and Applications
• Open-Source Software for Healthcare
Connected Health: Challenges
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• Assistive and Adaptive ubiquitous Computing
• Technologies for Healthcare
• Computer Games and Augmented reality for Healthcare
• Telemedicine
• E-Learning for Healthcare, Health Education
• Informatics and Quality of Care
• Applications of informatics and analytics tools in nutrition
Connected Health: Challenges
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• Not practical for daily use
• Slow alerting system lacking intelligence
• Expensive to implement
• Complexity of the system
• Flexibility
• Privacy
Connected Health: Challenges
Limitations of Existing Solutions
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Emerging Technologies:
Platforms and Tools
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“Spartan-6, Virtex-6, and 7 series FPGAs offer the performance of
dedicated ASICs and DSPs, with the added benefits of low NRE
cost, substantially reduced time to market, easy design portability,
and high I/O count with simplified PCB layout. In addition, Xilinx's
40 nm and upcoming 28 nm FPGA custom low power process,
coupled with leading edge power optimization tools offer
significantly lower power consumption than competing solutions.
All of these benefits enable portable ultrasound system
developers to improve patient care by rapidly deploying
systems that deliver the latest technology within budget and
power consumption constraints.”
www.xilinx.com
Xilinx FPGAs in Portable
Ultrasound Systems
Emerging Technologies
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© 2015 Abbes Amira
Excuse me for a
minute while I
reconfigure my
computer
Emerging Technologies
Reconfigurable Computing
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** ASIC’s, CPU’s, and DSP’s
• Time- to- market
• Performance
• Power
• Size/ weight
• Flexibility
• Life cycle cost
Using reconfigurable electronics to build connected health
systems with advantages over conventional technology **
in any of the areas of:
Emerging Technologies
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• Logic ports are connected to realize the right logic function
• Low delay (ns) input assertion and output generation
• High flexibility
• Customizable hardware
• X and Y are estimated at the same time
• Two different locations and hardware resources inside FPGA are used to generate X and Y
• Hardware structure is pre-defined
• An instruction per clock cycle
• Hardware is shared for generating out_1 and out_2
• High delay for output estimation
Memory
ALU I/O …
FPGA Microprocessors
Emerging Technologies
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FPGA Architectures
Emerging Technologies
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All Xilinx FPGAs contain the same basic resources
Logic Resources
• Slices (grouped into CLBs)
• Contain combinatorial logic and register resources
• Memory
• Multipliers
Interconnect Resources
• IOBs
Interface between the FPGA and the outside world
Other resources
• Global clock buffers, RISC ARM processors…etc
Emerging Technologies
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Zynq platform architecture
Emerging Technologies
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RC10 XUPV5-LX110T
Virtex-7 FPGA VC707
Emerging Technologies
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Zynq SoC prototyping board
Emerging Technologies
Zynq board for video processing
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Xilinx FPGAs
1
2
3
4
Software tools 4
3
1
2
Zynq
Emerging Technologies
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1
2
3
4
Software tools4
3
1
2
Xilinx FPGAs
Emerging Technologies
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• HDL (VHDL, Verilog)
• High Level Languages
• (Handel-C, SystemC, CatapultC, JHDL…etc)
• Schematic
• MATLAB-Simulink- Xilinx System Generator (XSG)
• AccelDSP (MATLAB)
• VIVADO HLS (AutoESL)
Emerging Technologies
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Compile
....00100110....
configure
Place & Route
Generate
+
Schematic
or HDL
Hardware Compilation
Handel-C Simulate
EDIF1
EDIF2
Emerging Technologies
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Emerging Technologies
New approach using Vivado HLS
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RCMAT: A Coprocessor for matrix
algorithms
Emerging Technologies
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Array Blocks Routines
Platform
Matrix multiplication
Matrix inversion
DSP, IP
Platform
Matrix transforms:
DCT, DWT, FFT
AP
Platform
Matrix decomposition:
SVD, QR, LU
Emerging Technologies
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DriverRoutines
Singular Value Decomposition
Finding EigenvectorsSolving Linear Equation
Matrix Invertion
Computational
Routines
LU, QR Factorisation
Bidiagonalisation, Jacobi Rotation
Array Block
RoutinesMatrix Multiplication
Matrix Addition
Design
Schematic, HDL, FSM
Systolic “Conv Arith”
Distributed-
Arithmetic
Implementation
CompilerTemplates
Hardware
FPGA, VLSI
Software
Libraries
ProgrammingLanguage
Level Examples
Real
World
Applications
Image processing
Kalman Filtering
ECG Monitoring
Fall Detection
VIVADO
HLS
JHDL
Handle C
Emerging Technologies
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Source: University of Denver Source: Ecole Polytechnique, Montreal
Emerging Technologies
Reconfigurable Computing
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Smartphone Point of care device
Source: University of Ulster, United Kingdom
Emerging Technologies
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Shimmer is a small wireless sensor platform that can
record and transmit physiological and kinematic data
in real-time. Designed as a wearable sensor, Shimmer
incorporates wireless ECG (Electrocardiogram), EMG
(Electromyography), GSR (Galvanic Skin Response-
skin conductance), Accelerometer,…
Source: http://www.shimmer-research.com/
Shimmer Platform
Emerging Technologies
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CPU
• Low-power TI MSP430F1611 microprocessor which
controls the operation of the device
• It configures and controls various integrated peripherals
through I/O pins, some of which are available on the
internal/external-expansion connectors
Emerging Technologies
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Computing Platform
• Run appropriate software
• Analyze the recorded data
• Analyze a person’s activities
• Intelligent alerting system
Emerging Technologies
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Emerging Technologies
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© 2015 Abbes Amira
Accelerometer Walk Data
0.00E+00
5.00E+00
1.00E+01
1.50E+01
2.00E+01
2.50E+01
3.00E+01
3.50E+01
1 153 305 457 609 761 913 1065 1217 1369 1521 1673 1825 1977 2129 2281 2433 2585 2737 2889 3041 3193 3345 3497
Sample Number @ 100sps
mg
X-Axis
Y-Axis
Z-Axis
Resultant waveforms from a walking motion for
the 3-axis accelerometer
Walking
Emerging Technologies
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Running
Accelerometer Run Data
0.00E+00
5.00E+00
1.00E+01
1.50E+01
2.00E+01
2.50E+01
3.00E+01
3.50E+01
4.00E+01
1 85 169 253 337 421 505 589 673 757 841 925 1009 1093 1177 1261 1345 1429 1513 1597 1681 1765 1849 1933
Sample Number @ 100 sps
mg
X-Axis
Y-Axis
Z-Axis
Resultant waveforms from a running motion for the 3-axis accelerometer
Emerging Technologies
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Jumping
Accelerometer Jump Data
0.00E+00
1.00E+01
2.00E+01
3.00E+01
4.00E+01
5.00E+01
6.00E+01
7.00E+01
1 777 1553 2329 3105 3881 4657 5433 6209 6985 7761 8537 9313 10089 10865 11641 12417 13193 13969 14745
Sample Number @ 100sps
mg
X-Axis
Y-Axis
Z-Axis
Resultant waveforms from a jumping motion
Emerging Technologies
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Vitalsens Device
ecg
respiration rate
temperature
SpO2
accelerometer
Vital signs measured can include:
• Simple heartbeat to full ECG
• Blood Oxygen
• Respiration
• Skin surface temperature
• Motion Detection
Intelligent, wearable, non invasive, wireless vital signs monitor
Source: http://www.intelesens.com/
Emerging Technologies
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• A reusable person-locating device
which is worn on the wrist, identifies
the wearer, is capable of locating the
wearer through GPS and
communicates their position and
status using GPRS communication
• Bio-sensing device which measures
human iso-potential signals and
derives a unique bio-measurement
from those electrical signals. This
has the potential to uniquely identify
the user of any piece of equipment
• Uses for this type of device include monitoring the
whereabouts of a person and for the tracking of patient in
hospitals
Emerging Technologies
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Emerging Technologies
Computing Platform
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Smart Connected Health Solutions
•Fall detection
•ECG monitoring
•Medical Imaging
•Other applications
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Falling and Importance of Monitoring
• Individuals at risk:
• Elderly
• Medical conditions
• Injuries caused:
• Broken bones
• Head trauma
• Individuals often unable to call for help
• Fall detection device can quickly raise alarm
Fall Detection
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Fall Detection Technology
• Visual camera-based
– High accuracy and high cost
• Wearable accelerometer-based
– Low accuracy and low cost
• We aim to improve upon wearable systems robustness
and accuracy for fall detection
Wearable Sensor Visual Camera
Fall Detection
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Proposed System
• Shimmer accelerometer device utilised
• Signal analysed with time, wavelet and classifier techniques
• Fall occurrence, strength and direction detected
Fall Detection
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Fall Data and Methodology
• 36 subjects
• 2 groups; wavelet and Principle Component
Analysis (PCA)
• Shimmer worn on centre chest position
• Activities of Daily Life (ADL) recorded
• Hard and soft directional falls recorded
Strong Front Fall Soft Front Fall
Fall Detection
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Fall Data and Methodology
• Subjects were split into two groups for independent analysis
and comparison of multiresolution and PCA fall detection and
diagnostics
• 25 subjects and their associated fall and ADL occurrences over
3 repetitions were combined to form group 1 for multiresolution,
threshold-based, analysis
• Group 2 consists of 11 subjects,139 falls and 84 ADL samples
which were used for PCA + decision tree classifier
• The fall data obtained from the Shimmer device was wirelessly
recorded and analyzed in LabView and Matlab
Fall Detection
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Multiresolution Wavelet Analysis
Group 1
• Biorthogonal (5-5) L2 wavelet applied
• Thresholds applied to categorise signal data:
– Time domain
• Fall occurrence; optimum threshold = maximum ADL
acceleration
– Wavelet domain
• Fall occurrence; optimum threshold = minimum average fall
acceleration
• Fall strength; threshold = maximum soft fall acceleration
– Wavelet and time signals compared for fall detection
• Logical AND or OR operators
Fall Detection
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PCA Classifier
Group 2
• All wavelet acceleration data evaluated by PCA
• Fall detection classifier tree obtained from PCA
• Wavelet acceleration signals applied to Boolean
classifier
• Classifier determines fall occurrence, strength and
direction
– Classifier can be calibrated with ADL data
Fall Detection
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Analysis Metrics• Recall (φ)
– Measure of falls detected from fall data
– Based on number of true falls detected (𝑇𝑝) and falls not detected
(𝐹𝑛)
φ=𝑇𝑝
𝑇𝑝 + 𝐹𝑛
• Precision (ψ)
– Measure of true falls detected, not ADL
– Based on number of true falls detected (𝑇𝑝) and ADLs detected as
falls (𝐹𝑝)
ψ=𝑇𝑝
𝑇𝑝 + 𝐹𝑝
• F-value (𝐹)
– Accuracy measure based on harmonic mean of recall and precision
𝐹=2φψ
φ+ψ63
Fall Detection
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Fall Detection Results
• AND comparator
• OR comparator
Fall Analysis Recall (%) Precision (%) F-Value (%)
Fall Detection
(AND Comparator)
83 94 88
Fall Detection
(OR Comparator)
98 88 93
Fall Strength 76 73 75
• Multiresolution fall detection
• Strict operator • High precision and low recall
• Flexible operator • High recall and low precision
Fall Detection
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• Additional ADL data used to calibrate classifier
• Calibration can improve precision
Fall Analysis Recall (%) Precision (%) F-Value (%)
Fall Detection
(Uncalibrated)
91 90 88
Fall Detection
(Calibrated)
87 92 87
Fall Strength 85 77 80
Fall Direction 81 83 87
• PCA classifier fall detection
Fall Detection
Fall Detection Results
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• Multiresolution Analysis
– Fall detection
• Can obtain high precision or high recall results only
• Comparator operators produce strict or flexible fall
detection
– Poor fall strength categorisation
• PCA classification tree
– Fall detection
• Good precision and good recall
• Can be calibrated to improve response with good data
selection
– Good fall strength categorisation
– Can obtain fall direction information
Fall Detection
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Automatic fall detection and Hardware Acceleration
Fall Detection
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© 2015 Abbes Amira
y
x
z
Signal
reconstruction
using OMP
Orientation
and
acceleration
estimation
module
Fall
detection
decision
module
Fall detection
response
Sparse 3D
Accelerometer
Signal
Orientation
Acceleration
Compressive Sensing
Fall Detection
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Compressive sensing
• Activity of Daily Living (ADL) training data
• Detects fall occurrence, strength and direction
Fall Detection
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© 2015 Abbes Amira
Compressive sensing
• Allows signal recovery from sparse data samples
– Less acceleration data sampled
– Less data transmitted
– Less power requirements
• Wavelet acceleration signal
– Thresholded; 45 largest nonzero coefficients
– Pre-known permutation index
– Gaussian sensing matrix
Fall Detection
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© 2015 Abbes Amira
Compressive sensing• Sparse signal recovery methods
– Matching Pursuit (MP) – 1993
» High iterations, low complexity
– Orthogonal MP (OMP) – 2007
» Low iterations, high complexity
– Regularised OMP (ROMP) – 2009
» Multiple magnitude-based column processing
– Stagewise OMP (StOMP) – 2012
» Multiple column, fixed iterations, high sparsity suited
Fall Detection
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Fall Data and Methodology
• 11 subjects
• 110 ADL samples recorded
• 140 hard and soft directional falls recorded
Strong Front Fall Soft Front Fall
Fall Detection
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Classifier Fall Detection Results
• Good rate of falls detected
• Accurately distinguish falls from ADL
• Additional data can be used to further calibrate
classifier
• Calibration can improve results
Fall Analysis Recall (%) Precision (%) F-Value (%)
Fall Detection 96 99 97
Fall Type 91 99 95
Fall Direction 87 98 93
Fall Detection
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Compressive Sensing Sparsity Effect
• Sparsity effect on sparse signal reconstruction
• Fall detection:
Recall Precision F-value
• 45 nonzero coefficients
• Sparsity decrease = response decrease
Fall Detection
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© 2015 Abbes Amira
H. Rabah, A. Amira, B.K. Mhanti, S. Almadeed, P.K. Meher “FPGA implementation of orthogonal matching
pursuit algorithm for compressive sensing reconstruction”, IEEE Transactions on VLSI, 2015 (available
online)
• Reconstruction failed with non-sparse signals (0.5 sparsity)
• StOMP and OMP obtained better responses
• Precision response maintained longest
Fall Detection
Compressive Sensing Sparsity Effect
R.M.Gibson, A. Amira, P. Casaseca, N. Ramzan and Z. Pervez “An Efficient User-Customisable
Multiresolution Classifier Fall Detection and Diagnostic System” The 26th International Conference on
Microelectronics (ICM), 14-17 December 2014, Doha, Qatar.
H.Rabah, A. Amira, and A.Ahmad “Design and Implementation of a Fall Detection System using
Compressive Sensing and Shimmer Technology” the 24th IEEE Conference on Microelectronics ICM2012,
Algiers, 17-20 December 2012.
A. Amira, N. Ramzan, C. Grecos, Q. Wang, P. Casaseca, Z. Pervez, X. Wang and C. Luo “A Reconfigurable
Supporting Connected Health Environment for People with Chronic diseases” book chapter (Chapter 17-
page 332) in Healthcare Informatics and Analytics: Emerging Issues and Trends" IGI Global 2014.
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A Wireless Reconfigurable
System for Falls Detection
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• Developing an efficient hardware architecture for 3D
accelerometer based fall detection approach
• Developing a novel fall detection algorithm with the best
effectiveness and complexity trade-off
• Using FPGAs as a low cost accelerator for falls detection
• Developing a real-time monitoring system for fall
detection using 3 axial accelerometer data
A Wireless Reconfigurable System for Falls Detection
Fall Detection
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A Wireless Reconfigurable System for Falls Detection
Fall Detection
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System block diagram Architecture
A Wireless Reconfigurable System for Falls Detection
Fall Detection
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© 2015 Abbes Amira
Task Software FPGA Speed
up
Orientation estimation 2052ns 12.2ns 168
Acceleration estimation 2388ns 12.2ns 195
Orientation & Acceleration estimation 3466ns 12.2ns 284
Task execution times and speedup
A Wireless Reconfigurable System for Falls Detection
Fall Detection
M. Neggazi, A.Amira and L. Hammami “Efficient Compressive Sensing on the Shimmer Platform for Fall
Detection”, the IEEE International Symposium on Circuits and Systems (ISCAS2014), 1-5 June 2014,
Melbourne, Australia.
M. Neggazi, A. Amira, L. Hammami “A Wireless Reconfigurable System for Falls Detection” the 11th
International Conference on Information Sciences, Signal Processing and their applications (ISSPA2012),
2-5 July 2012, Montreal, Canada.
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A. Ait Si Ali, M. Siupik, A. Amira, F. Bensaali and P. Casaseca, “HLS Based Hardware Acceleration on the
Zynq SoC: a Case Study for Fall Detection System,” The 11th ACM/IEEE International Conference on
Computer Systems and Applications, 10-13 November 2014, Doha, Qatar.
Maximum running Frequency: 300 MHz
PCA and Decision Tree Classifier
Fall Detection
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ECG Encryption using
VitalSens Technology
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To develop a concept demonstrator
for both wireless health-care monitoring and
individual identification using ECG,
with the aspects of
data storage and data security
ECG Encryption using Advanced Encryption
Standard (AES)
ECG Encryption
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© 2015 Abbes Amira
VitalSens VS100
+LM058 Bluetooth-Serial
adapter
Bluetooth
Flash memory holds data
VGA
VGA Screen
Flash memory holds AES
encrypted data
RC10 Board
ECG Encryption
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Expandability Scalability
Reliability
ECG Encryption
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Data from / toBluetooth-to-Serial Adapter
Output Datato Screen
Data Distribution Module
AES Encoder
Bluetooth Communication & Acquistion Module
Serial Interface Module
VitalSensProtocol Stack
LM058 Library
VGA Screen Displaying
People Recognition
Finite State Machinefor Communication
Store Data in Memory
ECG Encryption
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RC10 Board with LM058 Bluetooth-Serial adapter
VitalSens VS100
FPGA stores cipher-text with ECG data in
flash memory
Link option 1
Bluetooth Communication
& Acquistion
Data Distribution
ModuleAES Cipher
1
ECG Encryption
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RC10 board
VGA link
VGA screen
FPGA restores ECG data from
ciphertext
AES Decipher
VGA Displaying
Module2
ECG Encryption
A.Amira, M.Saghir, N.Ramzan, C. Grecos and F. Scherb “A Reconfigurable Wireless Environment for ECG
Monitoring and Encryption”, International Journal of Embedded and Real-time Communication systems,
Special issue on: Networked Embedded Systems- Design for Scalability and Heterogeneity. Volume 4,
Issue 3, 2013.
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ECG Encryption using AES Demo
ECG Encryption
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ECG Recognition using
VitalSens Technology
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• To develop a real time embedded system for people
identification using ECG signal
• To perform and evaluate a Matlab Simulation for the
identification process using Principle Component
Analysis (PCA) and different ECG sources
• To perform the ECG identification process on the
FPGA using Handel-C and Matlab simulation results
ECG Recognition
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Pre-processing
Feautre Vector
in Database
Classification
Verification
Access Denied
NO
Access Granted
YES
EnrolmentIdentification
ECG Sensor ECG Sensor
Feature
Extraction
Pre-processing
Feature
Extraction
ECG Recognition
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RC10 Board with bluetooth adaptorECG Sensor on human
chest
+
Database on
Flashmemory
Person ID on
7 Segment
Display
Identification
Algorithm on
FPGA
ECG Encryption
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FilteringDWT
CompletePCA
Create FlashData
User interface
CompletePCA
Load MIT-BIH ECG Data
Load VS ECG Data
MIT-BIH onlineECG Database
Recognition
ECG Recognition
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© 2015 Abbes Amira
RAM
ECG
sampling
Module
Computing
PCA Values
Module
Load
Matlab Data
from Flash
Module
Euclidian
Distance
Module
Display ID
Module
Bluetooth
and Data
Distribution
Module
VS100 + LM058
16 MB Flash Memory
Seven Segment Display
FPGA
ECG Recognition
P. Zicari, A. Amira, G.Fischer, J. Mclaughlin “An Embedded System for on Field Testing of Human
Identification Using ECG Biometric” the 11th International Conference on Information Sciences, Signal
Processing and their applications (ISSPA2012), 2-5 July 2012, Montreal, Canada.
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ECG Recognition Demo
ECG Recognition
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• Developing intelligent segmentation, filtering and
compression systems for 2D and 3D medical imaging
• Tumour definition for radiation therapy planning and
cancer diagnosis
• Efficient low power architectures for 3D medical
imaging
Medical image segmentation
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A. Amira, S. Chandrasekaran, D. Montgomery and I.S.Uzun “A Segmentation Concept for Positron
Emission Tomography Imaging Using Multiresolution Analysis” Neurocomputing, Special Issue on
Vision Research, Volume 71, Issues 10-12, pp 1954-1965, June 2008
D.Montgomery, A. Amira and H.Zaidi “Oncological PET Volume Segmentation Using a Combined
Multiscale and Statistical Model” Medical Physics (The American Association). 34 (2), February 2007.
Medical image segmentation
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Medical image segmentation
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• Input: is decomposed into a • Low-pass subband
• High-pass subband
• Where are low-pass and high-pass filter coefficient
• Haar filter:
• 9/7 Biorthogonal spline filter:
},...,,{ 110 Nxxxx
},...,,{ 12/10 Naaaa
ii gh ,
k
kknn xha 2
)5.0,5.0(
)5.0,5.0(
g
h
},...,,{ 12/10 Ncccc
k
kknn xgc 2
),,,,,,(
),,,,,,,,(
4321012
432101234
gggggggg
hhhhhhhhhh
Algorithms
Medical image segmentation
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Haar Wavelet Transform
A = [6, 4, 5, 9, 7, 5, 9, 3]
D1 = [5, 7, 6, 6, 1, -2, 1, 3]averages differences
D2 = [6, 6, -1, 0, 1, -2, 1, 3]averages
differences
Standard 2D Decomposition
Decompose all Rows
Decompose all Columns
Non-Standard 2D Decomposition
Decompose on a Row-Column,
Row-Column cycle
Medical image segmentation
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© 2015 Abbes Amira
DataProcessed
Data
Software Hardware
GUI- Host Application
Flash
MemoryFPGA
FPGA Board
Functions
Read ();
Haar ();
Write ();
Confirm ();
Configure ();
Host-FPGA Communication
Medical image segmentation
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Haar
Coefficients
Inputs
HWTF (N=8) based on distributed principles
ROM
+
-
+/-
+/-
x1x2
x5x6
x3x4
x7x8
+/-
SR2-1
Y
Medical image segmentation
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• PINLab chest/heart phantom
Four acquisitions with the inclusion of varying tumour inserts
Real PET data
Data set dimensionalities 128 x 128 x up to 117
Medical image segmentation
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(a) Original Phantom Image, (b) Thresholded Image T= 9000, (c) MRA Level 1,
(d) Reconstructed Image
Phantom Data
Medical image segmentation
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• The HWT hardware implementation based on pseudo code
using Handel-C requires 259 FPGA slices and operates
at a maximum frequency of about 67 MHz
• Parametrisable, scalable 16 bit 8x8 2-D has been used for
medical volume segmentation
• The hardware implementation of HWT clearly outperforms
the software implementation by a speedup factor of 5.5
times
Medical image segmentation
A.N. Sazish, Mhd S.Sharif and A. Amira “Hardware Implementation and Power Analysis of HWT for Medical
Imaging” The 16th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2009),
Hammamet, Tunisia, 13-16 December, 2009.
Mhd Sharif and A. Amira “An Intelligent System for PET Tumour Detection and Quantification” The IEEE
Conference on Image Processing (ICIP 2009), Cairo, 07-11 Nov 2009.
Mhd.Sharif, M. Abbod, A. Amira and H. Zaidi “Artificial Neural Network Based System for PET Volume
Segmentation” International Journal on Biomedical Imaging, Volume 2010, Article ID 105610, 2010.
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• Two architectures for 1-D DBWT
• Fully pipelined DBWT architecture,
• Hybrid DBWT architecture
• Two architectures for 2-D DBWT
• Separable 2-D DBWT architecture
• Non-separable 2-D DBWT architecture
• FPGA-based HDTV image/video compression
applications
Discrete Biorthogonal Wavelet Transform
Medical image segmentation
I.S.Uzun and A.Amira “A Framework for FPGA based Discrete Biorthogonal Wavelet Transforms
Implementation” Vision, Image and Signal Processing, IEE Proceedings -Special Issue on Rapid
Prototyping Systems- Volume 153, Issue 6, Page(s):721 - 734, Dec. 2006.
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1-D DBWT Pipelined Arch
PE1
PE2
PE3
PEK
x(n)
N0
N0/2
a1(n)
N0/4
a2(n)
N0/2K-1
aK-1(n)
d1(n)
d2(n)
N0/8
a3(n)
d3(n)
dK(n)
aK(n)
KK2
LM
2
LM1
4
LM 2
8
LM 3
• Arch-I consists of K PEs, each PE is devoted to compute
decomposition level k where 1≤k≤K.
• Because of decimation by two in DWT, each PE basically
has floor(L/2k) MACs. L is the filter length.
Medical image segmentation
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1-D DBWT Hybrid Arch
PE1 PE
2
RPA-
Scheduling
x(n)
N0
N0/2
a1(n)
N0/2K-1
aK(n)
d1(n)
d2(n),d3(n),..,dK(n)
2
LM1
L2M 2
The proposed architecture consists of 2 PEs
• PE1 computes the first level of decomposition
• PE2 computes the higher levels (k) of the
decomposition, where 2≤k≤K
Medical image segmentation
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2-D DBWT Separable Arch
• The separable 2-D DBWT architecture consists of a delay
line, a filter bank and a memory unit of J register blocks
(Rj) in order to store intermediate outputs.
Medical image segmentation
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2-D DBWT Non-Separable Arch
R1ODD
NP
0N
N N
N/2 N/2
N/2 N/2
N/4 N/4
N/4 N/4
S0
DEMUXEVEN
0
1
2
0 1 2
Input Image
(Even Rows)
[I] 2m
([I]2m
+[I]2m-8
)
([I]2m-2
+[I]2m-6
)
[I]2m-4
([LL1]2m
+[LL1]2m-8
)
( [LL2]2m
+[LL2]2m-8
)
([LL1]2m-2
+[LL1]2m-6
)
([LL2]2m-2
+[LL2]2m-6
)
[LL1]2m-4
[LL2]2m-4
[LL3]2m
LL{0,8}
/ HL{0,8}
LH{0,8}
/ HH{0,8}
LL{2,6}
/ HL{2,6}
LH{2,6}
/ HH{2,6}
LL4 / HL
4
VSER1EVEN VSER2
EVENVSER3
EVEN
Delay Line and
Symmetrical
Extention Router
(SER) Network
P2
S0
0
1
2
LH4
/ HH4
P4
S0
0
1
2
NP
1N
N
N/2 N/2
N/2
N/4 N/4
N/4
S0
DEMUXEVEN
0
1
2
0 1 2
Input Image
(Odd Rows)
([I]2m
+[I]2m-8
)
([I]2m-2
+[I]2m-6
)
([LL1]2m
+[LL1]2m-8
)
( [LL2]2m
+[LL2]2m-8
)
([LL1]2m-2
+[LL1]2m-6
)
([LL2]2m-2
+[LL2]2m-6
)
[LL3]2m+1
LL{1,7}
/ HL{1,7}
LH{1,7}
/ HH{1,7}
LL{3,5}
/ HL{3,5}
LH{3,5}
/ HH{3,5}
VSER1ODD VSER2
ODDVSER3
ODD
Delay Line and
Symmetrical
Extention Router
(SER) Network
P3
S0
0
1
2
+
S1
S2
LH1,2,3 /HH1,2,3
HL1,2,3
LL/HL
0
1
1
0
Row
Adder
LH{0,8}
/ HH{0,8}
LH{2,6}
/ HH{2,6}
LH4 / HH
4
LH{1,7}
/ HH{1,7}
LH{3,5}
/ HH{3,5}
+
Row
Adder
LL{0,8}
/ HL{0,8}
LL{2,6}
/ HL{2,6}
LL4
/ HL4
LL{1,7}
/ HL{1,7}
LL{3,5}
/ HL{3,5}
Qr[]
Qr[]
[I] 2m+1
R1EVEN
R2EVEN
R3EVEN
R2ODD
R3ODD
VSERjODD
: Vertical Symmetric Extension Router for ODD-
numbered of input data at jth decomposition level.
VSERjEVEN
: Vertical Symmetric Extension Router for EVEN-
numbered of input data at jth decomposition level.
RjODD/EVEN
: Row-delay circuit for jth decompsition level.
N : Row delay elements composed of N delay units.
Qr[] : Quantisation applied to subband outputs.
Medical image segmentation
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DBWT Results
Medical image segmentation
• Computation time for 1-D DBWT architectures is N0/2,
therefore it is at least twice faster than existing
architectures
• Very high data-throughput rates up to 320
MegaSamples/sec, with efficient hardware utilization
• The separable architecture requires less number of
multipliers compared to the non-separable architecture;
• The routing complexity of non-separable architecture is
less than separable architecture, it achieves better
maximum operating frequency; and
• The non-separable architecture requires less number of
ccs to compute the DBWT (2N2/3 versus 2N2)) and
achieves higher fmax, therefore it outperforms the
separable architecture in terms of computation time
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Medical image segmentation
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FPGA-based Compression
A single level of 9,7 filter
bank implementation
requires 462 slices (less
than %3 of slices
available on the FPGA
device) for a input data
throughput of 320
MegaSamples/sec
I.S.Uzun and A.Amira “Real-Time 2-D Wavelet Transform Implementation for HDTV Compression” Real-
Time Imaging, Special Issue on Spectral Imaging II, Volume 11, Issue 2, Pages 151-165, April 2005.
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3D Medical image compression
A. Ahmad, B. Krill, A. Amira and H. Rabah, “Efficient Architectures for 3-D HWT using Dynamic Partial
Reconfiguration”, Journal of Systems Architecture - Special Issue on Hardware/Software Co-Design,
Issue 8, Volume 56, pp 305-316, August 2010.
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A. Ahmad, B. Krill, A. Amira and H. Rabah, “Efficient Architectures for 3-D HWT using Dynamic Partial
Reconfiguration”, Journal of Systems Architecture - Special Issue on Hardware/Software Co-Design, Issue
8, Volume 56, pp 305-316, August 2010.
3D Medical image compression
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© 2015 Abbes AmiraOriginal 3-D image Compressed 3-D image
1D HWT T2 1D HWT1D HWT T1
Subimages (S0 – S7)
3D HWT coefficients
A
A
A
A
D
D
D
D
A
A
D
D
A
D
i(0)
i(1)
i(2)
i(N-1) o(N-1)
2 2 1
2
i ii
a aH
20.. 1Ni
22 2 1N i ii
H a a
20.. 1Ni
A
D
o(0)
...
N in
pu
ts s
am
ple
, N
=8
o(1)
o(2)
...
(a)
(b)
(c)
(d)
(e)
1D HWT 1D HWT 1D HWTT1 T2
Data fetch unit
Block RAMs
2
3
1
4Send data operation for transposition operation
Read data operation from memory
FPGA – Static area
Write the results to different
memory location
Send data operation to be proposed in 1D HWT
1D HWT Transpose
Data fetch unit
Block RAMs
Read data operation from
memory
Send data operation
for transposition operation Send data
operation to be proposed in 1D HWT
Write the results to different
memory location
2
3
4
1
Static area
Reconfigurable area
Reconfigurable area
FPGA
Xilinx University Program XUPV5 LX110T Development System
3D Medical image compression
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3D Medical image compression
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A. Amira and S. Chandrasekaran “Power Modelling and Efficient FPGA Implementation of FHT for Signal
Processing” IEEE Transactions on Very Large Scale Integration TVLSI, Vol 15 Number 3, pp 286-295,
March 2007.
Fast Hadamard Transform
04
7,8 iH
4
5,8 iH
4
7,8
4
5,8 ii HH
4
3,8
4
1,8 ii HH
4
7,8
4
3,8
4
1,8 iii HHH
4
5,8
4
3,8
4
1,8 iii HHH
4
7,8
4
5,8
4
3,8
4
1,8 iiii HHHH
04
8,8 iH
4
6,8 iH
4
8,8
4
6,8 ii HH
4
4,8
4
2,8 ii HH
4
8,8
4
4,8
4
2,8 iii HHH
4
6,8
4
4,8
4
2,8 iii HHH
4
8,8
4
6,8
4
4,8
4
2,8 iiii HHHH
mXX )( 21
4x16
Dec
oder
mXX )( 87
mXX )( 65
mXX )( 43
ROM's content for
i = 1,3,5,7
ROM's content for
i = 2,4,6,8
FF
SR
CI
RESULT
SHIFTACC
INVERTERINVER
T
TH
XHH
XHY
N
NN
N
4
24
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© 2015 Abbes Amira
Algorithm performance
Implementation performance
Feature Proposed
Structure
Structure
[Existing]
Structure
[Existing]
Computation
time
2n (2N-1)(n+log2N) (2nN)T
Design Features
(N=8, n=8)
Slices Speed
(MHz)
(Area/Speed) Ratio
Proposed
Architecture
124 90 1.37
Existing 136 45 3.02
Fast Hadamard Transform
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FPGA-based Compression
H. Rabah, A. Amira, B.K. Mohanti, S. Almadeed, P.K. Meher “FPGA implementation of orthogonal matching
pursuit algorithm for compressive sensing reconstruction”, IEEE Transaction on VLSI, 2015.
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F.Bensaali and A.Amira “Accelerating Color Space Conversion on Reconfigurable Hardware” Image and
Vision Computing, Vol 23, pp 935-942 (2005).
Color Space Conversation
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• Efficient architectures for
• the á trous wavelet transform
• Finite Radon transform
• Finite Ridgelet transform
• Building blocks of the Curvelet
transform and their FPGA
implementation
Spatial Domain Lena FRIT domain, p = 7
Spatial Domain Baboon FRIT domain, p = 31
S. Chandrasekaran, A. Amira A. Bermak and M. Shi “An Efficient VLSI Architecture and FPGA
Implementation of the Finite Ridglet Transform” Journal of Real-Time Image Processing, Vol 3, NO 3, pp
183-193, September 2008.
Curvelet Transform
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a’trous Wavelet Transform
Curvelet Transform
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Radon Transform
Curvelet Transform
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• Ridglet transform is precisely the application of a 1-
dimensional wavelet transform to the slices of the
Radon transform
Curvelet Transform
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Curvelet Transform
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• Implementations have been carried out fordifferent input image size where N=7,17 and 31for FRIT
• The Curvelet design has been implemented for atransform size of 289x289 and p = 17
• With 83MHz operating frequency and 3N2 + 3p3Nclock cycles computation time, the Curvelettransform with 3 resolution levels takesapproximately 57ms
• That gives a speed increase factor of 20compared to existing work
Curvelet Transform
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J.S.S.Kutty, F.Boussaid, A.Amira “A High Speed Configurable FPGA Architecture For Bilateral Filtering”
The International Conference on Image Processing (ICIP), 27-30 October 2014, Paris, France.
Image filtering
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Image filtering using FFT
I.S.Uzun and A.Amira “An FPGA-based Parametrisable System for High-Resolution Frequency Domain
Image Filtering” Journal of Circuits, Systems and Computers, Vol. 14, No. 5, pp 895-921 (2005).
I.S. Uzun, A. Amira, A. Bouridane “FPGA Implementations of Fast Fourier Transforms for Real- Time
Signal and Image Processing” IEE Proceedings on Vision, Image and Signal Processing, Volume 152,
Number 03, pp 283-296 - June 2005.
- Input Image
- Filter Coeffs.- Output Image
Point-to-Point
Multiplication
Virtex-2000E
FPGAParallel
2-D Forward FFT - Radix-2
- Radix-4
- Split-Radix
- FHT
Parallel
2-D Inverse FFT - Radix-2
- Radix-4
- Split-Radix
- FHT
RC1000 Dev. Board
SRAM
Bank 0
SRAM
Bank 1
Input/Output
Image
SRAM
Bank 2
SRAM
Bank 3
Filter
Coeffs.
Design Paremeters
- FFT Type
- Number of PEs
- Filter Type
*Filter params.
• Radix-2 is area efficient
• Radix-4 is best in terms ofcomputation time
• FHT has real kernel. It requires lessmemory
• Split-Radix is suitable for completepipelined architecture which is notsuitable for processing large FFTs
• Number of processors hassignificant effect on performance ofparallel 2-D FFT implementation
• Real-time 2-D FFT performance formatrix size: 128,256,512
• Near real-time 2-D FFT performancefor matrix size N =1024
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P. Meher, S. Chandrasekaran and A. Amira “FPGA Realization of FIR Filters by Efficient and Flexible
Systolization Using Distributed Arithmetic” IEEE Transactions on Signal Processing, VOL. 56, no. 7, pp
3009-3012, July 2008.
B. K. Mohanty, P. K. Meher, S. Al-Maadeed, and A. Amira “Memory Footprint Reduction for Power-
Efficient Realization of 2-D Finite Impulse Response Filters”, IEEE Transactions on Circuits and
Systems—I: REGULAR PAPERS, VOL. 61, No. 1, January 2014.
Image filtering
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A.AhmedSaid and A.Amira “Accelerating SVD on Reconfigurable Hardware For Image Denoising”
Proceedings of the IEEE International Conference on Image Processing (ICIP 2004), Singapore, October
24-27, 2004.
Image filtering NXN
SVD Reconstruction
Threshold
Noisy image Denoised image
FPGA
Partitioning of the
ImageComputation of SVD
output
blocks
D
.
.
.bank1
output
blocks
V
.
.
.bank2
output
blocks
U
.
.
.bank3
input
image
blocks
.
.
.bank0
Computation time of the SVD of one block
is 0.123ms (fmax=84.44 MHz) which
means 8130 blocks per second can be
processed.
X2 speedup
The use of a faster and a larger FPGA
(which can allow more parallelism), can
lead to higher performances
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A. Ahmad, A. Amira, H. Rabah and Y. Berviller, “Medical Image Denoising on FPGA using Finite Radon
Transform”, IET Image Processing – Volume 6, Issue 9, pp. 862 – 870, December 2012.
Image filtering
3-D
Transform
Quantisation/
selection
Entropy
coding
Buffers Buffers Buffers
Image
de-noising
z
Output: Bitstream
(Compressed
medical images)
Input: 3-D
medical images
Finite Radon
transformThresholding
Inverse finite
Radon transform
Buffers
Pre-processing Compression system
Sub-images [1]
Sub-images [n-1]
x
y
(a) (b)
Image noisy with σ2 = 0.16
29.91 dB
Image de-noising by FRAT, p = 7
(a)
34.48 dB
(b) (c)
Type Platform ReferencesMax. frequency
(MHz)Throughput
(MSPS)Area
(Slices)
Sequential
Virtex-II
Rahman & Wadawy (2004) 100.13 9.87 159Uzun & Amira (2005): Arch. 1 112.87 11.13 198Uzun & Amira (2005): Arch. 2 67.30 6.64 131Chandrasekaran et al. (2008) 79.97 37.32 215
Virtex-EChandrasekaran & Amira (2005) 69.00 6.90 345
Chandrasekaran et al. (2008) 94.46 45.01 245
Virtex-5Proposed - Loops rolled 174.30 0.12 669
Proposed - Loops unrolled 127.80 8.52 2,704
Pipelined Virtex-5Proposed - Loops rolled 161.40 13.31 2,044
Proposed - Loops unrolled 103.50 48.30 5,286BRAM-based Virtex-5 Proposed 188.90 6.30 637
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A. Amira, S. Chandrasekaran and D. Skinner “A Fast Hybrid LSI Approach for Intelligent Information Retrieval”
Proceedings of the 4th IET Visual Information Engineering 2007 Conference (VIE2007), London 25-27 July 2007.
Information retrieval using LSI
Document
Preprocessing
Term Document Matrix
(TDM) Generator
TDMQuery
Vector
Decomposition: SVD/
Haar
Visualisation
Engine
Document
Cosines
Results
Processor
Search Results
User Query
Update
Engine
Input
Database
Hardware
Acceleration
Parametrisable
Handel-C Code
Matrix
Param-
eters
DK4 EDIF Generation
Xilinx ISE P&R
Generate FPGA
Bitstream
Host Program
A. Alzu'bi, A. Amira and N. Ramzan “Semantic Content-based Image Retrieval: A Comprehensive Study”. Journal
of Visual Communication and Image Representation (Elsevier), 2015.
M. Al-Qahtani, A. Amira, N. Ramzan “An Efficient Information Retrieval Technique for
e-Health Systems” The 22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015,
London, 10-12 September 2015
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M. Neggazi, M. Bengherabi, Z.Boulkenafet and A.Amira “An Efficient FPGA Implementation of Gaussian
Mixture Models Based Classifier: Face Recognition Application”, the 9th IEEE International Workshop on
Systems, Signal and their Applications (WOSSPA2013), 12-15 May 2013, Algiers, Algeria.
GMM based Classifier
M. Shi, A. Bermak, S. Chandrasekaran, A. Amira and S. Brahim-Belhouari, “A Committee Machine Gas
Identification System Based on Dynamically Reconfigurable FPGA” the IEEE Sensors Journal, VOL. 8,
NO. 4, April 2008.
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© 2015 Abbes Amira
A. Al-Harami, A. Al-Mansoori, R. AlMesallam, U. Qidwai, A. Amira, “An Intelligent Sensing System for
Healthcare Applications using Real-time EMG and Gaze Fusion" The IEEE Technically Co-Sponsored SAI
Intelligent Systems Conference, London, 10-11 November 2015.
Robotics for Connected Health
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© 2015 Abbes Amira
2011 Gold Crest Award for Sentinus project on “No More Home Alone: A robotic application for connected
health applications”
Robotics for Connected Health
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© 2015 Abbes Amira
AI for Connected Health
L. Müller, S. Zagaria, A. Bernin, A. Amira, N. Ramzan, C. Grecos and F. Vogt “EmotionBike: A Study of
Provoking Emotions in Cycling Exergames”, 14th International Conference on Entertainment Computing
(ICEC), 2015, Trondheim, Norway
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© 2015 Abbes Amira
Conclusions
• Connected health concepts and challenges
• Some multiresolution and imaging algorithms have been
implemented and accelerated on reconfigurable
hardware
• Some applications have been addressed including fall
detection, ECG monitoring and medical imaging
• Design approaches and methodologies from Algorithm
to Power Modelling have been presented
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• Compressive sensing and Classification methods
• Co-design using the new FPGA platforms: partitioning the
design and implementation on the FPGA and ARM
processor
• Fall detection, gait analysis, ECG monitoring
• Medical devices with embedded internet services
• Pattern recognition for vital signs analysis and fusion
Ongoing Research
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© 2015 Abbes Amira
Data Fusion Processing Analysis
Decision
Making
Intelligent and Pervasive
Fusion and
Optimisation
Embedded and Wireless
Data
Retrieval
Distributed
And Wireless
Ongoing Research
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• Connected Health: How mobile phones, cloud, and big
data will reinvent healthcare?
• Social networking and internet services
• Towards prevention and management
• Training: transition from traditional to connected?
Ongoing Research
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© 2015 Abbes Amira
References and Acknowledgment
www.xilinx.com
Applications notes: DPR, ISE, Zynq, Family 7, Spartan 3
Xilinx university program
www.xilinx.com/university
PPT, Notes, Documentations, Code
www.mentor.com
Handel-C application notes
RC devices
Thanks to all my research team members and collaborators
University of the West Scotland (Visual Communication Cluster)
Qatar University (Embedded Computing Systems)
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© 2015 Abbes Amira
Thank You
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