digital communications lab - team: lcd - nist
TRANSCRIPT
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Digital Communications Lab - Team: LCD
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Our Project: ContactAR
Experiments Analysis SimulationData CT
Get a new Contact Tracing
Make other experiments
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TC4TL: Our Roadmap
NIST Development setNIST Test set
MITRE Range Angle (Structured)
NIST Training set
Preprocessing
Exploration
Preprocessing
Model Training
Predictions and Evaluation
Preprocessing
Exploration
Preprocessing
Model Training
Predictions and Evaluation
Preprocessing
Exploration
Preprocessing
Model Training
Predictions and Evaluation
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TC4TL DatasetsNIST Development set
NIST Training setNIST Test set
MITRE Range Angle (Structured)
0.9
1.2
1.5
2.4
2.7
3.0
3.6
4.5
1.8
Exact Distance [m]
0.9
1.2
1.5
2.4
2.7
3.0
3.6
4.5
1.8
Distance in a range [m] (fine_grain)
1.2
3.0
4.5
1.8
0.9
1.2
1.5
2.4
2.7
3.0
3.6
4.5
1.8
4.5
1.8
Distance in a range [m] (coarse_grain)
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NIST Development setTX
TXDeviceTXPowerTXCarryTXPose
RX
RXDeviceRXCarryRXPose
RSSI
Accelerometer_xAccelerometer_yAccelerometer_z
Gyroscope_xGyroscope_yGyroscope_zAttitude_pitchAttitude_rollAttitude_yaw
Gravity_xGravity_yGravity_z
Magnetic_field_xMagnetic_field_yMagnetic_field_z
Altitude_x,Altitude_ywindow / look
(4s)step_size_in_sec
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Data Preprocessing (Python-Pandas)
Imputation method Back-fill
a
Time
rssi values
sensor values
b a
Time
baaaa bbbb
Time feature 1(rssi)
feature 2(acceleration_x)
feature 3(acceleration_y)
feature 4(acceleration_z)
feature 5(gyroscope_x)
feature 6(...)
t1 rssi(t1)
t2 rssi(t2)
t3 rssi(t3)
t1 t2 t3 t1 t2 t3
1 Large Data file
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Data Preprocessing (Python-Pandas)
Features added as columns
TXDeviceTXCarryTXPose
RXDeviceRXCarryRXPose
window_number attenuationTXPower coarse_grain distance_in_meters
One Hot Encoding
TXCarry_hand
TXCarry_pocket
TXCarry_unknown
1 0 0
0 1 0
0 0 1
TXCarry
One Hot Encoding
TXPower - 41 - RSSIRSSI > 0 were removed
step_size_in_sec = Inf were removed
File
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Dataset Exploration
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Dataset Exploration
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Dataset Exploration
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Dataset Exploration
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Simple Linear Regression
each point is the mean attenuation value of 1 window
1 Feature: attenuation
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Several Linear Regressions
hand_hand_sitting_sitting_Y
hand_hand_sitting_sitting_N
hand_hand_standing_sitting_Y
hand_hand_standing_sitting_N
………..
TXCarry_RXCarry_TXPose_RXPose_coarsegrain
split in scenarios
M1
M2
M3
M4
M...
1 Model / scenario(1 Feature: attenuation)
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Score comparison
Several Linear Regressions
Simple Linear Regression
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RandomForestRegressor
GridSearch + Cross Validation All features: mean values / window
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RandomForestRegressor
Several Regressions
Score comparison
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Feature Importance Analysisusing: RSSI & attenuation
using: RSSI & attenuation & Altitude_y
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Place A -> Experiment 1 -> Distance 1Train Data
1
Test Data
1
Place B -> Experiment 2 -> Distance 2Train Data
2
Test Data
2
Place C -> Experiment 3 -> Distance 3
Train Data
3
Test Data
3
Altitude 1
Altitude 2
Altitude 3
Omid’s hypothesis
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MITRE Range Angle (Structured)
https://mitll.github.io/PACT/datasets.html#datasets-submit
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Data Preprocessing
Range 3Angle 0Angle 45Angle 90Angle 135Angle 180Angle 225Angle 270Angle 315
Range 4Angle 0Angle 45Angle 90Angle 135Angle 180Angle 225Angle 270Angle 315
Range 5Angle 0Angle 45Angle 90Angle 135Angle 180Angle 225Angle 270Angle 315
Range 6Angle 0Angle 45Angle 90Angle 135Angle 180Angle 225Angle 270Angle 315
Range 8Angle 0Angle 45Angle 90Angle 135Angle 180Angle 225Angle 270Angle 315
Range 10Angle 0Angle 45Angle 90Angle 135Angle 180Angle 225Angle 270Angle 315
Range 12Angle 0Angle 45Angle 90Angle 135Angle 180Angle 225Angle 270Angle 315
Range 15Angle 0Angle 45Angle 90Angle 135Angle 180Angle 225Angle 270Angle 315
0.9 m 4.5 m
coarse_grain exact distance
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Data Exploration
RSSI attenuation
0.9144 m
1.2192 m
1.524 m
2.4384 m
2.7432 m
3.048 m
3.6576 m
4.572 m
1.8288 m
Distance
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Data Exploration
(Varying Distancein each environment)
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Data Exploration
(Varying Anglein each environment)
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Simple Linear Regression
attenuation [dB]
Dis
tanc
e [m
]
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NIST Training set:Multiple RandomForestRegressors
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What we have left to do
● Try different aggregation techniques per file
● Try other Regression/Classification Algorithms
● Feature engineering with sensors data
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Thank you !!!
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BACKUP
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15500 files
10 files
hand_hand_sitting_sitting
hand_hand_sitting_standing
hand_hand_standing_sitting
hand_hand_standing_standing
pocket_hand_sitting_standing
pocket_hand_standing_standing
pocket_pocket_sitting_sitting
pocket_pocket_sitting_standing
pocket_pocket_standing_sitting
pocket_pocket_standing_standing
TXCarry_RXCarry_TXPose_RXPose
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10 files
hand_hand_sitting_sitting
hand_hand_sitting_standing
hand_hand_standing_sitting
hand_hand_standing_standing
pocket_hand_sitting_standing
pocket_hand_standing_standing
pocket_pocket_sitting_sitting
pocket_pocket_sitting_standing
pocket_pocket_standing_sitting
pocket_pocket_standing_standing
TXCarry_RXCarry_TXPose_RXPose
M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
LinearRegression, RandomForestRegressor
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M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
8000 filesPredictions
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Time
Atte
nuat
ion
15
14
16
11
12
13
17
percentil 50 / median
0.9144 m
1.2192 m
1.2 m
Distance fine_grain