, From regularized approaches to a constrained formulation, p.64

. .. ,

. Simulations and . .. Results, 70 4.4.2 Evaluation of the Constrained Robust Matrix Completion algorithm

. , Anomaly detection performance

. .. Conclusion,

. , Wireless sensor network architecture

. Repetti, Sparse seismic signal, 2015.

. , Data gathering process using SCS technique

. .. , The effect of anomalies on the DCT representation, p.33

, First sensor humidity and temperature readings over 300 time slots, p.37

. , From top to bottom: low rank feature-sparsity of DCT of all sensors' readings at the first time slot

, Anomaly detection performance on the Intel the humidity data, p.39

, Anomaly detection performance on the Intel temperature data, p.40

. , Left: on the Intel temperature data. Right: on the Intel humidity data

. , Anomaly detection and correction example. Top: on Intel temperature data. Bottom: on Intel humidity data

. , Anomaly detection and estimation on Intel humidity data using a centered anomaly distribution. Top: variance equals to 2. Bottom: variance equals to 1

, Anomaly detection and estimation on Intel temperature data using a normal anomaly probability distribution. Top: mean equals to 1 and variance equals to 2. Bottom: mean equals to 1 and variance equals to 2, p.56

, Anomaly detection performance on Intel temperature data, p.57

. .. , Anomaly detection performance on Intel humidity data, vol.58

. , Top: on Intel temperature data. Bottom: on Intel humidity data

. , The DCT of the data matrix. Top: Intel humidity dataset. Bottom: Intel temperature dataset

, Histogram of the difference between two consecutive. measurements: Top: Intel humidity dataset. Bottom: Intel temperature dataset, p.71

, Anomaly detection performance on Intel temperature data, 73 List of Figures

. .. , Anomaly detection performance on Intel humidity data, p.74

. , Right: on Intel temperature data. Left: on Intel humidity data

. , Data gathering process using PBCDG technique

. , Right: Intel humidity data, The spatial sparsity feature in: Left: Intel temperature data

. , The spatio-temporal sparsity feature in: Left: Intel temperature data. Right: Intel humidity data

. .. Dataset, 93 5.5 recovery performance on Intel temperature data for a noisy setting with. Top; SNR=5 db, Top: on Intel temperature dataset. Bottom: on Intel humidity, p.20

. , The effect of spikes on the power variation between two successive time slots. Left: on the temperature dataset using PBCDG and an SNR=20 db. Right: on the humidity dataset using ST-PBCDG and an SNR=20 db

. , Spike detection performance. Left: on the temperature dataset using PBCDG and an SNR=10 db. Right: on the humidity dataset using ST-PBCDG and an SNR=20 db

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