Real world Structural Health Monitoring (SHM) systems consist of sensors in the scale of hundreds, each sensor generating extremely large amounts of data, often arousing the issue of the cost associated with data transfer and storage. Sensor energy is a major component included in this cost factor, especially in Wireless Sensor Networks (WSN). Data compression is one of the techniques that is being explored to mitigate the effects of these issues. In contrast to traditional data compression techniques, Compressive Sensing (CS)-a very recent development-introduces the means of accurately reproducing a signal by acquiring much less number of samples than that defined by the Nyquist's theorem. CS achieves this task by exploiting the sparsity of the signal. By the reduced amount of data samples, CS may help reduce the energy consumption and storage costs associated with SHM systems. This paper investigates CS based data acquisition in SHM, in particular, the implications of CS on damage detection and localization. CS is implemented in a simulation environment to compress structural response data from a Reinforced Concrete (RC) structure. Promising results were obtained from the compressed data reconstruction process as well as the subsequent damage identification process using the reconstructed data. A reconstruction accuracy of 99% could be achieved at a Compression Ratio (CR) of 2.48 using the experimental data. Further analysis using the reconstructed signals provided accurate damage detection and localization results using two damage detection algorithms, showing that CS has not compromised the crucial information on structural damages during the compression process.

The computational cost of our algorithm is low as all features are derived from RR intervals and are processed by a single hidden layer neural network. This makes it potentially suitable for low-power devices.

IMPORTANCE The pathogenesis of transfusion-associated necrotizing enterocolitis remains elusive. Splanchnic hypoperfusion associated with packed red blood cell transfusion (PRBCT) and feeding has been implicated, but studies of splanchnic tissue oxygenation with respect to feeding plus PRBCT are lacking. OBJECTIVE To investigate the oxygen utilization efficiency of preterm gut and brain challenged with bolus feeding during anemia and after transfusion using near-infrared spectroscopy. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study conducted from September 1, 2014, to November 30, 2016, at a tertiary neonatal intensive care unit included 25 hemodynamically stable infants with gestational age less than 32 weeks, birth weight less than 1500 g, and postmenstrual age younger than 37 weeks. Data analysis was performed from August 1, 2017, to October 31, 2018. EXPOSURES Infants received PRBCT (15 mL/kg for 4 hours) and at least 120 mL/kg daily of second hourly bolus feedings. MAIN OUTCOMES AND MEASURES Splanchnic fractional tissue oxygen extraction (FTOEs) and cerebral fractional tissue oxygen extraction (FTOEc) measures were made during 75-minute feeding cycles that comprised a 15-minute preprandial feeding phase (FP0) and 4 contiguous 15-minute postprandial feeding phases (FP1, FP2, FP3, and FP4; each 15 minutes long). The intraindividual comparisons of feeding-related changes were evaluated during the pretransfusion epoch (TE0: 4 hours before onset of transfusion) and 3 TEs after transfusion (TE1: first 8 hours after PRBCT completion; TE2: 9-16 hours after PRBCT completion; and TE3: 17-24 hours after PRBCT completion). RESULTS Of 25 enrolled infants (13 [52%] female; median birth weight, 949 g [interquartile range {IQR}, 780-1100 g]; median gestational age, 26.9 weeks [IQR, 25.9-28.6 weeks]; median enrollment weight, 1670 g [IQR, 1357-1937 g]; and median postmenstrual age, 34 weeks [IQR, 32.9-35 weeks]), 1 infant was excluded because of corrupted near-infrared spectroscopy data. No overall association was found between FTOEs and FPs in a multivariable repeated-measures model that accounted for transfusion epochs (primary analysis approach) (FP0: mean estimate, 11.64; 95% CI, 9.55-13.73; FP1:

An automatic algorithm for processing simultaneously acquired electrocardiogram (ECG) and oximetry signals that identifies epochs of pure central apnoea, epochs containing obstructive apnoea and epochs of normal breathing is presented. The algorithm uses time and spectral features from the ECG derived heart-rate and respiration information, as well as features capturing desaturations from the oximeter sensor. Evaluation of performance of the system was achieved by using leave-one-record-out cross validation on the St. Vincent's University Hospital / University College Dublin Sleep Apnea Database from the Physionet collections of recorded physiologic signals. When classifying the three epoch types, our system achieved a specificity of 80%, a sensitivity to central apnoea of 44% and sensitivity to obstructive apnoea of 35%. A sensitivity of 81% was achieved when the central and obstructive epochs were combined into one class.

Structural Health Monitoring (SHM) and damage detection techniques have captured much interest and attention of researchers and structural engineers owing to their promising ability to provide spatial and quantitative information regarding structural damage and the performance of a structure during its life-cycle. With the development of smart sensors and communication technologies, Wireless Sensor Networks (WSN) has empowered the advancement in SHM. Recently, time series models have been widely used for structural damage detection due to the sensitivity of the model coefficients and residual errors to the damages in the structure. This paper presents a simple index that is computed using the Auto-Regressive (AR) model coefficients as an effective damage sensitive feature (DSF) for the detection of structural damage. Based on this feature, a damage identification method is developed. The Fisher information criterion of the computed DSF is used to statistically decide on the location of damage. This method has been implemented in a simulation environment and the verification of its accuracy in structural damage detection has been carried out experimentally. Experimental data is obtained using wireless sensors from a series of tests performed on a steel beam. The novel damage feature combined with the Fisher criterion for statistical evaluation has shown potential in effective structural damage detection.