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While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5–95% and the 25–75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

The rise of model-based and machine learning methods have created increasingly realistic opportunities to implement personalized, patient-specific mechanical ventilation (MV) in the ICU. These methods require monitoring of real-time patient ventilation waveform data (VWD) during MV treatment. However, there are relatively few non-invasive and/or non-proprietary systems to monitor and record patient-specific lung condition in real-time. In this paper, we present a CARE network data acquisition and monitoring system (CARENet) to automate data collection and to remotely monitor patient-specific lung condition and ventilation parameters. The automated system acquires VWD from a mechanical ventilator using a data acquisition device (DAQ), stores data in network-attached storage (NAS), and processes VWDs via a data management platform (DMP) web application. The web application enables real-time and retrospective model-based monitoring and analysis of clinical MV data. In particular, CARENet provides detailed breath-by-breath patient-specific respiratory mechanics, as well as the overall trends assessing changes in patient condition. Validation testing with a retrospective data set illustrated how breath-to-breath and time-varying patient-ventilator interaction during MV can be monitored, and, in turn, can be used to guide MV treatment. The network data acquisition system design presented is low-cost, open, and enables continuous, automated, scalable, real-time collection and analysis of waveform data, to help improve decision making, care, and outcomes in MV.

Mechanical ventilation is the primary form of care provided to respiratory failure patients. Limited guidelines and conflicting results from major clinical trials means selection of mechanical ventilation settings relies heavily on clinician experience and intuition. Determining optimal mechanical ventilation settings is therefore difficult, where non-optimal mechanical ventilation can be deleterious. To overcome these difficulties, this research proposes a model-based method to manage the wide range of possible mechanical ventilation settings, while also considering patient-specific conditions and responses.This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume.The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmHComparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.

Edge detection plays an important role in computer vision to extract object boundary. Multiscale edge detection method provides a variety of image features by different resolution at multiscale of edges. The method extracts coarse and fine structure edges simultaneously in an image. Due to this, the multiscale method enables more reliable edges are detected. Most of the multiscale methods are not translation invariant due to the decimated process. They mostly depend on the corresponding transform coefficients. These methods need more computation and a larger storage space. This study proposes a multiscale method that uses an average filter to smooth image at three different scales. Three different classical edge detectors namely Prewitt, Sobel and Laplacian were used to extract the edges from the smooth images. The edges extracted from the different scales of smooth images were then combined to form the multiscale edge detection. The performances of the multiscale images extracted from the three classical edge detectors were then compared and discussed.

Mechanical ventilation (MV) provides breathing support for acute respiratory distress syndrome (ARDS) patients in the intensive care unit, but is difficult to optimize. Too much, or too little of pressure or volume support can cause further ventilator-induced lung injury, increasing length of MV, cost and mortality. Patient-specific respiratory mechanics can help optimize MV settings. However, model-based estimation of respiratory mechanics is less accurate when patient exhibit un-modeled spontaneous breathing (SB) efforts on top of ventilator support. This study aims to estimate and quantify SB efforts by reconstructing the unaltered passive mechanics airway pressure using NARX model.Non-linear autoregressive (NARX) model is used to reconstruct missing airway pressure due to the presence of spontaneous breathing effort in mv patients. Then, the incidence of SB patients is estimated. The study uses a total of 10,000 breathing cycles collected from 10 ARDS patients from IIUM Hospital in Kuantan, Malaysia. In this study, there are 2 different ratios of training and validating methods. Firstly, the initial ratio used is 60:40 which indicates 600 breath cycles for training and remaining 400 breath cycles used for testing. Then, the ratio is varied using 70:30 ratio for training and testing data.The mean residual error between original airway pressure and reconstructed airway pressure is denoted as the magnitude of effort. The median and interquartile range of mean residual error for both ratio are 0.0557 [0.0230 - 0.0874] and 0.0534 [0.0219 - 0.0870] respectively for all patients. The results also show that Patient 2 has the highest percentage of SB incidence and Patient 10 with the lowest percentage of SB incidence which proved that NARX model is able to perform for both higher incidence of SB effort or when there is a lack of SB effort.This model is able to produce the SB incidence rate based on 10% threshold. Hence, the proposed NARX model is potentially useful to estimate and identify patient-specific SB effort, which has the potential to further assist clinical decisions and optimize MV settings.

Maintaining the quality of the water quality is one of the important aspects that play a substantial effect on the aquaculture industry especially in the tilapia industry. The quality of the water needs to be continuously monitored as any deviation from the allowed critical parameters such as water temperature and potential of hydrogen (pH) can cause unwanted scenarios such as disease, stress, higher mortality rate and profit loss. Currently, the monitoring process adopted by most fish breeders is done manually by using a portable sensor. This approach is found to be very tedious, ineffective use of manpower and time consuming particularly for the large-scale aquaculture industry. Hence, this research focuses on developing a simple, low-cost automated water quality monitoring system for the tilapia industry via LabVIEW software. The developed system will be able to monitor the parameter in real-time continuously with the capability of record and analyze each reading in a more efficient way. A data acquisition (DAQ) of NI myRIO-1900 is used as an interface between sensors and a monitoring station equipped with LabVIEW. Additionally, the developed system is equipped with an alarm system to alert the user when any deviation of the parameters occurs. Result shows that the system has a small range of average relative error of 4.28% and 6.22% for temperature and pH level respectively as compare to the portable sensor. Note that the errors are down to the selection of sensors. Furthermore, the developed prototype of the monitoring system has advantages in terms of its flexibility in extending the system with more sensors and allows a longer period of data collection without human intervention. The system is also upgradable with the integration of a control element to control the parameter when the monitored parameter is exceeded the threshold value. Succinctly, the system offers lots of advantages to the aquaculture industries with further improvement leads to better performance.

Mathematical modelling of respiratory system can guide clinicians in better monitoring and decision making for mechanically ventilated (MV) patients in intensive care unit (ICU). However, most mathematical models are develop for fully sedated patients and not particularly reliable to be applied for spontaneous breathing (SB) patients. Monitoring respiratory mechanics of SB patients requires invasive clinical protocols and equipment that are clinically too intensive to carry out. Previous study hypothesized that negative elastance occurred in SB patients due to the SB effort produced by the patient. Thus, this paper aims to further investigate the distribution of negative elastance in SB patients by extending the noninvasive time-varying elastance model. By capturing and reviewing the distribution of the negative elastance in SB patient, it can provide more consistent monitoring and decision making particularly for SB patients. Clinical data from 5 MV patients from Christchurch Hospital were used in this study. The area under the curve (AUC) for the time-varying elastance, Edrs, is estimated and analysed in each SB patient. The results are reported as median and interquartile range (IQR) for continuous data with a total of 82 hours. From the result, it was found that all patients have distribution of negative elastance with Patients 1 and 3 have higher distribution of negative elastance due to the SB effort. The median vaue for the negative elastance for all patients’ ranges from -0.66 cmH20.s/l to -2.27 cmH20.s/l. Negative elastance occurs when negative pressure is generated in the patient’s pleural space causing air volume to enter the lung. Thus, by capturing and reviewing the distribution of the negative elastance in SB patient, it can provide more consistent monitoring and decision making particularly for SB patients.

Background Modelling insulin secretion as a function of peripheral C-peptide levels by mathematical deconvolution is widespread. However, the measurement resolution for successful deconvolution and high cost of C-peptide assays means measurement of insulin secretion can only be undertaken in small scale research endeavours. This research models the nature of insulin secretion ( U N ) during the pathogenesis of type 2 diabetes. Methods A proportional-derivative U N model is based on the physiological, closed-loop insulin secretion response to increasing glucose ( ϕ D ) and glucose excursions ( ϕ P ). A total of 204 dynamic insulin sensitivity and secretion test (DISST) data sets from 68 participants in a 10-week dietary intervention trial were used to determine ϕ D and ϕ P values. The resulting gain values are used to classify subjects and thus the evaluation of U N over increasing insulin resistance. Results Participants with impaired fasting glucose ( G 0 >5.56 mmol L -1 ) had a lower median ϕ D value that becomes almost equal to ϕ P . In contrast, NGT participants ( G 0 -1 ), ϕ D that tended to be much greater than ϕ P . Thus, as the metabolic state of a participant moves from NGT to pre-diabetes, the participant is loses first phase insulin burst secretion. The resulting gains are classified by easily measured basal glucose. Conclusions The simplicity of this PD U N model in a DISST model framework provides clear relationship between the U N profile and the readily available metabolic state of each participant. These relationships could significantly improve the cost and resolution of model-based tests like the DISST.

Traffic congestion has become a serious issue due to the growing number of vehicles in Malaysia. Traffic light control system is widely used to control the flow of road junction. Currently, most of the traffic light system used pre-time and count down timers to control traffic flow. Due to the fixed-time setting, often the system unable to handle unexpected heavy traffic flows and cause traffic jam. Thus, there is a need of adaptive traffic signals which are able to do real time monitoring to control traffic light signal based on traffic density. This study proposed an adaptive traffic light control system that uses image processing and image matching technique in controlling the traffic in an effective manner by taking images of each lane at a junction. The density of traffic in the images at each junction are compared. Results show that more time are allocated for the vehicles on the densest road to pass compared to other less dense road. Edge operation detector is used to detect the density of traffic at each lane. In this study, a comparison study was carried out by applying five different edge detectors namely Roberts, Sobel, Log, Canny and active contour. Among these detectors, Canny edge operation detector has found to be the best as it could extract actual edges with average time of 0.453 second.

In aquaculture industry, measurement of fish length plays an important role in monitoring and determining fish growth, gender, age and reproduction. Currently, fish rearing industries are using measuring wooden board or acrylic plastic with the scale attached to the board itself. However, this method is causing a stress on the fish when measuring their length alive. Furthermore, manual measurement of fish length one by one using measurement board is time-consuming. Hence, this research presents an automated system that is able to measure fish length specifically tilapia fish by using image processing technique and Graphical User Interface (GUI). This automated GUI system is developed for end user and it is able to measure the length of tilapia automatically and accurately. The system is designed by using image processing tool, which consists of image segmentation and feature extraction to get the fish-shaped region. Based on the results, the GUI system is able to measure automatically and accurately the fish length based on the fish image. This developed system resulting with a 8.50% mean relative error and 91.50% accuracy of the tilapia fish length measurement for a 20 cm distance between the digital camera and the tilapia fish. These results show that the proposed system is capable to produce faster results and yield accurate length measurement of the tilapia fish. Thus, with this automated system, it is potentially assisting in fish reproduction and growth for aquaculture industry especially for tilapia industry.

Diabetes mellitus is a metabolic disorder that derived from the body’s inability to attain glucose homeostasis. Further increase in glucose level in the bloodstream, if left untreated, will lead to further metabolic disorder and complication such as amputation of a lower limb that caused by a foot ulcer. Approximately, 60% of diabetic foot ulcer comes from neuropathy. Thus, an early detection of foot ulcer would benefit particularly for diabetes individuals to reduce the risk of amputation. In this research, a plantar pressure sensor system is developed by using the Flexiforce sensor with data acquisition of NI-USB6008 as an interface to gather measurement data. There are four Flexiforce sensors that are placed at the most identified points at the right foot, which are heel, mid foot, fore foot and toe. In this pilot study, the right foot was chosen to be tested with this system. Further analysis was carried out to distinguish between 3 healthy individuals and 3 individuals with diabetes based on gathered plantar pressure value. A correlation study was performed between plantar pressure data and blood glucose data at t = 0 (G 0) to further justify the result before distinguishing the metabolic state of the individual. Although results show no significant different (p

Billing system technology in the market nowadays is less efficient in parallel with revolution of technology. Conventional method widely uses a barcode scanner that has disadvantages as it is a manual system and requires man power. Besides that, the conventional method does not store the information such as warranty of the product and the information of a buyer. Using the smart shop billing system using Radio Frequency Identification (RFID) reader and RFID tag, the system will become more efficient and automated. The system consists of two main elements which are passive RFID tag (125 kHz) and RFID reader (ID-20LA) that has similar function with a barcode tag and a barcode scanner. For the hardware components, the Espresso Lite V2.0 acts as a microcontroller and UC00C acts as Universal Asynchronous Receiver/Transmitter (UART) converter and Arduino IDE is used as a platform to link between hardware and software. The system is completed with the Internet of Things (IoT) technique to make sure the system can be operated wirelessly. As a result, all the information from the reader will be sent to Google spreadsheet as a cloud to store the information about the item to generate GUI bill. In conclusion this proposed system also uses a real time tracking.

Modelling the respiratory system of intensive care patients can enable individualized mechanical ventilation therapy and reduce ventilator induced lung injuries. However, spontaneous breathing (SB) efforts result in asynchronous pressure waveforms that mask underlying respiratory mechanics. In this study, a nonlinear auto-regressive (NARX) model was identified using a modified Gauss-Newton (GN) approach, and demonstrated on data from one SB patient. The NARX model uses three pressure dependent basis functions to capture respiratory system elastance, and contains a single resistance coefficient and positive end expiratory pressure (PEEP) coefficient. The modified GN method exponentially reduces the contribution of large residuals on the step in the coefficients at each GN iteration. This approach allows the model to effectively ignore the anomaly in the pressure waveform due to SB efforts, while successfully describing the shape of normal breathing cycles. This method has the potential to be used in the ICU to more robustly capture patient-specific behaviour, and thus enable clinicians to select optimal ventilator settings and improve patient care.

Endogenous insulin (U N ) secreted by pancreatic β-cells plays a leading role in glucose homeostasis. Pathological changes in U N can enable early diagnosis of metabolic dysfunction before the emergence of type 2 diabetes. The dynamic insulin sensitivity and secretion test (DISST) is a dynamic test that is able to quantify participant-specific insulin sensitivity (SI) values and U N profiles. Like most studies, the DISST uses direct inversion of C-peptide concentration measurements to quantify a U N profile which relies on the assumption that insulin and C-peptide are equimolarly secreted from β-cells. This study develops a proportional-derivative (PD) control model that defines U N as a function of glucose concentration to provide further insight and modeling capability for this prediabetic state. Results show that individuals with normal glucose tolerance (NGT) tend to have higher gain ratio compared to individuals with impaired fasting glucose (IFG) with median values of 19.11 and 2.79 min, respectively. In particular, the main difference between the U N profiles of NGT and IFG group lies within the derivative gain (σ 0 ), specifically in first phase secretion (U1). A higher value of σ O is needed in response to an abrupt increase in plasma glucose level. This proposed model offers model simplicity as well as a link between insulin secretion and glucose concentration that is able to provide more information in determining each participant's glycemic condition.

Modeling the respiratory system can aid clinical decision making during mechanical ventilation (MV) in the intensive care. However, most lung models only use airway pressure and flow data during inspiration phase of the breathing, and the data from expiration are often neglected. In this study, we hypothesized that the airway pressure and flow data during expiration have equal important information as the inspiratory data for respiratory mechanics modeling. In particular, expiratory time constant parameter, K, can provide unique information and relation with respiratory system elastance in MV patients. Two different respiratory models were investigated, with one model focusing on data from inspiratory cycle (Single compartment model) and the other focusing on expiratory cycle (Time constant model). The expiratory time constant model and single compartment model were evaluated based on 22 retrospective datasets of acute respiratory distress syndrome (ARDS) patients from two different cohorts. The expiration time constant model captured a moderate correlation with the lung elastance with R2= 0.568 in Cohort 1 and weak correlation with R2= 0.184 in Cohort 2, resulting in an overall of R2 = 0.435. Significant variations in lung resistance may lead to a poor correlation between K and lung elastance, Elung. Thus, the application of K as a surrogate to the respiratory elastance warrants further investigation.

Background. Respiratory mechanics models have the potential to guide mechanical ventilation. Airway branching models (ABMs) were developed from classical fluid mechanics models but do not provide accurate models ofin vivobehaviour. Hence, the ABM was improved to include patient-specific parameters and better model observed behaviour (ABMps).Methods. The airway pressure drop of the ABMps was compared with the well-accepted dynostatic algorithm (DSA) in patients diagnosed with acute respiratory distress syndrome (ARDS). A scaling factor (α) was used to equate the area under the pressure curve (AUC) from the ABMps to the AUC of the DSA and was linked to patient state.Results. The ABMps recorded a medianαvalue of 0.58 (IQR: 0.54–0.63; range: 0.45–0.66) for these ARDS patients. Significantly lowerαvalues were found for individuals with chronic obstructive pulmonary disease (P<0.001).Conclusion. The ABMps model allows the estimation of airway pressure drop at each bronchial generation with patient-specific physiological measurements and can be generated from data measured at the bedside. The distribution of patient-specificαvalues indicates that the overall ABM can be readily improved to better match observed data and capture patient condition.

Mechanical ventilation (MV) is one of the most common, but difficult, costly and variably delivered therapies in intensive care. Model-based estimated patient-specific respiratory elastance can be used to guide clinical staff in selecting positive end-expiratory pressure (PEEP) for MV patients. The Clinical Utilisation of Respiratory Elastance (CURE) trial investigates the potential to optimise PEEP in MV patients. A software system, CURE Soft, capable of providing real-time patient-specific respiratory elastance was developed for the purpose of CURE clinical trial. CURE Soft uses airway pressure and flow data from the ventilator to calculate respiratory elastance using a time-varying elastance model. The CURE Soft graphical user interface (GUI) was developed to provide patient-specific respiratory elastance in real time, history and response during a recruitment manoeuvre both of which offer significant clinical insight. CURE Soft was extensively tested on a mechanical test lung to verify the model-based elastance estimation and its robustness. Model fitting errors are low across a range of ventilation modes, showing it can successfully identify respiratory elastance. Real-time monitoring of respiratory elastance enables new insights into patient-specific lung condition, with no added patient burden. The additional insight available to clinicians will provide the information necessary for improved decision-making and patient outcomes.

Current practice in determining Mechanical Ventilation (MV) settings is highly variable with little consensus, forcing clinicians to rely on general approaches and clinical intuition. The Clinical Utilisation of Respiratory Elastance (CURE) system was developed to aid clinical determination of important MV settings by providing real-time patient-specific lung condition information at the patient bedside. The pilot clinical trials to investigate the performance and efficacy of this system are currently being carried out in the Christchurch Hospital ICU, New Zealand. This paper presents the CURE clinical trial protocol and its initial findings from the two patients recruited to date. In particular, this paper focuses on CURE's ability to determine patient-specific responses in real time to PEEP changes and recruitment manoeuvres (RM). The results from this study demonstrate the potential for CURE Soft to improve the reliability and ease with which clinicians make decisions about MV settings in the ICU.

Asynchronous events (AE) occur during mechanical ventilation (MV) therapy when the patient's breathing is not synchronised with the ventilator support. Frequent AE indicates sub-optimal ventilation therapy and may lead to further complications. Asynchrony Index (AI) gives the percentage of AEs as a percentage of total breaths, but is only assessed via manual scrutiny. Thus, there is a need to automate AE detection in real-time. A model-based approach using time-varying elastance to detect AEs is developed and retrospectively assessed in MV patients. Data from 14 mechanically ventilated respiratory failure patients, enrolled in an observational study in Christchurch Hospital, New Zealand were used to investigate the performance of the method. Patient data is sorted according to the ventilation mode used, and AI is calculated for each episode separately. The model-based approach accurately identifies AEs, and shown not to give false positive readings when compared to manual detection (gold standard). None of the ventilation modes give significantly different AI levels (P > 0.05). AI decreases when ventilation mode changes and increases overall time indicate worsen patient-ventilator interaction. The model-based method is able to successfully and accurately calculate AI. Real time use of this metric will enable patients with sub-optimal ventilator settings to be automatically identified for the first time and the settings adjusted as necessary, improving the efficacy of mechanical ventilation therapy, and providing a quantified metric to help guide MV care.

Patients with Acute Respiratory Distress Syndrome (ARDS) required mechanical ventilation (MV) for breathing support. However, some MV patients encountered spontaneous breathing (SB) efforts while fully sedated which can obscure the true underlying respiratory mechanics of these patients. Thus, a model-based method is required to reconstruct the missing pressure and calculate the breathing effort that produced by the patients without additional clinical protocols or invasive procedure. In this paper, results of spontaneous breathing effort in Malaysian critically-ill patients adopting the developed pressure reconstruction model are presented. By using the pressure reconstruction model, the SB affected pressure waveform is reconstructed to approximate true respiratory mechanics and quantifies the SB effort. The SB breathing efforts were computed and compared with the results from Christchurch Hospital, New Zealand. The substitute measure of SB effort can be indicated from the difference between the reconstructed and unreconstructed pressure. Results shows that all patients from both cohorts exhibited SB effort with the highest SB effort at 11.48% for Malaysian patient and 21.07% for Christchurch patient. Overall, the well-developed non-invasive pressure reconstruction method is able to measure the SB effort produced by Malaysian MV patients that help the clinicians in selecting the optimal MV setting. This first non-invasive guidance in selecting the optimal setting of MV in Malaysia is potentially reduced the ICU cost and improve the MV management in Malaysian hospital.

Mechanically ventilated spontaneous breathing (SB) patient produced entrained wave which is known as M-wave, even though in fully sedated condition, that create problem in estimating the true respiratory mechanics.Airway pressure reconstruction method is introduced in this paper, which able to reduce the variability in estimated mechanical properties that arise from patient-specific SB efforts, and do not represent variability in the underlying lung mechanics.More importantly, the airway pressure reconstruction can be used to estimate the SB efforts. These information can potentially be used to assess the level of patient asynchrony and may be clinically useful.This proof of concept study is thus, a first step to explore the possibility of this type of very simple reconstruction methods, which could be expanded to other ventilation modes as desired. BackgroundRespiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care. MethodsData from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort. ResultsThe pressure reconstruction method yielded a median AUC Edrs of 19.21 IQR: 16.30-22.47cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 IQR: 16.68-22.81cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort. ConclusionA simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care.

Respiratory mechanics models can be used to optimise patient-specific mechanical ventilation (MV), but are limited to fully sedated MV patients who are not spontaneously breathing. This research presents a non-invasive model-based method to determine respiratory mechanics of spontaneously breathing MV patients. Patient-specific respiratory mechanics of 22 spontaneously breathing patients are described using a single compartment lung model with time-varying elastance ( E drs ). The normalised E drs trajectories and the area under the curves ( AUCE drs ) are calculated using clinical data from 22 patients ventilated using pressure support (PS) and neurally adjusted ventilatory assist (NAVA). E drs trajectories are also compared between ventilation modes. E drs for PS ventilation were significantly higher compared to NAVA (p E drs trajectories were more variable during NAVA than PS (p AUCE drs > 25 cmH 2 Os/l. The AUCE drs is a surrogate of elastance, and thus can be used as a respiratory failure severity indicator. This non-invasive model-based approach captures unique dynamic respiratory mechanics for spontaneously breathing patients during PS and NAVA. The model is fully general and is applicable to both fully controlled and partially assisted MV modes, with the resulting potential to standardise treatment for all MV patients.

Introduction Model-based methods can be used to characterise patient-specific condition and response to mechanical ventilation (MV) during treatment for acute respiratory distress syndrome (ARDS). Conventional metrics of respiratory mechanics are based on inspiration only, neglecting data from the expiration cycle. However, it is hypothesised that expiratory data can be used to determine an alternative metric, offering another means to track patient condition and guide positive end expiratory pressure (PEEP) selection. Methods Three fully sedated, oleic acid induced ARDS piglets underwent three experimental phases. Phase 1 was a healthy state recruitment manoeuvre. Phase 2 was a progression from a healthy state to an oleic acid induced ARDS state. Phase 3 was an ARDS state recruitment manoeuvre. The expiratory time-constant model parameter was determined for every breathing cycle for each subject. Trends were compared to estimates of lung elastance determined by means of an end-inspiratory pause method and an integral-based method. All experimental procedures, protocols and the use of data in this study were reviewed and approved by the Ethics Committee of the University of Liege Medical Faculty. Results The overall median absolute percentage fitting error for the expiratory time-constant model across all three phases was less than 10 %; for each subject, indicating the capability of the model to capture the mechanics of breathing during expiration. Provided the respiratory resistance was constant, the model was able to adequately identify trends and fundamental changes in respiratory mechanics. Conclusion Overall, this is a proof of concept study that shows the potential of continuous monitoring of respiratory mechanics in clinical practice. Respiratory system mechanics vary with disease state development and in response to MV settings. Therefore, titrating PEEP to minimal elastance theoretically results in optimal PEEP selection. Trends matched clinical expectation demonstrating robustness and potential for guiding MV therapy. However, further research is required to confirm the use of such real-time methods in actual ARDS patients, both sedated and spontaneously breathing.

Physiological models of respiratory mechanics can be used to optimise mechanical ventilator settings to improve critically ill patient outcomes. Models are generally generated via either physical measurements or analogous behaviours that can model experimental outcomes. However, models derived solely from physical measurements are infrequently applied to clinical data.

Inefficient water usage during irrigation scheduling is a common crisis in agricultural sector. This crisis mostly occurs in time-controlled irrigation systems where timer will still turn on the sprinkler even on raining day. This research is proposed to overcome the mentioned crisis where the sprinkler is controlled by soil moisture sensor. In addition, this intelligence irrigation system also incorporated solar Photovoltaic (PV) and Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) software. Consequently, the system can operate independently and the performance of the irrigation system can be monitored remotely.

Dual-power PV-grid system was introduced to manipulate the lower tariff rate at off-peak period and to reduce the capital installation cost of PV energy system. In this paper, the system configuration, architecture and operation of the dual-power PV energy system are described and discussed. From the review of various multilevel inverter topologies and switching control strategies, the cascaded H-bridge utilizing sinusoidal pulse width modulation (SPWM) or closed loop current control has been identified as the most appropriate inverter topology and control scheme for the system.

Dual-tariff PV energy system was introduced to maneuver the lower tariff rate at off-peak period and to reduce the capital installation cost of PV energy system. In this paper, the system configuration, architecture and operation of the dual-tariff PV energy system are described and discussed. A 2.4 kW prototype system has been developed and run 24-hours to supply the load. Meteorological and electrical data was collected and prove the successful of such system. The system is expected to reduce 89% of electricity bill with estimated COE of RM3.813/kWh. It is hoped that the dual-tariff PV system can serve as an alternative implementation of standalone PV energy system.

This project presents a detail review of the subject fault diagnosis; feature extraction, dimensionality reduction and fault classification are being discussed. This project focuses on the faulty bearing which mainly caused by mass imbalance and axis misalignment. By analyzing the vibration signal obtained from the test rigs (rigs that are built to demonstrate the effect of faults in rotating machinery), it gives solid information concerning any faults within the rotating machinery.

In this paper, the single-phase matrix converter (SPMQ is presented to operate as an inverter with fully controllable regenerative capabilities. PWM technique was used to calculate the switch duty ratio to synthesize the output. Safe commutation strategy was implemented to avoid voltage spikes due to inductive load. Selected simulation and experimental results are presented to verify proposed operation.

Estimating respiratory mechanics of mechanically ventilated (MV) patients is unreliable when patients exhibit spontaneous breathing (SB) efforts on top of ventilator support. This reverse triggering effect [1] results in an M-wave shaped pressure wave. A model-based method to reconstruct the affected airway pressure curve is presented to enable estimation of the true underlying respiratory mechanics of these patients.