The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston's Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers' performances and with other integration methods to show the high quality of the proposed solution.

Gas diffusivity (Dp/D0: the ratio of gas diffusion coefficient, Dp, to gas diffusion in free air, D0) and air permeability (ka), and their variations with air-filled porosity () play key roles in surface gas exchange and vapor transport in permeable pavement and water-retentive pavement systems. This study carried out a series of laboratory measurements of Dp/D0 and ka for graded recycled concrete (RC) and clay brick (RCB) aggregates, and RC and RCB blended with autoclaved aerated concrete (AAC) grains at proportions of 20% and 40%. The tested samples were saturated after compaction, and the Dp/D0 and ka were measured during the drying process from saturation to air-dry. Results showed that Dp/D0 and ka of graded RC and RCB decreased most with increased proportions of AAC grains; in particular, the effect of AAC blending on Dp/D0() relationships decreased. For most tested samples, pore-connectivity factor and diffusion-based tortuosity (T) gradually increased with drying, and T values were decreased slightly by blending with AAC grains. Equivalent pore diameter for gas flow values of graded RC and RCB showed a clear decrease with drying; on the other hand, those of AAC blended RC and RCB did not depend on . Combining previous models and fitted T() relations for tested samples, the Dp/D0() relations and MLWs for ka() captured well the values of graded RC and RCB blended with AAC grains, implying that the previous gas transport parameter models would be useful for quick assessment of roadbed materials.

Purpose -The purpose of this paper is to design an intelligent ECG classifier using programmable IC technologies to implement many functional blocks of signal acquisition and processing in one compact device. The main microprocessor also simulates the TSK neuro-fuzzy classifier in testing mode to recognize the ECG beats. The design brings various theoretical solutions into practical applications. Design/methodology/approach -The ECG signals are acquired and pre-processed using the Field-Programmable Analog Array (FPAA) IC due to the ability of precise configuration of analog parameters. The R peak of the QRS complexes and a window of 300 ms of ECG signals around the R peak are detected. In this paper we have proposed a method to extract the signal features using the Hermite decomposition algorithm, which requires only a multiplication of two matrices. Based on the features vectors, the ECG beats are classified using a TSK neuro-fuzzy network, whose parameters are trained earlier on PC and downloaded into the device. The device performance was tested with the ECG signals from the MIT-BIH database to prove the correctness of the hardware implementations. Findings -The FPAA and Programmable System on Chip (PSoC) technologies allow us to integrate many signal processing blocks in a compact device. In this paper the device has the same performance in ECG signal processing and classifying as achieved on PC simulators. This confirms the correctness of the implementation. Research limitations/implications -The device was fully tested with the signals from the MIT-BIH databases. For new patients, we have tested the device in collecting the ECG signals and QRS detections. We have not created a new database of ECG signals, in which the beats are examined by doctors and annotated the type of the rhythm (normal or abnormal, which type of arrhythmia, etc.) so we have not tested the classification mode of the device on real ECG signals. Social implications -The compact design of an intelligent ECG classifier offers a portable solution for patients with heart diseases, which can help them to detect the arrhythmia on time when the doctors are not nearby. This type of device not only may help to improve the patients' safety but also contribute to the smart, inter-networked life style. Originality/value -The device integrate a number of solutions including software, hardware and algorithms into a single, compact device. Thank to the advance of programmable ICs such as FPAA and PSoC, the designed device can acquire one channel of ECG signals, extract the features and classify the arrhythmia type (if detected) using the neuro-fuzzy TSK network in online mode.

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Permeable pavement system (PPS) contributes to mitigating urban floods and urban heat islands (UHI). PPS has been widely used in developed countries for low-traffic areas, parking lots, and sidewalks. However, PPS has not received much attention in developing countries such as Vietnam. Monitoring studies on PPS mostly focus on hydrological and thermal performance related to volume, rate of flow, and surface temperature, but studies of water and heat balances are somewhat limited. This study, therefore, summarizes hydrological and thermal performance especially focusing water and heat balance in PPS, and the measurement techniques/instruments to construct a suitable monitoring system for application to pavement. In addition, challenges to the adoption of PPS in Vietnam were also investigated, fully considering technical standards/requirements and case studies in developed countries. The results showed that PPS contributes to flooding control due to a large infiltration/drainage component. Water vapor carries away a portion of the energy that arrives at the pavement surface, resulting in a reduction in sensible heat and UHI. Evaporation has been the most difficult component to accurately quantify. A lysimeter system is commonly used to measure components of water balance but cannot be easily integrated into a full-scale PPS installation. A monitoring vault can be used in full-scale applications, but a suitable method is required to accurately determine evaporation. The challenges/requirements for PPS adoption in Vietnam include site-specific, technical/engineered, economic, environmentally friendly, associated facilities, current policies, and awareness.