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Implementation of intelligent electronic needles is presented that can judge the physical conditions of a patient and be applied anytime anywhere with the information of pulse waves sent from a remote place through the web. The paper aims to discuss these issues.

The present research developed a web-based system of electronic acupuncture using fuzzy rules. Anybody can use his or her smart phone and select a file in the menu that automatically calculates optimal points and time of acupuncture.

A set of experiments found improvement of the time of acupuncture by 25 percent.

The present research developed a web-based system of electronic acupuncture using fuzzy rules. Anybody can use his or her smart phone and select a file in the menu that automatically calculates optimal points and time of acupuncture. A set of experiments found improvement of the time of acupuncture by 25 percent.

The purpose of this paper is to employ the acoustic wave propagation method for leakage detection in pipes. The first objective is to use acoustic finite element analysis (AFEA) method to simulate acoustic wave propagation and acoustic wave reflectometry in an intact pipe and in pipes with leaks of various sizes. This is followed by the second objective which is to validate the effectiveness and the practicability of the acoustic wave method via experimental testing. The third objective involves the decomposition and de-noising of the measured acoustic waves using stationary wavelet transform (SWT). It is shown that this approach, which is used for the first time on leakage detection in pipes, can be used to identify, locate and estimate the size of a leakage defect in a pipe.

The research work was designed inline with best practices and acceptable standards. The research methodology focusses on five basic areas: literature review; experimental measurements; simulations; data analysis and writing-up of the study with clear-cut communication of the findings. The approach used was acoustic wave propagation-based method in conjunction with SWT for leakage detection in fluid-filled pipe.

First, the simulation of acoustic wave propagation and acoustic wave reflectometry in fluid-filled pipes with and without leakage have great potential in leakage detection in pipeline systems and can detect very small leaks of 1 mm diameter. Second, the measured noise-contaminated acoustic wave propagation in a fluid-filled pipe can be successfully de-noised using the SWT method in order to clearly identify and locate leakage as little as 5 mm diameter in a pipe. Third, AFEA of a fluid-filled pipe can be achieved with the simulation of only the fluid content of the pipe and without the inclusion of the pipe in the model. This eliminates contact interaction of the solid pipe walls and the fluid, and as a consequence reduces computational time and resources. Fourth, the relationship of the ratio of the leakage diameter to the ratio of the first and second secondary wave amplitudes caused by the leakage can be represented by a second-order polynomial function. Fifth, the identification of leakage in a pipe is intuitive from mere comparison of the acoustic waveforms of an intact pipe with that of a pipe with a leakage.

The research work is a novelty and was developed from the scratch. The AFEA of acoustic wave propagation and acoustic wave reflectometry in a static fluid-filled pipe, and the SWT method have been used for the first time to detect, locate and estimate the size of a leakage in a fluid-filled pipe.

Wrist-cuff oscillometric blood pressure monitors are very popular in the portable medical device market. However, its accuracy has always been controversial. In addition to the oscillatory pressure pulse wave, the finger photoplethysmography (PPG) can provide information on blood pressure changes. A blood pressure measurement system integrating the information of pressure pulse wave and the finger PPG may improve measurement accuracy. Additionally, a neural network can synthesize the information of different types of signals and approximate the complex nonlinear relationship between inputs and outputs. The purpose of this study is to verify the hypothesis that a wrist-cuff device using a neural network for blood pressure estimation from both the oscillatory pressure pulse wave and PPG signal may improve the accuracy.

A PPG sensor was integrated into a wrist blood pressure monitor, so the finger PPG and the oscillatory pressure wave could be detected at the same time during the measurement. After the peak detection, curves were fitted to the data of pressure pulse amplitude and PPG pulse amplitude versus time. A genetic algorithm-back propagation neural network was constructed. Parameters of the curves were inputted into the neural network, the outputs of which were the measurement values of blood pressure. Blood pressure measurements of 145 subjects were obtained using a mercury sphygmomanometer, the developed device with the neural network algorithm and an Omron HEM-6111 blood pressure monitor for comparison.

For the systolic blood pressure (SBP), the difference between the proposed device and the mercury sphygmomanometer is 0.0062 ± 2.55 mmHg (mean ± SD) and the difference between the Omron device and the mercury sphygmomanometer is 1.13 ± 9.48 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and the proposed device was 0.28 ± 2.99 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and Omron HEM-6111 was −3.37 ± 7.53 mmHg.

Although the difference in the SBP error between the proposed device and Omron HEM-6111 was not remarkable, there was a significant difference between the proposed device and Omron HEM-6111 in the diastolic blood pressure error. The developed device showed an improved performance. This study was an attempt to enhance the accuracy of wrist-cuff oscillometric blood pressure monitors by using the finger PPG and the neural network. The hardware framework constructed in this study can improve the conventional wrist oscillometric sphygmomanometer and may be used for continuous measurement of blood pressure.

The purpose of this paper is to theoretically analyze and experimentally demonstrate the investigation on and optimization of a distributed optical fiber sensor based on phase-sensitive optical time domain reflectometer (F-OTDR) for disturbance detection.

The F-OTDR system is investigated and optimized in two aspects: the hardware parameter and the interrogation scheme.

Based on the optimized hardware and the new interrogation scheme, the performances of the F-OTDR system have been improved greatly, compared with conventional F-OTDR system. A location accuracy of 2 m and a signal-to-noise ratio (SNR) of 16 dB have been achieved under a spatial resolution of 8 m. On the other hand, four disturbances at four different locations have been detected and located simultaneously, which is the most effective detection system with the maximum detection capability reported to date, to the best of the authors’ knowledge.

Four disturbances at four different locations have been detected and located simultaneously, which is the most effective detection system with the maximum detection capability reported to date, to the best of the authors’ knowledge. With same hardware conditions, more existing disturbances can be detected by using the new interrogation scheme, which is helpful to reduce the miss report of disturbance.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of this paper is to present a system for automatic recognition of defects detected in non-conductive polymer composites using pulsed terahertz imaging.

On the beginning, non-destructive evaluation of composites using electromagnetic waves in terahertz frequency is shortly introduced. Next automatic defects recognition (ADR) algorithm is proposed, focussing on new features calculation. Dimensionality of features space is reduced by using principal component analysis. Finally, results of basalt fiber reinforced composite materials inspection and identification using artificial neural networks is presented and discussed.

It is possible to develop ADR system for non-destructive evaluation of dielectric materials using pulsed terahertz technique. New set of features in time and frequency domains is proposed and verified.

ADR in non-destructive testing is utilized in case of digital radiography and ultrasonic testing. Terahertz inspection with pulsed excitation is reported as a source of many useful information about the internal structure of the dielectric material. Up to now ADR based on terahertz non-destructive evaluation systems was not utilized.

Describes a new reliable low‐cost ultrasonic ranging system to assist in steering a powered wheelchair. Detection algorithms have been created and implemented on a micro controller based stand‐alone system suitable for a tele‐operated vehicle. The detection uses the gradient of the echo envelope and is resistant to noise and inconsistencies in the detection circuitry. The sensor array was considered as separate sensors, working independently so the system could quickly gather separate sets of range information. These sets were overlaid on to a 2D grid array. The new system is cheaper and simpler than available systems for powered wheelchairs.

This paper aims to provide an effective method so that the ultrasonic technique can be applied to the online debris particle detection. It proposes utilizing the waveshape features in discriminating the debris particle in lubricant.

The finite element model has been developed to investigate the scattering of the ultrasonic waves in lubricant containing single scatterer, such as the debris particle and the air bubble. The simulation results show that the results verify that different scatterers differ in the waveshape features. The static experiments were carried out on a specially fixture. The single spherical debris, long debris and air bubble were measured. The fast Fourier transform (FFT) method was applied to the analysis of the echo signals to obtain the features implicated in the waveshape.

The research of this paper verifies that different scatterers differ both in their shape features and in the FFT analysis features.

The rapid movement of the debris particles as well as the lubricant temperature may influence the measuring signals. Besides, the measuring signals are usually corrupted by noise, especially for the tiny debris. Therefore, researchers are encouraged to solve those problems further.

The paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.

The paper provides a promising way that the ultrasonic waveshape features can be utilized to the identify debris particle online.

Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users.

MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored.

According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems.

This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

The purpose of this paper is to describe the full‐wave modelling of pulsed terahertz systems utilized in non‐destructive testing.

At the outset, some basic information on the terahertz NDT are outlined and then, general remarks on its numerical modelling are presented. Frequency domain FEM and time domain FDTD analysis is carried out. Finally comparison of computed and measured signals is shown in order to prove numerical analysis correctness.

It is possible to model in a relatively simple way a terahertz system for nondestructive evaluation of dielectric materials. In contrast to other published work, the entire measuring setup is modelled, including photoconductive antenna with hemispherical lens, focusing lens and evaluated material with exemplary defect.

This paper gives a description of the terahertz non‐destructive testing system with comparison of simulated and measured results.