Assessment the Optimal Effect of Time of Repetition: Extrinsic Pulse Parameter on Gd-DTPA Enhanced, Spin-Echo T1-Weighted MR Images under Low Magnetic Field Strength

The contrast agent concentration, the time of repetition (TR) and magnetic field strength are significant parameters that influence for the accurate signal intensity (SI) in quantitative Magnetic Resonance Imaging (MRI). Therefore, this study was conducted to investigate and refine the dependence and the optimal effect of Time of Repetition (TR) on the relationship between signal intensity and Gd-DTPA (Gadolinium-diethylene-triaminepenta-acetic acid) concentration, after applying two-dimensional (2D) Spin Echo (SE) pulse sequence under low-field MRI. In addition to that, the optimal concentration of Gd-DTPA at given sequence parameters at low-field MRI was also evaluated. A water-filled phantom was constructed for a range of Gd-DTPA concentrations (0 - 6 mmol/L) and the mean signal intensities (SIs) were assessed in the defined region of interest on T1-weighted images with different TR values (40 - 2000 ms). The generated signal-concentration curves for Gd-DTPA revealed that increasing TR was associated with the increase of the overall SIs and the maximum relationship between SI to concentration. Moreover, the required Gd-DTPA concentration to produce the maximum SI was associated to decrease with the increase of TR. In addition to this, the application of beyond 100 ms TR values in this study with relatively higher concentrations (beyond 1 - 2 mmol/L) has resulted predominantly non-linear patterns in the signal-concentration curves and it appears the saturation or decay of the SIs due to T2 effect. From these results, it can be suggested that the selection of relatively lower Gd-DTPA concentration ( mmol/L) with less than 800 ms (<800 ms) TR values can produce a better linear relationship between the concertation and SIs in T1-weighted SE low field contrast-enhanced MRI. Furthermore, this study also outlined the significance and necessity of the optimization of TR in SE sequence in low field MRI prior to a particular examination.

Classification of Ultrasonic Signs Pre-processed by Fourier Transform through Artificial Neural Network Using the Echo Pulse Technique for the Identification of Defects in Welded Joints of Structural Steel

It is due to the need to ensure the security and integrity of equipment, that the non-destructive tests have been increasingly used in the industrial sector. Among these, the ultrasonic pulse echo technique is the most used in industry, mainly for its simplicity and efficiency. With one transducer only, it is possible to emit the ultrasonic and receive the echo pulse. The ANNs （artificial neural networks） are artificial intelligence techniques that, when properly trained, align themselves to inspection tests becoming a powerful tool in the detection and fault identification. In this work, the echo pulse technique was used to detect discontinuities in welds, where ANNs were fed from the information obtained by digital signal processing techniques （Fourier transform）, to identify and classify three distinct classes of defects. Results showed that with the combination of feature extraction by Fourier transformation and classification with neural networks, it is possible to obtain an automatic defect detection system in welded joints with average efficiency.

The Use of Ultrasound to Detect Sub-surface Defects in Aluminum Pieces

A normal ultrasonic transducer of 5 MHz is modified using different delay lines and wedges design （made from Perspex）. Such modification is for ameliorate the capability of the transducer to detect sub-surface flaws. Some prepared Aluminum pieces were subjected to different crosshead speed （200 and 350 ram/rain）. The ultrasonic pulse echo method was used at room temperature. The measurements have been done before and after each tensile stress. Flaws were found at different distances from the specimen＇s surface. Results showed that such delay lines and wedges were suitable to detect sub-surface flaws that were initiated under the effect of stress. In addition, a crosshead speed increment caused more sub-surface flaws formation.

Applications of Non-destructive Tests for Diagnosis of Heritage Buildings:Case Studies from Singapore and Malaysia

Non-destructive tests have been increasingly used in historic buildings not only for pre-restoration investigation but also as useful tools to evaluate the effectiveness of eventual interventions.These tests can complement and supplement destructive tests like laboratory analysis of extracted samples as long as a sound sampling plan has been put in place to commensurate with the pattern and extent of dilapidation and materials distribution.Common methods employed in projects in Singapore and Malaysia include the infrared thermography,ground penetrating radar,microwave moisture tomography and ultrasonic pulse echo tomography.The paper discusses about a selection of case studies,demonstrating the application of various techniques for the identification of hidden details,defects and deterioration,and moisture detection.

Unmanned vehicle dynamic obstacle detection,tracking and recognition method based on laser sensor

Purpose-In response to these shortcomings,this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approach-The existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate.The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle,and cannot meet the requirements of real traffic scene applications.Findings-First,based on the geometric features of dynamic obstacles,the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking;second,the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle,and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition.Finally,the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/value-The paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.The accuracy and effectiveness of the proposed method are verified by real vehicle tests.