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The purpose of this paper is to design and measure an H‐plane substrate integrated waveguide (SIW) 72 GHz backfired horn antenna chip. The SIW horn was fabricated on a standard 0.5‐μm GaAs process with substrate thickness of 100 μm.

Planar SIW horn design method with standard GaAs circuit design rule was adopted. The input reflection coefficient and output antenna gain was simulated at the FEM‐based 3D full‐wave EM solver, Ansoft HFSS and measured at the Agilent E8361C Network Analyzer and Cascade 110 GHz probe station.

The measured input −6 dB bandwidth is about 0.9 GHz at a center frequency of 72.39 GHz. The maximum antenna power gain extracted from the path loss at 72.39 GHz is about −3.64 dBi.

Thin substrate exhibits larger capacitance and energy stores rather than radiates. Flat cutting restricts the arc lens design and results in the radiation plane mismatches to the air. Simple taper transition design makes the input bandwidth much narrower. The problems can be further improved by selecting thicker substrate and the multi‐section input CPW GSG pads to microstrip transition.

Unlike the traditional anechoic chamber, the antenna measurement station is exposed to the open space and chip antenna was supported by the FR4 substrate and the metal probing station plate. A fully characterization of the antenna open space environment before the measurement is needed.

An H‐plane SIW 72 GHz horn antenna was designed and studied. The antenna was using the GaAs 0.5‐μm MMICs process design rule includes the SIW designed cylindrical metal bars all being restricted in standard rectangular shape. Compare to traditional bulky waveguide horn antenna, the antenna chip size is only 1.8×1.7 mm². The on‐wafer measurement is conducted to measure the input return loss and the maximum antenna power gain of the on‐chip antenna. The designed on‐chip SIW horn antenna is useful for the integrated design of the E band GaAs MMICs single‐chip RF transceiver.

In recent years, many development projects of the medical systems encounter difficulties and eventually fail. Failure is often due to very complicated and changeable medical procedures and the inconsistent understanding between system stakeholders, especially the healthcare providers, and information technology staff. Many research results also indicate that poor communication easily results in negative consequences during the implementation of the medical information system. To effectively overcome this obstacle, the purpose of this paper is to propose an enhanced Delphi method to assist in reaching consensus during the software development with some additional steps.

As an alternative to the traditional way to elicit pertinent feedback from respondents, the enhanced Delphi method stresses the systematic, flexible, and cyclic stages to construct a questionnaire with viewpoints from different types of panelists and a self-assessment procedure as a validating step to measure the improvements in the system implementation.

The better communication between the members of project team does increase the comprehensive assessment of a project.

Based on a practical case, the enhanced Delphi method really demonstrates good performance and effectiveness.

The purpose of this paper is to use vision stereo to simultaneously acquire image pairs under a normal environment. Then the methods of moving edges detection and moving target shifting are applied to reduce noise error in order to position a target efficiently. The target is then double confirmed via image merge and alignment. After positioning, the visual difference between the target and the image created by the stereo vision system is measured for alignment. Finally, the image depth of the target is calculated followed by real-time target tracking.

This study mainly applies Sobel image principle. In addition, moving edges detection and moving target shifting are also used to work with system multi-threading for improving image identification efficiency.

The results of the experiment suggest that real-time image tracking and positioning under a pre-set environment can be effectively improved. On the other hand, tracking and positioning are slightly affected under a normal environment. Errors of distance measurements occur because there is more noise existing.

This study mainly determines the movements and positioning of an object or a target via image. However, the stability of moving edges detection executed by the stereo vision system can be affected if the light sources in an environment are too strong or extreme.

So far the method of tracking and positioning a moving object has been applied to surveillance systems or the application which requires measuring and positioning under a normal environment. The method proposed by this study can also be used to construct a 3D environment.

The method proposed by this study can also be used to construct a 3D environment or tracking moving object to measure the distance.