The use of C-V techniques to investigate instability mechanisms in M-I-S structures

In the field of flat panel displays, the current leading technology is the Active Matrix liquid Crystal Display; this uses a-Si:H based thin film transistors (TFTs) as the switching element in each pixel. However, under gate bias a-Si:H TFTs suffer from instability, as is evidenced by a shift in the gate threshold voltage. The shift in the gate threshold voltage is generally measured from the gate transfer characteristics, after subjecting the TFT to prolonged gate bias. However, a major drawback of this measurement method is that it cannot distinguish whether the shift is caused by the change in the midgap states in the a-Si:H channel or by charge trapping in the gate insulator. In view of this, we have developed a capacitance-voltage (C-V) method to measure the shift in threshold voltage. We employ Metal-Insulator-Semiconductor (MIS) structures to investigate the threshold voltage shift as they are simpler to fabricate than TFTs. We have investigated a large of number Metal/a-Si:H/Si3N4/Si+n structures using our C-V technique. From, the C-V data for the MIS structures, we have found that the relationship between the thermal energy and threshold voltage shift is similar to that reported by Wehrspohn et. al in a-Si:H TFTs (J Appl. Phys, 144, 87, 2000). The a-Si:H and Si3N4 layers were grown using the radio-frequency plasma-enhanced chemical vapour deposition technique.

Development and evaluation of an alternative method for processing elastic-lidar return signals

A method for interpreting elastic-lidar return signals in heavily-polluted atmospheres is presented. It is based on an equation derived directly from the classic lidar equation, which highlights gradients of the atmospheric backscattering properties along the laser optical path. The method is evaluated by comparing its results with those obtained with the differential absorption technique. The results were obtained from locating and ranging measurements in pollutant plumes and contaminated environments around central México. © World Scientific Publishing Company.

Neuro-Fuzzy genetic system for selection of construction project managers

Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions

Automated 3D Vision Tracking for Project Control Support

On-site tracking in open construction sites is often difficult because of the large amounts of items that are present and need to be tracked. Additionally, the amounts of occlusions/obstructions present create a highly complex tracking environment. Existing tracking methods are based mainly on Radio Frequency technologies, including Global Positioning Systems (GPS), Radio Frequency Identification (RFID), Bluetooth and Wireless Fidelity (Wi-Fi, Ultra-Wideband, etc). These methods require considerable amounts of pre-processing time since they need to manually deploy tags and keep record of the items they are placed on. In construction sites with numerous entities, tags installation, maintenance and decommissioning become an issue since it increases the cost and time needed to implement these tracking methods. This paper presents a novel method for open site tracking with construction cameras based on machine vision. According to this method, video feed is collected from on site video cameras, and the user selects the entity he wishes to track. The entity is tracked in each video using 2D vision tracking. Epipolar geometry is then used to calculate the depth of the marked area to provide the 3D location of the entity. This method addresses the limitations of radio frequency methods by being unobtrusive and using inexpensive, and easy to deploy equipment. The method has been implemented in a C++ prototype and preliminary results indicate its effectiveness