Programmable Syringe Pump for Selective Micro Droplet Deposition

Micro/nanopatterns with micro deposition techniques have been used in various applications such as flexible electronic devices, biosensing, and biological tissue engineering. For depositing a small size of droplets that can be controlled, structured and patterned precisely is a very important process for microfabrication. In this study, we developed a low cost and simple system for fabricating micro/nanostructure by a selective micro deposition process using a syringe pump. This method is an additive fabrication method where selective droplet materials are released through a needle of the syringe pump. By translating the rotating stepper motor into a linear movement of the lead screw, it will press the plunger of the syringe and give a force to the fluid inside the syringe, hence a droplet can be injected out. The syringe pump system consists of a syringe, the mechanical unit, and the controller unit. A stepper motor, the lead screw, and the mechanical components are used for the mechanical unit. Arduino Uno microcontroller is used as the controller unit and can be programmed by the computer through GUI (Graphical User Interface). The input parameters, such as the push or pull of flow direction, flow rate, the droplet volume, and syringe size dimension can be inputted by the user as their desired value via keypad or the computer. The measurement results show that the syringe pump has characteristics: the maximum average error value of the measured volume is 2.5% and the maximum average error value of the measured flow rate is 14%. The benefits of a syringe pump for micro deposition can overcome photolithography weaknesses, which require an etching and stencil process in the manufacture of semiconductors. Combining two or more syringes into one system with different droplet materials can be used as a promising method for 3D microfabrication in the future.

A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging

A novel method was developed to track lung tumor motion in real time during radiation therapy with the purpose to allow target radiation dose escalation while simultaneously reducing the dose to sensitive structures, thereby increasing local control without increasing toxicity. This method analyzes beam’s eye view radiation therapy treatment megavoltage (MV) images with simulated digitally reconstructed radiographs (DRRs) as references. Instead of comparing global DRRs with projection images, this method incorporates a technique that divides the global composite DRR and the corresponding MV projection into sub-images called tiles. Registration is performed independently on tile pairs in order to reduce the effects of global discrepancies due to scattering or imaging modality differences. This algorithm was evaluated by phantom studies while simulated tumors were controlled to move with various patterns in a complex humanoid torso. Approximately 15,000 phantom MV images were acquired at nine gantry angles, with different tumors moving within ranges between 10 and 20 mm. Tumors were successfully identified on every projection with a total maximum/average error of 1.84/0.98 mm. This algorithm was also applied to over 5,000 frames of MV projections acquired during radiation therapy of five lung cancer patients. This tumor-tracking methodology is capable of accurately locating lung tumors during treatment without implanting any internal fiducial markers nor delivering extra imaging radiation doses.

Metrological Analysis Of Microsoft Kinect In The Context Of Object Localization

This paper presents a comprehensive metrological analysis of the Microsoft Kinect motion sensor performed using a proprietary flat marker. The designed marker was used to estimate its position in the external coordinate system associated with the sensor. The study includes calibration of the RGB and IR cameras, parameter identification and image registration. The metrological analysis is based on the data corrected for sensor optical distortions. From the metrological point of view, localization errors are related to the distance of an object from the sensor. Therefore, the rotation angles were determined and an accuracy assessment of the depth maps was performed. The analysis was carried out for the distances from the marker in the range of 0.8−1.65 m. The maximum average error was equal to 23 mm for the distance of 1.6 m.