One-step pipetting of barcoded planar microparticles into compact monolayer assembling chip for efficient readout of multiplexed immunoassay

Barcoded planar microparticles have many qualities suitable for developing cost-efficient multiplexed immunoassays. But at the translational research level, there are a number of technical aspects yet remain to be addressed which includes robustness and efficiency of the assay readout process. Assay readout process involves automated barcode identification and signal intensity values from each planar microparticle. For this, each microparticle has to be correctly aligned for correct barcode readout while being, ideally, compactly assembled for maximum microparticle imaging efficiency. To simultaneously achieve such alignment and assembly of microparticles but in a straightforward manner, we designed a microfluidic microparticle assembling chip that only requires a single pipetting step. Our design utilizes capillary flow based guided particle assembly, which allows maximum microparticle-based immunoassay readout efficiency. With the aid of image processing algorithms, we obtained good multiplex immunoassay readout accuracy similar to conventional imaging platforms. Our approach is applicable to both soft elastomer materials (e.g. PDMS) and rigid materials (e.g. polystyrene), the latter of which is frequently used for injection molding based mass production. We anticipate our device could help developing facile and user-friendly platform technologies based on barcoded planar microparticles.

Web Service for Point Cloud Supported Robot Programming Using Machine Learning

In industrial automation, the use of robots is already standard. But there is still a lot of room for further automation. One such place where improvements can be made is in the adjustment of a production system to new and unknown products. Currently, this task includes the reprogramming of the robot and a readjustment of the image processing algorithms if sensors are involved. This takes time, effort, and a specialist, something especially small and middle-sized companies shy away from. We propose to represent a physical production line with a digital twin, using the simulated production system to generate labeled data to be used for training in a deep learning component. An artificial neural network will be trained to both recognize and localize the observed products. This allows the production line to handle both known and unknown products more flexible. The deep learning component itself is located in a cloud and can be accessed through a web service, allowing any member of the staff to initiate the training, regardless of their programming skills. In summary, our approach addresses not only further automation in manufacturing but also the use of synthesized data for deep learning.

Comparative Analysis of White Blood Cell Cancer Detection Using Image Processing and IoT

Computerized analysis of white blood cells tumor such as Leukemia and Myeloma is an essential testing biomedical investigate point. Herein, a comparative analysis of image processing algorithms to detect the cancer is made and patient’s health is monitored using IOT also analyzed. The work could be useful for developing and exploring the new applications of image processing in IOT based systems.

Analisis Algoritma Shi-Tomasi Dalam Pengujian Citra Senyum Pada Wajah Manusia

Problems in image processing to obtain the best smile are strongly influenced by the quality, background, position, and lighting, so it is very necessary to have an analysis by utilizing existing image processing algorithms to get a system that can make the best smile selection, then the Shi-Tomasi Algorithm is used. the algorithm that is commonly used to detect the corners of the smile region in facial images. The Shi-Tomasi angle calculation processes the image effectively from a target image in the edge detection ballistic test, then a corner point check is carried out on the estimation of translational parameters with a recreation test on the translational component to identify the cause of damage to the image, it is necessary to find the edge points to identify objects with remove noise in the image. The results of the test with the shi-Tomasi algorithm were used to detect a good smile from 20 samples of human facial images with each sample having 5 different smile images, with test data totaling 100 smile images, the success of the Shi-Tomasi Algorithm in detecting a good smile reached an accuracy value of 95% using the Confusion Matrix, Precision, Recall and Accuracy Methods.

Compare the Similarity between Two Quantum Images

Comparing the similarity between digital images is an important subroutine in various image processing algorithms. In this study, we present three quantum algorithms for comparing the similarity between two quantum images. These algorithms apply to binary, grey and color images respectively. Without considering the image preparation, the proposed algorithms achieve exponential acceleration than the existing quantum and classical methods in complexity. At the end of this paper, an experiment based on the real quantum computer of IBMQ and simulations verify the effectiveness of the algorithms.

Detection of Pneumonia Using Chest X-Ray Images and Image Processing Algorithms - A Comparative Study

Utilizing exclusively picture handling procedures, this examination proposes an original strategy for distinguishing the presence of pneumonia mists in chest X-rays (CXR). Collected the several analogue chest CXRs from patients with normal and Pneumonia-infected lungs. Indigenous algorithms have been developed for cropping and for extraction of the lung region from the images. To detect pneumonia clouds first conducted the preprocessing of the image then used the image segmentation techniques like Otsu thresholding K-means clustering and global thresholding and then contour detection algorithm was applied which helped to detect lung boundary, the area’s ratio is used to classify the normal lung from pneumonia affected lung.