A Review on Automatic Bill of Material Generation and Visual Inspection on PCBs

Author(s):  
Mukhil Azhagan M. S ◽  
Dhwani Mehta ◽  
Hangwei Lu ◽  
Sudarshan Agrawal ◽  
Mark Tehranipoor ◽  
...  

Abstract Globalization and complexity of the PCB supply chain has made hardware assurance a challenging task. An automated system to extract the Bill of Materials (BoM) can save time and resources during the authentication process, however, there are numerous imaging modalities and image analysis techniques that can be used to create such a system. In this paper we review different imaging modalities and their pros and cons for automatic PCB inspection. In addition, image analysis techniques commonly used for such images are reviewed in a systematic way to provide a direction for future research in this area. Index Terms—Component Detection, PCB, Authentication, Image Analysis, Machine Learning

2021 ◽  
Vol 54 (3) ◽  
pp. 1-41
Author(s):  
Shyamali Mitra ◽  
Nibaran Das ◽  
Soumyajyoti Dey ◽  
Sukanta Chakraborty ◽  
Mita Nasipuri ◽  
...  

Cytology is a branch of pathology that deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions. In the present work, the term cytology is used to indicate solid organ cytology. Automation in cytology started in the early 1950s with an aim to reduce manual efforts in the diagnosis of cancer. The influx of intelligent systems with high computational power and improved specimen collection techniques helped to achieve technological heights in the cytology automation process. In the present survey, we focus on image analysis techniques paving the way to automation in cytology. We take a short tour of 17 types of solid organ cytology to explore various segmentation and/or classification techniques that evolved during the past three decades to automate cytology image analysis. It is observed that most of the works are aligned toward three types of cytology: Cervical, Breast, and Respiratory tract cytology. These are discussed elaborately in the article. Commercial systems developed during the period are also summarized to comprehend the overall growth in respective domains. Finally, we discuss different state-of-the-art methods and related challenges to provide prolific and competent future research directions in bringing cytology-based commercial systems into the mainstream.


Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 112 ◽  
Author(s):  
Andrzej Przybylak ◽  
Radosław Kozłowski ◽  
Ewa Osuch ◽  
Andrzej Osuch ◽  
Piotr Rybacki ◽  
...  

This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room.


Author(s):  
Grimur Tomasson ◽  
Gisli Kristjan Olafsson ◽  
Hlynur Sigurporsson ◽  
Bjorn Por Jonsson ◽  
Kristjan Runarsson ◽  
...  

2021 ◽  
Vol 69 (10) ◽  
pp. 627-631
Author(s):  
Abigail R. Bland ◽  
John C. Ashton

Histochemistry of tumor sections is a widely employed technique utilized to examine cell death in preclinical xenograft animal models of cancer. However, this is under the assumption that tumors are homogeneous, leading to practices such as automatic cell counting across the entire section. We have noted that in our experiments the core of the tumor is largely or partially necrotic, and lacks evidence of vascularization (in contrast to the outer areas of the tumor). We note that this can bias and confound immunohistochemical analyses that do not take care to sample areas of interest in a way to take this into account. Design-based stereology with image analysis techniques is an alternative process that could be used to measure the volume of the necrotic region compared to the volume of the whole tumor.


2021 ◽  
Vol 9 (1) ◽  
pp. 1406-1412
Author(s):  
K. Santhi, A. Rama Mohan Reddy

Cardiovascular disease (CVD) is one of the critical diseases and the most common cause of morbidity and mortality worldwide. Therefore, early detection and prediction of such a disease is extremely essential for a healthy life. Cardiac imaging plays an important role in the diagnosis of cardiovascular disease but its role has been limited to visual assessment of heart structure and its function. However, with the advanced techniques and tools of big data and machine learning, it become easier to clinician to diagnose the CVD. Stenosis with in the Coronary Arteries (CA) are often determined by using the Coronary Cine Angiogram (CCA). It comes under the invasive image modality. CCA is the effective method to detect and predict the stenosis. In this paper a coronary analysis automation method is proposed in disease diagnosis. The proposed method includes pre-processing, segmentation, identifying vessel path and statistical analysis.


Author(s):  
Debbie Care ◽  
Shirley Nichols ◽  
Derek Woodfield

The use of low-ionic-strength hydroponic culture and image analysis techniques to discriminate and isolate morphologically distinct, genetically differentiated root types within white clover is described. Advantages of this method include the ability to view the genetic expression of the root systems without the modifying effects of growth in soil, to examine the growth and structure of roots over time, and to store the images for further examination. It is recognised that although the root systems grow in three dimensions, they are constrained to two dimensions by the flatbed scanner. However, the morphological parameters determined by image analysis would not be altered whether this analysis was measured in two or three dimensions. Keywords: image analysis, root morphology, solution culture, Trifolium repens


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