Morphological operation on printed circuit board (PCB) reverse engineering using MATLAB

This paper introduces a boundary detector based on mathematical morphology using multi-direction structuring elements to detect the boundaries of an image, and then to make the image segmentation. This paper describes a modified technique based on gray-scale morphological gradient operations for finding the boundaries of an image. The boundaries finding algorithm, which uses a combination of morphological feature detection and a novel directional sensitive procedure to get the boundaries of an image. Its inherent direction sensitivity is better than the others. To improve the quality of the boundary image extracted, the noisy image is filtered by LUM and morphological filters. After boundary detection, disjoint boundaries are connected to form closed contours and result in a number of image regions. Finally, a region growing algorithm is used to produce regions and complete the image segmentation. Some experimental results are provided to show the validity of this method.

Mathematical morphological operations are commonly used as a tool in image processing for extracting image components that are useful in the representation and description of region shape. In this paper, six basic morphological operations are investigated to remove noise and enhance the appearance of binary images. Dilation, erosion, opening, closing, fill and majority operations are tested on twenty-five images and subjectively evaluated based on perceived quality of the enhanced images. Results of the experiments showed that noise can be effectively removed from binary images using combinations of erode-dilate operations. Also, the binary images are significantly enhanced using combinations of majority-close operations.

Diffusion coupling model based on neural oscillation synchronization theory, which can extract different targets with different gray levels from one, has been an important way for realizing image segmentation. This paper describes the diffusion coupling model and its algorithm and process for image segmentation. Simulation programs for supplementing this algorithm were compiled based on MATLAB platform. Experimental results on synthetic images, infrared images and brain magnetic resonance images show that, by setting reasonable algorithm parameters, the target image extraction and segmentation of different elements can be achieved. This image segmentation algorithm exhibits wide applications in medical image diagnosis, satellite image processing, traffic control, biometrics and others.

As part of several studies related to understanding many processes in bacteria (division, repair,), imaging allows to understand the dynamics of foci of bacterial chromosome. In this context, we have developed software to individualize the bacteria colonies, follow these bacteria and the foci referenced by bacteria and to analyze these dynamics. It comes to design and produce a tool for automatic processing of images dedicated to the description of the dynamics of the chromosome of the bacterium Escherichia coli. To achieve this goal, image segmentation is proposed for identification of bacteria on a second stack of images and the detection of genes for each bacterium. Then we use the method of active contours and the algorithm of the line of watershed to monitor bacteria throughout the pile for a whole cell cycle. Based on points of interest, mainly those with local extrema in terms of radius of curvature, we detect the new divisions and decide on new son. Various operations of mathematical morphology are used.

Image segmentation is a fundamental task in image analysis which is responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours. The goal of medical image segmentation is to partition a medical image in to separate regions, usually anatomic structures that are meaningful for a specific task. In many medical applications, such as diagnosis, surgery planning, and radiation treatment planning determining of the volume and position of an anatomic structure is required and plays a critical role in the treatment outcome.

The rapid development of information and communication technologies has driven the rapid growth of data traffic and the large requirement of high-speed optical transceiver modules. Usually, optical subassemblies through flexible printed circuit boards (PCBs) connect with the hard PCBs to compose as optical transceiver modules. Because of the impedance mismatch of connection between flexible PCB and hard PCB, the operation bandwidth of the module can be limited. In this paper, the design of connection between flexible PCB and hard PCB in optical subassembly modules will be investigated for increasing the operation speed of the module. First, the transmission bandwidth of flexible PCB is analyzed. High-frequency performances of three transmission line structures, micro-strip line, grounded coplanar waveguide, and top ground micro-strip line, are investigated in flexible PCB. High-frequency performances of flexible PCB with different bending types are also investigated. The flexible PCB has been verified to be suitable to use in 25 Gb/s applications. Next, the high-speed transmission performance is analyzed through the connection between flexible PCB and hard PCB, because the impedance mismatch usually occurs at the connection. The ground pads are added near the signal line of flexible PCB, and the ground pads are connected to the ground layer in PCB through ground vias. The anti-pad in hard PCB is added under the signal solder pad. The dip of S21 response can be eliminated by the optimized ground pad. The impedance mismatch can be reduced by the optimized anti-pad, the impedance increases from 40-Ω to 49-Ω at the connection between flexible PCB and hard PCB, and the transmission bandwidth can increase from 16.5 GHz to 23.5 GHz. TO-46 header is a common and low-cost packaging solution for DFB laser and photodiodes. The connection between flexible PCB and TO-46 header is also investigated. While the connection has a gap, the dip of frequency response will occur at lower frequency. While the connection is very close, the bandwidth can be larger than 25 GHz. The flexible PCB, hard PCB, and TO-46 header are combined together, and the 23 GHz transmission bandwidth can be obtained. The 25 Gb/s transmission eye diagram is also measured, and the eye height, rise time, fall time, and Q-factor are 268.14 mV, 13.78 ps, 13.56 ps, and 8.76, respectively. The combined flexible PCB, hard PCB, and TO-46 header are verified to be suitable to apply in 25 Gb/s optical subassembly modules, and will be able to be used in 100G Ethernet.

BackgroundHistopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited.ResultsIn this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset.ConclusionsThe framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.

This paper presents the development of an automated component detection system for quality control in Printed Circuit Boards (PCBs) by integrating the YOLO object detection algorithm with Internet of Things (IoT) real-time streaming on a Raspberry Pi platform. The proposed system aims to address the challenges associated with traditional manual inspection methods, including time inefficiency, human error, and limited accuracy in detecting faulty components. The YOLO model, renowned for its high-speed and accurate object detection capabilities, was trained to identify various PCB components and deployed on a Raspberry Pi due to its affordability, portability, and low power consumption. To enable real-time remote monitoring and analysis, IoT capabilities were incorporated using the MQTT protocol, allowing seamless data transmission to remote servers or devices. The experimental results demonstrated the effectiveness of the proposed system, achiev-ing an average detection accuracy of 95%, making it a reliable solution for real-time quality assurance in PCB manufacturing. The novelty of this study lies in the innovative integration of the YOLO algorithm with IoT technology on a cost-efficient platform, providing a scalable and practical solution for automating PCB inspection processes. This approach not only enhances inspection efficiency but also reduces operational costs, offering significant value to the electronics manufacturing industry. Future work will focus on scaling thesystem for broader applications and improving the detection capabilities for more complex PCB designs.

Herbarium specimen scans constitute a valuable source of raw data. Herbarium collections are gaining interest in the scientific community as their exploration can lead to understanding serious threats to biodiversity. Data derived from scanned specimen images can be analyzed to answer important questions such as how plants respond to climate change, how different species respond to biotic and abiotic influences, or what role a species plays within an ecosystem. However, exploiting such large collections is challenging and requires automatic processing. A promising solution lies in the use of computer-based processing techniques, such as Deep Learning (DL). But herbarium specimens can be difficult to process and analyze as they contain several kinds of visual noise, including information labels, scale bars, color palettes, envelopes containing seeds or other organs, collection-specific barcodes, stamps, and other notes that are placed on the mounting sheet. Moreover, the paper on which the specimens are mounted can degrade over time for multiple reasons, and often the paper's color darkens and, in some cases, approaches the color of the plants.

Though image segmentation is a fundamental task in image analysis; it plays a vital role in the area of image processing. Its value increases in case of medical diagnostics through medical images like X-ray, PET, CT and MRI. In this paper, an attempt is taken to analyse a CT brain image. It has been segmented for a particular patch in the brain CT image that may be one of the tumours in the brain. The purpose of segmentation is to partition an image into meaningful regions with respect to a particular application. Image segmentation is a method of separating the image from the background, read the contents and isolating it. In this paper both the concept of clustering and thresholding technique with edge based segmentation methods like sobel, prewitt edge detectors is applied. Then the result is optimized using GA for efficient minimization of the objective function and for improved classification of clusters. Further the segmented result is passed through a Gaussian filter to obtain a smoothed image.

Image segmentation is an important task in image analysis and processing. Many of the existing methods for segmenting a multi-component image (satellite or aerial) are very slow and require a priori knowledge of the image that could be difficult to obtain. Furthermore, the success of each of these methods depends on several factors, such as the characteristics of the acquired image, resolution limitations, intensity in-homogeneities and the percentage of imperfections induced by the process of image acquisition. Evolutionary programming(EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In this paper the new evolutionary programming is proposed to overcome the premature convergence. There are two step mutation in the new evolutionary programming. The first step is responsible for searching the whole space. The second is responsible for searching the local part in detail. The cooperation and specialization between different two step mutation are considered during the algorithm design. The new evolutionary programming can use in image segmentation and the experimental results show the new evolutionary programming is efficient.

The research object in the work is the printed circuit boards (PCB) Production technological process using the additive technology of photopolymer 3D printing. The existing problem is that the manufacturing process of single-sided and double-sided PCBs, simple in technology, from the third to the fifth accuracy class, requires the use of a large amount of consumables and technological equipment. In turn, this affects the cost of the product. The research subject is models and methods for manufacturing PCB using photopolymer 3D printing. In order to reduce the cost of materials: film or aerosol photoresist, as well as reduce the number of technological operations, applying photoresist and for the manufacture of PCBs stencils, it is proposed to use photopolymer 3D printing technologies for the manufacture of PCBs. The paper analyzes the costs of Plexiwire Resin Basic Orange Transparen photopolymer resin for the manufacture of single-sided PCBs and calculates the cost of the consumable (resin) compared to the costs of dry film photoresist. 60 % cost of consumables (photopolymer resin) compared to dry film photoresist for making single-sided PCBs. The work is aimed at determining the dependence of the geometric dimensions of the PСВs topology and the consumption of photopolymer resin on the technological parameters of photopolymer exposure. A regression correlation model of the dependence of resin consumption on exposure parameters has been developed and correlation coefficients have been calculated. It has been established that with an increase in the exposure time of the photopolymer resin, the consumption of the photopolymer resin increases and the deviation of the geometric dimensions of the PCBs topology increases, which in turn negatively affects the quality of the product. Therefore, using the obtained regression model, it is possible to calculate the influence of parameters on the PCB topology and reduce the deviation of conductor sizes and resin consumption.

Morphological openings and closings are basic operators in mathematical morphology. Morphological area openings and area closings have the same functions as standard morphological openings and closings but avoid distorting object boundaries. The conventional implementation of morphological area operators is based on the heap data structure. A new methodology for implementing morphological area operators, which relies on the disjoint set data structure, is proposed in this paper. The computational complexity of our method is O(N) where N is the image size, and not related to the area parameter. Experiments show that the new implementation of grayscale area operators runs faster than the conventional implementations. The computational complexity of binary area operators is also studied in this paper.

Printed Circuit Board (PCB) design reconstruction is essential for addressing part obsolescence, intellectual property recovery, compliance, quality assurance, and enhancing national capabilities. Traditional methods for PCB design extraction, both non-geometry-based and geometry-based, have limitations in accuracy, efficiency, and scalability. This paper presents an automated approach, combining image processing and machine learning, to achieve 3D semantic segmentation of PCB X-ray Computed Tomography (X-ray CT) images and subsequent netlist extraction. By employing a 3D U-Net architecture with a ResNet-18 backbone and training on synthetic data, we introduce a first-of-its-kind method for direct 3D semantic segmentation, significantly improving over previous efforts. Our approach eliminates the need for extensive labeled datasets by using inherently labeled synthetic data. Further, this method enhances ease of segmentation by significantly reducing or eliminating the preprocessing effort required for 2D image stacks. It also improves universality by expanding the scope of application beyond images with specific 2D stack criteria, segmenting the 3D image in its entirety. Additionally, this method enables the processing of images of PCBs that have undergone bending, which is common among PCBs with a thickness below a certain threshold. The implications of this approach extend beyond PCBs, finding applications in various physical and biological sciences where 3D image segmentation is crucial. This methodology includes high-resolution 3D imaging, watershed segmentation, machine learning-based semantic segmentation, and netlist extraction. Validation with both synthetic and real-world PCB datasets shows high accuracy and robustness, offering a scalable solution for PCB design reconstruction.

Medical image analysis plays a vital role in medical diagnostics, helping physicians monitor disease progression, select the most proper treatment plans in clinical practice, and in many other cases. In various image analysis tasks, image segmentation often serves as the first step. Later on, measurements extracted from segmented regions like intensity distribution, volume, and boundary properties provide a straightforward approach to inspect and interpret pathology. When rendered in 2D/3D/4D, the segmentations reveal non-trivial topological information. Furthermore, the extracted and quantized features can be combined with medical records to perform functional predictions especially when trained on data from large populations. However, traditional manual segmentation often requires topical expertise, suffers from inter- and intra-observer variations, and is overly time-consuming. Many semi-automated and automated algorithms have been proposed to reduce humans' interaction when generating accurate image segmentations, including active shape models, atlas based methods, graph based methods, and artificial neural networks. Among these methods, graph based methods have solid mathematical proof of global optimality and can be easily generalized to work on n-dimensional images, while they lack a good feature extractor to guide the necessary graph construction and define the underlying cost metrics. Recently, deep learning based methods have become prevalent and achieved new levels of the state-of-the-art accuracy in medical image segmentation. However, they are not explicitly topology-wise and require a large amount of annotations during training.