Due to advances in semiconductor processing technologies, each slice of a semiconductor is becoming denser and more complex, which can increase the number of surface defects. These defects should be caught early and correctly classified in order help identify the causes of these defects in the process and eventually help to improve the yield. In today’s semiconductor industry, visible surface defects are still being inspected manually, which may result in erroneous classification when the inspectors become tired or lose objectivity. This paper presents a vision-based machine-learning-based method to classify visible surface defects on semiconductor wafers. The proposed method uses deep learning convolutional neural networks to identify and classify four types of surface defects: center, local, random, and scrape. Experiments were performed to determine its accuracy. The experimental results showed that this method alone, without additional refinement, could reach a top accuracy in the range of 98% to 99%. Its performance in wafer-defect classification shows superior performance compared to other machine-learning methods investigated in the experiments.

In modern medicine, medical imaging technologies such as computed tomography (CT), X-ray, ultrasound, magnetic resonance imaging (MRI), nuclear medicine, etc., have been proven to provide useful diagnostic information by displaying areas of a lesion or tumor not visible to the human eye, and may also help provide additional recessive information by using modern data analysis methods. These methods, including Artificial Intelligence (AI) technologies, are based on deep learning architectures, and have shown remarkable results in recent studies. However, the lack of explanatory ability of connection-based, instead of algorithm-based, deep learning technologies is one of the main reasons for the delay in the acceptance of these technologies in the mainstream medical field. One of the recent methods that may offer the explanatory ability for the CNN classes of deep learning neural networks is the gradient-weighted class activation mapping (Grad-CAM) method, which produces heat-maps that may offer explanations of the classification results. There are already many studies in the literature that compare the objective metrics of Grad-CAM-generated heat-maps against other methods. However, the subjective evaluation of AI-based classification/prediction results using medical images by qualified personnel could potentially contribute more to the acceptance of AI than objective metrics. The purpose of this paper is to investigate whether and how the Grad-CAM heat-maps can help physicians and radiologists in making diagnoses by presenting the results from AI-based classifications as well as their associated Grad-CAM-generated heat-maps to a qualified radiologist. The results of this study show that the radiologist considers Grad-CAM-generated heat-maps to be generally helpful toward diagnosis.

By the standard of today’s image-guided surgery (IGS) technology, in order to check and verify the progress of the surgery, the surgeons still require divert their attention from the patients occasionally to check against the display. In this paper, a mixed-reality system for medical use is proposed that combines an Intel RealSense sensor with Microsoft’s Hololens head-mounted display system, for superimposing medical data onto the physical surface of a patient, so the surgeons do not need to divert their attention from their patients. The main idea of our proposed system is to display the 3D medical images of the patients on the actual patients themselves by placing the medical images and the patients in the same coordinate space. However, the virtual medical data may contain noises and outliers, so the transformation mapping function must be able to handle these problems. The transform function in our system is performed by the use of our proposed Denoised-Resampled-Weighted-and-Perturbed-Iterative Closest Points (DRWP-ICP) algorithm, which performs denoising and removal of outliers before aligning the pre-operative medical image data points to the patient’s physical surface position before displaying the result using the Microsoft HoloLens display system. The experimental results shows that our proposed mixed-reality system using DRWP-ICP is capable of performing accurate and robust mapping despite the presence of noise and outliers.

In various studies, problems with surgical instruments in the operating room are usually one of the major causes of delays and errors. It would be of great help, in surgery, to quickly and automatically identify and keep count of the surgical instruments in the operating room using only video information. In this study, the recognition rate of fourteen surgical instruments is studied using the Faster R-CNN, Mask R-CNN, and Single Shot Multi-Box Detectors, which are three deep learning networks in recent studies that exhibited near real-time object detection and identification performance. In our experimental studies using screen captures of real surgery video clips for training and testing, this study found that that acceptable accuracy and speed tradeoffs can be achieved by the Mask R-CNN classifier, which exhibited an overall average precision of 98.94% for all the instruments.

This paper investigates the use of an autonomous service robot in an indoor complex environment, such as a hospital ward or a retirement home. This type of service robot not only needs to plan and find paths around obstacles, but must also interact with caregivers or patients. This study presents a type of service robot that combines the image from a 3D depth camera with infrared sensors, and the inputs from multiple sonar sensors in an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based approach in path planning. In personal contacts, facial features are used to perform person recognition in order to discriminate between staff, patients, or a stranger. In the case of staff, the service robot can perform a follow-me function if requested. The robot can also use an additional feature which is to classify the person’s gender. The purpose of facial and gender recognition includes helping to present choices for suitable destinations to the user. Experiments were done in cramped but open spaces, as well as confined passages scenarios, and in almost all cases, the autonomous robots were able to reach their destinations.