Abstract: A first aspect of this disclosure is related to a computer-implemented method for identifying neuronal patterns in an image, comprising the steps: obtaining a first data set with Golgi-stained neuronal structures; based on the first data set, determining a first auxiliary data set, AR1, based on a first type of neuronal structure and a second auxiliary data set, AR2, based on a second type of neuronal structure; analyzing AR1 with a first method to identify information related to the first type of neuronal structure in AR1; analyzing AR2 with a second method to identify information related to the second type of neuronal structure in AR2; generating a second data set with the identified information related to the first and second type of neuronal structures.

Abstract: The disclosure provides an electronic fuse (eFuse) device and an operation method thereof. The eFuse device includes an eFuse, a readout circuit, a register, and a safety control device. The readout circuit reads out target data recorded by the eFuse to the register and the safety control device. The safety control device compares the target data provided by the readout circuit with the target data provided by the register to determine whether a soft error occurs in the target data stored in the register. When the soft error occurs in the target data stored in the register, the readout circuit reads out the target data recorded by the eFuse again to the register and the safety control device.

Abstract: A system for training a machine-learning algorithm for denoising images is configured to receive training data. The training data includes multiple image sets obtained from one or more series of consecutive images. Each image set includes a plurality of images obtained from a same series of consecutive images. The plurality of images of each image set includes an initial image, a middle image and a last image. The system is further configured to adjust weights of the machine-learning algorithm to obtain a trained machine-learning model, based on an output image of the machine-learning algorithm and a target image. The output image is obtained by using the initial image and the last image as input images. The target image is obtained from the middle image by applying a random shift to the middle image. The system is further configured to provide the trained machine-learning model.

Abstract: A ubiquitous auto calibration device is provided, which includes microcontroller unit, flex bus, image receiver image processing module, and an image output unit. The microcontroller unit is provided for receiving the electronic signal and performing a self-adjusting process to the electronic signal. The flex bus is connected with the microcontroller unit, and is provided for transmitting the electronic signal to the image processing module after performing the self-adjusting process. The image receiver is provided for receiving the image signal from the image receiving interface. The image processing module is provided for performing an image calibration process to the image signal, so that the image signal can obey the color temperature standard, Gamma value, uniformity and color gamut standards when the panel outputs the image signal.

Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Abstract: A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image prediction performs a deep image prediction learning on universal modality training images and corresponding desired modality prediction images to generate a deep image prediction model, which is applied to transform universal modality images into a high quality image mimicking a desired modality prediction image.

Abstract: A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image integration performs a deep image integration learning on multi-modality training images and corresponding desired integrated images to generate a deep image integration model, which is applied to transform multi-modality images into a high quality integrated image mimicking a desired integrated image.

Abstract: A ubiquitous auto calibration device is provided, which includes microcontroller unit, flex bus, image receiver image processing module, and an image output unit. The microcontroller unit is provided for receiving the electronic signal and performing a self-adjusting process to the electronic signal. The flex bus is connected with the microcontroller unit, and is provided for transmitting the electronic signal to the image processing module after performing the self-adjusting process. The image receiver is provided for receiving the image signal from the image receiving interface. The image processing module is provided for performing an image calibration process to the image signal, so that the image signal can obey the color temperature standard, Gamma value, uniformity and color gamut standards when the panel outputs the image signal.

Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Abstract: A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image integration performs a deep image integration learning on multi-modality training images and corresponding desired integrated images to generate a deep image integration model, which is applied to transform multi-modality images into a high quality integrated image mimicking a desired integrated image.

Abstract: A computerized teachable pattern scoring method receives a teaching image and region pattern labels. A region segmentation is performed using the teaching image to generate regions of interest output. A feature measurement is performed using the teaching image and the regions of interest to generate region features output. A pattern score learning is performed using the region features and the region pattern labels to generate pattern score recipe output. A computerized region classification method using the region features and the pattern score recipe to generate pattern scores output. A region classification is performed using the pattern scores and region features to generate region class output.

Abstract: A computerized teachable pattern scoring method receives a teaching image and region pattern labels. A region segmentation is performed using the teaching image to generate regions of interest output. A feature measurement is performed using the teaching image and the regions of interest to generate region features output. A pattern score learning is performed using the region features and the region pattern labels to generate pattern score recipe output. A computerized region classification method using the region features and the pattern score recipe to generate pattern scores output. A region classification is performed using the pattern scores and region features to generate region class output.

Abstract: A method for protecting a software authorization utilized in a software installed in a hardware device, which includes a timer to utilize a system time, and including a plurality of functions is disclosed. The method includes setting an authorized time, an accumulated authorization time, an accumulated running time and a last recorded time when the software is initially installed in the hardware device, operating a time-out check for determining whether the software authorization is expired or not according to the system time, the authorized time, the accumulated authorization time, the accumulated running time and the last recorded time when the software is initiated or the plurality of functions are initiated or terminated, and stopping the software from running in the hardware device when the software authorization is expired.

Abstract: A Recognition Frame presents multi-level application elements to the user simultaneously through a computer graphical user interface. The interface consists of an image display panel for displaying image channels; a data display panel for displaying object measurements and summary statistics; a configuration display panel for displaying recipe content; a master tab for selecting the panels. It also consists of a processing toolbar for context dependent processing tool display. The Recognition Frame further comprises a second side frame for data object display and charting. The second side frame has a tabular arrangement consisting of properties tab, controls tab, and charts tab. The Recognition Frame links application elements through a complex data model wherein interface display is automatically updated when one element is changed.

Abstract: A Recognition Frame presents multi-level application elements to the user simultaneously through a computer graphical user interface. The interface consists of an image display panel for displaying image channels; a data display panel for displaying object measurements and summary statistics; a configuration display panel for displaying recipe content; a master tab for selecting the panels. It also consists of a processing toolbar for context dependent processing tool display. The Recognition Frame further comprises a second side frame for data object display and charting. The second side frame has a tabular arrangement consisting of properties tab, controls tab, and charts tab. The Recognition Frame links application elements through a complex data model wherein interface display is automatically updated when one element is changed.

Abstract: A multilevel Chain-And-Tree model provides a framework for an image based decision system. The decision system enables separation of effects of defects within one component from other components within a common subject. The framework provides for linking of structure constraints of components of a common subject and for checking and resolving their consistency. The framework allows discrimination between subtle image changes and natural variations of the subject. The framework for standard data representation facilitates production process control.

Abstract: A systematic way of linking structure constraints of components of a common object and checking and resolving their inconsistency is used to improve detection results in image-based decision systems. A multilevel Chain-And-Tree (CAT) model is used to direct processing using both forward and backward scans through the related components. Since components occur as parts of an object, the context (relational structure) in which the component appears can be used to reduce noise and variation affects. In the method, object knowledge is translated into constraints between components. The constraints are used to enhance feature detection, defect detection, and measurement accuracy and consistency.

Abstract: A systematic way of linking structure constraints of components of a common object and checking and resolving their inconsistency is used to improve detection results in image-based decision systems. A multilevel Chain-And-Tree (CAT) model is used to direct processing using both forward and backward scans through the related components. Since components occur as parts of an object, the context (relational structure) in which the component appears can be used to reduce noise and variation affects. In the method, object knowledge is translated into constraints between components. The constraints are used to enhance feature detection, defect detection, and measurement accuracy and consistency.