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Computer memory systems
Published in Joseph D. Dumas, Computer Architecture, 2016
Another widely used virtual memory technique is called segmentation. A demand-segmented memory system maps memory in variable-length segments rather than fixed-size pages. Although it obviously requires hardware for implementation, segmentation is software-oriented in the sense that the length of the segments is determined by the structure of the code or data they contain rather than by hardware constraints, such as disk sector size. (There is always some maximum segment size due to hardware limitations, but it is typically much larger than the size of a page in a demand-paged system.) Because segments can vary in size, main memory is not divided into frames; segments can be loaded anywhere there is sufficient free memory for them to fit. Fragmentation problems are exactly the reverse of those encountered with paging. Because segments can vary in size, internal fragmentation is never a problem. However, when a segment is loaded, it is necessary to check the size of available memory areas (or other segments that might be displaced) to determine where the requested segment will fit. Invariably, over time there will arise some areas of main memory that do not provide a good fit for segments being loaded and thus remain unused; this is known as external fragmentation. Reclaiming these areas of memory involves relocating segments that have already been loaded, which uses up processor time and makes segmentation somewhat less efficient than paging.
E
Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
extension principle a basic identity for extending the domain of nonfuzzy or crisp relations to their fuzzy counterparts. external cavity klystron a klystron device in which the resonant cavities are located outside the vacuum envelope of the tube. external event event occurring outside the CPU and I/O modules of a computer system that results in a CPU interrupt. Examples include power fail interrupts, interval timer interrupts, and operator intervention interrupts. external fragmentation in segmentation, leaving small unusable areas of main memory that can occur after transferring segments into and out of the memory. external interrupt a signal requesting attention that is generated outside of the CPU. external memory puter. secondary memory of a com-
Lean Flow
Published in Jody Crane, Chuck Noon, The Definitive Guide to Emergency Department Operational Improvement, 2019
Segmentation can also be beneficial if the segmented processes are measurably more efficient than the processes without segmentation. Unfortunately, that’s not what we always see in the real world. Too often, ED personnel will tell us about their fast track and how well it performs. When details emerge, we find out that the fast track performs the same as the main side with respect to rooms to provider ratios, staff ratios, productivity, and LOS. With further questioning, they will admit to frequent imbalances of work between the two segments. In a case like this, the segmentation is simply “anti-pooling” and is hurting the overall flow of the system.
Automated Neural Network-based Survival Prediction of Glioblastoma Patients Using Pre-operative MRI and Clinical Data
Published in IETE Journal of Research, 2023
Gurinderjeet Kaur, Prashant Singh Rana, Vinay Arora
UNet design is a significant advancement in computer vision, revolutionizing segmentation in medical imaging and other domains. The UNet’s defining characteristic is the extended skip link between each level of contracting and growing paths. It is as though FCN is being dragged upward from both ends resulting in the U-shaped architecture. ResNet was another game-changing development in computer vision. Instead of learning unreferenced functions, ResNets (or residual networks) learn residual functions from the level inputs. Residual nets enable these layers to suit a more generic mapping instead of depending on a particular mapping for each layer. ResNet-50, for example, has 50 layers and is built utilizing residual blocks sequentially. The residual blocks and the identity mapping connections of Residual Network (ResNet) aided in constructing a deeper CNN, which delivered record-breaking classification results on the ImageNet dataset. By substituting ResBlock for convolutions on each level of UNet, we can get more outstanding performance than the original UNet. Figure 2, given below, illustrates the complete segmentation model architecture.
A Smart and Secured Approach for Children’s Health Monitoring Using Machine Learning Techniques Enhancing Data Privacy
Published in IETE Journal of Research, 2023
Labelling data for object recognition are challenging since there are several ways used to train algorithms that learn from data sets and anticipate the results. Image segmentation is a subset of digital image processing that focuses on dividing an image into distinct segments based on its characteristics and qualities. Image annotation is a way of labelling pictures including points of interest in order to make them identifiable to systems. The objective of image segmentation is to simplify or transform an image’s representation into something more relevant and easier to evaluate. It might be difficult to train an image segmentation model on new images, especially when you have to label your own data. The fundamental objective of image segmentation is to simplify the image so that it can be analysed more easily. Image segmentation is a method of breaking down a digital image into several subsets called Image segments, which serves to reduce the complexity of the image and make further processing or analysis of the image easier. In simpler terms, segmentation is the process of assigning labels to pixels. Image segmentation is commonly used to find objects and boundaries (lines, curves and so on) in images (Figure 7).
Deep Auto Encoder Based Extreme Learning System for Automatic Segmentation of Cervical Cells
Published in IETE Journal of Research, 2021
T. S. Sheela Shiney, R. Jemila Rose
Globally the fourth-largest female malignancy is cervical cancer. It is one of India’s 10 leading death causes. Overall, 80–90 percent of cervical cancers account for the most common cancer type in women [1,2]. This also coincides with the prevalence of Human Papilloma Virus (HPV) and Cervical Cancer in India, which accounts for over 90 percent of cases. The cervical cancer accounts the fourth most woman cancer in the world and the second most common cancer in India. The world’s burden of cervical cancer is 23 percent in India alone. The Cervical cancer has to be diagnosed and treated at earlier stage otherwise it may spread to other parts of the body, causing death. Segmentation is an important digital image processing technique that has been widely used in many fields. Throughout the area of biomedical applications, image segmentation plays a significant role. The radiologists used to separate the patient information into relevant regions through the segmentation technique. This technique is primarily used to identify the area of the tumor by segmenting the irregular microscopic input image.