A computer network based digital information library system employing authentication and encryption protocols for the secure transfer of digital information library programs to a client computer system and a mobile digital information playback device removably connectable to the client computer system. The present invention is a computer network based library and information delivery system for accessing and obtaining selected digital information files. The library and information delivery system comprises: 1) a library server having a plurality of digital information files; 2) a client computer system coupled to the library server over a network; and 3) a mobile device removably connectable to the client computer system, the client computer system including logic for requesting a download of a selected one or more of the digital information files from the library server, the client computer system further including logic for downloading the selected one or more of the digital information files to the mobile device.

Digital information library and delivery system with logic for generating files targeting a playback device

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. “Deep learning”, or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Overview of deep learning in medical imaging

When the two eyes view discrepant monocular stimuli, stable single vision gives way to alternating periods of monocular dominance; this is the well-known but little understood phenomenon of binocular rivalry. This article develops a neural theory of binocular rivalry that treats the phenomenon as the default outcome when binocular correspondence cannot be established. The theory posits the existence of monocular and binocular neurons arrayed within a functional processing module, with monocular neurons playing a crucial role in signaling the stimulus conditions instigating rivalry and generating inhibitory signals to implement suppression. Suppression is conceived as a local process happening in parallel over the entire cortical representation of the binocular visual field. The strength of inhibition causing suppression is related to the size of the pool of monocular neurons innervated by the suppressed eye, and the duration of a suppression phase is attributed to the strength of excitation generated by the suppressed stimulus. The theory is compared with three other contemporary theories of binocular rivalry. The article closes with a discussion of some of the unresolved problems related to the theory.

https://www.psy.gla.ac.uk/~martinl/Assets/MCMPS/Blake_89.pdf

A Neural Theory of Binocular Rivalry

Character recognition (CR) has been extensively studied in the last half century and has progressed to a level that is sufficient to produce technology-driven applications. Now, rapidly growing computational power is enabling the implementation of the present CR methodologies and is creating an increasing demand in many emerging application domains which require more advanced methodologies. This paper serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques, with their superiorities and weaknesses, are reviewed. Finally, the current status of CR is discussed and directions for future research are suggested. Special attention is given to off-line handwriting recognition, since this area requires more research in order to reach the ultimate goal of machine simulation of human reading.

An overview of character recognition focused on off-line handwriting

[서평]「Digital Video Processing」

A speech prosthesis has been developed based on the following idea. When a handicapped person such as a laryngectomee tries to speak in vain, the movements of the mouth, tongue, etc., are elicited. By detecting the movements, what he or she is trying to say can be determined. Then a speech synthesizer is driven to produce a voice of good quality.

A Speech Prosthesis Employing a Speech Synthesizer-Vowel Discrimination from Perioral Muscle Activities and Vowel Production

In binocular fusion, pairs of left and right stimuli yielding the same brightness perception constitute an equibrightness curve in a coordinate system whose ordinate and abscissa correspond to the left and right stimulus strengths. A neural network model is presented to elucidate the characteristics of the curve. According to the model, Fechner's paradox is due to the threshold characteristics of the neuron. If the shapes or movements are radically different between the left and right stimuli, the retinal rivalry is caused. That is, only the left stimulus is perceived at one moment and the right stimulus at another moment. The period of left or right eye dominance alternates randomly from time to time. The distribution of the period is approximate to the gamma distribution. In order to account for this fact, a neural network model is proposed, which consists of a pair of neurons receiving inputs with stochastic fluctuations. The computer simulation was carried out with satisfactory results. The model of retinal rivalry is integrated with that of brightness perception.

Neural models of brightness perception and retinal rivalry in binocular vision

In this paper, we describe experimental methods of recognizing the document structures of various types of documents in the framework of document understanding Namely, we interpret document structures with individually characterized document knowledge The document understanding process is divided into three procedures: the first is the recognition of document structures from a two-dimensional point of view; the second is the recognition of item relationships from a one-dimensional point of view; and the third is the recognition of characters from a zero-dimensional point of view The procedure for recognizing structures plays the most important role in document understanding This procedure extracts and classifies the logical item blocks from paper-based documents distinctly

Structure recognition methods for various types of documents

SUMMARY In this paper, we propose a new neural filter to which the features related to a given task are input, called a neural filter with features (NFF), to improve further the performance of the conventional neural filter.In order to handle the issue concerning the optimal selection of input features, we propose a framework composed of 1) manual selection of candidates for input features related to a given task and 2) training with automatically selection of the optimal input features required for achieving the given task.Experiments on the proposed framework with an application to improving the image quality of medical X-ray image sequences were performed.The experimental results demonstrated that the performance on edge-preserving smoothing of the NFF, obtained by the proposed framework, is superior to that of the conventional neural and dynamic filters.

Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences

Retinal velocity field generated by orthographic projection of a moving rigid object is studied. It is shown that, given a velocity field at one moment, the possible interpretation of motion and structure admits three degrees of freedom; two of them are due to freedom in choice of the position and the velocity of the object in the view direction, and the other is due to freedom in choice of thickness of the object. Explicit formulas are also obtained for representing the set of all possible interpretations. It is, furthermore, shown that the third degree of freedom, the freedom as to object thickness, can be removed if retinal position and velocities of three points at two moments are available.

Noboru Sugie

Papers

A Speech Prosthesis Employing a Speech Synthesizer-Vowel Discrimination from Perioral Muscle Activities and Vowel Production

Neural models of brightness perception and retinal rivalry in binocular vision

Structure recognition methods for various types of documents

Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences

Recovery of rigid structure from orthographically projected optical flow