Miracle-dz/UniFAR-FTUs huggingface dataset:
Contour detection based on horizontal interactions in primary visual cortex

Neurons in the primary visual cortex (V1) respond differently to a simple visual element presented in isolation from when it is embedded within a cluttered scene. The contextual influences are mediated by horizontal connections within V1. Inspired by these visual cortical mechanisms, a contour detection model is proposed. Different from previous researches, the facilitation and the suppression are unified modulated by surrounding elements in the image without separating regions of excitatory and inhibitory lateral connections specifically. The proposed model can selectively extract object contours while reducing non-meaningful texture edges and process angular visual features such as corners and T-junctions effectively. The experiments demonstrate the effectiveness of the proposed model.
Design of ion-implanted MOSFET's with very small physical dimensions

This paper considers the design, fabrication, and characterization of very small MOSFET switching devices suitable for digital integrated circuits using dimensions of the order of 1μ. Scaling relationships are presented which show how a conventional MOSFET can be reduced in size. An improved small device structure is presented that uses ion implantation to proVide shallow source and drain regions and a nonuniform substrate doping profile. Onedimensional models are used to predict the substrate doping profile and the corresponding threshold voltage versus source voltage characteristic. A two-dimensional current transport model is used to predict the relative degree of short-channel effects for different device parameter combinations. Polysilicon-gate MOSFETs with channel lengths as short as 0. 5μ were fabricated, and the device characteristics measured and compared with predicted values. Ibe performance improvement expected from using these very small devices in highly miniaturized integrated circuits is projected. Reprintedfrom the IEEE Journal of Solid-State Circuits, Vol. SC-9, October 1974, pp. 256-268. ]
A Quantitative method for the characterization of lytic metastases of the bone from radiographic images

The aim of our study was to assess the diagnostic usefulness of the gray level parameters to distinguish osteolytic lesions using radiological images. Materials and Methods. A retrospective study was carried out. A total of 76 skeletal radiographs of osteolytic metastases and 67 radiographs of multiple myeloma were used. The cases were classified into nonflat (MM1 and OL1) and flat bones (MM2 and OL2). These radiological images were analyzed by using a computerized method. The parameters calculated were mean, standard deviation, and coefficient of variation (MGL, SDGL, and CVGL) based on gray level histogram analysis of a region-of-interest. Diagnostic utility was quantified by measurement of parameters on osteolytic metastases and multiple myeloma, yielding quantification of area under the receiver operating characteristic (ROC) curve (AUC). Results. Flat bone groups (MM2 and OL2) showed significant differences in mean values of MGL (P = 0.048) and SDGL (P = 0.003). Their corresponding values of AUC were 0.758 for MGL and 0.883 for SDGL in flat bones. In nonflat bones these gray level parameters do not show diagnostic ability. Conclusion. The gray level parameters MGL and SDGL show a good discriminatory diagnostic ability to distinguish between multiple myeloma and lytic metastases in flat bones.
Real-time shot boundary detection for digital video camera using the MPEG-7 descriptor

The increased availability and usage of digital video lead to a need for automated video content analysis techniques. Most research on digital video content analysis includes automatic detection of the shot boundaries. However, those methods are not efficient in terms of computational time. In this paper, we propose the digital video camera system that can provide real-time shot boundary detection using the MPEG-7 descriptor. The video camera system is built so that MPEG-7 descriptors are extracted from frames of video. In this paper, the shot boundaries are achieved by measuring a distance of MPEG-7 descriptors for consecutive frames in real-time. Experimental results showed that the proposed video camera system provides fast and effective real-time shot boundary detection.
Acquiring first-order knowledge about air traffic control

Abstract This paper presents an application of knowledge intensive generalization to knowledge acquisition, in the domain of air traffic control. We explain why knowledge intensiveness and first-order logic are sometimes necessary, as for instance in the application field studied here. An obvious advantage of first-order logic is its power of expression, while an obvious drawback is long computation time. We also describe some less obvious advantages and drawbacks of first-order logic, especially when the knowledge must be expressed as Horn clauses to retain some computational efficiency. Finally, we emphasize the large translation problem that must be solved in order to allow an efficient interaction with the expert. Two translation phases are necessary. One goes from the expert's language to Horn clauses, the second one goes back from Horn clauses to the expert's language. The first one is necessary to ensure automatic learning, while the second one allows the expert to understand what has been learned. Both phases are far from trivial and ask for choices that must be made carefully in order to avoid losing significant information. One of our unexpected results is that the second translation phase plays the role of a validation step. It thus becomes a very efficient way to acquire knowledge the expert has problems formalizing. Using first-order logic does complicate things, but it provides, as a reward, a powerful way of extracting and validating the acquired knowledge, especially when the field expert is unable to express his knowledge in a simple way.
Pilot symbol initiated optimal decoder for the land mobile fading channel

We investigate the use of the expectation-maximization (EM) algorithm for estimating sequences of two-dimensional constellation signals transmitted over Gaussian fading channels with diversity reception. The algorithm performs coherent decoding with the use of pilot symbols. It is shown that the performance of the algorithm approaches that of the optimal maximum-likelihood (ML) receiver with linearly increasing complexity as a function of the sequence length. The uncoded performance as well as the coded performance are simulated, and compared with that of other algorithms.
Theory-Driven CALL and the Development Process.

Abstract This paper reflects upon the nature of theory‐driven CALL, particularly as it relates to the development process. Theoretical frameworks that have been used to motivate CALL projects are reviewed. Development processes are described using the literature from instructional design and CALL, and the findings of an international survey covering 18 countries and 104 experienced CALL practitioners. The Richards & Rodgers model (1986) is suggested as a suitable framework to describe both theory‐driven CALL, and projects that use as a point of departure a lower level of abstraction, e.g., curriculum specifications. Development processes are shown to range from those that are linear and systematic to those that are iterative and transformational. The implications for theory‐driven CALL are considered and the concept of ‘fit’ between the theoretical framework or pedagogy and the computer's capabilities is examined (see Wyatt, 1988). In addition, formalist and procedural approaches to CALL are discussed (se.
New results in fuzzy-set-based detection of land mines with GPR

Algorithms for detecting land mines using the GEO-CENTERS Energy Focusing Ground Penetrating Radar (EFGPR) are presented. Key elements of the system include normalization, down- and cross-track feature extraction, fuzzy set membership based confidence assignment, and false alarm testing via transition and number of hyperbolae features. The system has been implemented in real-time in the GEO- CENTERS Vehicle Mounted Mine Detection System and can be used to perform real-time land mine detection or to analyze data stored to disk. Results are presented on calibration lane data from Aberdeen Proving Grounds, Maryland and the Energetic Materials Research and Testing Center, New Mexico in the summer of 1998.
On The Approximation Capability of Neural Networks ‐ Dynamic System Modeling and Control

This paper discusses issues related to the approximation capability of neural networks in modeling and control. We show that neural networks are universal models and universal controllers for a class of nonlinear dynamic systems. That is, for a given dynamic system, there exists a neural network which can model the system to any degree of accuracy over time. Moreover, if the system to be controlled is stabilized by a continuous controller, then there exists a neural network which can approximate the controller such that the system controlled by the neural network is also stabilized with a given bound of output error.
Novel Algorithm for Calibrating Speed-Density Model Parameters in Mesoscopic Traffic Simulator

This article is on the basis of the classical speed-density model which combines with the complex road traffic flow, to raise a way that using the weighted regression to calibrate speed-density model parameters in mesoscopic traffic simulator. After processing detector data, the densities are taken as the variables; locally weighted regression is used to build a nonparameter relationship for the number of the traffic flow, to calibrate the vehicle speed. The test with a huge amount of factual data shows that the methods proposed in this paper outperforms the common optimal algorithm: simplex method in the vehicles speed estimation precision, and can accurately descript the dynamic change regularity of the road traffic flow.
Technological Convergence of IT and BT: Evidence from Patent Analysis

In recent innovation trends, one notable feature is the merging and overlapping of technologies: in other words, technological convergence. A key technological convergence is the fusion of biotechnology (BT) and information technology (IT). Major IT advances have led to innovative devices that allow us to advance BT. However, the lack of data on IT‐BT convergence is a major impediment: relatively little research has analyzed the inter‐disciplinary relationship of different industries. We propose a systematic approach to analyzing the technological convergence of BT and IT. Patent analysis, including citation and co‐classification analyses, was adopted as a main method to measure the convergence intensity and coverage, and two portfolio matrices were developed to manage the technological convergence. The contribution of this paper is that it provides practical evidences for IT‐BT convergence, based on quantitative data and systematic processes. This has managerial implications for each sector of IT and BT.
Traffic infrastructure inventory system by analysis image

This paper describes a computer vision system designed to perform an inventory of traffic signals. The system consists of five Ethernet synchronized cameras; the acquisition strategy allows us to take one image per camera every other second. We then use those five images to generate a panoramic image each second. Signal detection and recognition is carried out offline. Detection of traffic signal is done in the panoramic image using the Hough transform and enhancement of HSV color space. Traffic signal recognition is made by a combination of Haar wavelet and violates Jones classifier. Finally, we present experimental results using a database of one hundred images.
Quantization control for improvement of image quality compatible with MPEG2

A new quantization control method for a moving picture coding system, based on MPEG 2 video syntax, is discussed. The quantization control method consists of MB (macro block) activity calculation and rate control. In considering of human visual characteristics and coding efficiency, the quantization control method performs a high quality compression of the digital video signal. >
Effects on MR images compression in tissue classification quality

It is known that image compression is required to optimize the storage in memory. Moreover, transmission speed can be significantly improved. Lossless compression is used without controversy in medicine, though benefits are limited. If we compress images lossy, where image can not be totally recovered; we can only recover an approximation. In this point definition of "quality" is essential. What we understand for "quality"? How can we evaluate a compressed image? Quality in images is an attribute whit several definitions and interpretations, which actually depend on the posterior use we want to give them. This work proposes a quantitative analysis of quality for lossy compressed Magnetic Resonance (MR) images, and their influence in automatic tissue classification, accomplished with these images.
A cloud-based medical image repository

Many widely used digital medical image collections have been established but these are generally used as raw data sources without related image analysis toolsets. Providing associated functionality to allow specific types of operations to be performed on these images has proved beneficial in some cases (e.g. brain image registration and atlases). However, toolset development to provide generic image analysis functions on medical images has tended to be ad hoc, with Open Source options proliferating (e.g. ITK). Our Automated Medical Image Collection Annotation (AMICA) system is both an image repository, to which the research community can contribute image datasets, and a search/retrieval system that uses automated image annotation. AMICA was designed for the Windows Azure platform to leverage the flexibility and scalability of the cloud. It is intended that AMICA will expand beyond its initial pilot implementation (for brain CT, MR images) to accommodate a wide range of modalities and anatomical regions. This initiative aims to contribute to advances in clinical research by permitting a broader use and reuse of medical image data than is currently attainable. For example, cohort studies for cases with particular physiological or phenotypical profiles will be able to source and include enough cases to provide high statistical power, allowing more individualised risk factors to be assessed and thus allowing screening and staging processes to be optimised. Also, education, training and credentialing of clinicians in image interpretation, will be more effective because it will be possible to select instances of images with specific visual aspects, or correspond to types of cases where reading performance improvement is desirable.
Lessons from the academy: actuating active mass‐class information literacy instruction

Purpose – The purpose of this paper is to mitigate the dearth in the library literature of large‐class, active learning pedagogies, by providing examples of these instructional approaches used in mega‐classes within the science, humanities, and education fields, for application within the library environment, specifically in large information literacy instruction sessions.Design/methodology/approach – The author surveys both the library and science and education literatures, comparing coverage of active learning approaches in the professional literatures of each.Findings – Although the library literature enthusiastically supports active learning approaches to teaching information literacy in theory, there are statistically few librarians who employ these learning modalities in their teaching. There are hardly any examples in the library literature of librarians using large‐class, active‐learning pedagogies. This article discusses some of the reasons behind librarians' reluctance to use active learning tec.
Dynamic platform for moving organ imaging

A multimodality platform (CT, PET, Radiotherapy) has been developed in order to move phantoms (maximum weight: 70kg). This allows the study of the influence of motion on image quality. The translation system (160 mm in the z axis, maximal speed of 50 mm × s-1) was controlled by a computer via a NI Motion Controller PCI 7344 (National Instrument, TX, USA). As an initial experiment, an anthropomorphic cardiac CT phantom (QRM, Moehrendorf, Germany) was moved linearly with speeds of 5, 10 and 20 mm × s-1. Acquisitions were done on a Siemens Somatom Volume Zoom CT Scanner. To compare dynamic and static images, mutual information, correlation coefficient, standard deviation, volume computation and radiologist scoring were conducted. The mean position error of the platform was 0.1mm ± 0.04. Automatic evaluation of the image quality and/or the blurring is not easy. As predicted, we found an increase in artifacts with the speed of the phantom. The platform allows us to simulate physiological motions (respiratory and cardiac) in order to study their real influence on image quality and to correct them. We can already produce z axis physiological motion with the platform. More degrees of freedom (y and z rotations, x and y translations) will be added to improve the simulation of physiological motions.
Word sense disambiguation using word ontology and concept distribution

Abstract This paper presents a method of word sense disambiguation that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to “literally” and “regularly” express a “concept”. We apply set algebra to Wordnet's synsets cooperating with Wordnet's word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in Wordnet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts – we facilitate “concept distribution statistics” to determine the degree of semantic relatedness between two lexically expressed concepts. Human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is intended in a given context” is known as “Word Sense Disambiguation (WSD)”. The proposed method is not supervised, and does not require any manually created sense‐tagged training examples. The experimental results showed good performance on Semcor, a subset of the Brown corpus. We observe that measures of semantic relatedness are useful sources of information for word sense disambiguation.
Tools for dynamic network structures: GRAPE grammars

A theoretical framework based on attributed elementary programmed graph grammars (GRAPE grammars) that turns out to be equally well-suited for describing network dynamics, weight learning algorithms, and topology learning algorithms is proposed. It is shown how GRAPE grammars can be used to model neural networks. Special emphasis is placed on topology learning. It is concluded that GRAPE grammars offer a great potential for neural networks, giving the possibility of a common language suited for all kinds of neural networks. <<ETX>>
A hybrid Markov chain Monte Carlo method for generating permeability fields conditioned to multiwell pressure data and prior information

In order to properly evaluate the uncertainty in reservoir performance predictions, it is necessary to construct and sample the a posteriori probability density functions for the rock property fields. In this work, the a posteriori probability density function is constructed based on prior means and variograms (covariance function) for log-permeability and multiwell pressure data. Within the context of sampling the probability density function, we argue that the notion of equally probable realizations is the wrong paradigm for reservoir characterization. If the simulation of Gaussian random fields with a known variogram is the objective, it is shown that the variogram should not be incorporated directly into the objective function if simulated annealing is applied either to sample the a posteriori probability density function or to estimate a global minimum of the associated objective function. It is shown that the hybrid Markov chain Monte Carlo method provides a way to explore more fully the set of plausible log-permeability fields and does not suffer from the high rejection rates of more standard Markov chain Monte Carlo methods.
Evaluation of lossless compression of Teletext and graphics images for TV systems

The diversity in the nature of TV images is increasing due to the mixing of graphics data with natural scenery. In this paper we study the lossless coding of such graphics data with the objective to come to recommendations for video compression of such images. For this purpose, a comparison has been made between contour coding, template coding and run-length coding of graphical imagery. It is concluded that template coding is suitable for TXT-like images but it is not practical for more generic graphics images. For menus and similar imagery, contour and run-length coding provide interesting options.
DIFFERENTIAL CMOS CLASS-E RF POWER AMPLIFIER

This paper presents a differential CMOS class-E power amplifier that exhibits high linearity. The amplifier operates at 900 MHz from ± 2.0-V, and delivers 1-W output power with 80% drain efficiency. Simulation results show that 3 rd and 5 th IMD products of the proposed PA are lower than that of the conventional class-E amplifier by 18 dB and 13dB respectively.
A New Metric for Measuring Relatedness of ScientificPapers Based on Non-Textual Features

Measuring relatedness of two papers is an issue which arises in many applications, e.g., recommendation, clustering and classification of papers. In this paper, a digital library is modeled as a directed graph; each node representing three different types of entities: papers, authors, and venues, and each edge representing relationships between these entities. Based on this graph model, six different types of relations are considered between two papers, and a new metric is proposed for evaluating relatedness of the papers. This metric only focuses on the relational features, and does not consider textual features. We have used it in combination with a textual similarity measure in the context of citation recommendation systems. Experimental results show that using this metric can successfully improve the quality of the recommendations.
Research of Web Data Mining in Personalized Recommendation Service

To offer personalized recommendation service to web users, it adopts improved FP-Growth algorithm, introduces its implementation methods and directly applies it to the recessive knowledge mining of web site information category in details. Via mining the website data and analysing association rules, useful relavant knowledge is obtained, tendancy of website visiting can be prediced and personalized service will be prescribed which make website more friendly and satisfactory.