Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural scene representation for visualizing high-dimensional data in a large-scale time-critical environment

This paper reports the results of a project to develop an interactive visualization artifact to view data from a utility market as a natural scene. The visualization was the product of a series of experiments and prototypes, with the final design bearing structural resemblance to a decorated tree. It presents the raw data and its patterns of change within an integrated structure consisting of color-coded graphs, layers of pie charts, iconic markers, and parallel coordinates, while retaining overall comprehensibility. This paper details process based on the methodology or Robertson, a prototype implementation illustrated with an example walk through, and we comment on the merit of this approach.
Fast efficient algorithms for 3x3 ranked filters using finite-state machines

Median filters and ranked filters of ranks other than median have often been proposed or used to remove image noise as well as for other reasons. These are nonlinear operations, and often have relative long execution times, making them unsatisfactory for many speed-critical industrial applications. This paper builds on the earlier work of Mahmoodi and Waltz to provide efficient implementations of 3 X 3 ranked filters of ranks 1 (minimum), 2, 3, 4, 5 (median), 6, 7, 8, and 9 (maximum). These implementations are based on a partial realization of the SKIPSM (Separated- Kernel Image Processing using finite-State Machines) paradigm. A full SKIPSM realization is not possible because, except for the filters of ranks 1 and 9, these operations are not separable. This paper shows that, in spite of this lack of separability, the finite-state machine aspect of SKIPSM can be used to advantage. The emphasis is on software implementations, but implementation is pipelined hardware have also been demonstrated. In addition, a fast `full- SKIPSM' implementation of a slightly different ranked filter, sometimes called the `separable median' filter, is presented. This filter guarantees that the output pixels are of rank 4, 5, or 6. For typical noise-reduction applications, it is difficult to find a convincing argument that this filter is inferior in any meaningful way to the true median filter.
Multi-scale image analysis on the CNN Universal Machine

Algorithms for generating multi-scale representations of gray-scale images are presented. A number of possible approaches are described to produce low-pass and band-pass decompositions using simple analogic algorithms. It is also shown how the wavelet transform can be approximated with a similar technique and be used to obtain multi-level descriptions of the input data. This paper presents some methods how the cellular neural network (CNN) Universal Machine can be used effectively for generating multi-scale representations of gray-scale imagery.
Genetic task clustering for modular neural networks

This paper introduces a method to cluster subtasks of a complex task to be learned by neural networks. The main objectives are minimization of the epochs needed to train the clusters up to a specified error limit and to maximize the generalization rates of the trained networks. To cope with the combinatorial optimization problem involved a genetic algorithm is developed. It starts with a set of random clusters which are trained up to a stopping criterion. Based on a fitness measure derived from the training results, new clusters are assembled using genetic operators. The approach of this work is relevant for all problems being decomposable into distinct subtasks, for example in robotics and plant control, where piecewise control strategies can be learned, and in image processing. Simulations for letter recognition indicate that the method is superior to both training all tasks on large monolithic network and to training randomly assigned clusters on small modular networks.
Variable Geometry Chevrons for Jet Noise Reduction

Boeing is applying cutting edge smart material actuators to the next generation morphing technologies for aircraft. This effort has led to the Variable Geometry Chevrons (VGC), which utilize compact, light weight, and robust shape memory alloy (SMA) actuators. These actuators morph the shape of chevrons on a jet engine fan nozzle trailing edge in order to optimize acoustic and performance objectives at multiple flight conditions. We have completed a flight test of the VGC system on a Boeing 777-300ER with GE-115B engines. In this paper we describe the VGC design, development and performance during flight test. We demonstrated autonomous operation of the VGCs, which did not require a control system or aircraft power. The VGC concept demonstrated an exciting capability to optimize jet nozzle performance at multiple flight conditions. The VGC system provided a robust test vehicle to explore chevron configurations for community and shock-cell noise reduction. This capability was demonstrated with two examples of a parametric study which showed the influence of VGC configurations on community noise reduction and shock-cell generated cabin noise reduction during cruise.
Design and Implementation for the Secure ID Management System Based on Virtualization

In this paper, we study a solution to solve the authentication problem in the ID management system (IDMS). Our solution is based on the virtualization technology which allows data transfer between two servers in the absence of the network connection. In this paper, we propose a Virtual ID management system (VIDMS) that logically divides the login server and the authentication server in a physical system. The ID authentication process minimizes the security threat in the network since it can be performed using the virtual shared memory in the absence of the network connection. As compared with the existing IDMS, our solution improves the authentication performance (in terms of the speed).
SURF(Speeded Up Robust Features)와 Kalman Filter를 이용한 컬러 객체 추적 방법의 제안

객체 인식과 추적(object recognition and tracking)은 컴퓨터 비전의 중요 분야로써 작게는 동작 인식으로부터 크게는 우주 항공까지 그 활용 가능성이 무궁무진하다. 객체 인식의 정확도를 향상시키는 방법 중 하나는 방향, 스케일 그리고 가려짐에 강건한 컬러를 이용하는 것이다. 컬러를 이용함으로써 더 많은 특징점들을 추출하기 위한 계산 비용을 감소시킬 수 있다. 또한, 빠른 객체 인식을 위해 알고리즘의 정확도를 낮추는 것보다 객체의 위치를 예측하고 좀 더 작은 영역에서 인식을 수행하는 것이 더욱 효과적이다. 본 논문은, 인식 정확도를 향상시키기 위해 대표적인 객체 인식 알고리즘인 SURF와 컬러모델을 적용한 기술자(descriptor)를 사용하고, 움직임 예측 알고리즘인 Kalman filter를 결합하여 빠른 객체 추적 방법을 제안한다. 그 결과, 제안하는 방법은 다른 컬러를 갖는 같은 패턴의 객체들을 구분하고, 객체의 향후 움직임을 미리 예측한 관심영역(ROI)에서 인식을 수행함으로써 빠른 추적 결과를 보였다.
Group behavior models for learning in neural networks

A description is given of work on supervised learning algorithms for layered, feedforward neural networks, motivated by group behavior models developed to describe coordinated motion of flocks of birds and schools of fish, for example. The approach involves introducing additional hidden nodes while constraining the associated additional degrees of freedom to avoid excessive computation during the transient phase of learning and to avoid over parameterization asymptotically. If schools are rather widely dispersed. at least on the output side, it is expected that the school learning procedure will offer broader search capabilities because of the extra degrees of freedom available in early stages of the learning process. In the simplest selection strategy, the choice of units within schools is simply a cyclic one. Some caution should be exercised if this is used in conjunction with cyclic presentations of a finite set of training examples. <<ETX>>
Study on recognition algorithm for paper currency numbers based on neural network

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.
A 6-ns 4-mb Cmos Sram With Offset-voltage-insensitive Current Sense Amplifiers

A 4-Mb CMOS SRAM with 3.84 /spl mu/m/sup 2/ TFT load cells is fabricated using 0.25-/spl mu/m CMOS technology and achieves an address access time of 6 ns at a supply voltage of 2.7 V. The use of a current sense amplifier that is insensitive to its offset voltage enables the fast access time. A boosted cell array architecture allows low voltage operation of fast SRAM's using TFT load cells. >
Design and Realization of E-Learning Resource Storage System

The datacenter of one university owns massive E-Learning resources, and how to support safe and efficient access so as to support large-scale concurrent read and write protection has troubled the administrators of the campus datacenter. In this paper, we presented our E-learning resources storage system. The resources are stored in one HDFS(HADDOP Distributed File System) based storage systems, to effectively support the E-learning scenarios as small write-once files with large-scale concurrent read scenarios. Within the System, the E-learning data can be deployed redundantly across multiple storage nodes, which are distributed so as to improve read and write speed. Furthermore, when a single data node fails the system can also be recovered by redundant node data in the system and improve system security greatly. The system supports that the desktop systems, mobile platforms and web browser can access the system with cross-platform mechanism. The system adopts encryption mechanisms through the establishment of secure storage directory, and the user terminal can encrypts and unencrypts the user-files with offline mode to guarantee the safety of user data.
Window Interfaces A Taxonomy of Window Manager User Interfaces

This article presents a taxonomy for the user-visible parts of window managers. It is interesting that there are actually very few significant differences, and the differences can be classified in a taxonomy with fairly limited branching. This taxonomy should be useful in evaluating the similarities and differences of various window managers, and it will also serve as a guide for the issues that need to be addressed by designers of future window manager user interfaces. The advantages and disadvantages of the various options are also presented. Since many modern window managers allow the user interface to be customized to a large degree, it is important to study the choices available. A window manager is a software package that helps the user monitor and control different contexts by separating them physically onto different parts of one or more display screens. At its simplest, a window manager provides many separate terminals on the same screen, each with its own connection to a time-sharing computer. At its most advanced, a window manager supports many different activities, each of which uses many windows, and each window, in turn, can contain many different kinds of information including text, graphics, and even video. Window managers are sometimes implemented as part of a computer’s operating system and sometimes as a server that can be used if desired. They
The error term in formative measurement models: interpretation and modeling implications

Purpose – To clarify the nature of the error term in formative measurement models, as it had been misinterpreted in prior research.Design/methodology/approach – The error term in formative measurement models is analytically contrasted with the measurement errors typically found in reflective measurement models.Findings – It is demonstrated that, unlike in reflective measurement, the error term in formative models is not measurement error but rather a disturbance representing non‐modeled causes. It is also shown that, under certain circumstances, the inclusion of an error term is not necessary/appropriate.Research limitations/implications – Focus is only on first‐order measurement models; higher‐order specifications are not considered.Originality/value – The paper helps researchers in their initial specification of formative measurement models as well as their evaluation of the subsequent model estimates, leading to better specifications for formative constructs.
Acoustic and perceptual studies of Lombard speech: application to isolated-words automatic speech recognition

The purpose of this study was (1) to determine what are the acoustic-phonetic differences between speech produced in quiet and speech produced in noise (Lombard speech) and (2) to evaluate the influence of these differences on human listeners and automatic speech recognizers. The acoustical analyses, done at the phonetic level on about 40 parameters, showed significant differences in variability for male and female speakers. In addition to replicating previous studies, the authors investigated more parameters, and examined the influence of the Lombard effect on female speakers. Perceptual experiments, run for foreign listeners, exhibited a decrease of the intelligibility for some confusable subsets of the vocabulary studied. The findings are correlated with the performance of a DTW-based recognizer, and it is observed that recognition scores cannot necessarily be improved by reducing the variability for one specific parameter. It is also found that recognition scores are not directly related to the increase of the vocal effort and cannot be predicted from speech variability. <<ETX>>
Comparison of counter-measures against slow Rayleigh fading for TDMA systems

This paper compares the performance of three techniques for introducing tolerance to slow Rayleigh fading channels for TDMA fixed radio access schemes within a single cell: regular cyclic frequency hopping, adaptive modulation and coding, and frequency jumping when required. It is assumed for the channels considered that the fading rates are slow enough that simple feedback adaptive techniques are applicable. The theoretical maximum performance of each scheme is evaluated as a function of the number of conversations supportable and the average transmitted power required. (6 pages)
Aplicación de las redes neuronales pulsantes en el reconocimiento de patrones y análisis de imágenes

In this thesis we propose a method for the application of pulsed neural networks to problems in pattern recognition and image analysis, the architecture used for the neural network makes use of the Izhikevich neuronal model as processing unit; the unsupervised training and free parameters adjustment performed by a Differential Evolution algorithm programed in C++ language. Using a feedforward architecture with three layers: the input layer (E), intermediate layer (O) and output layer (S), the classification of different pattern types is achieved using the frequency obtained in the output layer of the network. The calculation of the current injected to the input neurons is realized by means of transformation functions which allow the pass of a feature vector in n-dimensional to a scalar value that facilitates separation. Proposes the use of the architecture and training method thresholding problem as features of images using descriptors of color and texture features.
Neural adaptive filters for estimating brainstem auditory evoked potential

This paper presents Neural Adaptive Filters (NAF's) for estimating brainstem auditory evoked potential (BAEP). The method is evaluated by using simulation and human data. It was observed that a significant improvement in waveform estimation, compared with the ensemble averaging and time varying adaptive filter (TVAF), can be achieved by the neural adaptive filters. In this work, a multilayer perceptron network (MLP) trained with the backpropagation learning rule and Radial Basis Function Network (RBFN) trained with stochastic gradient-based algorithm were employed for neural filter implementation. It was found that the RBFN give rises to improvements in BAEP estimation over the MLP.
A new technique creates realistic 3D free-form surfaces from photographs and paintings

This paper presents a novel approach for creating 3D free-form scene models from a single paiting or photograph with no prior knowledge about the shape. The new technique takes as input a sparse set of user-specified constraints, and generates a well-behaved 3D surface satisfying the parameters. As each constraint is specified, the system recalculates and displays the reconstruction in real time. In contrast to previous work in single view reconstruction, our technique enables high quality reconstruction of free-form curved surfaces. A key feature of the approach is a novel hierarchical transformation for accelerating convergence on a non-uniform, piecewise continuous grid. The technique is interactive and updates the model in real time as constraints are added, allowing fast reconstruction of photorealistic scene models. The approach is shown to yield high quality results.
The responsibility management system for collaborative meeting scheduling in the Distributed System Engineering project

This paper presents a role-based cooperative session scheduling system and its application to the cooperation scenario of the DSE project for design review activities in the space domain. Our approach allows generic group definition. In contrast to identity-based group definition we use a role-based approach, allowing exceptions due to changes of identities and locations to be handled. Our solution relies on open generic groups and has two advantages. On one hand, it allows dynamic sessions to be managed, allowing evolution of the supported activities. On the other, it allows an invitation mechanism to be implemented as in traditional planning-based session management systems.
SORTCHIP: a VLSI implementation of a hardware algorithm for continuous data sorting

We present a VLSI implementation of a hardware sorting algorithm for continuous data sorting. The device is able to continuously process an input data stream while producing a sorted output data stream. At each clock cycle, the device reads and processes a 48-bit word, 24 bits for the datum and 24 bits for the associated tag. The data stream is sorted according to the tags preserving the order of words with identical tags. Sequences up to 256 words are completely sorted and longer sequences are partially sorted. The maximum operation frequency is 50 Mwords/s. The architecture is based on a chain of identical elementary sorting units. A full custom design exploits the highly regular architecture to achieve high area and time performance. We describe the algorithm and give architectural details.
Sequential and parallel models in noncooperative games for decentralized decision making in vision systems

An examination is made of two models, one parallel and the other sequential, of decentralized decision-making in modular systems aimed at vision or potentially robotic tasks. The theoretical analysis and the experimental results from a two-module image analysis system reveal the critical dependence of the system behavior on the exact decision model. The experiments with the two-module vision system indicate that the parallel model leads to more robust performance for a certain class of multiple objective systems. <<ETX>>
Scalable IP switching based on optical interconnect

IP traffic on the Internet and enterprise networks has been growing exponentially in the last several years, and much attention is being focused on the use of IP multicast for real-time multimedia applications. The current soft and general-purpose CPU-based routers face great stress since they have great latency and low forwarding speeds. Based on the ASICs, layer 2 switching provides high-speed packet forwarding. Integrating high-speed of Layer 2 switching with the flexibility of Layer 3 routing, Layer 3 switching (IP switching) has been put forward in order to avoid the performance bottleneck associated with Layer 3 forwarding. In this paper, we present a prototype system of a scalable IP switching based on scalable ATM switching fabric and optical interconnect. The IP switching system mainly consists of the input/output interface unit, scalable ATM switching fabric and IP control component. Optical interconnects between the input fan-out stage and the interconnect stage, also the interconnect stage and the output concentration stage provide high-speed data paths. And the interconnect stage is composed of 16 X 16 CMOS-SEED ATM switching modules. With 64 ports of OC-12 interface, the maximum throughput of the prototype system is about 20 million packets per second (MPPS) for 256 bytes average packet length, and the packet loss ratio is less than 10e-9. Benefiting from the scalable architecture and the optical interconnect, this IP switching system can easily scale to very large network size.
Developing a virtual laboratory for a communication and computer networking course

Educational institutions face financial challenges in maintaining and upgrading traditional physical laboratories. Moreover, university students have challenges in understanding the theoretical issues involved in communication and networking courses. This paper presents a new computer-aided laboratory to help the students to enhance the practical view of communication and networking systems using computer modelling and simulation and to replace a physical laboratory. The new laboratory was build using a component-based approach that integrates modelling and simulation tools, namely MATLAB, SIMULINK, VPI and OPNET. These components were integrated through the development of graphical user interface. Through this interface, the student navigates through experiments of the laboratory. The new laboratory curricula and interface were tested on students and their feedback showed that the students were satisfied. The students gave comments and suggestions for future improvements.
The university for the new market of knowledge

NETTUNO's Distance Teaching Model, based upon a network of traditional universities, is achieving significant results. Not only has NETTUNO contributed to the adoption of technologies by the Universities, but it also helped teachers to achieve new didactic skills. This allowed traditional universities to develop new ways of transferring knowledge using television and the Internet. On line course contents and the way they are delivered depend on university professors, therefore the quality control is assured and users are guaranteed as “consumers” of educational products. NETTUNO demonstrated that today distance University, thanks to the new telematic and satellite technologies, can provide a valid answer to the needs of the new market of knowledge: it can put its quality control trademark upon products; guarantee the user; help traditional university changing from an isolated system into a system capable of updating itself and integrating all the knowledge available on the Web and allow the knowledge interchange and interactivity world‐wide.