Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional visualization of medical images over the internet

Three-dimensional visualization of medical images based on X-CT 2-D slice images requires a specialized medical imaging workstation and a trained operator to effectively generate 3-D images of the human anatomy. We therefore propose a system so that remote users can visualize and manipulate 3-D medical images without any specialized equipment nor all the slice data. The 3-D data set is pre-rendered at appropriately designed multiple viewpoints. The resulting images are compressed using the moving picture experts group (MPEG) standard and stored. The remote user can easily access the 3-D data for browsing and manipulation over the Internet using our newly developed 3-D viewer. The key features of our system are as follows: (1) the viewpoints for pre-rendering are spaced apart isotropically on the surface of the sphere, (2) the quantization level is determined at encode time to keep the PSNR of all images constant, (3) the encoder is designed to improve compression efficiency exploiting the similarity between adjacent viewpoint images and (4) the compressed 3-D dataset is streamed over the network so that the user can view the received data while the rest is being streamed.
A design for robust power system stabilizer by means of H∞ control and particle swarm optimization method

This paper proposes two types of PSS design methods that take into account robustness for comparably large power systems. The first one is a design method based on ∞ control theory and the second one is a parameter determination method for a standard PSS by using Particle Swarm Optimization (PSO). In order to deal with large-scale systems, a reduced model is developed to get the target system which preserves major oscillation modes only. The major oscillation modes are selected by using the residue concept, and the PSS is designed based on the target system. In order to verify effectiveness, the proposed methods are compared with the other previously proposed method based on a Genetic Algorithm (GA) through many numerical simulations. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(8): 34–43, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10132
Simple multiresolution approach for representing multiple regions of interest (ROIs)

A simple spatial-domain multiresolution scheme is presented for preserving multiple regions of interest (ROIs) in images. User-selected ROIs are maintained at high (original) resolution while peripheral areas are degraded. The presented method is based on the well-known MIP texture mapping algorithm used extensively in computer graphics. Most ROI schemes concentrate on preserving a single foveal region, usually attempting to match the visual acuity of the human visual system (HVS). The multiple ROI scheme presented here offers three variants of peripheral degradation, including linear and nonlinear resolution mapping, as well as a mapping matching HVS acuity. Degradation of image pixels is carried out relative to each ROI. A simple criterion is used to determine screen pixel membership in given image ROIs. Results suggest that the proposed multiple ROI representation scheme may be suitable for gaze-contingent displays as well as for encoding sparse images while optimizing compression and visual fidelity.
Fiberoptic receiver performance: A tutorial review

Abstract A tutorial review of fiberoptic receivers is presented, and their performance analyzed in terms of two quantities: quantum noise, a fundamental limit, and Noise Equivalent Power, or NEP, an engineering parameter. The NEP, a quantity specified by manufacturers as constant, is shown to vary with bandwith (data rate). The exact dependence of NEP on bandwidth is derived for two types of receivers: transimpedance and integrating, in both analog and digital modes. The results are applied to the calculation of the minimum required power for digital and analog receivers operation, and universal curves ofpower vs. bandwidth are presented. It is shown that the NEP specified by manufacturerscan be fitted to these universal curves, from which receiver performance can be predicted. For tutorial purposes, the treatment is self-contained and many well-known classical results such as signal-to-noise ratio of PCM signals and noise-equivalent circuits are re-derived from fundamental principles.
Short term load forecasting of Taiwan power system using a knowledge-based expert system

A knowledge-based expert system was developed for the short-term load forecasting of the Taiwan power system. The developed expert system, which was implemented on a personal computer, was written in PROLOG using a 5-year database. To benefit from the expert knowledge and experience of the system operator, eleven different shapes, each with different means of load calculations, were established. With these load shapes at hand, some peculiar load characteristics pertaining to the Taiwan Power Company can be taken into account. The special load types considered by the expert system include the extremely low load levels during the week of the Chinese New Year, the special load characteristics of the days following a tropical storm or a typhoon, the partial shutdown of certain factories on Saturdays, the special event caused by a holiday on Friday or on Tuesday, etc. A characteristic feature of the knowledge-based expert system is that it is easy to add new information and new rules to the knowledge base. To illustrate the effectiveness of the system, short-term load forecasting is performed on the Taiwan power system by using both the developed algorithm and the conventional Box-Jenkins statistical method. It is found that a mean absolute error of 2.52% for one year is achieved by the expert system approach as compared to an error of 3.86% by the statistical method. >
A hybrid neuro-fuzzy system for the classification of normal, fusion and PVC cardiac beats in the MIT-BIH database

Hybrid architectures for intelligent systems is as a new direction in the artificial intelligence research which aims at the development of the next generation of intelligent systems. Current research interests in this field focus on integrating neural networks and fuzzy logic to better exploit their strengths as well as expand their application domain. This paper presents a hybrid neuro-fuzzy system (HNFS) for the classification of normal, fusion and PVC (premature ventricular contraction) cardiac beats of patient 208 in the MIT-BIH Arrhythmia Database. The HFNS has a hierarchical topology consisting of three kinds of building blocks-fuzzy neural networks (FNNs), neural networks (NN) and fuzzy systems (FS). The FNNs and NNs are based on the feedforward backpropagation model. The FS is based on classical methods that measure the QRS area, height and RR interval. The first level of the HFNS accomplishes the task of classification of QRS complexes from leads MLII and VI into different classes. In case of the classification output being ambiguous, the QRS pattern is passed to the second HFNS level for final decision-making. A small inference system is developed to support the decision when the classification obtained from MLII and VI differs. Different configurations of FNNs, NNs and FS for both the first and the second level have been examined and tested. Classification results based on sensitivity and predictivity rates are presented and compared to previous approaches.
Systems research, genetic algorithms and information systems

Darwinian evolution and genetics have spawned a class of computational methods called evolutionary algorithms, and in particular, genetic algorithms. These evolutionary strategies provide new opportunities and challenges with ever-increasing applications in industry. In this paper, we propose that the proper context for a basic unifying theory of evolution for the emerging debate on the similarities and differences between biotic evolution and evolutionary algorithms is systems science. Recent changes in technology, coupled with developments in the field of artificial intelligence, promote the growth of enabling technologies, such as intelligent systems, in which we integrate genetic algorithms. Genetic algorithms are integrated with other artificial intelligence tools using a cooperating intelligent subsystem, which is integrated into the information systems of the organization. A portfolio of examples illustrating the evolving and expanding applications of genetic algorithms is included, as well as our computational experience with several commercially available genetic algorithm software. Copyright © 2000 John Wiley & Sons, Ltd.
Academic library benchmarking in The Netherlands: a comparative study

Purpose – This paper aims to describe some of the unique features of the Dutch academic library benchmarking system.Design/methodology/approach – The Dutch system is compared with similar projects in the USA, the UK and Germany.Findings – One of the most distinguishing features of the Dutch system is the method by which the results are analysed and presented. Absolute values of performance indicators are converted to median relative scores and, as a result, a graph can be created for each library that shows at a glance its relative position for a whole range of indicators.Practical implications – Similar benchmarking projects may benefit from the Dutch experience regarding the selection of raw data, the choice of performance indicators, the validity of the performance indicators, methods for analysis and presentation, and interpretation of the results.Originality/value – The paper provides detailed and critical information on a long‐running project. It questions the validity of some established performanc.
The Overload Reduction in SIP Servers through Exact Regulation of the Retransmission Timer of the Invite Message

To initiate voice, image, instant messaging and general multimedia communication, the Session communication must initiate between two participants. SIP (Session initiation protocol) is an application layer control, which task is creating management, and terminating this kind of Sessions. With regard to the independence of SIP from the Transport layer protocols, the SIP messages can be transferred on a variety of Transport layer protocols such as TCP or UDP. The mechanism of Retransmission, which has been embedded in SIP, is able to compensate the missing Packet loss, if needed. The application of this mechanism is when SIP messages are transmitted on an unreliable transmission layer protocol such as UDP. This mechanism, while facing with SIP proxy with overload, causes excessive filling of proxy queue, delays the increase of other contacts and adds the amount of the proxy overload. We in this article, while using UDP, as the Transport layer protocol, by regulating the Invite Retransmission Timer appropriately (T1), have improved the SIP functionality. Therefore, by proposing an Adaptive Timer of Invite message retransmission, we have tried to improve the time of Session initiation and as a result, improving the performance. The performance of the proposed SIP, by the SIPP software in a real network environment has been implemented and evaluated and its accuracy and performance has been demonstrated.
Evaluation of the Potential Nephroprotective and Antimicrobial Effect of Camellia sinensis Leaves versus Hibiscus sabdariffa (In Vivo and In Vitro Studies).

Green tea and hibiscus are widely consumed as traditional beverages in Yemen and some regional countries. They are relatively cheap and the belief is that they improve health state and cure many diseases. The aim of this study was to evaluate the potential protective and antibacterial activity of these two famous plants in vitro through measuring their antibacterial activity and in vivo through measuring nonenzymatic kidney markers dysfunction after induction of nephrotoxicity by gentamicin. Gram positive bacteria like MRSA (methicillin resistant Staphylococcus aureus) were isolated from hospitalized patients’ different sources (pus and wound) and Gram negative bacteria including E. coli and P. aeruginosa were used in vitro study. In addition, the efficacy of these plants was assessed in vivo through measuring nonenzymatic kidney markers including S. creatinine and S. urea. Green tea was shown antimicrobial activity against MRSA with inhibition zone 19.67 ± 0.33 mm and MIC 1.25 ± 0.00 mg/mL compared with standard reference (vancomycin) 18.00 ± 0.00 mg/mL. Hibiscus did not exhibit a similar effect. Both Hibiscus- and green tea-treated groups had nephroprotective effects as they reduced the elevation in nonenzymatic kidney markers. We conclude that green tea has dual effects: antimicrobial and nephroprotective.
Computer holography: 3D digital art based on high-definition CGH

Our recent works of high-definition computer-generated holograms (CGH) and the techniques used for the creation, such as the polygon-based method, silhouette method and digitized holography, are summarized and reviewed in this paper. The concept of computer holography is proposed in terms of integrating and crystalizing the techniques into novel digital art.
The edge extraction of agricultural crop leaf

In agricultural engineering, to ensure rational use of pesticide and improvement of crop production, computer image recognition technology is currently applied to help farmers to identify the degree of crop diseases. Considering the importance of feature extraction in this field, in this paper, we first present and discuss several widely used edge operator, including Sobel, Prewitt, Roberts, Canny and LoG. Furthermore, an experiment is conducted to compare performance and accuracy of five operators by applying them to a leaf image taken from agricultural crop for edge detection. The results of experiment show that, in practice, LoG edge operator is relatively a better choice and performs well for edge detection of agricultural crop leaf image.
MRI Denoising Based on a Non-Parametric Bayesian Image Sparse Representation Method

Magnetic Resonance images are often corrupted by Gaussian noise which highly affects the quality of MR images. In this paper, a Non-Parametric hierarchical Bayesian image sparse representation method is proposed to wipe out Gaussian distribution noise coupling in MR images. In this method a spike-slab prior is imposed on sparse coefficients, and a redundant dictionary is learned from the corrupted image. Experimental results show that the method not only improves the effect of MRI denoising, but also can obtain good estimation of the noise variance. Compared to non-local filter method, this model shows better visual quality as well as higher PSNR.
Modeling confusion for autonomous systems

Autonomous systems process sensory information to build representations of the external world, which serve as the basis for response decisions. These representations may be characterized by property lists, some of which are not direct sensor measurements, but inferred. Inferred properties are identified by classifiers or pattern recognition devices, which identify the existence of `fuzzy' concepts, such as `flying object. ' Fuzzy classifiers assign properties with infinite degrees of existence, represented by numbers on the closed unit interval [0,1], which raises issues not present with classifiers based on binary logic, in which properties either exist or do not. One of these issues is confusion. A system is said to be in a state of confusion when it is generating similar confidence factors for mutually exclusive properties. For example, the fuzzy concepts `civilian' and `military' may be properties of the object class `aircraft,' and a state of confusion exists if confidence factors for these properties are both relatively close. An autonomous system that reacts to aircraft needs an explicit representation of confusion to enable it to decide whether it should react to object instances in their present form or continue data gathering. We discuss approaches to modeling confusion using fuzzy logical operators and present illustrative examples of its application in multi-level classification.
Parameters in minimalist theory: The case of Scandinavian

Abstract The P&P theory of UG has come under heavy criticism, lately, from outside but also from inside generative grammar. The claim is that the search for ‘deep parameters’ underlying clusters of properties across languages has led nowhere, and should be given up. I have revisited a theory, now two decades old, which explained ten syntactic differences between Insular and Mainland Scandinavian as the effects of a single parametric difference (in a series of works by C. Platzack and A. Holmberg). The theory is shown to be fundamentally right, descriptively and theoretically. Later developments in generative theory only serve to sharpen the formulations, adding another layer of explanatory depth to the earlier theory. The conclusion is that there are parameters of the traditional kind. The problems encountered when the theory is tested on more distantly related languages is discussed on the basis of facts from Finnish. P&P theory is perfectly consistent with the minimalist approach to UG and variation when parameters are seen as points of under-specification in UG, and restrictions on variation are seen as, in part, third factor effects, in the sense of Chomsky (Linguistic Inquiry 36: 1–22, 2005).
Using classifier fusion to improve the performance of multiclass classification problems

The problem of multiclass classification is often modeled by breaking it down into a collection of binary classifiers, as opposed to jointly modeling all classes with a single primary classifier. Various methods can be found in the literature for decomposing the multiclass problem into a collection of binary classifiers. Typical algorithms that are studied here include each versus all remaining (EVAR), each versus all individually (EVAI), and output correction coding (OCC). With each of these methods a classifier fusion based decision rule is formulated utilizing the various binary classifiers to determine the correct classification of an unknown data point. For example, with EVAR the binary classifier with maximum output is chosen. For EVAI, the correct class is chosen using a majority voting rule, and with OCC a comparison algorithm based minimum Hamming distance metric is used. In this paper, it is demonstrated how these various methods perform utilizing the Bayesian Reduction Algorithm (BDRA) as the primary classifier. BDRA is a discrete data classification method that quantizes and reduces the dimensionality of feature data for best classification performance. In this case, BDRA is used to not only train the appropriate binary classifier pairs, but it is also used to train on the discrete classifier outputs to formulate the correct classification decision of unknown data points. In this way, it is demonstrated how to predict which binary classification based algorithm method (i.e., EVAR, EVAI, or OCC) performs best with BDRA. Experimental results are shown with real data sets taken from the Knowledge Extraction based on Evolutionary Learning (KEEL) and University of California at Irvine (UCI) Repositories of classifier Databases. In general, and for the data sets considered, it is shown that the best classification method, based on performance with unlabeled test observations, can be predicted form performance on labeled training data. Specifically, the best method is shown to have the least overall probability of error, and the binary classifiers have the least overall average quantization complexity.
A Triangulation and Guard Sufficiency Set of the Hierarchy of Simple Polygons

In this paper, we consider a characterization of a Guard Sufficiency Set(GSS) in the hierarchy of simple polygons. we propose the diagonals of a arbitrary triangulation of a polygon as a GSS when guards see the diagonals with completely visibility and both sides of the diagonal. we show that this can be a GSS in convex polygons, unimodal polygons, spiral polygons but this can not be a GSS in star-shaped polygons, monotone polygons, completely external visible polygons.
A novel algorithm for cut shot boundary detection

Cut is a common type of shot boundary. In previous literature, frame pair similarity (FPS) was usually used as building block of cut detector. For a given frame, one needs to determine what frame pairs in its adjacency to select and how to combine the FPS values. To do these, previous methods, including Cross Analysis, Full Analysis, Graph Cut, etc, resort to human experience and thus lead to inferior performance. Besides, they are susceptible to noise caused by flashlight or popup subtitle such that additional procedure is needed to suppress such noise. In this paper we propose a novel framework to address these problems. Both the frame pair selection and the similarity values combination are done via machine learning. Features insensitive to flashlight or popup subtitle are extracted by exploiting the color histogram based FPS. Experimental results on TRECVID2003~2005 datasets demonstrate the effectiveness of the proposed algorithm.
Living History: The Carnegie Library as Place in Ontario/ Histoire vivante : la bibliothèque Carnegie comme lieu en Ontario

Cet article réexamine les résultats d’une étude de cas menée en 2007 portant sur la bibliothèque Carnegie d’Owen Sound (Ontario), envisagée comme lieu dans l’espace. À partir d’une combinaison de méthodes provenant de la recherche historique et des études qualitatives de cas, et en utilisant la triade spatiale d’Henri Lefebvre comme cadre d’analyse, le chercheur compare certains aspects de l’espace de la bibliothèque tels que l’accessibilité, la supervision de l’espace de la bibliothèque, le sentiment de connexion du public avec l’extérieur, et le reflet de la communauté. L’article avance que la bibliothèque Carnegie d’Owen Sound représente, en tant que lieu, un nouveau modèle possible de bibliothèque publique digne d’étude : le bâtiment historique de la bibliothèque au sein de la communauté, un genre de bibliothèque qui n’est pas simplement un vestige du passé aujourd’hui désuet, mais au contraire un lieu ouvert et vibrant de vie au cœur de la communauté. The article reviews findings of a case study conducted in 2007 that examined the Owen Sound (Ontario) Carnegie Library as a place. Drawing from a combination of qualitative case study and historical research methods, and using Henri Lefebvre’s spatial triad as an analytical framework, the researcher compares such past and present aspects of the library space as accessibility, supervision of the library space, the patron’s sense of connection to the outside, and community reflection. The article suggests that the Owen Sound Carnegie Library as a place represents a possible new type of public library building worthy of further study: the historical library building in the community, a type of library that is not merely an obsolete relic of the past but is instead a vibrant and flexible place within the community.
Polarimetric Synthetic Aperture Radar data for Crop Cover Classification

The interest in crop inventory through the use of microwave sensors is on the rise owing to need for accurate crop forecast and the availability of multi polarization data. Till recently, the temporal amplitude data has been used for crop discrimination as well as acreage estimation. With the availability of dual and quadpol data, the differential response of crop geometry at various crop growth stages to various polarizations is being exploited for discrimination and classification of crops. An attempt has been made in the current study with RISAT1 and Radarsat2 C-band single, dual, fully and hybrid polarimetric data for crop inventory. The single date hybrid polarimetric data gave comparable results to the three date single polarization data as well as with the single date fully polarimetric data for crops like rice and cotton.