Miracle-dz/UniFAR-FTUs huggingface dataset:
Random forests for land cover classification

In recent years, a number of works reported the use of combination of multiple classifiers to produce a single classification and demonstrated significant performance improvement. The resulting classifier, referred to as an ensemble classifier, is a set of classifiers whose individual decisions are combined by weighted or unweighted voting to classify new examples. An ensembles are often more accurate than the individual classifiers that makes them up. In remote sensing Giacinto and Roli, 1997, Roli et al., 1997 report the use of ensemble of neural networks and the integration of classification results of different type of classifiers. Studies by growing an ensemble of decision trees and allowing them to vote for the most popular class reported a significant improvement in classification accuracy for land cover classification. This paper presents results obtained by random forests classifier, another technique of generating ensemble of classifiers and their performance is compared with the ensemble of decision tree classifiers. A classification accuracy of 88.32% is achieved by random forest classifier in comparison with 87.38% and 87.28% by decision tree ensemble created using boosting and bagging techniques. Further, study also suggests that bagging perform well in comparison with boosting in case of noise in training data.
A hybrid artificial neural network-dynamic programming approach for feeder capacitor scheduling

A hybrid artificial neural network (ANN) dynamic programming (DP) method for optimal feeder capacitor scheduling is presented in this paper. To overcome the time-consuming problem of full dynamic programming method, a strategy of ANN assisted partial DP is proposed. In this method, the DP procedures are performed on historical load data offline. The results are managed and valuable knowledge is extracted by using cluster algorithms. By the assistance of the extracted knowledge, a partial DP of reduced size is then performed online to give the optimal schedule for the forecasted load. Two types of clustering algorithms, hard clustering by Euclidean algorithm and soft clustering by an unsupervised learning neural network, are studied and compared in the paper. The effectiveness of the proposed algorithm is demonstrated by a typical feeder in Taipei City with its 365 days' load records. It is found that execution time of scheduling is highly reduced, while the cost is almost the same as the optimal one derived from full DP. >
Application of mathematical morphology to the extraction of craniofacial landmarks

An algorithm has been developed to extract craniofacial landmarks from skull X-rays. The locations of these landmarks are used by orthodontists in the diagnosis of anomalies and in the monitoring of treatments. The shape recognition algorithm is based on gray-scale mathematical morphology. A statistical approach was used to overcome subtle differences in skeletal topographies. Decomposition was used to desensitize the algorithm to size differences. A method for minimizing the search windows is presented. A system was trained to locate 20 landmarks. Preliminary tests on 30 X-rays showed a 80% recognition rate. <<ETX>>
Pictet-Spengler Reaction Revisited: Engineering of Tetherd Biheterocycles into Annulated Polyheterocycles

This micro review focuses on the application of the modified Pictet-Spengler reaction to the synthesis of diverse polyheterocy- cles with structural resemblance to natural products. The modified Pictet-Spengler reaction takes use of tethered biheterocycles compris- ing nucleophilic partner and a source for electrophilic partner followed by their condensation with aldehydes/ketones to furnish annulated polyheterocycles. Using this strategy engineering of numerous tethered biheterocycles into annulated polyheterocycles has been success- fully carried out using both 6-endo as well as 7-endo cyclizations.
Operational planning and performance measurement in the John Rylands University Library of Manchester

En octobre 1992, la Plan strategique 1992-1997 de la Bibliotheque universitaire John Rayland de Manchester au Royaume-Uni fut edite. Ce plan comprend des references a des indicateurs de performance dans le but de mesurer les activites de la bibliotheque. Ce plan a donc pour but d'etudier si la bibliotheque repond bien aux besoins de ses utilisateurs du point de vue de ses services mais aussi d'un point de vue financier. Cet article est donc un rapport dont le but est de souligner les succes mais aussi les diffcultes rencontrees par la bibliotheque. De plus, il propose des recommandations pratiques et realistes pour l'optimisation de l'utilisation de la bibliotheque
Temporal generalization of sub-pixel vegetation mapping with multiple machine learning and atmospheric correction algorithms

Temporal generalization allows a trained classification algorithm to be applied to multiple images across time to derive reliable classification map products. It is a challenging remote-sensing research topic since the results are dependent on the selection of atmospheric correction methods, classification algorithms, validation processes, and their varying combinations. This study examined the temporal generalization of sub-pixel vegetation mapping using multiple Landsat images (1990, 1996, 2004, and 2010). All Landsat images were processed with two atmospheric correction methods: simple dark object subtraction (DOS) and the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm. For the sub-pixel vegetation mapping of the 2004 Landsat image, we used high-resolution OrbView-3 images as a training/validation data set and compared three machine learning algorithms (neural networks, random forests, and classification and regression trees) for their classification performance. The trained classifiers were then applied to other Landsat images (1990, 1996, and 2010) to derive sub-pixel vegetation map products. For the 2004 Landsat image classification, cross-validation shows similar classification results for neural networks (root mean square error (RMSE) = 0.099) and random forests (RMSE = 0.100) algorithms, and both are better than classification and regression trees (RMSE = 0.123). Pseudo-invariant pixels between 2004 and 2010 were used as validation points to evaluate the temporal generalizability of classification algorithms. Simple DOS and LEDAPS atmospheric correction resulted in similar accuracy statistics. The neural-network-based classifier performed best in generating reliable sub-pixel vegetation map products across time.
An isopycnic ocean carbon cycle model

Abstract. The carbon cycle is a major forcing component in the global climate system. Modelling studies, aiming to explain recent and past climatic changes and to project future ones, increasingly include the interaction between the physical and biogeochemical systems. Their ocean components are generally z-coordinate models that are conceptually easy to use but that employ a vertical coordinate that is alien to the real ocean structure. Here, we present first results from a newly-developed isopycnic carbon cycle model and demonstrate the viability of using an isopycnic physical component for this purpose. As expected, the model represents well the interior ocean transport of biogeochemical tracers and produces realistic tracer distributions. Difficulties in employing a purely isopycnic coordinate lie mainly in the treatment of the surface boundary layer which is often represented by a bulk mixed layer. The most significant adjustments of the ocean biogeochemistry model HAMOCC, for use with an isopycnic coordinate, were in the representation of upper ocean biological production. We present a series of sensitivity studies exploring the effect of changes in biogeochemical and physical processes on export production and nutrient distribution. Apart from giving us pointers for further model development, they highlight the importance of preformed nutrient distributions in the Southern Ocean for global nutrient distributions. The sensitivity studies show that iron limitation for biological particle production, the treatment of light penetration for biological production, and the role of diapycnal mixing result in significant changes of nutrient distributions and liniting factors of biological production.
The estimation of parameters for stochastic differential equations using neural networks

In this article artificial neural network (ANN) is used to estimate parameters of stochastic differential equations (SDEs) given the discrete output variables of the equations. Some techniques are used to improve ANN performance in data preparation and ANN training procedure. In particular, an increase in the number of Wiener processes used to create training data set raises the accuracy of estimates significantly. The analysis of the results suggests that ANN can predict parameters of SDEs accurately under certain noise level regimes using an appropriate network architecture.
Probabilistic diagnostic reasoning: towards improving diagnostic efficiency

The author describes a new approximation method which can significantly improve the computational efficiency of Bayesian networks. He applies this technique to the diagnosis of acute abdominal pain, with good results. This approach is based on using a reduced set of the model parameters for diagnostic reasoning. The tradeoffs in diagnostic accuracy required to obtain increased computational efficiency (due to the smaller models) are carefully specified using a variety of statistical metrics. <<ETX>>
An Approach to Chinese Number Recognition

paper describes an approach to the symbol recognition of Chinese numerals. This approach uses techniques well established in digital image processing, including black/white conversion, digital binary morphology, pixel histograms for line counting, Zhang-Suen thinning for skeletonization, and other simple techniques such as median filtering. These techniques are combined to form a feature extraction algorithm. Once features are identified simple table lookups are preformed for number recognition. The table is constructed from known features in each of the non-complex and common Chinese numerals. Finally, results and conclusions are provided in regards to this algorithm along with ideas for future research. The algorithm used to recognize a numeral is based on what the author calls feature extraction. Feature extraction is composed of two feature types, point and line types. Feature point types are defined to be endpoints, corner points, 3-way intersections, and 4-way intersections within a numeral. Feature line types are defined as vertical and horizontal lines that make up the number. Once these features are identified, a simple table lookup is preformed. The table is constructed from the known features that exist in each Chinese numeral. The feature extraction algorithm consists of three overall phases; pre-processing, feature extraction, and post-processing. The pre-processing phase is used to set the image for feature extraction through the use of two techniques: simple black/white conversion and binary morphology. The feature extraction phase uses a line scanning technique for discovering feature lines, Zhang-Suen thinning, and finally connectivity calculations for feature point discovery. The post-processing phase includes orientation, point reconciliations, and feature matching via table lookup. The rest of this paper will describe these techniques in detail with results and conclusions provided afterwards.
Adaptive Sensitivity / Intelligent Scan Image Processor

Novel image processor architectures are presented. The architectures are designed to take advantage of the advanced concepts of Adaptive Sensitivity and Intelligent Scan, and, in particular, of the new sensors described in the companion paper. The processors are capable of manipulating selective visual data sets, which are generated by Intelligent Scan sensors. They are organized in feedback adaptive forms, exhibiting performance superior to conventional, feedforward architectures.
Modelling land-use changes using a novel vector-based geographic cellular automata

Cellular automata (CA) models have been increasingly used to simulate land-use changes due to their computational simplicity and their explicit representation of space and time. Typically, these models use the raster model, as defined in Geographic Information Systems, to represent geographic space. However, recent studies have demonstrated that raster-based CA are sensitive to spatial scale, i.e. cell size and neighborhood configuration. To overcome this limitation, a novel Vector-based Geographic Cellular Automata (VecGCA) model has been developed in which space is represented as a collection of geographic objects corresponding to meaningful entities of irregular shape and size composing a landscape. This paper presents a land-use change model using this new approach, tested on two study areas of different spatial complexity, in southern Quebec and in the Calgary region, respectively. The results obtained are compared to the patterns produced by a conventional raster-based CA and with land-use maps in each study area. They reveal that VecGCA generates an adequate evolution of the geometry of the objects composing the landscape and produces spatial patterns that are more similar to the land-use maps in each region.
Calibrated polarimetric SAR data for ship detection

The polarization information is investigated for ship detection using calibrated polarimetric Convair-680 SAR data. It is shown that at operational satellite SAR incidence angles (lower than 60/spl deg/), there is a significant improvement of ship-sea contrast when the full polarimetric information is used instead of the information provided by the scalar one channel polarization (HH, VV, or HV).
A 2 V 1.6 GHz BJT phase-locked loop

This paper describes the design of a BJT phase-locked loop (PLL) for /spl Sigma//spl Delta/ fractional-N frequency synthesis applications. Implemented in a 0.8 /spl mu/m BiCMOS technology, the PLL can operate up to 1.6 GHz, while consuming 225 mW of power from a single -2 V supply. The LC-tuned negative-resistance variable-frequency oscillator is integrated on the same chip. A low-voltage current-mode logic circuit configuration is used to minimize phase jitter and achieve low-voltage operation. Die size is 4300/spl times/4000 /spl mu/m/sup 2/.
How can the use of blog software facilitate the writing process of English language learners?

Blog use may offer instructors a helpful tool for teaching writing at the tertiary level in Teaching English as a Foreign Language (TEFL) classrooms. This article reports on a quasi-experimental study regarding the effect of blog-centered writing instruction on students' writing performance. Fifty intermediate English students at a Turkish university participated in the study. The control group (n = 23) received in-class process-oriented writing instruction and the experimental group (n = 27) integrated blogs into their writing processes by using a blog software. Based on the analyses of students' written work, the findings suggest that blog-integrated writing instruction might have resulted in a greater improvement in students' writing performance than merely in-class writing instruction. The study therefore supports the conclusion that English as a Foreign Language practitioners' use of blog software has potential to promote more effective writing instruction.
Suggestive modeling for machine vision

Traditional modeling techniques, with roots in CAD systems, do not provide a rich enough modeling environment for computer vision. The models themselves describe the structure rather than appearance of objects, and rarely provide facilities for the recording of the additional information required by a vision system. Encoding appearance explicitly ensures quick access and use of the model, and yields model features that correspond to observable data features. We describe the Suggestive Modelling System (SMS) which has been designed specifically for vision applications, combining the geometric object model with vision-specific annotations. Among SMS's features are: (1) A novel separation of surface shape, extent and position; (2) Encoding of underconstrained positions for subcomponents such as spheres and discs; (3) Incorporation of uncertain property values; (4) Cheap encoding of viewpoint- dependent information in addition to the body-centered model; (5) Hierarchical models; (6) Symbolic labels for each primitive; and (7) Parallel curve, surface, and volume-based representations simplify project management. We will describe how this approach reflects more faithfully the capabilities of current scene analysis algorithms than traditional methods. Results from the Imagine 2 vision system demonstrate the applicability of the models to complex real-world industrial inspection and recognition tasks. In addition a number of other vision-related applications in which the SMS paradigm has proved useful will be discussed.
Analysis and simulation of an inhibitive directional selective unit for computer vision

Directional selective unit, or velocity sensitive unit, is the mechanism in a vision system that responds to motion in a specific direction. This paper analyzes a model for directional selective unit proposed by Barlow and Leveck. This model employsthe inhibitory mechanism. Several parameters of the model are defined and examined. The analysis shows that the range of the response of the unit can be adjusted through the parameters, that the specific values of the parameters are not as important as the relative values of the parameters, and that a family of velocity-sensitive units with different ranges can be used to achieve velocity-specific detection.
Overlay of a wireless local loop system on CDMA2000 cellular system

The overlay of a wireless local loop system on the CDMA2000 cellular system is proposed. The CDMA2000 system uses 5, 10, 15 and 20 MHz of dispersion bandwidth and the WLL system uses 1.25 MHz of dispersion bandwidth. In this way, it is possible to have 3, 4, 8, 9, 12 and 16 frequency allocations for the WLL system depending on the different bandwidths of the CDMA2000 cellular system. As an overlay system the WLL can use only a fraction of the frequency allocations according to co-frequency allocation reuse distance, similar to FDMA and TDMA cellular systems. With this idea less interference is introduced in the WLL system (in the limit, the capacity will be similar to the isolated cell) keeping at the same value the interference introduced in the CDMA cellular system.
Less is more. Why Oberon beats mainstream in complex applications

The second part of the two-part account of the enabling technologies behind a successful completion of the Troitsk-nu-mass experiment, summarizes an almost 20 years' experience of using Oberon/Component Pascal in complex applications ranging from the cutting-edge computer algebra (b-quark decays etc.) to experimental data processing (neutrino mass etc.) to exploratory algorithm design work (the optimal jet finder etc.) to systematic computer science education (the international project Informatika-21).
A firewall concept for both control-flow and data-flow in regression integration testing

The authors present a methodology for regression testing and function or system testers. The methodology involves regression testing of modules where dependencies due to both control flow and data flow are taken into account. The control-flow dependency is modeled as a call graph and a firewall defined to include all affected modules which must be retested. Global variables are considered as the remaining data-flow dependency to be modeled. An approach to testing and regression testing of these global variables is given, and a firewall concept for the data-flow aspect of software change is defined. <<ETX>>
Arbitration schemes synthesis approach for multiprocessor systems

The aim of this paper is to present an arbiter synthesis approach stated as an arbitration scheme generation/selection problem. This approach may be easily inserted in an extendible system co-design methodology. It assumes that many co-design tasks have been performed, and it aims to automate the resolution of the access conflict problem that may be inevitably encountered whenever there is a resource contention in the system. The proposed approach allows us to transform a system composed of high-level processes that share the same channels and the same variables in a disordered manner into a set of processes executed by interconnected processors and hardware devices (e.g., ASIC, FPGA, etc.) that share the same resources such as memories and buses using the most efficient arbitration scheme. This approach is basically based on an algorithm that allows respectively the automatic generation/selection of the arbitration scheme, and the fixed priority assignment in a manner that minimizes the real-time cost.
A practical approach to system reliability growth modeling and improvement

In a product development process, to develop appropriate design validation and verification program for reliability assessment, one has to understand the functional behavior of the system, role of components in achieving required functions and failure modes if component/sub-system fails to perform required function. In this paper, the authors propose a simple and practical two-stage approach of system reliability growth modeling considering components, functions, and failure modes. The consideration of these three dimensions will help in uncovering the weak spots in design responsible for low system reliability. The proposed method assumes Weibull distribution as failure time distribution and reliability model is based on the Bayesian framework incorporating even fuzzy information. The fuzzy logic model that has been developed for this purpose is used to quantify the engineering judgment or fuzzy information of reliability improvement attributed to design changes or corrective actions. Uncertainty in data/information at component levels propagates to system level reliability and makes system reliability prediction highly unreliable. The paper suggests a variance reduction strategy to give more accurate system reliability predictions.
Monitoring Network-based Infrastructure ForForest Fire Detection

Information technology for design and reengineering of ground-based monitoring systems’ infrastructure was developed for forecasting and early detection of forest fires based on wireless networking, video surveillance and sensor networks. The technology is based on the methods of two-dimensional and three-dimensional coverage of multi-colored geometric objects, considering placement points and areas of surveillance, sensors, and network infrastructure as a whole. Heuristic procedures of a spatial covering of multicolor flat figures for various configurations are offered. Instrumental means of design and reengineering of ground-based monitoring systems, as well as a decision-making support, was developed to detect ignition centers and ensure fire-fighting.